Early changes in intervention coverage and mortality rates following the implementation of an integrated health system intervention in Madagascar

A realist synthesis to develop an explanatory model of how policy instruments impact child and maternal health outcomes

BACKGROUND

Child and maternal health, a key marker of overall health system performance, is a policy priority area by the World Health Organization and the United Nations, including the Sustainable Development Goals. Previous realist work has linked child and maternal health outcomes to globalization, political tradition, and the welfare state. It is important to explore the role of other key policy-related factors. This paper presents a realist synthesis, categorising policy instruments according to the established NATO model, to develop an explanatory model of how policy instruments impact child and maternal health outcomes.

METHODS

A systematic literature search was conducted to identify studies assessing the relationships between policy instruments and child and maternal health outcomes. Data were analysed using a realist framework. The first stage of the realist analysis process was to generate micro-theoretical initial programme theories for use in the theory adjudication process. Proposed theories were then adjudicated iteratively to produce a set of final programme theories.

FINDINGS

From a total of 43,415 unique records, 632 records proceeded to full-text screening and 138 papers were included in the review. Evidence from 132 studies was available to address this research question. Studies were published from 1995 to 2021; 76% assessed a single country, and 81% analysed data at the ecological level. Eighty-eight initial candidate programme theories were generated. Following theory adjudication, five final programme theories were supported. According to the NATO model, these were related to treasure, organisation, authority-treasure, and treasure-organisation instrument types.

CONCLUSIONS

This paper presents a realist synthesis to develop an explanatory model of how policy instruments impact child and maternal health outcomes from a large, systematically identified international body of evidence. Five final programme theories were supported, showing how policy instruments play an important yet context-dependent role in influencing child and maternal health outcomes.

Protocol-driven primary care and community linkage to reduce all-cause mortality in rural Zambia: a stepped-wedge cluster randomized trial

Introduction

While tremendous progress has been made in recent years to improve the health of people living in low- and middle-income countries (LMIC), significant challenges remain. Chief among these are poor health systems, which are often ill-equipped to respond to current challenges. It remains unclear whether intensive intervention at the health system level will result in improved outcomes, as there have been few rigorously designed comparative studies. We present results of a complex health system intervention that was implemented in Zambia using a cluster randomized design.

Methods

BHOMA was a complex health system intervention comprising intensive clinical training and quality improvement measures, support for commodities procurement, improved community outreach, and district level management support. The intervention was introduced as a stepped wedge cluster-randomized trial in 42 predominately rural health centers and their surrounding communities in Lusaka Province, Zambia. Baseline survey was conducted between January-May 2011, mid-line survey was conducted February-November, 2013 and Endline survey, February-November 2015.The primary outcome was all-cause mortality among those between 28 days and 60 years of age and assessed through community-based mortality surveys. Secondary outcomes included post-neonatal under-five mortality and service coverage scores. Service coverage scores were calculated across five domains (child preventative services; child treatment services; family planning; maternal health services, and adult health services). We fit Cox proportional hazards model with shared frailty at the cluster level for the primary analysis. Mortality rates were age-standardized using the WHO World Standard Population.

Results

Mortality declined substantially from 3.9 per 1,000 person-years in the pre-intervention period, to 1.5 per 1,000 person-years in the post intervention period. When we compared intervention and control periods, there were 174 deaths in 49,230 person years (age-standardized rate = 4.4 per 1,000 person-years) in the control phase and 277 deaths in 74,519 person years (age-standardized rate = 4.6 per 1,000 person-years) in the intervention phase. Overall, there was no evidence for an effect of the intervention in minimally-adjusted [hazard ratio (HR) = 1.18; 95% confidence interval (CI): 0.88, 1.56; value of p = 0.265], or adjusted (HR = 1.12; 95% CI: 0.84, 1.49; value of p = 0.443) analyses.Coverage scores that showed some evidence of changing with time since the cluster joined the intervention were: an increasing proportion of children sleeping under insecticide treated bed-net (value of p < 0.001); an increasing proportion of febrile children who received appropriate anti-malarial drugs (value of p = 0.039); and an increasing proportion of ever hypertensive adults with currently controlled hypertension (value of p = 0.047). No adjustments were made for multiple-testing and the overall coverage score showed no statistical evidence for a change over time (value of p = 0.308).

