Enhancing the routine health information system in rural southern Tanzania: successes, challenges and lessons learned

District health management and stillbirth recording and reporting: a qualitative study in the Ashanti Region of Ghana

BACKGROUND

Despite global efforts to reduce maternal and neonatal mortality, stillbirths remain a significant public health challenge in many low- and middle-income countries. District health systems, largely seen as the backbone of health systems, are pivotal in addressing the data gaps reported for stillbirths. Available, accurate and complete data is essential for District Health Management Teams (DHMTs) to understand the burden of stillbirths, evaluate interventions and tailor health facility support to address the complex challenges that contribute to stillbirths. This study aims to understand stillbirth recording and reporting in the Ashanti Region of Ghana from the perspective of DHMTs.

METHODS

The study was conducted in the Ashanti Region of Ghana. 15 members of the regional and district health directorates (RHD/DHD) participated in semi-structured interviews. Sampling was purposive, focusing on RHD/DHD members who interact with maternity services or stillbirth data. Thematic analyses were informed by an a priori framework, including theme 1) experiences, perceptions and attitudes; theme 2) stillbirth data use; and theme 3) leadership and support mechanisms, for stillbirth recording and reporting.

RESULTS

Under theme 1, stillbirth definitions varied among respondents, with 20 and 28 weeks commonly used. Fresh and macerated skin appearance was used to classify timing with limited knowledge of antepartum and intrapartum stillbirths. For theme 2, data quality checks, audits, and the district health information management system (DHIMS-2) data entry and review are functions played by the DHD. Midwives were blamed for data quality issues on omissions and misclassifications. Manual entry of data, data transfer from the facility to the DHD, limited knowledge of stillbirth terminology and periodic closure of the DHIMS-2 were seen to proliferate gaps in stillbirth recording and reporting. Under theme 3, perinatal audits were acknowledged as an enabler for stillbirth recording and reporting by the DHD, though audits are mandated for only late-gestational stillbirths (> 28 weeks). Engagement of other sectors, e.g., civil/vital registration and private health facilities, was seen as key in understanding the true population-level burden of stillbirths.

CONCLUSION

Effective district health management ensures that every stillbirth is accurately recorded, reported, and acted upon to drive improvements. A large need exists for capacity building on stillbirth definitions and data use. Recommendations are made, for example, terminology standardization and private sector engagement, aimed at reducing stillbirth rates in high-mortality settings such as Ghana.

Satisfaction of health informatics professionals with Ethiopian health system: the case of three zones in Ethiopia

BACKGROUND

The importance of the health information system faces multiple challenges such as supply, acceptance, and pressure from other professions in Ethiopia. Work-related challenges might cause low professional satisfaction and hinder service provision. There is a paucity of evidence for policy decisions to improve these challenges. Therefore, this study aims to assess Health Informatics professional satisfaction in the Ethiopian health system and associated factors to provide evidence for future improvements.

METHODS

We conducted an institutions-based cross-sectional study on health informatics professionals in three zones in Southern Ethiopia in 2020. We used a simple random sampling technique to select 215 participants. The local health officials were contacted regarding the research questions, and letters of permission were collected for data collection.

RESULTS

Out of 211(98%) Health Informatics professionals who accepted the interview, 50.8% (95%CI: 47.74%-53.86%) were satisfied. Age (AOR = 0.57; 95% CI: 0.53, 0.95), experience (AOR = 5; 95% CI: 1.50, 19.30), working time (AOR = 1.35; 95% CI: 1.10, 1.70), working as HMIS officers (AOR 2.30; 95% CI: 3.80, 13), single marital status (AOR = 9.60; 95% CI: 2.88, 32), and urban residence (AOR = 8.10; 95% CI: 2.95, 22) were some of the associated factors.

CONCLUSIONS

We found low satisfaction among health informatics professionals compared to other studies. It was suggested that the responsible bodies must keep experienced professionals and reduce pressure from other professions through panel discussions. Work departments and working hours need consideration, as they are the determinants of satisfaction. Improving educational opportunities and career structure is the potential implication area.

Developing a Sustainable Cardiovascular Disease Research Strategy in Tanzania Through Training: Leveraging From the East African Centre of Excellence in Cardiovascular Sciences Project

Introduction

Cardiovascular disease (CVD) contribute the largest mortality burden globally, with most of the deaths (80% of all deaths) occurring in low and middle-income countries (LMICs), including Tanzania. Despite the increasing burden, to date, CVD research output is still limited in Tanzania, as it is for many sub-Saharan Africa (SSA) countries. This trend hinders the establishment of locally informed CVD management and policy changes. Here, we aim to review the existing gaps while highlighting the available opportunities for a sustainable CVD research strategy in Tanzania.

Methods

A rapid review of available literature on CVD research in SSA was conducted, with emphasis on the contribution of Tanzania in the world literature of CVD. Through available literature, we identify strategic CVD research priorities in Tanzania and highlight challenges and opportunities for sustainable CVD research output.

Findings

Shortage of skilled researchers, inadequate research infrastructure, limited funding, and lack of organized research strategies at different levels (regional, country, and institutional) are among the existing key bottlenecks contributing to the low output of CVD research in Tanzania. There is generally strong global, regional and local political will to address the CVD epidemic. The establishment of the East African Centre of Excellence in Cardiovascular Sciences (EACoECVS) offers a unique opportunity for setting strategies and coordinating CVD research and training for Tanzania and the East African region.

