Fredrick Makumbi, Michael Friedman, Chijioke Okoro, Aloysius Mutebi, Fred Wabwire-Mangen
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HEALTH SECTOR HIV/AIDS RESPONSE REVIEW 2007 - 2010 HMIS Building Block
Fredrick Makumbi Michael Friedman Chijioke Okoro Aloysius Mutebi Fred Wabwire-Mangen
November 2010
August 2010
1 HEALTH SECTOR HIV/AIDS RESPONSE REVIEW 2010 HMIS BUILDING BLOCK
Fredrick Makumbi, Michael Friedman, Chijioke Okoro, Aloysius Mutebi, Fred Wabwire-Mangen
November 2010
Ministry of Health
Kampala, Uganda
2 TABLE OF CONTENTS
TABLE OF CONTENTS ...... 4
LIST OF TABLES ...... 6
LIST OF FIGURES ...... 7
ACKNOWLEDGEMENTS ...... 8
ACRONYMS AND ABBREVIATIONS ...... 9
EXECUTIVE SUMMARY ...... 10
1.0 INTRODUCTION ...... 14
1.1 BACKGROUND OF THE ASSESSMENT...... 14 1.2 RATIONALE AND SIGNIFICANCE OF THE HIS ASSESSMENT...... 15 1.3 SCOPE AND OBJECTIVES OF THE STUDY...... 16
2.0 METHODOLOGY ...... 17
2.1 DESIGN AND GENERAL APPROACH...... 17 2.2 STUDY POPULATION AND SAMPLING...... 17 2.2.1 SELECTION OF THE DISTRICT FIELD STUDY SITES...... 17 2.2.2 SAMPLING AND SELECTION OF KEY SERVICE PROVIDERS...... 18 2.3 METHODS AND TOOLS FOR DATA COLLECTION...... 18 2.3.1 DOCUMENT/DESK REVIEWS...... 18 2.3.2 KEY INFORMANT INTERVIEWS...... 19 2.3.3 STRUCTURED INTERVIEWS...... 20 2.4 DATA MANAGEMENT AND STATISTICAL ANALYSIS...... 21
3.0 STUDY FINDINGS ...... 22
3.1 FACILITY LEVEL RESULTS...... 22 3.1.1 DATA COLLECTION TOOLS AT HEALTH FACILITIES...... 23 3.1.2 DATA ANALYSIS AND REPORT GENERATION...... 25 3.1.3 HUMAN RESOURCES AND EQUIPMENT...... 26 3.1.4 ELECTRONIC MEDICAL RECORDS SYSTEMS USE AT THE DISTRICT HEALTH FACILITIES...... 28
3 3.1.5 CHALLENGES ENCOUNTERED AT FACILITY LEVEL IN KEEPING MEDICAL RECORDS...... 31 3.2 DISTRICT LEVEL RESULTS...... 35 3.2.1 HUMAN RESOURCES FOR DATA COLLECTION AND GENERATION AT DISTRICT LEVEL...... 35 3.2.2 SUBMISSION OF MONTHLY HMIS REPORTS...... 35 3.2.3 AVAILABILITY AND ADEQUACY OF STOCK FOR HMIS AND REGISTERS...... 35 3.2.4 CSO REGISTRATION AND THEIR REPORTING TO THE DISTRICTS...... 36 3.2.5 TIMELINESS, COMPLETENESS AND ACCURACY OF BOTH THE HMIS AND HIV/AIDS SPECIFIC MONTHLY SUMMARIES...... 36 3.2.6 REPORTING OF HMIS TO MOH AND FEEDBACK, AND USE OF HMIS FOR PLANNING...... 37 3.2.7 RESOURCES FOR THE FUNCTIONING OF HMIS AND PROGRAM DATA...... 37 3.3 NATIONAL LEVEL RESULTS...... 37 3.3.1 AREAS IN WHICH UGANDA IS DOING WELL IN DATA COLLECTION...... 37 STRENGTHS...... 37 3.3.2 WEAKNESSES/ CHALLENGES TO DATA COLLECTION...... 38 3.3.3 SUGGESTION FOR IMPROVEMENT AND ANTICIPATED CHALLENGES...... 39 3.3.4 AREAS IN WHICH UGANDA IS DOING POORLY IN DATA COLLECTION...... 39 3.4 INTEGRATION OF HIV DATA...... 40 3.4.1 WHAT UGANDA HAS DONE TO INTEGRATE HIV DATA...... 40 3.4.2 BARRIERS TO EFFECTIVE INTEGRATION...... 40 3.4.3 SPECIFIC ACTIONS TO BE TAKEN BY MOH...... 41 3.4.4 DATA USE AND POLICY DEVELOPMENT FROM COLLECTED DATA...... 41 3.5 DISCUSSING MAJOR STUDY QUESTIONS & EMERGING ISSUES...... 42
4.0 RECOMMENDATIONS ...... 44
4.1 RESOURCE ALLOCATION; BOTH FINANCIAL AND HUMAN...... 44 4.2 EMPOWER DISTRICTS TO REGISTER ALL CSO AND PRIVATE HEALTH PROVIDERS...... 44 4.3 CENTRALIZE ALL HIV/AIDS DATA SYSTEMS...... 45 4.4 FEEDBACK MECHANISM BE IMPROVED...... 45
5.0 CONCLUSIONS ...... 46
BIBLIOGRAPHY ...... 47
APPENDICES ...... 49
4 LIST OF TABLES
Table 3.1.1 Number of health facilities surveyed by level and ownership 21
Table 3.1.2 Current use of Registry for data collection by Facility-Level 22
Table 3.1.3 Mean number of Available and Required Records officer by facility characteristics...... 25
Table 3.1.4 Mean number of Available and Required Data analysis personnel by facility characteristics...... 25
5 LIST OF FIGURES
Figure 1 Map of Uganda showing district 81
6 Acknowledgements The AIDS Control Programme, Ministry of Health The Resource Centre, Ministry of Health The Uganda AIDS Commission, M&E Team The AIDS implementing partners The AIDS development Partners The District Chief Administrative offices and Health Offices, Arua, Gulu, Kampala, Kamuli, Kamwenge, Katakwi,Kiboga, Mbarara, Pader, Tororo The Health Facilities visited, Arua, Gulu, Kampala, Kamuli, Kamwenge, Katakwi,Kiboga, Mbarara, Pader, Tororo
This report was made possible through support provided by the MoH (Uganda), WHO (Uganda office), Contract No.XXXXXXXXXXX. The contents are the responsibility of the authors and do not necessarily reflect the views of funders or the MoH Copyright © 2010. Ministry of Health, Uganda government Suggested citation: Makumbi Fredrick, Friedman Michael, Chijioke Okoro, Mutebi Aloysius, Fred Wabwire-Mangen 2010. “HEALTH SECTOR HIV/AIDS RESPONSE REVIEW 2007 - 2010 HMIS Building Block,”
7 ACRONYMS AND ABBREVIATIONS HMIS Health Management Information system EMRS Electronic Medical Records System ACP STD/AIDS Control Program AIDS Acquired Immune Deficiency Syndrome ART Antiretroviral Therapy ARV Antiretroviral agent CDC Centers for Disease Control and Prevention CRS Catholic Relief Services DHO District Health Office FDA US Food and Drugs Administration HIV Human Immuno-deficiency virus HIS Health Information systems HMIS Health Management Information systems HSHASP HIV/AIDS Health Sector Strategic Plan 2007/8 – 200/10 HSSP – II Health Sector Strategic Plan II IEC Information, Education and Communication IP Implementing Partner JCRC Joint Clinical Research Centre MoH Ministry of Health NPSSP I National Pharmaceutical Sector Strategic Plan I NSP National HIV/AIDS Strategic Plan 2007-12 OI Opportunistic Infection PEP Post-Exposure Prophylaxis PEPFAR United States President’s Emergency Plan For AIDS Relief PMTCT Prevention of Mother to Child Transmission of HIV PMMP Performance Monitoring and Management Plan STI Sexually Transmitted Infection UNICEF United Nations Children’s Fund UNHASP Uganda National HIV & AIDS Strategic Plan 2007/8 - 2011/12 USAID United States Agency for International Development WHO World Health Organization
8 EXECUTIVE SUMMARY Background
Given Uganda’s efforts to have a coordinated national response to HIV epidemic, the HMIS health system which was established in 1985, was revised in 1997 to be inclusive in capturing HIV/AIDS related programme data from various levels of the national health system. Moreover, as the global response to HIV epidemic has expanded, other international organizations have implemented HIV prevention, care and treatment programs in Uganda, in line with the national strategy. However, there are gaps and areas in the HMIS that need strengthening, especially because the system is an integral part of the national health system in Uganda. The data, which move from health facility to central/national level, provides critical health staff, policy- makers and donors with information necessary for planning, decision-making and allocating resources. This system is essential in generating information that provides evidence-based picture for Uganda’s progress in the national HIV/AIDS response. Therefore, the objectives of HMIS block in the HEALTH SECTOR HIV/AIDS RESPONSE REVIEW 2007 – 2010 were to 1) assess availability and adequacy of HIV/AIDS related information gathering, processing and flow at various levels in the health system 2) determine if and how the data are utilized at various levels in the health system and 3) identify challenges to data collection, processing and reporting mechanisms, and integration of HIV/AIDS related data into HMIS or central HIV monitoring systems. The findings of this assessment will inform the strengthening and help to stream data collection processes, coordination of all HIV/AIDS related program information through the already established health management information system, where such improvement is needed.
Methods
The primary and secondary data were collected using qualitative and quantitative methods, specifically a document/desk review, structured interviews, key informant interviews. Additionally, site/facility assessments were carried out to capture information on availability of trained staff, equipment and the necessary infrastructure throughout all levels of the health system, to facilitate data collection and processing related to HMIS. A total of 62 facilities from 10 selected districts based on the HIV sero prevalence survey and HMIS performance league table were included in this assessment. A selection criterion for the included facilities was based
9 on services provided, but mainly focusing on HIV/AIDS core service areas of prevention, care and Treatment, Social support, and strengthening the service delivery systems.
Findings
HIV prevention: This HIV/AIDS service area especially the IEC and condom distribution did not have data collection tools available, nor indicators integrated into other data collection tools. Just about 30% of facilities that provide condoms (both facility-based and outreach services) and IEC, have related data collection tools available. Less than 70% of facilities who provide blood transfusion services and 45% of facilities providing PEP and MMC have related data collection tools available. However, over 90% of facilities that provide PMTCT services had data collection tools available. Facilities make monthly reports about HIV/AIDS indicators to the district. Over 80% of the facilities offering PMTCT and STI prevention services reported at least 50% of the time during the 12 months compared <10% of those offering IEC and condom distribution. Lack of data linkage into the HMIS tended to be the main reason of non-reporting of these indicators.
Most of the care and treatment programs had data collection tools available, majority reported at least 6 times in the 12 months prior to the assessment, and had their data lined to the HMIS. However data from PNFP and PHPs were not fully linked to HMIS.
Overall those HIV/AIDS program areas that are being supported by AIDS development partners have better outcomes in terms of data collection and reporting into the HMIS. Factors that may hinder better data collection and reporting include lack of equipment and infrastructure (computers and workspace), support materials (stationary and transport), and skilled. Integration of HIV indicators from all stakeholders in HIV/AIDS services is lacking, and the presence of “competing” information management systems (such as MEEPP) further weakens the ability of the HMIS to consolidate all data for the monitoring national response.
