1 1
Notes:

2
This report can be obtained from
University Research South Africa

Final Report, 08 December 2014
Mapping of Mineworkers and Ex-Mineworkers in Lesotho, South Africa and
Swaziland
Phase 1
Regional TB Service Delivery Framework

Contact Details:

Bay Technologies
P.O. Box 444, Pretoria, 0001
660 Mike Boulevard, Willow Acres Ext 12,
No 5 Silver Place, Silverlakes,
Pretoria
Email: tgqada@baytechnologies.co.za
Tel: +27 809 0171
Fax: 086 611 5078

3
PREAMBLE
Snapshot of Mining History in South Africa
Mining in South Africa has shaped the country culturally,
economically and politically. It directly contributed to the
establishment of the Johannesburg Stock Exchange
in the late 19th century, and today it still accounts for
a third of its market capitalisation (Source: Mining IQ
Mining Intelligence Database).

The history of mining in South Africa goes back as far as
the Dutch (Simon van der Stel, 1685) after the arrival in
the Cape. The pictures in Figure 1 and figure 3 reveals
some of the black, Chinese and white mine labourers in
a gold mine in South Africa from the early 1900’s; and
underground rock drillers in one of the highest exposure
to silica dust and risk of silicosis

By 1904, after the mining shutdown due to the Anglo
Boer War, the first 10,000 contracted Chinese workers
arrived to help rebuild the industry, and ensure low
labour prices. Whilst the Chinese were repatriated
Figure 1 – Mine Labourers Early 1900’s
by 1910, the demand for labour quickly brought large
Black, Chinese and White
labourers in a gold mine in South Africa, circa 1890 – 1923.
numbers of mineworkers from local and neighbouring ©Carpenter Collection, US Library of Congress
countries to the mines. They were treated as migrant
workers and placed in cramped mining accommodation.

The White Death
Silicosis on the Witwatersrand Gold Mines 1886 – 1910 by Elaine
Katz (1994), provides a historical account of the extent of silicosis
that ravaged the lungs of the early (white) miners. Katz notes in
the introduction to this seminal history of the early years of mining
in South Africa: “Although a great deal has been written about the
development of the Witwatersrand gold mining industry, only a tiny
slice has been devoted to its medical and health past….posterity has
been extraordinarily slow in acknowledging the devastation wrought
by the ‘white death’, or censuring those who did nothing to stop it”.

The first miners were predominantly foreign migrant workers from
England who returned home when ill. Eighty-five percent (85%) of
the white miners in the early years were British born and fifty-eight
percent (58%) of these men were from Cornwall. A full third from
the district of Redruth, Cornwall. Katz records that “Redruth was the
only foreign mining centre which compiled official silicosis mortality
statistics for the returned Witwatersrand rock drillers, who were
buried in the ‘rapidly filling graveyards’ of Cornwall”.

With the epidemiology of silicosis being more closely tracked by
doctors in Cornwall it was possible to conclude “between 1892 and
1910 almost an entire generation of professional miners from abroad Figure 2 – The ‘White’ Death
©Witwatersrand University Press, 1994.
died from an accelerated form of silicosis”. The fate of black South
African migrant mineworkers has, close to a hundred years later, yet
to be fully recorded. Decades upon decades have passed without homes in the labour-sending areas of the country, being counted in any
the graves of South African mineworkers, who have returned to their epidemiological reports. A grave is too late for any epidemiological inquiry

4
in the world’s deepest mining operations, TauTona
Mine (Western Deep No. 3 Shaft) at 3.9 kilometres.

Silica dust forms part of an ever-present potential
hazard for mineworkers resulting in the highest TB
incidence rates in the world. For instance, in South
Africa TB incidence is 2,500-3000/100,000 in the mines
while in general population it is 948/100,000 (Source:
World Bank data on National TB Incidence Rate
(2014)).

TB incidence among mineworkers is 10 times higher
than the WHO threshold for TB emergency: 250
per 100,000. This high incidence of TB amongst
mineworkers is driven by factors such as prolonged
exposure to silica dust, poor living conditions, lifestyle
and high HIV prevalence in the mining communities.

Figure 3 – Rock Drilling
One of humanity’s highest exposures to silica dust and risk of silicosis. Mineworkers frequently travel across provincial and
©Unknown national borders to visit their families. Their frequent
migratory movement elevates the risk of transmission of TB infection in
labour sending areas. In addition, this also adversely affects mineworkers’
adherence to TB treatment, and contributes to the incidence of drug
Migrant Workforce resistant TB.

Over the years, many mineworkers have returned to their labour sending
In 1920 the migrants from across the borders reached nearly 100,000 areas with an elevated risk of contracting TB, especially because of
(Figure 4). This number steadily rose and in the 1970’s gold mining silicosis, and over time, may contract TB or have a TB relapse. They then
in South Africa peaked to 265,000, contributing 68 per cent of global elevate the risk of their families contracting TB as well.
production for that year. The SA Chamber of Mines launched a drive
by 1974 to replace foreign migrants with South African workers. 1994 Current and ex-mineworkers are supposed to be screened regularly for
was the last year when African workers were classified separately from occupational illnesses such as TB and silicosis. Mineworkers and ex-
whites. mineworkers are eligible for financial compensation if they are confirmed
to have contracted TB as a result of their occupation. This would create
sufficient incentive for ex-mineworkers to go for TB screening on a
This mineworker labour force decline continued and reached a low of
regular basis. However, due to the administrative challenges in getting
406,994 in 2001, down from 721,000 in 1991. A slow turnaround to
compensation and lack of access to screening facilities, many current
around 524,632 by 2012 followed by about 498,634 in December 2013
and ex-mineworkers do not go for health screening.
as can be seen in Figure 5.

This mapping exercise is the first phase of the Regional TB Service
Over the years an estimated total of more than 4 million people worked in
Framework which seeks to reduce the risk of TB through multi-sectoral,
the industry, of which an estimated 2 million are still alive today.
multi-disciplinary and multi-country approach and the scope is to identify
geographic areas in which South African mines’ current mineworkers
The South African geophysical conditions was instrumental in making it
and ex-mineworkers are mostly located in Lesotho, South Africa and
possible to mine to depths not attainable elsewhere in the world; resulting
Swaziland.

Figure 4 – Population of Migrant Workers Figure 5 – Total Mineworkers (1000’s)
©URSA 2014 ©URSA 2014
5
ACKNOWLEDGEMENTS
A first note of acknowledgement and appreciation must go out to the URSA and the World Bank who made this Phase 1 Regional TB Service Delivery
Framework Mapping Exercise possible through the technical and financial support for which Bay Technologies is sincerely grateful.

Special gratitude is extended the URSA team led by Thulani Mbatha and Cindy Dladla and their Project Coordinators in Lesotho and Swaziland
Mankhala Lerotholi and Victoria Masuku for their tireless support and participation throughout the project which saw the completion of the mapping of
mineworkers and ex-mineworkers exercise a success.

The success of the mapping exercise was possible through the institutions and representatives who supplied data and special thanks is extended to
the Department. of Minerals & Resources, National Tuberculosis Programme (NTP) managers in the Ministries of Health in Lesotho and Swaziland,
NTP and Epidemiology and Surveillance in National Department of Health (NDoH), South Africa, Netcare Hospital Group, Swaziland Central Statistical
Offices, Land Administration Authority, Labour Health and Public Administration Consultancy, Lesotho Millennium Development Agency, Mineworkers
Development Agency, Rand Mutual Assurance, Harmony Gold, Sibanye Gold, Lonmin, Impala Platinum, Exxaro, AngloGold Ashanti, SASOL and
Medical Bureau for Occupational Disease (MBOD).

A special thanks to the whole team for their effective involvement and commendable input in the mapping exercise. The following put together the
mapping exercise report:
Thembekile Gqada Project Management and Leadership
Koos Brandt Principal Consultant and Project Leadership
Shane Athmaram GIS Maps and Data
Walter Grossmann Mining and Health Data Processing, Statistics and Graphs
Jeremey Padayachee GIS Consultant
Konrad Brandt (digitalk) Graphic Design and Communication

A special thanks to digitalk for making extra effort in desiging digital communication elements for the rpesentations, reports and for the effort to design
the layout for this report, and for assembling all the work and artefacts to produce this report, inclusive of the GIS Maps.

Lastly, appreciation is extended to all those, too numerous to mention individually, who contributed in different ways to the success of this project

6
EXECUTIVE SUMMARY
TB in the mines has been a challenge in the
sub-region for over 100 years. Although there
have been efforts at addressing it over the
years, none of these initiatives sufficiently
addressed the problem. A key constraint has
been that none of these efforts considered
the multi-sector and regional dynamics of the
challenge.

The Ministers of Health from Lesotho,
Mozambique, South Africa and Swaziland
region requested support from the World Bank
and other international development agencies
(DFID, Stop TB Partnership, PEPFAR, IOM,
etc.) to coordinate a regional effort in addressing
the TB epidemic in the mining sector and its
impact in the region. An important component
of this regional approach to TB management
is the Service Delivery Framework aimed at
reaching current mineworkers, ex-mineworkers Figure 6 – Detailed map of mapped area
©URSA 2014
and their families. Such a targeted framework
requires information on the location of current
mineworkers and ex-mineworkers, including
information on geographical location of health
facilities in the four countries. Methodology and mapping
University Research South Africa (URSA), the implementing partner on The methodology for this project was to obtain and consolidate
behalf of the World Bank is coordinating the response to TB in the mining information, and to map the information into GIS location based maps.
sector in South Africa, Swaziland, Lesotho and Mozambique. URSA The resulting data and maps will be handed over for further projects to
supports the governments, mining industry and civil society to implement use in planning work.
the TB Service Delivery Framework, which seeks to reduce the risk of
TB through multi-sectoral, multi-disciplinary and multi-country approach. The useful “available” information was identified and requested from
The primary focus of the Service Delivery Framework is to increase data sources. A few of the data source cooperated and provided the
awareness and knowledge of TB miners, ex-miners, and their families, information, some incomplete, and some did not respond. Smaller mining
with a focus on increasing case detection and improving the quality of companies were not approached for data.
care, community-based DOTS, TB epidemiology, quality assurance,
monitoring and evaluation (M&E), operational research, infection control After cleaning up data records, spelling and comparing to other summary
as well as MDR/XDR TB control and prevention. data sources, the data records for the mineworkers, ex-mineworkers and
families were loaded and sued to map density of mineworkers per sub-
Mapping Objective district. This resulted in a lot of detail maps, and the maps are available
in the GIS database for further planning. The health facility data were
cleaned up, remapped the coordinates for Swaziland data, and then
The fundamental challenge is that there is no single reliable source of
also loaded in the spatial database. District maps were generated for the
readily available, consolidated and adequate information currently.
requested areas, and the maps can be further used in planning.
Hence the need had been identified to embark on the Regional TB
Service Framework whose first phase is to map current mineworkers and
ex-mineworkers who are working or have worked in South African mines
Outcomes, Value Add and Benefits
and their families in relation to the geographic location of health facilities.
The mapping provides valuable information on TB in the mining sector
The objective is therefore to generate accurate, detailed, and up-to- within the region and can be used to improve access to services for
date information on the demographic characteristics of current and ex- mineworkers, ex-mineworkers, mining communities, and labour-sending
mineworkers and the availability of TB screening and treatment facilities countries. The information can be further used to; improve targeting of
in order to effectively coordinate and implement the regional TB response. current and ex-mineworkers for TB health services, define baseline data

7
for the development of an evaluation framework for the impact of the smaller percentage of the labour force, with local recruiting and local to the
regional response to TB as well as enhance country-level and regional mine sourcing and accommodation. This also mean that the mineworkers
decision-making around TB interventions. spend a larger percentage of time within the general community close to
the mine, and this could actually lead to a larger spread of TB closer to
This project provided the following deliverables: the mines. This could place a large additional burden on the public health
• Database of mines, mineworkers and health facilities facilities within the communities close to the mines.
• Consolidated spatial database of Lesotho, South Africa and


Swaziland
Density and geographical maps of mines, mineworkers, ex-
Challenges
mineworkers and families, and health facilities
Challenges in data collection and data accuracy, as well as participation
This established the capability to search, identify, select, group and analyse clearly indicated the challenges for any party who wants to deliver
geographical data for planning and managing TB related interventions services within the domain. Stronger cooperation should be obtained if
in mines and mineworker communities with an understanding of the success is sought, but the variety of parties with different objectives is
surrounding communities and health facilities. clearly a stumbling block in setting priorities.

The data recorded in the GIS database is sufficient to do detailed The data availability is clearly an issue, but more so the quality of the
planning maps, identifying mines and farms, and health facilities, and data actually obtained. Clearly there is a lack of data definition and
understanding the density of mine related populations within areas. terminology, but the lack of available data is clearly a contributing factor
Specific planned interventions could request detailed planning maps to obtaining compensation and services within this domain.
for specific areas, together with detailed numbers of related people and
health facilities. The nature of these plans would be innovative requiring Various recommendations were made, but primarily this relates to
GIS investigation and producing reports and maps that can be used to improving data quality, establishing cooperation, documenting a road
support the plans. map and communicating the road map, and establishing electronic
services to enable mineworker data and service records.
The maps provided here (Figure 6 and Figure 7 as examples) can be
studied by people within the domain to develop ideas for services. Once This is clearly a case of without proper data you can never measure
the idea is formulated, further detail GIS analysis can be done using the any progress. The mapping is but a small step in this direction, but the
GIS database created. findings of the data quality is a wake up call if services are planned for
this area.
A significant finding is the trend that the labour sending areas have a

Figure 7 – Sibanye – Family Members per Home Province/Country
©URSA
8
TABLE OF CONTENTS

PREAMBLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
EXECUTIVE SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
TABLE OF CONTENTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
LIST OF TABLES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
ACRONYMS AND ABBREVIATIONS. . . . . . . . . . . . . . . . . . . . . . . . 12
GLOSSARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
METHODOLOGY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
MINEWORKER MAPPING . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
HEALTH FACILITIES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
MINING COMMODITY REVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . 39
SUMMARY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Annexure A – Health Facilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Annexure B – Provincial Maps. . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Annexure C – Challenges and Actions. . . . . . . . . . . . . . . . . . . . . . . 161
Annexure D – Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

9
LIST OF FIGURES
Figure 1 – Mine Labourers Early 1900’s. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Figure 2 – The ‘White’ Death. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Figure 3 – Rock Drilling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Figure 4 – Population of Migrant Workers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Figure 5 – Total Mineworkers (1000’s). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Figure 6 – Detailed map of mapped area. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
Figure 7 – Sibanye – Family Members per Home Province/Country. . . . . . . . . . . . . . . . . . . . . . . . . . 8
Figure 8 – The Spread of TB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Figure 9 – The Explosion of TB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Figure 10 – The TB Incidence Rate Trend. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
Figure 11 – High Level Datamodel of Mineworker Information . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Figure 12 -A simplified explanation of GIS layers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Figure 13 -A sample GIS-mapped product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
Figure 14 – Lesotho, South Africa and Swaziland Administrative Districts . . . . . . . . . . . . . . . . . . . . . . 19
Figure 16 – Workforce Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Figure 15 – The Flow of Employment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Figure 17 – Reported and Mapped Mineworkers per Province . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Figure 18 – South Africa – Mineworkers per Province. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Figure 19 – Percentage of Mineworkers In Relation To The Total Population per Health Sub-District . . . . . . . 22
Figure 20 – South Africa – Mineworkers per District. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
Figure 21 – South Africa – Mineworkers per Mined Farm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
Figure 22 – South Africa – Contractors per Province. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Figure 23 – South Africa – Health Facility Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Figure 24 – Harmony, Sibanye, Exxaro and Lonmin – Mineworkers per Home Province/Country. . . . . . . . . . 28
Figure 25 – Harmony, Sibanye and Lonmin – Mineworkers per Home Province/Country . . . . . . . . . . . . . . 28
Figure 26 – Harmony, Sibanye, Exxaro and Lonmin – Mineworkers per Labour Home District/Country . . . . . . . 29
Figure 27 – Harmony, Sibanye and Lonmin – Mineworkers per Home District/Country . . . . . . . . . . . . . . . 30
Figure 28 – Harmony, Sibanye and Exxaro – Ex-Mineworkers per Home Province/Country . . . . . . . . . . . . . 33
Figure 29 – Harmony, Sibanye and Exxaro – Ex-Mineworkers per Home District/Country . . . . . . . . . . . . . . 33
Figure 30 – Harmony and Sibanye – Ex-Mineworkers per Home District/Country. . . . . . . . . . . . . . . . . . 34
Figure 31 – Harmony and Sibanye – Ex-Mineworkers per Home District/Country. . . . . . . . . . . . . . . . . . 34
Figure 32 – Sibanye – Family Members per Home Province/Country . . . . . . . . . . . . . . . . . . . . . . . . 35
Figure 33 – Sibanye – Family Members per Home District/Country . . . . . . . . . . . . . . . . . . . . . . . . . 36
Figure 34 Health Facility explanation map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Figure 36 – Mine Commodity Category Distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Figure 35 – Commodity/Workforce Distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Figure 37 – Mineworkers per Commodity (in1000’s). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Figure 38 – Mines and Mineworkers Distribution by Mine Size. . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Figure 39 – Gold -The breakdown of numbers related to the mining of Gold.. . . . . . . . . . . . . . . . . . . . 41
Figure 40 – Platinum Group Metals – The breakdown of numbers related to the mining of Platinum Group Metals.. 42
Figure 41 – Coal – The breakdown of numbers related to the mining of Coal.. . . . . . . . . . . . . . . . . . . . 43
Figure 42 – Iron – The breakdown of numbers related to the mining of Iron. . . . . . . . . . . . . . . . . . . . . . 44
Figure 44 – Chromium – The breakdown of numbers related to the mining of Chromium. . . . . . . . . . . . . . 44
Figure 43 – Sand, Clay and Stone – The breakdown of numbers related to the mining of Sand, Clay and Stone . . 45
Figure 45 – Diamond – The breakdown of numbers related to the mining of Diamonds. . . . . . . . . . . . . . . . 46
Figure 46 – Manganese – The breakdown of numbers related to the mining of Manganese. . . . . . . . . . . . . 47
Figure 47 – Randfontein detailed mapped area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

10
LIST OF TABLES
Table 1 – Summary of Demarcation Data Sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Table 2 – Analysis of mining related data received from data sources . . . . . . . . . . . . . . . . . . . . . . . . 17
Table 3 – Mapping ranges for classifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Table 4 – Health Facility Symbol Classifications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Table 5 – Mines and Mineworkers Reported – Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Table 6 – District Mineworker Statistics – DMR Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Table 7 – Summary per province of data provided by Exxaro, Harmony, Lonmin and Sibanye. . . . . . . . . . . 27
Table 8 – District data analysis of data provided by Exxaro, Harmony, Lonmin and Sibanye. . . . . . . . . . . . 31
Table 9 – Analysis of Sibanye Mineworker Marital Status. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Table 10 – Summary per province of Health Facility Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Table 11 – Classification of mining size by mineworker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
Table 12 – Diamond Sales Price per country in USD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

11
ACRONYMS AND ABBREVIATIONS

BEE Black Economic Empowerment (also Broad Based Black Economic Empowerment)
CHAI Clinton Health Access Initiative
DB Database
DFID Department For International Development (U.K.)
DMR Department of Mineral Resources
DoL Department of Labour
DOTS directly observed therapy short-course
ERD Entity-Relationship Diagram
GIS Geospatial Information System
GPS The Global Positioning System
ICT Information and Communication Technology
IOM International Organization for Migration
MDA Mineworkers Development Agency
MDR TB Multidrug-Resistant Tuberculosis
MoH Ministry of Health
NDA Non-Disclosure Agreement
NDoH National Department of Health
NTP National Tuberculosis Program
NUM National Union of Mineworkers
PEPFAR President’s Plan For AIDS Relief
PGM Platinum Group of Metals
POPI Protection of Personal Information Bill
RMA Rand Mutual Assurance
SOA Service Orientated Architecture
TB Tuberculosis
TEBA The Employment Bureau of Africa
TOR Terms of Reference
UCT University of Cape Town
URSA University Research South Africa
USD United States Dollars
WB the World Bank
WHO World Health Organization
XDR-TB Extensively drug-resistant tuberculosis
XML eXtensible Markup Language

12
GLOSSARY
Assumptions Migrant Worker
Accepted estimates of the existence of facts that were resolved using a Migrant means coming from another area to work in the mine location.
decision about the condition to overcome missing information in order to Typically it is assumed that migrant workers coming from outside South
undertake the investigation. African borders to work in South African mines would return after their
employment to the labour sending country.

