Health Information Audit Report South Africa

Health Information Audit Report South Africa Marian Loveday, Jackie Smith, Fiorenza Monticelli October 2006 Published by Health Systems Trust Health Systems Trust Tel: 031-307 2954 401 Maritime House Fax: 031-304 0775 Salmon Grove Email: hst@hst.org.za Victoria Embankment Web: http://www.hst.org.za Durban 4001 The National Health Information System Project is funded by the National Department of Health

Table of Contents List of Abbreviations... 3 Executive Summary... 4 Introduction... 5 Findings... 6 1. HUMAN RESOURCE ISSUES... 6 1.1. Employing Authority... 6 1.2. Vacancy Rate... 7 1.3. The Status of HIS posts... 7 1.4. Period in HIS position... 10 1.5. The amount of time staff responsible for HIS work spend on doing HIS-related work... 11 1.6. Level of post... 12 1.7. Occupational background of staff involved in HIS... 12 2. HUMAN RESOURCE SKILLS... 14 2.1. DHIS Training... 14 2.2. Length of training in the DHIS... 15 2.3. Skills in the use of the DHIS... 15 3. EQUIPMENT... 24 3.1 Types of computers accessible to HIS staff... 24 3.2. Computer capacity: Hard drive capacity... 25 3.3 Computer capacity: Memory capacity... 26 3.4. Software: MS Windows version... 27 3.5. Software: Version of MS Office... 27 3.6 Access to Printers... 28 3.7. Access of HIS staff members to email... 29 3.8. HIS staff with access to the provincial intranet... 30 3.9. HIS staff with access to the internet... 31 Discussion... 32 Recommendations... 33 Conclusion... 33 APPENDIX 1... 34 APPENDIX 2... 50 2

List of Abbreviations DHIS DIO DoH GB HIS HST IT KZN MB MS Sub-DIO District Health Information System District Information Officer Department of Health Gigabytes Health Information System Health Systems Trust Information Technology Kwa-Zulu Natal Megabytes Microsoft Sub-district information officer Legend for graphs showing all districts Free State Eastern Gauteng North West Northern KwaZulu-Natal Western Northern Mpumalanga Acknowledgements We would like to thank Naadira Karrim, Molefe Mahlatsi, Hlengiwe Ngcobo, Zimisele Ndlela and Riette Venter for their contribution to data collection and to all the Department of Health and Local Authority officials who assisted in the process and gave up their time to be interviewed. Thanks are also due to Sonja Venter and Rakshika Bhana who were responsible for the development of the interview tool and to the team leader, Ronel Visser, who guided this process and made sure that the all the pieces of the jigsaw fitted together. 3

Executive Summary In order to improve the quality of information it is critical that there are sufficiently well-trained staff members at provincial and district level that are able to process data that are collected and who are able to ensure that the quality is reliable. They need to have sufficient skills to do the work that is expected of them. They also need the appropriate IT equipment, hardware and software, as well as access to information to do their jobs effectively. From July 2006 to January 2007 audits in the different provinces of South Africa were carried out, to assess the staffing of the health information system sector and the availability and access to appropriate equipment. The following areas were included in the audit: human resource issues, human resources skills, IT equipment and access to information. This was a comprehensive audit and all available Health Information System (HIS) staff members throughout the country were interviewed. In total 677 HIS staff members were interviewed. The results highlighted several main areas including: Eighty seven percent of the HIS staff members interviewed were employed by provincial governments Three percent of the HIS posts were vacant. Thirty five percent of the HIS staff were not in official permanent HIS posts. Twenty percent of the HIS staff members had been in their posts for more than five years. A third of the HIS staff members had been in their posts for less than a year and 17% had been in their posts for less than six months. Forty two percent of the HIS staff members spent less than 80% of their time on HIS work. At a national level, less than half (45%) of the HIS staff members had received sufficient training (at least a week) to carry out their work. Nationally 35% of the HIS staff members had received no training in the DHIS software, and a further 20% had received training of less than a week, which would be considered inadequate. Twenty five HIS staff members did not have access to computers. Although many HIS staff had access to computers, many of these computers needed upgrading of hardware and software in order to function effectively. Seventy eight (12%) staff members did not have access to printers. A third of the HIS staff members did not have access to email, 39% did not have access to the intranet and half did not have internet access. 4

Introduction Background The establishment of a routine health information system (HIS) has been given high priority in the South African public health sector and a lot of money has been invested in this. However, the information generated by this system has not always been of high quality. This is one of the reasons why information is not optimally used by health managers at all levels of the health system for decision making to plan, manage, monitor and evaluate the health services. The lack of a national policy or guideline for human resources for HIS is thought to contribute to the poor quality of information. The provinces have each decided on their own job criteria, post levels, and job descriptions for HIS with negative consequences. Another factor thought to contribute to the poor information quality has been a lack of standardisation of the software and hardware necessary for processing the data. In order to quantify these differences in human resources and information technology (IT) hardware and software, the national Department of Health requested Health Systems Trust (HST), as part of their support to the development of the HIS system in the public sector, to carry out a provincial audit in each of the provinces. Methodology These provincial audits were carried out throughout the South Africa Department of Health. The data collected focused on human resource issues related to HIS, the skills of the staff working in HIS and the IT equipment available to them. Interviews were conducted telephonically by interns working for HST. A data collection tool was designed and the interns were trained in the use of the tool. The tool was first used in Gauteng following which it was adapted for use in the Eastern and piloted before being finalised. Data collection took place between October 2006 and January 2007. Data were captured immediately by the interviewers onto an Excel spread sheet. Pivot tables and graphs were generated, once the data had been checked and cleaned. Written permission for these audits were obtained from the relevant provincial heads before the audit began. HIS staff members at provincial and district levels assisted by checking the accuracy and validity of the data before pivot tables and graphs were generated. Six hundred and seventy seven (677) HIS staff members were interviewed. No staff handling the Comprehensive Care, Management and Treatment data (commonly referred to as HIV data) were included in the audit. 5

Findings The findings are presented in three sections: human resource issues, human resources skills IT equipment and access to information. 1. HUMAN RESOURCE ISSUES A number of variables were considered in the audit of human resources. These included: Employing authority Number of HIS staff members per province Vacancies Status of the HIS posts Period in HIS position Amount of time spent on HIS work Occupational background 1.1. Employing Authority The number of HIS staff members in the different provinces varied from 125 in Gauteng to seven in the Northern. The number of HIS staff members in the different districts varied from 55 in the City of Johannesburg to one person in each of the Northern districts. In Table 1 22 vacant HIS posts have been included with the 677 HIS staff members interviewed. The table shows that 87% of the HIS staff members interviewed were employed by provincial governments and 10% by local governments. In four of the nine provinces; KwaZulu-Natal, Mpumalanga, Northern and North West, all staff members were employed by the relevant provincial authority. In the remaining five provinces some HIS staff members were employed by local government. The number of local authority staff employed varied from 41 in Gauteng to one in the Free State. The proportion of local authority staff varied from 48% in Gauteng to 2% in the Free State. 6

Table 1: January 2007: Analysis of HIS staff and posts by province and level of government Free State Eastern Kwazulu Natal Limpopo Mpumalanga Northern North West Western Gauteng Total % Provincial Government (HIS staff) 60 101 108 113 59 7 50 29 84 611 87% Provincial Government (vacant posts) 0 0 13 0 8 1 0 0 0 22 3% Local Government 1 5 0 6 0 0 0 13 41 66 10% Total 61 106 121 119 67 8 50 42 125 699 100% 1.2. Vacancy Rate Table 1 shows the distribution of vacant posts across the provinces. All the vacant posts were provincial government posts. There were only three provinces with vacant posts and 13 of the 22 vacant posts were in KwaZulu- Natal. The vacancy rate in the rural nodes was marginally higher (5%) than the national average of 3%. (Appendix 1: Table 2) There were no vacant posts in the metros. (Appendix: Table 3) 1.3. The Status of HIS posts At the time of the audit there were 677 staff members doing HIS-related work. Not all the posts occupied by staff members doing HIS-related work were official permanent HIS posts. Almost two thirds of the staff members doing HIS-related work were permanently appointed to HIS-posts. (Appendix: Figure 1) Just less than a third of the staff doing HIS-related work were appointed to other posts such as clerical or nurse posts, but were doing HISrelated work. The remaining 7% of the staff members doing HIS-related work were appointed to vacant posts, acting in vacant posts or on contract. (A detailed breakdown of post status can be found in appendix 1 in Table 1). Across the country 38% of the HIS posts were neither official/permanent or were vacant. Figure 1 shows that the percentage of HIS posts per province that were neither official/ permanent or were vacant varied considerably. In the Free State 89% of the HIS posts were neither official/permanent or were vacant. In Mpumalanga two thirds of the HIS posts were neither official/permanent or were vacant. In the North West 16% and in KwaZulu- Natal 17% of the HIS posts were neither official/permanent or were vacant. In both the metros and rural nodes the average percentage of vacant and non-official posts was 38%. (Appendix 1: Tables 2 and 3) The rural node with the highest percentage of vacant and non-official posts was Thabo Mofutsanyana. 7

