Challenges in reducing maternal and neonatal mortality in Niger: an in-depth case study.

INTRODUCTION: Recent modelled estimates suggest that Niger made progress in maternal mortality since 2000. However, neonatal mortality has not declined since 2012 and maternal mortality estimates were based on limited data. We researched the drivers of progress and challenges. METHODS: We reviewed two decades of health policies, analysed mortality trends from United Nations data and six national household surveys between 1998 and 2021 and assessed coverage and inequalities of maternal and newborn health indicators. Quality of care was evaluated from health facility surveys in 2015 and 2019 and emergency obstetric assessments in 2011 and 2017. We determined the impact of intervention coverage on maternal and neonatal lives saved between 2000 and 2020. We interviewed 31 key informants to understand the factors underpinning policy implementation. RESULTS: Empirical maternal mortality ratio declined from 709 to 520 per 100 000 live births during 2000-2011, while neonatal mortality rate declined from 46 to 23 per 1000 live births during 2000-2012 then increased to 43 in 2018. Inequalities in neonatal mortality were reduced across socioeconomic and demographic strata. Key maternal and newborn health indicators improved over 2000-2012, except for caesarean sections, although the overall levels were low. Interventions delivered during childbirth saved most maternal and newborn lives. Progress came from health centre expansion, emergency care and the 2006 fee exemptions policy. During the past decade, challenges included expansion of emergency care, continued high fertility, security issues, financing and health workforce. Social determinants saw minimal change. CONCLUSIONS: Niger reduced maternal and neonatal mortality during 2000-2012, but progress has stalled. Further reductions require strategies targeting comprehensive care, referrals, quality of care, fertility reduction, social determinants and improved security nationwide.

Climate shocks and nutrition: The role of food security policies and programs in enhancing maternal and neonatal survival in Niger.

Niger is afflicted with high rates of poverty, high fertility rates, frequent environmental crises, and climate change. Recurrent droughts and floods have led to chronic food insecurity linked to poor maternal and neonatal nutrition outcomes in vulnerable regions. We analyzed maternal and neonatal nutrition trends and subnational variability between 2000 and 2021 with a focus on the implementation of policies and programs surrounding two acute climate shocks in 2005 and 2010. We used four sources of data: (a) national household surveys for maternal and newborn nutritional indicators allowing computation of trends and differences at national and regional levels; (b) document review of food security reports; (c) 30 key informant interviews and; (d) one focus group discussion. Many food security policies and nutrition programs were enacted from 2000 to 2020. Gains in maternal and neonatal nutrition indicators were more significant in targeted vulnerable regions of Maradi, Zinder, Tahoua and Tillabéri, from 2006 to 2021. However, poor access to financial resources for policy execution and suboptimal implementation of plans have hindered progress. In response to the chronic climate crisis over the last 20 years, the Nigerien government and program implementers have demonstrated their commitment to reducing food insecurity and enhancing resilience to climate shocks by adopting a deliberate multisectoral effort. However, there is more that can be achieved with a continued focus on vulnerable regions to build resilience, targeting high risk populations, and investing in infrastructure to improve health systems, food systems, agriculture systems, education systems, and social protection.

The Impact of Childhood Mortality on Fertility in Rural Tanzania: Evidence From the Ifakara and Rufiji Health and Demographic Surveillance Systems.

This manuscript examines the relationship between child mortality and subsequent fertility using longitudinal data on births and childhood deaths occurring among 15,291 Tanzanian mothers between 2000 and 2015. Generalized hazard regression analyses assess the effect of child loss on the hazard of conception, adjusting for child-level, mother-level, and contextual covariates. Results show that time to conception is most reduced if an index child dies during the subsequent birth interval, representing the combined effect of biological and volitional replacement. Deaths occurring during prior birth intervals were associated with accelerated time to conception during future intervals, consistent with hypothesized insurance effects of anticipating future child loss, but this effect is smaller than replacement effects. The analysis reveals that residence in areas of relatively high child mortality is associated with hastened parity progression, again consistent with the insurance hypothesis. Investigation of high-order interactions suggests that insurance effects tend to be greater in low-mortality communities, replacement effects tend to be stronger in high-mortality community contexts, and wealthier families tend to exhibit a weaker insurance response but a stronger replacement response to childhood mortality relative to poorer families.

The implementation and effectiveness of multi-tasked, paid community health workers on maternal and child health: A cluster-randomized pragmatic trial and qualitative process evaluation in Tanzania.

