Although the number of direct Ebola-related deaths from the 2013 to 2016 West African Ebola outbreak has been quantified, the number of indirect deaths, resulting from decreased utilization of routine health services, remains unknown. Such information is a key ingredient of health system resilience, essential for adequate allocation of resources to both 'crisis response activities' and 'core functions'. Taking stock of indirect deaths may also help the concept of health system resilience achieve political traction over the traditional approach of disease-specific surveillance. This study responds to these imperatives by quantifying the extent of the drop in utilization of essential reproductive, maternal and neonatal health services in Sierra Leone during the Ebola outbreak by using interrupted time-series regression to analyse Health Management Information System (HMIS) data. Using the Lives Saved Tool, we then model the implication of this decrease in utilization in terms of excess maternal and neonatal deaths, as well as stillbirths. We find that antenatal care coverage suffered from the largest decrease in coverage as a result of the Ebola epidemic, with an estimated 22 percentage point (p.p.) decrease in population coverage compared with the most conservative counterfactual scenario. Use of family planning, facility delivery and post-natal care services also decreased but to a lesser extent (-6, -8 and -13 p.p. respectively). This decrease in utilization of life-saving health services translates to 3600 additional maternal, neonatal and stillbirth deaths in the year 2014-15 under the most conservative scenario. In other words, we estimate that the indirect mortality effects of a crisis in the context of a health system lacking resilience may be as important as the direct mortality effects of the crisis itself.
Responses to survey questions about abortion are affected by a wide range of factors, including stigma, fear, and cultural norms. However, we know little about how interviewers might affect responses to survey questions on abortion. The aim of this study is to assess how interviewers affect the probability of women reporting abortions in nationally representative household surveys: Demographic and Health Surveys (DHS). We use cross-classified random intercepts at the level of the interviewer and the sampling cluster in a Bayesian framework to analyze the impact of interviewers on the probability of reporting abortions in 22 DHS conducted worldwide. Household surveys are the only available data we can use to study the determinants and pathways of abortion in detail and in a representative manner. Our analyses are motivated by improving our understanding of the reliability of these data. Results show an interviewer effect accounting for between 0.2% and 50% of the variance in the odds of a woman reporting ever having had an abortion, after women's demographic characteristics are controlled for. In contrast, sampling cluster effects are much lower in magnitude. Our findings suggest the need for additional effort in assessing the causes of abortion underreporting in household surveys, including interviewers' skills and characteristics. This study also has important implications for improving the collection of other sensitive demographic data (e.g., gender-based violence and sexual health). Data quality of responses to sensitive questions could be improved with more attention to interviewers—their recruitment, training, and characteristics. Future analyses will need to account for the role of interviewer to more fully understand possible data biases.
BackgroundA competent, enabled and efficiently deployed health workforce is crucial to the achievement of the health-related sustainable development goals (SDGs). Methods for workforce planning have tended to focus on ‘one size fits all’ benchmarks, but because populations vary in terms of their demography (e.g. fertility rates) and epidemiology (e.g. HIV prevalence), the level of need for sexual, reproductive, maternal, newborn and adolescent health (SRMNAH) workers also varies, as does the ideal composition of the workforce. In this paper, we aim to provide proof of concept for a new method of workforce planning which takes into account these variations, and allocates tasks to SRMNAH workers according to their competencies, so countries can assess not only the needed size of the SRMNAH workforce, but also its ideal composition (the ‘Dream Team’).MethodsAn adjusted service target model was developed, to estimate (i) the amount of health worker time needed to deliver essential SRMNAH care, and (ii) how many workers from different cadres would be required to meet this need if tasks were allocated according to competencies. The model was applied to six low- and middle-income countries, which varied in terms of current levels of need for health workers, geographical location and stage of economic development: Azerbaijan, Malawi, Myanmar, Peru, Uzbekistan and Zambia.ResultsCountries with high rates of fertility and/or HIV need more SRMNAH workers (e.g. Malawi and Zambia each need 44 per 10,000 women of reproductive age, compared with 20–27 in the other four countries). All six countries need between 1.7 and 1.9 midwives per 175 births, i.e. more than the established 1 per 175 births benchmark.ConclusionsThere is a need to move beyond universal benchmarks for SRMNAH workforce planning, by taking into account demography and epidemiology. The number and range of workers needed varies according to context. Allocation of tasks according to health worker competencies represents an efficient way to allocate resources and maximise quality of care, and therefore will be useful for countries working towards SDG targets. Midwives/nurse-midwives who are educated according to established global standards can meet 90% or more of the need, if they are part of a wider team operating within an enabled environment.
IntroductionThe growing use of Geographic Information Systems (GIS) to link population-level data to health facility data is key for the inclusion of health system environments in analyses of health disparities. However, such approaches commonly focus on just a couple of aspects of the health system environment and only report on the average and independent effect of each dimension.MethodsUsing GIS to link Demographic and Health Survey data on births (2008–13/14) to Service Availability and Readiness Assessment data on health facilities (2010) in Zambia, this paper rigorously measures the multiple dimensions of an accessible health system environment. Using multilevel Bayesian methods (multilevel analysis of individual heterogeneity and discriminatory accuracy), it investigates whether multidimensional health system environments defined with reference to both geographic and social location cut across individual-level and community-level heterogeneity to reliably predict facility delivery.ResultsRandom intercepts representing different health system environments have an intraclass correlation coefficient of 25%, which demonstrates high levels of discriminatory accuracy. Health system environments with four or more access barriers are particularly likely to predict lower than average access to facility delivery. Including barriers related to geographic location in the non-random part of the model results in a proportional change in variance of 74% relative to only 27% for barriers related to social discrimination.ConclusionsHealth system environments defined as a combination of geographic and social location can effectively distinguish between population groups with high versus low probabilities of access. Barriers related to geographic location appear more important than social discrimination in the context of Zambian maternal healthcare access. Under a progressive universalism approach, resources should be disproportionately invested in the worst health system environments.
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