Toward utilization of data for program management and evaluation: quality assessment of five years of health management information system data in Rwanda

Quality of routine health facility data for monitoring maternal, newborn and child health indicators: A desk review of DHIS2 data in Lumbini Province, Nepal

INTRODUCTION

Health-facility data serves as a primary source for monitoring service provision and guiding the attainment of health targets. District Health Information Software (DHIS2) is a free open software predominantly used in low and middle-income countries to manage the facility-based data and monitor program wise service delivery. Evidence suggests the lack of quality in the routine maternal and child health information, however there is no robust analysis to evaluate the extent of its inaccuracy. We aim to bridge this gap by accessing the quality of DHIS2 data reported by health facilities to monitor priority maternal, newborn and child health indicators in Lumbini Province, Nepal.

METHODS

A facility-based descriptive study design involving desk review of Maternal, Neonatal and Child Health (MNCH) data was used. In 2021/22, DHIS2 contained a total of 12873 reports in safe motherhood, 12182 reports in immunization, 12673 reports in nutrition and 12568 reports in IMNCI program in Lumbini Province. Of those, monthly aggregated DHIS2 data were downloaded at one time and included 23 priority maternal and child health related data items. Of these 23 items, nine were chosen to assess consistency over time and identify outliers in reference years. Twelve items were selected to examine consistency between related data, while five items were chosen to assess the external consistency of coverage rates. We reviewed the completeness, timeliness and consistency of these data items and considered the prospects for improvement.

RESULTS

The overall completeness of facility reporting was found within 98% to 100% while timeliness of facility reporting ranged from 94% to 96% in each Maternal, Newborn and Child Health (MNCH) datasets. DHIS2 reported data for all 9 MNCH data items are consistent over time in 4 of 12 districts as all the selected data items are within ±33% difference from the provincial ratio. Of the eight MNCH data items assessed, four districts reported ≥5% monthly values that were moderate outliers in a reference year with no extreme outliers in any districts. Consistency between six-pairs of data items that are expected to show similar patterns are compared and found that three pairs are within ±10% of each other in all 12 districts. Comparison between the coverage rates of selected tracer indicators fall within ±33% of the DHS survey result.

CONCLUSION

Given the WHO data quality guidance and national benchmark, facilities in the Lumbini province well maintained the completeness and timeliness of MNCH datasets. Nevertheless, there is room for improvement in maintaining consistency over time, plausibility and predicted relationship of reported data. Encouraging the promotion of data review through the data management committee, strengthening the system inbuilt data validation mechanism in DHIS2, and promoting routine data quality assessment systems should be greatly encouraged.

Utilization of routine health data and its determinants among healthcare workers in public health facilities of harari region, eastern Ethiopia

BACKGROUND

Routine health information is the pillar of the planning and management of health services and plays a vital role in effective and efficient health service delivery, decision making, and program improvement. Little is known about evidence-based actions to successively advance the use of information for decision making. Therefore, this study aimed to assess the level and determinants of routine health data utilization among health workers in public health facilities in the Harari region, Ethiopia.

METHODS

An institutional-based cross-sectional study design was used from June 1 to July 31, 2020. A total of 410 health care providers from two hospitals and five health centers were selected using a simple random sampling technique. Data were collected through a structured questionnaire complemented by an observational checklist. The collected data were thoroughly checked, coding, and entered into Epi-data version 4.6 before being transferred to Stata version 14 for analysis. Frequency and cross-tabulations were performed. To measure factors associated with routine use of health data, bivariate and multivariate logistic regression analyzes were performed. The odds ratio with a 95% CI was calculated, and then a p-value of less than 0.05 was considered significant.

RESULT

The general utilization of routine health data was 65.6%. The use of routine health data was significantly associated with healthcare workers who had a positive attitude towards data [AOR = 4 (2.3-6.9)], received training [AOR = 2.1 (1.3-3.6)], had supportive supervision [AOR = 3.6 (2.1-6.2)], received regular feedback [AOR = 2.9 (1.7-5.0)] and perceived a culture of information use [AOR = 2.5 (1.3-4.6)].

CONCLUSIONS

Sixty percent of health professionals had used routine health data utilization. Training, supervision, feedback, and the perceived culture of information were independently associated with the use of routine health data utilization. Therefore, it is critical to focus on improving data utilization practices by addressing factors that influence the use of routine health data.

District health information system (DHIS2) as integrated antimicrobial resistance surveillance platform: An exploratory qualitative investigation of the one health stakeholders' viewpoints in Ethiopia

INTRODUCTION

There is an unmet need for One Health (OH) surveillance and reporting systems for antimicrobial resistance (AMR) in resource poor settings. District health information system, version 2 (DHIS2), is a globally recognized digital surveillance platform which has not been widely utilized for AMR data yet. Our study aimed to understand the local stakeholders' viewpoints on DHIS2 as OH-AMR surveillance platform in Jimma, Ethiopia which will aid its further context specific establishment.

METHODS

We performed an exploratory qualitative study using semi-structured key informant interviews (KIIs) in Jimma Zone at Southwest Ethiopia. We interviewed 42 OH professionals between November 2020 and February 2021. Following verbatim transcription of the audio recordings of KIIs, we conducted thematic analysis.

RESULTS

We identified five major themes which are important for understanding the trajectory of OH-AMR surveillance in DHIS2 platform. The themes were: (1) Stakeholders' current knowledge on digital surveillance platforms including DHIS2. (2) Stakeholders' perception on digital surveillance platform including DHIS2. (3) Features suggested by stakeholders to be included in the surveillance platform. (4) Comments from stakeholders on system implementation challenges. (5) Stakeholders' perceived role in the process of implementation. Despite several barriers and challenges, most of the participants perceived and suggested DHIS2 as a suitable OH-AMR surveillance platform and were willing to contribute at their current professional roles.

CONCLUSIONS

Our study demonstrates the potential of the DHIS2 as a user friendly and acceptable interoperable platform for OH-AMR surveillance if the technology designers accommodate the stakeholders' concerns. Piloting at local level and using performance appraisal tool in all OH disciplines should be the next step before proceeding to workable format.

Development of novel composite data quality scores to evaluate facility-level data quality in electronic data in Kenya: a nationwide retrospective cohort study

BACKGROUND

In this evaluation, we aim to strengthen Routine Health Information Systems (RHIS) through the digitization of data quality assessment (DQA) processes. We leverage electronic data from the Kenya Health Information System (KHIS) which is based on the District Health Information System version 2 (DHIS2) to perform DQAs at scale. We provide a systematic guide to developing composite data quality scores and use these scores to assess data quality in Kenya.

METHODS

We evaluated 187 HIV care facilities with electronic medical records across Kenya. Using quarterly, longitudinal KHIS data from January 2011 to June 2018 (total N = 30 quarters), we extracted indicators encompassing general HIV services including services to prevent mother-to-child transmission (PMTCT). We assessed the accuracy (the extent to which data were correct and free of error) of these data using three data-driven composite scores: 1) completeness score; 2) consistency score; and 3) discrepancy score. Completeness refers to the presence of the appropriate amount of data. Consistency refers to uniformity of data across multiple indicators. Discrepancy (measured on a Z-scale) refers to the degree of alignment (or lack thereof) of data with rules that defined the possible valid values for the data.

RESULTS

A total of 5,610 unique facility-quarters were extracted from KHIS. The mean completeness score was 61.1% [standard deviation (SD) = 27%]. The mean consistency score was 80% (SD = 16.4%). The mean discrepancy score was 0.07 (SD = 0.22). A strong and positive correlation was identified between the consistency score and discrepancy score (correlation coefficient = 0.77), whereas the correlation of either score with the completeness score was low with a correlation coefficient of -0.12 (with consistency score) and -0.36 (with discrepancy score). General HIV indicators were more complete, but less consistent, and less plausible than PMTCT indicators.

CONCLUSION

We observed a lack of correlation between the completeness score and the other two scores. As such, for a holistic DQA, completeness assessment should be paired with the measurement of either consistency or discrepancy to reflect distinct dimensions of data quality. Given the complexity of the discrepancy score, we recommend the simpler consistency score, since they were highly correlated. Routine use of composite scores on KHIS data could enhance efficiencies in DQA at scale as digitization of health information expands and could be applied to other health sectors beyondHIV clinics.

