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Health management information system (HMIS) data verification: A case study in four districts in Rwanda.

ABSTRACT: INTRODUCTION:Reliable Health Management and Information System (HMIS) data can be used with minimal cost to identify areas for improvement and to measure impact of healthcare delivery. However, variable HMIS data quality in low- and middle-income countries limits its value in monitoring, evaluation and research. We aimed to review the quality of Rwandan HMIS data for maternal and newborn health (MNH) based on consistency of HMIS reports with facility source documents. METHODS:We conducted a cross-sectional study in 76 health facilities (HFs) in four Rwandan districts. For 14 MNH data elements, we compared HMIS data to facility register data recounted by study staff for a three-month period in 2017. A HF was excluded from a specific comparison if the service was not offered, source documents were unavailable or at least one HMIS report was missing for the study period. World Health Organization guidelines on HMIS data verification were used: a verification factor (VF) was defined as the ratio of register over HMIS data. A VF<0.90 or VF>1.10 indicated over- and under-reporting in HMIS, respectively. RESULTS:High proportions of HFs achieved acceptable VFs for data on the number of deliveries (98.7%;75/76), antenatal care (ANC1) new registrants (95.7%;66/69), live births (94.7%;72/76), and newborns who received first postnatal care within 24 hours (81.5%;53/65). This was slightly lower for the number of women who received iron/folic acid (78.3%;47/60) and tested for syphilis in ANC1 (67.6%;45/68) and was the lowest for the number of women with ANC1 standard visit (25.0%;17/68) and fourth standard visit (ANC4) (17.4%;12/69). The majority of HFs over-reported on ANC4 (76.8%;53/69) and ANC1 (64.7%;44/68) standard visits. CONCLUSION:There was variable HMIS data quality by data element, with some indicators with high quality and also consistency in reporting trends across districts. Over-reporting was observed for ANC-related data requiring more complex calculations, i.e., knowledge of gestational age, scheduling to determine ANC standard visits, as well as quality indicators in ANC. Ongoing data quality assessments and training to address gaps could help improve HMIS data quality.

SUBMITTER: Nshimyiryo A

PROVIDER: S-EPMC7367468 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Health management information system (HMIS) data verification: A case study in four districts in Rwanda.

Nshimyiryo Alphonse A Kirk Catherine M CM Sauer Sara M SM Ntawuyirusha Emmanuel E Muhire Andrew A Sayinzoga Felix F Hedt-Gauthier Bethany B

PloS one 20200717 7

<h4>Introduction</h4>Reliable Health Management and Information System (HMIS) data can be used with minimal cost to identify areas for improvement and to measure impact of healthcare delivery. However, variable HMIS data quality in low- and middle-income countries limits its value in monitoring, evaluation and research. We aimed to review the quality of Rwandan HMIS data for maternal and newborn health (MNH) based on consistency of HMIS reports with facility source documents.<h4>Methods</h4>We c ...[more]

PMID: 32678851

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Project description: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.

Project description:BackgroundQuality 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.MethodsA 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.ResultsHealth 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.ConclusionData 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.

Project description:BACKGROUND:As part of a partnership between the Institute for Healthcare Improvement and the Ethiopian Federal Ministry of Health, woreda-based quality improvement collaboratives took place between November 2016 and December 2017 aiming to accelerate reduction of maternal and neonatal mortality in Lemu Bilbilu, Tanqua Abergele and Duguna Fango woredas. Before starting the collaboratives, assessments found inaccuracies in core measures obtained from Health Management Information System reports. METHODS AND RESULTS:Building on the quality improvement collaborative design, data quality improvement activities were added and we used the World Health Organization review methodology to drive a verification factor for the core measures of number of pregnant women that received their first antenatal care visit, number of pregnant women that received antenatal care on at least four visits, number of pregnant women tested for syphilis and number of births attended by skilled health personnel. Impact of the data quality improvement was assessed using interrupted time series analysis. We found accurate data across all time periods for Tanqua Abergele. In Lemu Bilbilu and Duguna Fango, data quality improved for all core metrics over time. In Duguna Fango, the verification factor for number of pregnant women that received their first antenatal care visit improved from 0.794 (95%CI 0.753, 0.836; p<0.001) pre-intervention by 0.173 (95%CI 0.128, 0.219; p<0.001) during the collaborative; and the verification factor for number of pregnant women tested for syphilis improved from 0.472 (95%CI 0.390, 0.554; p<0.001) pre-intervention by 0.460 (95%CI 0.369, 0.552; p<0.001) during the collaborative. In Lemu Bilbilu, the verification factor for number of pregnant women receiving a fourth antenatal visit rose from 0.589 (95%CI 0.513, 0.664; p<0.001) at baseline by 0.358 (95%CI 0.258, 0.458; p<0.001) post-intervention; and skilled birth attendance rose from 0.917 (95%CI 0.869, 0.965) at baseline by 0.083 (95%CI 0.030, 0.136; p<0.001) during the collaborative. CONCLUSIONS:A Data quality improvement initiative embedded within woreda clinical improvement collaborative improved accuracy of data used to monitor maternal and newborn health services in Ethiopia.