Contractor
These are mineworkers classified by mining companies as contractors, Mineworker
typically contracting directly to the mining company or via a labour broker. Anybody who has been working in the mining industry, and who has
They can perform the same work as any other mineworker. been involved in mining operations. It excludes people working in supply
companies, or pure processing companies. Mineworkers are sub-classed
into Employees and Contractors.
Demarcation
A set of geographic information that describes a boundary to a land
parcel, where such land parcel can be a country, a province, region, New Industry Ex-Mineworker
district, farm, erf or any other arbitrary piece of land that has a defined These are mineworkers who found employment in any industry outside of
boundary. the primary mining industry. This could include mining related companies,
manufacturers of mining supplies, and mining processing plants,
excluded from primary mining activity. It could also be in any industry
Employee outside of mining, and these mineworkers would not be considered as
These are mineworkers currently in the employment of a mining company. unemployed.

Ex-Mineworkers Retired Ex-Mineworker
These are any persons, previously classified as mineworkers, but not These are mineworkers who have retired from working as a mineworker
currently employed as mineworkers. Ex-mineworkers are classified into at the retirement age prescribed by the mining company, typically around
Unemployed, New Industry, Medically Unfit and Retired. 65. Sometimes, voluntary early retirement programs, retrenchments etc.
change the retirement age. However, these are persons not considered
medically unfit, and also not unemployed with the idea to become
Farm employed again.
A land parcel that were originally allocated as a farming unit, and could
be sub-divided into smaller land parcels, and sub-divisions. The farms
originally held the mining rights to all minerals and metals under the Unemployed Ex-Mineworker
surface of the farm boundaries. Therefore farms were also traditionally This includes any mineworker, not classified as any of the other ex-
tightly connected to mines as mining companies acquired mining and mineworker sub-classifications, and not currently active as a mineworker.
exploration rights to minerals and metals on farms from the farm owners. Unemployed can be re-employed at any stage. Any mineworker
temporarily without an employment contract, from a mining company or
labour broker, is considered unemployed.
Health District
An area used to manage health delivery services. Mostly equal to
municipal boundary settings.

Health Facility
Any entity that provides primary health care services as an organisation,
but excluding medical practitioners

Medically Unfit Ex-Mineworker
These are any mineworkers who became medially unfit due to working
within the mining industry. The person could have become medically unfit
while working, or could have been unemployed and found medically unfit
on an entry examination, or during a routine examination. The person
could have become medically unfit to do his current employment in
another industry, but is only in this classification if that person’s cause
of medical unfitness makes him unfit for employment as a mineworker.

13
INTRODUCTION

Background
The World Bank, at the request of the governments of Lesotho, South
Africa, Swaziland and Mozambique, is convening a regional approach
to addressing the TB challenge. An important component of this
regional approach to TB management is the frequent screening and
treatment of both current mineworkers and ex-mineworkers. Such
a targeted approach requires information on where current and ex-
mineworkers are, and what TB screening and treatment facilities are
available to them. This information is currently inadequate.

Mineworker touch-points and
risk of spreading TB
Figure 8 – The Spread of TB
If TB is left untreated, each person with active TB will infect on average The incredible ‘multiplication’-factor with which TB spreads, and the
number of ‘touch-points’ a mineworker impacts on an almost-daily basis.
between ten and fifteen people every year (WHO). Figures 8 and
©digitalk
9 illustrate accelerated risk of TB spread from mineworker and ex-
mineworker it can have indirect touch points to around twenty thousand
people. It is estimated that one infected mineworker has the potential to
infect family members back home; these could be four children who in
turn are exposing about two hundred and fifty school friends; and the wife
could also possibly infect twenty other community friends. Considering
that there are approximately five hundred thousand current mineworkers
and about two million living ex-mineworkers, the potential touch points at
risk could rapidly escalate to enormous proportions.

Figure 10 show that in reality, this is already happening. The World Bank
statistics on the national TB incidence rate for the major labour sending
countries are still climbing. With a TB rate of 1,000 per 100,000 for South
Africa, this means around 550,000 people have TB in South Africa. The
estimated rate for mineworkers and ex-mineworkers is considered to be
more than 2,500 per 100,000, giving a speculative 62,500 mineworkers
and ex-mineworkers with active TB. If mining was the only active source
of TB, then the current effective multiplier would be around eight times Figure 9 – The Explosion of TB
already. The manner in which TB would explode if uncontrolled.
©digitalk
South Africa, Lesotho and Swaziland are among the top 10 High TB
Burden countries globally as shown in Figure 10. It is worthwhile to
note that our neighbours Namibia, Botswana and Zimbabwe all have a
downward trend suggesting that their handling of the disease is more
effective than our own. Australia, in the 1940’s, implemented stringent
dust standards and have a very low incidence rate of TB.

Purpose
The purpose of the mapping exercise is to provide valuable information
on the location of mineworkers, ex-mineworkers, their families, mines,
mining communities and health facilities in Lesotho, South Africa and
Swaziland. It is further understood that further phases, will establish
proper screening and integrated, harmonized surveillance systems, that
will establish ongoing organization and control over TB screening and
treatment for mineworkers and ex-mineworkers, and their families in the
Figure 10 – The TB Incidence Rate Trend
region. The trends for related countries

14
Objective Assumptions

The main objective is to generate accurate, detailed, and up to date The data obtained from the bulk of the mineworkers did not document
information on the demographic characteristics of current and ex- their home addresses, ie the labour origin or labour sending areas. The
mineworkers and the availability of TB screening and treatment data also did not actually provide an accommodation address for the
facilities in order to effectively coordinate and implement the regional mineworker while working at the mine. The assumption was made that
TB response. the mineworker needs to stay close to the mine, ie within the same district
as the mine.

Overall scope and tasks The family data was actually rather scarce and limited. An assumption
was made that the mineworker home address reflects the family home
address, and that the mineworker population could be used as a proxy for
The Consultancy firm was expected to work closely with the URSA a statistical sample of mineworker families.
and the NTP in Lesotho, South Africa and Swaziland to develop and
implement various innovative approaches to obtain data with the purpose A few mines who did report provide a significant number of records. It was
of accurately determine the numbers of mineworkers, ex-mineworkers
assumed that the data so provided could be used as a statistical sample
and their families and the areas where they are mostly located and the
for the distribution calculations.
geographic location of health facilities in the three countries.

The data required for this mapping is shown in the data model in figure
11. The data are concerned with where the mineworkers, ex-mineworkers
and families are mostly located. So the information required must be able Ethical considerations
to classify the sample data as mineworkers, ex-mineworkers and family,
and also provide information about their whereabouts, such as address Ethical protocol was adhered to through obtaining introduction letters
information. The information is required at an aggregated level, rather and permission from the relevant authorities prior to approaching data
than individual records. sources. We had undertaken to maintain the confidentiality of the
confidential data that has been supplied; and has been used for the
The health facilities need to be plotted at a specific location, enabling
purpose of this mapping exercise.
planning around the available health facilities within an area. Ideally this
would be at the GEO coordinate data level.
It is respected that the data supplied for this mapping belongs to the data
Mapping of this data is completed when the data is related to GIS providers.
reference data that can be loaded into a GIS software package. This was
done, and also a number of maps generated that can be used for various
of the intended planning and study areas.
Overview of the chapters

The rest of the document is constructed using the following chapters:

Methodology
In this chapter the approach to data collection, analysis, and mapping is
recorded and gives an overview of the data collected.

Mineworker Mapping
In this chapter the analysis of the mineworker data found is explained
and mapped. The main results for the mapping are in this chapter

Health Facilities
This chapter explain how the health facility data was classified and
mapped.

Mining Commodity Review
Although not part of the project request, this data was obtained and
analysed as it can help people doing work within this domain. The
employment and operational data is also summarised.

Annexures
Figure 11 – High Level Datamodel of Mineworker Information Various detail annexures of the health maps and density maps produced
@Own Work together with detail data tables.

15
METHODOLOGY

Introduction Table 1 – Summary of Demarcation Data Sources
The mapping exercise performed was based on Data Set South Lesotho Swaziland Comment
existing data collected from data sources, with Africa
some verification and cross checking amongst Digital Elevation Model CGIAR-CSI (Consortium for Spatial Information) Used as backdrop on maps
data sources and existing publications generally
Oceans Basemap ESRI
available.
International Boundary ESRI
Provincial Boundaries MDB MDB – Municipal Demarcation Board
The employment records obtained from
District Boundaries MDB LLAA DoL1 DoL – Department of Labour
Department of Minerals and Resources (DMR)
LLAA – Lesotho Land Administration Agency
were analysed and mapped to health districts
and sub districts, using the mine location as Sub-District Boundaries MDB

proxy for the mineworker living location. Farms & Subdivisions CSG CSG – Chief Surveyor-General
Towns/Cities MDB LLAA DoL1
The mineworkers, ex-mineworkers and Sub Places / Villages MDB LLAA SSA – Statistics South Africa
beneficiaries records obtained from the Roads MDB LLAA Geocommins (http://geocommons.com/overlays/6168)

mining companies were analysed and used to Rivers DoWAF LLAA DoWAF – Dep of Water Affairs and Forestry
extrapolate and plot labour sending areas for the
mineworkers, and hence areas where they and
their families mostly reside.

Health facilities were mapped from
existing data obtained from data A simplified explanation of GIS mapping
sources after summarising the
classifications of the health facilities Geographic Feature: Any item that is plotted
and selecting relevant health facilities. to enhance the content and detail of the map.
These are towns, rivers, roads, etc.
Data sources as shown in Table 2
were selected based on common Farm Layer: The concept of farms as land
knowledge of the industry and from parcels is used in South Africa extensively
information available about the NTP and mining rights were normally registered
program and participants. University against a farm. This project used the farm as
Research South Africa’s contacts an area that contains a mine. Farms can also
and knowledge of the environment be broken down further into subdivisions, or
and industry were “tapped” for parts of a farm.
identification of data sources. Data
sources within Lesotho and Swaziland Demarcation Layers: This shows country,
were also consulted for information. provinces and any other form of breaking
the land space up into smaller administrative

Data management
borders. In this project we used Country,
Provinces, Districts and Health Sub Districts

tools (SA only)

Base Terrain: This is actually a background
Data were collected using primarily image, geo referenced so that GIS data
Excel. The data were loaded and would correctly relate to items on the
structured using Microsoft Access pictures. It is typically used to give maps a
database tools, and reported using more realistic look. Everything shown on
Access SQL Queries and Excel Pivot higher layers are set with a transparency so
Tables for summaries. Some Visual that this background shows.
Basic functionalities were also coded
to restructure some data fields into
usable data.
Figure 12 -A simplified explanation of GIS layers

16
Table 2 – Analysis of mining related data received from data sources

Employee

Employer
End Date

Job Type
Division

District
Source Type Quantity Mapping Application

Gender
Identity

Home
Mine /

Natio-
Type

nality
Start
Date
DMR Mineworkers 498,634    1 Mineworker Distribution
Mines 1 702  2 Mineworkers Stats, Mine Locations
MBOD Claimants 193 700  Statistical Validity
Harmony Miners 34 757       Labour Sending, Family Distribution
Ex-miners 15 926       Ex Miner and Family Distribution
Sibanye Active 47 634       3 Labour Sending, Family Distribution
Terminations 69 825      3 Ex Miner and Family Distribution
Beneficiaries 100 000    3 Family Distribution
Lonmin Active 40 500       Labour Sending, Family Distribution
Impala Active 10 689    4    5
Exxaro Permanent 10 442        6 Labour Sending, Family Distribution
Non Permanent 14 260       6 Labour Sending, Family Distribution
Separations 25 465       6 Ex Miner and Family Distribution
AngloGold History 498 864 7 Not Used
Rand Mutual Employees 388 490 8 Statistical Validity
SASOL Mineworkers 245    9 Validation of Coal Distribution
Notes:
1. The DMR data does not show address information for the mineworkers. This data is for December 2013 and is reported to DMR monthly.
2. The mine list provided also shows Commodities, Mining Operation Type, and the mined farms and mining statistics classified by province and
commodity groups.
3. No addresses at the Mineworker record level. 100,000 beneficiaries records with incomplete addresses extrapolated to cover further family
members
4. The mining locations does not reflect the mining operations and farms where Lonmin operate
5. A very small percentage of the records reflect district data, and then it mostly reflects Rustenburg.
6. Exxaro does not use a labour sending area approach, and recruit locally and mineworkers stay around the mines. No address data that could
confirm this
7. The data reflects how many people were employed by Anglogold over time, but does not show how that is relevant to the current position and
the mapping study.
8. 305 registered mining companies who has insurance with Rand Mutual, and cover for 388,490 employees. It shows that classification around
the mining operation type, such as underground, deep underground are important for health and safety consideration.
9. Just country of origin was listed, and the employees are just the one from neighbouring countries. No district info provided,

The data records were compared to the
demarcation data and missing records were
cleaned up using mapping data available on
various mapping data tools, such as Planet
GIS, Google earth, Mapedia, Wikipedia.

GIS tools used
The data-sets were mapped using ArcGIS
for Desktop Version 10.2.2, a GIS software
considered an industry standard and utilized
by over 350,000 GIS practitioners worldwide.
ArcGIS for Desktop consists of ArcMap
which is the mapping and editing component;
ArcCatalog which is the file directory navigation

Figure 13 -A sample GIS-mapped product
17
component; and the geodatabase component which stores spatial data
in Microsoft Access. The spatial data used in this project was loaded and
Mapping Approach
stored in a personal geodatabase using ArcCatalog. The entire mapping,
editing of spatial data and spatial analysis for this project were done Shading is used to indicate relative density of the population size
using ArcMap. between geographical areas. For the mapping we distinguished between
the ranges as illustrated in Table 3.
Figures 12 and 13 describe the various layers used to make up a map
within the GIS and how they were used within the project. Each layer In each case a shading color is assigned to a range and generally a
is represented by data within a spatial database using geographic data darker color indicates a higher density (range) than a lighter color. In all
objects and shapes. Layers can be combined for display purposes from the density maps, a white color indicates not reported, or 0 mineworkers
the available data in the spatial database. reported within that specific area.
In Figure 13 the result of a typical GIS mapping is shown. It shows the
result obtained of plotting the Base Terrain, demarcations (at Health Sub-
District level, black outlines), the farms (yellow areas) where mines are
mapped to, some roads and rivers for reader orientation, a North Symbol
Health facilities
to orientate the map, and it also shows locations of health facilities, using
the red, green and blue plus symbols. The blue shaded areas represent
The format of supplied health facility data varied by supplying country but
Health Sub Districts, and the difference in the shading intensity shows
all data sets had the following in common:
the relative difference in number of mineworkers found in a Health Sub
• Facility name
District.
• Facility Type
• Latitude
For the results the decision was made to use two styles of maps. A density
• Longitude
map which shows on a scale the area where a higher concentration of
relevant people can be found and done at the sub-district, district and
Where data received was not in the same GEO Coordinate system the
province aggregation level. A second style of map used shows specific
data was recalculated and mapped to the relevant GEO coordinate
features, like health facilities at the coordinates where they can be found.
system used for the demarcation data. This did result in a few Swaziland
Health Facilities not mapping to the same locations normally indicated for
Data analysis them. The data was however not removed from the dataset.