Community health worker programs have proliferated worldwide based on evidence that they help prevent mortality, particularly among children. However, there is limited evidence from randomized studies on the processes and effectiveness of implementing community health worker programs through public health systems. This paper describes the results of a cluster-randomized pragmatic implementation trial (registration number ISRCTN96819844) and qualitative process evaluation of a community health worker program in Tanzania that was implemented from 2011-2015. Program effects on maternal, newborn and child health service utilization, childhood morbidity and sick childcare seeking were evaluated using difference-in-difference regression analysis with outcomes measured through pre- and post-intervention household surveys in intervention and comparison trial arms. A qualitative process evaluation was conducted between 2012 and 2014 and comprised of in-depth interviews and focus group discussions with community health workers, community members, facility-based health workers and staff of district health management teams. The community health worker program reduced incidence of illness and improved access to timely and appropriate curative care for children under five; however, there was no effect on facility-based maternal and newborn health service utilization. The positive outcomes occurred because of high levels of acceptability of community health workers within communities, as well as the durability of community health workers' motivation and confidence. Implementation factors that generated these effects were the engagement of communities in program startup; the training, remuneration and supervision of the community health workers from the local health system and community. The lack of program effects on maternal and newborn health service utilization at facilities were attributed to lapses in the availability of needed care at facilities. Strategies that strengthen and align communities' and health systems core capacities, and their ability to learn, adapt and integrate evidence-based interventions, are needed to maximize the health impact of community health workers.

From External to Local: Opportunities and Lessons Learned from Transitioning COMSA-Mozambique.

Donor transitions, where externally funded programs transfer to country ownership and management, are increasingly common. The Countrywide Mortality Surveillance for Action - Mozambique (COMSA) project established a nationwide surveillance system capturing vital events at the community level with funding from the Bill and Melinda Gates Foundation. COMSA was implemented in partnership between Johns Hopkins University (a U.S.-based academic institution) and the Instituto Nacional de Saúde (National Institute for Health) and Instituto Nacional de Estatística (National Institute for Statistics), two Mozambican public institutions. Midway through the project, the Gates Foundation directed COMSA's partners to develop and implement a transition plan that ensured COMSA's activities could be institutionalized after Gates Foundation funding ended. Here we describe the process and activities that COMSA underwent for transition planning, including stakeholder engagement and advocacy, securing financial commitments, documenting operational activities, capacity building, and supporting strategic planning. Facilitators included a project model that already embedded significant implementation and management responsibility with local agencies, high-level commitment to COMSA's activities from local stakeholders, establishing dedicated personnel and budget to manage transition, and fortuitous timing for financing. Challenges included needing to engage multiple government agencies to ensure buy-in, navigating tensions around future roles and responsibilities, reviewing and adjusting existing implementation structures, and the reality that this transition involved shifting financing from one development partner to another. Transition implementation was also constrained by the COVID-19 pandemic because key stakeholders were engaged in response efforts. COMSA's experience highlights lessons and threats for future programs facing donor transition in uncertain environments.

Multi-Cause Calibration of Verbal Autopsy-Based Cause-Specific Mortality Estimates of Children and Neonates in Mozambique.

The Countrywide Mortality Surveillance for Action platform is collecting verbal autopsy (VA) records from a nationally representative sample in Mozambique. These records are used to estimate the national and subnational cause-specific mortality fractions (CSMFs) for children (1-59 months) and neonates (1-28 days). Cross-tabulation of VA-based cause-of-death (COD) determination against that from the minimally invasive tissue sampling (MITS) from the Child Health and Mortality Prevention project revealed important misclassification errors for all the VA algorithms, which if not accounted for will lead to bias in the estimates of CSMF from VA. A recently proposed Bayesian VA-calibration method is used that accounts for this misclassification bias and produces calibrated estimates of CSMF. Both the VA-COD and the MITS-COD can be multi-cause (i.e., suggest more than one probable COD for some of the records). To fully use this probabilistic COD data, we use the multi-cause VA calibration. Two different computer-coded VA algorithms are considered-InSilicoVA and EAVA-and the final CSMF estimates are obtained using an ensemble calibration that uses data from both the algorithms. The calibrated estimates consistently offer a better fit to the data and reveal important changes in the CSMF for both children and neonates in Mozambique after accounting for VA misclassification bias.

