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ObjectiveTo assess existing knowledge related to methodological considerations for linking population-based surveys and health facility data to generate effective coverage estimates. Effective coverage estimates the proportion of individuals in need of an intervention who receive it with sufficient quality to achieve health benefit.DesignSystematic review of available literature.Data sourcesMedline, Carolina Population Health Center and Demographic and Health Survey publications and handsearch of related or referenced works of all articles included in full text review. The search included publications from 1 January 2000 to 29 March 2021.Eligibility criteriaPublications explicitly evaluating (1) the suitability of data, (2) the implications of the design of existing data sources and (3) the impact of choice of method for combining datasets to obtain linked coverage estimates.ResultsOf 3805 papers reviewed, 70 publications addressed relevant issues. Limited data suggest household surveys can be used to identify sources of care, but their validity in estimating intervention need was variable. Methods for collecting provider data and constructing quality indices were diverse and presented limitations. There was little empirical data supporting an association between structural, process and outcome quality. Few studies addressed the influence of the design of common data sources on linking analyses, including imprecise household geographical information system data, provider sampling design and estimate stability. The most consistent evidence suggested under certain conditions, combining data based on geographical proximity or administrative catchment (ecological linking) produced similar estimates to linking based on the specific provider utilised (exact match linking).ConclusionsLinking household and healthcare provider data can leverage existing data sources to generate more informative estimates of intervention coverage and care. However, existing evidence on methods for linking data for effective coverage estimation are variable and numerous methodological questions remain. There is need for additional research to develop evidence-based, standardised best practices for these analyses.

Objective To investigate whether sub-Saharan African countries have succeeded in reducing wealth-related inequalities in the coverage of reproductive, maternal, newborn and child health interventions. Methods We analysed survey data from 36 countries, grouped into Central, East, Southern and West Africa subregions, in which at least two surveys had been conducted since 1995. We calculated the composite coverage index, a function of essential maternal and child health intervention parameters. We adopted the wealth index, divided into quintiles from poorest to wealthiest, to investigate wealth-related inequalities in coverage. We quantified trends with time by calculating average annual change in index using a least-squares weighted regression. We calculated population attributable risk to measure the contribution of wealth to the coverage index. Findings We noted large differences between the four regions, with a median composite coverage index ranging from 50.8% for West Africa to 75.3% for Southern Africa. Wealth-related inequalities were prevalent in all subregions, and were highest for West Africa and lowest for Southern Africa. Absolute income was not a predictor of coverage, as we observed a higher coverage in Southern (around 70%) compared with Central and West (around 40%) subregions for the same income. Wealth-related inequalities in coverage were reduced by the greatest amount in Southern Africa, and we found no evidence of inequality reduction in Central Africa. Conclusion Our data show that most countries in sub-Saharan Africa have succeeded in reducing wealth-related inequalities in the coverage of essential health services, even in the presence of conflict, economic hardship or political instability. [phrase omitted] [phrase omitted] Objectif Determiner si les pays d'Afrique subsaharienne sont parvenus a reduire l'impact des inegalites de richesse sur la prise en charge des interventions de sante reproductives, maternelles et infantiles. Methodes Nous avons analyse des donnees d'enquetes menees dans 36 pays regroupe en sous-regions (Afrique centrale, Afrique de l'Est, Afrique australe et Afrique de l'Ouest). Au moins deux enquetes devaient dater d'apres 1995. Nous avons calcule l'indice de prise en charge composite en fonction de plusieurs parametres essentiels d'interventions de sante maternelle et infantile. Nous avons egalement adopte l'indice de richesse divise en quintiles, du plus pauvre au plus nanti, afin d'etudier l'impact des inegalites de richesse dans cette prise en charge. Nous avons quantifie les tendances par rapport au temps, en calculant l'evolution annuelle moyenne de l'indice a l'aide de la regression des moindres carres ponderes. Enfin, nous avons evalue le risque attribuable a la population pour mesurer la contribution de la richesse a l'indice de prise en charge. Resultats Nous avons remarque des differences considerables entre les quatre regions, avec un indice median de prise en charge composite allant de 50,8% en Afrique de l'Ouest a 75,3% en Afrique australe. Les inegalites liees a la richesse etaient tres repandues dans toutes les sous-regions; les plus fortes ont ete observees en Afrique de l'Ouest, les plus faibles en Afrique australe. Le revenu absolu n'est pas un indicateur valable car nous avons constate une meilleure prise en charge dans la sous-region d'Afrique australe (environ 70%) que dans celles d'Afrique centrale et d'Afrique de l'Ouest (environ 40%) pour des revenus equivalents. La majorite des inegalites de prise en charge liees a la richesse ont diminue en Afrique australe, et nous n'avons trouve aucune preuve de reduction des inegalites en Afrique centrale. Conclusion Nos donnees montrent que la plupart des pays d'Afrique subsaharienne ont reussi a attenuer les inegalites de prise en charge liees a la richesse pour les services de sante essentiels, meme en cas de conflit, de difficultes economiques ou d'instabilite politique. [phrase omitted] Objetivo Investigar si los paises del Africa subsahariana han logrado reducir las desigualdades relacionadas con la riqueza en la cobertura de las intervenciones sanitarias relativas a la salud reproductiva, materna, neonatal e infantil. Metodos Se analizaron los datos de las encuestas de 36 paises, agrupados en las subregiones de Africa central, oriental, meridional y occidental, en los que se habian realizado por lo menos dos encuestas desde 1995. Se calculo el indice compuesto de cobertura, en funcion de los parametros esenciales de las intervenciones sanitarias relativas a la salud maternoinfantil. Se adopto el indice de riqueza, dividido en quintiles de mas pobres a mas ricos, para investigar las desigualdades de cobertura relacionadas con la riqueza. Se cuantificaron las tendencias con el tiempo al calcular el cambio anual medio del indice por medio de una regresion por minimos cuadrados ponderados. Tambien se calculo el riesgo atribuible a la poblacion para medir la contribucion de la riqueza al indice de cobertura. Resultados Se observaron grandes diferencias entre las cuatro regiones, con un indice compuesto medio de cobertura que oscilaba entre el 50,8 % para el Africa occidental y el 75,3 % para el Africa meridional. Las desigualdades relacionadas con la riqueza prevalecian en todas las subregiones y eran mayores en el Africa occidental y menores en el Africa meridional. Los ingresos absolutos no eran un factor de prediccion de la cobertura, ya que se observo una mayor cobertura en las subregiones del sur (alrededor del 70 %) en comparacion con las subregiones central y occidental (alrededor del 40 %) para los mismos ingresos. Las desigualdades de cobertura relacionadas con la riqueza se redujeron en mayor medida en el Africa meridional, y no encontramos pruebas de reduccion de la desigualdad en el Africa central. Conclusion Estos datos muestran que la mayoria de los paises del Africa subsahariana han logrado reducir las desigualdades relacionadas con la riqueza en la cobertura de los servicios sanitarios esenciales, incluso en presencia de conflictos, dificultades economicas o inestabilidad politica., Introduction Reaching all women and children with essential reproductive, maternal, newborn and child health interventions is a critical part of universal health coverage, and is represented by the Global Strategy [...]