Figure 1: Percentage of HIS posts vacant or not official across the provinces a North West KZN Northern Limpopo Western Eastern Gauteng Mpumalanga Free State % HIS posts vacant or not official/permanent per province 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Figure 2 shows that in 13 districts no HIS posts were vacant and all HIS staff members were appointed to official permanent HIS posts. In contrast, in six districts there were no HIS staff members in official permanent HIS posts. Four of these districts were in the Free State and two in the Northern. In a further three districts more than 80% of the HIS posts were neither official/permanent or were vacant, three of these were in Mpumalanga and one in Gauteng. a Legend for graph on page 3 8

Figure 2: Percentage of HIS posts vacant or not official across the districts a Vacancy Rates per district Namakwa Thabo Mofutsanyane Xhariep Motheo Lejweleputswa Fezile Dabi Gert Sibande Nkangala Sedibeng Ehlanzeni Sekhukhune Cacadu City of Johannesburg Metsweding Amathole Mopani Capricorn Tshwane Ekurhuleni Town NM Umzinyathi Alfred Nzo Oliver Tambo Chris Hani Umgungundlovu Bojanala Platinum Amajuba Bophirima Overberg DM Central District Vhembe Umkhanyakude Nelson Mandela Metro Zululand Ukhahlamba Ehlanzeni (Buschbuckridge) Uthungulu Ethekwini Waterberg West Rand Eden DM West Coast DM Winelands DM Central Karoo DM Southern District Pixley ka Seme Frances Baard Kgalagadi Sisonke Ugu Uthukela Ilembe 0 10 20 30 40 50 60 70 80 90 100 Percentage 9

1.4. Period in HIS position Table 2 shows the length of time which HIS staff members had spent in their current position. Thirty one percent of the staff members had been in their position for longer than three years. Forty four percent of the HIS staff members had been in their positions for longer than two years. A third of the HIS staff members had been in their position less than a year, with 17% having been in their position for less than six months. Table 2: January 2007: Period in HIS position Period in current position Total Percentage 0-6 months 116 17% 6-12 months 118 17% 1-2 years 148 22% 2-3 years 87 13% 3-5 years 75 11% More than 5 years 132 20% Total 676* 100% *1 respondent missing Figure 3 shows the variations across the provinces when comparing the length of time HIS staff members have spent in positions. In Gauteng 29% of the HIS staff members had been in their position for longer than 3 years. In comparison, Northern had none and Limpopo had 4% of their staff members in HIS posts for longer than 3 years. Limpopo had the highest number of HIS staff members who had spent less than 6 months in their position. There were no HIS staff members in Northern who had been in their position for less than 6 months and in Mpumalanga and the North West only 3% of the staff members had been in their position less than 6 months. (A detailed breakdown of period in HIS position can be found in Appendix 1 in Table 4). 10

Figure 3: January 2007: Comparing period in HIS position across the provinces Period of Time in HIS positions across the Provinces 35% 30% 25% % 20% 15% 10% 5% 0% Eastern Free State Gauteng Kwazulu Natal Limpopo Mpumalang a North West Northern < 6 months 17% 6% 9% 22% 30% 3% 3% 0% 8% 6 months - 3 years 14% 8% 15% 17% 21% 8% 9% 2% 5% 3+ years 17% 13% 29% 11% 4% 13% 6% 0% 7% Western Information collected from HIS staff members in the rural nodes shows that they had spent a similar period of time in their position as their colleagues in the rest of the country. (Appendix: Table 5) In contrast information collected from the metros suggests that the HIS work-force in the metros is more stable than that of the rest of the country as half of the HIS staff members had been in their position for longer than three years. (Appendix 1: Table 6) 1.5. The amount of time staff responsible for HIS work spend on doing HIS-related work HIS staff members need to be able to work full-time on HIS-related work to ensure optimal data quality. Important activities such as giving regular feedback to facilities on data quality and trends and supporting information capacity development at a facility level cannot take place if HIS staff members are not employed full time in doing HIS-related work. Table 3 shows that 58% of the HIS staff members spend more than 80% of their time doing HIS-related work. Mpumalanga has the highest number of staff members (85%) who spend more than 80% of their time on HIS-related work. The Free State has the lowest number of staff members (25%) who spend more than 80% of their time doing HIS-related work. (A more detailed breakdown of the time spent by HIS staff doing HIS-related work can be seen in Appendix 1 as Table 7). 11

Table 3: January 2007: Percentage of time spent by HIS staff doing HISrelated work Eastern Free State Gauteng Kwazulu Natal Limpopo Mpumalanga North West Northern Western Total % of total > 80% 30 15 95 67 68 51 39 4 27 396 58 < 80% 76 46 30 41 51 9 11 2 15 281 42 Total 106 61 125 108 119 60 50 6 42 677 100 Information from the rural nodes shows that 46% of the HIS staff members interviewed spent more than 80% of their time on HIS-related work. (Appendix: Table 8) In the metros 72% of the HIS staff members spent more than 80% of their time on HIS-related work. (Appendix: Table 9) 1.6. Level of post (Appendix 1: Table 10) In 2005 HST and HISP made a proposal to the National DoH regarding human resource requirements for an effective HIS in South Africa. b In this document post levels for the different HIS posts were suggested. The suggestions are attached as Appendix 2. In this audit insufficient data was gathered to be able to determine if the HIS staff members interviewed were at the correct level required of their job. As set out in the proposal of 2005, level 5 is the lowest level suggested for HIS staff members. However, it is evident (Appendix 1: Table 10) that there are 36 posts at levels 3 and 4. A level 14 post is suggested for database developers/managers at a national level. However, at a sub-district level in Gauteng there are 3 HIS staff members employed in a level 14 posts. Standardisation and consistency across the country with regard to post levels is necessary to ensure the correct skills capacity at the correct level of the HIS. More in-depth investigation will be needed to make this possible. 1.7. Occupational background of staff involved in HIS Figure 4 illustrates the occupational backgrounds of HIS staff across the country. Forty percent of the HIS staff members had an administrative/clerical background and a third of the the HIS staff members had a nursing/medical background. A further 15% had a background in information technology and the remaining 13% had various other backgrounds. In the metros 17% of the HIS staff members had a nursing/medical background and 62% had an administrative/support background. (Appendix 1: Table 11) In comparison, in the rural nodes 43% of the HIS staff members had a nursing background. (Appendix 1: Table 12) In the rural nodes, the use of staff with a nursing/medical background to do HIS-related work needs to be b Proposal to the National DOH: Health Management Information System (HMIS) Human Resources (HR) Requirements. Prepared by the HST and HISP October 2005 12

carefully considered. Given the shortage of skilled health workers in the rural nodes, the appointment of HIS staff members with an administrative/support background may be more appropriate. Figure 4: January 2007: Occupational background of staff members involved in HIS work HIS Staff Occupational Background Nursing / Medical 32% Information Technology 15% Other 13% Administrative / Support 40% The table below shows that the majority of HIS staff members in the Free State, Eastern and Limpopo had a health background and just less than half the HIS staff members in Mpumalanga had a health background. In the other five provinces the majority of HIS staff members had an administrative or support background. The background of HIS staff members has implications for training. HIS staff members with a health background have an initial advantage as they understand the context within which they are working and much of the content is familiar, such as the definitions of the data elements. However as suggested above, the use of nursing or medical staff in areas in which their nursing/medical skills are in short supply must be carefully considered. Table 4: January 2007: Occupational backgrounds of HIS staff across the provinces Free State Eastern KwaZulu- Natal Limpopo Mpumalanga Northern North West Western Gauteng Total % Administrative / Support 11 33 65 11 28 3 18 21 78 268 40% Nursing / Medical 47 47 8 53 24 0 4 7 27 217 32% Information Technology 3 17 28 18 2 1 11 8 16 104 15% Other 0 9 7 37 5 3 17 6 4 88 13% 100 Total 61 106 108 119 59 7 50 42 125 677 % 13

2. HUMAN RESOURCE SKILLS 2.1. DHIS Training Table 5 shows that over a third of the HIS staff members interviewed had attended no formal training on DHIS software. There are two reasons why so many HIS staff members had received no training: In KwaZulu-Natal two thirds (79) of the HIS staff who were interviewed were hospital based facility information officers who had not been trained in the DHIS because the data collection system used by hospitals in the province was not the DHIS but the Patient Throughput Statistical System (PTSSH). In the Western, two of the four regions, use Sinjani, a web based software system instead of the standard DHIS 1.3 software. Consequently 13 HIS staff members had did not use the DHIS not been trained in the use of it. If these 92 HIS staff members who do not use the DHIS are excluded from the total, then three quarters of the HIS staff members who use the DHIS have been trained. However, in KwaZulu-Natal extensive training is presently required because the DHIS 1.4 will be rolled out to all hospitals in the province, because of the limitations of the PTSSH. In the Western no further training in the DHIS will be required as a decision has been taken that the Sinjani system is to be used throughout the province. Table 5 shows that in the North West province all 50 HIS staff members had been trained in the use of the DHIS software. In a further four provinces; the Free State, Mpumalanga, Northern and Gauteng 80% or more of the HIS staff members had been trained. Besides KwaZulu-Natal, over 40% of the HIS staff members in the Eastern and Limpopo require training in the DHIS software. In the metros, 38% of those interviewed had attended no formal training. (Appendix: Table 13) In the rural nodes half of those interviewed had attended no formal training on DHIS software. (Appendix: Table 14) Table 5: January 2007: Comparing the number of HIS staff who received training in the DHIS software across the provinces Province Yes No Total Free State 55 (90%) 6 (10%) 61 Eastern 63 (59%) 43 (41%) 106 Kwazulu-Natal 16 (15%) 92 (85%) 108 Limpopo 69 (58%) 50 (42%) 119 Mpumalanga 47 (80%) 12 (20%) 59 Northern 6 (86%) 1 (14%) 7 North West 50 (100%) 0 (0%) 50 Western 30 (71%) 12 (29%) 42 Gauteng 102 (82%) 23 (18%) 125 Total 438 (65%) 239 (35%) 677 14