Discussion
Data collection in the areas of HIV prevention and social services is still poor. Most of the focus has been put on Care and treatment especially ART, PMTCT and STI, because the core business of many private facilities is the provision of curative care.
10 Funding for many of the HIV/AIDS activities is contributed by the donors or AIDS development partners who have own reporting mechanisms. MEEP and Uganda AIDS Commission’s Performance Monitoring and Management Plan (PMMP) are some of the systems that could be running parallel to the HMIS partly because HMIS does not collect compressive data to satisfy these systems, or there is lack of coordination in the HIV/AIDS data flow. Absence of data on social services including orphanhood and vulnerable children is worrying and needs to be addressed. Although community based organizations are doing extensive work in these areas, lack of data in the main streamline HIV/AIDS datasets at the MoH may hinder proper linkages of such services and trends in HIV infections.
The electronic submission of HMIS reports to the national level suggests there is hope for a quicker turn around in giving feedback to the districts which may result in better quality data. However, weaknesses such as the existence of multiple HIV/AIDS data collection systems (such as PMMP-UAC, MEEP-PEPFAR) can lead to incomplete or overestimation of the HIV indicators Indicator definitions and extent of needed information per data collection system complicates the data gathering processes, and reporting.
District level CSO and private health providers not reporting data into the HMIS system potentially create underestimation of the national HIV response.
Recommendations:
Key areas of focus that can enhance improvement in data collection, processing and reporting may include; empowering districts to register all CSO and private health providers, and make it mandatory for these CSO to provide HIV/AIDS indicators from their services to the respective district departments; the government needs to urgently increase resource allocation to the HIV/AIDS sector especially in the areas of data collection and processing; Centralize all HIV/AIDS data systems other than independent systems by various implementers or sectors, and developing a feedback mechanism to the districts and lower levels that are generating the data for their proper planning, and improvement in data quality.
CONCLUSION
11 Data collection tools and HIV/AIDS indicators especially in the area of HIV prevention are lacking. Standardized data collection and reporting formats (especially indicators) for all stakeholders involved in the provision of HIV/AIDS care need to be agreed upon and implemented by all agencies; this may enhance the integration of HIV data and potentially reduce or eliminate the cumbersome task of duplicating data capture for various stakeholders at the health facilities. Lack of skilled human resource for data management may require recruitment of personal, and/or increased training of the available staff for the required skills.
12 1.0 INTRODUCTION
1.1 Background of the Assessment HIV/AIDS in Uganda has continued to spread all over the country registering epidemic peak of 18% in 1992 (MoH ANC Surveillance, 1995). The epidemic trend has changed including a precipitous decline in Sero-prevalence, captured through ANC surveillance, between 1992 and 2002, which later stabilized at prevalence between 6.1% and 6.5% during 2002-2005 period (MoH, 2005; Kirungi et al., 2006). For the first time, the overall national prevalence was estimated 6.4% among men and women aged 15-49 years in the 2004/05 HIV Sero-Behavioural Survey (UHSBS) (MoH and ORC Macro 2006).
In order to address the challenges of the HIV epidemic, the Uganda government embarked on a multi-sectoral approach to HIV prevalence but the health sector response accounts for more than 75% of the entire country response. However, health sector has mainly focused on health interventions (HIV prevention and treatment), with limited emphasis in areas such as impact mitigation for OVC. The response to HIV/AIDS started with formation of the National AIDS Control Program in the Ministry of Health, but the monitoring and evaluation of the impact of the various programs involving government and non-governmental stakeholders as well as public- private partnerships has been unclear with no proper indicators or inadequate flow of information. For example, a functional health management information system (HMIS), which incorporates all the data needed by program implementers, clinicians, health service users and policy makers to improve and protect population health, may not be receiving all the needed HIV/AIDS indicators for the monitoring of the HIV/AIDS response. Although HMIS exists, it is faced with key challenges including poor data quality such as accuracy, timeliness and lack of data collection tools, which makes it dysfunctional. A number of HIV/AIDs care, treatment and service providers are either not collecting or sending data to the coordinating units, and are not utilizing these data for performance monitoring as well as planning. This raises critical questions about the national health information systems for HIV and current overall capacity of the system to support HIV program efforts within the health sector. This assessment therefore focused at the process of information gathering, processing, reporting and utilization of HIV data at various levels of health facilities, as well as at the district and national levels in order to ascertain the HIV/AIDs response in Uganda.
13 1.2 Rationale and significance of the HIS assessment
Health management information system (HMIS) is important in assessing whether the key activities of a program are implemented as planned, and if such activities result into expected outcomes (Sally Stansfiled, 2005). In Uganda, Health Management Information System (HMIS) was initiated in January 1997 (MoH, 1997; GLADWIN, J 2003) after a review of the previous health information system (HIS) designed in 1985). The development of the HMIS was as a result of the deficiencies of the HIS that collected data in the health facilities, summarized at the district level and later forwarded to the MoH-the central point for data analyses. The HIS (1985) system left out vital management information, such as staffing levels, infrastructure, health facility management, medical equipment availability, financial information and drug management. The primary function of the Uganda HMIS is to establish and maintain a comprehensive source of health and management information for planning, monitoring and evaluation of the health sector strategic plan, with key focus on improving and strengthening: 1) data collection and compilation of health events; 2) data quality as measured by timeliness, completeness and accuracy; c) analyses, interpretation and utilisation; d) regular dissemination and feedback to all stakeholders; and; e) capacity building of health workers in aspects of data management, analysis and utilisation at all levels of health care, treatment and service delivery.
The role of HMIS within the National health systems is very crucial because information from the system can be used for program planning and monitoring purposes. The immediate users of such information are the health providers, NGOs, and policy makers at the MoH who coordinate the generation of such data. Data on services such as immunisation coverage, trends in epidemics such as cholera or Ebola, and other services like family planning FP and malaria can easily be monitored through the HMIS. Planning for both logistics and finances for health can be more appropriately supported by HMIS. The impact of government programs can easily be assessed using a functional HMIS. With the advent of HIV/AIDS, the government’s efforts to control the epidemic would therefore be more prudently assessed by utilizing the already established HMIS to monitor and evaluate, for further planning. Therefore, a review of HMIS in relation to the key indicators of HIV/AIDS response in the thematic areas is essential to ascertain how well Uganda is doing in terms of HIV/AIDS data collection and utilization at all the levels of HIV/AIDS care, treatment and service provision.
14 1.3 Scope and objectives of the study
The general objective of this assessment was to ascertain if and how HIV/AIDS related information flows, and its utilization as well as identifying challenges experienced from the source of data collection (health service provision points at community and facility levels), through the district up to national level as we try to understand the role of HMIS in Uganda’s HIV/AIDS response
The specific objectives of the HMIS block were; 1. To assess availability and adequacy of HIV/AIDS related information gathering mechanisms for the three thematic areas of prevention, care and treatment, and social support at the health service provision level (facility and community) and district level 2. To assess the adequacy of information processing and flow from various health facilities and HIV/AIDS care providers including flow of civil society organization (CSO) information into the HMIS 3. To determine if and how the data are utilized at the district or lower local levels, especially for planning purposes 4. To identify challenges to data collection, processing and reporting mechanisms, and integration of HIV/AIDS related data into HMIS or central HIV monitoring systems
15 2.0 METHODOLOGY
2.1 Design and general approach
This review was conducted using a cross-sectional design approach utilizing both qualitative and quantitative methods to gather data. For qualitative data collection key informant interviews (KIIs) were preferred to focus group discussions (FGDs) because FGDs were hard to organize due to the busy schedules of all potential participants. For the quantitative data collection, we administered self-assessment surveys as well as use of observational check lists at all levels; health facilities, district and national level. We also did a review of some key documents as shown below. Also, we carried out a review of the Health Sector HIV/AIDS Strategic Plan, 2007–2010 (HSHASP), especially the areas of prevention, care and treatment, and social support examining health services including: IEC/BCC, infection prevention and control, Prevention of Mother to Child Transmission (PMTCT), HIV Counselling and Testing (HCT), Sexually transmitted Infections (STIs), clinical management of HIV/AIDS, home-based care (including Palliative Care) and medical male circumcision (MMC).
2.2 Study population and sampling
2.2.1 Selection of the District Field Study Sites
This review was conducted in ten (10) districts based on the eight national sero-behavioural Survey regions as the primary categorization, indicating heterogeneity of HIV infection in Uganda by geographical areas. Districts were from all regions of Uganda, Central (Kiboga, and Kampala), Southwest (Mbarara), Western (Kamwenge), East Central (Kamuli), Eastern (Tororo), Northeast (Katakwi), North Central (Pader, and Gulu) and Northwest (Arua). However, Kampala and Gulu were selected because of their special circumstance: both areas are considered urban areas, both areas have high HIV prevalence (Kampala district due to population dynamics and Gulu district due to recent internal conflicts that had an impact on local health system). Other factors included as selection criteria were: i) ensure a balance in urban/rural divide in the eight districts, as defined by Uganda Bureau of Statistics (UBOS). Thus from rural (Kamwenge, Kiboga, Kamuli and Katakwi) and from urban (Arua, Mbarara, Tororo and Pader), ii) performance in reporting health information as defined by the league table, iii)
16 availability of at least one health facility (whether private or public) that is engaged in provision of HIV/AIDS care and services, and iv) duration of district existence that should be at least ten years because most newly-established tend to report to the districts they were formally part of.
2.2.2 Sampling and selection of key service providers
We compiled profiles for the ten (10) districts visited, indicating the number and level of health facilities, and classified them into public, private not for profit (PNFP), private for profit (PFP), and uniformed services (police, prisons and army). All HC IVs, and main hospitals were automatically included in the sample because they provide HIV/AIDS care and services especially ART and HCT. We however purposively selected a sample of HC III and HC IIs if they provided care and treatment services because this enabled us to assess HIV/AIDS services. Other providers selected into this exercise irrespective of their level of health system categorization but seen as a source of very useful information included Infectious Diseases Institute (IDI), The AIDS Service Organization (TASO), Joint Clinical Research Centre (JCRC), and Mildmay-Uganda. Some HIV/AIDS development partners were also included because of their role in HIV/AIDS service provision and subsequently their generation of data which links to the monitoring of the national HIV/AIDS response. This was important because lack of integration of the partner’s contribution into HMIS can result into inaccuracies in the assessment of the HIV/AIDS response. CDC, USAID and research partners such as IDI, JCRC, Demographic surveillance sites (DSS), Rakai Health Sciences Program (RHSP) and Medical Research Council (MRC) were some of the partners whose sites were included in the review.
2.3 Methods and tools for data collection Primary and secondary data were collected using quantitative and qualitative methods, to gather information from both the national health facilities and selected sites supported by some of the development partners. Because of the routine monitoring of HIV/AIDS program, it was prudent to include partner sites to assess how their information links into the health sector for the HIV/AIDS response. Document/desk reviews, key informant interview and structured interviews and site assessments were used to collect relevant data.