Conclusion

There is a light of hope for long-term sustainable CVD research output from Tanzania, taking advantage of the ongoing activities and plans for the evolving EACoECVS. The Tanzanian experience can be taken as a lesson for other SSA countries.

Exploring data quality and use of the routine health information system in Ethiopia: a mixed-methods study

OBJECTIVE

A routine health information system (RHIS) enables decision making in the healthcare system. We aimed to analyse data quality at the district and regional level and explore factors and perceptions affecting the quality and use of routine data.

DESIGN

This was a mixed-methods study. We used the WHO toolkit for analysing data quality and interviewed staff at the point of data generation and along with the flow of data. Data were analysed using the Performance of Routine Information System Management framework.

SETTING

This study was performed in eight districts in four regions of Ethiopia. The study was nested within a 2-year programme of the Operational Research and Coaching for government Analysts.

PARTICIPANTS

We visited 45 health posts, 1 district hospital, 16 health centres and 8 district offices for analysis of routine RHIS data and interviewed 117 staff members for the qualitative assessment.

OUTCOME MEASURES

We assessed availability of source documents, completeness, timeliness and accuracy of reporting of routine data, and explored data quality and use perceptions.

RESULTS

There was variable quality of both indicator and data element. Data on maternal health and immunisation were of higher quality than data on child nutrition. Issues ranged from simple organisational factors, such as availability of register books, to intricate technical issues, like complexity of indicators and choice of denominators based on population estimates. Respondents showed knowledge of the reporting procedures, but also demonstrated limited skills, lack of supportive supervision and reporting to please the next level. We saw limited examples of the use of data by the staff who were responsible for data reporting.

CONCLUSION

We identified important organisational, technical, behavioural and process factors that need further attention to improve the quality and use of RHIS data in Ethiopia.

Effectiveness of in-service training plus the collaborative improvement strategy on the quality of routine malaria surveillance data: results of a pilot study in Kayunga District, Uganda

BACKGROUND

Surveillance data are essential for malaria control, but quality is often poor. The aim of the study was to evaluate the effectiveness of the novel combination of training plus an innovative quality improvement method-collaborative improvement (CI)-on the quality of malaria surveillance data in Uganda.

METHODS

The intervention (training plus CI, or TCI), including brief in-service training and CI, was delivered in 5 health facilities (HFs) in Kayunga District from November 2015 to August 2016. HF teams monitored data quality, conducted plan-do-study-act cycles to test changes, attended periodic learning sessions, and received CI coaching. An independent evaluation was conducted to assess data completeness, accuracy, and timeliness. Using an interrupted time series design without a separate control group, data were abstracted from 156,707 outpatient department (OPD) records, laboratory registers, and aggregated monthly reports (MR) for 4 time periods: baseline-12 months, TCI scale-up-5 months; CI implementation-9 months; post-intervention-4 months. Monthly OPD register completeness was measured as the proportion of patient records with a malaria diagnosis with: (1) all data fields completed, and (2) all clinically-relevant fields completed. Accuracy was the relative difference between: (1) number of monthly malaria patients reported in OPD register versus MR, and (2) proportion of positive malaria tests reported in the laboratory register versus MR. Data were analysed with segmented linear regression modelling.

RESULTS

Data completeness increased substantially following TCI. Compared to baseline, all-field completeness increased by 60.1%-points (95% confidence interval [CI]: 46.9-73.2%) at mid-point, and clinically-relevant completeness increased by 61.6%-points (95% CI: 56.6-66.7%). A relative - 57.4%-point (95% confidence interval: - 105.5, - 9.3%) change, indicating an improvement in accuracy of malaria test positivity reporting, but no effect on data accuracy for monthly malaria patients, were observed. Cost per additional malaria patient, for whom complete clinically-relevant data were recorded in the OPD register, was $3.53 (95% confidence interval: $3.03, $4.15).

CONCLUSIONS

TCI improved malaria surveillance completeness considerably, with limited impact on accuracy. Although these results are promising, the intervention's effectiveness should be evaluated in more HFs, with longer follow-up, ideally in a randomized trial, before recommending CI for wide-scale use.

One country's journey to interoperability: Tanzania's experience developing and implementing a national health information exchange

BACKGROUND

Robust, flexible, and integrated health information (HIS) systems are essential to achieving national and international goals in health and development. Such systems are still uncommon in most low and middle income countries. This article describes a first-phase activity in Tanzania to integrate the country's vertical health management information system with the help of an interoperability layer that enables cross-program data exchange.

METHODS

From 2014 to 2019, the Tanzanian government and partners implemented a five-step procedure based on the "Mind the GAPS" (governance, architecture, program management, and standards) framework and using both proprietary and open-source tools. In collaboration with multiple stakeholders, the team developed the system to address major data challenges via four fully documented "use case scenarios" addressing data exchange among hospitals, between services and the supply chain, across digital data systems, and within the supply chain reporting system. This work included developing the architecture for health system data exchange, putting a middleware interoperability layer in place to facilitate the exchange, and training to support use of the system and the data it generates.