Effects of primary healthcare reform on routine health information systems (RHISs): a mixed-methods study in Lesotho

BACKGROUND AND OBJECTIVE

The Ministry of Health of Lesotho and Partners In Health piloted the Lesotho National Primary Health Care Reform (LPHCR) from July 2014 to June 2017 to improve quality and quantity of service delivery and enhance health system management. This initiative included improvement of routine health information systems (RHISs) to map disease burden and reinforce data utilisation for clinical quality improvement.

METHODS AND ANALYSIS

The WHO Data Quality Assurance framework's core indicators were used to compare the completeness of health data before versus after the LPHCR in 60 health centres and 6 hospitals across four districts. To examine change in data completeness, we conducted an interrupted time series analysis using multivariable logistic mixed-effects regression. Additionally, we conducted 25 key informant interviews with healthcare workers (HCWs) at the different levels of Lesotho's health system, following a purposive sampling approach. Interviews were analysed using deductive coding based on the Performance of Routine Information System Management framework, which inspected organisational, technical and behavioural factors influencing RHIS processes and outputs associated with the LPHCR.

RESULTS

In multivariable analyses, trends in monthly data completion rate were higher after versus before the LPHCR for: documenting first antenatal care visit (adjusted OR (AOR): 1.24, 95% CI: 1.14 to 1.36) and institutional delivery (AOR: 1.19, 95% CI: 1.07 to 1.32). When discussing processes, HCWs highlighted the value of establishing clear roles and responsibilities in reporting under a new organisational structure, improved community programmes among district health management teams, and enhanced data sharing and monitoring by districts.

CONCLUSION

The Ministry of Health had a strong data completion rate pre-LPHCR, which was sustained throughout the LPHCR despite increased service utilisation. The data completion rate was optimised through improved behavioural, technical and organisational factors introduced as part of the LPHCR.

Routine health information use among healthcare providers in Ethiopia: a systematic review and meta-analysis

INTRODUCTION

Healthcare policy formulation, programme planning, monitoring and evaluation, and healthcare service delivery as a whole are dependent on routinely generated health information in a healthcare setting. Several individual research articles on the utilisation of routine health information exist in Ethiopia; however, each of them revealed inconsistent findings.

OBJECTIVE

The main aim of this review was to combine the magnitude of routine health information use and its determinants among healthcare providers in Ethiopia.

METHODS

Databases and repositories such as PubMed, Global Health, Scopus, Embase, African journal online, Advanced Google Search and Google Scholar were searched from 20 to 26 August 2022.

RESULT

A total of 890 articles were searched but only 23 articles were included. A total of 8662 (96.3%) participants were included in the studies. The pooled prevalence of routine health information use was found to be 53.7% with 95% CI (47.45% to 59.95%). Training (adjusted OR (AOR)=1.56, 95% CI (1.12 to 2.18)), competency related to data management (AOR=1.94, 95% CI (1.35 to 2.8)), availability of standard guideline (AOR=1.66, 95% CI (1.38 to 1.99)), supportive supervision (AOR=2.07, 95% CI (1.55 to 2.76)) and feedback (AOR=2.20, 95% CI (1.30 to 3.71)) were significantly associated with routine health information use among healthcare providers at p value≤0.05 with 95% CI.

CONCLUSION

The use of routinely generated health information for evidence-based decision-making remains one of the most difficult problems in the health information system. The study's reviewers suggested that the appropriate health authorities in Ethiopia invest in enhancing the skills in using routinely generated health information.

PROSPERO REGISTRATION NUMBER

CRD42022352647.

Data quality assessment in emergency medical services: an objective approach

BACKGROUND

In emergency medical services, high quality data are of great importance for patient care. Due to the unique nature of this type of services, the purpose of this study was to assess data quality in emergency medical services using an objective approach.

METHODS

This was a retrospective quantitative study conducted in 2019. The research sample included the emergency medical records of patients who referred to three emergency departments by the pre-hospital emergency care services (n = 384). Initially a checklist was designed based on the data elements of the triage form, pre-hospital emergency care form, and emergency medical records. Then, data completeness, accuracy and timeliness were assessed.

RESULTS

Data completeness in the triage form, pre-hospital emergency care form, and emergency medical records was 52.3%, 70% and 57.3%, respectively. Regarding data accuracy, most of the data elements were consistent. Measuring data timeliness showed that in some cases, paper-based ordering and computer-based data entry was not sequential.

CONCLUSION

Data quality in emergency medical services was not satisfactory and there were some weaknesses in the documentation processes. The results of this study can inform the clinical and administrative staff to pay more attentions to these weaknesses and plan for data quality improvement.

Maternal and child health data quality in health care facilities at the Cape Coast Metropolis, Ghana

Background

The demand for quality maternal and child health (MCH) data is critical for tracking progress towards attainment of the Sustainable Development Goal 3. However, MCH cannot be adequately monitored where health data are inaccurate, incomplete, untimely, or inconsistent. Thus, this study assessed the level of MCH data quality.

Method

A facility-based cross-sectional study design was adopted, including a review of MCH service records. It was a stand-alone study involving 13 healthcare facilities of different levels that provided MCH services in the Cape Coast Metropolis. Data quality was assessed using the dimensions of accuracy, timeliness, completeness, and consistency. Health facilities registers were counted, collated, and compared with data on aggregate monthly forms, and a web-based data collation and reporting system, District Health Information System (DHIS2). The aggregate monthly forms were also compared with data in the DHIS2. Eight MCH variables were selected to assess data accuracy and consistency and two monthly reports were used to assess completeness and timeliness. Percentages and verification factor were estimated in the SPSS version 22 package.

Results

Data accuracy were recorded between the data sources: Registers and Forms, 102.1% (95% CI = 97.5%—106.7%); Registers and DHIS2, 102.4% (95% CI = 94.4%—110.4%); and Forms and DHIS2, 100.1% (95% CI = 96.4%—103.9%). Across the eight MCH variables, data were 93.2% (95% CI = 82.9%—103.5%) complete in Registers, 91.0% (95% CI = 79.5%—102.5%) in the Forms, and 94.9% (95% CI = 89.9%—99.9%) in DHIS2 database. On the average, 87.2% (95% CI = 80.5%—93.9%) of the facilities submitted their Monthly Midwife’s Returns reports on time, and Monthly Vaccination Report was 94% (95% CI = 89.3%—97.3%). The overall average data consistency was 93% (95% CI = 84%—102%).

Conclusion

Given the WHO standard for data quality, the level of MCH data quality in the health care facilities at the Cape Coast Metropolis, available through the DHIS2 is complete, reported on timely manner, consistent, and reflect accurately what exist in facility’s source document. Although there is evidence that data quality is good, there is still room for improvement in the quality of the data.

Perceptions on Data Quality, Use, and Management Following the Adoption of Tablet-Based Electronic Health Records: Results from a Pre-Post Survey with District Health Officers in Ghana

Purpose

An electronic health record (EHR) system known as the e-Tracker was introduced in community health facilities in Ghana and numerous advantages were expected for clinical staff, as evidenced by previous literature. However, little is known about public health officials’ views, specifically in low-resource settings. This study aimed to investigate the perceptions of district health officers on data quality, use, and management following the adoption of tablet-based electronic health records in Ghana.

Methods

A pre- and post-survey was conducted in two regions of Ghana that adopted the e-Tracker for the entire districts during the early stages of the national rollout. Sociodemographic information, internet connection environment, and perceptions on data quality, use, and management were measured. McNemar’s test and Wilcoxon Signed-Rank test were performed to identify changes in perceptions. Chi-square test and Mann–Whitney U-test were used to find any statistical differences in demographic characteristics between the two regions.

Results

Twenty-four out of 25 districts in Volta and 24 out of 26 districts in Eastern regions participated in both pre- and post-surveys, with a total of 73 participants. In terms of efficiency in data management, the district health officers reported reduced time commitment in data validation and aggregation. Within less than a year, however, no statistically significant improvement was found in data quality and the use of electronic data for relevant tasks.

Conclusion

A new EHR system in low-resource settings can rapidly improve efficiency in data management from the public health officials’ perspectives. Further impact evaluation is warranted to assess the long-term effect of the EHR system.

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.