Project description:BackgroundHealth management information systems (HMIS) are instrumental in addressing health delivery problems and strengthening health sectors by generating credible evidence about the health status of clients. There is paucity of studies which have explored possibilities for integrating family planning data from the public and private health sectors in Uganda's national HMIS. This study sought to investigate the facilitators, best practices and barriers of integrating family planning data into the district and national HMIS in Uganda.MethodsWe conducted a qualitative study in Kampala, Jinja, and Hoima Districts of Uganda, based on 16 key informant interviews and a multi-stakeholder dialogue workshop with 11 participants. Deductive and inductive thematic methods were used to analyze the data.ResultsThe technical facilitators of integrating family planning data from public and private facilities in the national and district HMIS were user-friendly software; web-based and integrated reporting; and availability of resources, including computers. Organizational facilitators included prioritizing family planning data; training staff; supportive supervision; and quarterly performance review meetings. Key behavioral facilitators were motivation and competence of staff. Collaborative networks with implementing partners were also found to be essential for improving performance and sustainability. Significant technical barriers included limited supply of computers in lower level health facilities, complex forms, double and therefore tedious entry of data, and web-reporting challenges. Organizational barriers included limited human resources; high levels of staff attrition in private facilities; inadequate training in data collection and use; poor culture of information use; and frequent stock outs of paper-based forms. Behavioral barriers were low use of family planning data for planning purposes by district and health facility staff.ConclusionFamily planning data collection and reporting are integrated in Uganda's district and national HMIS. Best practices included integrated reporting and performance review, among others. Limited priority and attention is given to family planning data collection at the facility and national levels. Data are not used by the health facilities that collect them. We recommend reviewing and tailoring data collection forms and ensuring their availability at health facilities. All staff involved in data reporting should be trained and regularly supervised.

Project description:Health management information system (HMIS) data are important for guiding the attainment of health targets in low- and middle-income countries. However, the quality of HMIS data is often poor. High-quality information is especially important for populations experiencing high burdens of disease and mortality, such as pregnant women, newborns, and children. The purpose of this study was to assess the quality of maternal and child health (MCH) data collected through the Ethiopian Ministry of Health's HMIS in three districts of Jimma Zone, Oromiya Region, Ethiopia over a 12-month period from July 2014 to June 2015. Considering data quality constructs from the World Health Organization's data quality report card, we appraised the completeness, timeliness, and internal consistency of eight key MCH indicators collected for all the primary health care units (PHCUs) located within three districts of Jimma Zone (Gomma, Kersa and Seka Chekorsa). We further evaluated the agreement between MCH service coverage estimates from the HMIS and estimates obtained from a population-based cross-sectional survey conducted with 3,784 women who were pregnant in the year preceding the survey, using Pearson correlation coefficients, intraclass correlation coefficients (ICC), and Bland-Altman plots. We found that the completeness and timeliness of facility reporting were highest in Gomma (75% and 70%, respectively) and lowest in Kersa (34% and 32%, respectively), and observed very few zero/missing values and moderate/extreme outliers for each MCH indicator. We found that the reporting of MCH indicators improved over time for all PHCUs, however the internal consistency between MCH indicators was low for several PHCUs. We found poor agreement between MCH estimates obtained from the HMIS and the survey, indicating that the HMIS may over-report the coverage of key MCH services, namely, antenatal care, skilled birth attendance and postnatal care. The quality of MCH data within the HMIS at the zonal level in Jimma, Ethiopia, could be improved to inform MCH research and programmatic efforts.

