Piloting the Inclusion of the Key Populations Unique Identifier Code in the South African Routine Health Information Management System: Protocol for a Multiphased Study.

BACKGROUND: The global community has set an ambitious goal to end HIV/AIDS as a public health threat by 2030. Significant progress has been achieved in pursuing these objectives; however, concerns remain regarding the lack of disaggregated routine data for key populations (KPs) for a targeted HIV response. KPs include female sex workers, transgender populations, gay men and other men who have sex with men, people who are incarcerated, and people who use drugs. From an epidemiological perspective, KPs play a fundamental role in shaping the dynamics of HIV transmission due to specific behaviors. In South Africa, routine health information management systems (RHIMS) do not include a unique identifier code (UIC) for KPs. The purpose of this protocol is to develop the framework for improved HIV monitoring and programming through piloting the inclusion of KPs UIC in the South African RHIMS. OBJECTIVE: This paper aims to describe the protocol for a multiphased study to pilot the inclusion of KPs UIC in RHIMS. METHODS: We will conduct a multiphased study to pilot the framework for the inclusion of KPs UIC in the RHIMS. The study has attained the University of Johannesburg Research Ethics Committee approval (REC-2518-2023). This study has four objectives, including a systematic review, according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (objective 1). Second, policy document review and in-depth stakeholder interviews using semistructured questionnaires (objective 2). Third, exploratory data analysis of deidentified HIV data sets (objective 3), and finally, piloting the framework to assess the feasibility of incorporating KPs UIC in RHIMS using findings from objectives 1, 2, and 3 (objective 4). Qualitative and quantitative data will be analyzed using ATLAS.ti (version 6; ATLAS.ti Scientific Software Development GmbH) and Python (version 3.8; Python Software Foundation) programming language, respectively. RESULTS: The results will encompass a systematic review of literature, qualitative interviews, and document reviews, along with exploratory analysis of deidentified routine program data and findings from the pilot study. The systematic review has been registered in PROSPERO (International Prospective Register of Systematic Reviews; CRD42023440656). Data collection is planned to commence in September 2024 and expected results for all objectives will be published by December 2025. CONCLUSIONS: The study will produce a framework to be recommended for the inclusion of the KP UIC national rollout. The study results will contribute to the knowledge base around the inclusion of KPs UIC in RHIMS data. TRIAL REGISTRATION: PROSPERO CRD42023440656; https://tinyurl.com/msnppany. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/55092.

Assessing intersectional gender analysis in Nepal's health management information system: a case study on tuberculosis for inclusive health systems.

BACKGROUND: Tuberculosis (TB) remains a major public health problem in Nepal, high in settings marked by prevalent gender and social inequities. Various social stratifiers intersect, either privileging or oppressing individuals based on their characteristics and contexts, thereby increasing risks, vulnerabilities and marganilisation associated with TB. This study aimed to assess the inclusiveness of gender and other social stratifiers in key health related national policies and the Health Management Information System (HMIS) of National Tuberculosis Programme (NTP) by conducting an intersectional analysis of TB cases recorded via HMIS. METHODS: A desk review of key policies and the NTP's HMIS was conducted. Retrospective intersectional analysis utilized two secondary data sources: annual NTP report (2017-2021) and records of 628 TB cases via HMIS 6.5 from two TB centres (2017/18-2018/19). Chi-square test and multi-variate analysis was used to assess the association between social stratifers and types of TB, registration category and treatment outcome. RESULTS: Gender, social inclusion and concept of intersectionality are incorporated into various health policies and strategies but lack effective implementation. NTP has initiated the collection of age, sex, ethnicity and location data since 2014/15 through the HMIS. However, only age and sex disaggregated data are routinely reported, leaving recorded social stratifiers of TB patients static without analysis and dissemination. Furthermore, findings from the intersectional analysis using TB secondary data, showed that male more than 25 years exhibited higher odds [adjusted odds ratio (aOR) = 4.95, 95% confidence interval (CI): 1.60-19.06, P = 0.01)] of successful outcome compared to male TB patients less than 25 years. Similarly, sex was significantly associated with types of TB (P < 0.05) whereas both age (P < 0.05) and sex (P < 0.05) were significantly associated with patient registration category (old/new cases). CONCLUSIONS: The results highlight inadequacy in the availability of social stratifiers in the routine HMIS. This limitation hampers the NTP's ability to conduct intersectional analyses, crucial for unveiling the roles of other social determinants of TB. Such limitation underscores the need for more disaggregated data in routine NTP to better inform policies and plans contributing to the development of a more responsive and equitable TB programme and effectively addressing disparities.