End Malaria Faster: Taking Lifesaving Tools Beyond “Access” to “Reach” All People in Need

To “reach the unreached” with preventive and curative malaria services, we must know which individuals and communities remain unreached and then bring tailored services from the clinic to the community and home.

The impact of Covid-19 on malaria services in three high endemic districts in Rwanda: a mixed-method study

Background

Rwanda has achieved impressive reductions in malaria morbidity and mortality over the past two decades. However, the disruption of essential services due to the current Covid-19 pandemic can lead to a reversal of these gains in malaria control unless targeted, evidence-based interventions are implemented to mitigate the impact of the pandemic. The extent to which malaria services have been disrupted has not been fully characterized. This study was conducted to assess the impact of Covid-19 on malaria services in Rwanda.

Methods

A mixed-methods study was conducted in three purposively selected districts in Rwanda. The quantitative data included malaria aggregated data reported at the health facility level and the community level. The data included the number of malaria tests, uncomplicated malaria cases, severe malaria cases, and malaria deaths. The qualitative data were collected using focus group discussions with community members and community health workers, as well as in-depth interviews with health care providers and staff working in the malaria programme. Interrupted time series analysis was conducted to compare changes in malaria presentations between the pre-Covid-19 period (January 2019 to February 2020) and Covid-19 period (from March 2020 to November 2020). The constant comparative method was used in qualitative thematic analysis.

Results

Compared to the pre-Covid-19 period, there was a monthly reduction in patients tested in health facilities of 4.32 per 1000 population and a monthly increase in patients tested in the community of 2.38 per 1000 population during the Covid-19 period. There was no change in the overall presentation rate for uncomplicated malaria. The was a monthly reduction in the proportion of severe malaria of 5.47 per 100,000 malaria cases. Additionally, although healthcare providers continued to provide malaria services, they were fearful that this would expose them and their families to Covid-19. Covid-19 mitigation measures limited the availability of transportation options for the community to seek care in health facilities and delayed the implementation of some key malaria interventions. The focus on Covid-19-related communication also reduced the amount of health information for other diseases provided to community members.

Conclusion

The Covid-19 pandemic resulted in patients increasingly seeking care in the community and poses challenges to maintaining delivery of malaria services in Rwanda. Interventions to mitigate these challenges should focus on strengthening programming for the community and home-based care models and integrating malaria messages into Covid-19-related communication. Additionally, implementation of the interrupted interventions should be timed and overlap with the malaria transmission season to mitigate Covid-19 consequences on malaria.

A data quality assessment of the first four years of malaria reporting in the Senegal DHIS2, 2014-2017

Background

As the global burden of malaria decreases, routine health information systems (RHIS) have become invaluable for monitoring progress towards elimination. The District Health Information System, version 2 (DHIS2) has been widely adopted across countries and is expected to increase the quality of reporting of RHIS. In this study, we evaluated the quality of reporting of key indicators of childhood malaria from January 2014 through December 2017, the first 4 years of DHIS2 implementation in Senegal.

Methods

Monthly data on the number of confirmed and suspected malaria cases as well as tests done were extracted from the Senegal DHIS2. Reporting completeness was measured as the number of monthly reports received divided by the expected number of reports in a given year. Completeness of indicator data was measured as the percentage of non-missing indicator values. We used a quasi-Poisson model with natural cubic spline terms of month of reporting to impute values missing at the facility level. We used the imputed values to take into account the percentage of malaria cases that were missed due to lack of reporting. Consistency was measured as the absence of moderate and extreme outliers, internal consistency between related indicators, and consistency of indicators over time.

Results

In contrast to public facilities of which 92.7% reported data in the DHIS2 system during the study period, only 15.3% of the private facilities used the reporting system. At the national level, completeness of facility reporting increased from 84.5% in 2014 to 97.5% in 2017. The percentage of expected malaria cases reported increased from 76.5% in 2014 to 94.7% in 2017. Over the study period, the percentage of malaria cases reported across all districts was on average 7.5% higher (P < 0.01) during the rainy season relative to the dry season. Reporting completeness rates were lower among hospitals compared to health centers and health posts. The incidence of moderate and extreme outlier values was 5.2 and 2.3%, respectively. The number of confirmed malaria cases increased by 15% whereas the numbers of suspected cases and tests conducted more than doubled from 2014 to 2017 likely due to a policy shift towards universal testing of pediatric febrile cases.

Conclusions

The quality of reporting for malaria indicators in the Senegal DHIS2 has improved over time and the data are suitable for use to monitor progress in malaria programs, with an understanding of their limitations. Senegalese health authorities should maintain the focus on broader adoption of DHIS2 reporting by private facilities, the sustainability of district-level data quality reviews, facility-level supervision and feedback mechanisms at all levels of the health system.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-021-07364-6.

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.

Impact of a Multifaceted Intervention to Improve Emergency Care on Newborn and Child Health Outcomes in Rwanda

Implementing context-appropriate neonatal and pediatric advanced life support management interventions has increasingly been recommended as one of the approaches to reduce under-five mortality in resource-constrained settings like Rwanda. One such intervention is ETAT+, which stands for Emergency Triage, Assessment and Treatment plus Admission care for severely ill newborns and children. In 2013, ETAT+ was implemented in Rwandan district hospitals. We evaluated the impact of the ETAT+ intervention on newborn and child health outcomes. We used monthly time series data from the DHIS2-enabled Rwanda Health Management Information System from 2012 to 2016 to examine neonatal and pediatric hospital mortality rate. Each hospital contributed data for 12 and 36 months before and after ETAT+ implementation, respectively. Using controlled interrupted time series analysis and segmented regression model, we estimated longitudinal changes in neonatal and pediatric hospital mortality rate in intervention hospitals relative to matched concurrent control hospitals. We also studied changes in case fatality rate specifically for ETAT+ targeted conditions. Our study cohort consisted of seven intervention hospitals and fourteen matched control hospitals contributing 142,424 neonatal and pediatric hospital admissions. After controlling for secular trends and autocorrelation, we found that the ETAT+ implementation had no statistically significant impact on the rate of all-cause neonatal and pediatric hospital mortality in intervention hospitals relative to control hospitals. However, the case fatality rate for ETAT+ targeted neonatal conditions decreased immediately following implementation by 5% (95% CI: -9.25, -0.77) and over time by 0.8% monthly (95% CI: -1.36, -0.25), in intervention hospitals compared with control hospitals. Case fatality rate for ETAT+ targeted pediatric conditions did not decrease following the ETAT+ implementation. While ETAT+ focuses on improving quality of hospital care for both newborns and children, we only found an impact on neonatal hospital mortality for ETAT+ targeted conditions that should be interpreted with caution given the relatively short pre-intervention period and potential regression to the mean.

Routine health information utilization and associated factors among health care workers in Ethiopia: A systematic review and meta-analysis

Background

Utilization of routine health information plays a vital role for the effectiveness of routine and programed decisions. A proper utilization of routine health information helps to make decisions based on evidence. Considerable studies have been done on the utilization of routine health information among health workers in Ethiopia, but inconsistent findings were reported. Thus, this study was conducted to determine the pooled utilization of routine health information and to identify associated factors among health workers in Ethiopia.

Methods

Search of PubMed, HINARI, Global Health, Scopus, EMBASE, web of science, and Google Scholar was conducted to identify relevant studies from October 24, 2020 to November 18, 2020. The Newcastle-Ottawa scale tool was used to assess the quality of included studies. Two reviewers extracted the data independently using a standardized data extraction format and exported to STATA software version 11 for meta-analysis. Heterogeneity among studies was checked using Cochrane Q and I² test statistics. The pooled estimate of utilization of routine health information was executed using a random effect model.

Results

After reviewing 22924 studies, 10 studies involving 4054 health workers were included for this review and meta-analysis. The pooled estimate of routine health information utilization among health workers in Ethiopia was 57.42% (95% CI: 41.48, 73.36). Supportive supervision (AOR = 2.25; 95% CI: 1.80, 2.82), regular feedback (AOR = 2.86; 95% CI: 1.60, 5.12), availability of standard guideline (AOR = 2.53; 95% CI: 1.80, 3.58), data management knowledge (AOR = 3.04; 95% CI: 1.75, 5.29) and training on health information (AOR = 3.45; 95% CI: 1.96, 6.07) were identified factors associated with utilization of routine health information.