Implementing the Countrywide Mortality Surveillance in Action in Mozambique: How Much Did It Cost?

Complete sample registration systems are almost inexistent in sub-Saharan Africa. The Countrywide Mortality Surveillance in Action (COMSA) project in Mozambique, a national mortality and cause of death surveillance system, was launched in January 2017, began data collection in March 2018, and covers over 800,000 population. The objectives of this analysis are to quantify the costs of establishing and maintaining the project between 2017 and 2020 and to assess the cost per output of the surveillance system using data from financial reports produced by the National Institute of Health in Mozambique. The program cost analysis consists of start-up (fixed) costs and average annual operating costs covering the period of maximum implementation in 700 clusters. The cost per output analysis quantifies the annual operating cost of surveillance outputs during the same period. Approximately two million dollars were spent on setting up the system, with infrastructure, technological investments, and training making up over 80% of these start-up costs. The average annual operating costs of maintaining COMSA was $984,771 per year, of which 66% were spent on wages and data collection incentives. The cost per output analysis indicates costs of $37-$42 per vital event captured in the surveillance system (deaths, pregnancies, pregnancy outcomes), $303-$340 per verbal and social autopsy conducted on a reported death, and a per capita cost of $1-$1.3. In conclusion, establishing COMSA required large costs associated with infrastructure and technological investments. However, the system offers long-term benefits for real-time data generation and informing government decision-making for health.

Completeness and Factors Affecting Community Workers' Reporting of Births and Deaths in the Countrywide Mortality Surveillance for Action in Mozambique.

Since March 2018, the Countrywide Mortality Surveillance for Action project, implemented as a national sample registration system by the Mozambique Instituto Nacional de Saude and the Instituto Nacional de Estatistica in 700 geographic clusters randomly distributed across the 11 provinces, has trained and deployed community surveillance agents (CSAs) to report births and deaths in each cluster prospectively. An independent, retrospective data collection was conducted to assess the completeness of surveillance data. Record linkage procedures were used to match households and vital events reported in the two data sources. We calculated birth and death reporting rates and used a regression model to determine factors associated with the likelihood of vital events being reported by the CSAs. Between March 2018 and December 2019, CSAs reported 54% of births (8,787/16,421) and 45% of deaths (1,726/3,867). Births of smaller cluster sizes (< 1,000 people) were more likely to be reported (adjusted odds ratio [aOR] = 1.45; 95% CI = 1.15-1.83) compared with those of larger cluster sizes (> 1,500 people). Deaths of rural clusters were more likely to be reported (aOR = 1.41; 95% CI = 1.07-1.85) than those of urban clusters. Adult deaths were more likely to be reported (aOR = 1.49; 95% CI = 1.10-2.02) than child deaths. Our findings suggest that a fully functioning sample vital registration system must adopt a dual system with high-quality surveys or other ways to estimate underregistration periodically, consider a smaller cluster size manageable by a community worker, and pay special attention to urban clusters as underreporting is larger.

Correcting for Verbal Autopsy Misclassification Bias in Cause-Specific Mortality Estimates.

Verbal autopsies (VAs) are extensively used to determine cause of death (COD) in many low- and middle-income countries. However, COD determination from VA can be inaccurate. Computer coded verbal autopsy (CCVA) algorithms used for this task are imperfect and misclassify COD for a large proportion of deaths. If not accounted for, this misclassification leads to biased estimates of cause-specific mortality fractions (CSMFs), a critical piece in health-policy making. Recent work has demonstrated that the knowledge of the CCVA misclassification rates can be used to calibrate raw VA-based CSMF estimates to account for the misclassification bias. In this manuscript, we review the current practices and issues with raw COD predictions from CCVA algorithms and provide a complete primer on how to use the VA calibration approach with the calibratedVA software to correct for verbal autopsy misclassification bias in cause-specific mortality estimates. We use calibratedVA to obtain CSMFs for child (1-59 months) and neonatal deaths using VA data from the Countrywide Mortality Surveillance for Action project in Mozambique.

Countrywide Mortality Surveillance for Action in Mozambique: Results from a National Sample-Based Vital Statistics System for Mortality and Cause of Death.