Adapting national data systems for donor transition: comparative analysis of experience from Georgia and China.

Health management information systems (HMISs) are essential in programme planning, budgeting, monitoring and evidence-informed decision-making. This paper focuses on donor transitions in two upper-middle-income countries, China and Georgia, and explores how national HMIS adaptations were made and what facilitated or limited successful and sustainable transitions. This comparative analytical case study uses a policy triangle framework and a mixed-methods approach to explore how and why adaptations in the HMIS occurred under the Gavi Alliance and the Global Fund-supported programmes in China and Georgia. A review of published and grey literature, key informant interviews and administrative data analysis informed the study findings. Contextual factors such as the global and country context, and health system and programme needs drove HMIS developments. Other factors included accountability on a national and international level; improvements in HMIS governance by establishing national regulations for clear mandates of data collection and reporting rules and creating institutional spaces for data use; investing in hardware, software and human resources to ensure regular and reliable data generation; and capacitating national players to use data in evidence-based decision-making for programme and transition planning, budgeting and outcome monitoring. Not all the HMIS initiatives supported by donors were sustained and transitioned. For the successful adaptation and sustainable transition, five interlinked and closely coordinated support areas need to be considered: (1) coupling programme design with a good understanding of the country context while considering domestic and external demands for information, (2) regulating appropriate governance and management arrangements enhancing country ownership, (3) avoiding silo HMIS solutions and taking integrative approach, (4) ensuring the transition of funding onto domestic budget and enforcing fulfilment of the government's financial commitments and finally (5) investing in technologies and skilled human resources for the HMIS throughout all levels of the health system. Neglecting any of these elements risks not delivering sustainable outcomes.

Review of the India Adolescent Health Strategy in the context of disease burden among adolescents.

Background: A nuanced understanding of the health needs of adolescents in the context of the India Adolescent Health Strategy (IAHS) is needed to inform policy interventions for improving the health and well-being of adolescents in India. Methods: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, we identified the top ten causes of years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs) disaggregated by sex and age group (10-14 and 15-19 years) for India and its states in 2019. To inform the IAHS of refinement or expansion in focus needed to improve adolescent health in India, we reviewed the extent to which the top 10 causes of disease burden are addressed in the IAHS, and the availability of and age- and sex-disaggregation in the service utilisation data for adolescents captured in the Adolescent Friendly Health Clinic monitoring information system (AFHC MIS) and Health Management Information System (HMIS). We also reviewed the availability of and age-and sex-disaggregation in the data capture at the population level for the IAHS outcome indicators in the data sources identified in the IAHS operational framework. Findings: Females in the 10-14 and 15-19 years age groups suffered 6.75 million and 9.25 million DALYs, respectively, 39.1% and 44.2% of which were YLLs; the corresponding DALYs for males were 6.71 million and 9.65 million (42.3% and 41.1% YLLs), respectively. Within the 6 thematic areas of the IAHS, most strategies and indicators identified are for sexual and reproductive health followed by nutrition, and broadly these conditions accounted for YLDs and not YLLs in adolescents. Significant gaps in the IAHS in comparison to the disease burden for fatal diseases and conditions were seen across injuries, communicable diseases, and non-communicable diseases. Injuries accounted for 65.9% and 45.3% of YLLs in males and females aged 15-19 years, and 40.8% in males aged 10-14 years. Specifically, road injuries (15.3%, 95% UI 11.0-18.0) and self-harm (11.3%, 95% UI 8.7-14.2) accounted for most of the injury deaths in 15-19 years whereas drowning (7.7% 95% UI 5.8-9.6) and road injuries (6.9%, 95% UI 4.7-8.6) accounted for the most injury deaths in 10-14 years males. However, only self-harm and gender-based violence are specifically addressed in the IAHS with non-specific interventions for other injuries. Diarrhoea, lower respiratory infections, malaria, encephalitis, tuberculosis, typhoid, cirrhosis, and hepatitis are the other disease conditions accounting for YLLs and DALYs in adolescents but these are neither addressed in the IAHS nor in service provision under the AFHC MIS. There is no age- or sex-disaggregation in the cause of death data captured in the HMIS to allow an understanding of mortality in adolescents. For the IAHS outcome indicators at the population level, data capture for the 10-14 years irrespective of sex was largely missing from the population surveys and none of the surveys captured data for either females or males aged 15-19 years for physical inactivity and mental health indicators. Interpretation: The considerable differences seen in the IAHS thematic focus as compared with the leading causes of fatal and non-fatal disease burden in adolescents in India, and in the availability of population-level data to monitor the outcome indicators of the IAHS can pose substantial limitations for improving adolescent health in India. The findings in this paper can be utilized by decision makers to refine action aimed at improving adolescent health and well-being. Funding: Bill & Melinda Gates Foundation.