Conclusion

We noted an overall reduction in post-neonatal under 60 mortality in the study communities during the period of our study, but this could not be attributed to the BHOMA intervention. Some improvements in service coverage scores were observed.

Clinical Trial Registration

clinicaltrials.gov, Identifier NCT01942278.

Geographic barriers to care persist at the community healthcare level: Evidence from rural Madagascar

Geographic distance is a critical barrier to healthcare access, particularly for rural communities with poor transportation infrastructure who rely on non-motorized transportation. There is broad consensus on the importance of community health workers (CHWs) to reduce the effects of geographic isolation on healthcare access. Due to a lack of fine-scale spatial data and individual patient records, little is known about the precise effects of CHWs on removing geographic barriers at this level of the healthcare system. Relying on a high-quality, crowd-sourced dataset that includes all paths and buildings in the area, we explored the impact of geographic distance from CHWs on the use of CHW services for children under 5 years in the rural district of Ifanadiana, southeastern Madagascar from 2018-2021. We then used this analysis to determine key features of an optimal geographic design of the CHW system, specifically optimizing a single CHW location or installing additional CHW sites. We found that consultation rates by CHWs decreased with increasing distance patients travel to the CHW by approximately 28.1% per km. The optimization exercise revealed that the majority of CHW sites (50/80) were already in an optimal location or shared an optimal location with a primary health clinic. Relocating the remaining CHW sites based on a geographic optimum was predicted to increase consultation rates by only 7.4%. On the other hand, adding a second CHW site was predicted to increase consultation rates by 31.5%, with a larger effect in more geographically dispersed catchments. Geographic distance remains a barrier at the level of the CHW, but optimizing CHW site location based on geography alone will not result in large gains in consultation rates. Rather, alternative strategies, such as the creation of additional CHW sites or the implementation of proactive care, should be considered.

Case fatality risk of diarrhoeal pathogens: a systematic review and meta-analysis

BACKGROUND

Estimates of the relative contribution of different pathogens to all-cause diarrhoea mortality are needed to inform global diarrhoea burden models and prioritize interventions. We aimed to investigate and estimate heterogeneity in the case fatality risk (CFR) of different diarrhoeal pathogens.

METHODS

We conducted a systematic review and meta-analysis of studies that reported cases and deaths for 15 enteric pathogens published between 1990 and 2019. The primary outcome was the pathogen-specific CFR stratified by age group, country-specific under-5 mortality rate, setting, study year and rotavirus vaccine introduction status. We developed fixed-effects and multilevel mixed-effects logistic regression models to estimate the pooled CFR overall and for each pathogen, controlling for potential predictors of heterogeneity.

RESULTS

A total of 416 studies met review criteria and were included in the analysis. The overall crude CFR for all pathogens was 0.65%, but there was considerable heterogeneity between and within studies. The overall CFR estimated from a random-effects model was 0.04% (95% CI: 0.026%-0.062%), whereas the pathogen-specific CFR estimates ranged from 0% to 2.7%. When pathogens were included as predictors of the CFR in the overall model, the highest and lowest odds ratios were found for enteropathogenic Escherichia coli (EPEC) [odds ratio (OR) = 3.0, 95% CI: 1.28-7.07] and rotavirus (OR = 0.23, 95% CI: 0.13-0.39), respectively.

CONCLUSION

We provide comprehensive estimates of the CFR across different diarrhoeal pathogens and highlight pathogens for which more studies are needed. The results motivate the need for diarrhoeal interventions and could help prioritize pathogens for vaccine development.

What are the trends in seeking health care for fever in children under-five in Sierra Leone? evidence from four population-based studies before and after the free health care initiative

Background

In 2010, the government of Sierra Leone implemented the Free Health Care Initiative (FHCI) in the country with the objective of reducing the high maternal, infant, and child mortality rates and improving general health indicators. The objective of this study was to assess the trends in the prevalence of health care-seeking and to identify the determinants of healthcare service utilization by caregivers of children younger than five years.

