A political economy analysis of strengthening health information system in Tanzania.

BACKGROUND: Many countries' health systems are implementing reforms to improve the functioning and performance of the Health Management Information System (HMIS) to facilitate evidence-based decisions for delivery of accessible and quality health services. However, in some countries such efforts and initiatives have led to a complex HMIS ecosystem characterized by multiple and fragmented sub-systems. We undertook an in-depth analysis of the HMIS ecosystem in Tanzania to inform the ongoing initiatives, by understanding the relationship and power differences among stakeholders, as well as drivers and barriers to HMIS investment and strengthening. METHODOLOGY: This was a qualitative research method incorporating data collection through document review and key informant interviews guided by political economy analytical framework. A total of 17 key informant interviews were conducted between April and May 2022. A thematic content analysis was used during data analysis. RESULTS: Good relationship between the government and stakeholders dealing/supporting HMIS ecosystem was noted as there are technical working groups which brings stakeholders together to discuss and harmonize HMIS activities. The 'need for the data' has been the driving force toward investment in the HMIS ecosystem. The analysis showed that the government is the main stakeholder within the HMIS ecosystem and responsible for identifying the needs for improvement and has the power to approve or reject systems which are not in line with the government priority as stipulated with the HMIS investment roadmap/strategy. Moreover, partners with long relationship are powerful in influencing HMIS investment decision-making compared to those who are recently coming to support. It was further noted shortage of staff with technical competence, inadequate financial resources, and the development of fact that some of the existing systems have not been developed to their full capacity and have hindered the whole systems' integration and interoperability exercise of ensuring integration and interoperability of the systems. CONCLUSION: A need-based assessment of staff capacity at the sub-national level is equally important to identify available capabilities and the knowledge gap to strengthen the HMIS ecosystem. Strong coordination of the ideas and resources intended to strengthen the HMIS ecosystem would help to reduce fragmentation. In addition, there is a need to mobilize resources within and outside the country to facilitate the integration and interoperability process smoothly.

Direct financial assistance for improved maternal and child health data: a pilot study supporting the health management information system in Malawi.

BACKGROUND: The health management information system (HMIS) is an integral component of a strong health care system. Despite its importance for decision-making, the quality of HMIS data remains of concern in low- and middle-income countries. To address challenges with the quality of maternal and child health (MCH) data gathered within Malawi's HMIS, we conducted a pilot study evaluating different support modalities to district-level HMIS offices. We hypothesized that providing regular, direct financial assistance to HMIS offices would enable staff to establish strategies and priorities based on local context, resulting in more accurate, timely, and complete MCH data. METHODS: The pilot intervention was implemented in Mwanza district, while Chikwawa, Neno, and Ntchisi districts served as control sites given support received from other institutions. The intervention consisted of providing direct financial assistance to Mwanza's HMIS office following the submission of detailed budgets and lists of planned activities. In the control districts, we performed interviews with the HMIS officers to track the HMIS-related activities. We evaluated the intervention by comparing data quality between the post- and pre-intervention periods in the intervention and control districts. Additionally, we conducted interviews with Mwanza's HMIS office staff to determine the acceptability and appropriateness of the intervention. RESULTS: Following the 10-month intervention period, we observed improvements in MCH data quality in Mwanza. The availability and completeness of MCH data collected in the registers increased by 22 and 18 percentage points, respectively. The consistency of MCH data between summary reports and electronic HMIS also improved. In contrast, 2/3 control districts noted minimal changes or reductions in data quality after 10 months. The qualitative interviews confirmed that, despite some challenges, the intervention was well received by the participating HMIS office. HMIS staff preferred our strategy to other conventional strategies that fail to give them the independence to make decisions. CONCLUSIONS: This pilot intervention demonstrated an alternative approach to support HMIS offices in their daily efforts to improve data quality. Given the Ministry of Health's (MoH) interest in strengthening its HMIS, our intervention provides a strategy that the MoH and local and international partners could consider to rapidly improve HMIS data with minimal oversight.

Did an Intervention Programme Aimed at Strengthening the Maternal and Child Health Services in Nigeria Improve the Completeness of Routine Health Data Within the Health Management Information System?