Conclusion

This systematic review and meta-analysis found that more than two-fifth of health workers did not use their routine health information. This study suggests the need to conduct regular supportive supervision, provision of training and capacity building, mentoring on competence of routine health information tasks, and strengthening regular feedback at all health facilities. In addition, improving the accessibility and availability of standard set of indicators is important to scale-up information use.

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.

Small-sample inference for cluster-based outcome-dependent sampling schemes in resource-limited settings: Investigating low birthweight in Rwanda

The neonatal mortality rate in Rwanda remains above the United Nations Sustainable Development Goal 3 target of 12 deaths per 1000 live births. As part of a larger effort to reduce preventable neonatal deaths in the country, we conducted a study to examine risk factors for low birthweight. The data were collected via a cost-efficient cluster-based outcome-dependent sampling (ODS) scheme wherein clusters of individuals (health centers) were selected on the basis of, in part, the outcome rate of the individuals. For a given data set collected via a cluster-based ODS scheme, estimation for a marginal model may proceed via inverse-probability-weighted generalized estimating equations, where the cluster-specific weights are the inverse probability of the health center's inclusion in the sample. In this paper, we provide a detailed treatment of the asymptotic properties of this estimator, together with an explicit expression for the asymptotic variance and a corresponding estimator. Furthermore, motivated by the study we conducted in Rwanda, we propose a number of small-sample bias corrections to both the point estimates and the standard error estimates. Through simulation, we show that applying these corrections when the number of clusters is small generally reduces the bias in the point estimates, and results in closer to nominal coverage. The proposed methods are applied to data from 18 health centers and 1 district hospital in Rwanda.

Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: an application of K-means clustering algorithm

Background

The ability to report complete, accurate and timely data by HIV care providers and other entities is a key aspect in monitoring trends in HIV prevention, treatment and care, hence contributing to its eradication. In many low-middle-income-countries (LMICs), aggregate HIV data reporting is done through the District Health Information Software 2 (DHIS2). Nevertheless, despite a long-standing requirement to report HIV-indicator data to DHIS2 in LMICs, few rigorous evaluations exist to evaluate adequacy of health facility reporting at meeting completeness and timeliness requirements over time. The aim of this study is to conduct a comprehensive assessment of the reporting status for HIV-indicators, from the time of DHIS2 implementation, using Kenya as a case study.

Methods

A retrospective observational study was conducted to assess reporting performance of health facilities providing any of the HIV services in all 47 counties in Kenya between 2011 and 2018. Using data extracted from DHIS2, K-means clustering algorithm was used to identify homogeneous groups of health facilities based on their performance in meeting timeliness and completeness facility reporting requirements for each of the six programmatic areas. Average silhouette coefficient was used in measuring the quality of the selected clusters.

Results

Based on percentage average facility reporting completeness and timeliness, four homogeneous groups of facilities were identified namely: best performers, average performers, poor performers and outlier performers. Apart from blood safety reports, a distinct pattern was observed in five of the remaining reports, with the proportion of best performing facilities increasing and the proportion of poor performing facilities decreasing over time. However, between 2016 and 2018, the proportion of best performers declined in some of the programmatic areas. Over the study period, no distinct pattern or trend in proportion changes was observed among facilities in the average and outlier groups.

Conclusions

The identified clusters revealed general improvements in reporting performance in the various reporting areas over time, but with noticeable decrease in some areas between 2016 and 2018. This signifies the need for continuous performance monitoring with possible integration of machine learning and visualization approaches into national HIV reporting systems.

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

BACKGROUND

Effective 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.

METHODS

This 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.

RESULTS

A 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.

CONCLUSION

There 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.

Spatial-temporal patterns of malaria incidence in Uganda using HMIS data from 2015 to 2019

BACKGROUND

As global progress to reduce malaria transmission continues, it is increasingly important to track changes in malaria incidence rather than prevalence. Risk estimates for Africa have largely underutilized available health management information systems (HMIS) data to monitor trends. This study uses national HMIS data, together with environmental and geographical data, to assess spatial-temporal patterns of malaria incidence at facility catchment level in Uganda, over a recent 5-year period.

METHODS

Data reported by 3446 health facilities in Uganda, between July 2015 and September 2019, was analysed. To assess the geographic accessibility of the health facilities network, AccessMod was employed to determine a three-hour cost-distance catchment around each facility. Using confirmed malaria cases and total catchment population by facility, an ecological Bayesian conditional autoregressive spatial-temporal Poisson model was fitted to generate monthly posterior incidence rate estimates, adjusted for caregiver education, rainfall, land surface temperature, night-time light (an indicator of urbanicity), and vegetation index.

RESULTS

An estimated 38.8 million (95% Credible Interval [CI]: 37.9-40.9) confirmed cases of malaria occurred over the period, with a national mean monthly incidence rate of 20.4 (95% CI: 19.9-21.5) cases per 1000, ranging from 8.9 (95% CI: 8.7-9.4) to 36.6 (95% CI: 35.7-38.5) across the study period. Strong seasonality was observed, with June-July experiencing highest peaks and February-March the lowest peaks. There was also considerable geographic heterogeneity in incidence, with health facility catchment relative risk during peak transmission months ranging from 0 to 50.5 (95% CI: 49.0-50.8) times higher than national average. Both districts and health facility catchments showed significant positive spatial autocorrelation; health facility catchments had global Moran's I = 0.3 (p < 0.001) and districts Moran's I = 0.4 (p < 0.001). Notably, significant clusters of high-risk health facility catchments were concentrated in Acholi, West Nile, Karamoja, and East Central - Busoga regions.

CONCLUSION

Findings showed clear countrywide spatial-temporal patterns with clustering of malaria risk across districts and health facility catchments within high risk regions, which can facilitate targeting of interventions to those areas at highest risk. Moreover, despite high and perennial transmission, seasonality for malaria incidence highlights the potential for optimal and timely implementation of targeted interventions.

Improving the quality of routine maternal and newborn data captured in primary health facilities in Gombe State, Northeastern Nigeria: a before-and-after study

OBJECTIVES

Primary objective: to assess nine data quality metrics for 14 maternal and newborn health data elements, following implementation of an integrated, district-focused data quality intervention.

SECONDARY OBJECTIVE

to consider whether assessing the data quality metrics beyond completeness and accuracy of facility reporting offered new insight into reviewing routine data quality.

DESIGN

Before-and-after study design.

SETTING

Primary health facilities in Gombe State, Northeastern Nigeria.

PARTICIPANTS

Monitoring and evaluation officers and maternal, newborn and child health coordinators for state-level and all 11 local government areas (district-equivalent) overseeing 492 primary care facilities offering maternal and newborn care services.

INTERVENTION

Between April 2017 and December 2018, we implemented an integrated data quality intervention which included: introduction of job aids and regular self-assessment of data quality, peer-review and feedback, learning workshops, work planning for improvement, and ongoing support through social media.

OUTCOME MEASURES

9 metrics for the data quality dimensions of completeness and timeliness, internal consistency of reported data, and external consistency.

RESULTS

The data quality intervention was associated with improvements in seven of nine data quality metrics assessed including availability and timeliness of reporting, completeness of data elements, accuracy of facility reporting, consistency between related data elements, and frequency of outliers reported. Improvement differed by data element type, with content of care and commodity-related data improving more than contact-related data. Increases in the consistency between related data elements demonstrated improved internal consistency within and across facility documentation.

CONCLUSIONS

An integrated district-focused data quality intervention-including regular self-assessment of data quality, peer-review and feedback, learning workshops, work planning for improvement, and ongoing support through social media-can increase the completeness, accuracy and internal consistency of facility-based routine data.

Improving quality and use of routine health information system data in low- and middle-income countries: A scoping review

BACKGROUND

A routine health information system is one of the essential components of a health system. Interventions to improve routine health information system data quality and use for decision-making in low- and middle-income countries differ in design, methods, and scope. There have been limited efforts to synthesise the knowledge across the currently available intervention studies. Thus, this scoping review synthesised published results from interventions that aimed at improving data quality and use in routine health information systems in low- and middle-income countries.