Data from the various data sources (Table 1 and 2) were analysed and For mapping purposes the health facilities were classified into 3 types
mapped as contained herein. The basic map shown in figure 14 forms (Table 4). This were done using either name or classification information
the backdrop for all the mapping. This illustrates the district breakdown already available. Each category were assigned a symbol and it was
for each province and also the region and provincial breakdown for the mapped against a clean but surface map to provide information reference
countries included in the study. for the health facilities within a rural context.

A further analysis of the business indicators per commodity and province
as reported by DMR (1991 through 2012) was also performed to show Table 4 – Health Facility Symbol Classifications
how the commodity groups differ and where the most economic and
labour activity is taking place. The results are documented per commodity Ref Category Symbol
group. 1 Hospital H
2 Clinic
+
3 Health Centre
t
Table 3 – Mapping ranges for classifications
Label Pronvincial Level District Level Sampling Distribution
Small** 1 – 10,000 1 – 5,000 < 1%
Medium 10,000 – 20,000 5,000 – 10,000 up to 3%
Large 20,000 – 50,000 10,000 – 50,000 up to 10%
Very Large > 50,000 > 50,000 > 10%
Notes
* – These ranges was indicated in the sampling as the most relevant distribution for a normal distribution over 4 categories.

** – The mining data supplied by the mining companies were treated as data samples and therefore ranegs are indicated as percentage distribu-
tions.

18
Figure 14 – Lesotho, South Africa and Swaziland Administrative Districts
©URSA

19
MINEWORKER MAPPING
Mineworker Classification
The project focused on mapping mineworkers and ex-mineworkers.
To properly do this a good understanding of the 2 classifications were
required. Figure 15 illustrates how mineworkers flow into the group of
mineworkers as new entrants, in a closed loop system, with just one
outflow. The only outflow from the system is when the mineworker
becomes deceased. The classification is built to address the issue that
a mineworker can still become sick from Silicosis and Tuberculosis long
after leaving the workplace where the condition has been contracted.
Leaving the industry to work in another industry, go farming, etc. does not
remove a mineworker from the grouping. Silicosis can take effect for up
to 20 years after being contracted, and TB for up to 15 years.

Another important consideration is the status of migrant worker or not.
Typically it is assumed that migrant workers coming from outside South
African borders to work in South African mines would return, after their Figure 15 – The Flow of Employment
Once a mineworker, one only leaves the “Ex-Mineworker” classification
employment, to the labour sending country. The employment structure is through death.
designed to promote this, but as with all structure there is no guarantee ©digitalk

are typically labourers who come to the mines to work, leaving their
families behind, and living in mine supplied accommodation, typically
hostels, while working on the South African mines.

However, this only supplies about half the workforce required by the
mines. Various other recruiting mechanisms and labour brokering
approaches are also used, including outsourcing of services. Mines
generally report to DMR all the contractors they employ, but outsourced
services can also included mineworkers that might not be reported to
DMR.

Non labour sending recruitment, and labour brokering, provide the
majority of mineworkers for the operating mines. DMR does not
distinguish between classes of labour, so the mineworker numbers also
include a portion for mine management. Important though is to note
that mineworkers can also live in non mine supplied accommodation,
with their families on a normal working arrangement. This means the
mineworker has a higher level of interaction with the community at large,
Figure 16 – Workforce Movement inclusive of family members. Typically these are not the mineworkers
The physical movement of the workforce between recruitment areas, exposed to the most risky jobs and workplaces as far as silicosis and TB
communities, accommodation and the mines.
is concerned, but it does not exclude them from the exposure either. This
©digitalk
brings about a further level of complexity when planning intervention for
that it is followed to the letter. The mineworkers move from location to the NTP.
location over time as illustrated in Figure 16. As TB is spread by humans
this movement pattern need to be highlighted as well. Whilst there could A total employment register for 2013 was obtained from DMR that,
be every intention for a mineworker to stay at one particular mine for his excluding Mining Agents, list 498,384 mineworkers as at end of December
complete mineworker employment, this is highly unlikely. Mines simply 2013. Informally, via UCT and CHAI, it was reported that TEBA records
stop operation, go into another operational mode, and are taken over by shows about 300,000 current active contracts, and about a total of 1.8
different companies changing the employment considerations. million individuals who delivered around 10 million contracts since 1973.
It is assumed this 1.8 million includes the current 300,000. Therefore
The mineworkers are sourced through a few mechanisms and they the ex-mineworkers from a TEBA perspective translate to roughly 1.5m
move between various locations over time. The migrant mineworkers ex-mineworkers. Generally the industry indicated around 2,000,000 ex-
are recruited from Labour Sending areas within Lesotho, Swaziland, mineworkers. It’s known that TEBA does not supply all the mineworkers,
Mozambique and other countries, and also from within South Africa from and it seems that TEBA could be supplying as much as 60%. Other
areas such as Eastern Cape, Kwazulu Natal, Mpumalanga, etc. These numbers however puts this slightly lower at just below 50%.

20
not being supplied by TEBA. The generally
stipulated number of 2,000,000 ex-mineworkers
seems to be reasonable, but could be more.

Given the TB incidence rate generally stipulated
for SA mineworkers, there could be a very good
chance that a very large portion of these ex-
mineworkers has silicosis, and/or tuberculosis.
Considering that there is no real study being done
on ex-mineworkers and TB, this could be a hidden
problem. Most of the analysis is normally focused
on current mineworkers as access to current
mineworkers is much easier as they are collocated.
Finding and analysing ex-mineworkers is a much
larger undertaking.

Figure 17 – Reported and Mapped Mineworkers per Province
©URSA

Mineworkers Data Overview

Between the 498,634(DMR) number and the 300,000 (TEBA) it can be The employment numbers reported to the DMR are done monthly. The
seen that there is about 200,000 difference. The project numbers actually December 2013 data is the youngest data and was selected as the most
indicate that the 2010 TEBA number was just 228,000. It is not sure recent and useful. This shows the distribution across provinces, as well
whether there was a significant increase towards TEBA employment as the differences between what was reported and how it distributed the
over the last 3 years. data, using the farm locations of the mines as location for the mines and
mineworkers. The reality is that sometimes mines have operations across
Assuming the 300,000 TEBA number it means that over this period, a provincial border, and is distributed across many farms. We followed a
since 1973, 1.5 million mineworkers has become ex-mineworkers. Again, process to distribute the mine’s employees evenly across the farms on
assuming that TEBA holds 60% of the workforce, the total number that which the mine operates. Hence there is a bit of a shift across provincial
borders between how we plotted the data and how it was summarised in
the DMR tables in Table 5 and Figure 17.
Table 5 – Mines and Mineworkers Reported – Summary
Mines Mineworkers There are also a small proportion of mines
Code Province Farms Farms Employ- Con- and employees that could not be mapped
Listed DMR Mapped
Reported Mapped ees tractors due to data errors, mines reporting labour,
EC Eastern Cape 156 99 80 1 612 1 311 1 219 92 but not on the DMR mine and farm list.
FS Free State 76 81 77 36 374 44 080 39 795 4 285
GT Gauteng 171 175 148 66 973 70 699 61 470 9 229 Around 30% of the mineworkers are contract
KZN KwaZulu-Natal 137 92 89 10 870 10 857 6 270 4 587 workers. The distribution of this is different
by commodity and by province, as shown in
LIM Limpopo 161 212 170 74 200 80 943 56 532 24 411
figure 22. Contract workers probably have
MP Mpumalanga 232 322 289 103 638 88 045 42 330 45 715
different living conditions from mineworkers,
NC Northern Cape 258 171 139 36 468 36 736 20 405 16 331 and therefore a slightly different exposure
NW North West 319 242 165 164 950 157 986 115 146 42 840 pattern and risk.
WC Western Cape 192 165 143 3 250 3 264 2 443 821
Unknown 18 299 The health districts used for the mapping
have their own population densities. The
Totals 1 720 1 559 1 300 498 634 493 921 345 610 148 311
density by itself drives infrastructure for
an area, health facilities and also demand
became ex-mineworkers could be 2.5 million. (Assuming the 228,000
for the services. Understanding the relative mineworkers to population
TEBA number this could mean around 3.28 million total that became ex-
density show how much of a relative load the mineworkers is vs. the
mineworkers over this period). The life expectancy or mortality statistics
overall population of that area. It is also interesting to note that in some
for this group of people is not known specifically, making it difficult to
health districts the mining population is more than 20% of the population
estimate the portion of ex-mineworkers still living today.
of that area. This information is reflected in the map in Figure 18.

There is also no clear indication that some of the TEBA mineworkers, no
In this map Figure 19 white areas indicate areas where no mineworkers
longer on contract at TEBA, have not moved to the 200,000 mineworkers
were reported, and hence the mineworkers were not compared to the
population of that area.

21
22
Figure 19 – Percentage of Mineworkers In Relation To The Total Population per Health Sub-District Figure 18 – South Africa – Mineworkers per Province
©URSA ©URSA
23
Figure 20 – South Africa – Mineworkers per District
Figure 21 – South Africa – Mineworkers per Mined Farm
©URSA

Mineworker Distribution sufficiently accurate for translating back to district though.

A further requirement was to also identify the contract workers within the
The DMR data was reported by mine. In order to map the mine data to mineworker group. The mines do report this number to the DMR, but
the GIS the farm names reported to the DMR was used. Since there is it should be noted that the contents of these numbers could be rather
no knowledge of the actual differences in mineworkers per farm, and difficult to interpret. From our analysis it was clear that it contains a mix
since mines report activity against many farms, an equal distribution bag, and depending on the mining operation, the definition could include
of the reported mine number against the reported farms were used. a wider group. In some cases this include fixed term employees, but in
Should a mine therefore report 1000 mineworkers and 10 farms, then others those are reported under employees. In some cases the mine
each farm would be allocated 100 mineworkers. As shown in the graph would include under contract workers all staff of all service providers who
in figure 17 this resulted in a bit of a shift in the number of mineworkers do enter the mine property to deliver services, and sometimes this is
reported per province, and the same would apply to districts. However, stretched as far as head office and administration professional services
to list the farm and not show the numbers broken down per farm, could such as ICT services.
also lead to inaccurate reporting to DMR.
In the attached map in Figure 22 the relative percentage of contractors
From the few mines that reported additional data it was determined per province is shown versus the total mineworkers in that province. From
that there is actually a very unequal distribution against the various this it is clear that Mpumalanga and Kwazulu Natal uses a large portion
locations. of contractors for their staff requirements compared to other provinces.
This trend was also confirmed by looking at industries where the trend
On the map in Figure 21 the farms on which mining activity was amongst the various commodity groups also differ largely.
reported is shown, using a range scale based on the number of
mineworkers allocated to the farm. In certain cases more than one A further requirement was to also map the health facilities in the 3
mine operates on the same farm. The farms labelled A in Figure 21 countries, Lesotho, South Africa and Swaziland, as a mechanism to
shows large land areas. This should not be confused for a large mining identify relative proximity to mineworker and ex-mineworker, and family
operation. This is primarily a mapping constraints in that a mine was proximity.
reported, but the farm identification was not precise enough to select a
small land parcel only. Hence the large land parcel is selected. This is The health facility locations were obtained from the data sources as
listed in the methodology section. The classification is a best attempt

24
Table 6 – District Mineworker Statistics – DMR Data
Province District District Miners Distribution Employees Contractors Males Females
Code
Eastern Cape BUF Buffalo City 136 0.03% 135 1 123 13
DC10 Cacadu 188 0.04% 165 23 166 22
DC12 Amathole 79 0.02% 61 18 72 7
DC13 Chris Hani 143 0.03% 123 20 109 34
DC14 Joe Gqabi 25 0.01% 25 – 22 3
DC15 O.R.Tambo 342 0.07% 342 – 293 49
DC44 Alfred Nzo 21 0.00% 15 6 19 2
NMA Nelson Mandela Bay 377 0.08% 353 24 304 73
Free State DC16 Xhariep 677 0.14% 677 – 593 84
DC18 Lejweleputswa 31 933 6.47% 29 285 2 648 28 558 3 375
DC19 Thabo Mofutsanyane 91 0.02% 68 23 78 13
DC20 Fezile Dabi 11 295 2.29% 9 701 1 594 9 837 1 458
MAN Mangaung 84 0.02% 64 20 75 9
Gauteng DC42 Sedibeng 275 0.06% 204 71 264 11
DC48 West Rand 56 707 11.48% 50 688 6 019 51 788 4 919
EKU Ekurhuleni 2 923 0.59% 2 537 386 2 555 368
JHB City of Johannesburg 7 417 1.50% 5 352 2 065 6 678 739
TSH City of Tshwane 3 377 0.68% 2 689 688 2 945 432
KwaZulu-Natal DC21 Ugu 480 0.10% 346 134 425 55
DC22 Umgungundlovu 181 0.04% 129 52 148 33
DC23 Uthukela 80 0.02% 66 14 61 19
DC24 Umzinyathi 489 0.10% 476 13 421 68
DC25 Amajuba 1 812 0.37% 958 854 1 603 209
DC26 Zululand 1 754 0.36% 970 784 1 508 246
DC27 Umkhanyakude 1 414 0.29% 496 918 1 207 207
DC28 Uthungulu 4 083 0.83% 2 383 1 700 3 427 656
DC29 iLembe 139 0.03% 123 16 132 7
DC43 Harry Gwala 108 0.02% 108 – 99 9
ETH eThekwini 317 0.06% 215 102 228 89
Limpopo DC33 Mopani 8 420 1.70% 4 944 3 476 7 567 853
DC34 Vhembe 4 182 0.85% 2 170 2 012 3 603 579
DC35 Capricorn 1 062 0.22% 580 482 926 136
DC36 Waterberg 33 481 6.78% 26 972 6 509 30 242 3 239
DC47 Sekhukhune 33 798 6.84% 21 866 11 932 29 545 4 253
Mpumalanga DC30 Gert Sibande 28 023 5.67% 14 347 13 676 25 559 2 464
DC31 Nkangala 54 438 11.02% 24 489 29 949 48 811 5 627
DC32 Ehlanzeni 5 584 1.13% 3 494 2 090 5 051 533
North West DC37 Bojanala 144 434 29.24% 103 195 41 239 131 189 13 245
DC38 Ngaka Modiri Molema 985 0.20% 665 320 937 48
DC39 Dr Ruth Segomotsi Mompati 328 0.07% 324 4 322 6
DC40 Dr Kenneth Kaunda 12 239 2.48% 10 962 1 277 10 835 1 404
Northern Cape DC45 John Taolo Gaetsewe 22 013 4.46% 11 399 10 614 19 736 2 277
DC6 Namakwa 2 730 0.55% 2 086 644 2 315 415
DC7 Pixley ka Seme 2 337 0.47% 1 294 1 043 2 082 255
DC8 Z F Mgcawu 6 550 1.33% 3 521 3 029 5 531 1 019
DC9 Frances Baard 3 106 0.63% 2 105 1 001 2 759 347
Western Cape CPT City of Cape Town 490 0.10% 450 40 429 61
DC1 West Coast 1 934 0.39% 1 198 736 1 757 177
DC2 Cape Winelands 208 0.04% 189 19 194 14
DC3 Overberg 159 0.03% 159 – 142 17
DC4 Eden 473 0.10% 447 26 424 49
Totals 493 921 100% 345 610 148 311 443 694 50 227

25
26
Figure 23 – South Africa – Health Facility Distribution Figure 22 – South Africa – Contractors per Province
©URSA ©URSA
and no classification of size or services were done. The classifications significant number of mineworkers and ex-mineworkers, and contrary to
were done using the names and existing reporting, and various facilities expectation, they also reported that the bulk of their mineworkers are
were excluded due to their specialist nature. For more detail on the health sourced from “local”, meaning close to the mine location, and they even
facilities refer to chapter on health facilities. reported that if the mineworker transfers to another mine, then he also
relocates. This position was basically confirmed by SASOL coal mining
The map shown in Figure 23 is a high level map showing density of which indicated a similar pattern. Unfortunately we did not obtain data
health facilities to mine densities, per province. from Anglo Coal, which could have a different approach to sourcing mine
workers due to their long standing relationship with TEBA.