BACKGROUND: During 2012-2015, the Federal Government of Nigeria launched the Subsidy Reinvestment and Empowerment Programme, a health system strengthening (HSS) programme with a Maternal and Child Health component (Subsidy Reinvestment and Empowerment Programme [SURE-P]/MCH), which was monitored using the Health Management Information Systems (HMIS) data reporting tools. Good quality data is essential for health policy and planning decisions yet, little is known on whether and how broad health systems strengthening programmes affect quality of data. This paper explores the effects of the SURE-P/MCH on completeness of MCH data in the National HMIS. METHODS: This mixed-methods study was undertaken in Anambra state, southeast Nigeria. A standardized proforma was used to collect facility-level data from the facility registers on MCH services to assess the completeness of data from 2 interventions and one control clusters. The facility data was collected to cover before, during, and after the SURE-P intervention activities. Qualitative in-depth interviews were conducted with purposefully-identified health facility workers to identify their views and experiences of changes in data quality throughout the above 3 periods. RESULTS: Quantitative analysis of the facility data showed that data completeness improved substantially, starting before SURE-P and continuing during SURE-P but across all clusters (ie, including the control). Also health workers felt data completeness were improved during the SURE-P, but declined with the cessation of the programme. We also found that challenges to data completeness are dependent on many variables including a high burden on providers for data collection, many variables to be filled in the data collection tools, and lack of health worker incentives. CONCLUSION: Quantitative analysis showed improved data completeness and health workers believed the SURE-P/MCH had contributed to the improvement. The functioning of national HMIS are inevitably linked with other health systems components. While health systems strengthening programmes have a great potential for improved overall systems performance, a more granular understanding of their implications on the specific components such as the resultant quality of HMIS data, is needed.

Estimation of health impact from digitalizing last-mile Logistics Management Information Systems (LMIS) in Ethiopia, Tanzania, and Mozambique: A Lives Saved Tool (LiST) model analysis.

BACKGROUND: Digital health has become a widely recognized approach to addressing a range of health needs, including advancing universal health coverage and achieving the Sustainable Development Goals. At present there is limited evidence on the impact of digital interventions on health outcomes. A growing body of peer-reviewed evidence on digitalizing last-mile electronic logistics management information systems (LMIS) presents an opportunity to estimate health impact. METHODS: The impact of LMIS on reductions in stockouts was estimated from primary data and peer-reviewed literature, with three scenarios of impact: 5% stockout reduction (conservative), 10% stockout reduction (base), and 15% stockout reduction (optimistic). Stockout reduction data was inverted to stock availability and improved coverage for vaccines and essential medicines using a 1:1 conversion factor. The Lives Saved Tool (LiST) model was used to estimate health impact from lives saved in newborns and children in Mozambique, Tanzania, and Ethiopia between 2022 and 2026 across the three scenarios. RESULTS: Improving coverage of vaccines with a digital LMIS intervention in the base scenario (conservative, optimistic) could prevent 4,924 (2,578-6,094), 3,998 (1,621-4,915), and 17,648 (12,656-22,776) deaths in Mozambique, Tanzania, and Ethiopia, respectively over the forecast timeframe. In addition, scaling up coverage of non-vaccine medications could prevent 17,044 (8,561-25,392), 21,772 (10,976-32,401), and 34,981 (17,543-52,194) deaths in Mozambique, Tanzania, and Ethiopia, respectively. In the base model scenario, the maximum percent reduction in deaths across all geographies was 1.6% for vaccines and 4.1% for non-vaccine medications. INTERPRETATION: This study projects that digitalization of last-mile LMIS would reduce child mortality by improving coverage of lifesaving health commodities. This analysis helps to build the evidence base around the benefits of deploying digital solutions to address health challenges. Findings should be interpreted carefully as stockout reduction estimates are derived from a small number of studies.

Data quality assessment and associated factors in the health management information system among health centers of Southern Ethiopia.

BACKGROUND: A well designed Health management information system is necessary for improving health service effectiveness and efficiency. It also helps to produce quality information and conduct evidence based monitoring, adjusting policy implementation and resource use. However, evidences show that data quality is poor and is not utilized for program decisions in Ethiopia especially at lower levels of the health care and it remains as a major challenge. METHOD: Facility based cross sectional study design was employed. A total of 18 health centers and 302 health professionals were selected by simple random sampling using lottery method from each selected health center. Data was collected by health professionals who were experienced and had training on HMIS tasks after the tools were pretested. Data quality was assessed using accuracy, completeness and timeliness dimensions. Seven indicators from national priority area were selected to assess data accuracy and monthly reports were used to assess completeness and timeliness. Statistical software SPSS version 20 for descriptive statistics and binary logistic regression was used for quantitative data analysis to identify candidate variable. RESULT: A total of 291 respondents were participated in the study with response rate of 96%. Overall average data quality was 82.5%. Accuracy, completeness and timeliness dimensions were 76%, 83.3 and 88.4 respectively which was lower than the national target. About 52.2% respondents were trained on HMIS, 62.5% had supervisory visits as per standard and only 55.3% got written feedback. Only 11% of facilities assigned health information technicians. Level of confidence [AOR = 1.75, 95% CI (0.99, 3.11)], filling registration or tally completely [AOR = 3.4, 95% CI (1.3, 8.7)], data quality check, supervision AOR = 1.7 95% CI (0.92, 2.63) and training [AOR = 1.89 95% CI (1.03, 3.45)] were significantly associated with data quality. CONCLUSION: This study found that the overall data quality was lower than the national target. Over reporting of all indicators were observed in all facilities. It needs major improvement on supervision quality, training status to increase confidence of individuals to do HMIS activities.