Methods

The analysis of health-care-seeking behavior was done using data from four population-based surveys in Sierra Leone before (2008) and after (2013, 2016, 2019) the FHCI was implemented. Care-seeking behavior was assessed with regard to caregivers seeking care for children under-five in the two weeks prior to each survey. We compared the percentages of healthcare-seeking behavior change and identify factors associated with healthcare-seeking using a modified Poisson regression model with generalized estimating equations.

Results

In 2008, a total of 1208 children with fever were recorded, compared with 2823 children in 2013, 1633 in 2016, and 1464 in 2019. Care-seeking for children with fever was lowest in 2008 (51%; 95% CI (46.4−55.5)) than in 2013 (71.5%; 95% CI (68.4−74.5)), 2016 (70.3%; 95% CI (66.6−73.8)), and 2019 (74.6%; 95% CI (71.6−77.3)) (p < 0.001). Care-seeking in 2013, 2016 and 2019 was at least 1.4 time higher than in 2008 (p < 0.001) after adjusting for mother’s age, wealth, religion, education level, household head and the child’s age. Care-seeking was lowest for children older than 12 months, mothers older than 35 years, children living in the poorest households, and in the northern region. A trend was observed for the sex of the household head. The level of care-seeking was lowest when the household head was a man.

Conclusions

The increase in healthcare-seeking for children under-five with fever followed the introduction of the FHCI in Sierra Leone. Care-seeking for fever varied by the child’s age, caregiver’s age, household wealth, the sex of the household head and region. Maintaining the FHCI with adequate strategies to address other barriers beyond financial ones is essential to reduce disparities between age groups, regions and, households.

Impact of health system strengthening on delivery strategies to improve child immunisation coverage and inequalities in rural Madagascar

Background

To reach global immunisation goals, national programmes need to balance routine immunisation at health facilities with vaccination campaigns and other outreach activities (eg, vaccination weeks), which boost coverage at particular times and help reduce geographical inequalities. However, where routine immunisation is weak, an over-reliance on vaccination campaigns may lead to heterogeneous coverage. Here, we assessed the impact of a health system strengthening (HSS) intervention on the relative contribution of routine immunisation and outreach activities to reach immunisation goals in rural Madagascar.

Methods

We obtained data from health centres in Ifanadiana district on the monthly number of recommended vaccines (BCG, measles, diphtheria, tetanus and pertussis (DTP) and polio) delivered to children, during 2014–2018. We also analysed data from a district-representative cohort carried out every 2 years in over 1500 households in 2014–2018. We compared changes inside and outside the HSS catchment in the delivery of recommended vaccines, population-level vaccination coverage, geographical and economic inequalities in coverage, and timeliness of vaccination. The impact of HSS was quantified via mixed-effects logistic regressions.

Results

The HSS intervention was associated with a significant increase in immunisation rates (OR between 1.22 for measles and 1.49 for DTP), which diminished over time. Outreach activities were associated with a doubling in immunisation rates, but their effect was smaller in the HSS catchment. Analysis of cohort data revealed that HSS was associated with higher vaccination coverage (OR between 1.18 per year of HSS for measles and 1.43 for BCG), a reduction in economic inequality, and a higher proportion of timely vaccinations. Yet, the lower contribution of outreach activities in the HSS catchment was associated with persistent inequalities in geographical coverage, which prevented achieving international coverage targets.

Conclusion

Investment in stronger primary care systems can improve vaccination coverage, reduce inequalities and improve the timeliness of vaccination via increases in routine immunisations.

An Integrated Primary Care Initiative for Child Health in Northern Togo

OBJECTIVES

To determine if the Integrated Community-Based Health Systems-Strengthening (ICBHSS) initiative was effective in expanding health coverage, improving care quality, and reducing child mortality in Togo.

METHODS

Population-representative cross-sectional household surveys adapted from the Demographic Household Survey and Multiple Indicator Cluster Surveys were conducted at baseline (2015) and then annually (2016-2020) in 4 ICBHSS catchment sites in Kara, Togo. The primary outcome was under-5 mortality, with health service coverage and health-seeking behavior as secondary outcomes. Costing analyses were calculated by using "top-down" methodology with audited financial statements and programmatic data.