Mineworkers Family Distribution Since it was suspected that this pattern does not always hold, a mapping
Figure 25 where Exxaro data has been excluded was also performed. In
this case the expected pattern of Eastern Cape, Free State, North West
Data was obtained from a few of the bigger mines in a usable format, and Lesotho as major feeding areas do appear.
with varying degrees of accuracy, completeness and relevance. The
basics of this has already been illustrated in the methodology chapter. Note also that in these maps the neighbouring countries were not treated
This data was used to map two elements not possible to map with the at a district level. They were treated as equivalent to provinces vs SA
DMR data. The first being the Ex-Mineworkers and the second the Family provinces.
whereabouts. This data is summarised in Tables 7 and 8.
The concept of a labour sending area was also further explored. The
Since only one company actually reported beneficiary details, similar to expectation was that especially for Gold mining the “bulk” of the
family details, the mineworker data reported was used as a statistical mineworkers would be coming from traditional labour sending areas.
sample for the families. Thus the Mineworker data should be interpreted In fact, especially using Harmony’s data, it was determined that a huge
as where the mineworker would be staying when not working, and hence percentage of mineworkers have probably relocated (or reported home
this should be close to where the family of that mineworker would be. addresses as such) to communities close to the mining locations. This
can be understood especially from a South African context, as the labour
From Figure 24 it can be seen that when using the data provided by can move freely between the labour sending and mining areas. This is
the mines, the provinces that supply the bulk of the mineworkers are different for labour coming from neighbouring countries where border
Limpopo, Eastern Cape, Mpumalanga and Free State. On this a note control plays a role in restricting relocation of the families.
should be taken that Exxaro, a major coal mine operation, reported a

Totals Exxaro Harmony Lonmin Sibanye
PROVINCE Emp Con Ex Min Emp Con Ex Min Emp Con Ex Min Emp Con Emp Con Ex Min Ben
Botswana 462 2 112 47 37 31 2 384 75 1721

Eastern Cape 19785 2404 4086 6423 796 3361 8704 1533 4658 75 725 21244

Free State 13485 3258 5047 10059 2688 4760 1763 472 1663 98 287 5817

Gauteng 6767 1887 2622 532 2 338 2638 796 1812 1579 910 2018 179 472 5736

KwaZulu- 3763 374 766 39 18 979 182 456 793 167 1952 25 292 9892
Natal
Lesotho 11183 826 3902 5511 210 3465 1621 596 4051 20 437 18294

Limpopo 7166 7082 16240 5132 6478 15885 387 131 254 1202 465 445 8 101 1933

Mozambique 8047 1908 1123 1365 288 821 2671 1556 4011 64 302 1710

Mpumalanga 6750 7305 9425 5278 6825 9073 407 145 297 628 322 437 13 55 20245

North West 9036 6676 445 664 599 346 7954 6022 418 55 99 76

Northern 375 52 25 50 14 21 298 37 27 1 4 1359
Cape
Swaziland 2034 118 368 327 18 230 614 95 1093 5 138 7398

Western 27 20 18 7 13 12 9 7 11 6 27
Cape
Zimbabwe 15 102 1 4 13 98 1 9
Totals 88895 32014 44179 10981 13305 25314 28865 5884 15872 27880 12282 21169 543 2993 95461
Table 7 – Summary per province of data provided by Exxaro, Harmony, Lonmin and Sibanye

27
28
Figure 25 – Harmony, Sibanye and Lonmin – Mineworkers per Home Province/Country Figure 24 – Harmony, Sibanye, Exxaro and Lonmin – Mineworkers per Home Province/Country
©URSA ©URSA
29
Figure 26 – Harmony, Sibanye, Exxaro and Lonmin – Mineworkers per Labour Home District/Country
©URSA
30
Figure 27 – Harmony, Sibanye and Lonmin – Mineworkers per Home District/Country
©URSA
Table 8 – District data analysis of data provided by Exxaro, Harmony, Lonmin and Sibanye

Area District District Name Totals Miners per Mining Company Beneficiaries

Code

Miners Dist Exxaro Harmony Lonmin Sibanye Sibanye

BO BA01 Botswana 576 0.35% 84 33 459 1721

Eastern Cape BUF Buffalo City 739 0.45% 306 261 172 452

DC10 Cacadu 36 0.02% 11 14 11 44

DC12 Amathole 4789 2.90% 2067 1852 870 3129

DC13 Chris Hani 3535 2.14% 1911 882 742 2674

DC14 Joe Gqabi 2197 1.33% 639 1165 393 1456

DC15 O.R.Tambo 10450 6.33% 3642 4919 1889 7843

DC44 Alfred Nzo 4488 2.72% 1989 1129 1370 5629

NMA Nelson Mandela Bay 41 0.02% 15 15 11 17

Free State DC16 Xhariep 325 0.20% 181 93 51 155

DC18 Lejweleputswa 16736 10.14% 14878 459 1399 3847

DC19 Thabo Mofutsanyane 2220 1.34% 1328 538 354 1030

DC20 Fezile Dabi 355 0.22% 197 121 37 101

MAN Mangaung 2154 1.30% 923 1024 207 684

Gauteng DC42 Sedibeng 498 0.30% 173 199 126 318

DC48 West Rand 5187 3.14% 2789 321 2077 4425

EKU Ekurhuleni 868 0.53% 196 563 109 270

JHB City of Johannesburg 2774 1.68% 1935 561 278 554

TSH City of Tshwane 1949 1.18% 872 153 845 79 169

KwaZulu-Natal DC21 Ugu 439 0.27% 183 197 59 239

DC22 Umgungundlovu 113 0.07% 35 54 24 77

DC23 Uthukela 58 0.04% 34 17 7 23

DC24 Umzinyathi 165 0.10% 37 17 111 514

DC25 Amajuba 160 0.10% 57 32 34 37 106

DC26 Zululand 1157 0.70% 315 150 692 3212

DC27 Umkhanyakude 1240 0.75% 401 123 716 3325

DC28 Uthungulu 459 0.28% 196 55 208 903

DC29 iLembe 16 0.01% 5 2 9 19

DC43 Harry Gwala 899 0.54% 318 210 371 1405

ETH eThekwini 197 0.12% 61 101 35 69

Lesotho BB01 Butha Buthe 1088 0.66% 548 142 398 1709

BE01 Berea 1809 1.10% 1054 227 528 2184

LE01 Leribe 3223 1.95% 1719 556 948 4103

MF01 Mafeteng 2752 1.67% 1701 325 726 2671

MH01 Mohale’s Hoek 1782 1.08% 1233 549 2093

MO01 Mokhotlong 346 0.21% 194 49 103 496

MS01 Maseru 3317 2.01% 1893 719 705 2692

QN01 Qacha’s Nek 417 0.25% 218 81 118 559

QT01 Quthing 819 0.50% 460 58 301 1166

TT01 Thaba Tseka 358 0.22% 166 60 132 621

Limpopo DC33 Mopani 5410 3.28% 4394 293 555 168 679

DC34 Vhembe 3057 1.85% 2560 151 154 192 706

DC35 Capricorn 934 0.57% 246 562 126 369

DC36 Waterberg 20760 12.58% 20541 30 147 42 121

DC47 Sekhukhune 327 0.20% 52 249 26 58

Mpuma langa DC30 Gert Sibande 359 0.22% 235 52 72 204

DC31 Nkangala 21305 12.91% 21176 22 85 22 34

DC32 Ehlanzeni 1816 1.10% 592 813 411 1472

MOZ MOZ1 Mozambique 11078 6.71% 2474 4227 4377 20245

31
Area District District Name Totals Miners per Mining Company Beneficiaries

Code Miners Dist Exxaro Harmony Lonmin Sibanye Sibanye

North West DC37 Bojanala 11779 7.13% 203 11512 64 107

DC38 Ngaka Modiri Molema 1688 1.02% 564 965 159 399

DC39 Dr Ruth Segomotsi Mompati 1156 0.70% 225 817 114 327

DC40 Dr Kenneth Kaunda 1534 0.93% 617 682 235 526

Northern Cape DC45 John Taolo Gaetsewe 363 0.22% 53 290 20 49

DC6 Namakwa 0.00%

DC7 Pixley ka Seme 6 0.00% 6

DC8 Z F Mgcawu 5 0.00% 2 3

DC9 Frances Baard 78 0.05% 30 36 12 27

Swaziland SW01 HHOHHO 403 0.24% 145 40 218 1407

SW02 MANZINI 540 0.33% 212 36 292 1910

SW03 SHISELWENI 547 0.33% 191 15 341 2222

SW04 LUBOMBO 1030 0.62% 27 618 385 1859

Western Cape CPT City of Cape Town 29 0.02% 15 6 8 11

DC2 Cape Winelands 21 0.01% 14 4 3 6

DC3 Overberg 1 0.00% 1

DC4 Eden 12 0.01% 3 3 6 10

DC5 Central Karoo 2 0.00% 2

ZIM ZI01 Zimbabwe 117 0.07% 5 111 1 9

Totals 165088 100% 49600 50621 40162 24705 95461

Ex-Mineworkers Distribution Family Distribution
Ex-mineworker data is not maintained in most cases, especially not The dependents, beneficiary and family data was not readily available.
beyond the termination or separations from the mining company. Only Sibanye Gold provided this, and they provided a set of precisely
Hence the data so obtained cannot be considered as a database of 100,000 records. The 100,000 records relates to around 24,700 mostly
ex-mineworkers. However, as a proxy for ex-mineworker statistics this mineworkers. The beneficiaries also did not all have addresses and
is quite a substantial dataset. Again there are ex-mineworker records the address data so obtained were cleaned up and eventually 95,461
reported by Exxaro and mapping was done using the set with and beneficiary records were connected to districts, and sub-districts as may
without Exxaro data. be the case. This beneficiary address data was also used to populate
some of the Sibanye Mineworker and Ex-Mineworker records, as they all
Also take note that for ex-mineworkers Lonmin did not provide any data. were supplied without addresses.
The maps excluding Exxaro is thus focused on Harmony and Sibanye,
which are major gold mining operations. While most of these records relate to wives and children, there was also
a significant set relating to other members such as parents, in-laws,
Using the complete set Figures 28 and 29, Limpopo, Mpumalanga girlfriends, brothers, sisters, etc. Generally this raises the question that if
and Free State are prominent as the larger labour supply areas. With the TB benefits are extended to the family, who would be included in the
Exxaro excluded, Figure 30 and 31, this changes significantly to Free family that would qualify for screening and benefits.
State, Eastern Cape and Lesotho. From a focus area perspective, the
TB prevalence is considered higher in the Gold and PGM sector. Thus
excluding Exxaro which is primarily a coal miner also make sense in At the provincial level, the major areas sending labour are Eastern Cape,
that respect. Mozambique and Lesotho, Figures 32 and 33.

Various maps are thus produced with and without Exxaro data to Considering that the Sibanye Employee, Contractor and Ex-Mineworker
understand the effect. The idea that all the mineworkers are recruited records indicated 60,935 married people, the 24,700 matched families
from around the mines, and stays in own accommodation, also probably to mineworkers is rather low and incomplete. However, it is a significant
mean they are not really part of the target group. In this sense it could sample and clearly indicates a specific spread of people. (Table 9)
be that amongst the relevant mines the TB incidence rate is significantly
higher than normally assumed. The families were also mapped to sub-district level where possible and
then summarized to district and provincial level. The statistics were listed
with the other district statistics and a map is shown in Figures 32 and 33.

32
33
Figure 29 – Harmony, Sibanye and Exxaro – Ex-Mineworkers per Home District/Country Figure 28 – Harmony, Sibanye and Exxaro – Ex-Mineworkers per Home Province/Country
©URSA ©URSA
34
This one looks wrong
Figure 31 – Harmony and Sibanye – Ex-Mineworkers per Home District/Country Figure 30 – Harmony and Sibanye – Ex-Mineworkers per Home District/Country
©URSA ©URSA
Figure 32 – Sibanye – Family Members per Home Province/Country
©URSA

Table 9 – Analysis of Sibanye Mineworker Marital Status
Status Employee ExMineworker Contractor Total
Divorced 442 845 70 1357
Married 21933 36526 2476 60935
Single 16858 28686 5168 50712
Separated 41 135 5 181
Traditional Marriage 58 10 3 71
Unknown 53 635 25 713
Widowed 408 619 46 1073
Totals 39793 67456 7793

35
36
Figure 33 – Sibanye – Family Members per Home District/Country
©URSA
HEALTH FACILITIES
Overview Table 10 – Summary per province of Health Facility Statistics

Country Area Total Clinics Health Hospitals Laboratories
The data for health facilities were obtained from Centres
the data sources as listed in the methodology
section. The data received were accepted as
+ t H
accurate as long as the data had properly formed Lesotho Berea 24 2 18 4
GEO Coordinates. No attempt other than a cursory Lesotho Butha Buthe 16 1 13 2
inspection of the data was made to compare the Lesotho Leribe 34 5 26 3
data either via sampling or otherwise, to the real Lesotho Mafeteng 21 2 18 1
world physical entities.
Lesotho Maseru 70 24 38 8
Lesotho Mohale’s Hoek 18 17 1
We assumed that the data supplied by the
Departments were accurate and complete Lesotho Mokhotlong 12 1 10 1
and represent the health facilities that would Lesotho Qacha’s Nek 13 1 10 2
be considered in further planning of the NTP Lesotho Quthing 10 1 8 1
programs. Lesotho Thaba Tseka 18 16 2
South Africa Eastern Cape 806 659 100 47
At this stage no data on the actual capacity
South Africa Free state 272 221 43 8
or capability of the health facilities to do TB
South Africa Gauteng 424 281 2 115 26
screening, examination or treatment was
performed or requested from the data sources. South Africa Kwazulu Natal 747 552 2 141 52
South Africa Limpopo 469 386 1 46 36
The medical facilities list were provided by the South Africa Mpumalanga 288 229 39 20
respective departments of health of Lesotho, South Africa Northern Cape 152 110 2 35 5
Swaziland and South Africa. Not all provided
South Africa North West 313 259 2 35 17
facilities records had geographic coordinates and
South Africa Western Cape 350 244 7 84 15
subsequently not all could be mapped. This is
summarized in the table 10. Swaziland HHOHHO 67 62 2 2 1
Swaziland LUBOMBO 99 92 2 4 1
Lesotho and Swaziland included regional Swaziland MANZINI 34 31 2 1
information in their respective data sets. South Swaziland SHISELWENI 47 44 1 2
African provincial data was derived from provided 4304 3207 197 672 228
coordinates. All subsequent mapping in this
section will use facilities with known coordinates.
names to go with the numbered labels. This is shown here for some of
Only South African data included information about private or public the health facilities around Maseru City,
health facilities. Some South African facility names are semicolon
delimited, meaning that a coordinate pair may contain more than one At this level of detail it is difficult to distinguish health facilities that are
facility names. These were counted as a single facility. within the same town or city, as the scale is too small and the facility
coordinates essentially overlay on top of each other. The GIS database
Facility types for Lesotho were supplied, but for South Africa and can be consulted for more specific details regarding the specific location
Swaziland these were derived from the facility names. To enable of a health facility.
standardized mapping amongst the respective countries, facilities were
classified as Hospitals, clinics, health centers (Lesotho only), laboratory The summary statistics indicate that the health facilities that were
(South Africa only), and “other”. For countries where the type was derived considered for mapping included statistics for laboratories. Other
from the name, the above order was used to establish the type, with health facilities were excluded if they were not related to the intended
precedence for types earlier in the list. For instance if a record contains TB interventions. The laboratories were not plotted on the sample
both the word clinic and hospital, it would be classified as a hospital. The maps included here, since these maps were prepared for printing and
following table summarizes facility types by country and province. distribution to mineworkers, and the laboratories were not considered for
that printing. The data is however in the database and can be plotted
The symbology used in the mapping in figure 34 is shown in the legend easily.
on the maps. On the maps rivers and roads were included for better
references of location of health facilities. The sample map here shows a The villages and sub-places (suburbs, townships, settlements) are all
zoomed state to illustrate elements of these group of maps. The ARCGIS included on the spatial database, but again were not plotted as at this
labelling placement strategy used resolves placing the labels and when level of detail it would overcrowd the map and obscure the information
the density of the labels are to tight, it will generate numbers and a list of of interest.

37
Figure 34 Health Facility explanation map
Own Work

38
MINING COMMODITY REVIEW
South Africa’s mineral wealth has been built on the country’s enormous more complex, as it has an element of local vs. international, but also
resources and it accounts for 96 percent of the known global reserves of an element of primary vs. processed. As our project were concerned
the Platinum Group Metals, 85 percent of Chromium, 26 of Vanadium and with mineworkers, we restricted it to a simplistic primary production, total
12 percent of Gold reserves. The mining industry is one of the key sectors sales and international sales. We also considered that the TB problem is
with potential for substantial contribution to economic growth, job creation probably more prevalent in underground mining, therefore we included
and transformation. some analysis of the underground portion of the mining.
Ever since the late 1800’s, mining activity in South Africa has
been a major player in terms of revenue and employment. The
mining activity can be measured in production and sales, both
local usage and international sales, and also reserves that
could still be mined. For our purposes we selected to illustrate
3 elements of significance. Firstly we looked at the employment
data because the focus of the project is an understanding of
the distribution of mineworkers, ex-mineworkers and their
families. We therefore collected data on the distribution as at
end of 2013, and we also included some data on the trend of
the mineworker population. There have been some significant
shifts in these employment numbers over time, and it requires
an understanding of the employment differences in the various
mining commodities to appreciate the trends in the employment
numbers. We only recorded employment trends as reflected in
DMR data from 1991 till 2013.

There is a general perception that Gold and Diamond mining
is the major player in South Africa. Whilst these commodities
founded the industry and were the cornerstone for many years, Figure 35 – Commodity/Workforce Distribution
at least 3 other commodities are challenging those positions in The distribution of the mining workforce over the different commodities
recent years. It really depends on how one looks at size. ©digitalk
The DMR reported 498,000 mineworkers as at the end of
December 2013. According to primary mining activity of the mining This commodity analysis is not to create a perception of importance,
operations reporting the data, the distribution is as shown here in. It but could be relevant for further planning purposes. Clearly there are
indicates that when considering employment numbers, the Platinum differences between the commodities, and maybe addressing the TB
Group of Metals are the largest employment group, employing nearly burden requires adjusted approaches for the different commodities.
40% of all mineworkers. Together with Gold and Coal this makes for
80% of all mineworkers employed (Figure 35). However, there is another The DMR data shows 1702 registered mines. These mines are
perspective also. We already mentioned world reserve position earlier, distributed by commodity and category, and the graph in 36 illustrates
but we should also consider production and sales. Sales are rather the percentage of mines in each category of mining as reported by DMR
and were listed in each commodity group. It is clear that
Gold is mostly underground mining whilst Iron is totally
open cast mining.

When mines report to DMR they show a list of categories
they operate within. Since the mines do not report only
one category, a type was assigned for classification.
Underground mining is probably the worst for causing
silicosis and spreading TB, and therefore any mine
reporting UNDERGROUND as a category, was
considered as UNDERGROUND. Secondly, OPENCAST
mines are probably also very dusty, and hence were
considered worst than SURFACE as a category.
Therefore mines were classified as UNDERGROUND
overrides all other types, OPENCAST overrides the
remainder, and SURFACE overrides AT SEA.