METHOD

We included articles on intervention studies that aimed to improve data quality and use within routine health information systems in low- and middle-income countries, published in English from January 2008 to February 2020. We searched the literature in the databases Medline/PubMed, Web of Science, Embase, and Global Health. After a meticulous screening, we identified 20 articles on data quality and 16 on data use. We prepared and presented the results as a narrative.

RESULTS

Most of the studies were from Sub-Saharan Africa and designed as case studies. Interventions enhancing the quality of data targeted health facilities and staff within districts, and district health managers for improved data use. Combinations of technology enhancement along with capacity building activities, and data quality assessment and feedback system were found useful in improving data quality. Interventions facilitating data availability combined with technology enhancement increased the use of data for planning.

CONCLUSION

The studies in this scoping review showed that a combination of interventions, addressing both behavioural and technical factors, improved data quality and use. Interventions addressing organisational factors were non-existent, but these factors were reported to pose challenges to the implementation and performance of reported interventions.

Bayesian spatio-temporal modeling of malaria risk in Rwanda

Every year, 435,000 people worldwide die from Malaria, mainly in Africa and Asia. However, malaria is a curable and preventable disease. Most countries are developing malaria elimination plans to meet sustainable development goal three, target 3.3, which includes ending the epidemic of malaria by 2030. Rwanda, through the malaria strategic plan 2012-2018 set a target to reduce malaria incidence by 42% from 2012 to 2018. Assessing the health policy and taking a decision using the incidence rate approach is becoming more challenging. We are proposing suitable statistical methods that handle spatial structure and uncertainty on the relative risk that is relevant to National Malaria Control Program. We used a spatio-temporal model to estimate the excess probability for decision making at a small area on evaluating reduction of incidence. SIR and BYM models were developed using routine data from Health facilities for the period from 2012 to 2018 in Rwanda. The fitted model was used to generate relative risk (RR) estimates comparing the risk with the malaria risk in 2012, and to assess the probability of attaining the set target goal per area. The results showed an overall increase in malaria in 2013 to 2018 as compared to 2012. Ofall sectors in Rwanda, 47.36% failed to meet targeted reduction in incidence from 2012 to 2018. Our approach of using excess probability method to evaluate attainment of target or identifying threshold is a relevant statistical method, which will enable the Rwandan Government to sustain malaria control and monitor the effectiveness of targeted interventions.

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

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.

Evaluation of quality and use of health management information system in primary health care units of east Wollega zone, Oromia regional state, Ethiopia

Background

Health care practice relies on evidence-based decisions and needs the use of quality health care data. Health management information system (HMIS) is among the core elements of health system building blocks. In our study setting, there is a lack of adequate information on the quality of health information data. This study aimed at exploring the quality of health management information system data in terms of timeliness, completeness, and accuracy. The specific objectives were to evaluate the quality and use of the health management information system in Primary health care units of East Wollega zone, Ethiopia.

Methods

A cross-sectional study was conducted from April to June 2016 on 316 health professionals/health information technicians. The sample was obtained by simple random sampling technique. Qualitative data were obtained from 16 purposefully selected key informants by Focus group discussion (FGD). We observed 50 selected health facilities using an observation checklist. We analyzed quantitative data by SPSS version 20 using descriptive and logistic regression analysis techniques. we applied a thematic analysis approach to analyze qualitative data.

Results

Timeliness of report, registration completeness, report completeness, and data accuracy level of the selected facilities were 70, 78.2, 86, and 48%, respectively. All results are below the expected national standards. Commonly reported reasons for the poor practice of data quality were; poor support of management, lack of accountability for the false report, poor supportive supervision, and lack of separate and responsible unit for health information management.

Conclusion

The Health information management system is poorly coordinated at the primary health units. Accountability should be assured through continuous in-service training, supportive supervision, and concrete feedbacks. Electronic management of health information should be available in primary health care units.

Practical Implications of a Relationship between Health Management Information System and Community Cohort-Based Malaria Incidence Rates

Global malaria burden is reducing with effective control interventions, and surveillance is vital to maintain progress. Health management information system (HMIS) data provide a powerful surveillance tool; however, its estimates of burden need to be better understood for effectiveness. We aimed to investigate the relationship between HMIS and cohort incidence rates and identify sources of bias in HMIS-based incidence. Malaria incidence was estimated using HMIS data from 15 health facilities in three subcounties in Uganda. This was compared with a gold standard of representative cohort studies conducted in children aged 0.5 to < 11 years, followed concurrently in these sites. Between October 2011 and September 2014, 153,079 children were captured through HMISs and 995 followed up through enhanced community cohorts in Walukuba, Kihihi, and Nagongera subcounties. Although HMISs substantially underestimated malaria incidence in all sites compared with data from the cohort studies, there was a strong linear relationship between these rates in the lower transmission settings (Walukuba and Kihihi), but not the lowest HMIS performance highest transmission site (Nagongera), with calendar year as a significant modifier. Although health facility accessibility, availability, and recording completeness were associated with HMIS incidence, they were not significantly associated with bias in estimates from any site. Health management information systems still require improvements; however, their strong predictive power of unbiased malaria burden when improved highlights the important role they could play as a cost-effective tool for monitoring trends and estimating impact of control interventions. This has important implications for malaria control in low-resource, high-burden countries.

Expanding home-based management of malaria to all age groups in Rwanda: analysis of acceptability and facility-level time-series data

Background

In response to a resurgence of malaria in Rwanda, home-based management (HBM) was expanded to enable community-health workers (CHWs) to provide malaria treatment to patients of all ages. We assessed the effect of the expanded HBM program on malaria case presentations at health facilities.

Methods

Services provided by CHWs and health facility presentations among individuals >5 y of age were considered. Presentations to CHWs were analyzed descriptively to assess acceptability and segmented regression modeling using facility-level data was employed to compare changes between the pre- and postintervention periods for intervention and control districts.

Results

Individuals >5 y of age readily accessed malaria diagnosis and treatment services from CHWs. Severe and uncomplicated malaria increased in the postintervention period for both the intervention and control districts. Presentations for uncomplicated malaria increased in the intervention and control districts to a similar degree. Severe cases increased to a greater degree in the intervention districts immediately after HBM was expanded compared with controls, but the monthly rate of increase was lower in the intervention districts.

Conclusions

Services were shifted to CHWs, as demonstrated by the number of individuals treated through the expanded program. The rate of severe malaria increased immediately after implementation within intervention districts relative to controls, potentially because of enhanced case-finding. The rate of increase in severe cases was lower in the intervention districts comparatively, likely due to expedited treatment.

Optimizing data quality of pharmaceutical information systems in public health care in resource limited settings

BACKGROUND

Robust pharmaceutical management information systems (PMIS) strengthen healthcare planning and delivery. Few nationwide studies in resource limited settings in Africa validate the data quality of PMIS in public healthcare.

OBJECTIVE

To determine predictors and quality of data in a nationwide PMIS database in Namibia.

METHODS

A population-level analysis of the quality of data i.e. completeness, accuracy and consistency in a nationwide PMIS database, 2007-2015. Data quality of the PMIS was determined by three domains, completeness, accuracy and consistency. Data completeness was determined by level of missing data in SPSSv25, with acceptable level set at <5%. Data accuracy was determined by proportion of PMIS indicators with extreme outliers. Data consistence was determined by patterns of missingness, i.e. random or systematic. Predictors of data quality were determined using logistic regression modelling.

RESULTS

A total of 544 entries and 12 indicators were registered in the PMIS at 38 public health facilities. All the PMIS indicators had missing data and 50% (n = 6) had inaccurate data i.e. extreme values. The data for most PMIS indicators (75%, n = 12) were consistent with the pattern of missing completely at random (MCAR, i.e. missingness <5%). Incompleteness of PMIS data was highest for average number of prescriptions 6%, annual expenditure per capita for pharmaceuticals 5% and population per pharmacist's assistant 5%. The main predictors of poor quality of PMIS data were year of reporting of PMIS data (p = 0.035), level of health facility (p < 0.001), vital reference materials available at the pharmacy (p = 0.002), and pharmacists' posts filled (p = 0.013).

CONCLUSIONS

The data quality of PMIS in public health care in Namibia is sub-optimal and widely varies by reporting period, level of health facility and region. The integration of data quality assurance systems is required to strengthen quality of PMIS data to optimize quality of PMIS data in public health care.