2.3.1 Document/desk reviews A desk review of secondary sources (published in peer reviewed journals, reports and some unpublished documents) related to health information systems or data management in the
17 health sector was done. In the document review, we assessed how the various key players in monitoring HIV/AIDS programmes at both national and local (district) levels link with the health sector, used HMIS. The following documents (mainly obtained from the MoH) were critical in informing this assessment, providing information pertaining to HIV/AIDS response: 1. Health Sector HIV/AIDS Strategic Plan 2. National HIV/AIDS Strategic Plan, 2007/08- 2011/12 3. Uganda AIDS Commission Frameworks from 2005-2009 4. Surveillance report, 2009 5. World Health Organization 2004:National Aids Programs: A Guide To Monitoring and Evaluating HIV/AIDs Care And Support 6. World Health Organization 2004; Developing Health Management Information Systems: A Practical Guide For Developing Countries.
2.3.2 Key Informant Interviews
We conducted key informant interviews with the monitoring and evaluation (M&E), facility-level staff HIV-focal persons or responsible officers in charge of data management and analysis such as records assistants, district-level staff (HMIS focal persons, members of District Health Team, DHO and focal persons for various HIV/AIDS programs and HMIS) and national-staff managing HIV/AIDS programs and national-level HMIS and M&E staff responsible for data. This was done to enhance the understanding of how various bodies, at all levels in the health system, manage the process of collecting data for their indicators, reporting, coordinating and disseminating HIV/AIDS data, and using the data for planning and decision making. Some of the organizations implementing or coordinating HIV/AIDS that were visited for interviews with the key responsible officers at the national level were; 1. AIDS Control Program (ACP) 2. Uganda AIDS Commission (UAC) 3. Ministry of Health (MoH, HMIS) 4. Vertical programs offering HIV care services (e.g. ART, CDC) 5. National AIDS information Centre (NADIC) 6. UNFPA
18 2.3.3 Structured interviews
In-depth structured interviews were conducted with personnel, such as information (records) officers, responsible for routine health information and data collection, reporting, and analysis at the health facility-level (both private and public health facilities) and district- level (HMIS focal persons, Assistant Chief administrative Officers-staff in charge of HIV/AIDS). Self-assessment mini-questionnaires were the primary tools for data collection in this exercise with the focus on assessing data collection, data management, and reporting processes, flow and use of HIV/AIDS information. Data on the availability, capture and adequacy of key HIV/AIDS indicators in the area of Prevention, Care and Treatment and social services were obtained. District planners and other officers at the district level were asked about use of HIV/AIDS data for their planning and decision making. Regional/Referral hospitals, do not report their HMIS to the districts they are located within, were included in assessment and data was obtained using the same tools to ascertain how they link/report their health information into the national HMIS.
Health and CSO facility assessments: A short structured questionnaire for the health facilities and district level CSO was administered to the health facility in-charges or CSO unit in-charges. The tool covered three areas of facility assessment in relationship to the HIV-indicators of Prevention, care and treatment, and social support. The areas assessed were 1) data collection tools and Reporting systems (with focus on availability of registry and staff responsible, number of monthly summaries in past 12 months, annual summaries in past 3 yrs, data integration into HMIS, monthly reports to health sub- district [HSD] and to the DHO), 2) Data analysis, interpretation, challenges and recommendations (focusing on display of analyzed data, use of data as part of quality assurance and control processes), and 3) Human resource and equipment (focusing on required and recommended numbers of skilled staff, and currently available and functional equipment such as computers).
All the tools were developed in collaboration with stakeholders, especially the MoH (ACP) staff, pre-tested and piloted in Kayunga district in December 2009, prior to data collection from the ten districts during February 2010. Data were collected by research assistants who were trained for 3 days in preparation of this assessment.
19 2.4 Data management and statistical analysis
For the qualitative methods, all KIIs were tape recorded and carefully transcribed verbatim. Manifest content analysis technique (computer aided) was used. The initial step in analysis was to carefully read through the typed transcripts. They were then logged into ATLAS.ti qualitative data management software to systematically extract meaningful pieces of data based on the objectives of the review. Emerging themes were also identified depending on the study objectives. Query reports were run and then carefully read to summarize key messages that guided report writing. Key quotes from the KIIs were highlighted and are presented in the report verbatim. We performed triangulation of qualitative and quantitative to enhance the evidence for the assessment objectives from the two methodologies. Quantitative data were captured in screens designed in EpiData. The data analyses were mainly descriptive providing summary statistics and proportions for the categorical variables, as well as means (with standard deviations) for continuous variables. Differences in proportions were assessed using chi-square, while differences in continuous variables between any two or more categories were assessed using Wilcoxon signed-rank test. Graphs were also included to illustrate some of the findings.
20 3.0 STUDY FINDINGS This section looks at the results of the assessment, covering facility, district and national level findings.
3.1 Facility level results
Table 1 shows the overall distribution of the health facilities by level in each of the ten (10) districts visited districts, forming part of the 62 selected facilities for participation in this assessment. Focus has been put on assessing the ability of these health facilities by level (HC- III, IV and hospitals) and by Ownership (Public, PHP, PNFP and Others) to collect, analyze, report and use HIV/AIDS data in the monitoring of the National response.
Table 3.1.1 Number of health facilities surveyed by level and ownership
Number of facilities Proportion, %
Total 62 100.0
Level
HC II 1 1.6
HC III 20 32.3
HC IV 12 19.4
Hospital 22 35.5
Private 7 11.3
Type of facilities
Other (NGO/CSO) 2 3.2
PHP 6 9.7
PNFP 18 29.0
Public 36 58.1
21 3.1.1 Data Collection Tools at health facilities HIV prevention
Analysis for the availability of data collection tools (registries) by level of facility shows that the proportion of facilities with available data collection tools for Information, Education and communication (IEC), Condom (Condom distribution-static and outreach), PEP (Universal precautions and Post Exposure Prophylaxis) and MMC was very low compared to facilities reporting tools for PMTCT and STI . Almost all hospitals, 95%[20/21], reported availability of tools to collect PMTCT data, but only 36% (8/22) reported available tools for capturing data on condoms. Such divergent differences in availability of data collection tools for HIV prevention by type of prevention (PMTCT and STI, compared to Condoms and IEC) are consistent at all levels of health facilities (Hospital, HC IV-II) visited, and by ownership (private and public); for example none of the PHP reported tools for IEC, or condom distribution (static or outreach) although availability of PMTCT and STI tools did not differ across facility ownership. Private/CSO facilities such as IDI and TASO that offer HIV/AIDS service have tools availability for condoms and STI, but not Blood transfusion, IEC, PEP and MMC, areas which may not be core for such organizations.
Table 3.1.2 Current use of Registry for data collection by Facility-Level
Level of Facility
Hospital HC IV HC III HC II *Private Total providers
Total 22 (35.5) 12 (19.4) 20(32.3) 01(1.6) 07(11.3) 62
Area1: prevention
IEC 7/21 (33.3) 1/10 (10) 2/17(11.8) 1 2/7(28.6)
Condom-static 8/22(36.3) 3/9(35.3) 7/17(41.2) 0 3/6(50)
Condom-Outreach 5/21(25.8) 3/9(35.3) 5/17(29.4) 0 3/6(50)
PMTCT 20/21(95.2) 11/11(100) 19/19(100) 1(100) 4/7(57.1)
Blood transfusion 13/20(65.0) 2/8(25.0) 0/15(0) 0 1/7(14.3)
Post exposure 9/20(45.0) 2/10(20.0) 5/16(31.2) 1(100) 3/7(42.9) prophylaxis
Management of 16/19(84.2) 8/10(80.0) 13/17(76.5) 1 6/7(85.7)
22 Sexually Transmitted Infections
Medical Male 9/20(45.0) 1/10(10.0) 1/15(6.7) 0 1/7(14.3) circumcision
Area2: care and treatment
ART/Treatment 21/22(95.4) 8/11(72.7) 8/17(47.1) 1(100) 7/7(100) coverage
HCT-static 21/22(95.4) 11/11(100) 17/18(94.4) 1 6/7(85.7)
HCT-outreach 14/21(66.7) 9/9(100) 8/15(53.3) 1 3/7(42.9)
Treatment and 15/21(71.4) 6/8(75.0) 10/15(66.7) 1 4/6(66.7) prophylaxis for opportunistic infections in general
Treatment and 21/22(95.4) 9/11(81.8) 14/17(82.4) 1 5/7(71.4) prophylaxis for opportunistic infections (TB)
Home-based Care 5/20(25.0) 3/9(33.3) 2/15(13.3) 1 3/7(42.9)
NON-HIV SERVICES
Malaria 20/20 (100) 10/11 (91) 20/20 (100) 1/1 4/7 (57)
TB 22/22 (100) 11/11 (100) 16/19(84.2) 1/1 4/7 (57)
Maternal health 20/22 (91) 10/11 (91) 20/20 (100) 1/1 3/7 (42.9)
Immunization 20/22 (91) 10/11 (91) 18/18 (100) 1 1/7 (14.3)
Note: * Case medical clinic, IDI, JCRC, Kadic health services Nakulabye, Naguru medical lab, Taso and TASO Mulago
Care and treatment
Almost all hospitals, 95% (21/22) and about three quarters 72% (8/11) of HC IV had ART registries for data collection. This observation was also consistent for HCT- Static and Treatment and prophylaxis for opportunistic infections (TB). However, only 25% (5/20) and 33% (3/9) hospitals and HC IVs, respectively, had registries for Home-based Care. On the whole, a lower proportion of facilities at HC III levels or below compared to HC IV and above reported availability of registries for data collection. For example just about half of the HC III 47% (8/17)
23 had ART registries compared to almost all hospitals 95% (21/22). Less than two thirds of facilities did not have registries for HCTOUT (HCT-Outreach) and HMBC.
Non-HIV services
Data collection tools (Registries) for the non-HIV services, Malaria, TB, MCH (Maternal health and Immunization) are available at almost all levels of facilities (Hospitals, HC IV and III). However, the private facilities are not yet doing well in this area. For example only 1/7 and just about half 4/7 of private facilities had registries for immunizations and malaria, respectively (such services may not be their core health provision services for private facilities offering HIV/AIDS care and services).
3.1.2 Data Analysis and Report Generation HIV prevention
Analysis and report generation at facility level was very limited. For example, in the past 12 months prior to the survey, on average for every 5 facilities one or none had at least 6 reports for IEC, condoms, PEP). The expectation was that every month, there should be a report. However, as earlier observed these same areas of HIV prevention lacked data collection tools. However, analysis and report generation for the PMTCT (18/22 hospitals; 11/12 HC IV; 16/20 HC IIIs) and STI (16/22 hospitals; 10/12 HC IV; 14/20 HC IIIs) had at least 6 reports in the 12 months prior to the survey.
Care and treatment
Reporting of care and treatment for at least 6 times in the past 12 months was more common for the ART services at hospitals (20/22) and HC-IVs (8/12) but not at HC III (6/20); HCT-static as well as TB treatment was also common, at least 4 in 5 facilities produced/generated 6 or more reports in past 12 months. Also observed was that just about half of the facilities had at least 6 reports on the TTTINF (Treatment of infections) and HCT-outreach in the past 12 months; while Home based care was persistently low
Non-HIV services
Almost all facilities had at least 6/12 reports in the past 12 months. However, HC-III reporting was still below that of hospitals and HC-IVs.