2.2. Length of training in the DHIS Training of at least a week is needed to provide HIS staff members with the skills necessary to do their work competently. Table 6 shows that besides the 35% of the HIS staff members nationally who had received no training in the DHIS software, a further 20% had received training of less than a week, which would be considered inadequate. At a national level, less than half (45%) of the HIS staff members had received sufficient training (at least a week) to carry out their work. In the metros the average number of HIS staff members who had received adequate training was similar to that at a national level. In the rural nodes only a third of the HIS staff members had received sufficient training (of at least a week) to carry out their work. (For a detailed breakdown per metro and rural node see Tables 15 and 16 in Appendix 1). Table 6: January 2007: Length of training on DHIS software Length of DHIS software training National Total National % Metro Total Metro % Rural Nodes Total Rural Nodes % No training 239 35% 54 38% 52 50% Training of less than a week 134 20% 22 15% 19 18% Training of 1 week or more 304 45% 68 47% 33 32% Total 677 100% 144 100% 104 100% 2.3. Skills in the use of the DHIS Staff members interviewed were asked to rank their level of skills as either good/average or poor/none for four components of DHIS work which have to be performed regularly. These included: Data capturing, importing and exporting data; Standard and used defined reports/reporting module Validation, regression analysis, assessing data quality. Pivot tables and graphs. Table 7 shows the level of skills as rated by the HIS staff. c It was very encouraging to note that at least two thirds of the HIS staff members rated their skills for all four components of DHIS work as good or average. For two components of the DHIS work, namely standard used and defined reports and the use or pivot tables and graphs, a third of the HIS staff members rated their skills as poor/none. c The 79 HIS staff members from KwaZulu-Natal and 13 HIS staff members from the Western who were using different data capturing packages are included in this section. 15

Table 7: January 2007: Level of skills of HIS staff* DHIS data capturing, import and export % Standard and user defined reports % Validation, regression analysis, assess data quality. % Use of pivot tables and graphs Good/Average 522 79 432 65 476 71 424 64 Poor/None 141 21 231 35 187 29 239 36 Total 663 100 633 100 663 100 663 100 * 13 respondents did not respond Table 8 shows the variation in self assessed skills in DHIS data capturing, importing and exporting data across the provinces. A high percentage of HIS staff members in the Northern, North West, Western and Gauteng rated their skills as good/average. KwaZulu-Natal and the Eastern were the two provinces where the most staff rated their skills rated their skills as poor/none. d Table 8: January 2007: Comparing self assessed skills in DHIS data capturing, importing and export data across the provinces % Eastern Free State KwaZulu- Natal Limpopo Mpumalanga Northern North West Western Gauteng Good/Average (%) 62 87 50 88 81 100 100 91 90 Poor/None (%) 38 13 50 12 19 0 0 9 10 Total 100 100 100 100 100 100 100 100 100 Figure 5 shows the percentage of staff who rated their skills in data capturing, importing and exporting data as poor or none. Nine of the 15 districts which scored highest for this were in KwaZulu-Natal. e The remaining 6 were in the Eastern. In 16 districts all HIS staff members rated their skills as good/average. In four of the metros, 80% or more of the staff rated their skills in data capturing, importing and exporting data as good/average. However in Nelson Mandela Bay Metro a third of the staff members rated their skills as poor/none and in ethekweni 57% of the staff members rated their skills as poor/none. (Appendix 1: Table 17). In eight of the 11 rural nodes all the HIS staff members rated their skills as good/average. (Appendix 1: Table 18) There were five HIS staff members in the rural nodes who rated their skills as poor/none. Two of these staff members were in Alfred Nzo and Chris Hani and the remaining one was in Thabo Mofutsanyane. d In KwaZulu-Natal at the time of the audit 79 HIS staff members were not using the DHIS. 16

Figure 5: Percentage of poor or no skills in data capturing, importing and exporting per district a Poor or No skills in Data Capture, import and export Amajuba Sisonke Umzinyathi Ugu Ethekwini Cacadu Umgungundlovu Uthukela Amathole Alfred Nzo Ilembe Uthungulu Chris Hani Nelson Mandela Metro Xhariep Overberg DM Ekurhuleni Capricorn Lejweleputswa Fezile Dabi Nkangala Zululand Waterberg Ukhahlamba Gert Sibande Ehlanzeni City of Johannesburg Town NM Oliver Tambo Ehlanzeni (Buschbuckridge) Thabo Mofutsanyane Vhembe Mopani Tshwane West Rand West Coast DM Southern District Sekhukhune Sedibeng Pixley ka Seme Motheo Metsweding Kgalagadi Frances Baard Eden DM Umkhanyakude Central District Winelands DM Bophirima Bojanala Platinum 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage 17

Table 9 shows the variation in self assessed skills in standard and user defined reports across the provinces. It also shows that the percentage of HIS staff members who rated their skills as poor/none was lower than that for data capturing, exporting and importing. Almost all the HIS staff members in the North West province rated their skills as good or average. Up to half of the staff members in four provinces rated their skills in standard and user defined reports as poor/none. Mpumalanga had the most staff members who rated their skills as poor/none. In Figure 6 below the percentage of HIS staff members with poor or no skills in data capturing, importing and exporting per district is shown. Eight of the 15 districts which scored highest for this were in KwaZulu-Natal. e Of the remaining 6 districts 3 were in the Eastern, 2 in Mpumalanga and 1 in the Free State. In 12 districts all HIS staff members rated their skills as good/average. In both the metros and rural nodes a quarter of the HIS staff members rated their skills as poor/none. ethekweni had the most HIS staff members out of all the metros who rated their skills as poor/none (66%). (Appendix 1: Table 19) Of the 13 HIS staff members in the rural nodes who rated their skills as poor/none, six were in Thabo Mofutsanyane and two in Chris Hani. (Appendix 1: Table 20) Table 9: January 2007: Comparing self assessed skills in standard and user defined reports/reporting modules across the provinces Eastern Free State KwaZulu- Natal Limpopo Mpumalanga Northern North West Western Gauteng Good/Average 50 52 40 83 37 83 96 76 85 Poor/None 50 48 60 17 63 17 4 24 15 Total 100 100 100 100 100 100 100 100 100 18

Figure 6: Percentage of poor or no skills in standard and user defined reports/reporting modules per district a Poor or no skills in Standard and User defined Reports Amajuba Gert Sibande Ehlanzeni Sisonke Ukhahlamba Umzinyathi Xhariep Ethekwini Ugu Umgungundlovu Amathole Zululand Ilembe Cacadu Fezile Dabi Uthukela Thabo Mofutsanyane Lejweleputswa Motheo Alfred Nzo Chris Hani Sedibeng Overberg DM Uthungulu Oliver Tambo Nelson Mandela Metro Town NM Waterberg Mopani Ekurhuleni Nkangala Capricorn City of Johannesburg Ehlanzeni (Buschbuckridge) Southern District Bojanala Platinum Vhembe Tshwane West Rand West Coast DM Sekhukhune Pixley ka Seme Metsweding Kgalagadi Frances Baard Eden DM Umkhanyakude Central District Winelands DM Bophirima 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage 19

Table 10 shows the skills in validation, regression analysis and assessing data quality as analysed across the provinces. Over 70% of the HIS staff members in all the provinces except the Eastern and KwaZulu-Natal rated their skills in validation, regression analysis and assessing data quality as good/average. e The low skills level in the Eastern province is a matter of concern given that the DHIS was initiated by the Equity Project in the province and reflects the instability of the HIS workforce in the province. Figure 7 shows the percentage of HIS staff members with poor or no skills in validation, regression analysis and assessing data quality per district. Eight of the 10 districts which scored highest for this were in KwaZulu-Natal. e The remaining two districts were in Eastern. In 13 districts all HIS staff members rated their skills as good/average. An average of 72% of HIS staff members in the metros rated their skills as good/average, the same as that for the country as a whole. (Appendix 1: Table 21) However, in ethekweni metro, 71% of the HIS staff members rated their skills as poor/none in this component of DHIS work. The average score of HIS staff members in the metros who rated their skills as good/average was 86%, which was higher than that of the rest of the country. Fourteen percent of the HIS staff members in the rural nodes rated their skills as poor/none. (Appendix 1: Table 22) Table 10: January 2007: Comparing self assessed skills in validation, regression analysis and assessing data quality across the provinces Eastern Free State KwaZulu- Natal Limpopo Mpumalanga Northern North West Western Gauteng Good/Average 57 82 38 83 71 83 98 85 82 Poor/None 43 18 62 17 29 17 2 15 18 Total 100 100 100 100 100 100 100 100 100 e In KwaZulu-Natal at the time of the audit 79 HIS staff members were not using the DHIS. 20