RESULTS

Tanzania successfully completed the five-step procedure for all four use cases. Data exchange is currently enabled among 15 separate information systems, and has resulted in improved data availability and significant time savings. The government has adopted the health information exchange within the national strategy for health care information, and the system is being operated and managed by Tanzanian officials.

CONCLUSION

Developing an integrated HIS requires a significant time investment; but ultimately benefit both programs and patients. Tanzania's experience may interest countries that are developing their HIS programs.

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.

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

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

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

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.

Routine health information system in the health facilities in Yaoundé-Cameroon: assessing the gaps for strengthening

BACKGROUND

Management of health data and its use for informed-decision making is a challenging health sector aspect in developing countries. Monitoring and evaluation of health interventions for meeting health-related Sustainable Development Goals (SDGs), and Cameroon Health Sector Strategy (HSS) targets is facilitated through evidence-based decision-making and public health action. Thus, a routine health information system (RHIS) producing quality data is imperative. The objective of this study was to assess the RHIS in the health facilities (HFs) in Yaoundé in order to identify gaps and weaknesses and to propose measures for strengthening.

METHODS

A health facility-based cross-sectional descriptive study was carried out in the six health districts (HDs) of Yaoundé; followed by a qualitative aspect consisting of in-depth interviews of key informants at the Regional Health Office. HFs were selected using a stratified sampling method with probability proportional to the size of each HD. Data were collected (one respondent per HF) using the World Health Organization and MEASURE Evaluation RHIS rapid assessment tool. Data were entered into Microsoft Excel 2013 and analyzed with IBM-SPSS version 20.

RESULTS

A total of 111 HFs were selected for the study. Respondents aged 24-60 years with an average of 38.3 ± 9.3 years; 58 (52.3%) male and 53(47.7%) female. Heads of HFs and persons in charge of statistics/data management were most represented with 45.0% and 21.6% respectively. All the twelve subdomains of the RHIS were adequately functioning at between 7 and 30%. These included Human Resources (7%), Data Analysis (10%), Information and Communication Technology (11%), Standards and System Design (15%), Policies and Planning (15%), Information Dissemination (16%), Data Demand and Use (16%), Management (18%), Data Needs (18%), Data Quality Assurance (20%), Collection and Management of Individual Client Data (26%), Collection, Management, and Reporting of Aggregated Facility Data (30%).

CONCLUSIONS

The level of functioning of subdomains of the RHIS in Yaoundé was low; thus, immediate and district-specific strengthening actions should be implemented if health-related SDGs and HSS targets are to be met. A nation-wide assessment should be carried out in order to understand the determinants of these poor performances and to strengthen the RHIS.

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.

Predictors of electronic health management information system for improving the quality of care for women and people with disabilities

The electronic Hospital management Information System (eHMIS) can improve care for vulnerable patients, help collect important disability and maternal health data, and improve the hospital’s overall data management. This study assessed the use of HMIS and factors influencing the usage and behavioral intentions to use the eHMIS at the Comprehensive Community Based Rehabilitation in Tanzania (CCBRT). We investigated both clinical and non-clinical staff (n=69) using tablets and online surveys, with a rate of return of 71.9%. The ICT staff were also interviewed to supplement data from the survey. The survey questionnaire was guided by the updated Information System Success (ISS) model. Most health workers (81.2%) used the eHMIS several times a day to support different decision-making activities. The better educated personnel were more likely to enter data more effectively into eHMIS as compared to their counterparts. Among six predictors, self-efficacy positively influenced self-reported use and user satisfaction of HMIS, while service quality negatively affected self-reported use of eHMIS. The system quality positively influenced health workers to be satisfied with the eHMIS, and user satisfaction positively influenced continued usage intention of the eHMIS. Both user satisfaction and continued usage intention were positively related to individual impact of eHMIS. Individual impact had positive effects on organizational impact of eHMIS. This is a comprehensive study conducted in Tanzania regarding the implementation of eHMIS, and factors influencing post-adoptive use of HMIS to improve quality of care of women and people with disabilities.

A model to strengthen utility of quality pharmaceutical health systems data in resource-limited settings

Background:

Limited utility of quality health data undermines efforts to strengthen healthcare delivery, particularly in resource-limited settings. Few studies model the effective utility of quality pharmaceutical information system (PIS) data in sub-Saharan Africa, typified with weak health systems.

Aim:

To develop a model and guidelines for strengthening utility of quality PIS data in public healthcare in Namibia, a resource-limited setting.

Methods:

A qualitative model based on Dickoff et al. practice-oriented theory, Chinn and Jacobs’ systematic approach to theory, and applied consensus techniques. Data from nationwide studies on quality and utility of PIS data in public healthcare conducted between 2018 and March 2020 informed the development of the model concepts. Pharmaceutical and public health systems experts validated the final model.