Integration of HIV/AIDS data into HMIS
24 HIV prevention
Majority of the HIV prevention services are not integrated into HMIS by level or ownership of facility, except for PMTCT (18/22 hospitals; 10/12 HC-IV and 16/20 HC-III) and STI (16/22 hospitals; 7/12 HC-IV; and 14/20 HC-III). Almost all facilities do not have condom, IEC, MMC and PEP data into HMIS; for example, just about 1 in 3 public facilities reported integration of condoms into HMIS while almost none of the PHP reported integration of any prevention item into HMIS (PHP may not be filing HMIS data).
Care and treatment
In the hospitals, HMIS integration of ART (18/22), HCT-static (16/22) and TB treatment and prophylaxis (20/22) were the most commonly sited while HC-IV sited HCT-Static and TB, and HC-III sited HCT-Static most. Only 7/12 HC-IV and 5/20 HC-III reported integration of ART. Non-HIV services were integrated by almost all the hospitals and by about 3 in 4 HC-IVs and HC-III. On the other hand (by ownership), over half of the public facilities reported integration of ART 58.3% (21/36), HCT-Static 77.8% (28/36) and TTTB 72.2% (26/36) while 66.7% (12/18) of PNFP reported integration for HCT-Static and TTTB. Majority of the PNFP and PHP did report integration of ART.
3.1.3 Human Resources and Equipment
Questions on how well equipped the facilities were in terms of both human resources and computers, that can enable data collection, analysis as well as report writing were asked. Table 3.1.3 shows the mean number of required and available information/records officers (RO) by characteristic of the facility. The number of required records officers needed to perform the roles of data collection is significantly smaller than what is available, by level of facility as well as by ownership. These differences were statistically significant by level of facility,and by ownership except at PNP. Variability in the numbers of required and available RO by facility level and ownership was high. The high coefficient of variations (CV=Standard deviation/Mean) suggests unequal distribution of ROs by facility levels or ownership. For example, the CV for available RO in the public facilities was 1.62 and 1.81 in PNFP. These important human resources gaps are consistent with what the key informants pointed out as some of the challenges the facilities have in performing data collection, analysis and reporting responsibilities.
25 Table 3.1.3 Mean number of Available and Required Records officer by facility characteristics
Facility Characteristics Number Available Required difference Wilcoxon , N signed-rank test, Mean (SD) Mean (SD) p-value
Level of Facility
HC-III 20 0.40 (0.50) 1.45 (0.60) -1.10 (0.69) 0.0001
HC-IV 12 1.00 (0.00) 1.67 (0.78) -0.67 (0.78) 0.0154
Hospital 19 2.47 (2.00) 4.58 (4.40) -2.11 (3.40) 0.0005
Ownership of Facility
PHP 5 1.2 (0.84) 2.0 (1.00) -0.80 (1.3) 0.2164
PNFP 14 2.7 (4.90) 2.4 (1.99) 0.29 (4.0) 0.0434
Public 34 1.3 (2.10) 2.8 (3.60) -1.60 (2.67) 0.0000
Table 3.1.4 Mean number of Available and Required Data analysis personnel by facility characteristics
Facility Characteristics Number, Available Required difference Wilcoxon signed- N rank test, p-value Mean (SD) Mean (SD)
Level of Facility
HC-III 16 0.25 (0.45) 1.38 (0.72) -1.13 (0.72) 0.0006
HC-IV 10 0.60 (0.70) 1.40 (1.00) -0.80 (0.63) 0.0092
Hospital 14 1.07 (0.83) 2.64 (2.41) -1.57 (2.31) 0.0023
Ownership of Facility
PHP 4 0.25 (0.5 2.5 (1.29) -2.25 (0.96) 0.0656
PNFP 11 1.2(1.08) 2.0 (1.00) -0.82 (0.60) 0.0053
Public 27 0.48 (0.51) 1.70 (1.86) -1.22 (1.72) 0.0000
26 Table 10 (Appendix) shows number and proportion of available records officers that are trained in data management. Limited training for ROs has created a huge human resource gap in all facilities especially in hospitals and HC IV where only 67% and 59% respectively, are trained. It is clear that in PHPs very few available records staff trained in data collection. In public facilities very small proportion of ROs was trained. These findings may suggest that quality of data collection may be compromised in a substantial number of facilities due to insufficient number of trained staff.
Similarly, Table 12 (Appendix) shows the number and proportion of personnel with ability to perform data analysis and report generation. The KII revealed that most of the trainings are on the job and are done by implementing partners especially PMTCT and ART services for reporting and partner accountability because they need indicators in these areas for their reports as well as accountability. The need to train more staff in data management and analysis skills and to recruit more skilled personnel was emphasized by the respondents.
Table 13 (Appendix) shows availability of computers versus what is required by level and ownership of facility. Availability of computers and printers versus the required numbers is really so low at almost all levels of facilities and ownership except for the PNFPs, and the two partner facilities under category “Other”. Of the required number of computers in public facilities, only 28.8% (30/104) were available. Similar findings were observed in the hospitals 38.2% (42/110) and HC-II 7.9% (3/38). Lack of such equipment negatively impacts of data management, analysis as well as report generation; which ultimately hampers data utilization for planning and monitoring at facility levels.
3.1.4 Electronic Medical records systems use at the district health facilities The availability and use of electronic medical records systems (EMRS) at the various health facilities was assessed to ascertain how the systems, where available, work, or what it would take to establish such a system where non-existent. Below are the findings from the KIIs.
All facilities keep medical records for inpatients, outpatients, HIV/AIDS, non HIV/AIDS, accident and emergencies plus other kinds of patients, but these are mainly paper based and not electronic systems.
27 Identification of patients at facilities
All KIIs reported that their facilities have some kind of patient identification. Some numbers are department specific while others are unique to specific at time of reporting the ailment, while some are based on type of illness/condition. However, HIV/AIDS clients are assigned lifetime unique identifiers; such assignment of unique identifiers helps to monitor HIV/AIDs individual patients while in care, and may help to avoid duplication of number of patients in care. Although such identifications are, the whole patient records are not electronic and this makes searching for patients records and providing them services inefficient thus leading to patient delay.
For all public facilities, the identification (ID) numbers are only allocated uniquely for the same disease or condition. Patients presenting more than once in the same month but with a different condition or disease are presented with new ID numbers. Therefore, a patient can have more than one ID in the same month. Patients are therefore not followed for development of varying conditions because they never get unique identifications, but rather each condition is identified by an ID. This approach suggests that the analysis of the IDs can indicate number of conditions reported in a given month but not the frequency of occurrence or episodes of a given condition.
Most of the PNFP facilities have department specific unique numbers. For example different unique numbers are generated for such departments as out-patient department (OPD), In- patients, Maternal and Child Health (MCH). However, in all facilities, HIV/AIDS clients have unique ID numbers that are assigned for their entire lifetime while attending a specific clinic for the clients in public facilities.
“HIV/AIDS patients after testing positive are generally identified by a code in computer and an individual is uniquely identified by initials and a number which is entered once in the computer, otherwise the rest of the patients are entered in the respective registers and identified by their names, age, sex, and address plus numbers in registers which is written on their medical forms which are supposed to be brought in during their next visit” - (Key informant, HC IV public)
Type of Medical record system (paper or computerised)
Most of the key informants in PNFP and private facilities reported that they had both a paper- based and a computerized medical record system (EMRS). However, the computerized systems in place are stand-alone and not networked and need lots of improvement. Presence
28 of computerized system may increase the efficiency of patient record management, but lack of a networked system needs to be addressed.
“ The information is both computerized and paper work. This computer information is not networked, it is a stand-alone system. Every department has to collect its data and send it to the central records department for storage and analysis” – (Key informant, Mission hospital).
However, key informants from public facilities reported to have only a paper-based manual medical record system, except for Mbarara regional Hospital, Ntwetwe HC IV and Bukomero HC IV who reported having both paper and computerized systems in place. These public facilitates have been supported by organizations funding in HIV/AIDS care services and research and have provided computers for this system; Ntwetwe and Bukomera HC-IVs are supported by IDI.
The variance in the type of records management by facility ownership suggests that data analysis and management may be harder to achieve in the public compared to private facilities; yet public facilities, which have a larger patient-load, may not be properly linked into the main stream health management systems thus leading to underestimatation of health statistics from public facilities.
Use of patient information It was reported that the patient information is mainly used for planning purposes (projecting drug requirements and ordering medical supplies), disease surveillance , accountability and monthly reporting, patient follow-up, research and training students (for facilities used by academic institutions). However, no clear differences in use of such data by either level of facility or ownership status were observed.
“It is used in follow up and treatment of patients, ordering of drugs whereby drugs are ordered depending on number of patients who consume it. It also helps in planning and decision-making both at facility and district levels.” - (Key informant, HC IV public)
“We use this information to analyze some situations like in epidemic outbreak, trends of certain diseases, disease burden is also assessed.... for weekly surveillance, if you see certain diseases like dysentery reoccurring, you have to be alert and you need to know the cause of that dysentery and inform the surveillance focal person and also use it for management” – (Deputy in charge, HC III public)
29 3.1.5 Challenges encountered at facility level in keeping medical records There are enormous challenges in keeping medical records, especially where it is still paper- based. Key informants reported inadequate staffing and skills gap especially in computing proficiency as the primary big problems. In terms of manpower, there are no records officers/assistants in many facilities, and where available, many of the personnel dealing with records lack required computer skills in data handling.
Also, there is limited filing space for the bulky paper-based medical records, lack of supplies (storage files, storage cabinets and paper). Retrieval of data is very hard and cumbersome, in some cases the files are lost or misplaced making it hard to analyse the data. Lack of steady electricity, mainly in rural health centres, means that the few personnel who were trained in computing cannot put their skills to use because computer cannot be accessed.
In places where some kind of a computer-based records system is available (in some private and a few public facilities), the computers are not networked which makes it hard to collect data from various departments. There are no proper backup systems or servers, which limits the storage of large amounts of data over time and increases the risk of losing this critical information. Some Key informants reported loss of data at their health facilities. Overall, computers are reportedly still very few in facilities. Lack of ability to update or have anti-virus software on these available computers increases the risk of data loss or corruption of data files in many of the facilities.
For some facilities, the multiple reporting formats by different partners (funders) create overload for the people in charge of records. There is a need for standardization or stream-lining of the record forms. These observations strongly suggest that data and health information management need to urgently be addressed.
“...you know partners are the ones helping us, Ministry of health is not helping us in providing this stationery and the whatever, because of the little support they give us they also put something to make sure that when we get their support from them they give us indicators we are supposed to fill in using their own formats. So however much we give them the monthly reports, every month they make sure that their indicators are also captured in a separate place” – (Focal person, public hospital)
“ First of all we don’t have the records officer and most of our information tends to get misplaced. We are not sometimes supplied with adequate recording materials for recording like
30 books. We are not sometimes trained on computers because here we have our computer in which we could feed in our information other than relying on paper work” - (In charge, field military Hospital)
“First of all we even don’t have an office. When I say we don’t have an office where I can keep my documents, even when the cupboard itself I share with maternity people, so all my documents are with maternity. We use the same cupboard with the midwife.... even when I want to do my things there are times I do under the veranda when it is a busy place like this, you don’t have a place to sit, but if I want to do my work I have to sit out under the tree.” – (Information Assistant, HC III public)
How to improve on the medical record system The medical record system can be improved through 1) computerising all the manual records systems and networking these electronic systems so each department can access information easily, 2) training of personnel who manage the data, in computing skills and data handling, 3) recruiting more skilled personnel to handle the data and use it appropriately, providing more computers where the system is partially computerised . Also, rural facilities need a steady power supply to be able to support the computerised system that is being recommended. There must also be a sufficient back-up system for these records to prevent loss of information.