RESULTS

There were 10 022 household surveys completed from 2015 to 2020. At baseline (2015), under-5 mortality was 51.1 per 1000 live births (95% confidence interval [CI]: 35.5-66.8), and at the study end period (2020), under-5 mortality was 35.8 (95% CI: 23.4-48.2). From 2015 to 2020, home-based treatment by a community health worker increased from 24.1% (95% CI: 21.9%-26.4%) to 45.7% (95% CI: 43.3%-48.2%), and respondents reporting prenatal care in the first trimester likewise increased (37.5% to 50.1%). Among respondents who sought care for a child with fever, presenting for care within 1 day increased from 51.9% (95% CI: 47.1%-56.6%) in 2015 to 80.3% (95% CI: 74.6%-85.0%) in 2020. The estimated annual additional intervention cost was $8.84 per person.

CONCLUSIONS

Our findings suggest that the ICBHSS initiative, a bundle of evidence-based interventions implemented with a community-based strategy, improves care access and quality and was associated with reduction in child mortality.

District-level health system strengthening for universal health coverage: evidence from a longitudinal cohort study in rural Madagascar, 2014-2018

Introduction

Despite renewed commitment to universal health coverage and health system strengthening (HSS) to improve access to primary care, there is insufficient evidence to guide their design and implementation. To address this, we conducted an impact evaluation of an ongoing HSS initiative in rural Madagascar, combining data from a longitudinal cohort and primary health centres.

Methods

We carried out a district representative household survey at the start of the HSS intervention in 2014 in over 1500 households in Ifanadiana district, and conducted follow-up surveys at 2 and 4 years. At each time point, we estimated maternal, newborn and child health coverage; economic and geographical inequalities in coverage; and child mortality rates; both in the HSS intervention and control catchments. We used logistic regression models to evaluate changes associated with exposure to the HSS intervention. We also estimated changes in health centre per capita utilisation during 2013 to 2018.

Results

Child mortality rates decreased faster in the HSS than in the control catchment. We observed significant improvements in care seeking for children under 5 years of age (OR 1.23; 95% CI 1.05 to 1.44) and individuals of all ages (OR 1.37, 95% CI 1.19 to 1.58), but no significant differences in maternal care coverage. Economic inequalities in most coverage indicators were reduced, while geographical inequalities worsened in nearly half of the indicators.

Conclusion

The results demonstrate improvements in care seeking and economic inequalities linked to the early stages of a HSS intervention in rural Madagascar. Additional improvements in this context of persistent geographical inequalities will require a stronger focus on community health.

Variation in predicted COVID-19 risk among lemurs and lorises

The novel coronavirus SARS-CoV-2, which in humans leads to the disease COVID-19, has caused global disruption and more than 2 million fatalities since it first emerged in late 2019. As we write, infection rates are at their highest point globally and are rising extremely rapidly in some areas due to more infectious variants. The primary target of SARS-CoV-2 is the cellular receptor angiotensin-converting enzyme-2 (ACE2). Recent sequence analyses of the ACE2 gene predict that many nonhuman primates are also likely to be highly susceptible to infection. However, the anticipated risk is not equal across the Order. Furthermore, some taxonomic groups show high ACE2 amino acid conservation, while others exhibit high variability at this locus. As an example of the latter, analyses of strepsirrhine primate ACE2 sequences to date indicate large variation among lemurs and lorises compared to other primate clades despite low sampling effort. Here, we report ACE2 gene and protein sequences for 71 individual strepsirrhines, spanning 51 species and 19 genera. Our study reinforces previous results while finding additional variability in other strepsirrhine species, and suggests several clades of lemurs have high potential susceptibility to SARS-CoV-2 infection. Troublingly, some species, including the rare and endangered aye-aye (Daubentonia madagascariensis), as well as those in the genera Avahi and Propithecus, may be at high risk. Given that lemurs are endemic to Madagascar and among the primates at highest risk of extinction globally, further understanding of the potential threat of COVID-19 to their health should be a conservation priority. All feasible actions should be taken to limit their exposure to SARS-CoV-2.

