Income inequality and health: lessons from a refugee residential assignment program

Causal Assessment of Income Inequality on Self-Rated Health and All-Cause Mortality: A Systematic Review and Meta-Analysis

Policy Points Income is thought to impact a broad range of health outcomes. However, whether income inequality (how unequal the distribution of income is in a population) has an additional impact on health is extensively debated. Studies that use multilevel data, which have recently increased in popularity, are necessary to separate the contextual effects of income inequality on health from the effects of individual income on health. Our systematic review found only small associations between income inequality and poor self-rated health and all-cause mortality. The available evidence does not suggest causality, although it remains methodologically flawed and limited, with very few studies using natural experimental approaches or examining income inequality at the national level.

CONTEXT

Whether income inequality has a direct effect on health or is only associated because of the effect of individual income has long been debated. We aimed to understand the association between income inequality and self-rated health (SRH) and all-cause mortality (mortality) and assess if these relationships are likely to be causal.

METHODS

We searched Medline, ISI Web of Science, Embase, and EconLit (PROSPERO: CRD42021252791) for studies considering income inequality and SRH or mortality using multilevel data and adjusting for individual-level socioeconomic position. We calculated pooled odds ratios (ORs) for poor SRH and relative risk ratios (RRs) for mortality from random-effects meta-analyses. We critically appraised included studies using the Risk of Bias in Nonrandomized Studies - of Interventions tool. We assessed certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation framework and causality using Bradford Hill (BH) viewpoints.

FINDINGS

The primary meta-analyses included 2,916,576 participants in 38 cross-sectional studies assessing SRH and 10,727,470 participants in 14 cohort studies of mortality. Per 0.05-unit increase in the Gini coefficient, a measure of income inequality, the ORs and RRs (95% confidence intervals) for SRH and mortality were 1.06 (1.03-1.08) and 1.02 (1.00-1.04), respectively. A total of 63.2% of SRH and 50.0% of mortality studies were at serious risk of bias (RoB), resulting in very low and low certainty ratings, respectively. For SRH and mortality, we did not identify relevant evidence to assess the specificity or, for SRH only, the experiment BH viewpoints; evidence for strength of association and dose-response gradient was inconclusive because of the high RoB; we found evidence in support of temporality and plausibility.

CONCLUSIONS

Increased income inequality is only marginally associated with SRH and mortality, but the current evidence base is too methodologically limited to support a causal relationship. To address the gaps we identified, future research should focus on income inequality measured at the national level and addressing confounding with natural experiment approaches.

Context, health and migration: a systematic review of natural experiments

Background

Migration health research pays little attention to the places into which people migrate. Studies on health effects of contextual factors are often limited because of the ability of individuals to self-select their environment, but natural experiments may allow for the causal effect of contexts to be examined. The objective was to synthesise the evidence on contextual health effects from natural experiments among migrant groups.

Methods

We performed a systematic review of natural experiments among migrant populations in PubMed/MEDLINE, The Cochrane Library, Web of Science, CINAHL and Google Scholar for literature published until 13 October 2022. 5870 articles were screened in duplicate using the following inclusion criteria: quantitative natural experiment design, migrant population, context factor as treatment variable and health or healthcare outcome variable. Synthesis without meta-analysis was performed following quality appraisal using the EPHPP tool for quantitative studies and data extraction (PROSPERO: CRD42020169236).

Findings

The 46 included articles provide evidence for negative effects of neighbourhood disadvantage on physical health and mortality, while finding mixed effects on mental health. Articles comparing migrants with those that stayed behind demonstrate detrimental effects of migration and adverse post-migratory contexts on physical health and mortality, while demonstrating favourable effects for mental and child health. Natural experiments of policy environments indicate the negative impacts of restrictive migration and social policies on healthcare utilization, mental health and mortality, as well as the positive health effects when restrictions are lifted.

Interpretation

Natural experiments complement observational studies and provide robust evidence to advocate for more inclusive migration, health and social policies as well as neighbourhood improvement programmes. In order to strengthen the methodological approach, future research utilising natural experiments should be more explicit in the mechanisms underlying the experiment and provide details on potential causal mechanisms for the observed effects.

Funding

German Science Foundation (FOR: 2928/GZ: BO5233/1-1).