While the manual paper-based system is still in place, the responsible authorities should ensure provision of adequate required HMIS supplies like forms and necessary stationery. The limited space for storage and filing cabinets need to be improved upon to ease retrieval, minimise file misplacement and loss of data.
“It would be good if power was available because some staff members were trained by MoH in some computer programs. They may even forget because they are not practicing” – (In charge, public HC IV)
31 “The medial records can be improved when computerized. This will help to keep the records properly and they can be easily retrieved whenever necessary.” (In charge, public HCIII)
“Just computerize the system, because with the electronic system data is easy to store, not bulky, easy to retire and fast to process.” - (Mission Hospital)
“...we need to train first the in-charge or the record assistants in computer skills. ... and we need to train both in computer skills and E-HMIS so that they can collect, enter, analyze and use their data at their level because what we are advocating for is that each facility should start using their data they produce for proper planning” – (HMIS focal person)
“It can be improved by computerizing the system and provide tools that capture all the necessary data, also by training staffs in proper records keeping” – (Key Informant, HC III)
“ The medical records can be improved by computerizing the records, train people in data management, regularize the supply of HMIS material. The government should provide computers and train staff in record keeping” – (public HCIV)
What it would take to computerise the paper-based medical record system
Extracts from KIIs indicate that many components are necessary in order to computerise the paper-based system that currently exists.
32 First, there is need to purchase computers and related accessories (printers, UPS systems, storage files/external hard drives). Installation of this equipment at the facilities, training staff in software for data entry, analysis and management of data handling, and providing internet connection to send data to the next levels in reporting. Recruitment of additional staff fully trained in data management, or training the already existing staff in skills the fully enable them to function, and hiring information Technology officers (ITO) at hospital and/or Health Sub-District (HSD) levels to provide IT support to personnel in the lower-level facilities in that catchment area. There also need to re-organize the record office so they are fully functional including creating proper storage and ensuring reliable power supply so that access to the computerized system is uninterrupted. These suggestions were echoed by key informants across all facilities and levels.
“ At the district and hospital level we need to have a biostatistician and a medical records officer and an IT officer, and then we need a records officer or assistant and even the hospital instead of having a records officer on top of that....” – (District HMIS focal person)
“To computerize the system, we would need to procure more computers, install/connect them and train personnel to enter the data and analyse it.” – (Principle Nursing Officer, Mission hospital)
“For the information system to be computerised there is need for power (either solar or Hydro-power), need for training of staff in computer skills and records and more space to accommodate records and computers.” – (Key informant, HC III, PNFP)
33 Views on current computer system as an effective way of using resources
Most of the Key informants from facilities where there is some kind of computerized records system agreed that this is the most effective way of utilizing resources. They report that, it is easier to enter and retrieve information anytime it is needed, easier to make modifications that are required for decision making and safer way of keeping records. They, however suggest improvements to be done to the current computer system to increase capacity to handle all clients/patients in all departments.
“Well that’s true, because by now even when asked in one minute, I can tell you how many patients we admitted last week. And say if I want to go monthly I can just still use the system to identify how many patients, how many died last month, how many where discharged, how many escaped... this is effective because I can know in every department things are now centred here, and I can tell the management we are going, this is the trend, this is the problem we are facing,... so this is an effective system.” (HMIS focal person, mission hospital)
“The computer system currently being used was a donation, but even if it was procured by the hospital it is the most effective way of keeping information and therefore it would have been the effective use of resources.” – (Key informant, District hospital)
3.2 District level results
3.2.1 Human resources for data collection and generation at district level Almost all districts reported the presence of focal persons for HMIS, Surveillance, and HIV/AIDS, PMTCT, HCT, Condom and TB/Leprosy all related to providing data for the HIV/AIDS responses; only Arua and Gulu had focal persons for the condom program. The reported 113 number of trained/qualified records assistants is very small compared to the 1,328 number of facilities in the visited districts. This means 1 of 10 facilities has a qualified records assistant suggesting a huge lack of human resource in this critical area of data collection. This is a big gap in the HMIS system, at the district level.
34 3.2.2 Submission of monthly HMIS reports The frequency of HMIS report submission in all the 10 participating districts was poor; only 6 districts have been able to make a monthly submission of their HMIS reports in the past 12 months. The frequency of submission of the monthly HMIS may be affected by the presence of a functional HMIS dataset. Therefore assessment for the presence of an electronic HMIS dataset showed that almost all districts had this dataset, but two of the districts HMIS datasets were non-functional at the time of the survey. Submission of the monthly reports was mainly done electronically, either by email (8/10 districts) or flash drive (1/10 districts).
3.2.3 Availability and Adequacy of stock for HMIS and registers “Adequacy” meant availability of data collection forms in sufficient numbers for all the facilities, but was left to the respondents to define adequacy either as perceived or actual. These data collection forms to capture all the data needed for routine periodic reporting on the indicators of HIV/AIDS. All the districts, except two, reported adequate supply of registers and reporting forms for special programs (PMTCT, HCT, ART), but half of the districts lacked adequate supply of HMIS reporting forms and registers due to stock outs. Stock out of data collection forms was mainly attributed to low or lack of allocation of funds for data management by the district; districts are decentralized and do their own financial planning and budget allocations for the various departments. Although HMIS forms and other registers are standardized MoH forms, two districts were using non-standardized forms including exercise books as a way to improvise and ensure continuity of data capture when the MoH data collection forms were unavailable.
3.2.4 CSO registration and their reporting to the districts
Although most HIV/AIDS programmes and services are being implemented by CSO at community and facility levels, important data gathered from their services are not fully linked to the HMIS/district systems because they are not registered at the district. For example, only four districts had registered CSOs in the areas of prevention, treatment and care, social support and capacity building (Table 6). For CSO that provide data to the district, this was not routine and was mainly through their annual reports to the district. These poor reporting behaviours strongly suggest that a lot of critical HIV/AIDS related information from CSO activities is not available to the districts for monitoring of the HIV/AIDS response as well as planning purpose (Table 6).
35 3.2.5 Timeliness, completeness and accuracy of both the HMIS and HIV/AIDS specific monthly summaries The district HMIS focal persons receive data summaries from health facilities and collate these data before onward reporting to the MoH Resource Center. The MoH Resource Center is responsible for receipt of these data at the national level, by the 28th day of every month. Therefore, timeliness, completeness and accuracy of both the HMIS and HIV/AIDS specific monthly summaries at the district level is important to ensure timely submission to the MoH. In almost all districts, except one, over 50% of the health facilities were reported as being timely in sending their summaries to the district, or having completed and accurate data. There was little variance between regular HMIS and HIV/AIDS specific summaries except for a couple of districts (Table 7). Some of the challenges sited for poor performance in this area included lack of transport to deliver reports, low staffing levels to collect data, lack of coordination in data gathering, and high turnover of health workers responsible for these activities. The other key challenges that affected timeliness, accurate and completeness of data included having too many documents or reports that needed to be filled by the limited available health workers.
3.2.6 Reporting of HMIS to MoH and feedback, and use of HMIS for planning
Although some districts send HMIS reports on a monthly basis to the MoH, only two districts received feedback from the MoH every month; however, information on the type of feedback received was not obtained (Table 10). HMIS, routinely-collected data from HIV/AIDS programs (PMTCT, ART and HCT), AIDS indicator surveys and sentinel surveillance are the most commonly used by almost all districts for planning at the district level. Other data sources are not frequently used by the majority of districts for planning purposes (Table 11).
3.2.7 Resources for the functioning of HMIS and program data Facilitation for the movement of HMIS and programme data from health facility to HSD and district level is still inadequate or non-existent in almost all districts. Districts receive summaries of HMIS and program data from the health facilities, through the HSD. However, only 5/10 districts reported that their HSD are actively involved in organizing HMIS and program reports prior to sending them to the districts. This level of non-involvement of HSD in the reporting mechanism may have consequences of data quality.
36 3.3 National level results
The national level findings were mainly obtained from Key informant interviews, and are therefore provided below in narrative and key emerging issues per sub area indicated.
3.3.1 Areas in which Uganda is doing well in data collection
Strengths A few Key informants noted the following as strong areas regarding data collection.
There is HMIS in place which uses good standardized forms for data collection on HIV response and capturess detailed data on PMTCT especially from the public health facilities.
There are established population-based surveys such as the Uganda HIV/AIDS Sero- Behavioural Surveys (UHSBS), and sentinel surveys and other studies like the cohort studies, all of which generate HIV data and both complement validate the traditional HMIS data. ACP has also set up data collection systems parallel to HMIS and although the systems make data collection labour intensive and duplicative at times; they also function as validation of the HMIS data.
Data from all the US-funded HIV/AIDS program implementers is collected routinely and successfully in a separate system
HIV programs (HCT, ART & PMTCT) have focal persons who are trained to routinely monitor the HIV activities through data collection and reporting
3.3.2 Weaknesses/ challenges to data collection Most Key informants had reservations about Uganda’s progress in data collection because the available systems still have challenges that need to be addressed so the system may function properly.
They noted that HMIS data is very limited in scope and does not include relevant details that can help in fully understanding of the HIV epidemic. For example very little is captured on paediatric HIV and ART use. Secondly, data from the private facilities are often not included in HMIS, therefore this makes the national picture is incomplete.
37 This quotation highlights this challenge:
“ The fact is that we have national system for data collection through HIMS and also the coordinating implementation partners who are largely coordinated by MEEPP... I think It has been very successful. HMIS has also not done badly but the biggest challenge is that HMIS is very limited in terms of data they collected only a few aspects and their reports are very limited to data every month but when you look at their reports, their information Is shallow for example like laboratory tests, they just ask you how many lab tests were done, but they do not go into things like the discordance, the couple things and you know all these things are very relevant in terms of HIV/AIDS data.” – (KI, National Level)
They also reported that the data collected are underutilized for improving systems and daily operations. There are also difficulties in collecting data efficiently because of different reporting periods and formats required by various HIV/AIDS funding partners.
Coordination of data collection from different stakeholders especially the organizations that deal with the (non-health) psycho-social support component of the HIV response is not strong enough. The issue of funders having non-uniform reporting formats and indicators and interpretation of indicators is still a challenge.
“when you go to the government health facility, you find that there are number of different partners working in the same facility and you find that all these partners are doing HIV activities using different ways of collecting their own data. You find that there is no coordination mechanism in their system of collecting data. So to me that is a very big drawback” (KI, National Level partner)
Lack of a central data collection system makes the evidence of the HIV epidemic questionable and difficult to pool together.
“There is the existence of the parallel systems. You can find Mildmay has its tools, JCRC has its tools, TASO has its tools; all these are just picking indicators from the national framework but tearing it to the needs of the organization in capturing all the relevant HIV information.” (KI, National Level partner)
National M&E framework has not been operationalized even though stakeholders were told about it a couple of years ago.
38 3.3.3 Suggestion for improvement and anticipated challenges Harmonized reporting formats and periods, although this may take time because different organizations and health institutions are at different levels of development and may not have the same expertise and technology.