A quality assessment of Health Management Information System (HMIS) data for maternal and child health in Jimma Zone, Ethiopia

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.

Quality of routine facility data for monitoring priority maternal and newborn indicators in DHIS2: A case study from Gombe State, Nigeria

INTRODUCTION

Routine health information systems are critical for monitoring service delivery. District Heath Information System, version 2 (DHIS2) is an open source software platform used in more than 60 countries, on which global initiatives increasingly rely for such monitoring. We used facility-reported data in DHIS2 for Gombe State, north-eastern Nigeria, to present a case study of data quality to monitor priority maternal and neonatal health indicators.

METHODS

For all health facilities in DHIS2 offering antenatal and postnatal care services (n = 497) and labor and delivery services (n = 486), we assessed the quality of data for July 2016-June 2017 according to the World Health Organization data quality review guidance. Using data from DHIS2 as well as external facility-level and population-level household surveys, we reviewed three data quality dimensions-completeness and timeliness, internal consistency, and external consistency-and considered the opportunities for improvement.

RESULTS

Of 14 priority maternal and neonatal health indicators that could be tracked through facility-based data, 12 were included in Gombe's DHIS2. During July 2016-June 2017, facility-reported data in DHIS2 were incomplete at least 40% of the time, under-reported 10%-60% of the events documented in facility registers, and showed inconsistencies over time, between related indicators, and with an external data source. The best quality data elements were those that aligned with Gombe's health program priorities, particularly older health programs, and those that reflected contact indicators rather than indicators related to the provision of commodities or content of care.

CONCLUSION

This case study from Gombe State, Nigeria, demonstrates the high potential for effective monitoring of maternal and neonatal health using DHIS2. However, coordinated action at multiple levels of the health system is needed to maximize reporting of existing data; rationalize data flow; routinize data quality review, feedback, and supervision; and ensure ongoing maintenance of DHIS2.

The impact of an mHealth monitoring system on health care utilization by mothers and children: an evaluation using routine health information in Rwanda

Maternal and child mortality rates remain unacceptably high globally, particularly in sub-Saharan Africa. A popular approach to counter these high rates is interventions delivered using mobile phones (mHealth). However, few mHealth interventions have been implemented nationwide and there has been little evaluation of their effectiveness, particularly at scale. Therefore, we evaluated the Rwanda RapidSMS programme—one of the few mHealth programmes in Africa that is currently operating nationwide. Using interrupted time series analysis and monthly data routinely reported by public health centres (n = 461) between 2012 and 2016, we studied the impact of RapidSMS on four indicators: completion of four antenatal care visits, deliveries in a health facility, postnatal care visits and malnutrition screening. We stratified all analyses based on whether the district received concurrent additional supports, including staff and equipment (10 out of 30 Districts). We found that community health workers in Rwanda sent more than 9.3 million messages using RapidSMS, suggesting the programme was successfully implemented. We found that the implementation of the RapidSMS system combined with additional support including training, supervision and equipment provision increased the use of maternal and child health services. In contrast, implementing the RapidSMS system alone was ineffective. This suggests that mHealth programmes alone may be insufficient to improve the use of health services. Instead, they should be considered as a part of more comprehensive interventions that provide the necessary equipment and health system capacity to support them.

Quality improvement and emerging global health priorities

Quality improvement approaches can strengthen action on a range of global health priorities. Quality improvement efforts are uniquely placed to reorient care delivery systems towards integrated people-centred health services and strengthen health systems to achieve Universal Health Coverage (UHC). This article makes the case for addressing shortfalls of previous agendas by articulating the critical role of quality improvement in the Sustainable Development Goal era. Quality improvement can stimulate convergence between health security and health systems; address global health security priorities through participatory quality improvement approaches; and improve health outcomes at all levels of the health system. Entry points for action include the linkage with antimicrobial resistance and the contentious issue of the health of migrants. The work required includes focussed attention on the continuum of national quality policy formulation, implementation and learning; alongside strengthening the measurement-improvement linkage. Quality improvement plays a key role in strengthening health systems to achieve UHC.

Routine health information utilization and associated factors among health care professionals working at public health institution in North Gondar, Northwest Ethiopia

BACKGROUND

Routine health information systems (RHIS) are vital for the acquisition of data for health sector planning, monitoring, and evaluation. However, in developing countries the insufficient quality of the data produced by RHIS limits their usefulness in decision-making. As routine health information utilization is still low in Ethiopia, this study aimed to assess the magnitude of routine health data utilization and associated factors among health care professionals in some public health institutions in North Gondar, northwest Ethiopia.

METHODS

An institution based cross-sectional study was conducted from March to April2017, at public health institutions of North Gondar Zone, northwest Ethiopia. A total of 720 health care professionals were selected from public health institutions using the multi-stage sampling technique. Data were collected using a structured self-administered questionnaire and an observational checklist, cleaned, coded, and entered into Epi-info version 3.5.3 and transferred into SPSS version 20 for further statistical analysis. In the multiple logistic regression analysis, a less than 0.05 P-vale was considered statistically significant.

RESULT

In this study, the level of good routine health information utilization among health professionals was 78.5% (95% CI: 73.2%, 84.3%). According to the multivariable logistic regression analysis, sex (AOR = 2.19, 95% CI: 1.47, 3.27), type of institution (AOR = 3.57, 95% CI: 2.39, 5.32), standard indicators (AOR = 3.28, 95% CI: 1.90, 5.65), data analysis skills (AOR = 1.90, 95% CI: 1.12, 3.23), and good governance (AOR = 1.97, 95% CI: 1.31, 2.95), were found significantly associated with a good level of health information utilization.

CONCLUSION

Over three-fourths of the health care professionals working at public health institutions of North Gondar utilized health information better than the respondents in previous studies. Sex, type of institution, standard indicators, data analysis skills, and governance were factors related to routine health information utilization. Therefore, standard indicators, data analysis skills and good governance are highly recommended for improving routine health data utilization of health care professionals working at public health institutions.

Can training of health care workers improve data management practice in health management information systems: a case study of primary health care facilities in Kaduna State, Nigeria

Introduction

Reliable and accurate public health information is essential for monitoring, evaluating and improving the delivery of healthcare services. The objective of this study was to assess the effect of training health care workers on data management practice in health management information systems in primary health care (PHC) centers in Kaduna state.

Methods

The study was quasi-experimental with baseline, intervention and end point components. It was carried out in two local government areas, a study and a control. Eleven PHC facilities were selected in each LGA. The intervention was carried out among 76 PHC workers in the study LGA. Data were collected using a health facility checklist and a focused group discussion (FGD) guide. Data analysis was done using SPSS version 20.0 and statistical significance of the difference between baseline and end-line data were determined using chi-square or fisher's exact test where applicable at p < 0.05.

Results

There was a statistically significant increase in completeness of reporting (p = 0.02), overall accuracy rate (p < 0.001), timeliness rate of reporting (p = <0.001) and feedback (p = 0.012). No improvement was found in the control group. During the baseline FGDs, PHC workers in both study and control LGAs expressed difficulty in filling registers/forms, data analysis and use of data. At end point, those in the study LGA said their practice had improved but those in the control LGA still expressed difficulty in data management.

Conclusion

Health management information system training achieved an improvement in the data management practice of PHC workers. In-service training and re-training should be done to improve data management practice of health workers.

A Comparison of Health Achievements in Rwanda and Burundi

Strong primary health care systems are essential for implementing universal health coverage and fulfilling health rights entitlements, but disagreement exists over how best to create them. Comparing countries with similar histories, lifestyle practices, and geography but divergent health outcomes can yield insights into possible mechanisms for improvement. Rwanda and Burundi are two such countries. Both faced protracted periods of violence in the 1990s, leading to significant societal upheaval. In subsequent years, Rwanda's improvement in health has been far greater than Burundi's. To understand how this divergence occurred, we studied trends in life expectancy following the periods of instability in both countries, as well as the health policies implemented after these conflicts. We used the World Bank's World Development Indicators to assess trends in life expectancy in the two countries and then evaluated health policy reforms using Walt and Gilson's framework. Following both countries' implementation of health sector policies in 2005, we found a statistically significant increase in life expectancy in Rwanda after adjusting for GDP per capita (14.7 years, 95% CI: 11.4-18.0), relative to Burundi (4.6 years, 95% CI: 1.8-7.5). Strong public sector leadership, investments in health information systems, equity-driven policies, and the use of foreign aid to invest in local capacity helped Rwanda achieve greater health gains compared to Burundi.