Socio-demographic, not environmental, risk factors explain fine-scale spatial patterns of diarrhoeal disease in Ifanadiana, rural Madagascar

Precision health mapping is a technique that uses spatial relationships between socio-ecological variables and disease to map the spatial distribution of disease, particularly for diseases with strong environmental signatures, such as diarrhoeal disease (DD). While some studies use GPS-tagged location data, other precision health mapping efforts rely heavily on data collected at coarse-spatial scales and may not produce operationally relevant predictions at fine enough spatio-temporal scales to inform local health programmes. We use two fine-scale health datasets collected in a rural district of Madagascar to identify socio-ecological covariates associated with childhood DD. We constructed generalized linear mixed models including socio-demographic, climatic and landcover variables and estimated variable importance via multi-model inference. We find that socio-demographic variables, and not environmental variables, are strong predictors of the spatial distribution of disease risk at both individual and commune-level (cluster of villages) spatial scales. Climatic variables predicted strong seasonality in DD, with the highest incidence in colder, drier months, but did not explain spatial patterns. Interestingly, the occurrence of a national holiday was highly predictive of increased DD incidence, highlighting the need for including cultural factors in modelling efforts. Our findings suggest that precision health mapping efforts that do not include socio-demographic covariates may have reduced explanatory power at the local scale. More research is needed to better define the set of conditions under which the application of precision health mapping can be operationally useful to local public health professionals.

Research priorities in maternal and neonatal health in Africa: results using the Child Health and Nutrition Research Initiative method involving over 900 experts across the continent

Background: Africa will miss the maternal and neonatal health (MNH) Sustainable Development Goals (SDGs) targets if the current trajectory is followed. The African Academy of Sciences has formed an expert maternal and newborn health group to discuss actions to improve MNH SDG targets. The team, among other recommendations, chose to implement an MNH research prioritization exercise for Africa covering four grand challenge areas. Methods: The team used the Child Health and Nutrition Research Initiative (CHNRI) research prioritization method to identify research priorities in maternal and newborn health in Africa. From 609 research options, a ranking of the top 46 research questions was achieved. Research priority scores and agreement statistics were calculated, with sub-analysis possible for the regions of East Africa, West Africa and those living out of the continent.  Results: The top research priorities generally fell into (i) improving identification of high-risk mothers and newborns, or diagnosis of high-risk conditions in mothers and newborns to improve health outcomes; (ii) improving access to treatment through improving incentives to attract and retain skilled health workers in remote, rural areas, improving emergency transport, and assessing health systems' readiness; and (iii) improving uptake of proven existing interventions such as Kangaroo Mother Care. Conclusions: The research priorities emphasized building interventions that improved access to quality healthcare in the lowest possible units of the provision of MNH interventions. The lists prioritized participation of communities in delivering MNH interventions. The current burden of disease from MNCH in Africa aligns well with the list of priorities listed from this exercise but provides extra insights into current needs by African practitioners. The MNCH Africa expert group believes that the recommendations from this work should be implemented by multisectoral teams as soon as possible to provide adequate lead time for results of the succeeding programmes to be seen before 2030.

Estimating the local spatio-temporal distribution of malaria from routine health information systems in areas of low health care access and reporting

BACKGROUND

Reliable surveillance systems are essential for identifying disease outbreaks and allocating resources to ensure universal access to diagnostics and treatment for endemic diseases. Yet, most countries with high disease burdens rely entirely on facility-based passive surveillance systems, which miss the vast majority of cases in rural settings with low access to health care. This is especially true for malaria, for which the World Health Organization estimates that routine surveillance detects only 14% of global cases. The goal of this study was to develop a novel method to obtain accurate estimates of disease spatio-temporal incidence at very local scales from routine passive surveillance, less biased by populations' financial and geographic access to care.