Project description:BackgroundMadagascar's Malaria National Strategic Plan 2018-2022 calls for progressive malaria elimination beginning in low-incidence districts (< 1 case/1000 population). Optimizing access to prompt diagnosis and quality treatment and improving outbreak detection and response will be critical to success. A malaria elimination readiness assessment (MERA) was performed in health facilities (HFs) of selected districts targeted for malaria elimination.MethodsA mixed methods survey was performed in September 2018 in five districts of Madagascar. Randomly selected HFs were assessed for availability of malaria commodities and frequency of training and supervision conducted. Health providers (HPs) and community health volunteers (CHVs) were interviewed, and outpatient consultations at HFs were observed. To evaluate elimination readiness, a composite score ranging from 0 to 100 was designed from all study tools and addressed four domains: (1) resource availability, (2) case management (CM), (3) data management and use, and (4) training, supervision, and technical assistance; scores were calculated for each HF catchment area and district based on survey responses. Stakeholder interviews on malaria elimination planning were conducted at national, regional and district levels.ResultsA quarter of the 35 HFs surveyed had no rapid diagnostic tests (RDTs). Of 129 patients with reported or recorded fever among 300 consultations observed, HPs tested 56 (43%) for malaria. Three-quarters of the 35 HF managers reviewed data for trends. Only 68% of 41 HPs reported receiving malaria-specific training. Of 34 CHVs surveyed, 24% reported that treating fever was no longer among their responsibilities. Among treating CHVs, 13 (50%) reported having RDTs, and 11 (42%) had anti-malarials available. The average district elimination readiness score was 52 out of 100, ranging from 48 to 57 across districts. Stakeholders identified several challenges to commodity management, malaria CM, and epidemic response related to lack of training and funding disruptions.ConclusionThis evaluation highlighted gaps in malaria CM and elimination readiness in Madagascar to address during elimination planning. Strategies are needed that include training, commodity provision, supervision, and support for CHVs. The MERA can be repeated to assess progress in filling identified gaps and is a feasible tool that could be used to assess elimination targets in other countries.

Project description:Mortality statistics form a crucial component of national Health Management Information Systems (HMIS). However, there are limitations in the availability and quality of mortality data at national level in Viet Nam. This study assessed the completeness of recorded deaths and the reliability of recorded causes of death (COD) in the A6 death registers in the national routine HMIS in Viet Nam.1477 identified deaths in 2014 were reviewed in two provinces. A capture-recapture method was applied to assess the completeness of the A6 death registers. 1365 household verbal autopsy (VA) interviews were successfully conducted, and these were reviewed by physicians who assigned multiple and underlying cause of death (UCOD). These UCODs from VA were then compared with the CODs recorded in the A6 death registers, using kappa scores to assess the reliability of the A6 death register diagnoses. The overall completeness of the A6 death registers in the two provinces was 89.3% (95%CI: 87.8-90.8). No COD recorded in the A6 death registers demonstrated good reliability. There is very low reliability in recording of cardiovascular deaths (kappa for stroke = 0.47 and kappa for ischaemic heart diseases = 0.42) and diabetes (kappa = 0.33). The reporting of deaths due to road traffic accidents, HIV and some cancers are at a moderate level of reliability with kappa scores ranging between 0.57-0.69 (p<0.01). VA methods identify more specific COD than the A6 death registers, and also allow identification of multiple CODs.The study results suggest that data completeness in HMIS A6 death registers in the study sample of communes was relatively high (nearly 90%), but triangulation with death records from other sources would improve the completeness of this system. Further, there is an urgent need to enhance the reliability of COD recorded in the A6 death registers, for which VA methods could be effective. Focussed consultation among stakeholders is needed to develop a suitable mechanism and process for integrating VA methods into the national routine HMIS A6 death registers in Viet Nam.

Dataset Information

Health management information system (HMIS) data verification: A case study in four districts in Rwanda.

Publications

Health management information system (HMIS) data verification: A case study in four districts in Rwanda.

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