Using agent-based modelling to test hypotheses on the role of neighbourhood social mechanisms in the development of small-area health inequalities.

BACKGROUND: Small-area health inequalities may originate from differentials in the spatial distribution of environmental stressors on health. The role played by neighbourhood social mechanisms on small-area health inequalities is difficult to evaluate. We demonstrate that agent-based modelling (ABM) is a useful technique to overcome existing limitations. It allows testing hypotheses that social contagion has the potential to modify the effects of environmental stressors by reducing or increasing small-area health inequalities. METHODS: Parameters defining the strength of the effect of social contagion on health behaviour were used together with a stochastic model to obtain for every year the health outcome of every agent based on health the previous year, environmental stressors and health behaviour. Unequal spatial distribution of stressors was operationalised with spatial correlation structure. We measured changes in health inequalities using parameters of the spatial correlation structure of health after 10 years. In a further round of simulations, social contagion depended on the environmental stressors. RESULTS: A social contagion mechanism led to a reduction of small-area health inequalities together with an increase in the spatial reach of the effect of environmental stressors. An association between environmental stressor and social contagion mechanism led to a stronger localisation of the effect of environmental stressors. CONCLUSIONS: Hypotheses about the role of neighbourhood social mechanisms can be tested using ABM. The respective models provide a better understanding of mechanisms in the causal chain between environmental stressors and health inequalities. This can pave the way to the development of a new type of neighbourhood-based intervention informed by social mechanisms.

Does social cohesion mediate neighbourhood effects on mental and physical health? Longitudinal analysis using German Socio-Economic Panel data.

BACKGROUND: Neighbourhood has risen as a relevant determinant of health. While there is substantial evidence that environmental factors affect health, far less evidence of the role of social mechanisms in the causal chain between neighbourhood characteristics and health is available. METHOD: To evaluate the role of social cohesion as a mediator between four different neighbourhood characteristics and health using data from German Socio-Economic-Panel (SOEP), a longitudinal mediation analysis was performed. Multilevel linear regression models adjusted for socio-economic variables involved three time points and two measures of physical and mental health (physical and mental component scores (PCS and MCS) of the SF12 Questionnaire. Participants were followed-up for 4 and 10 year starting in 2004. RESULTS: A total of 15,518 measures of MCS and PCS on 10,013 participants living in 4985 households were included. After adjusting for values of MCS and PCS at baseline and demographic/socio-economic variables, social cohesion was a significant positive predictor of both MCS and PCS (ß-coefficient MCS: 1.57 (0.27); PCS: 1.50 (0.24)). Interaction between social cohesion and follow-up were significant for PCS. The effect of environmental and built characteristics on health was consistently mediated by social cohesion with proportion varying between 10 and 23%. DISCUSSION: We show that social cohesion is part of the causal chain between environmental and built characteristics of a neighbourhood and health, with increasing mediation effect over time for physical health. Social mechanisms should be considered when studying the effect of neighbourhood characteristics on health inequalities making social cohesion as a legitimate target of public health interventions at neighbourhood level.

Do social factors and country of origin contribute towards explaining a "Latina paradox" among immigrant women giving birth in Germany?

BACKGROUND: The "Latina paradox" describes the unexpected association between immigrant status, which is often correlated to low socioeconomic status, and low prevalence of unfavourable birth outcomes. Social (e.g. culture, religion) and/or non-social factors related to country of origin are potentially responsible for this paradox. METHODS: Questionnaire survey of 6413 women delivering in three large obstetric hospitals in Berlin (Germany) covering socioeconomic and migration status, country of origin (Turkey, Lebanon), and acculturation. Data was linked with routine obstetric data. Logistic regressions were performed to assess the effect of acculturation, affinity to religion and country of origin on preterm birth and small-for-gestational-age (SGA). RESULTS: Immigrant women with a low level of acculturation (reference) were less likely to have a preterm birth than those who were highly acculturated (aOR: 1.62, 95%CI: 1.01-2.59), as were women from Turkey compared to non-immigrants (aOR: 0.49, 95%CI: 0.33-0.73). For SGA, we found no epidemiologic paradox; conversely, women from Lebanon had a higher chance (aOR: 1.72, 95%CI: 1.27-2.34) of SGA. Affinity to religion had no influence on birth outcomes. CONCLUSIONS: There is evidence that low acculturation (but not affinity to religion) contributes towards explaining the epidemiologic paradox with regard to preterm birth, emphasising the influence of socioeconomic characteristics on birth outcomes. The influence of Turkish origin on preterm birth and Lebanese origin on SGA suggests that non-social factors relating to the country of origin are also at play in explaining birth outcome differences, and that the direction of the effect varies depending on the country of origin and the outcome.

Social Mechanisms in Epidemiological Publications on Small-Area Health Inequalities-A Scoping Review.

Background: Small-area social mechanisms-social processes involving the social environment around the place of residence-may be playing a role in the production of health inequalities. Understanding how small-area health inequalities (social environment affects health and consequently contribute to inequalities between areas) are generated and the role of social mechanisms in this process may help defining interventions to reduce inequalities. In mediation and pathway analyses, social mechanisms need to be treated as processes or factors. We aimed to identify which types of social mechanisms explaining the process leading from small-area characteristics to health inequalities have been considered and investigated in epidemiological publications and to establish how they have been operationalized. Methods: We performed a scoping review for social mechanisms in the context of small-area health inequalities in the database PubMed. Epidemiological publications identified were categorized according to the typology proposed by Galster (social networks, social contagion, collective socialization, social cohesion, competition, relative deprivation, and parental mediation). Furthermore, we assessed whether the mechanisms were operationalized at the micro or macro level and whether mechanisms were considered as processes or merely as exposure factors. Results: We retrieved 1,019 studies, 15 thereof were included in our analysis. Eight forms of operationalization were found in the category social networks and another nine in the category social cohesion. Other categories were hardly represented. Furthermore, all studies were cross sectional and did not consider mechanisms as processes. Except for one, all studies treated mechanisms merely as factors whose respective association to health outcomes was tested. Conclusion: In epidemiological publications, social mechanisms in studies on small-area effects on health inequalities are not operationalized as processes in which these mechanisms would play a role. Rather, the focus is on studying associations. To understand the production of health inequalities and the causal effect of social mechanisms on health, it is necessary to analyze mechanisms as processes. For this purpose, methods such as complex system modeling should be considered.