Harmonizing all the key indicators is important but this may actually pause serious challenges in their measurements and interpretation since different players collect data for different uses
Strengthening the coordination of the data collection by ministry of Health and ensuring compliance by all implementers/stakeholders
“There is need to marry all the fragmented parallel systems of data collection into a single system of quality data collection. That includes; collection, access, and utilization. For example, all the multitudes of research projects should be able to really be feeding into the MoH so that one can access the HIV epidemic information from a single source” (KI, National Level)
3.3.4 Areas in which Uganda is doing poorly in data collection No electronic data records in public health facilities. Data collection is mainly through use of paper-forms which poses of a problem of lack of accuracy and timeliness of the data and loss of the completed paper forms.
“where we are doing poorly I think is the utilization of the information, because yes we collect but we are not utilizing so much, and apart from utilization of course I will talk about computerization, because we are computerized and I would want sites to be computerized, because we have limited resources, sites are not computerized and we have manual reports, so if computerization would be done, we would be on a greater level especially where we have double counting computerization would clear that incident” (KI, National Level, Partner)
Inadequate skills and resources such as computers and personnel for data collection
Lack of integrated system for data collection between local and central sectors
Behavioural data and psychosocial support data is largely not captured
39 Data utilization is poor, even the data that is collected is not utilized to monitor performance and quality of services
Lack of a unique national identifier for HIV/AIDS patients leads to double counting and overutilization of services by some patients. Also, makes monitoring of progress very problematic. E.g. some clients may be accessing ARVs from one partner but because the other provider gives food and basics, they also register to receive the same
3.4 Integration of HIV data
3.4.1 What Uganda has done to integrate HIV data Uganda according to key informants has included PMTCT, ART and HCT indicators in the HMIS data collection tools which has improved on the information generated. HIV organizations are also being encouraged to use the HMIS data collection tools as their standard forms. This should help with uniformity in the generated information.
3.4.2 Barriers to effective integration Lack of properly trained personnel in data handling leading to incomplete and inaccurate data
Financial resources to put the necessary equipment like computers in place and to pay well trained personnel to work on a well integrated data collation system
Lack of leadership in coordination of the data collection process from UAC and MoH
The integration has not been done effectively because the HIV is majorly funded by donors and these donors have different interests and objectives.
Integration of data is a good idea because it is more cost effectively. Scattered data is very expensive in collecting and collating it together and this delays policy making. What we need is to establish a system of data collection, access and utilization. It will also help solve the problem of sustainability in our resource limited setting, the effort and resources used in the parallel systems can then be turned toward supporting the system. ”Definitely, it means we will get one point to get the information. Currently if I am looking at what the current indicators in the NSP, where do I go for this information? Who do I call up to get me the information to say how many women for example have been able to access ANC and are found to be positive? Do I call the MoH? Do I call the Uganda AIDS Commission? Who
40 do I call to give me that data? So we need to have one data base which is user friendly.” – (KI, National Level)
3.4.3 Specific Actions to be taken by MoH Set standard guidelines and timelines for reporting to be followed by the public and private health facilities and HIV care organizations
Work with donor agencies to come up with a comprehensive data collection and reporting format
Build capacity of health personnel at the district level to collect and manage the data
Emphasis should be placed on coordination of all the data collection activities by the MoH, need to ensure that there are operational protocols to guide the process
Involvement of research and teaching institutions in data analysis, utilization and policy formulation
3.4.4 Data use and policy development from collected data Major Policy developed from hard data collected through national HMIS in Uganda
The upcoming policy of male medical circumcision as a prevention strategy for HIV came from the scientific evidence generated by researchers
The policy of routine HIV testing as opposed to the traditional VCT grew from data collected in health care settings
The TB/HIV collaboration policy where universal HIV testing for all TB patients is recommended developed from data showing that prevalence of HIV infection was very high among TB patients
Policy change in education, awareness and knowledge about HIV to reach individuals or couples with information rather than via mass media arose from the results from the modes of transmission study
The policy to keep the CD4 cut off for ART initiation at 250 cells/µl instead of the 350 cells/µl recommended by WHO was based on the national data that showed that the current unmet need was still high
41 The current policy to focus on social networks was derived from data that showed that marriage was a risk factor for HIV infection in Uganda
The policy to change the PMTCT regimen from Niverapine only to a combination regimen
3.5 DISCUSSING MAJOR STUDY QUESTIONS & EMERGING ISSUES
Data collection in the areas of HIV prevention and social services is still low. Most of the focus has been put on Care and treatment especially ART, PMTCT and STI. The core business of many private facilities is the provision of curative care. Also, many AIDS development partners are engaged in care and services and usually need data on monitoring and accountability which may be tagged on indicators that are not necessarily consistent with what the government needs to monitor the national response.
Funding for many of the HIV/AIDS activities is contributed by the donors or AIDS development partners who have own reporting mechanisms. MEEPS (Kalisbala Samuel, 2010) and Uganda AIDS Commission’s Performance Monitoring and Management Plan (PMMP) are some of the systems that could be running parallel to the HMIS partly because HMIS does not collect comprehensive data to satisfy these systems, or there is lack of coordination in the HIV/AIDS data flow. Absence of data on social services including orphan-hood and vulnerable children is worrying and needs to be addresses. Although community based organizations are doing extensive work in these areas, the lack of data in the main streamline HIV/AIDS data the MoH may hinder proper linkages of such services and trends in HIV infections. For example people receiving HCT and ART are captured by the HMIS but OVC and services offered are usually under the docket of the Ministry of gender.
A number of strengths are already in existence that can enhance the data collection, processing and utilization at the health facility, district and national levels. The existence of HMIS in all districts, irrespective of its current weaknesses, is an important opportunity to capture data necessary for monitoring the national response. The HMIS already has established reporting mechanisms right from the health facility through the health sub-districts, districts to the MoH. Although staff may not be in sufficient numbers, almost all districts visited have at least one
42 qualified personal in human resources to handle data management from HMIS. Such qualified staff are also available at higher levels (MoH Resources center), though they are not sufficient to handle to volumes of data coming into the systems. Utility of data/information is already appreciated; for example, the interest in assessing how well the country is doing in monitoring of HIV-response is a clear indicator that these data can be used after collection, for planning purposes at all levels including district and national.
Availability of electronic HMIS dataset, and the mode of submission of HMIS reports, which is mainly electronic through use of emails, suggests there can be a quick turn around in data processing. However, email submission suggests incurring internet costs but this is less time consuming and faster than travel to physically hand deliver the reports. This increased use of email for HMIS report submission by the majority of districts indicates the increasing use and access to internet. Therefore, with the accessibility of the internet, there is hope that more timely report submission and potential faster response to the district can be achieved.
Although there are strengths to improvement of data collection and processing, some weaknesses still exist, that calls for urgent attention before the ideal data system can be achieved. There exist multiple data collection systems for the HIV data (such as PMMP-UAC, MEEP-PEPFAR), which creates challenges with data collection, collation and coordination at health facility, especially. There is inefficient use of the already limited resources (human and financial) due to increased workload which may compromise data quality as well as turnout. Indicator definitions and extent of needed information per data collection system complicates the data gathering processes, and reporting. Many of these challenges have been highlighted in prior studies assessing HMIS (Kintu Peter et al., 2005). These challenges will certainly need to be streamlined.
Although some districts indicated utilization of HIOV/AIDS data for planning purposes, there is still a high proportion of health facilities not yet utilizing these data decision making due to lack of skills to analyze and interpret the collected information. CSO and private health providers do not all have data captured in the HMIS system, which potentially creates underestimation of the national HIV response.
43 4.0 RECOMMENDATIONS
4.1 Resource allocation; both financial and human In order to significantly improved the HMIS systems, the government needs to budget and allocate financial resources that will provide training to staff in addition to equipping health facilities through districts which will strengthen the collection, analysis, and use of reliable, quality health information for informed decision-making, starting from the lower levels (health facilities) up to the national level. Government could either mandate of instruct government funded institutions of higher learning like Makerere University to offer technical skills development to districts and MoH departments dealing with data management of HMIS. Although money is very important and necessary for the improvement in information management, it is not sufficient as it will have to invested in a coordinated manner to avoid dupilication of activities as well as avoiding unnecessary fragmentation (Carla AbouZahr and Ties Boerma, 2005)
4.2 Empower districts to register all CSO and private health providers The government needs to empower or strengthen the districts to register all CSO or private health providers before operating in the various districts. Annual renewal of licenses to operate in the districts should be pegged to performance in terms of collecting and providing data on health to the district health information offices. For example, CSOs at the district should report their data and information through the line departments in the district. Community-based CSOs should report to the Community-Based Services Department while health facility based CSOs should report to the district health office.
4.3 Centralize all HIV/AIDS data systems Various organizations have systems for management data, but are not linked thus making it impossible to assess HIV/AIDS national response. Government should develop policies or strong guidelines to streamline all agencies (public and private) that offer HIV/AIDS care and services to make mandatory reporting of all data into a central
44 system. These requirements could be part of MoU with all agencies prior to obtaining permission to conduct business in the country. For example the NGOs being funded by AIDS development partners could be required to report to their line sectors at the national level. Such suggestions will therefore require deliberate action within the Ministries to develop this information loop.
4.4 Feedback mechanism be improved Almost all districts do not receive feed back on submitted data to the MoH. This needs to be improved by sending at least quarterly summaries of findings, as well as challenges found in the submitted data. The MoH could hold quarterly regional workshops for HMIS personnel and other information system managers for skills building as well as feedback mechanisms, as a way of improving timeliness, completeness and data accuracy as well as proving important basic analysis skills to empower data managers provide district and facility level analyses interpretation for planning and decision making at those levels. Also, MoH could borrow from MEEPP about mechanisms used to provide feedback to PEPFAR implementing partners, which is created for improvement in data quality collected by MEEPP (Kalibala, Samuel. 2010).
5.0 CONCLUSIONS Availability of data collection tools (such as standardized registries) is wanting and requires urgent attention especially in HIV prevention at all levels (hospitals and Health centres) and ownership (public and private) of health facilities if improvement in data collection is to be made.
Human resource for data management, analysis and reporting needs to be urgently strengthen through recruit of skills personal, and/or increased training of the available staff in these skills. Critical equipment such as computers and printers need to be purchased, and have staff trained in their use as to strengthen the data analysis and report generation, right from the health facility levels up to the district.
45 Supervision of health facilities and other providers of HIV/AIDS services, especially at the community level need to be developed or strengthened to ensure that all important data are captured and submitted through the HMIS system to enable coordinated management of these data.
More HIV/AIDS indicators especially in the area of HIV prevention need to be integrated into HMIS, and data on these indicators need to be reported by all providers of HIV/AIDS irrespective of the level or ownership of the health facility.
Standardized data collection and reporting formats (especially indicators) for all stakeholders involved in the provision of HIV/AIDS care need to be agreed upon and implemented by all agencies. This may enhance the integration of HIV data and potentially reduce or eliminate the cumbersome task of duplicating capturing data for various stakeholders at the health facilities, which is currently a big deficiency in the utilization of available scare human resources at these sites.
HIV is majorly funded by donors who may have different interests and objectives from those of government. Therefore, government needs to commit more financial and skilled human resources into data management so as to strengthen integration of HIV.