Figure 7: Percentage of poor or no skills in validation, regression analysis and assessing data quality per district a Poor on No skills in Validation, regression analysis and assessment of data quality Amajuba Sisonke Umzinyathi Ukhahlamba Ethekwini Uthungulu Ugu Umgungundlovu Ilembe Cacadu Amathole Uthukela Lejweleputswa Alfred Nzo Sedibeng Nkangala Zululand Nelson Mandela Metro Chris Hani Waterberg Fezile Dabi Gert Sibande Ekurhuleni Town NM Oliver Tambo Ehlanzeni (Buschbuckridge) Overberg DM Ehlanzeni Capricorn City of Johannesburg Mopani Xhariep Motheo Vhembe Thabo Mofutsanyane Bophirima Tshwane West Rand West Coast DM Southern District Sekhukhune Pixley ka Seme Metsweding Kgalagadi Frances Baard Eden DM Umkhanyakude Central District Winelands DM Bojanala Platinum 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage 21

Table 11 shows self-assessed skills of HIS staff in the use of pivot tables and graphs. In four provinces: the Northern, North West, Western and Gauteng over three quarters of the staff rated their skills in the use of pivot tables and graphs as good/average. If KwaZulu-Natal is excluded e in three provinces: the Eastern, Free State and Mpumalanga over 40% of the HIS rated their skills as poor/none. Figure 8 shows the percentage of HIS staff members with poor or no skills in validation, regression analysis and assessing data quality per district. Nine of the 10 districts which scored highest for this were in KwaZulu-Natal. e The remaining district was in the Eastern. In 11 districts all HIS staff members rated their skills as good/average. The metro average of HIS staff for self-assessed skills in the use of pivot tables and graphs was higher than that of the rest of the country. (Appendix 1: Table 23) In all metros except ethekweni at least two thirds of the HIS staff rated their skills level in the use of pivot tables and graphs as good/average. However, in ethekweni almost three quarters of the HIS staff members rated their skills as poor/none. The average score of HIS staff in the rural nodes for self-assessed skills in the use of pivot tables and graphs was higher than that of the rest of the country. (Appendix 1: Table 24) (Only in Thabo Mofutsanyane where over half of the HIS staff members rated their skills as poor/none was the score lower than that of the national average. In contrast, in eight of the rural nodes all HIS staff members rated their skills as good/average. Table 11: January 2007: Comparing self assessed skills in the use of pivot tables and graphs across the provinces Eastern Free State KwaZulu- Natal Limpopo Mpumalanga Northern North West Western Gauteng Good/Average 55 60 36 65 59 83 98 76 80 Poor/None 45 40 64 34 41 17 2 24 20 Total 100 100 100 100 100 100 100 100 100 22

Figure 8: Percentage of poor or no skills in the use of pivot tables and graphs per district a Poor or No skills in the use of Pivot Tables Amajuba Sisonke Umzinyathi Ethekwini Uthungulu Umgungundlovu Ugu Cacadu Zululand Ilembe Ehlanzeni (Buschbuckridge) Fezile Dabi Thabo Mofutsanyane Amathole Uthukela Waterberg Lejweleputswa Alfred Nzo Ukhahlamba Chris Hani Nkangala Vhembe Ekurhuleni Ehlanzeni Town NM Xhariep Sedibeng Nelson Mandela Metro Motheo Mopani Gert Sibande Capricorn Oliver Tambo Overberg DM Umkhanyakude City of Johannesburg Tshwane Southern District Bojanala Platinum West Rand West Coast DM Sekhukhune Pixley ka Seme Metsweding Kgalagadi Frances Baard Eden DM Central District Winelands DM Bophirima 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Percentage 23

3. EQUIPMENT The major focus of the equipment audit was to determine whether DHIS version 1.4. (DHIS 1.4) could be run on the computers used by HIS staff and therefore the questions in this part of the audit were designed to determine whether the capacity of the computers and the software in use met the requirements needed for DHIS 1.4 to run effectively. 3.1 Types of computers accessible to HIS staff Each and every HIS staff members needs to have a dedicated computer. Ninety six percent of the HIS staff members had access to a computer, 25 did not. (Appendix 1: Table 25) Of those who did not have a computer 14 were in the Eastern and five each in Mpumalanga and Gauteng. One HIS staff member in KwaZulu-Natal did not have a dedicated computer. In Figure 9 the percentage of HIS staff members with no access to computers across the provinces is shown. In the Eastern 13% of the HIS staff members do not have no access to a computer of their own. Figure 9: Percentage of HIS staff members with no access to computers across the provinces a No Access to computer per province Eastern Mpumalanga Gauteng Kwazulu Natal Western North West Northern Limpopo Free State 0 2 4 6 8 10 12 14 Percentage Figure 10 shows the percentage of HIS staff members across the districts who did not have access to computers at the time of the audit. Five of the 10 districts where HIS staff members did not have access to computers were in the Eastern and three were in Mpumalanga. In Amathole district as many as 27% of the HIS staff members did not have access to their own computer. The percentage of HIS staff members in the metros and rural nodes who did not have access to computers was similar to the national average. (Appendix 1: Tables 26 and 27) In the metros six HIS staff members did not computers, five of these were in the city of Joburg and one in the Nelson Mandela Bay Metro. In the rural nodes three HIS staff members in rural nodes in the 24

Eastern did not have computers, two of these staff members were in Chris Hani and one in O.R. Tambo. Figure 10: Percentage of HIS staff members with no access to computers across the districts a No Access to computers per district Amathole Gert Sibande Nelson Mandela Metro Oliver Tambo Chris Hani City of Johannesburg Nkangala Ehlanzeni Umgungundlovu Cacadu 0 5 10 15 20 25 30 3.2. Computer capacity: Hard drive capacity A hard drive capacity of at least 80 Gigabytes (GB) is needed to run DHIS effectively at a district level. At a provincial level, a hard drive capacity of 120 GB is recommended to handle large data files. For a number of reasons information on the hard drive capacity of 16 computers in the country was not available at the time the audit was undertaken. (Appendix 1: Table 31) The hard drive capacity of 636 computers was assessed. Fifteen percent (97) of the computers used by HIS staff members throughout the country needed their hard drive capacity increased. Thirty two computers at a provincial level needed their hard drive capacity upgraded to 120 GB and 22 computers at a district level needed their hard drive capacity upgraded to 80 GB. (Appendix 1: Table 28) Figure 11 shows that at a provincial level Mpumalanga had the most computers that needed upgrading. In contrast, Limpopo and Gauteng had no computers at a provincial level which needed upgrading. At a district level Gauteng had the most computers (18) which needed upgrading, whereas the Northern had none. 25

In the metros, 22 computers needed their hard drive capacity increased and 15 computers needed upgrading in the rural nodes. (Appendix 1: Table 32) The metros with the most computers that needed upgrading were the cities of Johannesburg and Town and Ekhuruleni. Greater Sekhukhune was the rural node with the most computers that needed upgrading. (Appendix 1: Table 33) Figure 11: January 2007: Comparison of provincial and district level computers requiring an upgrade of their hard drive capacity Comparision of provincial and district level computers needing upgrading to their hard drive capacity 20 18 16 14 Number 12 10 8 Provincial level computers with less than 120G hard drive capacity District level Computers with less than 80G hard drive capacity 6 4 2 0 Free State Eastern Gauteng Kwazulu Natal Limpopo Mpumalanga Northern North West Western 3.3 Computer capacity: Memory capacity At a district, sub-district and hospital level a memory capacity of 500 megabytes (MB) should be able to support the DHIS 1.4, but only in the short term. Ideally the memory of all district level computers should be upgraded to 1 GB. Provincial level computers should however have a memory capacity of 2 GB to handle large combined data files. For a number of reasons information on the memory capacity of nine computers in the country was not available at the time the audit was undertaken. (Appendix 1: Table 31) The memory capacity of 643 computers was assessed. Twenty four percent (152) of the computers used by HIS staff members throughout the country required upgrading of their memory capacity. Sixty computers at a provincial level needed their memory capacity upgraded to 2 GB and 92 computers at a district level required memory capacity upgrading to 1 GB. (Appendix 1: Table 29) In the metros, 32 computers required memory capacity upgrading. Ten of these computers were in the City of Johannesburg and eight in Ekhuruleni. 26

