The impact of austerity on children: Uncovering effect heterogeneity by political, economic, and family factors in low- and middle-income countries.

Which children are most vulnerable when their government imposes austerity? Research tends to focus on either the political-economic level or the family level. Using a sample of nearly two million children in 67 countries, this study synthesizes theories from family sociology and political science to examine the heterogeneous effects on child poverty of economic shocks following the implementation of an International Monetary Fund (IMF) program. To discover effect heterogeneity, we apply machine learning to policy evaluation. We find that children's average probability of falling into poverty increases by 14 percentage points. We find substantial effect heterogeneity, with family wealth and governments' education spending as the two most important moderators. In contrast to studies that emphasize the vulnerability of low-income families, we find that middle-class children face an equally high risk of poverty. Our results show that synthesizing family and political factors yield deeper knowledge of how economic shocks affect children.

Uncovering Heterogeneous Associations Between Disaster-Related Trauma and Subsequent Functional Limitations: A Machine-Learning Approach.

This study examined heterogeneity in the association between disaster-related home loss and functional limitations of older adults, and identified characteristics of vulnerable subpopulations. Data were from a prospective cohort study of Japanese older survivors of the 2011 Japan Earthquake. Complete home loss was objectively assessed. Outcomes in 2013 (n = 3,350) and 2016 (n = 2,664) included certified physical disability levels, self-reported activities of daily living, and instrumental activities of daily living. We estimated population average associations between home loss and functional limitations via targeted maximum likelihood estimation with SuperLearning and its heterogeneity via the generalized random forest algorithm. We adjusted for 55 characteristics of survivors from the baseline survey conducted 7 months before the disaster. While home loss was consistently associated with increased functional limitations on average, there was evidence of effect heterogeneity for all outcomes. Comparing the most and least vulnerable groups, the most vulnerable group tended to be older, not married, living alone, and not working, with preexisting health problems before the disaster. Individuals who were less educated but had higher income also appeared vulnerable for some outcomes. Our inductive approach for effect heterogeneity using machine learning algorithm uncovered large and complex heterogeneity in postdisaster functional limitations among Japanese older survivors.

Long-Term Associations between Disaster-Related Home Loss and Health and Well-Being of Older Survivors: Nine Years after the 2011 Great East Japan Earthquake and Tsunami.

BACKGROUND: Little research has examined associations between disaster-related home loss and multiple domains of health and well-being, with extended long-term follow-up and comprehensive adjustment for pre-disaster characteristics of survivors. OBJECTIVES: We examined the longitudinal associations between disaster-induced home loss and 34 indicators of health and well-being, assessed ∼9y post-disaster. METHODS: We used data from a preexisting cohort study of Japanese older adults in an area directly impacted by the 2011 Japan Earthquake (n=3,350 and n=2,028, depending on the outcomes). The study was initiated in 2010, and disaster-related home loss status was measured in 2013 retrospectively. The 34 outcomes were assessed in 2020 and covered dimensions of physical health, mental health, health behaviors/sleep, social well-being, cognitive social capital, subjective well-being, and prosocial/altruistic behaviors. We estimated the associations between disaster-related home loss and the outcomes, using targeted maximum likelihood estimation and SuperLearner. We adjusted for pre-disaster characteristics from the wave conducted 7 months before the disaster (i.e., 2010), including prior outcome values that were available. RESULTS: After Bonferroni correction for multiple testing, we found that home loss (vs. no home loss) was associated with increased posttraumatic stress symptoms (standardized difference=0.50; 95% CI: 0.35, 0.65), increased daily sleepiness (0.38; 95% CI: 0.21, 0.54), lower trust in the community (-0.36; 95% CI: -0.53, -0.18), lower community attachment (-0.60; 95% CI: -0.75, -0.45), and lower prosociality (-0.39; 95% CI: -0.55, -0.24). We found modest evidence for the associations with increased depressive symptoms, increased hopelessness, more chronic conditions, higher body mass index, lower perceived mutual help in the community, and decreased happiness. There was little evidence for associations with the remaining 23 outcomes. DISCUSSION: Home loss due to a disaster may have long-lasting adverse impacts on the cognitive social capital, mental health, and prosociality of older adult survivors. https://doi.org/10.1289/EHP10903.