46 BIBLIOGRAPHY 1. Health Sector Strategic Plan II (HSSP II), 2. Health Sector HIV/AIDS Strategic Plan (HSHASP), 2007 - 2012 3. National HIV/AIDS Strategic Plan, 2007/08- 2011/12 4. HIV/AIDS Monitoring and evaluating a guide for districts 5. Health Sector Strategic Plan 2005/06 – 2009/10, Midterm Review Report; MoH, October 2008 6. Report of the Monitoring and Evaluation Systems Strengthening Exercise; (Joint HIV/AIDS, 7. Annual Health Sector performance Report, Financial Year 2006/2007; October 2007 8. M & E Self-assessment tool, Final Draft Version 1; July 2006 9. Health Sector Strategic Plan 11 2005/06 – 2009/2010 Volume 1; MoH 10. Tracking HIV Drug Resistance in Facilities Providing Antiretroviral Therapy in Uganda, Report of Assessment of Early Warning Indicators for Antiretroviral Drug Resistance:2008; STD/ACP MoH; September 2009 11. National AIDS programmes: a guide to monitoring and evaluating HIV/AIDS care and support (World Health Organization.) 12. Assessing the National Health Information System An Assessment Tool VERSION 4.00 Health metrics network (WHO, 2008) 13. Developing Health Management Information Systems:A PRACTICAL GUIDE FOR DEVELOPING COUNTRIES (WHO, 2004) 14. Health management information systems; Eldis Health Key Issues (http://www.google.co.ug/#hl=en&biw=1024&bih=537&q=Health+management+information+s ystems&aq=f&aqi=&aql=&oq=&gs_rfai=&fp=915773cb171fe031) December 2 2010 15. Kalibala, Samuel. 2010. “Monitoring and Evaluation of the Emergency Plan Progress(MEEPP): End-of-project evaluation,” Final Report. New York: Population Council 16. Makumbi F, Mayega R, Kisitu et al. “Technical assistance to the Uganda AIDS Commission for operationallisation of the performance monitoring and management plan,” Final Report. New York: Population Council 2009 17. THE UGANDA HIV/AIDS STATUS REPORT JULY 2004 – DECEMBER 2005, Uganda AIDS Commission March 2006 18. Sally Stansfiled Structuring Information and incentives to improve health ;Bulletin of World health organization, August 2005, 83(8)
47 19. Carla AbouZahr and Ties Boerma Health information systems: the foundations of public health Bulletin of the World Health Organization | August 2005, 83 (8) 20. Kintu, Peter; Nanyunja, Miriam; Nzabanita, Amos & Magoola, Ruth Health information systems - Development of HMIS in poor countries: Uganda as a case study; Health Policy and Development, Vol. 3, No. 1, 2005, pp. 46-53 21. GLADWIN, J, RA DIXON, TD WILSON Implementing a new health management information system in Uganda HEALTH POLICY AND PLANNING; 18(2): 214–224 Health Policy and Planning 18(2); 2003 22. Assessment of the Health Information System in Uganda; Under guidance of the Ministry of Health Resource Centre (Health Information and ICT) June 2007
48 APPENDICES
Table 4.1.1 to 4.1.13 are for the district level self-assessment
Table 4.1.1 Number of health facilities by district visited
District Hospitals HC_IV HC_III HC_II Trained/qualified records assistants
Overall 28 22 143 1135 113
Arua 3 3 17 20 16
Gulu 4 2 14 34 15
Kampala 8 2 21 855 -
Kamuli 2 3 14 51 20
Kamwenge 0 2 9 33 6
Katakwi 0 1 7 12 6
Kiboga 1 2 13 - 3
Mbarara 5 3 15 53 12
Pader 2 1 16 37 18
Tororo 3 3 17 40 17
49 Table 4.1.2 Number of HMIS reports submitted in the past 12 months and method of submission
District HMIS to Method of submission MoH/ACP
2=Email, 1=flash drive
Arua 12 2
Gulu 5 2
Kampala 12 1
Kamuli - 2 Kamwenge 12 2
Katakwi 12 2
Kiboga - 2
Mbarara 12 2
Pader 6 2
Tororo 12 0
50 Table 4.1.3 Mean score on adequacy of HIV/AIDS prevention, Treatment, social services and service delivery at health facility level
Prevention Treatment Social services Service delivery
District
Overall 2.8 3.1 1.1 2.6
Arua 1.5 3.5 0 4.0
Gulu 2.7 3.0 .- 2.8
Kampala 2.8 2.8 0.1 3.0
Kamuli 4.0 4.0 3 2.0
Kamwenge 2.2 2.8 2.1 3.2
Katakwi 4.0 3.0 0 0
Kiboga 2.7 1.8 0 1.0
Mbarara 3.2 2.8 3.1 3.0
Pader 0.8 4.0 0 4.0
Tororo 4.0 3.0 .- .
Table 4.1.4 Mean score on adequacy of HIV/AIDS prevention, Treatment, social services and service delivery at Non-health facility level
Prevention Treatment Social services Service delivery
51 District
Overall 1.2 0.75 1.0 1.2
Arua 0 0 0 0
Gulu 0.7 0 2 0
Kampala 0 0 0 0
Kamuli 4 0 0 2.4
Kamwenge 1.8 1.3 1.9 3
Katakwi 0 1.3 0 0
Kiboga . . . .
Mbarara 2 2.7 3.4 3.2
Pader . . . .
Tororo . . . .
52 Table 4.1.5 Score on adequacy of HIV/AIDS prevention, Treatment, social support and Capacity building and systems strengthening at Non-health facility level implemented by CSO
District Prevention Care and Social Capacity building and Treatment support systems strengthening (IEC, Condom Distribution)
Arua 0 0 0 0
Gulu 4 4 4 4
Kampala 0 0 0 0
Kamuli 4 4 4 4
Kamwenge 2 2 1 1
Katakwi 0 0 0 0
Kiboga . . . .
Mbarara 1 2 2 3
Pader . . . .
Tororo . . . .
Table 4.1.6 CSO registration at district level implementing HIV/AIDS related interventions
Total registered Number providing:
District CSOs Routine reports Non-routine reports
Arua 0 0 0
Gulu 14 7 7
Kampala 0 0 0
Kamuli 11 3 8
Kamwenge 0 0 0
Katakwi 23 5 0
Kiboga 0 0 0
Mbarara 36 3 33
Pader 0 0 0
53 Tororo 0 0 0
54 Table 4.1.7 Proportion of health facilities reporting HMIS and HIV/AIDS specific monthly summaries by timeliness, completeness and accuracy
HMIS HIV/AIDS specific
District Timeliness Completeness Accuracy Timeliness completeness Accuracy
Arua 52 76 99 52 76 99
Gulu 65 85 98 65 85 90
Kampala 0 0 0 0 0 0
Kamuli 74 80 0 33 33 0
Kamwenge 95 80 60 90 90 70
Katakwi 20 20 20 18 18 18
Kiboga 65 80 75 60 50 60
Mbarara 64 40 74 64 50 50
Pader 78 64 64 90 90 90
Tororo 100 100 80 100 100 100
Table 4.1.8 Proportion of health facilities reporting specific monthly summaries by timeliness, completeness
District Timeliness Completeness
55 PMTCT ART HCT TB/Leprosy PMTCT ART
Arua 52 52 52 52 76 76
Gulu 60 80 65 68 90 90
Kampala ------
Kamuli 21 7 25 100
Kamwenge 90 90 90 90 95 100
Katakwi 11 1 18 11 11 1
Kiboga 80 30 85 40 50 60
Mbarara 95 70 80 - - -
Pader 90 90 90 90 90 90
Tororo 20 38 - 25 99
56 Table 4.1.9 Proportion of health facilities reporting specific monthly summaries by accuracy
District Accuracy
PMTCT ART HCT TB/Leprosy
Arua 100 100 100 100
Gulu 100 80 70 90
Kampala - - - -
Kamuli - - - -
Kamwenge 80 90 90 60
Katakwi 11 1 18 11
Kiboga 70 60 70 40
Mbarara 98 98 98 90
Pader 90 90 90 90
Tororo 100 38 100 25
Table 4.1.10 HMIS monthly reports and feedback from MoH/ACP past 12 months
District Number of HMIS Number of Times get feedback from
57 monthly reports MoH/ACP
Arua 4 1
Gulu 12 1
Kampala - -
Kamuli 12 12
Kamwenge 12 12
Katakwi 1 1
Kiboga 12 1
Mbarara - -
Pader 12 2
Tororo 12 1
58 Table 4.1.11 Frequency of use of HIV/AIDS data sources for backing up district level planning and program monitoring
District HMIS Special National Censu UDHS AIDS Special MARPS programs survey s indicator Surveys (PMTCT, survey ART, HCT)
Arua 3 3 2 1 1 1 1 1
Gulu 3 3 2 1 2 1 1 0
Kampala 3 3 3 3 3 3 3 3
Kamuli 3 3 - - -
Kamwenge 3 3 2 1 0 3 3 2
Katakwi 3 3 3 0 0 - - -
Kiboga 2 2 0 3 3 3 0 1
Mbarara 3 3 2 1 2 3 2 1
Pader 3 3 3 3 3 3 3 3
Tororo 3 3 3 3 0 3 2 0
Note: 3: Always, 2: Sometimes, 1: Rarely, 0: Never
District Service LQAS/ Special Sentinel Condom Provision district level Cohort surveillan Surveys Assessmen outcome studies ce ts surveys
59 Arua 0 0 0 2 0
Gulu 1 1 0 2 0
Kampala 3 3 3 3 3
Kamuli 0 0 0 - -
Kamwenge 2 3 1 3 3
Katakwi 0 0 0 - -
Kiboga 1 1 2 3 1
Mbarara 2 2 2 3 2
Pader 3 3 3 3 3
Tororo 0 0 0 2 3
Note: 3: Always, 2: Sometimes, 1: Rarely, 0: Never
60 Table 4.1.12 Adequacies of resources to effectively perform the following actives
Facilitating the movement of HMIS and Supportive supervision of programme data from:
District Health units to the HSDs to the HMIS PMTCT, ART, & HCT HSD district
Arua 0 1 0 1
Gulu 1 1 2 2
Kampala 0 2 3 3
Kamuli 3 3 4 4
Kamwenge 2 3 3 4
Katakwi 2 4 3 4
Kiboga 0 0 0 0
Mbarara 2 2 2 2
Pader 2 2 3 3
Tororo 0 1 3 3
Note: 0-non existent, 1-very inadequate, 2: inadequate, 3: adequate, 4: very adequate