(Appendix 1: Table 32) In the rural nodes seven computers across various rural districts needed their hard drive capacity increased. (Appendix 1: Table 33) 3.4. Software: MS Windows version For DHIS 1.4 to function effectively on a computer, the computer must have either the XP, 2002 or 2000 version of MS Windows software installed. Twenty four computers used by HIS staff members across the country needed a version of windows that could support DHIS 1.4 effectively. (Appendix 1: Table 30) Of these 24 computers, seven were in the Free State and KwaZulu-Natal and six in the Eastern. The remaining four were scattered in Limpopo, Mpumalanga, North West and Gauteng. In the metros four computers needed a different version of windows installed. Three of these computers were in ethekweni and one in the City of Johannesburg. (Appendix 1: Table 32) In the rural nodes three computers needed a different version of windows installed, two in Thabo Mofutsanyana and the remaining one in Chris Hani. (Appendix 1: Table 33) 3.5. Software: Version of MS Office The versions of MS Office recommended to run DHIS 1.4 effectively are MS Office 2002, XP and MS Office 2003. MS Office 2000 and MS Office 98 have some major software-based limitations related to Pivot Table functionality. As an interim measure, DHIS 1.4 will operate on these two versions for lower level users (districts, hospitals and sub-districts) but this will definitely hinder their delivering on their responsibilities. For higher data volume users (provincial and national HIS units) MS Office version 2003 is definitely recommended to achieve reasonable operating speeds and to avoid out of memory blockages. For a number of reasons information on the version of MS office in 7 computers in the country was not available at the time the audit was undertaken. (Appendix 1: Table 31) Across the country as a whole, 182 computers would need an upgraded version of MS office installed to support the DHIS 1.4. This is more than a quarter of the computers used by HIS staff members throughout the country. (Appendix 1: Table 30) Figure 12 shows that at the time of the audit there were four provinces: Free State, Eastern, Northern and Western where more than half the computers used by HIS staff members in the province would need an upgraded version of MS office. In contrast, in Mpumalanga only 2% of the computers in the province would need an upgraded version of MS office. In the metros, 31 computers would need to have an upgraded version of MS office installed. The City of Johannesburg was the metro which had the most computers needing upgrading. (Appendix 1: Table 32) In the rural nodes, 47 computers needed to have an upgraded version of MS office installed. (Appendix 1: Table 33) The four Eastern rural nodes accounted for 34 27

of the rural node computers needing an upgraded version of MS office installed. In the Eastern, the most computers (12) needing an upgraded version of MS office installed were in Chris Hani district. Figure 12: January 2007: Comparison of the number and percentage of computers needing an upgraded version of MS Office installed Computers needing MS office upgrade 90 80 70 60 50 40 No. computers needing an MS office upgrade % computers needing an MS office upgrade 30 20 10 0 Free State Eastern Kwazulu Natal Limpopo Mpumalanga Northern North West Gauteng Western 3.6 Access to Printers All HIS staff members need access to a functional printer. In Figure 13 the percentage of HIS staff members who do not have access to printers from their own computers is compared. Across the country 12% of the HIS staff members do not have access to printers. Mpumalanga is the province with the most HIS staff members who do not have access to printers (one quarter of the staff). In the Eastern a fifth of the HIS staff members do not have access to printers. (The detailed provincial table is in Appendix 1 as Table 34). The average number of HIS staff members who do not have printers in the metros and rural nodes is similar to the national average. In Nelson Mandela Bay Metro, third of the HIS staff members have no access to printers. In contrast, in the Cities of Town, Tshwane and ethekwini, less than 5% of the HIS staff members have access to printers. (Appendix 1: Table 35) In three rural nodes Alfred Nzo, Chris Hani and Umzinyathi almost a quarter of the HIS staff members do not have access to printers. (Appendix 1: Table 36) 28

Figure 13: Percentage of HIS staff members across the provinces with no printers Percentage of HIS staff members with no printers 30 25 20 % no printers 15 10 5 0 Northern North West Western KwaZulu- Natal Free State Limpopo Gauteng Eastern Mpumalanga Average 3.7. Access of HIS staff members to email To facilitate the effective use of DHIS (e.g. importing and exporting of data) every person working in HIS should have access to email at their own computer. In Figure 14 the percentage of HIS staff members who do not have access to email from their own computers is compared. Across the country over a third of the HIS staff members do not have access to email. There is considerable variation in the number of HIS staff members with email access across the provinces. This varies from two thirds of the HIS staff members in KwaZulu- Natal without email access, to a mere 2% of the HIS staff members in the Western who have no email access. (The detailed provincial table is in the appendix as Table 37). A quarter of the HIS staff members do not have email access in the metros. This varies from almost two thirds in ethekweni to 4% in the City of Town. In contrast, almost two thirds of the HIS staff members in the rural nodes do not have email access. (Appendix 1: Tables 38 and 39) 29

Figure 14: Comparing percentage of HIS staff members with email access across the provinces Percentage of HIS staff members with no email access 70 60 50 % No email access 40 30 20 10 0 KwaZulu- Natal Limpopo Eastern Northern Free State Gauteng Mpumalanga North West Western National average 3.8. HIS staff with access to the provincial intranet Access to the provincial intranet is essential for HIS staff members. In most provinces the provincial intranet is accessible for provincial government employees only. Table 12 shows that 265 (39%) of the HIS staff members throughout the country did not have access to the provincial intranet at the time of this baseline audit. Although 60 of these HIS staff members were employed by local government, the remaining 205 should have intranet access. In four provinces, namely Eastern, Limpopo, Northern and Gauteng more than 50% of the staff did not have intranet access. In comparison, in the North West 4% of the HIS staff members had no access. In the metros more than half of the HIS staff members did not have intranet access. (Appendix 1: Table 38) The percentage of HIS staff members with provincial intranet access varied from a high of 85% in the City of Johannesburg (where many of the HIS staff members are local government employees) to a low of 9% in ethekweni. In the rural nodes, 48% of the HIS staff members did not have intranet access. (Appendix 1: Table 39) More than three quarters of the HIS staff members in Alfred Nzo, Kgalagadi and O.R. Tambo districts had no intranet access. In contrast, all staff in the Central Karoo and Umkhanyakude districts had intranet access. 30

Table 12: January 2007: Provincial comparison of HIS staff members with no access to intranet or internet from their own computers Province Authority No intranet No internet Free State Local Authority 1 1 Provincial Government 12 45 Free State Total 13 (21%) 46 (75%) Eastern Local Authority 4 5 Provincial Government 62 74 Eastern Total 66 (62%) 79 (74%) Kwazulu Natal Provincial Government 12 22 KwaZulu-Natal Total 12 (11%) 22 (20%) Limpopo Local Authority 5 5 Provincial Government 60 77 Limpopo Total 65 (55%) 82 (69%) Mpumalanga Provincial Government 11 41 Mpumalanga Total 11 (19%) 41 (69%) Northern Provincial Government 4 3 Northern Total 4 (66%) 3 (50%) North West Provincial Government 6 2 North West Total 6 (4%) 2 (4%) Western Local Authority 13 10 Provincial Government 1 16 Western Total 14 (33%) 26 (62%) Gauteng Local Authority 37 20 Provincial Government 37 20 Gauteng Total 74 (59%) 40 (32%) Total 265 (39%) 341 (50%) 3.9. HIS staff with access to the internet Access to the internet is useful for HIS staff members as updated versions of DHIS are accessible through the internet. Updates for other software programmes are also available on the internet. In Table 12 the number and percentage of HIS staff members with access to the internet is tabulated. At the time of this audit half of the HIS workforce did not have internet access. This varied from three quarters in the Free State and Eastern provinces to a low of 4% in the North West province. In the metros and rural nodes 39% and 65% of the HIS staff members respectively did not have access to the internet. (Appendix 1: Tables 38 and 39) The range of no internet access across the metros varied from a high of 71% in the City of Town to a low of 13% in the City of Tshwane. In 8 of the rural nodes more than 70% of the HIS staff members did not have internet access. 31

Discussion In order to improve the quality of information it is critical that the HIS workforce is both stable and motivated. HIS staff members need to be appointed to official permanent posts if they are to remain in their positions; acquire skills, practice these and begin to improve the quality of the data for which they are responsible. In the rural nodes in particular, care must be taken when formalising HIS posts as the use of people with a nursing or medical background as HIS staff members when their medical skills are in short supply, is possibly not a wise use of scarce resources. However, the creation of full time permanent HIS posts do not automatically guarantee good quality data. Training, ongoing training, regular supervision and monitoring are needed at all levels of the HIS to improve the quality of data. In addition, all levels of managers must insist on having the most recent data and information available which they must use to improve the functioning of the health service for which they are responsible. HIS staff members cannot function unless they have computers with the necessary capacity and software to perform the tasks of DHIS 1.4. They also require email, internet and intranet access. The access of HIS staff members to email, the internet and the intranet was lower in the rural nodes than in the rest of the country. This reflects the poorer level of infrastructure in the rural nodes. 32