METHODS

We use a geographically explicit dataset with residences of the 73,022 malaria cases confirmed at health centers in the Ifanadiana District in Madagascar from 2014 to 2017. Malaria incidence was adjusted to account for underreporting due to stock-outs of rapid diagnostic tests and variable access to healthcare. A benchmark multiplier was combined with a health care utilization index obtained from statistical models of non-malaria patients. Variations to the multiplier and several strategies for pooling neighboring communities together were explored to allow for fine-tuning of the final estimates. Separate analyses were carried out for individuals of all ages and for children under five. Cross-validation criteria were developed based on overall incidence, trends in financial and geographical access to health care, and consistency with geographic distribution in a district-representative cohort. The most plausible sets of estimates were then identified based on these criteria.

RESULTS

Passive surveillance was estimated to have missed about 4 in every 5 malaria cases among all individuals and 2 out of every 3 cases among children under five. Adjusted malaria estimates were less biased by differences in populations' financial and geographic access to care. Average adjusted monthly malaria incidence was nearly four times higher during the high transmission season than during the low transmission season. By gathering patient-level data and removing systematic biases in the dataset, the spatial resolution of passive malaria surveillance was improved over ten-fold. Geographic distribution in the adjusted dataset revealed high transmission clusters in low elevation areas in the northeast and southeast of the district that were stable across seasons and transmission years.

CONCLUSIONS

Understanding local disease dynamics from routine passive surveillance data can be a key step towards achieving universal access to diagnostics and treatment. Methods presented here could be scaled-up thanks to the increasing availability of e-health disease surveillance platforms for malaria and other diseases across the developing world.

Variation in predicted COVID-19 risk among lemurs and lorises

The novel coronavirus SARS-CoV-2, which in humans leads to the disease COVID-19, has caused global disruption and more than 1.5 million fatalities since it first emerged in late 2019. As we write, infection rates are currently at their highest point globally and are rising extremely rapidly in some areas due to more infectious variants. The primary viral target is the cellular receptor angiotensin-converting enzyme-2 (ACE2). Recent sequence analyses of the ACE2 gene predicts that many nonhuman primates are also likely to be highly susceptible to infection. However, the anticipated risk is not equal across the Order. Furthermore, some taxonomic groups show high ACE2 amino acid conservation, while others exhibit high variability at this locus. As an example of the latter, analyses of strepsirrhine primate ACE2 sequences to date indicate large variation among lemurs and lorises compared to other primate clades despite low sampling effort. Here, we report ACE2 gene and protein sequences for 71 individual strepsirrhines, spanning 51 species and 19 genera. Our study reinforces previous results and finds additional variability in other strepsirrhine species, and suggests several clades of lemurs have high potential susceptibility to SARS-CoV-2 infection. Troublingly, some species, including the rare and Endangered aye-aye (Daubentonia madagascariensis), as well as those in the genera Avahi and Propithecus, may be at high risk. Given that lemurs are endemic to Madagascar and among the primates at highest risk of extinction globally, further understanding of the potential threat of COVID-19 to their health should be a conservation priority. All feasible actions should be taken to limit their exposure to SARS-CoV-2.

Networks of Care in Rural Madagascar for Achieving Universal Health Coverage in Ifanadiana District

Health care is most effective when a patient's basic primary care needs are met as close to home as possible, with advanced care accessible when needed. In Ifanadiana District, Madagascar, a collaboration between the Ministry of Public Health (MoPH) and PIVOT, a non-governmental organization (NGO), fosters Networks of Care (NOC) to support high-quality, patient-centered care. The district's health system has three levels of care: community, health center, district hospital; a regional hospital is available for tertiary care services. We explore the MoPH/PIVOT collaboration through a case study which focuses on noteworthy elements of the collaboration across the four NOC domains: (I) agreement and enabling environment, (II) operational standards, (III) quality, efficiency, and responsibility, (IV) learning and adaptation. Under Domain I, we describe formal agreements between the MoPH and PIVOT and the process for engaging communities in creating effective NOC. Domain II discusses patient referral across levels of the health system and improvements to facility readiness and service availability. Under Domain III the collaboration prioritizes communication and supervision to support clinical quality, and social support for patients. Domain IV focuses on evaluation, research, and the use of data to modify programs to better meet community needs. The case study, organized by the domains of the NOC framework, demonstrates that a collaboration between the MoPH and an NGO can create effective NOC in a remote district with limited accessibility and advance the country's agenda to achieve universal health coverage.