The impact of routine data quality assessments on electronic medical record data quality in Kenya

Background

Routine Data Quality Assessments (RDQAs) were developed to measure and improve facility-level electronic medical record (EMR) data quality. We assessed if RDQAs were associated with improvements in data quality in KenyaEMR, an HIV care and treatment EMR used at 341 facilities in Kenya.

Methods

RDQAs assess data quality by comparing information recorded in paper records to KenyaEMR. RDQAs are conducted during a one-day site visit, where approximately 100 records are randomly selected and 24 data elements are reviewed to assess data completeness and concordance. Results are immediately provided to facility staff and action plans are developed for data quality improvement. For facilities that had received more than one RDQA (baseline and follow-up), we used generalized estimating equation models to determine if data completeness or concordance improved from the baseline to the follow-up RDQAs.

Results

27 facilities received two RDQAs and were included in the analysis, with 2369 and 2355 records reviewed from baseline and follow-up RDQAs, respectively. The frequency of missing data in KenyaEMR declined from the baseline (31% missing) to the follow-up (13% missing) RDQAs. After adjusting for facility characteristics, records from follow-up RDQAs had 0.43-times the risk (95% CI: 0.32–0.58) of having at least one missing value among nine required data elements compared to records from baseline RDQAs. Using a scale with one point awarded for each of 20 data elements with concordant values in paper records and KenyaEMR, we found that data concordance improved from baseline (11.9/20) to follow-up (13.6/20) RDQAs, with the mean concordance score increasing by 1.79 (95% CI: 0.25–3.33).

Conclusions

This manuscript demonstrates that RDQAs can be implemented on a large scale and used to identify EMR data quality problems. RDQAs were associated with meaningful improvements in data quality and could be adapted for implementation in other settings.

Economic evaluation of a mentorship and enhanced supervision program to improve quality of integrated management of childhood illness care in rural Rwanda

Background

Integrated management of childhood illness (IMCI) can reduce under-5 morbidity and mortality in low-income settings. A program to strengthen IMCI practices through Mentorship and Enhanced Supervision at Health centers (MESH) was implemented in two rural districts in eastern Rwanda in 2010.

Methods

We estimated cost per improvement in quality of care as measured by the difference in correct diagnosis and correct treatment at baseline and 12 months of MESH. Costs of developing and implementing MESH were estimated in 2011 United States Dollars (USD) from the provider perspective using both top-down and bottom-up approaches, from programmatic financial records and site-level data. Improvement in quality of care attributed to MESH was measured through case management observations (n = 292 cases at baseline, 413 cases at 12 months), with outcomes from the intervention already published. Sensitivity analyses were conducted to assess uncertainty under different assumptions of quality of care and patient volume.

Results

The total annual cost of MESH was US$ 27,955.74 and the average cost added by MESH per IMCI patient was US$1.06. Salary and benefits accounted for the majority of total annual costs (US$22,400 /year). Improvements in quality of care after 12 months of MESH implementation cost US$2.95 per additional child correctly diagnosed and $5.30 per additional child correctly treated.

Conclusions

The incremental costs per additional child correctly diagnosed and child correctly treated suggest that MESH could be an affordable method for improving IMCI quality of care elsewhere in Rwanda and similar settings. Integrating MESH into existing supervision systems would further reduce costs, increasing potential for spread.

Data-driven quality improvement in low-and middle-income country health systems: lessons from seven years of implementation experience across Mozambique, Rwanda, and Zambia

BACKGROUND

Well-functioning health systems need to utilize data at all levels, from the provider, to local and national-level decision makers, in order to make evidence-based and needed adjustments to improve the quality of care provided. Over the last 7 years, the Doris Duke Charitable Foundation's African Health Initiative funded health systems strengthening projects at the facility, district, and/or provincial level to improve population health. Increasing data-driven decision making was a common strategy in Mozambique, Rwanda and Zambia. This paper describes the similar and divergent approaches to increase data-driven quality of care improvements (QI) and implementation challenge and opportunities encountered in these three countries.

METHODS

Eight semi-structured in-depth interviews (IDIs) were administered to program staff working in each country. IDIs for this paper included principal investigators of each project, key program implementers (medically-trained support staff, data managers and statisticians, and country directors), as well as Ministry of Health counterparts. IDI data were collected through field notes; interviews were not audio recorded. Data were analyzed using thematic analysis but no systematic coding was conducted. IDIs were supplemented through donor report abstractions, a structured questionnaire, one-on-one phone calls, and email exchanges with country program leaders to clarify and expand on key themes emerging from IDIs.

RESULTS

Project successes ranged from over 450 collaborative action-plans developed, implemented, and evaluated in Mozambique, to an increase from <10% to >80% of basic clinical protocols followed in intervention facilities in rural Zambia, and a shift from a lack of awareness of health data among health system staff to collaborative ownership of data and using data to drive change in Rwanda.

CONCLUSION

Based on common successes across the country experiences, we recommend future data-driven QI interventions begin with data quality assessments to promote that rapid health system improvement is possible, ensure confidence in available data, serve as the first step in data-driven targeted improvements, and improve staff data analysis and visualization skills. Explicit Ministry of Health collaborative engagement can ensure performance review is collaborative and internally-driven rather than viewed as an external "audit."

Improving data quality across 3 sub-Saharan African countries using the Consolidated Framework for Implementation Research (CFIR): results from the African Health Initiative

Background

High-quality data are critical to inform, monitor and manage health programs. Over the seven-year African Health Initiative of the Doris Duke Charitable Foundation, three of the five Population Health Implementation and Training (PHIT) partnership projects in Mozambique, Rwanda, and Zambia introduced strategies to improve the quality and evaluation of routinely-collected data at the primary health care level, and stimulate its use in evidence-based decision-making. Using the Consolidated Framework for Implementation Research (CFIR) as a guide, this paper: 1) describes and categorizes data quality assessment and improvement activities of the projects, and 2) identifies core intervention components and implementation strategy adaptations introduced to improve data quality in each setting.

Methods

The CFIR was adapted through a qualitative theme reduction process involving discussions with key informants from each project, who identified two domains and ten constructs most relevant to the study aim of describing and comparing each country’s data quality assessment approach and implementation process. Data were collected on each project’s data quality improvement strategies, activities implemented, and results via a semi-structured questionnaire with closed and open-ended items administered to health management information systems leads in each country, with complementary data abstraction from project reports.

Results

Across the three projects, intervention components that aligned with user priorities and government systems were perceived to be relatively advantageous, and more readily adapted and adopted. Activities that both assessed and improved data quality (including data quality assessments, mentorship and supportive supervision, establishment and/or strengthening of electronic medical record systems), received higher ranking scores from respondents.

Conclusion

Our findings suggest that, at a minimum, successful data quality improvement efforts should include routine audits linked to ongoing, on-the-job mentoring at the point of service. This pairing of interventions engages health workers in data collection, cleaning, and analysis of real-world data, and thus provides important skills building with on-site mentoring. The effect of these core components is strengthened by performance review meetings that unify multiple health system levels (provincial, district, facility, and community) to assess data quality, highlight areas of weakness, and plan improvements.

The national burden of influenza-associated severe acute respiratory illness hospitalization in Rwanda, 2012-2014

Background

Estimates of influenza‐associated hospitalization are severely limited in low‐ and middle‐income countries, especially in Africa.

Objectives

To estimate the national number of influenza‐associated severe acute respiratory illness (SARI) hospitalization in Rwanda.

Methods

We multiplied the influenza virus detection rate from influenza surveillance conducted at 6 sentinel hospitals by the national number of respiratory hospitalization obtained from passive surveillance after adjusting for underreporting and reclassification of any respiratory hospitalizations as SARI during 2012‐2014. The population at risk was obtained from projections of the 2012 census. Bootstrapping was used for the calculation of confidence intervals (CI) to account for the uncertainty associated with all levels of adjustment. Rates were expressed per 100 000 population. A sensitivity analysis using a different estimation approach was also conducted.