Recommendations The National Department of Health needs to develop a policy document on human resource requirements for an effective HIS workforce. The recommendations suggested in 2005 by HST and HISP could form a framework for this. b Issues which would need to be addressed in this document would include: The ideal number of HIS staff members required at the different levels of the health information system; The post levels for the different levels of workers; The length of training in the DHIS (at least one week) should be stipulated; Target dates by which provinces need to stabilise their HIS workforce and appoint HIS staff members to official permanent HIS posts need to be discussed and confirmed with the different provinces. A basic job description for HIS staff members to ensure they spend at least 80% of their time doing HIS-related work; All HIS staff members need access to a computer with A version of windows which can support the DHIS 1.4., (Windows XP professional is recommended) Sufficient hard drive capacity, Sufficient memory capacity, A version of Microsoft Office which can support DHIS 1.4 (MS Office 2003 or MS Office XP), A printer, Access to email, intranet and internet from their own computer. Conclusion The aim of this baseline audit is to provide a picture of the state of HIS in South Africa at the moment. It is hoped it will provide a basis from which the national Department of Health can develop a policy document which can be used as a guideline by provinces as they seek to improve the quality of the information emanating from their health information system. 33

APPENDIX 1 Table 1: January 2007: Status of HIS Posts Province Vacancies Province Region District Sub-district Hospital CHC Total % Eastern Acting (No post) 1 0 2 31 3 37 35 Acting (Vacant) 0 0 2 0 0 2 2 Filled 4 3 17 43 0 67 63 EC Total 5 3 21 74 3 106 100 Free State Acting (No post) 0 5 15 31 3 54 89 Filled 7 0 0 0 0 7 11 FS Total 7 5 15 31 3 61 100 Gauteng Acting (No post) 0 2 28 23 0 53 42.4 Contract 2 1 0 0 0 3 2 Filled 10 22 27 10 0 69 55.2 Gauteng Total 12 25 55 33 0 125 100 Kwazulu Natal Acting (No post) 0 0 0 4 1 5 4 Acting (Vacant) 0 0 0 2 1 3 2 Filled 3 17 0 68 12 100 83 Vacant 5 0 0 7 1 13 11 KZN Total 8 17 0 81 15 121 100 Limpopo Acting (No post) 0 10 0 17 0 27 23 Acting (Vacant) 0 2 0 2 0 4 3 Filled 1 14 0 72 0 87 73 Internship 0 0 0 1 0 1 1 Limpopo Total 1 26 92 0 119 100 Mpumalanga Acting (No post) 0 0 16 20 0 36 54 Filled 8 4 5 6 0 23 34 Vacant 2 1 2 3 0 8 12 MP Total 10 5 23 29 0 67 100 North West Acting (No post) 0 0 0 3 0 3 6 Acting (Vacant) 0 0 3 2 0 5 10 Filled 4 4 18 16 0 42 84 NW Total 4 4 21 21 0 50 100 Northern Filled 2 4 0 1 0 7 88 Vacant 0 1 0 0 0 1 12 NC Total 2 5 0 1 0 8 100 Western Acting (No post) 0 1 0 0 3 1 5 12 Acting (Vacant) 0 1 0 1 5 0 7 17 Filled 2 3 9 11 5 0 30 71 WC Total 2 5 9 12 13 1 42 100 Total 52 5 98 147 375 22 699 34

Figure 1: January 2007: Status of HIS posts throughout South Africa Acting (No Post) 31% Filled 62% Acting (Vacant) 3% Contract 1% Vacant 3% Table 2: January 2007: Rural nodes: Status of HIS posts Subdistrict Rural Nodes Vacancies District Hospital CHC Total Alfred Nzo Acting (No post) 3 3 Filled 1 4 1 6 Central Karoo Filled 1 1 Chris Hani Acting (No post) 4 4 Acting (Vacant) 2 2 Filled 4 11 15 Kgalagadi Filled 1 1 O. R. Tambo Acting (No post) 1 2 3 Filled 2 4 6 Greater Sekhukhune Acting (No post) 2 4 6 Acting (Vacant) 1 1 Filled 3 1 4 Thabo Mofutsanyane Acting (No post) 1 3 10 14 Ukhahlamba Acting (No post) 1 1 Filled 6 6 Umkhanyakude Filled 1 4 5 Vacant 1 1 Umzinyathi Filled 3 5 8 Vacant 4 4 Ugu Filled 2 4 6 Zululand Acting (Vacant) 1 1 Filled 1 9 1 11 Vacant 1 1 Total 16 16 77 1 110 35

Table 3: January 2007: Metros: Status of HIS posts Metros Vacancies District Hospital Subdistrict CHC Total City of Town Acting (No post) 3 1 4 Acting (Vacant) 3 1 4 Filled 6 4 6 16 City of Johannesburg Acting (No post) 6 24 30 Filled 8 5 12 25 Ekurhuleni Acting (No post) 7 7 Filled 6 4 3 13 Ethekwini Acting (No post) 1 1 Acting (Vacant) 1 1 Filled 3 14 4 21 Nelson Mandela Bay Metro Acting (No post) 1 1 Filled 1 4 5 City of Tshwane Acting (No post) 5 1 6 Filled 3 1 6 10 Total 27 58 53 6 144 Table 4: January 2007: Comparing period in HIS position across the provinces 6 Province < 6 months % months - 3 years 3+ years Grand Total Eastern 20 17% 50 14% 36 17% 106 16% Free State 7 6% 28 8% 26 13% 61 9% Gauteng 11 9% 54 15% 60 29% 125 18% Kwazulu- Natal 26 22% 60 17% 22 11% 108 16% Limpopo 35 30% 75 21% 8 4% 118 17% Mpumalanga 4 3% 28 8% 27 13% 59 9% North West 4 3% 33 9% 13 6% 50 7% Northern 0 0% 7 2% 0 0% 7 1% Western 9 8% 18 5% 15 7% 42 6% Total Table 5: January 2007- Rural Nodes: Period in HIS position District < 6 months 3 + years 6 months - 3 years Total Alfred Nzo 4 3 2 9 Central Karoo 0 0 1 1 Chris Hani 4 6 11 21 Kgalagadi 0 0 1 1 O.R. Tambo 1 3 5 9 Greater Sekhukhune 1 5 5 11 Thabo Mofutsanyane 3 5 6 14 Ukhahlamba 2 2 3 7 Umkhanyakude 0 1 4 5 Umzinyathi 2 0 6 8 Ugu 1 1 4 6 Zululand 2 3 7 12 Total 20 (19%) 29 (28%) 55 104 36

Table 6: January 2007: Metros: Period in HIS position Metro < 6 months 3 + years 6 months - 3 years Total City of Town 5 7 12 24 City of Johannesburg 5 34 16 55 Ekurhuleni 5 8 7 20 ethekwini 8 11 4 23 Nelson Mandela Bay Metro 3 2 1 6 City of Tshwane 0 9 7 16 Total 26 (18%) 71 (49%) 47 144 Table 7: January 2007: Comparing time spent doing HIS-related work (detail) Eastern Free State Gauteng KwaZulu - Natal Limpopo Mpumala nga North West Northern Western Total > 80% 30 15 95 67 68 50 39 5 27 396 51% - 80% 31 8 17 30 30 7 6 0 11 140 21% - 50% 29 16 12 9 11 0 5 0 2 84 0-20% 16 22 1 2 10 2 0 2 2 57 Total 106 61 125 108 119 59 50 7 42 677 Table 8: January 2007: Rural Nodes: Percentage of time spent doing HIS-related work Rural Node > 80% < 80% Total Alfred Nzo 5 4 9 Central Karoo 1 0 1 Chris Hani 5 16 21 Kgalagadi 0 1 1 O. R. Tambo 6 3 9 Greater Sekhukhune 9 2 11 Thabo Mofutsanyane 1 13 14 Ukhahlamba 0 7 7 Umkhanyakude 5 0 5 Umzinyathi 6 2 8 Ugu 3 3 6 Zululand 7 5 12 Total 48 (46%) 56 (54%) 104 Table 9: January 2007: Metros: Percentage of time spent doing HISrelated work Metro > 80% < 80% Total City of Town 18 6 24 City of Johannesburg 43 12 55 Ekurhuleni 17 3 20 ethekwini 12 11 23 Nelson Mandela Bay Metro 3 3 6 City of Tshwane 10 6 16 Total 103 (72%) 41 (28%) 144 37

Table 10: January 2007: Level of posts of HIS staff members Province Level Level 3 Level 4 Level 5 Level 6 Level 7 Level 8 Level 9 Level 10 Level 11 Level 12 Level 13 Level 14 Total Free State Provincial 0 0 0 1 0 4 1 1 0 0 0 0 7 FS Total 0 0 0 1 0 4 1 1 0 0 0 0 7 Eastern Hospital 0 8 3 7 2 10 6 2 4 0 0 0 42 Sub-Dist 0 0 1 0 1 1 12 2 0 0 0 0 17 District 0 0 0 0 0 1 2 0 0 0 0 3 Provincial 0 0 0 1 0 0 0 0 3 0 0 0 4 EC Total 0 8 4 8 3 11 19 6 7 0 0 0 66 Kwazulu Natal Hospital 6 2 1 0 58 0 0 0 1 0 0 0 68 District 3 0 0 1 2 0 0 11 0 0 0 0 17 Provincial 0 0 0 0 1 0 0 1 1 0 0 0 3 CHC 1 0 0 1 10 0 0 0 0 0 0 0 12 KZN Total 10 2 1 2 71 0 0 12 2 0 0 0 100 Limpopo Hospital 0 1 0 0 11 22 22 6 4 0 0 0 66 District 0 0 0 0 5 3 3 0 0 0 0 0 11 LP Total 0 1 0 0 16 25 25 6 4 0 0 0 77 Mpumalanga Hospital 0 0 0 0 2 1 1 1 1 0 0 0 6 Sub-district 1 0 0 0 1 0 1 1 1 0 0 0 5 District 0 0 0 0 0 1 3 0 0 0 4 Provincial 0 0 0 0 2 2 2 2 0 0 0 8 MP Total 1 0 0 0 5 2 7 4 4 0 0 0 23 Northern Hospital 0 0 0 0 0 0 0 0 1 0 0 0 1 District 0 0 0 0 3 0 0 0 0 0 0 0 3 Provincial 1 0 0 0 1 0 0 0 0 0 0 0 2 NC Total 1 0 0 0 4 0 0 0 1 0 0 0 6 North West Hospital 0 0 0 0 15 1 0 0 0 0 0 0 16 Sub-district 0 0 1 2 14 1 0 0 0 0 0 0 18 District 0 0 0 0 0 0 4 0 0 0 0 0 4 Provincial 0 0 0 0 2 1 1 0 0 0 0 0 4 38