Documenting the development, adoption and pre-ebola implementation of Liberia's integrated disease surveillance and response (IDSR) strategy.

BACKGROUND: In the immediate aftermath of a 14-year civil conflict that disrupted the health system, Liberia adopted the internationally recommended integrated disease surveillance and response (IDSR) strategy in 2004. Despite this, Liberia was among the three West African countries ravaged by the worst Ebola epidemic in history from 2014 to 2016. This paper describes successes, failures, strengths, and weaknesses in the development, adoption, and implementation of IDSR following the civil war and up until the outbreak of Ebola, from 2004 to early 2014. METHODS: We reviewed 112 official Government documents and peer-reviewed articles and conducted 29 in-depth interviews with key informants from December 2021 to March 2022 to gain perspectives on IDSR in the post-conflict and pre-Ebola era in Liberia. We assessed the core and supportive functions of IDSR, such as notification of priority diseases, confirmation, reporting, analysis, investigation, response, feedback, monitoring, staff training, supervision, communication, and financial resources. Data were triangulated and presented via emerging themes and in-depth accounts to describe the context of IDSR introduction and implementation, and the barriers surrounding it. RESULTS: Despite the adoption of the IDSR framework, Liberia failed to secure the resources-human, logistical, and financial-to support effective implementation over the 10-year period. Documents and interview reports demonstrate numerous challenges prior to Ebola: the surveillance system lacked key components of IDSR including laboratory testing capacity, disease reporting, risk communication, community engagement, and staff supervision systems. Insufficient financial support and an abundance of vertical programs further impeded progress. In-depth accounts by donors and key governmental informants demonstrate that although the system had a role in detecting Ebola in Liberia, it could not respond effectively to control the disease. CONCLUSION: Our findings suggest that post-war, Liberia's health system intended to prioritize epidemic preparedness and response with the adoption of IDSR. However, insufficient investment and systems development meant IDSR was not well implemented, leaving the country vulnerable to the devastating impact of the Ebola epidemic.

Strengthening routine data reporting in private hospitals in Lagos, Nigeria.

The availability of routine health information is critical for effective health planning, especially in resource-limited countries. Nigeria adopted the web-based District Health Information System (DHIS) to harmonize the collection, analysis and storage of data for informed decision-making. However, only 44% of all private hospitals in Lagos State reported to the DHIS despite constituting 90% of all health facilities in the state. To bridge this gap, this study implemented targeted interventions. This paper describes (1) the implemented interventions, (2) the effects of the interventions on data reporting on DHIS during the intervention period and (3) the evaluation of data reporting on DHIS after the intervention period in select private hospitals in Lagos State. A five-pronged intervention was implemented in 55 private hospitals (intervention hospitals), which entailed stakeholder engagement, on-the-job training, in-facility mentoring and the provision of data tools and job aids, to improve data reporting on DHIS from 2014 to 2017. A controlled before-and-after study design was employed to assess the effectiveness of the implemented interventions. A comparable cohort of 55 non-intervention private hospitals was selected, and data were extracted from both groups. Data analysis was conducted using paired and independent t-tests to assess the effect and measure the difference between both groups of hospitals, respectively. An average increase of 65.28% (P < 0.01) in reporting rate and 50.31% (P < 0.01) in the timeliness of reporting on DHIS was seen among intervention hospitals. Similarly, the difference between intervention and non-intervention hospitals post-intervention was significantly different for both data reporting (mean difference = -22.38, P < 0.01) and timeliness (mean difference = -18.81, P < 0.01), respectively. Furthermore, a sustained improvement in data reporting and timeliness of reporting on DHIS was observed among intervention hospitals 24 months after interventions. Thus, implementing targeted interventions can strengthen routine data reporting for better performance and informed decision-making.