Results:

Overall, four preliminary national studies that recruited 58 PIS focal persons at 38 public health facilities and national level informed the development of four model concepts. The model describes concepts on access, management, dissemination, and utility of quality PIS data. Activities to implement the model in practice include grass-root integration of real-time automated pharmaceutical intelligence systems to collect, consolidate, monitor, and report PIS data. Strengthening coordination, human resources, and technical capacity through support supervisory systems at grass-root facilities are key activities. PIS focal persons at health facility and national level are agents to implement these activities among recipients, that is, healthcare professionals at points of care. Guidelines for implementation of the model at point of care are included. Experts described the model as clear, simple, comprehensive, and integration of pharmaceutical intelligence systems at point of care as novel and of importance to enhance utility of quality PIS data in resource-limited settings.

Conclusion:

While utility of quality PIS data is limited in Namibia, advantages of the model are encouraging, toward building resilient pharmaceutical intelligence systems at grass roots in resource-limited countries, where there are not only weak health systems, but high burden of misuse of medicines.

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.

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.

Financial cost analysis of a strategy to improve the quality of administrative vaccination data in Uganda

BACKGROUND

High-quality vaccination data are critical to planning, implementation and evaluation of immunization programs. However, sub-optimal administrative vaccination data quality in low- and middle-income countries persist for heterogeneous reasons, though most relate to organizational factors and human behavior. The nationwide Data Improvement Team (DIT) strategy in Uganda aimed to strengthen human resource capacity to generate quality administrative vaccination data at the health facility.

METHODS

A financial cost analysis of the Uganda DIT strategy (2014-2016) was conducted from the program funder perspective. Activity-based micro-costing from funder financial and program monitoring records was used to estimate total and unit costs by program area (in 2016 US dollars). Hypothetical scenarios were developed to illustrate potential approaches to reducing costs.

RESULTS

Over 25 months the DIT strategy was implemented in all 116 operational districts and 3443 (89%) health facilities in Uganda at a total financial cost of US $575 275. Training and deployment of DITs accounted for the highest proportion of expenditure across program areas (69%). Transport, per diems, lodging, and honoraria for DIT members and national supervisors were the main cost drivers of the strategy. Deployment of 557 DIT members cost US $839 per DIT member, US $4 030 per district, and US $136 per health facility. The estimated opportunity cost of government staff time wasn't a major cost driver (2.5%) of total cost.

CONCLUSION

The results provide the first estimates of the magnitude and drivers of cost to implement a national workforce capacity building strategy to improve administrative vaccination data quality in a low- or middle-income country. Financial costs are a critical input to combine with future outcome data to describe the cost of strategies relative to performance outcomes. The operational costs of the strategy were modest (0.5-1.6%) relative to the estimated operational costs of Uganda's national immunization program.

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.

Targeting Pregnant Women for Malaria Surveillance

Women attending antenatal care (ANC) are a generally healthy, easy-access population, contributing valuable data for infectious disease surveillance at the community level. ANC-based malaria surveillance would provide a routine measure of the malaria burden in pregnancy, which countries lack, whilst potentially improving pregnancy outcomes. It could also offer contemporary information on temporal trends and the geographic distribution of malaria burden as well as intervention coverage in the population to guide resource allocation and to assess progress towards elimination. Here, we review the factors underlying the relationship between Plasmodium falciparum in pregnancy and in the community, and outline strengths and limitations of an ANC-based surveillance in sub-Saharan Africa, its potential role within wider malaria surveillance systems, and subsequent programmatic applications.

Facilitators, best practices and barriers to integrating family planning data in Uganda's health management information system

Background

Health management information systems (HMIS) are instrumental in addressing health delivery problems and strengthening health sectors by generating credible evidence about the health status of clients. There is paucity of studies which have explored possibilities for integrating family planning data from the public and private health sectors in Uganda’s national HMIS. This study sought to investigate the facilitators, best practices and barriers of integrating family planning data into the district and national HMIS in Uganda.

Methods

We conducted a qualitative study in Kampala, Jinja, and Hoima Districts of Uganda, based on 16 key informant interviews and a multi-stakeholder dialogue workshop with 11 participants. Deductive and inductive thematic methods were used to analyze the data.

Results

The technical facilitators of integrating family planning data from public and private facilities in the national and district HMIS were user-friendly software; web-based and integrated reporting; and availability of resources, including computers. Organizational facilitators included prioritizing family planning data; training staff; supportive supervision; and quarterly performance review meetings. Key behavioral facilitators were motivation and competence of staff. Collaborative networks with implementing partners were also found to be essential for improving performance and sustainability.

Significant technical barriers included limited supply of computers in lower level health facilities, complex forms, double and therefore tedious entry of data, and web-reporting challenges. Organizational barriers included limited human resources; high levels of staff attrition in private facilities; inadequate training in data collection and use; poor culture of information use; and frequent stock outs of paper-based forms. Behavioral barriers were low use of family planning data for planning purposes by district and health facility staff.

Conclusion

Family planning data collection and reporting are integrated in Uganda’s district and national HMIS. Best practices included integrated reporting and performance review, among others. Limited priority and attention is given to family planning data collection at the facility and national levels. Data are not used by the health facilities that collect them. We recommend reviewing and tailoring data collection forms and ensuring their availability at health facilities. All staff involved in data reporting should be trained and regularly supervised.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4151-9) contains supplementary material, which is available to authorized users.

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.