NW Total 0 0 1 2 31 3 5 0 0 0 0 0 42 Western Hospital 0 0 0 0 4 0 1 0 0 0 0 0 5 Sub-district 0 6 0 0 5 0 0 0 0 0 0 0 11 District 0 1 5 0 0 0 2 1 0 0 0 0 9 Provincial 0 0 0 0 0 0 0 0 1 0 1 2 Regional 0 0 0 0 1 0 2 0 0 0 0 0 3 WC Total 0 7 5 0 10 0 5 1 1 0 1 0 30 Gauteng Hospital 0 0 0 3 3 2 0 2 0 0 0 0 10 Sub-district 4 0 2 3 6 4 3 2 0 0 0 3 27 District 0 2 3 5 2 3 4 1 1 1 0 0 22 Provincial 0 0 0 0 0 4 0 3 1 0 2 0 10 GP Total 4 2 5 11 11 13 7 8 2 1 2 3 69 Grand Total 16 20 16 24 151 58 69 38 21 1 3 3 420 Table 11: January 2007: Metros: Occupational background of HIS staff members Metro Administrative / Support Information Technology Nursing / Medical Other Total City of Town 13 3 2 6 24 City of Johannesburg 33 4 16 2 55 Ekurhuleni 15 5 0 0 20 ethekwini 18 4 0 1 23 Nelson Mandela Bay Metro 0 2 3 1 6 City of Tshwane 11 2 3 0 16 Grand Total 90 20 24 10 144 39

Table 12: January 2007: Rural Nodes: Occupational background of HIS staff members Rural Node Administrative / Support Information Technology Nursing / Medical Other Total Alfred Nzo 3 1 4 1 9 Central Karoo 0 0 1 0 1 Chris Hani 9 0 9 3 21 Kgalagadi 1 0 0 0 1 O. R. Tambo 4 2 2 1 9 Greater Sekhukhune 0 0 11 0 11 Thabo Mofutsanyane 1 0 13 0 14 Ukhahlamba 2 1 3 1 7 Umkhanyakude 3 0 1 1 5 Umzinyathi 7 0 0 1 8 Ugu 6 0 0 0 6 Zululand 4 6 1 1 12 Total 40 10 45 9 104 Table 13: January 2007: Metros: Number of staff who received training in the DHIS software Metro No Yes Total City of Town 11 13 24 City of Johannesburg 6 49 55 Ekurhuleni 13 7 20 ethekwini 20 3 23 Nelson Mandela Bay Metro 3 3 6 City of Tshwane 1 15 16 Grand Total 54 (38%) 90 (62%) 144 Table 14: January 2007: Rural Nodes: Number of staff who received training in the DHIS software Rural Node No Yes Total Alfred Nzo 2 7 9 Central Karoo 1 0 1 Chris Hani 9 12 21 Kgalagadi 0 1 1 O. R. Tambo 4 5 9 Greater Sekhukhune 4 7 11 Thabo Mofutsanyane 0 14 14 Ukhahlamba 5 2 7 Umkhanyakude 4 1 5 Umzinyathi 7 1 8 Ugu 5 1 6 Zululand 11 1 12 Total 52 (50%) 52 (50%) 104 40

Table 15: January 2007: Metros: Length of training in the DHIS software 1 week or Metro < 1 week more Total City of Town 4 9 13 City of Johannesburg 12 37 49 Ekurhuleni 0 7 7 ethekwini 0 3 3 Nelson Mandela Bay Metro 1 2 3 City of Tshwane 5 10 15 Total 22 68 90 Table 16: January 2007: Rural nodes: Length of training in the DHIS software 1 week or Rural Nodes < 1 week more Total Alfred Nzo 6 1 7 Chris Hani 3 9 12 Kgalagadi 1 0 1 O. R. Tambo 2 3 5 Greater Sekhukhune 0 7 7 Thabo Mofutsanyane 5 9 14 Ukhahlamba 1 1 2 Umkhanyakude 0 1 1 Umzinyathi 1 0 1 Ugu 0 1 1 Zululand 0 1 1 Grand Total 19 33 52 Table 17: January 2007: Metros: Self assessed skills in DHIS data capturing, import and export in percentages Metros Good / Average Poor / None City of Town 88 12 City of Johannesburg 87 13 Ekurhuleni 80 20 ethekwini 43 57 Nelson Mandela Bay Metro 67 33 City of Tshwane 94 6 Metro average 79 21 41

Table 18: January 2007: Rural nodes: Self assessed skills in DHIS data capturing, import and export in percentages Rural Nodes Good / Average Poor / None Alfred Nzo 71 29 Chris Hani 83 17 Kgalagadi 100 0 O. R. Tambo 100 0 Greater Sekhukhune 100 0 Thabo Mofutsanyane 92 8 Ukhahlamba 100 0 Umkhanyakude 100 0 Umzinyathi 100 0 Ugu 100 0 Zululand 100 0 Rural node average 90 10 Table 19: January 2007: Metros Standard and user defined reports/ reporting module Metros Good / Average Poor / None City of Town 69 31 City of Johannesburg 87 13 Ekurhuleni 80 20 ethekwini 33 66 Nelson Mandela Bay Metro 66 33 City of Tshwane 94 6 Metro average 75 25 Table 20: January 2007: Rural Nodes Standard and user defined reports/ reporting module District Good/Average Poor/None Alfred Nzo 71 29 Chris Hani 75 25 Kgalagadi 100 0 O. R. Tambo 100 0 Greater Sekhukhune 100 0 Thabo Mofutsanyane 54 46 Ukhahlamba 0 100 Umkhanyakude 100 0 Umzinyathi 100 0 Ugu 100 0 Zululand 1 0 Rural node average 75 25 42

Table 21: January 2007: Metros: Validation, regression analysis and assessing data quality Metros Good / Average Poor / None City of Town 75 25 City of Johannesburg 82 18 Ekurhuleni 75 25 ethekwini 29 71 Nelson Mandela Bay Metro 66 33 City of Tshwane 94 6 Metro average 72 28 Table 22: January 2007: Rural Nodes: Validation, regression analysis and assessing data quality District Good / Average Poor / None Alfred Nzo 72 18 Chris Hani 83 17 Kgalagadi 100 0 O. R. Tambo 100 0 Greater Sekhukhune 100 0 Thabo Mofutsanyane 92 8 Ukhahlamba 0 100 Umkhanyakude 100 0 Umzinyathi 100 0 Ugu 100 0 Zululand 100 0 Rural node average 86 14 Table 23: January 2007: Metros: Use of pivot tables and graphs Metros Good / Average Poor / None City of Town 63 37 City of Johannesburg 84 16 Ekurhuleni 60 40 ethekwini 40 60 Nelson Mandela Bay Metro 66 33 City of Tshwane 88 12 Metro average 69 31 43

Table 24: January 2007: Rural Nodes: Use of pivot tables and graphs District Good / Average Poor / None Alfred Nzo 71 29 Chris Hani 83 17 Kgalagadi 100 0 O. R. Tambo 100 0 Greater Sekhukhune 100 0 Thabo Mofutsanyane 46 54 Ukhahlamba 100 0 Umkhanyakude 100 0 Umzinyathi 100 0 Ugu 100 0 Zululand 100 0 Rural node average 78 22 Table 25: January 2007: Access of HIS staff members to computers Province Computer No Computer Total Free State 61 0 61 Eastern 92 14 106 KwaZulu-Natal 108 1 109 Limpopo 119 0 119 Mpumalanga 54 5 59 Northern 6 0 6 North West 50 0 50 Western 42 0 42 Gauteng 120 5 125 Total 652 (96%) 25 (4%) 677 Table 26: January 2007: Access of HIS staff members in the metros to computers Metros Computer No Computer Total City of Town 24 0 24 City of Johannesburg 50 5 55 Ekurhuleni 20 0 20 ethekwini 23 0 23 Nelson Mandela Bay Metro 5 1 6 City of Tshwane 16 0 16 Total 138 (96%) 6 (4%) 144 44

