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

Could an obstacle course help us make access to healthcare fairer?

I research maternal health inequalities in Zambia using mixed methods

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 past 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 analyse the impact of interviewers on the probability of reporting past abortions in twenty-two 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 an abortion, after controlling for women’s demographic characteristics. 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 the possible data biases

Challenging categorical thinking: a mixed methods approach to explaining health inequalities

&ldquo;Categorical thinking&rdquo; 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&nbsp;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, &ldquo;health service environments&rdquo;, explain a large share of SES inequalities in maternal healthcare access. Women's work, marital status, and levels of &ldquo;autonomy&rdquo; 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&rsquo; and 'development&rsquo;. 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.</p

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

Context and heterogeneity: a novel approach to explaining maternal health inequalities in Zambia

Quantitative data from low- and middle-income countries show that inequalities in skilled birth attendance and health facility birth remain higher than inequalities in access to all other primary care interventions. Improving maternal health equity is increasingly prioritised in key policy, advocacy and accountability frameworks, such as the Global Strategy for Women’s, Children’s and Adolescents’ Health, the Sustainable Development Goals, and Countdown to 2030. However, we lack theoretically grounded evidence on why inequalities in healthcare access and experience persist, without which effective policies cannot be developed. This thesis by papers demonstrates a novel approach to the empirical explanation of maternal health inequalities, using the case study of Zambia. This thesis’ approach is rooted in social epidemiological, feminist, and sociological theories and makes use of mixed methods, including Bayesian multilevel modelling, interaction effects, decomposition, and analysis of in-depth interviews. This thesis advances our understanding of inequalities by theorising, measuring, and analysing the context in which individuals operate, instead of essentialising individual-level characteristics. Using multilevel models, I analyse the power of multidimensional health service environments to predict access to a health facility birth in Paper 11. Rather than solely defining the context geographically, I combine geographic characteristics (distance to any health facility, to a midwife, and/or to a hospital capable of conducting Caesarean sections) with social characteristics we know are discriminated against in the Zambian health system (being poor, having many children). I find that multidimensional health service environments have high discriminatory accuracy in the Zambian context. Social context is further explored in Paper 32, which analyses the role of social exclusion, shame and stigma in shaping women’s experiences of pregnancy and childbirth, particularly in their relationship with the health facility. I demonstrate that health facility rules play a key role in perpetuating social exclusion and reinforcing unequal power relations, both between patients and health workers, and among patients themselves. Paper 4 uses decomposition analysis to explore the extent to which health service environments are unequally distributed across more vs. less advantaged groups. I show that these environments explain a large share of socio-economic inequalities. This thesis also critically examines the assumption that policies, environments and individual characteristics have the same meaning and effect for different socio-economic groups. In Paper 23, I explore whether the association between facing a specific healthcare access barrier and having a facility birth differs according to how many other barriers a person faces. I find that for three out of the six barriers defined, the association is weaker the more other barriers are present. I theorise the implication of this finding for policies that seek to remove one barrier at a time in order to reduce inequalities and propose and formalise a new hypothesis I call: “The Concurrent Barrier Hypothesis”. In Paper 32, I show that while facility rules can be unequally applied, social exclusion works more strongly through “institutional bias”, in that the rules are harder to follow for women with fewer economic or social resources. In Paper 4, I use Kitagawa-Oaxaca-Blinder decomposition to investigate whether health service environments and individual or household attributes have different effects on access to facility birth depending on socio-economic status. I find that many individual characteristics (such as marital status, autonomy, and employment) have contradictory meanings across different socio-economic groups. This thesis not only contributes to the field of global health empirically, but brings a number of conceptual contributions by (1) Modelling an abstract and multidimensional social structure using random effects; (2) Developing and testing a new hypothesis on the unintended consequences of assumed pro-equity health policies; (3) Suggesting that the global discourse around disrespectful maternity care should be modified to include routine practices such as health facility rules; and (4) Proposing a novel empirical approach for quantitative researchers of global health inequalities to avoid “categorical thinking” (the practice of treating social categories as de-contextualised, natural, and internally homogeneous). The thesis concludes that the manner in which we conduct research matters for the policy and politics of maternal health, particularly from a social justice perspective

Does collective bargaining reduce health inequalities between labour market insiders and outsiders?

Collective bargaining institutions are correlated with better population health. However, there are still major gaps in our understanding regarding the impact of collective bargaining on health inequalities, particularly between labour market ‘insiders’ and ‘outsiders’. In this study, we investigate the effect of collective bargaining coverage on individuals’ self-rated health, and whether the impact varies according to labour market status. We use four waves of the European Values Survey (1981–2018) and three-level nested random intercept models across 33 OECD and European countries (N = 66 301). We find that stronger and more inclusive collective bargaining institutions reduce health inequalities between the unemployed and the employed by disproportionately improving the health of the unemployed. This study implies that targeting the political institutions that shape the distribution of power and resources is important for reducing health inequalities.</p