Sub-Saharan Africa lacks timely, reliable, and accurate national data on mortality and causes of death (CODs). In 2018 Mozambique launched a sample registration system (Countrywide Mortality Surveillance for Action [COMSA]-Mozambique), which collects continuous birth, death, and COD data from 700 randomly selected clusters, a nationally representative population of 828,663 persons. Verbal and social autopsy interviews are conducted for COD determination. We analyzed data collected in 2019-2020 to report mortality rates and cause-specific fractions. Cause-specific results were generated using computer-coded verbal autopsy (CCVA) algorithms for deaths among those age 5 years and older. For under-five deaths, the accuracy of CCVA results was increased through calibration with data from minimally invasive tissue sampling. Neonatal and under-five mortality rates were, respectively, 23 (95% CI: 18-28) and 80 (95% CI: 69-91) deaths per 1,000 live births. Mortality rates per 1,000 were 18 (95% CI: 14-21) among age 5-14 years, 26 (95% CI: 20-31) among age 15-24 years, 258 (95% CI: 230-287) among age 25-59 years, and 531 (95% CI: 490-572) among age 60+ years. Urban areas had lower mortality rates than rural areas among children under 15 but not among adults. Deaths due to infections were substantial across all ages. Other predominant causes by age group were prematurity and intrapartum-related events among neonates; diarrhea, malaria, and lower respiratory infections among children 1-59 months; injury, malaria, and diarrhea among children 5-14 years; HIV, injury, and cancer among those age 15-59 years; and cancer and cardiovascular disease at age 60+ years. The COMSA-Mozambique platform offers a rich and unique system for mortality and COD determination and monitoring and an opportunity to build a comprehensive surveillance system.

From External to Local: Opportunities and Lessons Learned from Transitioning COMSA-Mozambique

Donor transitions, where externally funded programs transfer to country ownership and management, are increasingly common. The Countrywide Mortality Surveillance for Action - Mozambique (COMSA) project established a nationwide surveillance system capturing vital events at the community level with funding from the Bill and Melinda Gates Foundation. COMSA was implemented in partnership between Johns Hopkins University (a U.S.-based academic institution) and the Instituto Nacional de Saúde (National Institute for Health) and Instituto Nacional de Estatística (National Institute for Statistics), two Mozambican public institutions. Midway through the project, the Gates Foundation directed COMSA's partners to develop and implement a transition plan that ensured COMSA's activities could be institutionalized after Gates Foundation funding ended. Here we describe the process and activities that COMSA underwent for transition planning, including stakeholder engagement and advocacy, securing financial commitments, documenting operational activities, capacity building, and supporting strategic planning. Facilitators included a project model that already embedded significant implementation and management responsibility with local agencies, high-level commitment to COMSA's activities from local stakeholders, establishing dedicated personnel and budget to manage transition, and fortuitous timing for financing. Challenges included needing to engage multiple government agencies to ensure buy-in, navigating tensions around future roles and responsibilities, reviewing and adjusting existing implementation structures, and the reality that this transition involved shifting financing from one development partner to another. Transition implementation was also constrained by the COVID-19 pandemic because key stakeholders were engaged in response efforts. COMSA's experience highlights lessons and threats for future programs facing donor transition in uncertain environments.

Countrywide Mortality Surveillance for Action in Mozambique: Results from a National Sample-Based Vital Statistics System for Mortality and Cause of Death

Sub-Saharan Africa lacks timely, reliable, and accurate national data on mortality and causes of death (CODs). In 2018 Mozambique launched a sample registration system (Countrywide Mortality Surveillance for Action [COMSA]-Mozambique), which collects continuous birth, death, and COD data from 700 randomly selected clusters, a nationally representative population of 828,663 persons. Verbal and social autopsy interviews are conducted for COD determination. We analyzed data collected in 2019-2020 to report mortality rates and cause-specific fractions. Cause-specific results were generated using computer-coded verbal autopsy (CCVA) algorithms for deaths among those age 5 years and older. For under-five deaths, the accuracy of CCVA results was increased through calibration with data from minimally invasive tissue sampling. Neonatal and under-five mortality rates were, respectively, 23 (95% CI: 18-28) and 80 (95% CI: 69-91) deaths per 1,000 live births. Mortality rates per 1,000 were 18 (95% CI: 14-21) among age 5-14 years, 26 (95% CI: 20-31) among age 15-24 years, 258 (95% CI: 230-287) among age 25-59 years, and 531 (95% CI: 490-572) among age 60+ years. Urban areas had lower mortality rates than rural areas among children under 15 but not among adults. Deaths due to infections were substantial across all ages. Other predominant causes by age group were prematurity and intrapartum-related events among neonates; diarrhea, malaria, and lower respiratory infections among children 1-59 months; injury, malaria, and diarrhea among children 5-14 years; HIV, injury, and cancer among those age 15-59 years; and cancer and cardiovascular disease at age 60+ years. The COMSA-Mozambique platform offers a rich and unique system for mortality and COD determination and monitoring and an opportunity to build a comprehensive surveillance system.

