What the percentage of births in facilities does not measure: readiness for emergency obstetric care and referral in Senegal

Cavallaro, Francesca; Beňová, Lenka; Dioukhane, El Hadji; Wong, Kerry L. M.; Sheppard, Paula; Faye, Adama; Radovich, Emma; Dumont, Alexandre; Mbengue, Ababacar; Ronsmans, Carine; Martínez-Álvarez, Melisa

doi:10.1136/bmjgh-2019-001915

Cited by 8 publications

(11 citation statements)

References 47 publications

(63 reference statements)

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“…Likewise, women are frequently transferred out of the private sector to government facilities when complications arise, or when fetal death is diagnosed. Reducing stillbirths in government facilities will not only require improvements in the quality of care delivered at the facility, but also strengthening the referral pathways from lower level or private facilities to ensure that women and their babies receive timely emergency interventions if required [ 41 , 42 ].…”

Section: Discussionmentioning

confidence: 99%

Stillbirth maternity care measurement and associated factors in population-based surveys: EN-INDEPTH study

Stefano

Bottecchia

Yargawa

et al. 2021

Popul Health Metrics

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Background Household surveys remain important sources of maternal and child health data, but until now, standard surveys such as Demographic and Health Surveys (DHS) have not collected information on maternity care for women who have experienced a stillbirth. Thus, nationally representative data are lacking to inform programmes to address the millions of stillbirths which occur annually. Methods The EN-INDEPTH population-based survey of women of reproductive age was undertaken in five Health and Demographic Surveillance System sites in Bangladesh, Ethiopia, Ghana, Guinea-Bissau and Uganda (2017–2018). All women answered a full birth history with additional questions on pregnancy losses (FBH+) or full pregnancy history (FPH). A sub-sample, including all women reporting a recent stillbirth or neonatal death, was asked additional maternity care questions. These were evaluated using descriptive measures. Associations between stillbirth and maternal socio-demographic characteristics, babies’ characteristics and maternity care use were assessed using a weighted logistic regression model for women in the FBH+ group. Results A total of 15,591 women reporting a birth since 1 January 2012 answered maternity care questions. Completeness was very high (> 99%), with similar proportions of responses for both live and stillbirths. Amongst the 14,991 births in the FBH+ group, poorer wealth status, higher parity, large perceived baby size-at-birth, preterm or post-term birth, birth in a government hospital compared to other locations and vaginal birth were associated with increased risk of stillbirth after adjusting for potential confounding factors. Regarding association with reported postnatal care, women with a stillbirth were more likely to report hospital stays of > 1 day. However, women with a stillbirth were less likely to report having received a postnatal check compared to those with a live birth. Conclusions Women who had experienced stillbirth were able to respond to questions about pregnancy and birth, and we found no reason to omit questions to these women in household surveys. Our analysis identified several potentially modifiable factors associated with stillbirth, adding to the evidence-base for policy and action in low- and middle-income contexts. Including these questions in DHS-8 would lead to increased availability of population-level data to inform action to end preventable stillbirths.

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Section: Discussionmentioning

confidence: 99%

Stillbirth maternity care measurement and associated factors in population-based surveys: EN-INDEPTH study

Stefano

Bottecchia

Yargawa

et al. 2021

Popul Health Metrics

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“…Looking at the geographic accessibility during inter health facility referral, the median distance between health facilities that can(not) perform caesareans section was 32km (ie, 41-minute driving time) in Senegal [ 63 ]. On the contrary, in two regions of Ethiopia (Tigray and Amhara), the average time between the two facilities was as high as two hours [ 64 ].…”