A roadmap for using DHIS2 data to track progress in key health indicators in the Global South: experience from sub-saharan Africa.

High quality health data as collected by health management information systems (HMIS) is an important building block of national health systems. District Health Information System 2 (DHIS2) software is an innovation in data management and monitoring for strengthening HMIS that has been widely implemented in low and middle-income countries in the last decade. However, analysts and decision-makers still face significant challenges in fully utilizing the capabilities of DHIS2 data to pursue national and international health agendas. We aimed to (i) identify the most relevant health indicators captured by DHIS2 for tracking progress towards the Sustainable Development goals in sub-Saharan African countries and (ii) present a clear roadmap for improving DHIS2 data quality and consistency, with a special focus on immediately actionable solutions. We identified that key indicators in child and maternal health (e.g. vaccine coverage, maternal deaths) are currently being tracked in the DHIS2 of most countries, while other indicators (e.g. HIV/AIDS) would benefit from streamlining the number of indicators collected and standardizing case definitions. Common data issues included unreliable denominators for calculation of incidence, differences in reporting among health facilities, and programmatic differences in data quality. We proposed solutions for many common data pitfalls at the analysis level, including standardized data cleaning pipelines, k-means clustering to identify high performing health facilities in terms of data quality, and imputation methods. While we focus on immediately actionable solutions for DHIS2 analysts, improvements at the point of data collection are the most rigorous. By investing in improving data quality and monitoring, countries can leverage the current global attention on health data to strengthen HMIS and progress towards national and international health priorities.

Impact of COVID-19 on utilization of maternal and child health services in India: Health management information system data analysis.

Background/Objectives: Studies globally have documented the impact of COVID 19 on maternal and newborn health services. This study assesses the impact of COVID-19 on essential maternal and child health (MCH) services in India based on the national Health Management Information System (HMIS). Methods: Present retrospective study used secondary data analysis upon the routinely collected data accessed from Health Management Information System. Microdata on maternal and newborn indicators was extracted for all states between April and June during 2019, 2020 and 2021. Relative change for each indicator were taken into consideration for the year 2020 and 2021; with respect to the outcomes in 2019. Results: Compared to 2019, antenatal care registrations saw a decline in all states for both periods in 2020 and 2021 except for Sikkim, Telangana, Maharashtra and Andhra Pradesh. Similarly, the relative changes in 2019 pertaining to the proportion of pregnant women provided with emergency obstetric care for pregnancy complications registered a decline in all states except for Himachal Pradesh, Telangana and Arunachal Pradesh. There was a decreasing trend noted in institutional deliveries in 2020 and 2021 among all major states. However, an increasing trend was seen in the number of immunization sessions held among all major states. Conclusion: The study demonstrates a disruption in service delivery during the lockdown period in the first wave and the peak of the second wave. Further qualitative studies need to be undertaken to generate evidence for maintaining continuum of care during a pandemic situation.

Digital Technologies to Enhance Infectious Disease Surveillance in Tanzania: A Scoping Review.

Mobile phones and computer-based applications can speed up disease outbreak detection and control. Hence, it is not surprising that stakeholders in the health sector are becoming more interested in funding these technologies in Tanzania, Africa, where outbreaks occur frequently. The objective of this situational review is, therefore, to summarize available literature on the application of mobile phones and computer-based technologies for infectious disease surveillance in Tanzania and to inform on existing gaps. Four databases were searched-Cumulative Index to Nursing and Allied Health Literature (CINAHL), Excerpta Medica Database (Embase), PubMed, and Scopus-yielding a total of 145 publications. In addition, 26 publications were obtained from the Google search engine. Inclusion and exclusion criteria were met by 35 papers: they described mobile phone-based and computer-based systems designed for infectious disease surveillance in Tanzania, were published in English between 2012 and 2022, and had full texts that could be read online. The publications discussed 13 technologies, of which 8 were for community-based surveillance, 2 were for facility-based surveillance, and 3 combined both forms of surveillance. Most of them were designed for reporting purposes and lacked interoperability features. While undoubtedly useful, the stand-alone character limits their impact on public health surveillance.