Uncovering heterogeneous associations of disaster-related traumatic experiences with subsequent mental health problems: A machine learning approach.

AIM: Understanding the differential mental health effects of traumatic experiences is important to identify particularly vulnerable subpopulations. We examined the heterogeneous associations between disaster-related traumatic experiences and postdisaster mental health, using a novel machine learning-based causal inference approach. METHODS: Data were from a prospective cohort study of Japanese older adults in an area severely affected by the 2011 Great East Japan Earthquake. The baseline survey was conducted 7 months before the disaster and the 2 follow-up surveys were conducted 2.5 and 5.5 years after (n = 1150 to n = 1644 depending on the exposure-outcome combinations). As disaster-related traumatic experiences, we assessed complete home loss and loss of loved ones. Using the generalized random forest algorithm, we estimated conditional average treatment effects (CATEs) of the disaster damages on postdisaster mental health outcomes to examine the heterogeneous associations by 51 predisaster characteristics of the individuals. RESULTS: We found that, even when there was no population average association between disaster-related trauma and subsequent mental health outcomes, some subgroups experienced severe impacts. We also identified and compared characteristics of the most and least vulnerable groups (ie, top versus bottom deciles of the estimated CATEs). While there were some unique patterns specific to each exposure-outcome combination, the most vulnerable group tended to be from lower socioeconomic backgrounds with preexisting depressive symptoms for many exposure-outcome combinations. CONCLUSIONS: We found considerable heterogeneity in the association between disaster-related traumatic experiences and subsequent mental health problems.

Heterogeneity in cognitive disability after a major disaster: A natural experiment study.

Cognitive disability following traumatic experiences of disaster has been documented; however, little is known about heterogeneity in the association across individuals. In this natural experiment study of approximately 3000 Japanese older adults in an area directly affected by the 2011 Great East Japan Earthquake, the baseline survey was established 7 months before the 2011 earthquake. To inductively identify heterogeneity in postdisaster cognitive disability by predisaster characteristics, we applied a machine learning­based causal inference approach­generalized random forest. We identified strong evidence for heterogeneity in the association between home loss and cognitive disability objectively assessed 2.5 and 5.5 years after the 2011 earthquake. The subgroups with the strongest disaster-dementia associations tended to be from low socioeconomic backgrounds and have predisaster health problems. The study demonstrated that some subpopulations are particularly prone to experience cognitive disability after disasters, which could be overlooked in studies assessing population average associations only.

A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects.

BACKGROUND: Machine learning (ML) has spread rapidly from computer science to several disciplines. Given the predictive capacity of ML, it offers new opportunities for health, behavioral, and social scientists. However, it remains unclear how and to what extent ML is being used in studies of social determinants of health (SDH). METHODS: Using four search engines, we conducted a scoping review of studies that used ML to study SDH (published before May 1, 2020). Two independent reviewers analyzed the relevant studies. For each study, we identified the research questions, Results, data, and algorithms. We synthesized our findings in a narrative report. RESULTS: Of the initial 8097 hits, we identified 82 relevant studies. The number of publications has risen during the past decade. More than half of the studies (n = 46) used US data. About 80% (n = 66) utilized surveys, and 70% (n = 57) employed ML for common prediction tasks. Although the number of studies in ML and SDH is growing rapidly, only a few studies used ML to improve causal inference, curate data, or identify social bias in predictions (i.e., algorithmic fairness). CONCLUSIONS: While ML equips researchers with new ways to measure health outcomes and their determinants from non-conventional sources such as text, audio, and image data, most studies still rely on traditional surveys. Although there are no guarantees that ML will lead to better social epidemiological research, the potential for innovation in SDH research is evident as a result of harnessing the predictive power of ML for causality, data curation, or algorithmic fairness.

Estimating the Impact of Sustained Social Participation on Depressive Symptoms in Older Adults.