Section: Resultsmentioning

confidence: 99%

How applicable is geospatial analysis in maternal and neonatal health in sub-Saharan Africa? A systematic review

et al. 2022

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Background Sub-Saharan Africa (SSA) has the world's highest maternal and neonatal morbidity and mortality and has shown the slowest progress in reducing them. In addition, there is substantial inequality in terms of maternal and neonatal morbidity and mortality in the region. Geospatial studies can help prioritize scarce resources by pinpointing priority areas for implementation. This systematic review was conducted to explore the application of geospatial analysis to maternal and neonatal morbidity and mortality in SSA. Methods A systematic search of PubMed, SCOPUS, EMBASE, and Web of Science databases was performed. All observational and qualitative studies that reported on maternal or neonatal health outcomes were included if they used a spatial analysis technique and were conducted in a SSA country. After removing duplicates, two reviewers independently reviewed each study's abstract and full text for inclusion. Furthermore, the quality of the studies was assessed using the Joanna Briggs Institute (JBI) critical appraisal checklists. Finally, due to the heterogeneity of studies, narrative synthesis was used to summarize the main findings, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline was strictly followed to report the review results. A total of 56 studies were included in the review. Results We found that geospatial analysis was used to identify inequalities in maternal and neonatal morbidity, mortality, and health care utilization and to identify gaps in the availability and geographic accessibility of maternal health facilities. In addition, we identified a few studies that used geospatial analysis for modelling intervention areas. We also detected challenges and shortcomings, such as unrealistic assumptions used by geospatial models and a shortage of reliable, up-to-date, small-scale georeferenced data. Conclusions The use of geospatial analysis for maternal and neonatal health in SSA is still limited, and more detailed spatial data are required to exploit the potential of geospatial technologies fully.

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“…In Senegal, we also excluded health huts, as the assessment instrument differ for these single-room facilities and did not record elements of process quality of care. However, health huts are often in the front-line in the management of sick children in remote rural areas and studying their referral practices to larger health facilities would be an important undertaking (Cavallaro et al ., 2020). Second, we estimated process quality metrics from survey instruments using two different assessment methods (direct observation of consultations vs clinical vignettes), although previous studies have shown that there is a difference between what health providers know and what they do (Peabody et al ., 2000; Leonard and Masatu, 2005).…”

Section: Discussionmentioning

confidence: 99%

A methodological framework to assess temporal trends and sub-national disparities in healthcare quality metrics using facility surveys, with applications to sick-child care in Kenya, Senegal, and Tanzania

Fullman

Leslie

Eliakimu

et al. 2022

Preprint

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Monitoring healthcare quality at a subnational resolution is key to identify and resolve geographic inequities and ensure that no sub-population is left behind. Yet, health facility surveys are typically not powered to report reliable estimates at a subnational scale.In this study, we present a framework to fill this gap and jointly analyse publicly available facility survey data, allowing exploration of temporal trends and subnational disparities in healthcare quality metrics. Specifically, our Bayesian hierarchical model includes random effects to account for differences between survey instruments; space-time processes to leverage correlations in space and time; and covariates to incorporate auxiliary information. We apply this framework to Kenya, Senegal, and Tanzania - three countries with at least four rounds of standardized facility surveys each – and estimate the readiness and process quality of sick-child care over time and across subnational areas.These estimates of readiness and process quality of care over time and at a fine spatial resolution show uneven progress in improving facility-based service provision in Kenya, Senegal, and Tanzania. For instance, while national gains in overall readiness of care improved in Tanzania, geographic inequities persisted; in contrast, Senegal, and Kenya experienced stagnation in overall readiness at the national level, but disparities grew across subnational areas. Overall, providers adhered to about one-third of the clinical guidelines for managing sick-child illnesses at the national level. Yet across subnational units, such adherence greatly varied (e.g., 25% to 85% between counties of Kenya in 2020).Our new approach enables identifies precise estimation of changes in the spatial distribution of healthcare quality metrics over time, at a a programmatic spatial resolution, and with accompanying uncertainty estimates. Use of our framework will provide new insights at a policy-relevant spatial resolution for national and regional decision-makers, and international funders.

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What the percentage of births in facilities does not measure: readiness for emergency obstetric care and referral in Senegal

Cited by 8 publications

References 47 publications

Stillbirth maternity care measurement and associated factors in population-based surveys: EN-INDEPTH study

Stillbirth maternity care measurement and associated factors in population-based surveys: EN-INDEPTH study

How applicable is geospatial analysis in maternal and neonatal health in sub-Saharan Africa? A systematic review

A methodological framework to assess temporal trends and sub-national disparities in healthcare quality metrics using facility surveys, with applications to sick-child care in Kenya, Senegal, and Tanzania

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