Table 4.1.13 Proportion of health workers that can make primary diagnoses of new HIV cases and suspected new HIV cases that are investigated with laboratory results
61 District Health workers HIV cases with lab results
Arua 50 99
Gulu 50 14
Kampala - -
Kamuli 60 100
Kamwenge 60 80
Katakwi 90 99
Kiboga 99 99
Mbarara 35 64
Pader 60 99
Tororo - -
Table 4.1 to 4.13 are for health facility assessment
Table 4.2: Current use of Registry for data collection by Facility ownership
Ownership of Facility
Public PHP PNFP *Other Total
Total 36 () 6 () 18() 02(1.6) 62
62 Area1: prevention
IEC 5/32 0/5 8/17 0/2
Condom-static 16/32 0/5 3/16 2/2
Condom-OutR 11/32 0/4 3/16 2/2
PMTCT 33/34 3/5 18/18 1/2
Blood transf. 8/29 2/5 6/15 0/2
PEP 10/32 1/4 9/16 0/2
STI mgt 25/31 3/4 14/17 2/2
MMC 5/30 2/5 5/16 0/2
Area2: care and treatment
ART 25/33 5/5 13/18 2/2
HCTSTAT 33/34 5/5 17/18 1/2
HCTOUT 20/29 2/5 12/17 1/2
TTINF 21/29 2/4 12/17 1/1
TTTB 30/33 3/5 15/18 2/2
HMBC 6/29 0/4 7/17 1/2
NON-HIV SERVICES Public PHP PNFP *Other
Malaria 33/33 4/6 16/17 1/2
TB 34/34 3/6 15/17 1/2
Maternal health 34/35 4/6 15/17 0/2
Immunization 33/34 3/5 12/17 0/0
Note: * IDI and TasoGulu
63 Table 4.3: Number of monthly reports in past 12 months level of facility
Number of monthly Hospital HC HC III HC II *Private reports in past 12m IV provider s
Total, N 22 12 20 01 07
Area 1: prevention
IEC
0-3 1 1
4-5 0 1
6+ 4 2 1 2
Condom-static
0-3 2 0
4-5 1
6+ 5 5 4 2
Condom-OutReach
0-3 1
4-5
6+ 4 5 3 2
PMTCT
0-3 2
4-5
6+ 18 11 16 1 3
Blood transfusion
0-3 2
4-5
6+ 10 2 1
64 PEP
0-3 1 2 2
4-5 1
6+ 7 2 2 1 3
Management of STI
0-3
4-5
6+ 16 10 14 1 4
Medical male circumcision
0-3 3 1 0
4-5
6+ 4 1
Area 2:
Care & Treatment
ART
0-3 1
4-5
6+ 20 8 6 1 7
HCT-STATIC
0-3 1
4-5
6+ 16 10 16 1 3
HCT-OUTREACH
0-3 1 1
4-5
65 6+ 10 8 7 1 3
Treatment of Infection
0-3 1 1
4-5
6+ 11 4 10 1 4
TB treatment
0-3 1
4-5
6+ 19 9 11 1 5
Home Based Care
0-3 1 1
4-5 1
6+ 1 1 1 1 3
NON-HIV SERVICES
Malaria
0-3 0 1 0 0
4-5 0 0 0 0
6+ 18 9 17 1
TB
0-3 0 0 0 1
4-5 0 0 0 0
6+ 20 10 15 0
Maternal health
0-3 0 0 0 0
4-5 0 0 0 0
66 6+ 19 9 16 1
Immunization
0-3 0 0 0 0
4-5 0 0 0 0
6+ 19 9 16 0
67 Table 4.4: Number of monthly reports in past 12 months ownership of facility
Number of monthly reports in past Public PHP PNFP *Other 12m
Total, N 36 6 18 02
IEC
0-3 1 1
4-5 1
6+ 3 6
Condom-static
0-3 2
4-5 1
6+ 12 3 1
Condom-OutReach
0-3 1
4-5
6+ 10 3 1
PMTCT
0-3 2
4-5
6+ 29 3 16 1
Blood transfusion
0-3 2
4-5
6+ 6 2 5
PEP
0-3 5
4-5 1
68 6+ 7 1 7
Management of STI
0-3
4-5
6+ 27 3 14 1
Medical male circumcision
0-3 0 1
4-5
6+ 2 3
ART 2
0-3 1
4-5
6+ 24 5 11
HCT-STATIC
0-3 1
4-5
6+ 28 3 15
HCT-OUTReach
0-3 2
4-5
6+ 16 2 10 1
Treatment of Infection
0-3 2
4-5
6+ 17 1 10 2
TB treatment
69 0-3 1
4-5
6+ 26 3 14 2
Home Based Care
0-3 1 1
4-5 1
6+ 3 3 1
NON-HIV SERVICES Public PHP PNFP *Other
Malaria
0-3 1 0 0 0
4-5 0 0 0 0
6+ 30 2 14 1
TB
0-3 0 0 1 0
4-5 0 0 0 0
6+ 32 3 12 1
Maternal health
0-3 0 0 0
4-5 0 0 0
6+ 30 2 14
Immunization
0-3 0 0 0
4-5 0 0 0
6+ 30 3 11
Note: * IDI and TasoGulu
70 Table 4.5 Integration of facility data into HMIS by level of facility
Integration in HMIS Hospital HC IV HC III HC II *Private Yes/total providers
Total 22 12 20 01 07
Area 1: Prevention
IEC 2 0 3 0 0
Condom-static 4 4 4 1
Condom-OutReach 2 4 4 1
PMTCT 18 10 16 1 1
Blood transfusion 10 1 0
PEP 5 1 2 0 0
STI mgt 16 7 14 1 1
MMC 4 1 0
Area 2: care& treatment
ART 18 7 5 1 1
HCTSTAT 16 9 16 1 0
HCTOUT 11 7 6 1 1
TTINF 12 4 8 1 1
TTTB 20 8 11 1 1
HMBC 0 1 3 0 1
NON-HIV SERVICES Hospital HC IV HC III HC II *Private providers
Malaria 18 9 17 1 -
TB 21 9 15 1 -
Maternal health 19 9 16 1 -
Immunization 19 9 16 - -
71 Note: * Case medical clinic, IDI, JCRC, Kadic health services Nakulabye, Naguru medical lab, Taso and Tasomulago
72 Table 4.6 Integration of facility data into HMIS by ownership of facility
Integration in HMIS Public PHP PNFP *Other Yes/total
Total 36 6 18 02
Area 1: Prevention
IEC 2 0 3 0
Condom-static 12 0 0 1
Condom-OutR 10 0 0 1
PMTCT 29 2 14 1
Blood transf. 5 0 5 0
PEP 5 0 3 0
STI mgt 25 2 11 1
MMC 3 0 2 0
Area 2: care& treatment
ART 21 2 8 1
HCTSTAT 28 2 12 0
HCTOUT 16 2 7 1
TTINF 15 1 9 1
TTTB 26 2 12 1
HMBC 3 0 1 1
NON-HIV SERVICES
Malaria 30 2 12 0
TB 32 3 11 1
Maternal health 30 2 12 0
Immunization 30 3 11 0
73 Note: * Case medical clinic, IDI, JCRC, Kadic health services Nakulabye, Naguru medical lab, Taso and Tasomulago
74 Table 4.7 Data display by level of health facility
HC II HC III HC IV Hospital Private
Total 01 20 12 22 07
Area 1: Prevention
IEC 0 4 1 2
Condom-static 0 1 1 0
Condom-OutR 0 1 0 0
PMTCT 0 3 5 10
Blood transf. 0 0 0 1
PEP 0 1 1 2
STI mgt 0 4 1 6
MMC 0 0 0 1
Area 2:Care& treatment
ART 0 0 2 7
HCTSTAT 0 2 4 7
HCTOUT 0 3 4 4
TTINF 0 2 1 3
TTTB 0 1 3 7
HMBC 0 1 1 1
NON-HIV SERVICES
Malaria 0 6 7 13
TB 0 5 8 13
Maternal health 0 6 7 12
Immunization 0 5 7 13
Table 4.8 Data display by ownership of health facility
Other PHP PNFP Public
Total 02 6 18 36
75 Area 1: Prevention
IEC 0 0 6 2
Condom-static 1 0 2 1
Condom-Outreach 1 0 1 1
PMTCT 1 0 9 9
Blood transfusion 0 0 2 0
Post exposure prophylaxis 1 0 5 0
Management of Sexually Transmitted Infections 1 0 8 4
Medical Male circumcision 0 0 1 0
Area 2:Care& treatment
ART 2 2 8 2
HCT-static 0 1 9 5
HCT-outreach 1 1 7 5
Treatment and prophylaxis for opportunistic infections in general 2 0 7 1
Treatment and prophylaxis for opportunistic infections (TB) 1 1 7 4
Home-based Care 1 0 5 0
NON-HIV SERVICES
Malaria 0 0 9 17
TB 1 0 10 17
Maternal health 0 0 9 16
Immunization 0 0 7 17
Table 4.9 Mean number of Available and Required Records officer by facility characteristics
Facility Characteristics Number Available Required difference Wilcoxon , N signed-rank test,
76 Mean (SD) Mean (SD) p-value
Level of Facility
HC-III 20 0.40 (0.50) 1.45 (0.60) -1.10 (0.69) 0.0001
HC-IV 12 1.00 (0.00) 1.67 (0.78) -0.67 (0.78) 0.0154
Hospital 19 2.47 (2.00) 4.58 (4.40) -2.11 (3.40) 0.0005
Ownership of Facility
PHP 5 1.2 (0.84) 2.0 (1.00) -0.80 (1.3) 0.2164
PNFP 14 2.7 (4.90) 2.4 (1.99) 0.29 (4.0) 0.0434
Public 34 1.3 (2.10) 2.8 (3.60) -1.60 (2.67) 0.0000
Table 4.10 Number and proportion of trained records officer by level and ownership
Proportion Facility Characteristic Number available Number trained trained, %
Level of Facility
HC III 8 6 75.0
HC IV 12 8 66.7
Hospital 54 32 59.3
77 Ownership of Facility
Other 16 16 100.0
PHP 8 1 12.5
PNFP 55 47 85.5
Public 47 30 63.8
Table 4.11 Mean number of Available and Required Data analysis personnel by facility characteristics
Facility Characteristics Number Available Required difference Wilcoxon signed- , N rank test, p-value Mean (SD) Mean (SD)
Level of Facility
HC-III 16 0.25 (0.45) 1.38 (0.72) -1.13 (0.72) 0.0006
HC-IV 10 0.60 (0.70) 1.40 (1.00) -0.80 (0.63) 0.0092
Hospital 14 1.07 (0.83) 2.64 (2.41) -1.57 (2.31) 0.0023
78 Ownership of Facility
PHP 4 0.25 (0.5 2.5 (1.29) -2.25 (0.96) 0.0656
PNFP 11 1.2(1.08) 2.0 (1.00) -0.82 (0.60) 0.0053
Public 27 0.48 (0.51) 1.70 (1.86) -1.22 (1.72) 0.0000
Table 4.12 Number and proportion of trained Data analysis personnel by level and ownership
Proportion Facility Characteristic Number available Number trained trained, %
Level of Facility
HC III 4 3 75.0
HC IV 6 4 66.7
Hospital 24 17 70.8
Ownership of Facility
79 Other 2 2 100.0
PHP 1 1 100.0
PNFP* 27 33 122.2
Public 17 11 64.7
Note * Apparent inconsistencies in data
Table 4.13 Number and proportion of available equipment by level and ownership
Proportion Facility Characteristic Number available Number required available, %
Computers
Level of Facility
HC III 3 38 7.9
HC IV 19 31 61.3
Hospital 42 110 38.2
Printers
HC III 1 22 4.5
HC IV 5 13 38.5
80 Hospital 19 50 38.0
Ownership of Facility Computers
Other 56 20 280.0
PHP 14 36 38.9
PNFP 71 41 173.2
Public 30 104 28.8
Printers
Other 4 0
PHP 2 7 28.6
PNFP 12 23 52.2
Public 15 56 26.8
Figure 1 Map of Uganda showing district
81 82