Cost and cost drivers associated with setting-up a prime vendor system to complement the national medicines supply chain in Tanzania

Introduction

Economic analysis of supply chain management interventions to improve the availability of healthcare commodities at healthcare facilities is important in generating evidence for decision-makers. The current study assesses the cost and cost drivers for setting-up a public-private partnership programme in Tanzania in which all public healthcare facility orders for complementary medicines are pooled at the district level, and then purchased from one contracted supplier, the prime vendor (referred to as ‘Jazia Prime Vendor System’ (Jazia PVS)).

Methods

Financial and economic costs of Jazia PVS were collected retrospectively and using the ingredients approach. The financial costs were spread over the implementation period of January 2014–July 2019. In addition, we estimated the financial rollout costs of Jazia PVS to the other 23 regions in the country over 2 years (2018–2019). A multivariate sensitivity analysis was conducted on the estimates.

Results

Jazia PVS start-up and recurrent financial costs amounted to US$2 170 989.74 and US$709 302.32, respectively. The main cost drivers were costs for short-term experts, training of staff and healthcare workers and the Jazia PVS technical and board management activities. The start-up financial cost per facility was US$2819.47 and cost per capita was US$0.37.

Conclusion

In conclusion, the study provides useful information on the cost and cost drivers for setting-up a complementary pharmaceutical supply system to complement an existing system in low-income settings. Despite the substantial costs incurred in the initial investment and operations of the Jazia PVS, the new framework is effective in achieving the desired purpose of improving availability of healthcare commodities.

Improving geographical accessibility modeling for operational use by local health actors

BACKGROUND

Geographical accessibility to health facilities remains one of the main barriers to access care in rural areas of the developing world. Although methods and tools exist to model geographic accessibility, the lack of basic geographic information prevents their widespread use at the local level for targeted program implementation. The aim of this study was to develop very precise, context-specific estimates of geographic accessibility to care in a rural district of Madagascar to help with the design and implementation of interventions that improve access for remote populations.

METHODS

We used a participatory approach to map all the paths, residential areas, buildings and rice fields on OpenStreetMap (OSM). We estimated shortest routes from every household in the District to the nearest primary health care center (PHC) and community health site (CHS) with the Open Source Routing Machine (OSMR) tool. Then, we used remote sensing methods to obtain a high resolution land cover map, a digital elevation model and rainfall data to model travel speed. Travel speed models were calibrated with field data obtained by GPS tracking in a sample of 168 walking routes. Model results were used to predict travel time to seek care at PHCs and CHSs for all the shortest routes estimated earlier. Finally, we integrated geographical accessibility results into an e-health platform developed with R Shiny.

RESULTS

We mapped over 100,000 buildings, 23,000 km of footpaths, and 4925 residential areas throughout Ifanadiana district; these data are freely available on OSM. We found that over three quarters of the population lived more than one hour away from a PHC, and 10-15% lived more than 1 h away from a CHS. Moreover, we identified areas in the North and East of the district where the nearest PHC was further than 5 h away, and vulnerable populations across the district with poor geographical access (> 1 h) to both PHCs and CHSs.

CONCLUSION

Our study demonstrates how to improve geographical accessibility modeling so that results can be context-specific and operationally actionable by local health actors. The importance of such approaches is paramount for achieving universal health coverage (UHC) in rural areas throughout the world.

Inequalities in cause-specific mortality in children and adolescents in the Moramanga health survey, Madagascar

OBJECTIVES

One child or young adolescent dies every 10 min in Madagascar and large disparities in survival persist. We estimated cause-specific mortality in a cohort of children aged 0-14 in the Moramanga district and explored how causes of death shape these inequalities.

METHODS

Children were followed prospectively between 2012 and 2017. Causes of death were established based on verbal autopsies. Incidence rate ratios were estimated in Poisson regression models.

RESULTS

The risk of dying before age 15 was 68.1 per thousand live births. Risks of dying were highest in the first year of life (31.2‰) and lowest in children aged 10-14 (6.4‰). The male-to-female sex ratios of mortality increased with age and reached 2.3 among adolescents aged 10-14. Communicable, nutritional and neonatal causes accounted for 79.5% of deaths below age 5 and 47.0% above age 5. Mortality was positively associated with household poverty, lack of education of the household head, and rural residence.