BACKGROUND: Social participation has been suggested as a means to prevent depressive symptoms. However, it remains unclear whether a one-time boost suffices or whether participation needs to be sustained over time for long-term prevention. We estimated the impacts of alternative hypothetical interventions in social participation on subsequent depressive symptoms among older adults. METHODS: Data were from a nationwide prospective cohort study of Japanese older adults ≥65 years of age (n = 32,748). We analyzed social participation (1) as a baseline exposure from 2010 (approximating a one-time boost intervention) and (2) as a time-varying exposure from 2010 and 2013 (approximating a sustained intervention). We defined binary depressive symptoms in 2016 using the Geriatric Depression Scale. We used the doubly robust targeted maximum likelihood estimation to address time-dependent confounding. RESULTS: The magnitude of the association between sustained participation and the lower prevalence of depressive symptoms was larger than the association observed for baseline participation only (e.g., prevalence ratio [PR] for participation in any activity = 0.83 [95% confidence interval = 0.79, 0.88] vs. 0.90 [0.87, 0.94]). For activities with a lower proportion of consistent participation over time (e.g., senior clubs), there was little evidence of an association between baseline participation and subsequent depressive symptoms, while an association for sustained participation was evident (e.g., PR for senior clubs = 0.96 [0.90, 1.02] vs. 0.88 [0.79, 0.97]). Participation at baseline but withholding participation in 2013 was not associated with subsequent depressive symptoms. CONCLUSIONS: Sustained social participation may be more strongly associated with fewer depressive symptoms among older adults.

Predicting women's height from their socioeconomic status: A machine learning approach.

The social determinants of health literature routinely deploy socio-economic status (SES) as a key factor in accounting for women's height-an established indicator of human welfare at the population level-using traditional regression. However, this literature lacks a systematic identification of the predictive power of SES as well as the possible non-linear relationships between the measures of SES (education, occupation, and material wealth) in predicting variation in women's height. This study aims to evaluate this predictive power. We used the Demographic and Health Surveys (DHS) from 66 low- and middle-income countries (women = 1,273,644), sampled between 1994 and 2016. The analysis consisted of training seven machine-learning algorithms of different function classes and assessing their predictive power out-of-sample, vis-à-vis OLS regression. In an OLS framework, SES accounts for 0.7%, R2, of the total variance in women's height (from σOLSFix2 = 31.82 to σOLSSES2 = 31.57), adjusting for country, community, and sampling year fixed effects. The country-specific variances range from as low as 25.10 units in Egypt to as high as 74.46 units in Sao Tome and Principe. With the same set of SES measures, the best performing learner, a Bayesian neural net, produces a predictive variance of σBnnSES2 = 31.52. This is a negligible improvement in variance explained by 0.3% (σBnnSES2-σOLSSES2). Given our selection of algorithms, our findings indicate no relevant non-linear relationships between SES and women's height, and also the predictive limits of SES. We recommend that scholars report both the average effect of SES on health outcomes as well as its contribution to the variance explained. This will improve our understanding of how key social and economic factors affect health, deepening our understanding of the social determinants of health.

The influence of maternal agency on severe child undernutrition in conflict-ridden Nigeria: Modeling heterogeneous treatment effects with machine learning.

Nigeria is one of the fastest growing African economies, yet struggles with armed conflict, poverty, and morbidity. An area of high concern is how this situation affects vulnerable families and their children. A key pathway in improving the situation for children in times of conflict is to reinforce maternal agency, for instance, through education. However, the state of the art of research lacks a clear understanding of how many years of education is needed before children benefit. Due to mother's differing social context and ability, the effect of maternal education varies. We study the heterogeneous treatment effects of maternal agency, here operationalized as length of education, on severe child undernutrition in the context of armed conflict. We deploy a repeated cross-sectional study design, using the Nigeria 2008 and 2013 Demographic and Health Survey (DHS). The sample covers 25,917 children and their respective mothers. A key methodological challenge is to estimate this heterogeneity inductively. The causal inference literature proposes a machine learning approach, Bayesian Additive Regression Trees (BART), as a promising avenue to overcome this challenge. Based on BART-estimation of the Conditional Average Treatment Effect (CATE) this study confirms earlier findings in that maternal education decreases severe child undernutrition, but only when mothers acquire an education that lasts more than the country's compulsory 9 years; that is 10 years of education and higher. This protective effect remains even during the exposure of armed conflict.

Structural adjustment, state capacity and child health: evidence from IMF programmes.