Multi-Cause Calibration of Verbal Autopsy-Based Cause-Specific Mortality Estimates of Children and Neonates in Mozambique

The Countrywide Mortality Surveillance for Action platform is collecting verbal autopsy (VA) records from a nationally representative sample in Mozambique. These records are used to estimate the national and subnational cause-specific mortality fractions (CSMFs) for children (1-59 months) and neonates (1-28 days). Cross-tabulation of VA-based cause-of-death (COD) determination against that from the minimally invasive tissue sampling (MITS) from the Child Health and Mortality Prevention project revealed important misclassification errors for all the VA algorithms, which if not accounted for will lead to bias in the estimates of CSMF from VA. A recently proposed Bayesian VA-calibration method is used that accounts for this misclassification bias and produces calibrated estimates of CSMF. Both the VA-COD and the MITS-COD can be multi-cause (i.e., suggest more than one probable COD for some of the records). To fully use this probabilistic COD data, we use the multi-cause VA calibration. Two different computer-coded VA algorithms are considered-InSilicoVA and EAVA-and the final CSMF estimates are obtained using an ensemble calibration that uses data from both the algorithms. The calibrated estimates consistently offer a better fit to the data and reveal important changes in the CSMF for both children and neonates in Mozambique after accounting for VA misclassification bias.

Correcting for Verbal Autopsy Misclassification Bias in Cause-Specific Mortality Estimates

Verbal autopsies (VAs) are extensively used to determine cause of death (COD) in many low- and middle-income countries. However, COD determination from VA can be inaccurate. Computer coded verbal autopsy (CCVA) algorithms used for this task are imperfect and misclassify COD for a large proportion of deaths. If not accounted for, this misclassification leads to biased estimates of cause-specific mortality fractions (CSMFs), a critical piece in health-policy making. Recent work has demonstrated that the knowledge of the CCVA misclassification rates can be used to calibrate raw VA-based CSMF estimates to account for the misclassification bias. In this manuscript, we review the current practices and issues with raw COD predictions from CCVA algorithms and provide a complete primer on how to use the VA calibration approach with the calibratedVA software to correct for verbal autopsy misclassification bias in cause-specific mortality estimates. We use calibratedVA to obtain CSMFs for child (1-59 months) and neonatal deaths using VA data from the Countrywide Mortality Surveillance for Action project in Mozambique.

Countrywide Mortality Surveillance for Action in Mozambique: Results from a National Sample-Based Vital Statistics System for Mortality and Cause of Death

Sub-Saharan Africa lacks timely, reliable, and accurate national data on mortality and causes of death (CODs). In 2018 Mozambique launched a sample registration system (Countrywide Mortality Surveillance for Action [COMSA]-Mozambique), which collects continuous birth, death, and COD data from 700 randomly selected clusters, a nationally representative population of 828,663 persons. Verbal and social autopsy interviews are conducted for COD determination. We analyzed data collected in 2019­2020 to report mortality rates and cause-specific fractions. Causespecific results were generated using computer-coded verbal autopsy (CCVA) algorithms for deaths among those age 5 years and older. For under-five deaths, the accuracy of CCVA results was increased through calibration with data from minimally invasive tissue sampling. Neonatal and under-five mortality rates were, respectively, 23 (95% CI: 18­28) and 80 (95% CI: 69­91) deaths per 1,000 live births. Mortality rates per 1,000 were 18 (95% CI: 14­21) among age 5­14 years, 26 (95% CI: 20­31) among age 15­24 years, 258 (95% CI: 230­287) among age 25­59 years, and 531 (95% CI: 490­572) among age 601 years. Urban areas had lower mortality rates than rural areas among children under 15 but not among adults. Deaths due to infections were substantial across all ages. Other predominant causes by age group were prematurity and intrapartum-related events among neonates; diarrhea, malaria, and lower respiratory infections among children 1­59 months; injury, malaria, and diarrhea among children 5­14 years; HIV, injury, and cancer among those age 15­59 years; and cancer and cardiovascular disease at age 601 years. The COMSA-Mozambique platform offers a rich and unique system for mortality and COD determination and monitoring and an opportunity to build a comprehensive surveillance system.