Data quality and associated factors in the health management information system at health centers in Shashogo district, Hadiya zone, southern Ethiopia, 2021.

BACKGROUND: Poor quality routine data contributes to poor decision-making, inefficient resource allocation, loss of confidence in the health system, and may threaten the validity of impact evaluations. For several reasons in most developing countries, the routine health information systems in those countries are described as ineffective. Hence, the aim of this study is to determine the quality of data and associated factors in the routine health management information system in health centers of Shashogo district, Hadiya Zone. METHODS: A facility-based cross-sectional study was conducted from June 1, 2021, to July 1, 2021, and 300 participants were involved in the study through simple random sampling. The data was collected with a self-administered questionnaire by trained data collectors. After checking its completeness, the data was entered into EPI data version 3.1 and exported to SPSS version 25 for statistical analysis. Finally, variables with p < 0.05 during multivariable analysis were considered significant variables. RESULT: A total of 300(100%) participant were included in the interview and HMIS data quality was 83% in Shashogo district health centers. The data quality in terms of accuracy, completeness, and timeliness was 79%, 86%, and 84%, respectively. Conducting supportive supervision [AOR 3.5 (1.4, 8.9)], checking accuracy [AOR 1.3 (1.5, 3.5)], filling registrations [AOR 2.7 (1.44, 7.7)], and confidence level [AOR 1.9 (1.55, 3.35)] were all rated positively found to be factors associated with data quality. CONCLUSION: The overall level of data quality in Shashogo district health centers was found to be below the national expectation level. All dimensions of data quality in the district were below 90% in data accuracy, content completeness, and timeliness of data. Conducting supportive supervision, checking accuracy, filling registrations and confidence level were found to be factors associated with data quality. Hence, all stakeholders should give all necessary support to improve data quality in routine health information systems to truly attain the goal of providing good quality data for the decision-making process by considering the identified factors.

Perceived Usefulness, Competency, and Associated Factors in Using District Health Information System Data Among District Health Managers in Tanzania: Cross-sectional Study.

BACKGROUND: Tanzania introduced District Health Information Software (version 2; DHIS2) in 2013 to support existing health management information systems and to improve data quality and use. However, to achieve these objectives, it is imperative to build human resource capabilities to address the challenges of new technologies, especially in resource-constrained countries. OBJECTIVE: This study aimed to determine the perceived usefulness, competency, and associated factors in using DHIS2 data among district health managers (DHMs) in Tanzania. METHODS: This descriptive cross-sectional study used a quantitative approach, which involved using a self-administered web-based questionnaire. This study was conducted between April and September 2019. We included all core and co-opted members of the council or district health management teams (DHMTs) from all 186 districts in the country. Frequency and bivariate analyses were conducted, and the differences among categories were measured by using a chi-square test. P values of <.05 were considered significant. RESULTS: A total of 2667 (77.96%) of the expected 3421 DHMs responded, of which 2598 (97.41%) consented and completed the questionnaires. Overall, the DHMs were satisfied with DHIS2 (2074/2596, 79.83%) because of workload reduction (2123/2598, 81.72%), the ease of learning (1953/2598, 75.17%), and enhanced data use (2239/2598, 86.18%). Although only half of the managers had user accounts (1380/2598, 53.12%) and were trained on DHIS2 data analysis (1237/2598, 47.61%), most claimed to have average to advanced skills in data validation (1774/2598, 68.28%), data visualization (1563/2598, 60.16%), and DHIS2 data use (1321/2598, 50.85%). The biggest challenges facing DHMs included the use of a paper-based system as the primary data source (1890/2598, 72.75%) and slow internet speed (1552/2598, 59.74%). Core members were more confident in using DHIS2 compared with other members (P=.004), whereas program coordinators were found to receive more training on data analysis and use (P=.001) and were more confident in using DHIS2 data compared with other DHMT members (P=.001). CONCLUSIONS: This study showed that DHMs have appreciable competencies in using the DHIS2 and its data. However, their skill levels have not been commensurate with the duration of DHIS2 use. This study recommends improvements in the access to and use of DHIS2 data. More training on data use is required and should involve using cost-effective approaches to include both the core and noncore members of the DHMTs. Moreover, enhancing the culture and capacity of data use will ensure the better management and accountability of health system performance.

