When do democratic transitions reduce or increase child mortality? Exploring the role of non-violent resistance.

What explains variation across countries in the effect of democratization on child mortality rates? Democratic transitions, on average, improve health outcomes but there is substantial variation across countries in whether democratization leads to lower-than-expected child mortality post-transition. As yet, there is no convincing quantitative explanation for this variation. In this paper, we argue that whether you have a protest-led or violence-led democratic transition alters the trajectory of child mortality post-transition. Our paper makes two contributions. First, we offer a more detailed account of how the type of resistance movement promoting regime change affects health post-transition. We also draw on novel data to categorise the movements producing democratic transitions as violent or peaceful, moving beyond earlier work which operationalised peaceful democratizations in terms of battle-related deaths. Second, we extend earlier research by examining whether the nature of the democratization movement constitutes a necessary cause of higher or lower-than-expected child mortality following democratization. Across 51 transitions, countries that have a protest-led transition have lower-than-expected child mortality rates after the transition to democracy than countries with other kinds of movements (ß = -0.17, p = 0.003). Countries with violence-led transitions, meanwhile, have, on average, higher-than-expected child mortality rates after their transition (ß = 0.20, p = 0.001). These associations hold when we adjust for covariates (including all possible combinations of various confounding variables). We also find evidence that protest-led transitions may be a necessary condition for avoiding increased child mortality post-transition. Finally, we conduct a deviant case analysis of transitions that appear to be contrary to our theory, finding that these cases are likely instances of measurement error. Democratization may not always improve health, but such health improvements are more likely when regime change is protest-led. This is because such movements are more likely to build broad coalitions committed to consensual politics post-transition, a critical feature of successful democracies.

Challenging categorical thinking: A mixed methods approach to explaining health inequalities.

"Categorical thinking" in social science research has been widely criticised by feminist scholars for conceptualising social categories as natural, de-contextualised, and internally homogeneous. This paper develops and applies a mixed-methods approach to the study of health inequalities, using social categories meaningfully in order to challenge categorical thinking. The approach is demonstrated through a case study of socio-economic (SES) inequalities in maternal healthcare access in Zambia. This paper's approach responds to the research agenda set by intersectional social epidemiologists by considering potential heterogeneity within categories, but also by exploring the context-specific meaning of categories, examining explanations at multiple levels, and interpreting results according to mutually constitutive social processes. The study finds that meso-level institutions, "health service environments", explain a large share of SES inequalities in maternal healthcare access. Women's work, marital status, and levels of "autonomy" have heterogeneous implications for healthcare access across SES categories. Disadvantaged categories and their reproductive behaviours are stigmatised as 'backwards', in contrast to advantaged categories and their behaviours, which are associated with 'modernity' and 'development'. Challenging categorical thinking has important implications for social justice and health, by rejecting framings of a specific category as problematic or non-compliant, highlighting the possibility of change, and emphasising the political and structural nature of progress.

Depends Who's Asking: Interviewer Effects in Demographic and Health Surveys Abortion Data.

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.

Women who break the rules: Social exclusion and inequities in pregnancy and childbirth experiences in Zambia.

Health inequities are a growing concern in low- and middle-income countries, but reducing them requires a better understanding of underlying mechanisms. This study is based on 42 semi-structured interviews conducted in June 2018 with women who gave birth in the previous year, across rural and urban clinic sites in Mansa district, Zambia. Findings show that health facility rules regulating women's behaviour during pregnancy and childbirth create inequities in women's maternity experiences. The rules and their application can be understood as a form of social exclusion, discriminating against women with fewer financial and social resources. This study extends existing frameworks of social exclusion by demonstrating that the rules do not only originate in, but also reinforce, the structural processes that underpin inequitable social institutions. Legitimising the rules supports a moral order where women with fewer resources are constructed as "bad women", while efforts to follow the rules widen existing power differentials between socially excluded women and others. This study's findings have implications for the literature on reversed accountability and the unintended consequences of global and national safe motherhood targets, and for our understanding of disrespectful maternity care.

The predictive power of health system environments: a novel approach for explaining inequalities in access to maternal healthcare.

INTRODUCTION: The 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. METHODS: Using 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. RESULTS: Random 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. CONCLUSIONS: Health 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.

Counting indirect crisis-related deaths in the context of a low-resilience health system: the case of maternal and neonatal health during the Ebola epidemic in Sierra Leone.

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.

The 'Dream Team' for sexual, reproductive, maternal, newborn and adolescent health: an adjusted service target model to estimate the ideal mix of health care professionals to cover population need.

BACKGROUND: A 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'). METHODS: An 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. RESULTS: Countries 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. CONCLUSIONS: There 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.

Evidence for action on improving the maternal and newborn health workforce: The basis for quality care.

Ambitious new goals to end preventable maternal and newborn deaths will not only require increased coverage but also improved quality of care. Unfortunately, current levels of quality in the delivery of maternal and newborn care are low in high-burden countries, for reasons that are intimately linked with inadequate planning and management of the maternal and newborn health workforce. The Global Strategy on Human Resources for Health is a key opportunity to strengthen global and country-level accountability frameworks for the health workforce and its capacity to deliver quality care. In order to succeed, maternal and newborn health specialists must embrace this strategy and its linkages with the new Global Strategy for Women's, Children's, and Adolescents' Health; action is needed across high- and low-income countries; and any accountability framework must be underpinned by ambitious, measurable indicators and strengthened data collection on human resources for health.