Behavioral Determinants of Routine Health Information System Data Use in Senegal: A Qualitative Inquiry Based on the Integrated Behavioral Model.

Routine health information system (RHIS) data are essential in driving decision making and planning in health systems as well as health programs. However, despite their importance, these data are underutilized, and the underlying individual-level facilitators and barriers to use remain understudied. In this research, we applied the Integrated Behavior Model (IBM) to examine how attitudes toward RHIS data, perceived norms concerning RHIS data use, and the ability to use RHIS data influence the demand and use of RHIS data among stakeholders in Senegal. Using data from interviews with respondents working at national levels of malaria, HIV, and TB control programs in Senegal, we used a framework analysis approach to apply the IBM behavioral constructs and identify their linkages to RHIS data use. We found that attitudes about the quality, availability, and relevance of RHIS data for decision making were important in driving data use among respondents. Institutional expectations, organizational protocols, policies, and practices around RHIS data ultimately shape social norms around the use of the data. Although we found that perceived ability and self-efficacy to use RHIS data were not barriers to RHIS data use among stakeholders at the strategic levels of their respective organizations, these were reported to be barriers at lower levels of the health system. Low perceived control of the RHIS data production process ultimately reduced RHIS data use for decision making among the strategic-level respondents. We recommend context-specific reexamination of existing RHIS interventions with a renewed emphasis on behavioral aspects of data use. The IBM can help guide practitioners, policy makers, and academics to address multiple socioecological factors that influence data use behavior when recommending RHIS and data use solutions.

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.

Key informant perspectives on the challenges and opportunities for using routine health data for decision-making in Senegal.

BACKGROUND: Increasing the performance of routine health information systems (RHIS) is an important policy priority both globally and in Senegal. As RHIS data become increasingly important in driving decision-making in Senegal, it is imperative to understand the factors that determine their use. METHODS: Semi-structured interviews were conducted with 18 high- and mid-level key informants active in the malaria, tuberculosis and HIV programmatic areas in Senegal. Key informants were employed in the relevant divisions of the Senegal Ministry of Health or nongovernmental / civil society organizations. We asked respondents questions related to the flow, quality and use of RHIS data in their organizations. A framework approach was used to analyze the qualitative data. RESULTS: Although the respondents worked at the strategic levels of their respective organizations, they consistently indicated that data quality and data use issues began at the operational level of the health system before the data made its way to the central level. We classify the main identified barriers and facilitators to the use of routine data into six categories and attempt to describe their interrelated nature. We find that data quality is a central and direct determinant of RHIS data use. We report that a number of upstream factors in the Senegal context interact to influence the quality of routine data produced. We identify the sociopolitical, financial and system design determinants of RHIS data collection, dissemination and use. We also discuss the organizational and infrastructural factors that influence the use of RHIS data. CONCLUSIONS: We recommend specific prescriptive actions with potential to improve RHIS performance in Senegal, the quality of the data produced and their use. These actions include addressing sociopolitical factors that often interrupt RHIS functioning in Senegal, supporting and motivating staff that maintain RHIS data systems as well as ensuring RHIS data completeness and representativeness. We argue for improved coordination between the various stakeholders in order to streamline RHIS data processes and improve transparency. Finally, we recommend the promotion of a sustained culture of data quality assessment and use.

Errors in reported ages and dates in surveys of adult mortality: A record linkage study in Niakhar (Senegal).

Sibling survival histories are a major source of adult mortality estimates in countries with incomplete death registration. We evaluate age and date reporting errors in sibling histories collected during a validation study in the Niakhar Health and Demographic Surveillance System (Senegal). Participants were randomly assigned to either the Demographic and Health Survey questionnaire or a questionnaire incorporating an event history calendar, recall cues, and increased probing strategies. We linked 60-62 per cent of survey reports of siblings to the reference database using manual and probabilistic approaches. Both questionnaires showed high sensitivity (>96 per cent) and specificity (>97 per cent) in recording siblings' vital status. Respondents underestimated the age of living siblings, and age at and time since death of deceased siblings. These reporting errors introduced downward biases in mortality estimates. The revised questionnaire improved reporting of age of living siblings but not of age at or timing of deaths.