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. Key messages: Niger is ranked as the most vulnerable country in the world for exposure and sensitivity to the negative effects of disasters and climate change. Acute environmental shocks in 2005 and 2010 elevated government and development partner responses to the 'food security crises'.Multisectoral policy and program investments in vulnerable regions played a meaningful role in improving food security and subsequently maternal and neonatal nutrition but progress is slow or stagnant.The Nigerien government, program implementers, and donors should prioritize investments in infrastructure and shock resilience to improve health systems, food systems, agriculture systems, education systems, and social protection, particularly in high risk populations. [ABSTRACT FROM AUTHOR]

Quantification learning is the task of prevalence estimation for a test population using predictions from a classifier trained on a different population. Quantification methods assume that the sensitivities and specificities of the classifier are either perfect or transportable from the training to the test population. These assumptions are inappropriate in the presence of dataset shift, when the misclassification rates in the training population are not representative of those for the test population. Quantification under dataset shift has been addressed only for single-class (categorical) predictions and assuming perfect knowledge of the true labels on a small subset of the test population. We propose generalized Bayes quantification learning (GBQL) that uses the entire compositional predictions from probabilistic classifiers and allows for uncertainty in true class labels for the limited labeled test data. Instead of positing a full model, we use a model-free Bayesian estimating equation approach to compositional data using Kullback–Leibler loss-functions based only on a first-moment assumption. The idea will be useful in Bayesian compositional data analysis in general as it is robust to different generating mechanisms for compositional data and allows 0's and 1's in the compositional outputs thereby including categorical outputs as a special case. We show how our method yields existing quantification approaches as special cases. Extension to an ensemble GBQL that uses predictions from multiple classifiers yielding inference robust to inclusion of a poor classifier is discussed. We outline a fast and efficient Gibbs sampler using a rounding and coarsening approximation to the loss functions. We establish posterior consistency, asymptotic normality and valid coverage of interval estimates from GBQL, which to our knowledge are the first theoretical results for a quantification approach in the presence of local labeled data. We also establish finite sample posterior concentration rate. Empirical performance of GBQL is demonstrated through simulations and analysis of real data with evident dataset shift. for this article are available online. [ABSTRACT FROM AUTHOR]