CONCLUSIONS

Interventions should be designed with an equity lens to reduce large disparities in survival and be tailored to the needs of each age-group.

Assessing trends in the content of maternal and child care following a health system strengthening initiative in rural Madagascar: A longitudinal cohort study

BACKGROUND

In order to reach the health-related Sustainable Development Goals (SDGs) by 2030, gains attained in access to primary healthcare must be matched by gains in the quality of services delivered. Despite the broad consensus around the need to address quality, studies on the impact of health system strengthening (HSS) have focused predominantly on measures of healthcare access. Here, we examine changes in the content of maternal and child care as a proxy for healthcare quality, to better evaluate the effectiveness of an HSS intervention in a rural district of Madagascar. The intervention aimed at improving system readiness at all levels of care (community health, primary health centers, district hospital) through facility renovations, staffing, equipment, and training, while removing logistical and financial barriers to medical care (e.g., ambulance network and user-fee exemptions).

METHODS AND FINDINGS

We carried out a district-representative open longitudinal cohort study, with surveys administered to 1,522 households in the Ifanadiana district of Madagascar at the start of the HSS intervention in 2014, and again to 1,514 households in 2016. We examined changes in healthcare seeking behavior and outputs for sick-child care among children <5 years old, as well as for antenatal care and perinatal care among women aged 15-49. We used a difference-in-differences (DiD) analysis to compare trends between the intervention group (i.e., people living inside the HSS catchment area) and the non-intervention comparison group (i.e., the rest of the district). In addition, we used health facility-based surveys, monitoring service availability and readiness, to assess changes in the operational capacities of facilities supported by the intervention. The cohort study included 657 and 411 children (mean age = 2 years) reported to be ill in the 2014 and 2016 surveys, respectively (27.8% and 23.8% in the intervention group for each survey), as well as 552 and 524 women (mean age = 28 years) reported to have a live birth within the previous two years in the 2014 and 2016 surveys, respectively (31.5% and 29.6% in the intervention group for each survey). Over the two-year study period, the proportion of people who reported seeking care at health facilities experienced a relative change of +51.2% (from 41.4% in 2014 to 62.5% in 2016) and -7.1% (from 30.0% to 27.9%) in the intervention and non-intervention groups, respectively, for sick-child care (DiD p-value = 0.01); +11.4% (from 78.3% to 87.2%), and +10.3% (from 67.3% to 74.2%) for antenatal care (p-value = 0.75); and +66.2% (from 23.1% to 38.3%) and +28.9% (from 13.9% to 17.9%) for perinatal care (p-value = 0.13). Most indicators of care content, including rates of medication prescription and diagnostic test administration, appeared to increase more in the intervention compared to in the non-intervention group for the three areas of care we assessed. The reported prescription rate for oral rehydration therapy among children with diarrhea changed by +68.5% (from 29.6% to 49.9%) and -23.2% (from 17.8% to 13.7%) in the intervention and non-intervention groups, respectively (p-value = 0.05). However, trends observed in the care content varied widely by indicator and did not always match the large apparent increases observed in care seeking behavior, particularly for antenatal care, reflecting important gaps in the provision of essential health services for individuals who sought care. The main limitation of this study is that the intervention catchment was not randomly allocated, and some demographic indicators were better for this group at baseline than for the rest of the district, which could have impacted the trends observed.

CONCLUSION

Using a district-representative longitudinal cohort to assess the content of care delivered to the population, we found a substantial increase over the two-year study period in the prescription rate for ill children and in all World Health Organization (WHO)-recommended perinatal care outputs assessed in the intervention group, with more modest changes observed in the non-intervention group. Despite improvements associated with the HSS intervention, this study highlights the need for further quality improvement in certain areas of the district's healthcare system. We show how content of care, measured through standard population-based surveys, can be used as a component of HSS impact evaluations, enabling healthcare leaders to track progress as well as identify and address specific gaps in the provision of services that extend beyond care access.