Experiences in running a complex electronic data capture system using mobile phones in a large-scale population trial in southern Nepal

The increasing availability and capabilities of mobile phones make them a feasible means of data collection. Electronic Data Capture (EDC) systems have been used widely for public health monitoring and surveillance activities, but documentation of their use in complicated research studies requiring multiple systems is limited. This paper shares our experiences of designing and implementing a complex multi-component EDC system for a community-based four-armed cluster-Randomised Controlled Trial in the rural plains of Nepal, to help other researchers planning to use EDC for complex studies in low-income settings. We designed and implemented three interrelated mobile phone data collection systems to enrol and follow-up pregnant women (trial participants), and to support the implementation of trial interventions (women's groups, food and cash transfers). 720 field staff used basic phones to send simple coded text messages, 539 women's group facilitators used Android smartphones with Open Data Kit Collect, and 112 Interviewers, Coordinators and Supervisors used smartphones with CommCare. Barcoded photo ID cards encoded with participant information were generated for each enrolled woman. Automated systems were developed to download, recode and merge data for nearly real-time access by researchers. The systems were successfully rolled out and used by 1371 staff. A total of 25,089 pregnant women were enrolled, and 17,839 follow-up forms completed. Women's group facilitators recorded 5717 women's groups and the distribution of 14,647 food and 13,482 cash transfers. Using EDC sped up data collection and processing, although time needed for programming and set-up delayed the study inception. EDC using three interlinked mobile data management systems (FrontlineSMS, ODK and CommCare) was a feasible and effective method of data capture in a complex large-scale trial in the plains of Nepal. Despite challenges including prolonged set-up times, the systems met multiple data collection needs for users with varying levels of literacy and experience.

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.

District decision-making for health in low-income settings: a feasibility study of a data-informed platform for health in India, Nigeria and Ethiopia

Low-resource settings often have limited use of local data for health system planning and decision-making. To promote local data use for decision-making and priority setting, we propose an adapted framework: a data-informed platform for health (DIPH) aimed at guiding coordination, bringing together key data from the public and private sectors on inputs and processes. In working to transform this framework from a concept to a health systems initiative, we undertook a series of implementation research activities including background assessment, testing and scaling up of the intervention. This first paper of four reports the feasibility of the approach in a district health systems context in five districts of India, Nigeria and Ethiopia. We selected five districts using predefined criteria and in collaboration with governments. After scoping visits, an in-depth field visit included interviews with key health stakeholders, focus group discussions with service-delivery staff and record review. For analysis, we used five dimensions of feasibility research based on the TELOS framework: technology and systems, economic, legal and political, operational and scheduling feasibility. We found no standardized process for data-based district level decision-making, and substantial obstacles in all three countries. Compared with study areas in Ethiopia and Nigeria, the health system in Uttar Pradesh is relatively amenable to the DIPH, having relative strengths in infrastructure, technological and technical expertise, and financial resources, as well as a district-level stakeholder forum. However, a key challenge is the absence of an effective legal framework for engagement with India’s extensive private health sector. While priority-setting may depend on factors beyond better use of local data, we conclude that a formative phase of intervention development and pilot-testing is warranted as a next step.

Data for Program Management: An Accuracy Assessment of Data Collected in Household Registers by Community Health Workers in Southern Kayonza, Rwanda

Community health workers (CHWs) collect data for routine services, surveys and research in their communities. However, quality of these data is largely unknown. Utilizing poor quality data can result in inefficient resource use, misinformation about system gaps, and poor program management and effectiveness. This study aims to measure CHW data accuracy, defined as agreement between household registers compared to household member interview and client records in one district in Eastern province, Rwanda. We used cluster-lot quality assurance sampling to randomly sample six CHWs per cell and six households per CHW. We classified cells as having 'poor' or 'good' accuracy for household registers for five indicators, calculating point estimates of percent of households with accurate data by health center. We evaluated 204 CHW registers and 1,224 households for accuracy across 34 cells in southern Kayonza. Point estimates across health centers ranged from 79 to 100% for individual indicators and 61 to 72% for the composite indicator. Recording error appeared random for all but the widely under-reported number of women on modern family planning method. Overall, accuracy was largely 'good' across cells, with varying results by indicator. Program managers should identify optimum thresholds for 'good' data quality and interventions to reach them according to data use. Decreasing variability and improving quality will facilitate potential of these routinely-collected data to be more meaningful for community health program management. We encourage further studies assessing CHW data quality and the impact training, supervision and other strategies have on improving it.

Updating a patient-level ART database covering remote health facilities in Zomba district, Malawi: lessons learned

SETTING

A non-governmental organization, Dignitas International, working in partnership with the Ministry of Health in Malawi, adopted innovative, low-technology methods to collect, capture, and manage patient-level antiretroviral therapy (ART) data in a district database covering 26 remote low-resource facilities in Zomba District, Malawi.

OBJECTIVE

To establish a longitudinal, observational database of routinely collected program data that could serve as a program monitoring and evaluation tool as well as a platform to conduct effective operational research.

DESIGN

This article describes the processes developed for digital capture of paper-based ART clinical records at health facilities and updating them in a central electronic database. It documents and focuses on lessons learned during the implementation and review of processes.

CONCLUSIONS

Data quality can only be ensured with regular review of, and compliance with, clearly delineated workflow protocols and adequate staffing and supervision. Through the implementation of this procedure, we expect to improve data quality, completeness, and use of routine ART clinical data in low-resource settings.