It should be noted that these categories are applicable
to the mines themselves, and unfortunately do not give
Figure 36 – Mine Commodity Category Distribution
©URSA
39
Table 11 – Classification of mining size by mineworker

Range Less than 100 101 to 1000 1001 to 5000 5,001 to 10,000 > 10,000*
Label Very Small Small Medium Large Very Large
Mineworkers 12,295 54,960 171,023 92,775 151,720
Mineworker % 2.5% # 11.4% 35.4% 19.2% 31.4%
Mines 696 161 81 13 8
Mines % ** 72.6% 16.8% 8.4% 1.4% 0.8%
Notes
# – The mineworkers % is based on 482,773 mineworkers, from mines that could be mapped
* – The graphs shows these in 2 groups of between 10,000 and 20,000 and above 20,000
** – This percentage is based on just the mines reporting 1 or more mineworkers

a breakdown of where the mineworkers
operate. It is likely that TB intervention
planning would be influenced by mining
category. The underground numbers
Commodity Mines Reported
should not be considered as describing
GOLD 54 42
absolute numbers of mineworkers that
PGM 54 52
do underground mining.
COAL 139

CHROME 41
Figure 36 shows the mining category
MANGANESE 27
distribution within a commodity.
OTHER 107
This clearly illustrates that within the
gold commodity, as many as 97% DIAMONDS 389

of mineworkers work in mines that SAND, CLAY, STONE 878

are listing UNDERGROUND as the IRON 13

category, and 100% of mineworkers Total Mines 1702

in the iron commodity work in the
OPENCAST category.

It is important to note that DMR Figure 37 – Mineworkers per Commodity (in1000’s)
reporting does not require actually ©URSA
stipulating who works underground and
who does not, etc. The best position
that can be glanced from this data
is that gold mining mostly involves
underground mining, vs. iron that does
not. If relevant to NTP intervention
planning this should be considered with
this in mind.

Note: The DMR data reports some
mines with more than one category,
and the mineworkers reported therefore
cannot be differentiated. This also
leads to numbers that at first glance are
doubtful. To get a better understanding
of this, a separate table with mines and
their classification has been included,
especially where the mine does not
conform to conventional wisdom in
terms of primary type.
Figure 38 – Mines and Mineworkers Distribution by Mine Size
©URSA

In Figures 37 and 38 mineworkers are distributed per commodity them they however only account for just over 10% of the number of
where the commodity is the primary commodity of the mine where mines reporting employees. This by no means indicates that the other
they are employed. It can be clearly seen that the 3 commodities of commodities are unimportant or could be ignored. The 20% of employees
gold, platinum and coal employs the bulk of the mineworkers. Between in the other mining commodities are just more dispersed, have differing

40
conditions, etc. and would therefore probably need more focused interventions.

In Figure 38 an analysis is done to show how the mine size and number of mines per size correlates. The mines shown here reflects that a very large
portion of the registered mines, 761 out of 1720, reported 0 mineworkers or did not report at all. This means that less than 65% (941) of the registered
mines employ all the mineworkers accounted for.
For further analysis we considered the following sizing spread, including only mines that actually reported one or more mineworkers, and using the
number of mineworkers as the sizing factor.

Considering just the mines with mineworkers, it should further be noted that a further 696 (72.6%) mines can be considered as very small in terms of
mineworkers, employing less than 100 mineworkers each, for a total of 12,295 (2.5%) mineworkers. The number of small mines makes for a further
16.8% of the mines and bringing the total of mineworkers in these first 2 categories to nearly 90% of mines, but with just 14% of mineworkers. The
medium to very large mines therefore account for just 10% of the mines, but for 86% of the mineworkers.

Gold
South Africa’s gold mining industry has been the mainstay for many years. Over the last 20 years however, due to various factors, the production and
employment in this sector has been reduced drastically, from around 450,000 out of a total of 700,000 mineworkers (around 64% in 1995) to as low
as 124,000 in 2013 or just 26% of the total mineworkers.

Figure 39 – Gold -The breakdown of numbers related to the mining of Gold.
©digitalk

By the end of 2013 there were only 41 mines reporting employees to DMR. A year before it was around 53 mines in 2012. South Africa still holds
around 12% of the world’s reserves of gold. Despite this decline, the industry is still quite big and is earning substantial income for South Africa. The
restructuring and decline does however create issue for the NTP and should be recognised as a potential issue of responsibility. A recent outcry was
heard when talks of Anglogold Ashanti doing a sales of its local mines became known.

The underground numbers listed shows that most of the mining operation is done in underground mines.

The mini mine belt maps shown in the infographic indicates where mining activity for this commodity is mostly taking place. In each case a shading
color is assigned to a range and generally a darker color indicates a higher density (range) than a lighter color. In all the density maps, a white color
indicates not reported, or 0 mineworkers reported within that specific area.

41
Platinum Group Metals
The platinum group of metals consists of Platinum, Palladium and Rhodium. This set of metals is the new mainstay within the South African Mining
Industry. The bulk of the demand for these materials comes from the vehicle industry for auto catalysts. Car exhaust standards drive the demand. A
secondary market is from jewellery.

Figure 40 – Platinum Group Metals – The breakdown of numbers related to the mining of Platinum Group Metals.
©digitalk

This commodity group employs the most mineworkers in South Africa, and the mineworkers are mostly underground workers. The peak of employment
was reached around 2008, and has maintained that level of employment for the last 6 years. Production is slightly down, mostly due to the major
labour unrest of the last few years in this sector.

The 52 mines that were mapped in this commodity group shows that nearly all activity takes place in the Northern part of South Africa. The mines in
this industry typically employ a large workforce per mine.

This sector employs around 137,000 employees via normal recruitment practices, and a further 54,000 as contractors. Female employees make for
around 10% of the total employees.

South Africa’s reserves in these commodities far outstrip all other countries. The demand for the commodity drives the higher prices, also due to unrest
and uncertainty in supply, mostly connected to labour unrest.

42
Coal
South Africa has a strong coal mining industry with demand driven from local energy demands and international sales. South Africa’s energy generation
is mostly coal based, and the new power stations coming on board over the next couple of years will drive this demand even higher.

Figure 41 – Coal – The breakdown of numbers related to the mining of Coal.
©digitalk

As a producer, the coal industry pips PGM and Gold. However, export sales are lower since the local demand takes nearly 50% of the production.

The coal industry is somewhat more mechanised and opencast but still has a large underground contingent. Distribution of the coal production is
mostly in the eastern and northern regions of South Africa.

Coal, together with Gold and PGM, employs around 80% of the total mineworker population. The trend for employment and production is upward
since the early 2000’s.

43
Figure 42 – Iron – The breakdown of numbers related to the mining of Iron.
©digitalk

Iron
Iron is part of the ferrous group of metals, which also include Chrome, Manganese and Vanadium. Altogether South Africa is a major player in these
metals. Steel production drives the use of these metals, and is believed to continue driving the demand, with China a major consumer of this. As an
Iron reserve, South Africa is very small, holding less than 1%, putting it 12th. However, as an exporter, South Africa ranks 3rd in the world (7th as a
producer). Generally the mining is not underground, and the mines are not very large in terms of mineworkers employed, with an average of around
1600 mineworkers per mine. These mines are characterised by large mechanical equipment, and opencast mining. Interesting is the nearly equal split
between employees and contractors active.

Chromium
Chromium, Chrome Ore, or Chromite are used primarily in ferrochrome production, making iron products into steel, stainless steel, etc. South Africa
holds 74% of the 12 billion tons of reserve. Output has risen over the last few years. The steady rise in this mining activity is also reflected in the
mineworkers employed, and the more than 70% of the activity is at underground mines.

Figure 44 – Chromium – The breakdown of numbers related to the mining of Chromium
©digitalk

44
Sand, Clay & Stone
Sand Clay and Stone is a very large group of commodities and mines (516 reported mineworkers) that on average employ very few mineworkers per
mine. The distribution of the mining operations is very dispersed across the country. This area is very mechanised, using machinery that causes a lot
of dust. Hence the silicosis threat is rather large, albeit that the mineworkers in each are rather small.

Every operation that wants to take sand, stone, clay from the earth for commercial purposes, need to register their operation with DMR as a mine.
Stone such as gravel, of any size, and building materials are generally classified under this group.

Figure 43 – Sand, Clay and Stone – The breakdown of numbers related to the mining of Sand, Clay and Stone
©digitalk

45
Diamond

Figure 45 – Diamond – The breakdown of numbers related to the mining of Diamonds.
©digitalk

Diamond is arguably the first commodity that started the rush of mineworkers to South Africa. Compared to Gold, PGM and Coal, the Diamond
industry employs very few mineworkers. It should also be noted that the DMR lists a very large number of active and operating diamond mines, but
very few of these have a significant number of mineworkers. Diamonds are mined underground, on the surface, opencast and even at sea.
The diamond pricing Table 12 paints a very interesting picture of diamond quality. Lesotho diamonds are rare, the production small, and fetch very
high prices compared to the rest. South Africa weighs in at second place with quality in this list, with Congo DRC coming in with a very low price per
carat compared to the rest. Lesotho diamond fetch more than 100 times the Congo diamond price.

Country 2009 2010 2012
Botswana $81.13 $117,55 $145,32
Congo $10.60 $8,61 $8,51
Lesotho $1 455.00 $1,816.63 $629,43
Russia $67.24 $68,25 $83,03
South Africa $144.23 $202,92 $142,64

Table 12 – Diamond Sales Price per country in USD

46
Manganese
This commodity seems to attract and use a higher than average percentage of contract works, being the only commodity group where the contractors
exceed the employed mineworkers. South Africa accounts for 75% of the world reserves, and the production is growing at around 8.7 percent
annually. Some new mines have been commissioned in this area, which will further accelerate the production, and simultaneously the employment.
Generally not large employers per mine, it is underground and as this sector grows, it can be expected that the employment will also grow.

Most of the mining is located in the Northern Cape and some very small numbers elsewhere.

Figure 46 – Manganese – The breakdown of numbers related to the mining of Manganese.
©digitalk

47
SUMMARY
Capabilities Established •
to mines
Health facilities loaded into the spatial database
• A tool to further analyse the provided data
• Capability to draw detail section of the maps, farms, mineworker
The project established capability through data in a spatial database that
densities, etc.
provides the ability to
• Identify and locate mines and the farms they operate on
• Analysis of mineworker densities and concentrations as a source Analysis of Mineworker Data
of TB
• Analysis of ex-mineworkers and families as density and
concentrations as a spread area for TB The actively reported mineworkers are reported to DMR as 498,634
• Identification of health facility locations and proximity to mines • The generally accepted number of 500,000 mineworkers contains
and surrounding communities all workers at mines, but excludes workers in related industries
• Identify residential locations in proximity to mines and health • The generally accepted number of 500,000 mineworkers could
facilities be slightly low if all contract workers are considered as there are
• Analyse mineworker vs general population density in planning mines not reporting all classes of contract workers
areas • The generally reported number of 2000 mines in South Africa
• Group and analyse mines, mineworkers, health districts and does not agree with the DMR statistics.
health facility data to strategise planning, prioritise actions and • Active mines reporting mineworkers are just 959.
allocate resources • Only 263 mines have 100 or more mineworkers.
• Use location based data to evaluate intervention Proposals and • TEBA data will only represent approximately 50% of the problem
Interventions aimed at specific locations or areas statement regarding ex-mineworkers and families
• Enabling selection of health facilities to assess, for capacity and • Various data sources were analysed and a better
capability, based on mineworkers in the area understanding of data sources were obtained
• Capability to plan any other mineworker related intervention
planning
Mines and Mineworkers Totals
In various newspapers and presentations, mention is made of around
500,000 mineworkers and more than 2,000 mines in South Africa. During
Mapping of Data this project data from DMR was used primarily and from this it was
concluded that:
• The DMR data have a register of only 1720 mines
• Of the 1720 mines 761 does not report any employees, meaning
• Mapped mines (and their farms) reporting having mineworkers
they are dormant or not active
• Density maps of mineworkers within South Africa against health
• Of the remaining 959 mines 696 are mines with less than 100
sub-districts
mineworkers.
• Density maps of mineworkers separated into employees and
• The remaining 263 mines therefore employs the bulk of the
contractors
mineworkers.
• Density maps broken down by mining commodity
• This means that future planning should probably focus on this
• Density maps for mineworker recruitment areas
reduce set of mines.
• Density maps for ex-mineworkers locations
• As at December 2013, 498,634 mineworkers were reported,
• Density maps for mineworker families as represented by
which is close to the 500,000 number
beneficiaries
• Of the 498,634 reported mineworkers, some 149,944 were
• Consolidated maps of health facilities in Lesotho, South Africa
reported as contractors. At least one mining company providing
and Swaziland
detail records shows that they only report around 60% of the
possible mineworkers to DMR. This could result in a significant

Spatial Database & Arc Reader •
change in DMR numbers.
The DMR data reflects mineworkers as reported by
the mines, and includes all mineworkers at a mine,
• A consolidated spatial database for Lesotho, South Africa and independent of any classifications. It is not always clear
Swaziland that every report stipulates this as being the case, and this
• Terrain and demarcation data loaded into spatial database makes comparison to some earlier reports rather difficult.
• Mines location data and characteristics loaded and linked to
demarcation data
• Mineworkers data loaded into the spatial database and linked

48
Mineworkers and TEBA Detail Planning GIS Map
There is the notion that TEBA represents the bulk of the mineworkers. In Figure 47 a detailed map of a smaller area is shown. The previous
It was rather difficult to confirm this due to TEBA’s refusal to provide maps shown in the report shows areas and analyses at a much more
information. Using the Migrant Population of 2010 as reported by TEBA, summarised level. The GIS tools and Spatial Database provided do
and noting that the total DMR numbers between 2010 and 2013 are however provide a lot more detail that can be worked with to achieve the
roughly on par, the reported TEBA total is possibly 228,456 (as opposed benefits described before.
to a claimed 300,000) as used in Department of Health and World Bank
reports. This means that around 267,700 mineworkers are not recruited The map shows Randfontein and Mogale City Health Sub Districts
or employed via TEBA. The parties who supply these mineworkers are labelled, with Westonaria (bottom right) and Merafong City (bottom left)
not any sizable single entity, but a large group of entities. Any future shown partially, but not labelled. The area boundaries are shown as dark
plans addressing this area should actually get an understanding of this black lines. Of the areas, Merafong City have the most mineworkers
area to ensure according to the darker shading, and Mogale City and Randfontein
implementable interventions. roughly the same, but less than Merafong and Westonaria..

With TEBA data being less than complete, or representative, and TEBA Various farms are shown as yellow areas. Instead of showing the farm
recruiting only “migrant” labour, there should be action plans to also names on the farms, the mine names operating on those farms were
understand the distribution of the larger 267,000 mineworker population used as labels for the farms. In this way the map illustrates that some
portion in terms of origin, job types, age, home residence, originating mines, like Kloof Gold Mine is spread over a number of farms. In this
address, dependents, service span, life expectancy, and similar particular case Kloof Gold Mine is shown on 11 farms. Each of the 11
significant data. farms shows 552 mineworkers. As explained before the mineworkers
for a mine were distributed evenly across the farms for a mine. It can
Even considering that TEBA focus on Gold, PGM and Coal, which therefore also be noted here that some of Kloof Gold Mine farms falls into
represents 80% of mineworkers, the TEBA numbers fall short. Taking Westonaria Health Sub District, whilst at least one of the farms fall into
into account that this area employs around 112,000 contractors for these Randfontein Health Sub District.
commodity based mines, there is a large portion of mineworkers not
working via TEBA. The light green areas indicate residential areas. Within the spatial
database they are called sub-places. These are communities with mostly
non-mineworkers. Census data can be applied to this to show how many
TEBA Data Ownership people live in each community.

TEBA is a recruiting agency, recruiting specifically for the mines. They
also provide further services to the mines related to the employees. It is
rather troublesome to get to grips with the position that the mines do not
get a complete record of the mineworker, even if they are the employer.
Some mine HR department stipulated that they do not have the “home”
address for the employee, i.e. his address where he is actually from. The
mines seem to be actually handing the function to TEBA. However, it is
not clear if they do this for all mineworkers, employees and contractors.
TEBA clearly indicate they do not supply any contractors, and they do not
employ mineworkers and supply as a labour broker.

This position is rather conflicting as the mineworker data ownership and
access should be made available along with the mineworker that gets
employed at the mine. It seems that this fragmented position would be a
stumbling block for any future integrated framework.

Commodity Based Analysis
Implementing a better understanding of the remaining commodities,
especially since gold mine employment is rapidly reducing, could be
very useful. Not only does it put some perspective on the mineworker
numbers, it also bring an understanding to the future of the numbers.
Certain commodities are rapidly growing. Previously it was considered
that all mineworkers are the same, exposed to the same conditions,
come from the same origins, and gold mining is the bad guy in term of
TB. This mapping of data clearly brings a more objective perspective.
Commodities like Chromium and Manganese are the new kids in terms
of growth. A strong focus should be on ensuring that whilst this is growing
and profitable proper practices are implemented from early on.

Figure 47 – Randfontein detailed mapped area
©URSA

49
This map also shows some health facilities (the white H on a red box
as hospitals, and the green crosses as clinics). In this case the health
Road map
facility labels were not all shown, but the identification data is in the spatial A road map is a mechanism that can be used to explain to stakeholders
database and can easily be shown for further referencing. the road that is being taken. The road map should be rather clear and
easily distributed. It should contain enough information that stakeholders
In this case only two major roads the N14 and N12, were shown. However, can determine timing, duration and sequence of events. It is typically
all access roads are loaded in the spatial database and can be used for high level and multi year planning orientated. The detail behind any of
further planning. Some rivers, dotted blue line, are also shown in blue the items should be available somewhere for interested parties to further
writing. investigate.

This map is just a small section from the detailed maps which can then be The road map should be built from a proper strategic and tactical planning
used for more detailed analysis and planning. session and should be updated regularly to illustrate progress. Typically
it can be represented in a few diagrams, with interdepencies and relative
Using the powerful GIS analysis tools, areas can be selected to do scale. The near term items could show more detail and as one approach
groupings for just some health districts, mines, or any other area for which new time periods, those periods can be detailed a bit more.
numbers exist, and these can be summed and shown graphically or in
charts on the maps.
Economic impact
Distance information, between two areas, or specific points can be
determined using the GIS tools. This can help plan practical interventions The TB spread is not only touching the obvious health environment. The
taking distance into account. economic impact is also rather big.