Establishing evidence-based decision-making mechanism in a health eco-system and its linkages with health service coverage in 25 high-priority districts of Uttar Pradesh, India.

BACKGROUND: Achievement of successful health outcomes depends on evidence-based programming and implementation of effective health interventions. Routine Health Management Information System is one of the most valuable data sets to support evidence-based programming, however, evidence on systemic use of routine monitoring data for problem-solving and improving health outcomes remain negligible. We attempt to understand the effects of systematic evidence-based review mechanism on improving health outcomes in Uttar Pradesh, India. METHODS: Data comes from decision-tracking system and routine health management information system for period Nov-2017 to Mar-2019 covering 6963 health facilities across 25 high-priority districts of the state. Decision-tracking data captured pattern of decisions taken, actions planned and completed, while the latter one provided information on service coverage outcomes over time. Three service coverage indicators, namely, pregnant women receiving 4 or more times ANC and haemoglobin testing during pregnancy, delivered at the health facility, and receive post-partum care within 48 h of delivery were used as outcomes. Univariate and bivariate analyses were conducted. RESULTS: Total 412 decisions were taken during the study reference period and a majority were related to ante-natal care services (31%) followed by delivery (16%) and post-natal services (16%). About 21% decisions-taken were focused on improving data quality. By 1 year, 67% of actions planned based on these decisions were completed, 26% were in progress, and the remaining 7% were not completed. We found that, over a year, districts witnessing > 20 percentage-point increase in outcomes were also the districts with significantly higher action completion rates (> 80%) compared to the districts with < 10 percentage-point increase in outcomes having completion of action plans around 50-70%. CONCLUSIONS: Findings revealed a significantly higher improvement in coverage outcomes among the districts which used routine health management data to conduct monthly review meetings and had high actions completion rates. A data-based review-mechanisms could specifically identify programmatic gaps in service delivery leading to strategic decision making by district authorities to bridge the programmatic gaps. Going forward, establishing systematic evidence-based review platforms can be an important strategy to improve health outcomes and promote the use of routine health monitoring system data in any setting.

Gaps in the coverage of vitamin K1 prophylaxis among newborns in India: insights from secondary analysis of data from the Health Management Information System.

OBJECTIVE: Despite operational guidelines, anecdotal evidence suggests that newborn vitamin K1 prophylaxis is not practiced routinely in India. This study determined the coverage of vitamin K1 prophylaxis among newborns in the country. DESIGN: Nationwide cross-sectional data on live births and newborns receiving vitamin K1 during the 2019-2020 reporting period were abstracted from the Health Management Information System (HMIS). The coverage estimates of newborn vitamin K1 prophylaxis were derived nationally and also for individual states and union territories (UT). Additionally, coverage heterogeneities were investigated using classifiers, viz. geography, socio-demographic index (SDI), special developmental categories and institutional birth rate (IBR). SETTING: India. PARTICIPANTS: 20 208 804 newborns documented with HMIS. RESULTS: Vitamin K1 was administered to overall 62·36 % newborns (95 % CI: 62·34 to 62·38 %). The Central zone (49·0 %), low SDI states (54·39 %), Empowered Action Group states (53·32 %) and states with low IBR (44·69 %) had the lowest coverage amongst their respective groupings. Across the individual states and UT, the coverage ranged widely from 22·18 % (in Tripura) to 99·38 % (in Puducherry), exhibiting considerable variability (coefficient of variation: 33·74 %) and inequality (Gini coefficient: 0·17). While the coverage in eight states/UT (i.e. Arunachal Pradesh, Manipur, Nagaland, Tripura, Uttar Pradesh, Uttarakhand, Telangana and Andaman & Nicobar Islands) was below 50 %; only five states/UT (i.e. Chandigarh, Gujarat, Goa, Puducherry and Tamil Nadu) achieved above 90 % coverage. CONCLUSION: Vitamin K1 prophylaxis was not practiced in more than one-third newborns in India. It calls for identifying the barriers, addressing the gaps and implementing newborn vitamin K1 prophylaxis more effectively throughout the country.