Innovating to enhance clinical data management using non-commercial and open source solutions across a multi-center network supporting inpatient pediatric care and research in Kenya

Objective To share approaches and innovations adopted to deliver a relatively inexpensive clinical data management (CDM) framework within a low-income setting that aims to deliver quality pediatric data useful for supporting research, strengthening the information culture and informing improvement efforts in local clinical practice.

Materials and methods The authors implemented a CDM framework to support a Clinical Information Network (CIN) using Research Electronic Data Capture (REDCap), a noncommercial software solution designed for rapid development and deployment of electronic data capture tools. It was used for collection of standardized data from case records of multiple hospitals’ pediatric wards. R, an open-source statistical language, was used for data quality enhancement, analysis, and report generation for the hospitals.

Results In the first year of CIN, the authors have developed innovative solutions to support the implementation of a secure, rapid pediatric data collection system spanning 14 hospital sites with stringent data quality checks. Data have been collated on over 37 000 admission episodes, with considerable improvement in clinical documentation of admissions observed. Using meta-programming techniques in R, coupled with branching logic, randomization, data lookup, and Application Programming Interface (API) features offered by REDCap, CDM tasks were configured and automated to ensure quality data was delivered for clinical improvement and research use.

Conclusion A low-cost clinically focused but geographically dispersed quality CDM (Clinical Data Management) in a long-term, multi-site, and real world context can be achieved and sustained and challenges can be overcome through thoughtful design and implementation of open-source tools for handling data and supporting research.

Completeness and accuracy of data transfer of routine maternal health services data in the greater Accra region

Background

High quality routine health system data is essential for tracking progress towards attainment of the Millennium Development Goals 4 & 5. This study aimed to determine the completeness and accuracy of transfer of routine maternal health service data at health facility, district and regional levels of the Greater Accra Region of Ghana.

Methods

A cross sectional study was conducted using secondary data comprised of routine health information data collected at facility level for the first quarter of 2012. Twelve health facilities were selected using a multistage sampling method. Data relating to antenatal care and delivery were assessed for completeness and accuracy of data transfer. Primary source data from health facility level (registers and record notebooks where health information data are initially entered) , used as the reference data, were counted, collated, and compared with aggregate data on aggregate forms compiled from these sources by health facility staff. The primary source data was also compared with data in the district health information management system (DHIMS–II), a web-based data collation and reporting system. Percentage completeness and percentage error in data transfer were estimated.

Results

Data for all 5,537 antenatal registrants and 3, 466 deliveries recorded into the primary source for the first quarter of 2012 were assessed. Completeness was best for age data, followed by data on parity and hemoglobin at registration. Mean completeness of the facility level aggregate data for the data sampled, was 94.3% (95% CI = 90.6% – 98.0%) and 100.0% respectively for the aggregate form and DHIMS-II database. Mean error in data transfer was 1.0% (95% CI = 0.8% - 1.2%). Percentage error comparing aggregate form data and DHIMS-II data respectively to the primary source data ranged from 0.0% to 4.9% respectively, while percentage error comparing the DHIMS-II data to aggregate form data, was generally very low or 0.0%.

Conclusion

Routine maternal health services data in the Greater Accra region, available at the district level through the DHIMS-II system is complete when compared to facility level primary source data and reliable for use.

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

BACKGROUND

Health data can be useful for effective service delivery, decision making, and evaluating existing programs in order to maintain high quality of healthcare. Studies have shown variability in data quality from national health management information systems (HMISs) in sub-Saharan Africa which threatens utility of these data as a tool to improve health systems. The purpose of this study is to assess the quality of Rwanda's HMIS data over a 5-year period.

METHODS

The World Health Organization (WHO) data quality report card framework was used to assess the quality of HMIS data captured from 2008 to 2012 and is a census of all 495 publicly funded health facilities in Rwanda. Factors assessed included completeness and internal consistency of 10 indicators selected based on WHO recommendations and priority areas for the Rwanda national health sector. Completeness was measured as percentage of non-missing reports. Consistency was measured as the absence of extreme outliers, internal consistency between related indicators, and consistency of indicators over time. These assessments were done at the district and national level.

RESULTS

Nationally, the average monthly district reporting completeness rate was 98% across 10 key indicators from 2008 to 2012. Completeness of indicator data increased over time: 2008, 88%; 2009, 91%; 2010, 89%; 2011, 90%; and 2012, 95% (p<0.0001). Comparing 2011 and 2012 health events to the mean of the three preceding years, service output increased from 3% (2011) to 9% (2012). Eighty-three percent of districts reported ratios between related indicators (ANC/DTP1, DTP1/DTP3) consistent with HMIS national ratios. Conclusion and policy implications: Our findings suggest that HMIS data quality in Rwanda has been improving over time. We recommend maintaining these assessments to identify remaining gaps in data quality and that results are shared publicly to support increased use of HMIS data.