The GIS tools are also rather powerful to locate a specific mine, and then At first there is the direct and visible impact of compensation to be paid.
show the communities in close proximity, or finding a specific community There is also the rather direct impact of recruiting and training someone
and show the mines in close proximity. else to take over the work.

By selecting a specific medical facility, and then deciding on a radius There is a temporarily loss in production as a less qualified person is
around it as a feeding area, the GIS tools can show the number of taking over the work and needs to get up to speed.
mineworkers that could potentially want to use that health facility.
Then there is the indirect economic cost of the productive capacity of a
By selecting a specific location where some mineworkers reside, the GIS person cut short. We expect that every able-bodied person will contribute
tool can determine the distances to the nearest health facilities, either as through his labour to the economy for at least 30 years. This is what is
the crow flies, or via the road network. needed to grow a society and prosper. Cutting a person’s productive life
short by as much as 20 years is a tremendous drain on the society.

Challenges and Recommendations This message should be included as a further motivation for focused and
sustained action to address the TB Burden.

The project experienced various challenges and found ways to overcome
some of them. Some challenges were not directly addressable.
Recommendations for addressing them are noted in a separate annexure.
Primary data collection
It has been recommended that primary data be collected from mineworkers
The project also noted some challenges or issues that could become
and mines. The project uncovered that the idea to rely on TEBA data is
challenges for the NTP moving forward, and some ideas on those are
not only risky, it is also misleading. TEBA data only represent a certain
also noted here.
section, albeit large, of the total mineworker population. Even if it does
include the high risk gold and PGM mineworkers, it does not cover
In the main the project experienced problems collecting data from sources,
the contract workers and other potential mineworkers not from labour
even if it was reasonable to expect those data sources to have the
sending areas.
data. The data was sometimes just not forthcoming, but also of a rather
poor quality in terms of accuracy, causing extensive data cleanup work
The best way to collect primary data is naturally to have a complete set
required. This make the data rather impractical for planning, but more so
of data. Whilst planning can be done on statistical sampling, databases
for actual case management, be it treatment or claim related.
can not work on sampled data. Specific records should be maintained

A secondary problem was a communication and awareness problem
observed with many potential participants and volunteers. The people The best way to collect such data is through ongoing interfaces to existing
were just not aware of all the actions being planned and undertaken, and systems, and to have process to augment the data with more complete
was impatient and sometimes negative about the actual work being done data elements. Failing the existence of sufficient data elements within
as they did not understand or believe in how the activities fit into the areas existing systems to interface to, the only alternative is to have processes
they are interested in. that collect such data at key points in existing process. This is a lengthy
approach, but if not started will never deliver any value outcome.
3 specific recommendation are made here.

50
Annexure A – Health Facilities
This annexure gives detail maps per district or region within Lesotho, South Africa and Swaziland of the location of health facilities within the areas

The maps are group by country, province or region, and district, with sub district detail shown where applicable

Individual maps are shown for each province or country to orientate the position of the district maps within the province or district.

Province Code District Page Province Code District Page
Lesotho BE01 Berea 52 Limpopo DC35 Capricorn 96
BB01 Butha Buthe DC33 Mopani
LE01 Leribe DC47 Sekhukhune
MF01 Mafeteng DC34 Vhembe
MS01 Maseru DC36 Waterberg
MH01 Mohale’s Hoek Mpumalanga DC32 Ehlanzeni 102
MO01 Mohlokong DC30 Gert Sibande
QN01 Qacha’s Nek DC31 Nkangala
QT01 Quthing North West DC37 Bojanala 106
TT01 Thaba Tseka DC40 Dr Kenneth Kaunda
Eastern Cape DC44 Alfred Nzo 63 DC39 Dr Ruth Segomotsi
DC12 Amathole Mompati
BUF Buffalo City DC38 Ngaka Modiri Molema
DC10 Cacadu Northern Cape DC6 Namakwa 111
DC13 Chris Hani DC9 Frances Baard
DC14 Joe Gqabi DC45 John Taolo Gaetsewe
NMA Nelson Mandela Bay DC7 Pixley ka Seme
DC15 O.R.Tambo DC8 Z F Mgcawu
Free State DC20 Fezile Dabi 72 Western Cape DC2 Cape Winelands 117
DC18 Lejweleputswa DC5 Central Karoo
MAN Mangaung CPT City of Cape Town
DC19 Thabo Mofutsanyane DC4 Eden
DC16 Xhariep DC3 Overberg
Gauteng JHB City of Johannesburg 78 DC1 West Coast
TSH City of Tshwane Swaziland SW01 HHOHHO 124
EKU Ekurhuleni SW04 Lubombo
DC42 Sedibeng SW02 Manzini
DC48 West Rand SW03 Shiselweni
KwaZulu-Natal DC25 Amajuba 84
ETH eThekwini
This symbology was used through out the mapping.
DC43 Harry Gwala
DC29 iLembe Ref Category Symbol
DC21 Ugu
1 Hospital H
DC22 Umgungundlovu
DC27 Umkhanyakude
2 Clinic
+
3 Health Centre
DC24 Umzinyathi
t
DC23 Uthukela
DC28 Uthungulu
DC26 Zululand

51
Lesotho

Country Region Code Total Clinic Health Centre Hospital Laboratory
Lesotho Berea BE01 24 2 18 4
Lesotho Butha Buthe BB01 16 1 13 2
Lesotho Leribe LE01 34 5 26 3
Lesotho Mafeteng MF01 21 2 18 1
Lesotho Maseru MS01 70 24 38 8
Lesotho Mohale’s Hoek MH01 18 17 1
Lesotho Mokhotlong MO01 12 1 10 1
Lesotho Qacha’s Nek QN01 13 1 10 2
Lesotho Quthing QT01 10 1 8 1
Lesotho Thaba Tseka TT01 18 16 2

52
53
54
55
56
57
58
59
60
61
62
Eastern Cape

Province District Code Total Clinic Health Centre Hospital Laboratory
Eastern Cape Alfred Nzo DC44 79 66 6 7
Eastern Cape Amathole DC12 163 142 14 7
Eastern Cape Buffalo City BUF 88 72 12 4
Eastern Cape Cacadu DC10 76 55 16 5
Eastern Cape Chris Hani DC13 152 127 17 8
Eastern Cape Joe Gqabi DC14 54 42 10 2
Eastern Cape Nelson Mandela Bay NMA 57 41 12 4
Eastern Cape O.R.Tambo DC15 137 114 13 10

63
64
65
66
67
68
69
70
71
Free State

Province District Code Total Clinic Health Centre Hospital Laboratory
Free State Fezile Dabi DC20 41 32 8 1
Free State Lejweleputswa DC18 55 45 9 1
Free State Mangaung MAN 63 48 11 4
Free State Thabo Mofutsanyane DC19 90 76 12 2
Free State Xhariep DC16 23 20 3

72
73
74
75
76
77
Gauteng

Province District Code Total Clinic Health Centre Hospital Laboratory
Gauteng City of Johannesburg JHB 145 104 1 33 7
Gauteng City of Tshwane TSH 112 60 43 9
Gauteng Ekurhuleni EKU 77 48 24 5
Gauteng Sedibeng DC42 43 35 6 2
Gauteng West Rand DC48 47 34 1 9 3

78
79
80
81
82
83
Kwazulu Natal

Province District Code Total Clinic Health Hospital Laboratory
Centre
Kwazulu Natal Amajuba DC25 29 22 5 2
Kwazulu Natal eThekwini ETH 197 128 1 57 11
Kwazulu Natal Harry Gwala DC43 52 39 1 8 4
Kwazulu Natal iLembe DC29 32 24 4 4
Kwazulu Natal Ugu DC21 63 49 10 4
Kwazulu Natal Umgungundlovu DC22 73 53 17 3
Kwazulu Natal Umkhanyakude DC27 61 51 5 5
Kwazulu Natal Umzinyathi DC24 52 43 5 4
Kwazulu Natal Uthukela DC23 41 33 5 3
Kwazulu Natal Uthungulu DC28 70 51 12 7
Kwazulu Natal Zululand DC26 77 59 13 5

84
85
86
87
88
89
90
91
92
93
94
95
LIMPOPO

Province District Code Total Clinic Health Centre Hospital Laboratory
Limpopo Capricorn DC35 99 80 11 8
Limpopo Mopani DC33 101 82 11 8
Limpopo Sekhukhune DC47 77 64 7 6
Limpopo Vhembe DC34 124 109 1 7 7
Limpopo Waterberg DC36 68 51 10 7

96
97
98
99
100
101
Mpumalanga
Province District Code Total Clinic Health Centre Hospital Laboratory
Mpumalanga Ehlanzeni DC32 132 109 15 8
Mpumalanga Gert Sibande DC30 78 58 13 7
Mpumalanga Nkangala DC31 78 62 11 5

102
103
104
105
North West Province

Province District Code Total Clinic Health Centre Hospital Laboratory
North West Bojanala DC37 124 109 9 6
North West Dr Kenneth Kaunda DC40 50 36 11 3
North West Dr Ruth Segomotsi Mompati DC39 54 43 7 4
North West Ngaka Modiri Molema DC38 85 71 2 8 4

106
107
108
109
110
Northern Cape
Province District Code Total Clinic Health Centre Hospital Laboratory
Northern Cape Frances Baard DC9 31 22 8 1
Northern Cape John Taolo Gaetsewe DC45 40 35 1 3 1
Northern Cape Namakwa DC6 26 16 1 8 1
Northern Cape Pixley ka Seme DC7 36 26 9 1
Northern Cape Z F Mgcawu DC8 19 11 7 1

111
112
113
114
115
116
Western Cape
Province District Code Total Clinic Health Centre Hospital Laboratory
Western Cape Cape Winelands DC2 63 47 1 13 2
Western Cape Central Karoo DC5 13 7 5 1
Western Cape City of Cape Town CPT 158 106 5 40 7
Western Cape Eden DC4 48 33 11 4
Western Cape Overberg DC3 32 26 1 5
Western Cape West Coast DC1 36 25 10 1

117
118
119
120
121
122
123
Swaziland
District Code Total Clinic Health Centre Hospital Laboratory
HHOHHO SW01 67 62 2 2 1
LUBOMBO SW04 99 92 2 4 1
MANZINI SW02 34 31 2 1
SHISELWENI SW03 47 44 1 2

124
125
126
127
128
Annexure B – Provincial Maps
The provincial maps on the following pages shows mineworker distribution in terms of employees, contractors, and also the relative density within the
province. The maps also show where the farms within the health districts are located.

In the attached data tables the following meaning should be given to the columns

Code and Name for the District
Code and Name for the Sub District

DMR: This columns shows the total mineworkers as reported to DMR, that through the mapping, including the statistical spread over farms mined
by a mine, that through the mine farm position aggregated the mineworkers into this sub-district. This therefore represents where the mineworkers
themselves mostly are

Exxaro, Harmony, Sibanye and Lonmin: This includes mineworkers (employees and contractors) as well as ex-mineworkers reported by the four
companies for which addresses could be mapped to sub-districts after data clean up. As such it is not a record of how many people are present within
an area, but it is a sample which can indicate where the families of the mineworkers and ex-mineworkers should be.

Beneficiaries. This data was reported by Sibanye and represent a sampling of 100,000 beneficiaries with addresses and this reflects relatively where
family members should be mostly.

The maps provided in each case shows the density of where mineworkers are mostly working, including pie chart of the relative mix in terms of
employee and contractor. This is done using the DMR data supplied. The color ranges is absolute in terms of numbers vs similar sub-districts also
in other provinces.

The second map shows the beneficiaries per sub district. This data is percentage distribution within the province, across the sub-districts. This is a
first position about where families mostly are.

The third map shows the mineworkers per labour sending area, or where the mineworkers are from. The percentage shown is percentage distribution
within the province, across the sub-districts. This gives an alternate or confirming view about where the families of mineworkers and ex-mineworkers
are, including where the ex-mineworkers themselves also mostly are.

The fourth map shows the farms within the province that have been listed as being mined. It should be noted that not the whole are shown is a mine, it
is a farm on which a mine are being operated. Secondly, the ranges shown gives an indication on which farms the mineworkers mostly are. However,
this has been statistically modified with a strategy to allocate equal distributions of mineworkers for a mine across all the farms included in its mining.
This also includes all mining types and activity.

The additional table shown gives information about the commodities being mined in the province.

129
Eastern Cape
Code District Code Sub District DMR Exxaro Harmony Sibanye Lonmin Benefeciaries

BUF Buffalo City BUF Buffalo City 136 306 172 261 452
DC10 Cacadu EC101 Camdeboo 36 1 3
DC10 Cacadu EC102 Blue Crane Route 16 2 1 5
DC10 Cacadu EC103 Ikwezi 5
DC10 Cacadu EC104 Makana 24 5 5 4 21
DC10 Cacadu EC105 Ndlambe 42 3 3 6 11
DC10 Cacadu EC106 Sundays River Valley 9
DC10 Cacadu EC107 Baviaans 1 1
DC10 Cacadu EC108 Kouga 46 1
DC10 Cacadu EC109 Kou-Kamma 10 1 3 3
DC12 Amathole EC121 Mbhashe 1034 325 1396 1263
DC12 Amathole EC122 Mnquma 788 212 333 766
DC12 Amathole EC123 Great Kei 34
DC12 Amathole EC124 Amahlathi 43 98 54 75 186
DC12 Amathole EC126 Ngqushwa 54 134 21 457
DC12 Amathole EC127 Nkonkobe 2 87 144 27 453
DC12 Amathole EC128 Nxuba 6 1 4
DC13 Chris Hani EC131 Inxuba Yethemba 27 4 3 4
DC13 Chris Hani EC132 Tsolwana 18 7 2 2 2
DC13 Chris Hani EC133 Inkwanca 2 27 3 6 17
DC13 Chris Hani EC134 Lukanji 50 129 73 73 232
DC13 Chris Hani EC135 Intsika Yethu 562 208 193 740
DC13 Chris Hani EC136 Emalahleni 46 226 95 59 385
DC13 Chris Hani EC137 Engcobo 723 296 433 1066
DC13 Chris Hani EC138 Sakhisizwe 233 63 116 229
DC14 Joe Gqabi EC141 Elundini 264 184 141 760
DC14 Joe Gqabi EC142 Senqu 337 193 556 646
DC14 Joe Gqabi EC143 Maletswai 25 25 12 461 40
DC14 Joe Gqabi EC144 Gariep 13 4 7 10
DC15 O.R.Tambo EC153 Ngquza Hill 6 1127 762 1163 3430
DC15 O.R.Tambo EC154 Port St Johns 9 229 112 246 405
DC15 O.R.Tambo EC155 Nyandeni 853 465 1555 1961
DC15 O.R.Tambo EC156 Mhlontlo 307 647 227 669 828
DC15 O.R.Tambo EC157 King Sabata Dalindyebo 20 786 323 1286 1219
DC44 Alfred Nzo EC441 Matatiele 21 605 476 419 1832
DC44 Alfred Nzo EC442 Umzimvubu 398 260 360 1030
DC44 Alfred Nzo EC443 Mbizana 626 634 350 2767
DC44 Alfred Nzo EC444 Ntabankulu 360
NMA Nelson Mandela Bay NMA Nelson Mandela Bay 377 15 11 15 17
Totals 1 311 10 580 5 459 10 237 21 245

Commodity Employees Contractors Mineworkers Mines The Eastern Cape generally have very little mining activity, and the
OTHER 99 4 103 2 maps shows no areas of high density of mine employees. This is as
SAND, CLAY, STONE 391 70 461 36 expected because this is a labour sending area, and the employees are
Grand Total 490 74 564 38 working in the high density areas elsewhere. If more mining activity were
happening here, the Eastern Cape would not have become a labour
sending area.