BACKGROUND: Consensus is growing that policy reform programmes by the International Monetary Fund (IMF)-an international organization mandated with upholding global financial stability and assisting countries in economic turmoil-produce adverse effects on public health. However, this consensus is unclear about which policies of these programmes underlie these effects. This article fills parts of this gap by examining the impact of four kinds of IMF policies (fiscal policy, public-sector employment, privatization of state-owned enterprises and price liberalization) on public-health expenditure, child vaccination and child mortality. METHODS: We conducted time-series cross-section analyses for up to 128 developing countries over the 1980-2014 period using observational data on health outcomes and IMF conditionality for different policy areas. IMF effectiveness research faces two types of potential biases: self-selection into IMF programmes and IMF policy conditions. We deployed instrumental variables in a seemingly unrelated regression framework to address both types of endogeneity, besides traditional remedies such as the use of fixed effects on countries and years. RESULTS: IMF policy conditions on public-sector employment are negatively related to child health. A change from the minimum to the maximum number of such policy conditions decreases vaccination (which ranges from 0 to 100) by 10.97% [95% confidence interval (CI): 1.16 to 20.79]. This effect is robust against different sets of control variables. In addition, IMF programmes increase the share of government expenditure devoted to public health in developing countries by 0.91 percentage points (95% CI: 0.15 to 1.68). CONCLUSIONS: These findings suggest that IMF policies-particularly those that require public-sector reforms-undermine health by weakening the capacity of states to deliver vaccination. Therefore, international financial institutions need to increase their awareness of the public-health impact of their policy prescriptions. Strengthening state capacity in times of economic crisis would ensure that increased health spending also delivers quality healthcare.

The impact of rising food prices on obesity in women: a longitudinal analysis of 31 low-income and middle-income countries from 2000 to 2014.

OBJECTIVES: To examine whether changes in food prices are associated with changes in obesity prevalence among women in developing countries, and assess effect modification by individual socioeconomic status (SES). METHODS: Longitudinal study of country-level food price inflation temporally and geographically linked to anthropometric data on non-pregnant adult women (n = 295,984) in 31 low-income and middle-income countries over the 2000-2014 time period, using separate multivariable multilevel growth models of five SES indicators. Post-estimation analysis computed the relationship between food price inflation and predicted mean probabilities of being obese, by SES. RESULTS: Rising food price inflation was strongly associated with women's obesity prevalence, and SES consistently modified the relationship. Regardless of indicator used, higher food price inflation was positively associated with obesity among women in top SES categories, but was flat or negative among women in low SES categories, averaging over time. The SES differences were widest across educational strata and were most pronounced when food price inflation was highest. Overall, for every 1-unit increase in food price inflation, predicted mean obesity prevalence was between 0.02 and 0.06 percentage points greater in women of high SES compared to low SES women. CONCLUSION: There is a strong link between food price inflation and obesity in adult women in developing countries which is clearly modified by individuals' SES. Greater food price inflation was associated with greater obesity prevalence only among women in higher SES groups, who may be net food buyers most at risk of obesity in low-income and middle-income countries.

The association of minimum wage change on child nutritional status in LMICs: A quasi-experimental multi-country study.

There is recognition that social protection policies such as raising the minimum wage can favourably impact health, but little evidence links minimum wage increases to child health outcomes. We used multi-year data (2003-2012) on national minimum wages linked to individual-level data from the Demographic and Health Surveys (DHS) from 23 low- and middle-income countries (LMICs) that had least two DHS surveys to establish pre- and post-observation periods. Over a pre- and post-interval ranging from 4 to 8 years, we examined minimum wage growth and four nutritional status outcomes among children under 5 years: stunting, wasting, underweight, and anthropometric failure. Using a differences-in-differences framework with country and time-fixed effects, a 10% increase in minimum wage growth over time was associated with a 0.5 percentage point decline in stunting (-0.054, 95% CI (-0.084,-0.025)), and a 0.3 percentage point decline in failure (-0.031, 95% CI (-0.057,-0.005)). We did not observe statistically significant associations between minimum wage growth and underweight or wasting. We found similar results for the poorest households working in non-agricultural and non-professional jobs, where minimum wage growth may have the most leverage. Modest increases in minimum wage over a 4- to 8-year period might be effective in reducing child undernutrition in LMICs.

Impact of International Monetary Fund programs on child health.