Data quality assessment in the routine health information system: an application of the Lot Quality Assurance Sampling in Benin

Health information systems in developing countries are often faulted for the poor quality of the data generated and for the insufficient means implemented to improve system performance. This study examined data quality in the Routine Health Information System in Benin in 2012 and carried out a cross-sectional evaluation of the quality of the data using the Lot Quality Assurance Sampling method. The results confirm the insufficient quality of the data based on three criteria: completeness, reliability and accuracy. However, differences can be seen as the shortcomings are less significant for financial data and for immunization data. The method is simple, fast and can be proposed for current use at operational level as a data quality control tool during the production stage.

Physician tracking in sub-Saharan Africa: current initiatives and opportunities

BACKGROUND

Physician tracking systems are critical for health workforce planning as well as for activities to ensure quality health care - such as physician regulation, education, and emergency response. However, information on current systems for physician tracking in sub-Saharan Africa is limited. The objective of this study is to provide information on the current state of physician tracking systems in the region, highlighting emerging themes and innovative practices.

METHODS

This study included a review of the literature, an online search for physician licensing systems, and a document review of publicly available physician registration forms for sub-Saharan African countries. Primary data on physician tracking activities was collected as part of the Medical Education Partnership Initiative (MEPI) - through two rounds over two years of annual surveys to 13 medical schools in 12 sub-Saharan countries. Two innovations were identified during two MEPI school site visits in Uganda and Ghana.

RESULTS

Out of twelve countries, nine had existing frameworks for physician tracking through licensing requirements. Most countries collected basic demographic information: name, address, date of birth, nationality/citizenship, and training institution. Practice information was less frequently collected. The most frequently collected practice fields were specialty/degree and current title/position. Location of employment and name and sector of current employer were less frequently collected. Many medical schools are taking steps to implement graduate tracking systems. We also highlight two innovative practices: mobile technology access to physician registries in Uganda and MDNet, a public-private partnership providing free mobile-to-mobile voice and text messages to all doctors registered with the Ghana Medical Association.

CONCLUSION

While physician tracking systems vary widely between countries and a number of challenges remain, there appears to be increasing interest in developing these systems and many innovative developments in the area. Opportunities exist to expand these systems in a more coordinated manner that will ultimately lead to better workforce planning, implementation of the workforce, and better health.

Major improvements in the quality of malaria case-management under the "test and treat" policy in Kenya

Background

Monitoring implementation of the “test and treat” case-management policy for malaria is an important component of all malaria control programmes in Africa. Unfortunately, routine information systems are commonly deficient to provide necessary information. Using health facility surveys we monitored health systems readiness and malaria case-management practices prior to and following implementation of the 2010 “test and treat” policy in Kenya.

Methods/Findings

Between 2010 and 2013 six national, cross-sectional, health facility surveys were undertaken. The number of facilities assessed ranged between 172 and 176, health workers interviewed between 216 and 237 and outpatient consultations for febrile patients evaluated between 1,208 and 2,408 across six surveys. Comparing baseline and the last survey results, all readiness indicators showed significant (p<0.005) improvements: availability of parasitological diagnosis (55.2% to 90.7%); RDT availability (7.5% to 69.8%); total artemether-lumefantrine (AL) stock-out (27.2% to 7.0%); stock-out of one or more AL packs (59.5% to 21.6%); training coverage (0 to 50.2%); guidelines access (0 to 58.1%) and supervision (17.9% to 30.8%). Testing increased by 34.0% (23.9% to 57.9%; p<0.001) while testing and treatment according to test result increased by 34.2% (15.7% to 49.9%; p<0.001). Treatment adherence for test positive patients improved from 83.3% to 90.3% (p = 0.138) and for test negative patients from 47.9% to 83.4% (p<0.001). Significant testing and treatment improvements were observed in children and adults. There was no difference in practices with respect to the type and result of malaria test (RDT vs microscopy). Of eight dosing, dispensing and counseling tasks, improvements were observed for four tasks. Overall AL use for febrile patients decreased from 63.5% to 35.6% (p<0.001).

Conclusions

Major improvements in the implementation of “test and treat” policy were observed in Kenya. Some gaps towards universal targets still remained. Other countries facing similar needs and challenges may consider health facility surveys to monitor malaria case-management.

Monitoring child survival in 'real time' using routine health facility records: results from Malawi

Objectives

Few developing countries have the accurate civil registration systems needed to track progress in child survival. However, the health information systems in most of these countries do record facility births and deaths, at least in principle. We used data from two districts of Malawi to test a method for monitoring child mortality based on adjusting health facility records for incomplete coverage.

Methods

Trained researchers collected reports of monthly births and deaths among children younger than 5 years from all health facilities in Balaka and Salima districts of Malawi in 2010–2011. We estimated the proportion of births and deaths occurring in health facilities, respectively, from the 2010 Demographic and Health Survey and a household mortality survey conducted between October 2011 and February 2012. We used these proportions to adjust the health facility data to estimate the actual numbers of births and deaths. The survey also provided ‘gold-standard’ measures of under-five mortality.

Results

Annual under-five mortality rates generated by adjusting health facility data were between 35% and 65% of those estimated by the gold-standard survey in Balaka, and 46% and 50% in Salima for four overlapping 12-month periods in 2010–2011. The ratios of adjusted health facility rates to gold-standard rates increased sharply over the four periods in Balaka, but remained relatively stable in Salima.