Results

SARI cases accounted for 70.6% (9759/13 813) of respiratory admissions at selected hospitals: 77.2% (6783/8786) and 59.2% (2976/5028) among individuals aged <5 and ≥5 years, respectively. Overall, among SARI cases tested, the influenza virus detection rate was 6.3% (190/3022): 5.7% (127/2220) and 7.8% (63/802) among individuals aged <5 and ≥5 years, respectively. The estimated mean annual national number of influenza‐associated SARI hospitalizations was 3663 (95% CI: 2930‐4395—rate: 34.7; 95% CI: 25.4‐47.7): 2637 (95% CI: 2110‐3164—rate: 168.7; 95% CI: 135.0‐202.4) among children aged <5 years and 1026 (95% CI: 821‐1231—rate: 11.3; 95% CI: 9.0‐13.6) among individuals aged ≥5 years. The estimates obtained from both approaches were not statistically different (overlapping CIs).

Conclusions

The burden of influenza‐associated SARI hospitalizations was substantial and was highest among children aged <5 years.

Completeness of malaria indicator data reporting via the District Health Information Software 2 in Kenya, 2011-2015

Background

Health facility-based data reported through routine health information systems form the primary data source for programmatic monitoring and evaluation in most developing countries. The adoption of District Health Information Software (DHIS2) has contributed to improved availability of routine health facility-based data in many low-income countries. An assessment of malaria indicators data reported by health facilities in Kenya during the first 5 years of implementation of DHIS2, from January 2011 to December 2015, was conducted.

Methods

Data on 19 malaria indicators reported monthly by health facilities were extracted from the online Kenya DHIS2 database. Completeness of reporting was analysed for each of the 19 malaria indicators and expressed as the percentage of data values actually reported over the expected number; all health facilities were expected to report data for each indicator for all 12 months in a year.

Results

Malaria indicators data were analysed for 6235 public and 3143 private health facilities. Between 2011 and 2015, completeness of reporting in the public sector increased significantly for confirmed malaria cases across all age categories (26.5–41.9%, p < 0.0001, in children aged <5 years; 30.6–51.4%, p < 0.0001, in persons aged ≥5 years). Completeness of reporting of new antenatal care (ANC) clients increased from 53.7 to 70.5%, p < 0.0001). Completeness of reporting of intermittent preventive treatment in pregnancy (IPTp) decreased from 64.8 to 53.7%, p < 0.0001 for dose 1 and from 64.6 to 53.4%, p < 0.0001 for dose 2. Data on malaria tests performed and test results were not available in DHIS2 from 2011 to 2014. In 2015, sparse data on microscopy (11.5% for children aged <5 years; 11.8% for persons aged ≥5 years) and malaria rapid diagnostic tests (RDTs) (8.1% for all ages) were reported. In the private sector, completeness of reporting increased significantly for confirmed malaria cases across all age categories (16.7–23.1%, p < 0.0001, in children aged <5 years; 19.4–28.6%, p < 0.0001, in persons aged ≥5 years). Completeness of reporting also improved for new ANC clients (16.2–23.6%, p < 0.0001), and for IPTp doses 1 and 2 (16.6–20.2%, p < 0.0001 and 15.5–20.5%, p < 0.0001, respectively). In 2015, less than 3% of data values for malaria tests performed were reported in DHIS2 from the private sector.

Conclusions

There have been sustained improvements in the completeness of data reported for most key malaria indicators since the adoption of DHIS2 in Kenya in 2011. However, major data gaps were identified for the malaria-test indicator and overall low reporting across all indicators from private health facilities. A package of proven DHIS2 implementation interventions and performance-based incentives should be considered to improve private-sector data reporting.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-017-1973-y) contains supplementary material, which is available to authorized users.

Impact of a district-wide health center strengthening intervention on healthcare utilization in rural Rwanda: Use of interrupted time series analysis

Background

Evaluations of health systems strengthening (HSS) interventions using observational data are rarely used for causal inference due to limited data availability. Routinely collected national data allow use of quasi-experimental designs such as interrupted time series (ITS). Rwanda has invested in a robust electronic health management information system (HMIS) that captures monthly healthcare utilization data. We used ITS to evaluate impact of an HSS intervention to improve primary health care facility readiness on health service utilization in two rural districts of Rwanda.

Methods

We used controlled ITS analysis to compare changes in healthcare utilization at health centers (HC) that received the intervention (n = 13) to propensity score matched non-intervention health centers in Rwanda (n = 86) from January 2008 to December 2012. HC support included infrastructure renovation, salary support, medical equipment, referral network strengthening, and clinical training. Baseline quarterly mean outpatient visit rates and population density were used to model propensity scores. The intervention began in May 2010 and was implemented over a twelve-month period. We used monthly healthcare utilization data from the national Rwandan HMIS to study changes in the (1) number of facility deliveries per 10,000 women, (2) number of referrals for high risk pregnancy per 100,000 women, and (3) the number of outpatient visits performed per 1,000 catchment population.

Results

PHIT HC experienced significantly higher monthly delivery rates post-HSS during the April-June season than comparison (3.19/10,000, 95% CI: [0.27, 6.10]). In 2010, this represented a 13% relative increase, and in 2011, this represented a 23% relative increase. The post-HSS change in monthly rate of high-risk pregnancies referred increased slightly in intervention compared to control HC (0.03/10,000, 95% CI: [-0.007, 0.06]). There was a small immediate post-HSS increase in outpatient visit rates in intervention compared to control HC (6.64/1,000, 95% CI: [-13.52, 26.81]).

Conclusion

We failed to find strong evidence of post-HSS increases in outpatient visit rates or referral rates at health centers, which could be explained by small sample size and high baseline nation-wide health service coverage. However, our findings demonstrate that high quality routinely collected health facility data combined with ITS can be used for rigorous policy evaluation in resource-limited settings.

Assessing Lymphatic Filariasis Data Quality in Endemic Communities in Ghana, Using the Neglected Tropical Diseases Data Quality Assessment Tool for Preventive Chemotherapy

Background

The activities of the Global Programme for the Elimination of Lymphatic Filariasis have been in operation since the year 2000, with Mass Drug Administration (MDA) undertaken yearly in disease endemic communities. Information collected during MDA–such as population demographics, age, sex, drugs used and remaining, and therapeutic and geographic coverage–can be used to assess the quality of the data reported. To assist country programmes in evaluating the information reported, the WHO, in collaboration with NTD partners, including ENVISION/RTI, developed an NTD Data Quality Assessment (DQA) tool, for use by programmes. This study was undertaken to evaluate the tool and assess the quality of data reported in some endemic communities in Ghana.

Methods

A cross sectional study, involving review of data registers and interview of drug distributors, disease control officers, and health information officers using the NTD DQA tool, was carried out in selected communities in three LF endemic Districts in Ghana. Data registers for service delivery points were obtained from District health office for assessment. The assessment verified reported results in comparison with recounted values for five indicators: number of tablets received, number of tablets used, number of tablets remaining, MDA coverage, and population treated. Furthermore, drug distributors, disease control officers, and health information officers (at the first data aggregation level), were interviewed, using the DQA tool, to determine the performance of the functional areas of the data management system.

Findings

The results showed that over 60% of the data reported were inaccurate, and exposed the challenges and limitations of the data management system. The DQA tool is a very useful monitoring and evaluation (M&E) tool that can be used to elucidate and address data quality issues in various NTD control programmes.

The Global Programme for the Elimination of Lymphatic Filariasis has been conducting yearly treatment of entire communities in endemic countries since the year 2000. During the treatments various information is collected on the populations, number of medicine tablets distributed and remaining, the number of people treated, etc. that can be used to evaluate the performance of the lymphatic filariasis control programme. For example, information on the number of people treated in a District gives an indication of the success of the programme. In line with this, the World Health Organization in collaboration with other agencies developed a tool for Neglected Tropical Diseases (NTD) to help national control programmes assemble and analyse their data. This study was undertaken to evaluate this tool and the information collected from some endemic communities in Ghana. Community registers were reviewed and personnel involved in drug distribution in the communities were interviewed to collect the necessary information. The results showed that more than half of the data reported in the endemic communities surveyed were inaccurate. It also revealed some weaknesses in the data management and reporting system. The tool, however, is good for identifying and quantifying the magnitude of the challenges encountered in the information management for NTD programmes, especially at peripheral levels.