130
131
132
Free State

Code District Code Sub District DMR Exxaro Harmony Sibanye Lonmin Benefeciaries
DC16 Xhariep FS161 Letsemeng 501 5 1
DC16 Xhariep FS162 Kopanong 166 15 4 6 12
DC16 Xhariep FS163 Mohokare 10 74 21 60 58
DC16 Xhariep FS164 Naledi 87 26 26 85
DC18 Lejweleputswa FS181 Masilonyana 8894 506 247 23 758
DC18 Lejweleputswa FS182 Tokologo 18 5 1 1 1
DC18 Lejweleputswa FS183 Tswelopele 105 15 5 43
DC18 Lejweleputswa FS184 Matjhabeng 23021 14030 1112 393 2993
DC18 Lejweleputswa FS185 Nala 232 24 37 52
DC19 Thabo Mofutsanyane FS191 Setsoto 26 363 110 196 390
DC19 Thabo Mofutsanyane FS192 Dihlabeng 35 95 28 201 77
DC19 Thabo Mofutsanyane FS193 Nketoana 25 1 3 4
DC19 Thabo Mofutsanyane FS194 Maluti a Phofung 30 733 194 103 491
DC19 Thabo Mofutsanyane FS195 Phumelela 2 2 2 9
DC19 Thabo Mofutsanyane FS196 Mantsopa 110 19 33 59
DC20 Fezile Dabi FS201 Moqhaka 8178 143 20 103 52
DC20 Fezile Dabi FS203 Ngwathe 874 49 15 11 42
DC20 Fezile Dabi FS204 Metsimaholo 2243 1
DC20 Fezile Dabi FS205 Mafube 4 2 7 7
MAN Mangaung MAN Mangaung 84 923 207 1024 684

Commodity Employees Contractors Mineworkers Mines
The Free State is one of the major gold mining areas in South
Africa. Nearly 33% of gold mineworkers are employed here. The
COAL 1 574 593 2 167 2
mining activity is concentrated around a few major areas, and is
DIAMONDS 1 431 526 1 957 9
mostly serviced using employees. Although it shows only 4 gold
GOLD 36 256 3 119 39 374 4 mines, these mines are generally large to very large, and spans
OTHER 4 – 4 2 many farm locations.
SAND, CLAY, STONE 346 28 374 13
Grand Total 39 611 4 266 43 876 30

133
134
135
Gauteng

Code District Code Sub District DMR Exxaro Harmony Sibanye Lonmin Benefe-
ciaries
EKU Ekurhuleni EKU Ekurhuleni 2923 196 109 563 270
DC42 Sedibeng GT421 Emfuleni 10 171 119 199 309
DC42 Sedibeng GT422 Midvaal 171 1 1
DC42 Sedibeng GT423 Lesedi 94 2 6 8
DC48 West Rand GT481 Mogale City 2460 189 83 73 155
DC48 West Rand GT482 Randfontein 1217 774 243 62 460
DC48 West Rand GT483 Westonaria 18834 256 701 42 1466
DC48 West Rand GT484 Merafong City 34196 1570 1050 144 2344
JHB City of Johannesburg JHB City of Johannesburg 7417 1935 278 561 554
TSH City of Tshwane TSH City of Tshwane 3377 872 153 79 845 169

Commodity Employees Contractors Mineworkers Mines
Gauteng has historically been the hub of the mining, and still
houses administration for a large proportion of the mines.
COAL 47 43 90 1
Gold is still the major commodity, and over 50% of all gold
DIAMONDS 1 363 362 1 725 2
mineworkers are located in Gauteng.
GOLD 57 581 8 080 65 661 16
MANGANESE 4 – 4 0
OTHER 233 – 233 1
PGM 28 964 991 3
SAND, CLAY, STONE 1 940 559 2 499 59
Grand Total 61 195 10 008 71 202 81

136
137
138
Kwazulu Natal
Code District Code Sub District DMR Exxaro Harmony Sibanye Lonmin Benef.
ETH eThekwini ETH eThekwini 317 61 35 101 69
DC21 Ugu KZN211 Vulamehlo 1 9
DC21 Ugu KZN212 Umdoni 16 21 3 11 12
DC21 Ugu KZN214 uMuziwabantu 18 98 28 137 129
DC21 Ugu KZN215 Ezinqoleni 363 35 14 22 50
DC21 Ugu KZN216 Hibiscus Coast 83 29 13 27 39
DC22 Umgungundlovu KZN221 uMshwathi 10
DC22 Umgungundlovu KZN222 uMngeni 42 1 4 1
DC22 Umgungundlovu KZN224 Impendle 1 3 10
DC22 Umgungundlovu KZN225 The Msunduzi 97 34 20 50 66
DC22 Umgungundlovu KZN226 Mkhambathini 32
DC23 Uthukela KZN232 Emnambithi/Ladysmith 32 17 5 8 12
DC23 Uthukela KZN234 Umtshezi 48 4 2 4 11
DC23 Uthukela KZN235 Okhahlamba 13 5
DC24 Umzinyathi KZN241 Endumeni 489 2 6 1 23
DC24 Umzinyathi KZN242 Nqutu 19 50 9 227
DC24 Umzinyathi KZN244 Msinga 12 39 4 200
DC24 Umzinyathi KZN245 Umvoti 4 16 3 64
DC25 Amajuba KZN252 Newcastle 216 25 35 31 97
DC25 Amajuba KZN253 Emadlangeni 439 7 1
DC25 Amajuba KZN254 Dannhauser 1157 57 2 2 9
DC26 Zululand KZN261 eDumbe 5 7 5 26
DC26 Zululand KZN262 uPhongolo 85 31 48 14 155
DC26 Zululand KZN263 Abaqulusi 835 30 57 18 240
DC26 Zululand KZN265 Nongoma 789 192 440 78 2180
DC26 Zululand KZN266 Ulundi 45 57 140 35 611
DC27 Umkhanyakude KZN271 Umhlabuyalingana 23 6 26
DC27 Umkhanyakude KZN272 Jozini 15 312 611 105 2894
DC27 Umkhanyakude KZN273 The Big 5 False Bay 460 13 24 3 119
DC27 Umkhanyakude KZN274 Hlabisa 6 7 1 34
DC27 Umkhanyakude KZN275 Mtubatuba 939 47 68 14 252
DC28 Uthungulu KZN281 Mfolozi 3633
DC28 Uthungulu KZN282 uMhlathuze 447 48 66 41 234
DC28 Uthungulu KZN284 uMlalazi 3 115 71 5 312
DC28 Uthungulu KZN285 Mthonjaneni 19 52 9 260
DC28 Uthungulu KZN286 Nkandla 14 19 97
DC29 iLembe KZN291 Mandeni 8
DC29 iLembe KZN292 KwaDukuza 131 5 9 19
DC29 iLembe KZN293 Ndwedwe 1
DC29 iLembe KZN294 Maphumulo 1
DC43 Harry Gwala KZN431 Ingwe 1 9 3 8 12
DC43 Harry Gwala KZN432 Kwa Sani 1 1 1
DC43 Harry Gwala KZN433 Greater Kokstad 107 13 11 11 24
DC43 Harry Gwala KZN434 Ubuhlebezwe 57 46 49 183
DC43 Harry Gwala KZN435 Umzimkhulu 238 310 142 1185

Kwazulu Natal is rather low on mining activity with the major portion of Commodity Employees Contractors Mineworkers Mines
its mine mineworkers involved in coal mining. It is also interesting to
COAL 2 560 2 204 4 764 9
note that nearly half of these are contractors. Generally KZN is more
GOLD 85 – 85 1
of a labour sending area than an actual user of mineworkers.
OTHER 2 481 1 931 4 412 3
SAND, CLAY, STONE 907 344 1 251 30
Grand Total 6 033 4 479 10 512 43

139
140
141
Limpopo

Code District Code Sub District DMR Exxaro Harmony Sibanye Lonmin Benef.
DC33 Mopani LIM331 Greater Giyani 92 91 252 359
DC33 Mopani LIM332 Greater Letaba 1 1
DC33 Mopani LIM333 Greater Tzaneen 78 189 156 73 193 303
DC33 Mopani LIM334 Ba-Phalaborwa 8286 4205 36 3 55 16
DC33 Mopani LIM335 Maruleng 56 9
DC34 Vhembe LIM341 Musina 4092 2560 5 5 8 14
DC34 Vhembe LIM342 Mutale 15 24 5 109
DC34 Vhembe LIM343 Thulamela 20 72 122 91 461
DC34 Vhembe LIM344 Makhado 70 59 41 105 122
DC35 Capricorn LIM351 Blouberg 1 6 6 23
DC35 Capricorn LIM352 Aganang 2 4
DC35 Capricorn LIM353 Molemole 65 7 2 2
DC35 Capricorn LIM354 Polokwane 631 226 114 528 334
DC35 Capricorn LIM355 Lepele-Nkumpi 365 7 2 34 6
DC36 Waterberg LIM361 Thabazimbi 27218 6 8 44 19
DC36 Waterberg LIM362 Lephalale 3193 20541 1 5 5 13
DC36 Waterberg LIM364 Mookgopong 84 1 3 3
DC36 Waterberg LIM365 Modimolle 15 1 3 3
DC36 Waterberg LIM366 Bela-Bela 4 4 2 8 2
DC36 Waterberg LIM367 Mogalakwena 2967 19 25 84 81
DC47 Sekhukhune LIM471 Ephraim Mogale 93
DC47 Sekhukhune LIM472 Elias Motsoaledi 781 18 15 119 29
DC47 Sekhukhune LIM473 Makhuduthamaga 11 6 32 6
DC47 Sekhukhune LIM474 Fetakgomo 6572
DC47 Sekhukhune LIM475 Greater Tubatse 26352 23 5 98 23

Commodity Employees Contractors Mineworkers Mines
Limpopo is an area vast and active in mining activity. It ranks
3rd, after North West and Mpumalanga in terms of mineworkers
CHROME 5 738 2 546 8 284 10
employed. PGM is the largest by far, and more than 25% of the
COAL 3 378 884 4 262 5
PGM mineworkers are located in Limpopo. It should be noted
DIAMONDS 1 350 1 595 2 945 2 though that the PGM activity is rather adjacent to the North West
IRON 1 044 1 242 2 286 4 PGM mines and the coal activity in Mpumalanga.
OTHER 4 972 3 417 8 389 10
PGM 38 198 14 171 52 368 15
SAND, CLAY, 1 801 309 2 110 60
STONE
Grand Total 56 481 24 164 80 645 105

142
143
144
Mpumalanga
Code District Code Sub District DMR Exxaro Harmony Sibanye Lonmin Benefeciaries
DC30 Gert Sibande MP301 Chief Albert Luthuli 1108 3 2 5
DC30 Gert Sibande MP302 Msukaligwa 3041 26 15 11 34
DC30 Gert Sibande MP303 Mkhondo 1642 18 25 17 71
DC30 Gert Sibande MP304 Dr Pixley Ka Isaka Seme 838 1 1
DC30 Gert Sibande MP305 Lekwa 1913 8 2 3 5
DC30 Gert Sibande MP306 Dipaleseng 79 6 24 82
DC30 Gert Sibande MP307 Govan Mbeki 19402 177 3 18 6
DC31 Nkangala MP311 Victor Khanye 8671 4260 3 1 6 4
DC31 Nkangala MP312 Emalahleni 23978 10422 13 15 60 23
DC31 Nkangala MP313 Steve Tshwete 15882 4228 6 2 13 2
DC31 Nkangala MP314 Emakhazeni 5040 2266 5
DC31 Nkangala MP315 Thembisile 867 1
DC31 Nkangala MP316 Dr JS Moroka 3 1 3
DC32 Ehlanzeni MP321 Thaba Chweu 2464 5 8 7 16
DC32 Ehlanzeni MP322 Mbombela 162 189 163 341 578
DC32 Ehlanzeni MP323 Umjindi 2309 44 19 18 59
DC32 Ehlanzeni MP324 Nkomazi 649 55 82 33 251
DC32 Ehlanzeni MP325 Bushbuckridge 299 139 414 568

Commodity Employees Contractors Mineworkers Mines
Mpumalanga is traditionally known for its coal mines. These coal
mines are also the major reason for where Eskom has traditionally
COAL 34 393 38 845 73 238 94
placed its coal burning power stations. There are also some gold
GOLD 4 570 1 201 5 771 8
and a little PGM activity.
OTHER 1 064 2 155 3 219 3
PGM 146 1 259 1 405 1
SAND, CLAY, STONE 983 188 1 171 31
Grand Total 41 156 43 648 84 804 138

145
146
147
Northern Cape
Code District Code Sub District DMR Exxaro Harmony Sibanye Lonmin Benefeciaries
DC6 Namakwa NC061 Richtersveld 487
DC6 Namakwa NC062 Nama Khoi 155
DC6 Namakwa NC064 Kamiesberg 511
DC6 Namakwa NC065 Hantam 46
DC6 Namakwa NC067 Khâi-Ma 1531
DC7 Pixley ka Seme NC071 Ubuntu 12 1
DC7 Pixley ka Seme NC072 Umsobomvu 63 1
DC7 Pixley ka Seme NC073 Emthanjeni 34 1
DC7 Pixley ka Seme NC074 Kareeberg 1018
DC7 Pixley ka Seme NC075 Renosterberg 63
DC7 Pixley ka Seme NC076 Thembelihle 30
DC7 Pixley ka Seme NC077 Siyathemba 402 3
DC7 Pixley ka Seme NC078 Siyancuma 715
DC8 Z F Mgcawu NC081 Mier 17
DC8 Z F Mgcawu NC082 Kai !Garib 49
DC8 Z F Mgcawu NC083 ‘//Khara Hais 82 2 2
DC8 Z F Mgcawu NC085 Tsantsabane 4497 1
DC8 Z F Mgcawu NC086 Kgatelopele 1905
DC9 Frances Baard NC091 Sol Plaatjie 1596 28 10 26 18
DC9 Frances Baard NC092 Dikgatlong 1301 3
DC9 Frances Baard NC093 Magareng 9 1
DC9 Frances Baard NC094 Phokwane 200 2 2 6 9
DC45 John Taolo NC451 Joe Morolong 9522
Gaetsewe
DC45 John Taolo NC452 Ga-Segonyana 53 20 290 49
Gaetsewe
DC45 John Taolo NC453 Gamagara 12491
Gaetsewe

Commodity Employees Contractors Mineworkers Mines The Northern Cape has limited mining activity. The bulk of
DIAMONDS 2 691 1 569 4 260 20 this is driven by the diamond industry, but from a mineworker
activity the iron mines top the list. Nearly all iron mining is
IRON 9 131 8 195 17 326 7
located in this province. Manganese, although relatively small
MANGANESE 4 253 5 284 9 537 13
in numbers, also are nearly exclusive to this province.
OTHER 969 614 1 583 9
SAND, CLAY, STONE 827 20 847 14
Grand Total 17 871 15 682 33 553 62

148
149
150
North West
Code District Code Sub District DMR Exxaro Harmony Sibanye Lonmin Benefeciaries
DC37 Bojanala NW372 Local Municipality of 32989 56 10 4350 27
Madibeng
DC37 Bojanala NW373 Rustenburg 106590 140 53 6214 79
DC37 Bojanala NW374 Kgetlengrivier 513 1 6 1
DC37 Bojanala NW375 Moses Kotane 4342 7 942
DC38 Ngaka Modiri Molema NW381 Ratlou 396 1 4
DC38 Ngaka Modiri Molema NW382 Tswaing 61 42 19 44 35
DC38 Ngaka Modiri Molema NW383 Mafikeng 65 387 85 527 221
DC38 Ngaka Modiri Molema NW384 Ditsobotla 269 81 32 62 84
DC38 Ngaka Modiri Molema NW385 Ramotshere Moiloa 194 54 22 332 55
DC39 Dr Ruth Segomotsi Mompati NW392 Naledi 26 56 34 325 79
DC39 Dr Ruth Segomotsi Mompati NW393 Mamusa 187 22 4 38 23
DC39 Dr Ruth Segomotsi Mompati NW394 Greater Taung 4 93 60 340 180
DC39 Dr Ruth Segomotsi Mompati NW396 Lekwa-Teemane 111 6 6 10 8
DC39 Dr Ruth Segomotsi Mompati NW397 Kagisano/Molopo 48 10 104 37
DC40 Dr Kenneth Kaunda NW401 Ventersdorp 141 33 22 19 62
DC40 Dr Kenneth Kaunda NW402 Tlokwe City Council 141 106 67 26 123
DC40 Dr Kenneth Kaunda NW403 City of Matlosana 11480 463 131 637 310
DC40 Dr Kenneth Kaunda NW404 Maquassi Hills 477 15 15 31

Commodity Employees Contractors Mineworkers Mines North West is clearly the new mining hub, with more than 30% of all
mineworkers active in this province. The bulk of these are actually
CHROME 5 038 4 500 9 538 17
related to PGM, with also nearly 11,000 active in gold mining.
DIAMONDS 1 247 166 1 413 35
Like gold, PGM are deep underground structures, and the same
GOLD 10 094 665 10 758 2 risks associated traditionally with gold can be associated with PGM.
MANGANESE 9 – 9 1
OTHER 280 466 746 5
PGM 95 033 35 642 130 675 22
SAND, CLAY, 1 800 75 1 875 36
STONE
Grand Total 113 500 41 513 155 014 117

151
152
153
Western Cape

Code District Code Sub District DMR Exxaro Harmony Sibanye Lonmin Benefeciaries
CPT City of Cape Town CPT City of Cape Town 490 15 8 6 11
DC1 West Coast WC011 Matzikama 1492
DC1 West Coast WC012 Cederberg 4
DC1 West Coast WC013 Bergrivier 168
DC1 West Coast WC014 Saldanha Bay 90
DC1 West Coast WC015 Swartland 180
DC2 Cape Winelands WC022 Witzenberg 7
DC2 Cape Winelands WC023 Drakenstein 17 3
DC2 Cape Winelands WC024 Stellenbosch 34 11 2 1 5
DC2 Cape Winelands WC025 Breede Valley 83 1
DC2 Cape Winelands WC026 Langeberg 67 1 2 1
DC3 Overberg WC031 Theewaterskloof 25
DC3 Overberg WC032 Overstrand 63
DC3 Overberg WC033 Cape Agulhas 70 1
DC3 Overberg WC034 Swellendam 1
DC4 Eden WC041 Kannaland 2 3 2 3
DC4 Eden WC042 Hessequa 79 2 1 3
DC4 Eden WC043 Mossel Bay 284 1
DC4 Eden WC044 George 43 1 1 1
DC4 Eden WC045 Oudtshoorn 17
DC4 Eden WC047 Bitou 35
DC4 Eden WC048 Knysna 13 1 3
DC5 Central Karoo WC051 Laingsburg 2

Commodity Employees Contractors Mineworkers Mines
The Western Cape is the smallest of all in terms of mining
activity. No large mines in this area, in terms of mineworkers.
DIAMONDS – 37 37 1
MANGANESE 7 2 9 1
OTHER 866 498 1 364 7
SAND, CLAY, 993 200 1 193 86
STONE
Grand Total 1 866 737 2 603 95

154
155
156
Lesotho
Code Sub District DMR Exxaro Harmony Sibanye Lonmin Benefeciaries
BB01 Butha Buthe 548 398 142 1709
BE01 Berea 1054 528 227 2184
LE01 Leribe 1719 948 556 4103
MF01 Mafeteng 1701 726 325 2671
MH01 Mohale’s Hoek 1233 549 2093
MO01 Mohlokong 194 103 49 496
MS01 Maseru 1893 705 719 2692
QN01 Qacha’s Nek 218 118 81 559
QT01 Quthing 460 301 58 1166
TT01 Thaba Tseka 166 132 60 621

157
158
Swaziland

Code Sub District DMR Exxaro Harmony Sibanye Lonmin Benefeciaries
SW01 HHOHHO 145 218 40 1407
SW02 Manzini 212 292 36 1910
SW03 Shiselweni 191 341 15 2222
SW04 Lubombo 27 385 618 1859

159
160
Annexure C – Challenges and Actions
Ref Challenge Action Taken Recommendation
1 Data
Some challenges pertaining to the data that was not accessible during the mapping exercise which has led to less than complete and
accurate have been recorded here
1.1 Accuracy
Inaccurate data causes various problems, not least, of which are rework, data cleanup, and cause for doubt in results. When there is
reason to doubt the accuracy of data, it is wise if possible to rather address the data concern. However, when there is a huge bulk of data
it could be impractical to perform a clean up or rework of the data.
1.1.1 Mine locations are not provided as Mine location data were inferred from farm Mines do not occupy full farms, and separate GEO
geo spatial data data provided spatial data should be collected for mines, mine
licenses, mining rights etc. Not in scope of this
project
1.1.2 Mine locations are provided via a Perform a manual search and select of DMR should probably obtain a standard list of farm
list of farms that are not standardised mine locations against a farms database, names, and conduct a data clean up. Not in scope
and aligned with demarcation data also using Google maps, Google Earth, of this project
and other Internet mapping sources to re-
late the given farm name back to a unique
GEO Reference ID
1.1.3 Mine locations: Farm names could Farms not mapped were excluded from Additional effort to correct this is probably not justi-
not be found or mapped accurately the mapping of a mine. Additional effort fied as the number of mineworkers excluded by
was spend on finding at least farm for the process accounts to a low % that is not likely to
a mine, but if none found the mine was influence planning and coordination
excluded.
1.1.4 Mineworker statistics are reported An average distribution of mineworkers to 1. DMR as collector could request more detailed
against the mine, not the work loca- all reported farms were used data per operating location, possibly shafts, plants,
tion etc.