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.

Health management information system (HMIS) data quality and associated factors in Massaguet district, Chad.

BACKGROUND: Quality data from Health Management Information Systems (HMIS) are important for tracking the effectiveness of malaria control interventions. However, HMIS data in many resource-limited settings do not currently meet standards set by the World Health Organization (WHO). We aimed to assess HMIS data quality and associated factors in Chad. METHODS: A cross-sectional study was conducted in 14 health facilities in Massaguet district. Data on children under 15 years were obtained from the HMIS and from the external patient register covering the period January-December 2018. An additional questionnaire was administered to 16 health centre managers to collect data on contextual variables. Patient registry data were aggregated and compared with the HMIS database at district and health centre level. Completeness and accuracy indicators were calculated as per WHO guidelines. Multivariate logistic regressions were performed on the Verification Factor for attendance, suspected and confirmed malaria cases for three age groups (1 to < 12 months, 1 to < 5 years and 5 to < 15 years) to identify associations between health centre characteristics and data accuracy. RESULTS: Health centres achieved a high level of data completeness in HMIS. Malaria data were over-reported in HMIS for children aged under 15 years. There was an association between workload and higher odds of inaccuracy in reporting of attendance among children aged 1 to < 5 years (Odds ratio [OR]: 10.57, 95% CI 2.32-48.19) and 5- < 15 years (OR: 6.64, 95% CI 1.38-32.04). Similar association was found between workload and stock-outs in register books, and inaccuracy in reporting of malaria confirmed cases. Meanwhile, we found that presence of a health technician, and of dedicated staff for data management, were associated with lower inaccuracy in reporting of clinic attendance in children aged under five years. CONCLUSION: Data completeness was high while the accuracy was low. Factors associated with data inaccuracy included high workload and the unavailability of required data collection tools. The results suggest that improvement in working conditions for clinic personnel may improve HMIS data quality. Upgrading from paper-based forms to a web-based HMIS may provide a solution for improving data accuracy and its utility for future evaluations of health interventions. Results from this study can inform the Ministry of Health and it partners on the precautions to be taken in the use of HMIS data and inform initiatives for improving its quality.

Addressing missing values in routine health information system data: an evaluation of imputation methods using data from the Democratic Republic of the Congo during the COVID-19 pandemic.

BACKGROUND: Poor data quality is limiting the use of data sourced from routine health information systems (RHIS), especially in low- and middle-income countries. An important component of this data quality issue comes from missing values, where health facilities, for a variety of reasons, fail to report to the central system. METHODS: Using data from the health management information system in the Democratic Republic of the Congo and the advent of COVID-19 pandemic as an illustrative case study, we implemented seven commonly used imputation methods and evaluated their performance in terms of minimizing bias in imputed values and parameter estimates generated through subsequent analytical techniques, namely segmented regression, which is widely used in interrupted time series studies, and pre-post-comparisons through paired Wilcoxon rank-sum tests. We also examined the performance of these imputation methods under different missing mechanisms and tested their stability to changes in the data. RESULTS: For regression analyses, there were no substantial differences found in the coefficient estimates generated from all methods except mean imputation and exclusion and interpolation when the data contained less than 20% missing values. However, as the missing proportion grew, k-NN started to produce biased estimates. Machine learning algorithms, i.e. missForest and k-NN, were also found to lack robustness to small changes in the data or consecutive missingness. On the other hand, multiple imputation methods generated the overall most unbiased estimates and were the most robust to all changes in data. They also produced smaller standard errors than single imputations. For pre-post-comparisons, all methods produced p values less than 0.01, regardless of the amount of missingness introduced, suggesting low sensitivity of Wilcoxon rank-sum tests to the imputation method used. CONCLUSIONS: We recommend the use of multiple imputation in addressing missing values in RHIS datasets and appropriate handling of data structure to minimize imputation standard errors. In cases where necessary computing resources are unavailable for multiple imputation, one may consider seasonal decomposition as the next best method. Mean imputation and exclusion and interpolation, however, always produced biased and misleading results in the subsequent analyses, and thus, their use in the handling of missing values should be discouraged.

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.

The chunampet community health information management system: A health and demographic surveillance system from a rural South India.

BACKGROUND: Developed countries have strong health and demographic surveillance system (HDSS), whereas there is a dearth of such system in developing countries like India. India depends on national surveys and individual studies for public health information. At present All India Institute of Medical Sciences - New Delhi HDSS and Vadu HDSS are well established HDSS in India. MATERIALS AND METHODS: We developed a HDSS in a remote rural area of South India and named as Community Health Information Management System (CHIMS) This covered 20 villages around Rural Health Training Centre - Chunampet. We collected the family and demographic information from March 2018 to October 2018. Pregnancy, birth, under-five and mortality data were collected once in every 3 months with the help of interns, Medical Social Workers. Data collection done using CHIMS Guide and entered in EpiData software. EpiAnalysis, Quantum Geographic Information System, Dropbox were the other freely available software used in this program. RESULTS: CHIMS HDSS covered 14924 individuals belonging to 4486 households in the surrounding twenty villages. Population density was 213/km2. CHIMS consumed very limited resources in terms of workforce, materials, and transport. CHIMS database was used as a baseline database for many other studies. This CHIMS HDSS helped in many publications, postgraduate thesis dissertations and mainly attracted many extramural research funds from leading government Research Institutes from India. CONCLUSION: CHIMS proved to be a robust surveillance system in providing vital public health information about the community and attracted more extramural funds to the institute.

Adherence to clinical guidelines in integration of mental health services into primary health care in Mbarara, southwestern Uganda: a medical records review.

BACKGROUND: The Ugandan Ministry of Health decentralized mental healthcare to the district level; developed the Uganda Clinical Guidelines (UCG); and trained primary health care (PHC) providers in identification, management, and referral of individuals with common mental disorders. This was intended to promote integration of mental health services into PHC in the country. 'Common mental disorders' here refers to mental, neurological and substance use conditions as indicated in the UCG. However, the extent of integration of mental health into general healthcare remains unknown. This study aimed to establish the level of adherence of PHC providers to the UCG in the identification and management of mental disorders. METHODS: This was a prospective medical record review of patient information collected in November and December 2018, and March and April 2019 at two health centers (III and IV) in southwestern Uganda. Data (health facility level; sex and age of the patient; and mental disorder diagnosis, management) was collected using a checklist. Continuous data was analyzed using means and standard deviation while categorical data was analyzed using Chi-square. Multivariable logistic regression analysis was performed to establish predictors of PHC provider adherence to the clinical guidelines on integration of mental health services into PHC. The analysis was conducted at a 95% level of significance. RESULTS: Of the 6093 records of patients at the study health facilities during the study period, 146 (2.4%) had a mental or neurological disorder diagnosis. The commonly diagnosed disorders were epilepsy 91 (1.5%) and bipolar 25 (0.4%). The most prescribed medications were carbamazepine 65 (44.5%), and phenobarbital 26 (17.8%). The medicines inappropriately prescribed at health center III for a mental diagnosis included chlorpromazine for epilepsy 3 (2.1%) and haloperidol for epilepsy 1 (0.7%). Female gender (aOR: 0.52, 95% CI 0.39-0.69) and age 61+ years (aOR: 3.02, 95% CI 1.40-6.49) were predictors of a mental disorder entry into the HMIS register. CONCLUSION: There was a noticeable change of practice by PHC providers in integrating mental health services in routine care as reflected by the rise in the number of mental disorders diagnosed and treated and entered into the modified paper based HMIS registers.

Associations between Natural Resource Extraction and Incidence of Acute and Chronic Health Conditions: Evidence from Tanzania.

Natural resource extraction projects are often accompanied by complex environmental and social-ecological changes. In this paper, we evaluated the association between commodity extraction and the incidence of diseases. We retrieved council (district)-level outpatient data from all public and private health facilities from the District Health Information System (DHIS2). We combined this information with population data from the 2012 national population census and a geocoded list of resource extraction projects from the Geological Survey of Tanzania (GST). We used Poisson regression with random effects and cluster-robust standard errors to estimate the district-level associations between the presence of three types of commodity extraction (metals, gemstone, and construction materials) and the total number of patients in each disease category in each year. Metal extraction was associated with reduced incidence of several diseases, including chronic diseases (IRR = 0.61, CI: 0.47-0.80), mental health disorders (IRR = 0.66, CI: 0.47-0.92), and undernutrition (IRR = 0.69, CI: 0.55-0.88). Extraction of construction materials was associated with an increased incidence of chronic diseases (IRR = 1.47, CI: 1.15-1.87). This study found that the presence of natural resources commodity extraction is significantly associated with changes in disease-specific patient volumes reported in Tanzania's DHIS2. These associations differed substantially between commodities, with the most protective effects shown from metal extraction.

Opening the 'black box' of collaborative improvement: a qualitative evaluation of a pilot intervention to improve quality of malaria surveillance data in public health centres in Uganda.

BACKGROUND: Demand for high-quality surveillance data for malaria, and other diseases, is greater than ever before. In Uganda, the primary source of malaria surveillance data is the Health Management Information System (HMIS). However, HMIS data may be incomplete, inaccurate or delayed. Collaborative improvement (CI) is a quality improvement intervention developed in high-income countries, which has been advocated for low-resource settings. In Kayunga, Uganda, a pilot study of CI was conducted in five public health centres, documenting a positive effect on the quality of HMIS and malaria surveillance data. A qualitative evaluation was conducted concurrently to investigate the mechanisms of effect and unintended consequences of the intervention, aiming to inform future implementation of CI. METHODS: The study intervention targeted health workers, including brief in-service training, plus CI with 'plan-do-study-act' (PDSA) cycles emphasizing self-reflection and group action, periodic learning sessions, and coaching from a CI mentor. Health workers collected data on standard HMIS out-patient registers. The qualitative evaluation (July 2015 to September 2016) included ethnographic observations at each health centre (over 12-14 weeks), in-depth interviews with health workers and stakeholders (n = 20), and focus group discussions with health workers (n = 6). RESULTS: The results suggest that the intervention did facilitate improvement in data quality, but through unexpected mechanisms. The CI intervention was implemented as planned, but the PDSA cycles were driven largely by the CI mentor, not the health workers. In this context, characterized by a rigid hierarchy within the health system of limited culture of self-reflection and inadequate training and supervision, CI became an effective form of high-quality training with frequent supervisory visits. Health workers appeared motivated to improve data collection habits by their loyalty to the CI mentor and the potential for economic benefits, rather than a desire for self-improvement. CONCLUSIONS: CI is a promising method of quality improvement and could have a positive impact on malaria surveillance data. However, successful scale-up of CI in similar settings may require deployment of highly skilled mentors. Further research, focusing on the effectiveness of 'real world' mentors using robust study designs, will be required to determine whether CI can be translated effectively and sustainably to low-resource settings.

Data utilisation and factors influencing the performance of the health management information system in Tanzania.

BACKGROUND: Health Management Information System (HMIS) is a set of data regularly collected at health care facilities to meet the needs of statistics on health services. This study aimed to determine the utilisation of HMIS data and factors influencing the health system's performance at the district and primary health care facility levels in Tanzania. METHODS: This cross-sectional study was carried out in 11 districts and involved 115 health care facilities in Tanzania. Data were collected using a semi-structured questionnaire administered to health workers at facility and district levels and documented using an observational checklist. Thematic content analysis approach was used to synthesise and triangulate the responses and observations to extract essential information. RESULTS: A total of 93 healthcare facility workers and 13 district officials were interviewed. About two-thirds (60%) of the facility respondents reported using the HMIS data, while only five out of 13 district respondents (38.5%) reported analysing HMIS data routinely. The HMIS data were mainly used for comparing performance in terms of services coverage (53%), monitoring of disease trends over time (50%), and providing evidence for community health education and promotion programmes (55%). The majority (41.4%) of the facility's personnel had not received any training on data management related to HMIS during the past 12 months prior to the survey. Less than half (42%) of the health facilities had received supervisory visits from the district office 3 months before this assessment. Nine district respondents (69.2%) reported systematically receiving feedback on the quality of their reports monthly and quarterly from higher authorities. Patient load was described to affect staff performance on data collection and management frequently. CONCLUSION: Inadequate analysis and poor data utilisation practices were common in most districts and health facilities in Tanzania. Inadequate human and financial resources, lack of incentives and supervision, and lack of standard operating procedures on data management were the significant challenges affecting the HMIS performance in Tanzania.