Table 27: January 2007: Access of HIS staff members in the rural nodes to computers Rural Nodes Computer No Computer Total Alfred Nzo 9 0 9 Central Karoo 1 0 1 Chris Hani 19 2 21 Kgalagadi 1 0 1 O.R. Tambo 8 1 9 Greater Sekhukhune 11 0 11 Thabo Mofutsanyane 14 0 14 Ukhahlamba 7 0 7 Umkhanyakude 5 0 5 Umzinyathi 8 0 8 Ugu 6 0 6 Zululand 12 0 12 Total 101 (97%) 3 (3%) 104 Table 28: January 2007: Computers needing upgrading due to limited hard drive capacity at a provincial and district level Provincial level computers with less than 120G hard drive capacity District level Computers with less than 80G hard drive capacity Free State 7 7 Eastern 4 1 Gauteng 0 18 KwaZulu-Natal 5 12 Limpopo 0 15 Mpumalanga 8 3 Northern 2 0 North West 5 2 Western 1 7 Total 32 65 Table 29: January 2007: Computers needing upgrading due to limited memory capacity at a provincial and district level Provincial level District level Province computers with computers with Memory capacity Memory capacity less than 2 GB less than 1 GB Free State 12 7 Eastern 5 3 Gauteng 0 26 Kwazulu Natal 10 15 Limpopo 0 21 Mpumalanga 18 3 Northern 5 0 North West 7 8 Western 3 9 Grand Total 60 92 45

Table 30: January 2007: Computers needing upgrading across the provinces Windows less than Windows 2000 No. of computers needing an MS office upgrade % computers needing an upgraded version of MS office Free State 7 32 52 Eastern 6 56 61 KwaZulu-Natal 7 21 19 Limpopo 1 19 16 Mpumalanga 1 1 2 Northern 0 5 83 North West 1 15 30 Gauteng 1 10 8 Western 0 23 55 Total 24 182 28 Table 31: Missing data for computer upgrading from across the country Version of Windows Version of MS Office Hard Drive Capacity Memory Capacity Free State 0 0 0 0 Eastern 0 0 1 1 KwaZulu-Natal 0 0 0 0 Limpopo 0 0 3 2 Mpumalanga 1 0 0 0 Northern 0 0 0 0 North West 6 0 11 5 Gauteng 0 0 1 1 Western 0 0 0 0 Grand Total 7 0 16 9 Table 32: January 2007: Computers needing upgrading across the metros Version of Windows Version of MS Office Hard Drive Capacity Memory Capacity City of Town 0 6 6 6 City of Johannesburg 1 14 7 10 Ekurhuleni 0 0 6 8 ethekwini 3 8 3 3 Nelson Mandela Bay Metro 0 0 0 2 City of Tshwane 0 3 0 3 Total 4 31 22 32 46

Table 33: January 2007: Computers needing upgrading across the rural nodes Version of Windows Version of MS Office Hard Drive Capacity Memory Capacity Alfred Nzo 0 6 1 0 Central Karoo 0 0 1 1 Chris Hani 1 12 0 0 Kgalagadi 0 0 1 0 OR Tambo 0 10 0 0 Greater Sekhukhune 0 4 5 1 Thabo Mofutsanyane 2 5 1 1 Ukhahlamba 0 6 0 0 Umkhanyakude 0 1 1 2 Umzinyathi 0 1 3 1 Ugu 0 1 1 1 Zululand 0 1 1 1 Total 3 47 15 7 Table 34: January 2007: HIS staff members with no printers across the provinces Province No printers % No printers Free State 4 6 Eastern 22 21 KwaZulu-Natal 4 4 Limpopo 11 9 Mpumalanga 14 24 North West 1 2 Western 1 2 Gauteng 21 17 Northern 0 0 Total 78 12% Table 35: January 2007: HIS staff members with no printers across the metros Metros No printers % no printers City of Town 1 4 City of Johannesburg 9 16 Ekurhuleni 2 10 ethekwini 1 4 Nelson Mandela Bay Metro 2 33 City of Tshwane 0 0 Total 15 10% 47

Table 36: January 2007: HIS staff members with no printers across the rural nodes Rural Node No printer % no printers Alfred Nzo 2 22 Central Karoo 0 0 Chris Hani 5 24 Kgalagadi 0 0 O.R. Tambo 1 11 Greater Sekhukhune 0 0 Thabo Mofutsanyane 1 7 Ukhahlamba 1 14 Umkhanyakude 0 0 Umzinyathi 2 22 Ugu 0 0 Zululand 0 0 Total 12 12% Table 37: January 2007: Provincial comparison of HIS staff members with no access to email, intranet or internet from their own computers Province Authority No email No intranet No internet Free State Local Authority 1 1 1 Provincial Government 14 12 45 Free State Total 15 (24%) 13 (21%) 46 (75%) Eastern Local Authority 4 4 5 Provincial Government 54 62 74 Eastern Total 58 (55%) 66 (62%) 79 (74%) Kwazulu Natal Provincial Government 72 12 22 KwaZulu-Natal Total 72 (66%) 12 (11%) 22 (20%) Limpopo Local Authority 5 5 5 Provincial Government 66 60 77 Limpopo Total 71 (60%) 65 (55%) 82 (69%) Mpumalanga Provincial Government 8 11 41 Mpumalanga Total 8 (14%) 11 (19%) 41 (69%) Northern Provincial Government 2 4 3 Northern Total 2 (33%) 4 (66%) 3 (50%) North West Provincial Government 2 6 2 North West Total 2 (4%) 6 (4%) 2 (4%) Western Local Authority 0 13 10 Provincial Government 1 1 16 Western Total 1 (2%) 14 (33%) 26 (62%) Gauteng Local Authority 4 37 20 Provincial Government 20 37 20 Gauteng Total 24 (20%) 74 (59%) 40 (32%) Total 253 (37%) 265 (39%) 341 (50%) 48

Table 38: January 2007: Comparison across the metros of HIS staff members with no access to email, intranet or internet from their own computers No Metro email No intranet No internet City of Town 1(4 %) 14(58%) 17(71%) City of Johannesburg 12(22%) 47(85%) 26(47%) Ekurhuleni 3(15%) 4(20%) 4(20%) ethekwini 15(65%) 2(9%) 3(13%) Nelson Mandela Bay Metro 2(33%) 2(33%) 4(67%) City of Tshwane 2(13%) 9(56%) 2(13%) Total 35(24%) 78(54%) 56(39%) Table 39: January 2007: Comparison across the rural nodes of HIS staff members with no access to email, intranet or internet from their own computers Rural nodes No email access No intranet No internet Alfred Nzo 7(78%) 7(78%) 8(89%) Central Karoo 0(0%) 0(0%) 1(100%) Chris Hani 13(62%) 15(71%) 15(71%) Kgalagadi 1(100%) 1(100%) 1(100%) O.R. Tambo 7(78%) 7(78%) 8(89%) Greater Sekhukhune 9(82%) 7(64%) 8(73%) Thabo Mofutsanyane 2(14%) 2(14%) 13(93%) Ukhahlamba 3(43%) 4(57%) 6(86%) Umkhanyakude 3(60%) 0(0%) 0(0%) Umzinyathi 6(75%) 2(25%) 3(38%) Ugu 5(83%) 0(0%) 0(0%) Zululand 10(83%) 5(42%) 5(42%) Grand Total 66(63%) 50(48%) 68(65%) 49

APPENDIX 2 From the proposal to the National DOH: Health Management Information System (HMIS) Human Resources (HR) Requirements. Prepared by the HST and HISP October 2005 Proposed HR structure for Information Management The following post structure is proposed (the institutional types are following the recent submission on facility definitions): PHC facilities Level 1 hospitals, step down facilities Level 2 and 3 Hospitals Sub district office District/Regional Offices Provincial Office g National Information Cluster National Health Programs - Post Number of personnel Post level Data captured at sub district office f Clinical Data Officer 1 per 150 beds 5-7 Clinical Data Manager ½ time AD or fulltime (if > 300 beds) 9-10 Information Manager Nil Clinical Data Officer 1 per 150 beds 5-7 Clinical Data Manager 1 AD per 300 beds 9-10 Information Manager 1 9-10 Clinical Data Officer 1 per 20 PHC facilities 5-7 Clinical Data Manager 1 AD per 30 facilities 9-10 Information Manager 1 AD 9-10 Clinical Data Officer 1 5-7 2AD ( 1 PHC & Clinical Data Manager hospital, 1 other 9-10 routine, HR & finance) Information Manager 1 9-11 Clinical Data Officer 1 5-7 4 (1 PHC, 1 hospital, 1 Clinical Data Manager other routine, 1 HR and 11-12 finance data) Information Manager 1 13 Database Developer/ Manager 1 13 Clinical Data Officer 1 5-7 4 (1 PHC, 1 hospital, 1 Clinical Data Manager other routine, 1 HR and 11-12 finance data) Information Manager 1 13 Database Developer/ Manager 1 13-14 Each Cluster Clinical Data Manager 2 1@11-12; 1@ 9-10 f Big PHC facilities might have the capacity and the equipment to capture data on site if so, a post of clinical data officer should be considered. g Note that some of the smaller provinces (population wise) may not be able to have such senior posts they should adjust the post level accordingly 50