Using independent cross-sectional survey data to predict post-migration health trajectories among refugees by estimating transition probabilities and their variances

Health research is often concerned with the transition of health conditions and their relation with given exposures, therefore requiring longitudinal data. However, such data is not always available and resource-intensive to collect. Our aim is to use a pseudo-panel of independent cross-sectional data (e.g., data of T 0 $T_0$ and T 1 $T_1$ ) to extrapolate and approximate longitudinal health trajectories ( T 0 $T_0$ - T 1 $T_1$ ). Methods will be illustrated by examples of studying contextual effects on health among refugees by calculating transition probabilities with associated variances. The data consist of two cross-sectional health surveys among randomly selected refugee samples in reception ( T 0 $T_0$ ) and accommodation centers ( T 1 $T_1$ ) located in Germany's third-largest federal state. Self-reported measures of physical and mental health, health-related quality of life, health care access, and unmet medical needs of 560 refugees were collected. Missing data were imputed by multiple imputation. For each imputed data set, transition probabilities were calculated based on (i) probabilistic discrete event systems with Moore-Penrose generalized inverse matrix method (PDES-MP) and (ii) propensity score matching (PSM). By application of sampling approaches, exploiting the fact that status membership is multinomially distributed, results of both methods were pooled by Rubin's Rule, accounting for within and between-imputation variance. Most of the analyzed estimates of the transition probabilities and their variances are comparable between both methods. However, it seems that they handle sparse cells differently: either assigning an average value for the transition probability for all states with high certainty (i) or assigning a more extreme value for the transition probability with large variance estimate (ii).

Health-Care Utilization of Refugees: Evidence from Austria

European countries have experienced significant inflows of migrants in the past decade, including many refugees from regions engaged in armed conflicts. Although previous research on migrant health has largely focused on economic migration, empirical evidence on refugee health is sparse. This article uses administrative data from Austria to differentiate between economic migrants and refugees and to analyze both groups’ health-care expenditures in comparison to natives. We contribute to the literature on migrant health in several dimensions. First, we follow economic migrants and refugees over the first five years after arrival and show different health-care expenditure patterns among migration groups. In contrast to patterns for economic migrants, we find substantially higher health-care expenditures for refugees compared to natives, especially in the first year after arrival. This difference is not explained by specific diseases or individual refugee groups, indicating refugees’ generally inferior health status in the first years of settlement. Second, we focus on the health effects of granting asylum and find that the expenditure differences decrease after a positive asylum decision. In the last part, by using refugees’ quasi-random placement as a natural experiment, we show that the local health-care sector's characteristics do not have a significant effect on expenditure levels. The findings highlight the importance of distinguishing between migrant groups in the analysis of health-care utilization and show that the time spent in the host country, as well as legal status, have a substantial impact on migrants’ health-care utilization.

Long-term effects of neighbourhood deprivation on diabetes risk: quasi-experimental evidence from a refugee dispersal policy in Sweden

BACKGROUND

Although studies have shown associations between neighbourhood quality and chronic disease outcomes, such associations are potentially confounded by the selection of different types of people into different neighbourhood environments. We sought to identify the causal effects of neighbourhood deprivation on type 2 diabetes risk, by comparing refugees in Sweden who were actively dispersed by government policy to low-deprivation, moderate-deprivation, or high-deprivation neighbourhoods.

METHODS

In this quasi-experimental study, we analysed national register data for refugees who arrived in Sweden aged 25-50 years, at a time when the government policy involved quasi-random dispersal of refugees to neighbourhoods with different levels of poverty and unemployment, schooling, and social welfare participation. Individuals in our sample were assigned to a neighbourhood categorised as high deprivation (≥1 SD above the mean), moderate deprivation (within 1 SD of the mean), or low deprivation (≥1 SD below the mean). The primary outcome was new diagnosis of type 2 diabetes between Jan 1, 2002, and Dec 31, 2010. We used multivariate logistic and linear regressions to assess the effects of neighbourhood deprivation on diabetes risk, controlling for potential confounders affecting neighbourhood assignment and assessing effects of cumulative exposure to different neighbourhood conditions.

FINDINGS

We included data for 61 386 refugees who arrived in Sweden during 1987-91 and who were assigned to one of 4833 neighbourhoods. Being assigned to an area deemed high deprivation versus low deprivation was associated with an increased risk of diabetes (odds ratio [OR] 1·22, 95% CI 1·07-1·38; p=0·001). In analyses that included fixed effects for assigned municipality, the increased diabetes risk was estimated to be 0·85 percentage points (95% CI -0·030 to 1·728; p=0·058). Neighbourhood effects grew over time such that 5 years of additional exposure to high-deprivation versus low-deprivation neighbourhoods was associated with a 9% increase in diabetes risk.

INTERPRETATION

This study makes use of a pre-existing governmental natural experiment to show that neighbourhood deprivation increased the risk of diabetes in refugees in Sweden. This finding has heightened importance in the context of the current refugee crisis in Europe.

FUNDING

US National Heart, Lung, and Blood Institute, US National Center for Advancing Translational Sciences, US National Institute on Minority Health and Health Disparities, Swedish Research Council.