2. Collect secondary data from ten mines through
data requests

1.1.5 Mineworker data from DMR does not Current mineworkers have been mapped 1. Collect primary data through sampling
reflect mineworker living address. using the physical location of the mines
2. Implement record keeping to track accurate ad-
and not the actual residential location
dresses
since the secondary data did not contain
this variable 3. Primary data collected from major mines to
understand distribution mineworkers around mine
location
1.1.6 Rand Mutual and Harmony mine Data received from mining parties was Standards to be implemented, especially for system
data rather difficult to reconcile with DMR data interfacing and integration.
as different identification or no codifica-
tion was used. Matching on text and using
computer coding took rather big efforts
1.1.7 Health facility names in one text Counted as one for now, at same coordi- Needs cleanup for future projects
list, separated by delimiters where nate
more than one facility at the same
coordinates
1.2 Completeness
Incomplete data impose a limit to what can be achieved with the data. Incomplete can be in terms of the population, in which case esti-
mation can complete the data. It can also be in terms of data attributes or elements, in which case a certain attribute of the population is
completely unknown, This cannot be fixed by estimation alone, at least a sample of representative data is required.

161
Ref Challenge Action Taken Recommendation
1.2.1 Mineworker sending area address 1. Consulted mining companies and HR Collect primary data through sampling
where the mineworkers originate from for the data. TEBA seemed to be the only
(town of labour sending country) and source of data
where their families mostly reside
Obtained data from some mine and
mapped the data
1.2.2 Where mineworker family mostly Obtained data from some mines and Use primary collected mineworker data to estimate
reside mapped the data this
1.2.3 Ex-Mineworker sending area Obtained data from some mines and Use primary collected mineworker data to estimate
address (town of labour sending mapped the data this
country)
1.2.4 Where ex-mineworker family mostly Obtained data from some mines and Can use proxy of ex-mineworkers location
reside mapped the data
1.2.5 Number of mineworkers’ family Obtained data from some mines and Collect primary data through sampling to improve
members mapped the data quality of data
1.2.6 Estimated number ex-mineworkers’ Obtained data from some mines and Use primary collected mineworker data to estimate
family members mapped the data this
1.3 Additional Data
In order to effectively manage TB for the mining community and to reduce the spread thereof, some key elements may need to be cap-
tured and these may include the following. This section contains various elements which were not part of the mapping exercise but which
would be helpful to very helpful to guide NTP planning and interventions
1.3.1 Mineworker statistics are reported An indication was done that a large pro- 1. Collect primary data from Mines
against the mine, not the working portion of the mineworkers at a mine are
2. Collect primary data through sampling
conditions (Underground, Surface, subjected to the most arduous of the pos-
Dust, etc.) sible conditions. This is not near accurate
for proper planning purposes, and depend-
ing on the level of planning undertaken
should be corrected.
1.3.2 Mineworker age information was not This data can be very useful to understand 1. Collect primary data from Mines
available shifts in the population, the expected age
2. Collect primary data through sampling
of exiting the occupation, etc. Also future
interventions would want to understand
the impact of the intervention and be able
to measure back to a baseline
1.3.3 Mineworker’s duration of service at When estimating the total ex-mineworkers 1. Collect primary data from Mines
the mine was not available a service span of 20 years was considered
2. Collect primary data through sampling
as an average. The case studies seen
reported, indicates this could be as low as
15 years, as most of the case studies con-
tracted silicosis in 10 years, even starting
as physically strong young adults.

This could lead to a significantly higher
ex-mineworker population
1.3.4 Mineworker mine mortality rate not In the early years mining was a more 1. Collect primary data from Mines
collected. This refers to the mortality “dangerous” environment from a safety
2. Collect data from Chamber of Mines
at work perspective, and the deaths were more
related to accidents. Statistics from Cham- 2. Collect primary data through sampling
ber of Mines suggest a consistent rate
over time. This number has an influence
on the size of the ex-mineworker popula-
tion.

162
Ref Challenge Action Taken Recommendation
1.3.5 Ex-mineworker life expectancy not The Ex-mineworker with TB (and/or Sili- 1. Collect primary data from sending areas
known cosis) is an immediate health burden, but
through sampling
is also an economic burden in that he or
she cannot contribute towards economic
growth and wealth. Understanding the
duration of life after leaving mining could
be very useful for NTP planning purposes
1.3.6 Mining employment service record This information needs to be accurately 1. Collect primary data from Mines
recorded to inform health policies to be
aligned with preventative measures in the
mining industry
1.3.7 The housing condition of the ac- The level of control and prevention of 1. Collect primary data from Mines
commodation where the mineworker TB infections could be influenced by the
2. Collect primary data through sampling
is living residential settings such as:

Stand alone house in urban area

Stand alone house in township or rural
area

Informal Settlement

Communal hostel

This data would be very useful to track as
baseline and for planning
1.3.8 Capacity of Health Facilities Public It would be important to assess their 1. Define planning approach
and private health facilities have been capacity to handle TB cases based on the
2. Document and agree features and information
mapped in Lesotho, South Africa and population of mineworkers, ex-minework-
needs
Swaziland to the district and regional ers and their families in the respective
level areas. The specific information needs and 3. Obtain data from relevant government depart-
definitions have to be developed with an ments and private sector
understanding of planning for interven-
tions. This has been stipulated as a next
phase.
1.4 Available Data Analysed
The DMR provided data wider than the project’s immediate requirements. In discussion with potential users of the mapping report it
became clear that a good understanding at a summarised level could be very helpful.
1.4.1 Mining commodity The mining commodity was not part of Consider when planning and tracking the mine-
the mapping, but it signifies very different worker
mining and working conditions and could
be relevant to future NTP studies.

According to Anglo their coal mines show
an incidence rate of 330/100,000 whereas
PGM is 1400.
1.4.2 Underground indication The working conditions of each contract Consider when planning and tracking the mine-
the mineworker is on keeps changing. This worker
could be important. We mapped the worst
case workplace but as a workplace for all
the mineworkers at the mine

163
Ref Challenge Action Taken Recommendation
1.4.3 Commercial trends The various commodities have signifi- The economic indicators give an indication of the
cantly different trends. Gold is reducing sector’s growth and strength, and should be consid-
employment and even number of mines ered when planning
and production drastically, but the actual
commercial value of the gold sales is still
going up, and gold is still very much on par
with PGM as a foreign exchange earner.
2 Communication
2.1 Standard Concepts Concepts such as mineworker, employee, A glossary of terms should be established and
contractor, and TB Incidence rate were maintained for these terms.
found to be used inconsistently.
2.2 Economic Impact vs. Health Impact It is generally understood that TB requires Build consistent communication and spread the
health funding and compensation. There is message. It touches everyone.
a basic understanding that mines loose in
production. Very little recognition is given
to notion that a person’s economic contri-
bution is curtailed by TB thereby affecting
everyone
3 Participation
3.1 Data sources not participating Various data sources stated that the data 1. Collect primary data from Mines
is at TEBA. In reality TEBA represents a
2. Collect primary data through sampling
big dataset, but at the same time not a
complete picture. 3. Improve the scope of the data

The issue was escalated to URSA and
WB. Alternate requests to mines indicated
that they do not obtain all the data from
TEBA that TEBA records
3.2 Bay Tech unknown to data sources Letters of introduction were drafted by Continue this practise
URSA and communicated to all potential
data sources. This worked in most cases
3.3 Data confidentiality Some data sources requested confidenti- Ensure a standard version exist and provide this
ality agreements to be put in place regard- early.
ing the data before they would provide
the data. A NDA was provided and signed
between URSA and a mine. This could
delay the task, as legal departments need
to get involved.
3.4 Various parties wanted to assist with We repeatedly had to inform parties on An official road map of all the interventions planned
the identification of ex-mineworkers at the TOR as they had a rather different and when they could occur, interdependencies etc.
the ground level understanding of mapping to include track- with a detailed website as backup could be very
ing and plotting of individuals. The overall useful
approach was that further phases could
address that.
3.5 Some parties wanted to have a role Parties with information wanted to have a 1.An official road map of all the interventions
on the project role on the project, in exchange for their planned and when they could occur, interdepen-
data. Other parties wanted to have their dencies etc. with a detailed website as backup
activities funded in exchange for data. could be very useful

2. An official mechanism for participation should be
documented

164
Ref Challenge Action Taken Recommendation
3.6 Some parties were negative about the We repeatedly had to inform parties on An official road map of all the interventions planned
project as they were not involved, or the TOR as they had a rather different and when they could occur, interdependencies etc.
the project was not performing tasks understanding of mapping to include track- with a detailed website as backup could be very
they thought is part of mapping ing and plotting of individuals. The overall useful
approach was that further phases could
address that.
4 Process
4.1 Maintaining medical certifications It was noted that the process to get medi- A medical certification capability is needed that can
cal certification could take extremely long. be shared by parties, is centrally hosted, but shared
It also seemed that the record keeping and by countries.
access to records was rather complicated
and unreliable.
3.2 Speed up process The process is not trusted, is overly costly, Use workflow technology to control the process and
and the compensation can arrive too late track the action across entities
to make a difference
3.3 Statistics The interested parties and stakeholders Collect meaningful operation and statistical data to
have no quality data to base decisions and ensure the process flows efficiently
action plans on
4.4 Donor Access The donors and coordinators do not have Provide up to date data access to donor and stake-
access to relevant data to manage the holders to monitor and drive the processes
outcomes
4.5 Accountability It is generally speculated that the funding Ensure that the process management reflects the
for efforts and actions in this space are accountability across parties so the blame game
wasted and does not reach the intended can be stopped.
beneficiaries. The relevant parties who
is supposed to make things happen are
blamed for spending the funding on the
processes and not deliver the benefits

165
Annexure D – Data Sources
Organisation Contact Person Data Requirements Progress / Action Plan
South Africa
TEBA SA Dr Graham Herbert Number of Active Mineworkers, ex-Mineworkers and Data Not Received
their dependents summarised by town of Residence (in
Ms Nobesuthu Motsepe South Africa, Swaziland, Lesotho)
Dept. of Miner- Ms Nkhesani Masekoa Count of Mineworkers summarised by Mine and Nation- Data Received
als & Resources ality
(DMR)
Mine Data List with location if possible
National Depart of Dr Lindiwe Mvusi Public Health Facilities List including GIS Location Data Data Received
Health – SA and classification of health facility
Mildred Phangiso (PA)
Depart of Health Dr Barry Kistnasamy MBOD Mapping Report of Claimants Data Received
Netcare Mande Toubkin Public and Private Health facilities Data Received
Rand Mutual Jeoffrey Raselabe They have records of about 140 major mines and ap- Data Received
proximately 390 000 mineworkers.

Anglogold Ashanti Dr. James Steele Number of Active Mineworkers (Permanent or Contrac- Data Received
tor) ex-Mineworkers and their dependents summarised
by town of Residence (in South Africa, Swaziland,
Lesotho)
Sibanye Stella Nthimbane Number of Active Mineworkers (Permanent or Contrac- Data Received
tor) ex-Mineworkers and their dependents summarised
Dr Jameson Malemela by town of Residence (in South Africa, Swaziland,
Lesotho)
Harmony Dr Tumi Legobye Number of Active Mineworkers (Permanent or Contrac- Data Received
tor) ex-Mineworkers and their dependents summarised
by town of Residence (in South Africa, Swaziland,
Lesotho)
Goldfields Dr. Khutso Setati Number of Active Mineworkers (Permanent or Contrac- Data Received
tor) ex-Mineworkers and their dependents summarised
by town of Residence (in South Africa, Swaziland,
Lesotho)
Implats Dr Steenkamp Number of Active Mineworkers (Permanent or Contrac- Data Received
tor) ex-Mineworkers and their dependents summarised
Johan Vanemmenes by town of Residence (in South Africa, Swaziland,
Lesotho)
Lonmin Dr Bongani Nene Number of Active Mineworkers (Permanent or Contrac- Data Received
tor) ex-Mineworkers and their dependents summarised
Dr Marie Vermaak by town of Residence (in South Africa, Swaziland,
Lesotho)
Exxaro Naresh Singh Number of Active Mineworkers (Permanent or Contrac- Data Received
tor) ex-Mineworkers and their dependents summarised
by town of Residence (in South Africa, Swaziland,
Lesotho)
Sasol Mining Dr Obed Mphofu Number of Active Mineworkers (Permanent or Contrac- Data Received
tor) ex-Mineworkers and their dependents summarised
Mr Gabriel Morodi by town of Residence (in South Africa, Swaziland,
Lesotho)

166
Organisation Contact Person Data Requirements Progress / Action Plan
Swaziland

Central Statistical Demarcation data Data Received
Mr Amos Zwane
Offices
Ms Rejoice Nkambule Health Facilities List including GIS Location Data Data Received
The Ministry of
Health

Lesotho
Ministry of Health – Mr Lefu Manyokole Health Facilities List including GIS Location Data Data Received
Lesotho
Dr McPherson,

Dr Letsie

Dr Maama

Land Administra- Mr. Mahashe Chaka Demarcation data Data Received
tion Authority
(LAA)

Lesotho Millen- Mrs Sophia Mohapi Data of Health facilities that they renovated Data Received
nium Development
Agency

167
References

AIDS and Rights Alliance for Southern Africa. Tuberculosis and migrant labour in Southern Africa, 2008. Available at http://www.tac.org.za/community/
files/Mines,_TB_and_Southern_Africa.pdf.

Buckley AR, Kimerling AJ, Phillip C. Muehrcke PC, Juliana O. Muehrcke JO (2009), Map Use: Reading and Analysis 6th edition, ESRI Press.

Chamber of Mines of South Africa. Chamber of Mines. Facts and Figures, 2013. Available at http://chamberofmines.org.za/media-room/facts-and-
figures.

Daily Maverick. Black, Chinese and white labourers in a gold mine in South Africa, circa 1890 – 1923. Available at http://www.dailymaverick.co.za/
specialarticle/2014-03-14-coughing-up-for-gold/?chapter=3#.VH2rsMlGLdk.

Department of Health. Tuberculosis Strategic Plan for South Africa 2007-2011, South Africa, 2007. Available at http://www.info.gov.za/view/
DownloadFileAction?id=72544.

Department of Health. Report on TB in the mining industry, South Africa, 2010.

Department of Health. Occupational Diseases in Mines and Works Amendment Act, No. 208 of 1993, South Africa, 1993.

Department of Labour. Compensation for Occupational Injuries and Diseases Act, No. 130 of 1993, South Africa, 1993.

Department of Labour. Labour Migration and South Africa: Towards a fairer deal for migrants in the South Africa Economy, Labour Market Review,
South Africa, 2007.

Department of Labour. Occupational Health and Safety Act, No. 85 of 1993, South Africa, 1993.

Department of Minerals Resources. Mine Health and Safety Act, No. 29 of 1996, South Africa, 1996.

Digitalk Pty Ltd. InDesign formatting and layout

Jaine Roberts (2009). The Hidden Epidemic amongst Former Miners: Silicosis, Tuberculosis and the Occupational Diseases in Mines and Works Act
in the Eastern Cape, South Africa. Published by Health Systems Trust. Available at http://www.hst.org.za/uploads/files/ODMWA.pdf.

Katz, E., (1994). The White Death: Silicosis on the Witwatersrand Gold Mines, 1886-1910, Witwatersrand University Press.

International Labour Organization. Guidelines for workplace TB control activities. Report. Geneva, Switzerland: WHO; 2003. Available at http://www.
weforum.org/pdf/Initiatives/GHI_Guidelines_WHO_TB.pdf

Mining IQ Mining Intelligence Database (http://www.projectsiq.co.za/mining-in-south-africa.htm).

National Tuberculosis Program Managers of Lesotho, Mozambique, South Africa and Swaziland, the World Bank, and the World Health Organization.
A framework for the harmonized management of Tuberculosis in the mining sector, 2014.

Statistics South Africa. General Household Survey, Pretoria, 2012.

Prof. Rodney Ehrlich (2012). The crisis of tuberculosis and silicosis in the South African mining industry. Centre for Occupational and Environmental
Health Research, University of Cape Town.

World Bank. Overview Benefits and Costs of Reducing Tuberculosis among Southern Africa’s Mineworkers, 2014. Available at http://www.health-e.
org.za/wp-content/uploads/2014/04/World-Bank-economic-analysis-on-addressing-TB-in-the-mines-brief.pdf

168
Notes:

169
InDesign formatting and layout performed by digitalk Pty Ltd

170

Leave a Comment

Your email address will not be published. Required fields are marked *

%d bloggers like this: