Routine Health Management Information System Research Articles

A time series analysis of disruptions to maternal and child health care in northern Togo during the COVID-19 pandemic in the context of an integrated primary care program

There are significant concerns that COVID-19-related disruptions in routine health care will reverse the gains made over the past 2 decades in reducing maternal and under-5 mortality in low- and middle-income countries. The objective of this study was to examine the extent of disruptions in community-based maternal and child health services and explore community perceptions and experiences with health care use in the year following the start of the COVID-19 pandemic in one district in northern Togo, West Africa. This study included analysis of multiple types of data including: (1) routine health management information system data from 18 public sector health centers, (2) programmatic data collected through an integrated health center- and community-based primary care program at 5 health centers, and (3) semi-structured interviews with health center managers, community health workers (CHWs), and community members at those 5 health centers. We found only short-term declines in service utilization coinciding with the COVID-19 pandemic and no decline in the delivery of community-based care by CHWs. Qualitative data were consistent with the quantitative results, reporting sustained use of health-care services. Multilevel factors related to the continued provision of care during the COVID-19 pandemic included regular and clear communication from CHWs as trusted community sources, risk minimization at the health-center level, continued provision of community-based care by CHWs, and collaboration between community leaders and health-care workers to limit COVID-19 transmission. Findings demonstrate resiliency of an integrated primary care system when equipped with an adequately trained, supervised, and supplied health workforce, implementation of infection prevention and control measures, communication by trusted community sources, and adaptations to health-care delivery that enable the continued provision of care.

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

BackgroundTuberculosis (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.MethodsA 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.ResultsGender, 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).ConclusionsThe 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.

Malaria trends in districts that were targeted and not-targeted for seasonal malaria chemoprevention in children under 5 years of age in Guinea, 2014–2021

BackgroundSeasonal malaria chemoprevention (SMC) is a main intervention to prevent and reduce childhood malaria. Since 2015, Guinea has implemented SMC targeting children aged 3–59 months (CU5) in districts with high...

COVID-19 outbreak control strategies and their impact on the provision of essential health services in Ghana: An exploratory-sequential study.

The COVID-19 pandemic has led to substantial interruptions in critical health services, with 90% of countries reporting interruptions in routine vaccinations, maternal health care and chronic disease management. The use of non-pharmaceutical interventions (NPIs) such as lockdowns and self-isolation had implications on the provision of essential health services (EHS). We investigated exemplary COVID-19 outbreak control strategies and explored the extent to which the adoption of these NPIs affected the provision of EHS including immunization coverage and facility-based deliveries. Finally, we document core health system strategies and practices adopted to maintain EHS during the early phase of the pandemic. This study used an explanatory sequential study design. First, we utilized data from routine health management information systems to quantify the impact of the pandemic on the provision of EHS using interrupted time series models. Second, we explored exemplary strategies and health system initiatives that were adopted to prevent the spread of COVID-19 infections while maintaining the provision of EHS using in-depth interviews with key informants including policymakers and healthcare providers. The COVID-19 pandemic and the interventions that were implemented disrupted the provision of EHS. In the first month of the COVID-19 pandemic, Oral Polio and pentavalent vaccination coverage reduced by 15.2% [95% CI = -22.61, -7.87, p<0.001] and 12.4% [95% CI = 17.68, -7.13; p<0.001] respectively. The exemplary strategies adopted in maintaining the provision of EHS while also responding to the spread of infections include the development of new policy guidelines that were disseminated with modified service delivery models, new treatment and prevention guidelines, the use of telemedicine and medical drones to provide EHS and facilitate rapid testing of suspected cases. The implementation of different NPIs during the peak phase of the pandemic disrupted the provision of EHS. However, the Ministry of Health leveraged the resilient health system and deployed efficient, all-inclusive, and integrated infectious disease management and infection prevention control strategies to maintain the provision of EHS while responding to the spread of infections.

Access and utilization of health services improved by trained posyandu cadres in rural Indonesia

Abstract Posyandu stands for ‘Pos Pelayanan Terpadu’ in Bahasa, meaning ‘Integrated Service Post,’ a community-based health center to provide a range of services for mothers and under five children in Indonesia. Each Posyandu center s served by five posyandu cadres who are community-based health workers and responsible to deliver essential health services and counseling to their communities. In Central Lombok district, Indonesia, we facilitated building capacity and skill of posyandu cadres for counseling and health education and to encourage the community to attend Posyandu center as well as to seek care from nearby Puskesmas and Polindes (other community based static health facilities). During January - August 2022, we trained 1100 posyandu cadres and equipped them with pictorial cards with health education and counseling messages. We retrieved monthly attendance and care seeking data from Puskesmas, Polindes and Posyandu centers through routine health management information system for January 2022 to February 2023 and we compared pre-training (January-August 2022) and post-training (September 2022-February 2023) data. Monthly average number of pregnant women and sick children who were brought to nearby Puskesmas and Polindes for care during pre-training period was 1553 which was significantly increased to 1844 during post-training period (p &amp;lt; 0.001). We also observed statistically significant ((p &amp;lt; 0.001) increase in average monthly attendance of mothers and children at Posyandu centres (from 12,751 in pre-training period to 14,677 in post-training period). This incremental attendance at Posyandu centers and increased care seeking from Puskesmas and Polindes can potentially be attributable to counseling-education session by trained and skilled posyandu cadres and it reconfirms the worthiness of additional investment for capacity and skill building of posyandu cadres to improve community health in rural Indonesia. Key messages • Posyandu cadres have potential strength to make significant improvement in community health care service delivery by educating and counseling communities in rural Indonesia. • Investment on capacity building and training of posyandu cadres can pay off by improving access and utilization of health care services by pregnant women and children.

Why district assemblies disburse resources to district health systems for service delivery at district level in the context of decentralization: a comparative study of two districts in the Volta Region of Ghana.

To explore why the District Assembly disburses financial and other resources to the District Health System. Multiple case study with a single unit of analysis (holistic) using quantitative and qualitative methods of data collection involving a desk review, analysis of routine health management information system data and key informant interviews. Two districts in the Volta Region of Ghana. Twelve key officials of each district assembly and the district health system (24 total) who had worked in the district at least a year or more. None. Both District Assemblies had moderate decision space which was influenced by their capacity, power and contextual factors like politics, economics, legal and situational factors. Disbursement of financial and other resources to the District Health Systems was influenced by financial capacity, use of power by stakeholders, context and the decision space of the District Assembly. Political actors appeared to have more power in resource disbursement decision making than community members and technocrats in a context of resource constraints and inadequate funding. The funding available was used predominantly for capital investments, mainly construction of Community Based Health Planning and Services (CHPS) compounds. It is important to make policies that will regulate the relative power among the political appointees like the District Chief Executives (DCEs), public and civil servants in decentralized departments and agencies and Community members to make resource disbursement more sensitive to communities and decentralized departments.

Predicting Treatment Interruption Among People Living With HIV in Nigeria: Machine Learning Approach

Background Antiretroviral therapy (ART) has transformed HIV from a fatal illness to a chronic disease. Given the high rate of treatment interruptions, HIV programs use a range of approaches to support individuals in adhering to ART and in re-engaging those who interrupt treatment. These interventions can often be time-consuming and costly, and thus providing for all may not be sustainable. Objective This study aims to describe our experiences developing a machine learning (ML) model to predict interruption in treatment (IIT) at 30 days among people living with HIV newly enrolled on ART in Nigeria and our integration of the model into the routine information system. In addition, we collected health workers’ perceptions and use of the model’s outputs for case management. Methods Routine program data collected from January 2005 through February 2021 was used to train and test an ML model (boosting tree and Extreme Gradient Boosting) to predict future IIT. Data were randomly sampled using an 80/20 split into training and test data sets, respectively. Model performance was estimated using sensitivity, specificity, and positive and negative predictive values. Variables considered to be highly associated with treatment interruption were preselected by a group of HIV prevention researchers, program experts, and biostatisticians for inclusion in the model. Individuals were defined as having IIT if they were provided a 30-day supply of antiretrovirals but did not return for a refill within 28 days of their scheduled follow-up visit date. Outputs from the ML model were shared weekly with health care workers at selected facilities. Results After data cleaning, complete data for 136,747 clients were used for the analysis. The percentage of IIT cases decreased from 58.6% (36,663/61,864) before 2017 to 14.2% (3690/28,046) from October 2019 through February 2021. Overall IIT was higher among clients who were sicker at enrollment. Other factors that were significantly associated with IIT included pregnancy and breastfeeding status and facility characteristics (location, service level, and service type). Several models were initially developed; the selected model had a sensitivity of 81%, specificity of 88%, positive predictive value of 83%, and negative predictive value of 87%, and was successfully integrated into the national electronic medical records database. During field-testing, the majority of users reported that an IIT prediction tool could lead to proactive steps for preventing IIT and improving patient outcomes. Conclusions High-performing ML models to identify patients with HIV at risk of IIT can be developed using routinely collected service delivery data and integrated into routine health management information systems. Machine learning can improve the targeting of interventions through differentiated models of care before patients interrupt treatment, resulting in increased cost-effectiveness and improved patient outcomes.

Uptake of reproductive, maternal and child health services during the first year of the COVID-19 pandemic in Uganda: A mixed methods study.

Use of reproductive health (RH), maternal, newborn and child health (MNCH) services in Uganda is suboptimal. Reasons for this are complex; however, service-delivery factors such as availability, quality, staffing, and supplies, contribute substantially to low uptake. The COVID-19 pandemic threatened to exacerbate existing challenges to delivery and use of high-quality RH and MNCH services. We conducted a mixed methods study, combining secondary analysis of routine electronic health management information system (eHMIS) data with exploratory key informant interviews (KII) to examine changes in health service uptake over the course of the pandemic and to understand service delivery adaptations implemented in response. We analyzed eHMIS data for four services (family planning, facility-based deliveries, antenatal visits, and immunization for children by one year), comparing them across four time periods: pre-COVID-19, partial lockdown, total lockdown and post lockdown. Additionally, KIIs were used to document adaptations made for continuity of health services. Use of services declined substantially during total lockdown; however, rebounded quickly to earlier observed levels, during the post lockdown for all four services, especially for immunization for children by one year. KIIs identified several health services delivery adaptations. At the community level, these included: community outreaches, training some mothers as community liaisons to encourage others to seek health services, and support from local leaders to create call centers to facilitate clients transport during travel restrictions. Health facilities creatively used space to accommodate social distancing and shifted providers' roles. District leadership reassigned health workers to facilities closest to their homes, provided vehicle passes to staff, and ambulances to transport pregnant women in critical need. WhatsApp groups facilitated communication at district level and enabled redistribution of supplies. Ministry of Health produced critical guidelines for continuity of health services. Implementing partners provided and redistributed commodities and personal protective equipment, and provided technical support, training and transport.

Estimating maternal mortality and identifying causes of maternal deaths in Jammu and Kashmir: a two-year epidemiological study

Background: An accurate estimate of the maternal mortality ratio and its underlying causes is crucial for the effective planning of maternal health services. The aim of the study was to estimate the maternal mortality ratio (MMR) and identify causes of maternal deaths in Jammu and Kashmir. A subnational geographical area in India. Methods: The study included maternal deaths from April 2020 to March 2022 among residents of Jammu and Kashmir. We enumerated all maternal deaths and recorded their socio-clinical parameters from interviews with family members. The cause of death was inferred from the medical records and verbal autopsy. Live births data for the corresponding period was obtained from the routine health management information system. We calculated the maternal mortality ratio using a standard formula. Results: A total of 185 maternal deaths and 3.97 lakh live births were recorded over the two-year study period. It translated into an MMR of 47 per lakh live births, exhibiting noteworthy variability between districts. Postpartum haemorrhage (PPH) and hypertensive disorders of pregnancy accounted for a substantial proportion of maternal deaths, representing 44.3% (82/185) of cases. Sepsis and other obstetric complications were identified as the cause of death in 15.7% (29/185) and 19.5% (36/185) of maternal deaths, respectively. Furthermore, the majority of maternal deaths occurred among women between 25-30 years of age. Approximately two-thirds of maternal deaths occurred during the post-partum period. Conclusions: In summary, our study reveals that the maternal mortality rate (MMR) in Jammu and Kashmir falls below the sustainable development goal target, albeit with marked inter-district variability pointing to disparities in healthcare delivery. We recommend expanding the coverage of evidence-based interventions aimed at reducing the incidence of postpartum haemorrhage, eclampsia, and other obstetric complications to improve maternal health outcomes.

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

BackgroundPoor 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.MethodsA 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.ResultA 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.ConclusionThe 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.

Assessment of maternal and child health care services performance in the context of COVID-19 pandemic in Addis Ababa, Ethiopia: evidence from routine service data

BackgroundIn many settings, health care service provision has been modified to managing COVID-19 cases, and this has been affecting the provision of maternal and child health services. The aim of this study was to assess trends in selected maternal and child health services performance in the context of COVID-19 pandemic.MethodsA cross-sectional data review was conducted in Addis Ababa, Ethiopia from April to May 2021. Routine health management information system database was reviewed from Addis Ababa Health Bureau for the period from July 2019 to March 2021 across all quarters. Proportion and mean with standard deviation were computed. T-test was used to assess statistically significant differences in services mean performance.ResultsPostnatal care visit, new contraceptives accepters, safe abortion care and number of under-5 years old children treated for pneumonia significantly decreased by 9.3% (p-value 0.04), 20.3% (p-value 0.004), 23.7% (p-value 0.01) and 77.2% (p-value < 0.001), respectively during the first 8 months of the COVID-19 pandemic compared to the previous 8 months’ average performance. The trends in Antenatal care first visit, new contraceptive accepters, pentavalent-3 vaccination and under-five children treated for pneumonia began to decline in January to March 2020, a quarter when the COVID-19 pandemic began; with accelerated declines in April to June 2020 following national lockdown. The trends for the stated services began to increase during July–September 2020, the last quarter of national lockdown. Contraceptive accepters and pentavalent-1 vaccination continued to decline and showed no recovery until January–March 2021 when this study was completed.ConclusionsMost of the maternal and child health services performance declined following the onset of COVID-19 pandemic and national lockdown, and most of the services began recovering during July–September 2020, the last quarter of national lockdown. However, new and repeat contraceptive accepters and pentavalent-1 recipients continue to decline and show no recovery during end of the study period. Implementing COVID-19 prevention measures and assuring the community about the safety of service delivery is imperative to ensure continuity of the maternal and child health services. Regular monitoring and evaluation of services performance is required to identify slowly recovering services and respond to potentially volatile changes during the COVID-19 pandemic.

Innovative approach to improve information accuracy in a two-district cross-sectional study in Bihar, India

ObjectiveCombine Health Management Information Systems (HMIS) and probability survey data using the statistical annealing technique (AT) to produce more accurate health coverage estimates than either source of data and a...

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

BackgroundAchievement 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.MethodsData 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.ResultsTotal 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%.ConclusionsFindings 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.

Patient volume and quality of primary care in Ethiopia: findings from the routine health information system and the 2014 Service Provision Assessment survey

BackgroundSeveral studies have reported inadequate levels of quality of care in the Ethiopian health system. Facility characteristics associated with better quality remain unclear. Understanding associations between patient volumes and quality of care could help organize service delivery and potentially improve patient outcomes.MethodsUsing data from the routine health management information system (HMIS) and the 2014 Ethiopian Service Provision Assessment survey + we assessed associations between daily total outpatient volumes and quality of services. Quality of care at the facility level was estimated as the average of five measures of provider knowledge (clinical vignettes on malaria and tuberculosis) and competence (observations of family planning, antenatal care and sick child care consultations). We used linear regression models adjusted for several facility-level confounders and region fixed effects with log-transformed patient volume fitted as a linear spline. We repeated analyses for the association between volume of antenatal care visits and quality.ResultsOur analysis included 424 facilities including 270 health centers, 45 primary hospitals and 109 general hospitals in Ethiopia. Quality was low across all facilities ranging from only 18 to 56% with a mean score of 38%. Outpatient volume varied from less than one patient per day to 581. We found a small but statistically significant association between volume and quality which appeared non-linear, with an inverted U-shape. Among facilities seeing less than 90.6 outpatients per day, quality increased with greater patient volumes. Among facilities seeing 90.6 or more outpatients per day, quality decreased with greater patient volumes. We found a similar association between volume and quality of antenatal care visits.ConclusionsHealth care utilization and quality must be improved throughout the health system in Ethiopia. Our results are suggestive of a potential U-shape association between volume and quality of primary care services. Understanding the links between volume of patients and quality of care may provide insights for organizing service delivery in Ethiopia and similar contexts.

Barriers and enablers to routine register data collection for newborns and mothers: EN-BIRTH multi-country validation study

BackgroundPolicymakers need regular high-quality coverage data on care around the time of birth to accelerate progress for ending preventable maternal and newborn deaths and stillbirths. With increasing facility births, routine Health Management Information System (HMIS) data have potential to track coverage. Identifying barriers and enablers faced by frontline health workers recording HMIS source data in registers is important to improve data for use.MethodsThe EN-BIRTH study was a mixed-methods observational study in five hospitals in Bangladesh, Nepal and Tanzania to assess measurement validity for selected Every Newborn coverage indicators. We described data elements required in labour ward registers to track these indicators. To evaluate barriers and enablers for correct recording of data in registers, we designed three interview tools: a) semi-structured in-depth interview (IDI) guide b) semi-structured focus group discussion (FGD) guide, and c) checklist assessing care-to-documentation. We interviewed two groups of respondents (January 2018–March 2019): hospital nurse-midwives and doctors who fill ward registers after birth (n = 40 IDI and n = 5 FGD); and data collectors (n = 65). Qualitative data were analysed thematically by categorising pre-identified codes. Common emerging themes of barriers or enablers across all five hospitals were identified relating to three conceptual framework categories.ResultsSimilar themes emerged as both barriers and enablers. First, register design was recognised as crucial, yet perceived as complex, and not always standardised for necessary data elements. Second, register filling was performed by over-stretched nurse-midwives with variable training, limited supervision, and availability of logistical resources. Documentation complexity across parallel documents was time-consuming and delayed because of low staff numbers. Complete data were valued more than correct data. Third, use of register data included clinical handover and monthly reporting, but little feedback was given from data users.ConclusionHealth workers invest major time recording register data for maternal and newborn core health indicators. Improving data quality requires standardised register designs streamlined to capture only necessary data elements. Consistent implementation processes are also needed. Two-way feedback between HMIS levels is critical to improve performance and accurately track progress towards agreed health goals.

Routine health management information system data in Ethiopia: consistency, trends, and challenges

ABSTRACT Background: Ethiopia is investing in the routine Health Management Information System. Improved routine data are needed for decision-making in the health sector. Objective: To analyse the quality of the routine Health Management Information System data and triangulate with other sources, such as the Demographic and Health Surveys. Methods: We analysed national Health Management Information System data on 19 indicators of maternal health, neonatal survival, immunization, child nutrition, malaria, and tuberculosis over the 2012–2018 time period. The analyses were conducted by 38 analysts from the Ministry of Health, Ethiopia, and two government agencies who participated in the Operational Research and Coaching for Analysts (ORCA) project between June 2018 and June 2020. Using a World Health Organization Data Quality Review toolkit, we assessed indicator definitions, completeness, internal consistency over time and between related indicators, and external consistency compared with other data sources. Results: Several services reported coverage of above 100%. For many indicators, denominators were based on poor-quality population data estimates. Data on individual vaccinations had relatively good internal consistency. In contrast, there was low external consistency for data on fully vaccinated children, with the routine Health Management Information System showing 89% coverage but the Demographic and Health Survey estimate at 39%. Maternal health indicators displayed increasing coverage over time. Indicators on child nutrition, malaria, and tuberculosis were less consistent. Data on neonatal mortality were incomplete and operationalised as mortality on day 0–6. Our comparisons with survey and population projections indicated that one in eight early neonatal deaths were reported in the routine Health Management Information System. Data quality varied between regions. Conclusions: The quality of routine data gathered in the health system needs further attention. We suggest regular triangulation with data from other sources. We recommend addressing the denominator issues, reducing the complexity of indicators, and aligning indicators to international definitions.

Data quality of the routine health management information system at the primary healthcare facility and district levels in Tanzania

BackgroundEffective planning for disease prevention and control requires accurate, adequately-analysed, interpreted and communicated data. In recent years, efforts have been put in strengthening health management information systems (HMIS) in Sub-Saharan Africa to improve data accessibility to decision-makers. This study assessed the quality of routine HMIS data at primary healthcare facility (HF) and district levels in Tanzania.MethodsThis cross-sectional study involved reviews of documents, information systems and databases, and collection of primary data from facility-level registers, tally sheets and monthly summary reports. Thirty-four indicators from Outpatient, Inpatient, Antenatal care, Family Planning, Post-natal care, Labour and Delivery, and Provider-Initiated Testing and Counselling service areas were assessed. Indicator records were tracked and compared across the process of data collection, compilation and submission to the district office. Copies of monthly report forms submitted by facilities to the district were also reviewed. The availability and utilization of HMIS tools were assessed, while completeness and data accuracy levels were quantified for each phase of the reporting system.ResultsA total of 115 HFs (including hospitals, health centres, dispensaries) in 11 districts were involved. Registers (availability rate = 91.1%; interquartile range (IQR) 66.7–100%) and report forms (86.9%; IQR 62.2–100%) were the most utilized tools. There was a limited use of tally-sheets (77.8%; IQR 35.6–100%). Tools availability at the dispensary was 91.1%, health centre 82.2% and hospital 77.8%, and was low in urban districts. The availability rate at the district level was 65% (IQR 48–75%). Wrongly filled or empty cells in registers and poor adherence to the coding procedures were observed. Reports were highly over-represented in comparison to registers’ records, with large differences observed at the HF phase of the reporting system. The OPD and IPD areas indicated the highest levels of mismatch between data source and district office. Indicators with large number of clients, multiple variables, disease categorization, or those linked with dispensing medicine performed poorly.ConclusionThere are high variations in the tool utilisation and data accuracy at facility and district levels. The routine HMIS is weak and data at district level inaccurately reflects what is available at the source. These results highlight the need to design tailored and inter-service strategies for improving data quality.

Accelerating the performance of district health systems towards achieving UHC via twinning partnerships

BackgroundA twinning partnership is a formal and substantive collaboration between two districts to improve their performance in providing primary healthcare services. The ‘win-win’ twinning partnership pairs are categorized under relatively high and low-performing districts. The purpose of this formative evaluation is to use the empirically derived systems model as an analytical framework to systematically document the inputs, throughputs and outputs of the twinning partnership strategy.MethodsThis explanatory sequential mixed method study design was conducted from October 2018 to September 2019, in Amhara, Oromia, Southern, Nations, Nationalities and Peoples’ (SNNP) and Tigray regions. The quantitative research approach used an observational design which consists of three measurements: at baseline (October 2018), midterm (March 2019) and end-line (September 2019), and the qualitative approach employed a case study. Qualitative data was collected using interviewer-guided semi-structured interview tools. The data were transcribed verbatim, translated from Amharic and Afan Oromo into English and analyzed through a theoretical framework named the Bergen Model of Collaborative Functioning (BMCF). Quantitative data were extracted from routine health management information system. The results are presented as averages, percentages and graphs. To claim statistical significance, non-parametric tests: Friedman test at (p < 0.05) and Wilcoxon signed ranks test (p < 0.017) were analyzed.ResultsThe District Health System Performance (DHSP) was determined using data collected from eight districts. At baseline, the mean DHSP score was 50.97, at midterm, it was 60.3 and at end-line, it was 72.07. There was a strong degree and statistically significant relationship between baseline, midterm and end-line DHSP scores (r > 0.978**), using the Friedman test χ2(2) = 16.000, p = 0.001. Post hoc analysis using Wilcoxon signed-rank test was conducted with a Bonferroni correction and the results elicit higher DHSP values from baseline to midterm and from midterm to end-line with significance level set at p < 0.017. The qualitative results of the case study revealed that scanning the mission of the twinning partnership and focusing on a shared vision coupled with mobilizing internal and external resources were the fundamental input elements for successful twinning partnerships at the district level. In addition, the context of pursuing Universal Health Coverage (UHC) through achieving transformed districts can be enhanced through deploying skilled and knowledgeable leadership, defining clear roles and responsibilities for all stakeholders, forming agreed detailed action plans, and effective communication that leads to additive results and synergy. The twinning partnership implementing districts benefit from the formal relationship and accelerate their performances towards meeting the criteria of transformed districts in Ethiopia.ConclusionsTwinning partnerships help to accelerate the health system’s performance in achieving the district transformation criteria. Therefore, scaling up the implementation of the twinning partnership strategy is recommended.

Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components

Liver Segmentation on a Variety of Computed Tomography (CT) Images Based on Convolutional Neural Networks Combined with Connected Components

Quality of malaria data in public health facilities in three provinces of Mozambique.

Malaria data reported through Mozambique's routine health information system are used to guide the implementation of prevention and control activities. Although previous studies have identified issues with the quality of aggregated data reported from public health facilities in the country, no studies have evaluated the quality of routine indicators recorded in health facility registries. This study addresses this issue by comparing indicators calculated from data from exit interviews and re-examinations of patients with data based on registry records from health facilities in order to measure the quality of registry data and data reporting in three provinces in Mozambique. Data were collected from 1,840 outpatients from 117 health facilities in Maputo, Zambézia, and Cabo Delgado Provinces interviewed and examined as part of a malaria-specific health facility survey. Key indicators based on exit interview / re-examination data were compared to the same indicators based on records from health facility registries. Multivariable regression was performed to identify factors associated with indicators matching in re-examination / exit interview data and health facility registries. Aggregated indicators abstracted from facility registries were compared to those reported through the routine health management information system (HMIS) for the same time period. Sensitivity of exit interview / re-examination data compared with those recorded in facility registries was low for all indicators in all facilities. The lowest sensitivities were in Maputo, where the sensitivity for recording negative RDT results was 9.7%. The highest sensitivity was for recording positive RDT results in Cabo Delgado, at 75%. Multivariable analysis of factors associated with agreement between gold standard and registry data showed patients were less likely to be asked about having a fever in the triage ward in Maputo and Cabo Delgado (adjusted Odds Ratio 0.75 and 0.39 respectively), and in the outpatient ward in Cabo Delgado (aOR = 0.37), compared with the emergency department. Patients with positive RDT were also more likely to have RDT results recorded in all three provinces when patients had been managed according to national treatment guidelines during initial examination. Comparison of retrospective data abstracted from facility registries to HMIS data showed discrepancies in all three provinces. The proportion of outpatient cases with suspected and confirmed malaria were similar in registry and HMIS data across all provinces (a relatively low difference between registry and HMIS data of 3% in Maputo and Zambézia), though the total number of all-cause outpatient cases was consistently higher in the HMIS. The largest difference was in Maputo, where a total of 87,992 all-cause outpatient cases were reported in HMIS, compared with a total of 42,431 abstracted from facility registries. This study shows that care should be taken in interpreting trends based solely on routine data due to data quality issues, though the discrepancy in all-cause outpatient cases may be indicative that register availability and storage are important factors. As such, simple steps such as providing consistent access and storage of registers that include reporting of patient fever symptoms might improve the quality of routine data recorded at health facilities.