Conclusions

Even in Malawi, where high proportions of births and deaths occur in health facilities compared with other countries in sub-Saharan Africa, routine Health Management Information Systems data on births and deaths cannot be used at present to estimate annual trends in under-five mortality.

Strengthening integrated primary health care in Sofala, Mozambique

Background

Large increases in health sector investment and policies favoring upgrading and expanding the public sector health network have prioritized maternal and child health in Mozambique and, over the past decade, Mozambique has achieved substantial improvements in maternal and child health indicators. Over this same period, the government of Mozambique has continued to decentralize the management of public sector resources to the district level, including in the health sector, with the aim of bringing decision-making and resources closer to service beneficiaries. Weak district level management capacity has hindered the decentralization process, and building this capacity is an important link to ensure that resources translate to improved service delivery and further improvements in population health. A consortium of the Ministry of Health, Health Alliance International, Eduardo Mondlane University, and the University of Washington are implementing a health systems strengthening model in Sofala Province, central Mozambique.

Description of implementation

The Mozambique Population Health Implementation and Training (PHIT) Partnership focuses on improving the quality of routine data and its use through appropriate tools to facilitate decision making by health system managers; strengthening management and planning capacity and funding district health plans; and building capacity for operations research to guide system-strengthening efforts. This seven-year effort covers all 13 districts and 146 health facilities in Sofala Province.

Evaluation design

A quasi-experimental controlled time-series design will be used to assess the overall impact of the partnership strategy on under-5 mortality by examining changes in mortality pre- and post-implementation in Sofala Province compared with neighboring Manica Province. The evaluation will compare a broad range of input, process, output, and outcome variables to strengthen the plausibility that the partnership strategy led to health system improvements and subsequent population health impact.

Discussion

The Mozambique PHIT Partnership expects to provide evidence on the effect of efforts to improve data quality coupled with the introduction of tools, training, and supervision to improve evidence-based decision making. This contribution to the knowledge base on what works to enhance health systems is highly replicable for rapid scale-up to other provinces in Mozambique, as well as other sub-Saharan African countries with limited resources and a commitment to comprehensive primary health care.

Ownership and use of mobile phones among health workers, caregivers of sick children and adult patients in Kenya: cross-sectional national survey

Background

The rapid growth in mobile phone penetration and use of Short Message Service (SMS) has been seen as a potential solution to improve medical and public health practice in Africa. Several studies have shown effectiveness of SMS interventions to improve health workers’ practices, patients’ adherence to medications and availability of health facility commodities. To inform policy makers about the feasibility of facility-based SMS interventions, the coverage data on mobile phone ownership and SMS use among health workers and patients are needed.

Methods

In 2012, a national, cross-sectional, cluster sample survey was undertaken at 172 public health facilities in Kenya. Outpatient health workers and caregivers of sick children and adult patients were interviewed. The main outcomes were personal ownership of mobile phones and use of SMS among phone owners. The predictors analysis examined factors influencing phone ownership and SMS use.

Results

The analysis included 219 health workers and 1,177 patients’ respondents (767 caregivers and 410 adult patients). All health workers possessed personal mobile phones and 98.6% used SMS. Among patients’ respondents, 61.2% owned phones and 71.4% of phone owners used SMS. The phone ownership and SMS use was similar between caregivers of sick children and adult patients. The respondents who were male, more educated, literate and living in urban area were significantly more likely to own the phone and use SMS. The youngest respondents were less likely to own phones, however when the phones were owned, younger age groups were more likely to use SMS. Respondents living in wealthier areas were more likely to own phones; however when phones are owned no significant association between the poverty and SMS use was observed.

Conclusions

Mobile phone ownership and SMS use is ubiquitous among Kenyan health workers in the public sector. Among patients they serve the coverage in phone ownership and SMS use is lower and disparities exist with respect to gender, age, education, literacy, urbanization and poverty. Some of the disparities on SMS use can be addressed through the modalities of mHealth interventions and enhanced implementation processes while further growth in mobile phone penetration is needed to reduce the ownership gap.

Utilizing community health worker data for program management and evaluation: systems for data quality assessments and baseline results from Rwanda

Community health workers (CHWs) have and continue to play a pivotal role in health services delivery in many resource-constrained environments. The data routinely generated through these programs are increasingly relied upon for providing information for program management, evaluation and quality assurance. However, there are few published results on the quality of CHW-generated data, and what information exists suggests quality is low. An ongoing challenge is the lack of routine systems for CHW data quality assessments (DQAs). In this paper, we describe a system developed for CHW DQAs and results of the first formal assessment in southern Kayonza, Rwanda, May-June 2011. We discuss considerations for other programs interested in adopting such systems. While the results identified gaps in the current data quality, the assessment also identified opportunities for strengthening the data to ensure suitable levels of quality for use in management and evaluation.

A systematic review of healthcare applications for smartphones

BACKGROUND

Advanced mobile communications and portable computation are now combined in handheld devices called "smartphones", which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category.

METHODS

In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches.

RESULTS

A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students.

CONCLUSIONS

Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.

Mobile phone text messaging: tool for malaria control in Africa

Dejan Zurovac and colleagues discuss six areas where text messaging could improve the delivery of health services and health outcomes in malaria in Africa.