Parental education is located at the center of global efforts to improve child health. In a developing-country context, the International Monetary Fund (IMF) plays a crucial role in determining how governments allocate scarce resources to education and public health interventions. Under reforms mandated by IMF structural adjustment programs, it may become harder for parents to reap the benefits of their education due to wage contraction, welfare retrenchment, and generalized social insecurity. This study assesses how the protective effect of education changes under IMF programs, and thus how parents' ability to guard their children's health is affected by structural adjustment. We combine cross-sectional stratified data (countries, 67; children, 1,941,734) from the Demographic and Health Surveys and the Multiple Indicator Cluster Surveys. The sample represents ∼2.8 billion (about 50%) of the world's population in year 2000. Based on multilevel models, our findings reveal that programs reduce the protective effect of parental education on child health, especially in rural areas. For instance, in the absence of IMF programs, living in an household with educated parents reduces the odds of child malnourishment by 38% [odds ratio (OR), 0.62; 95% CI, 0.66-0.58]; in the presence of programs, this drops to 21% (OR, 0.79; 95% CI, 0.86-0.74). In other words, the presence of IMF conditionality decreases the protective effect of parents' education on child malnourishment by no less than 17%. We observe similar adverse effects in sanitation, shelter, and health care access (including immunization), but a beneficial effect in countering water deprivation.

Correction: What Is the Association between Absolute Child Poverty, Poor Governance, and Natural Disasters? A Global Comparison of Some of the Realities of Climate Change.

[This corrects the article DOI: 10.1371/journal.pone.0153296.].

What Is the Association between Absolute Child Poverty, Poor Governance, and Natural Disasters? A Global Comparison of Some of the Realities of Climate Change.

The paper explores the degree to which exposure to natural disasters and poor governance (quality of governance) is associated with absolute child poverty in sixty-seven middle- and low-income countries. The data is representative for about 2.8 billion of the world´s population. Institutionalist tend to argue that many of society's ills, including poverty, derive from fragile or inefficient institutions. However, our findings show that although increasing quality of government tends to be associated with less poverty, the negative effects of natural disasters on child poverty are independent of a country´s institutional efficiency. Increasing disaster victims (killed and affected) is associated with higher rates of child poverty. A child´s estimated odds ratio to be in a state of absolute poverty increases by about a factor of 5.7 [95% CI: 1.7 to 18.7] when the average yearly toll of disasters in the child´s country increases by one on a log-10 scale. Better governance correlates with less child poverty, but it does not modify the correlation between child poverty and natural disasters. The results are based on hierarchical regression models that partition the variance into three parts: child, household, and country. The models were cross-sectional and based on observational data from the Demographic Health Survey and the Multiple Indicator Cluster Survey, which were collected at the beginning of the twenty-first millennium. The Sustainable Development Goals are a principle declaration to halt climate change, but they lack a clear plan on how the burden of this change should be shared by the global community. Based on our results, we suggest that the development agencies should take this into account and to articulate more equitable global policies to protect the most vulnerable, specifically children.

Examining the changing profile of undernutrition in the context of food price rises and greater inequality.

This paper examines how the profile of undernutrition among children in two African countries (Ethiopia and Nigeria) changed over the period of the 2007/08 food, fuel and financial crisis. Using the Composite Index of Anthropometric Failure (CIAF), an indicator which allows for a comprehensive assessment of undernutrition in young children, we examine what changes occurred in the composition of undernutrition, and how these changes were distributed amongst children in different socio-economic groups. This is important as certain combinations of anthropometric failure (AF), especially the experience of multiple failures (dual and triple combinations of AF) are associated with higher morbidity and mortality risks, and are also related to poverty. Our hypothesis is that increases in food prices during the crisis contributed to an increase in inequality, which may have resulted in concurrent increases in the prevalence of more damaging forms of undernutrition amongst poorer children. While both countries witnessed large increases in food prices, the effects were quite different. Ethiopia managed reduce the prevalence of multiple anthropometric failure between 2005 and 2011 across most groups and regions. By contrast, in Nigeria prevalence increased between 2008 and 2013, and particularly so in the poorer, northern states. The countries studied applied quite different policies in response to food price increases, with the results from Ethiopia demonstrating that protectionist public health and nutrition interventions can mitigate the impacts of price increases on poor children.