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Social network analysis predicts health behaviours and self-reported health in African villages.

ABSTRACT: The provision of healthcare in rural African communities is a highly complex and largely unsolved problem. Two main difficulties are the identification of individuals that are most likely affected by disease and the prediction of responses to health interventions. Social networks have been shown to capture health outcomes in a variety of contexts. Yet, it is an open question as to what extent social network analysis can identify and distinguish among households that are most likely to report poor health and those most likely to respond to positive behavioural influences. We use data from seven highly remote, post-conflict villages in Liberia and compare two prominent network measures: in-degree and betweenness. We define in-degree as the frequency in which members from one household are named by another household as a friends. Betweenness is defined as the proportion of shortest friendship paths between any two households in a network that traverses a particular household. We find that in-degree explains the number of ill family members, whereas betweenness explains engagement in preventative health. In-degree and betweenness independently explained self-reported health and behaviour, respectively. Further, we find that betweenness predicts susceptibility to, instead of influence over, good health behaviours. The results suggest that targeting households based on network measures rather than health status may be effective for promoting the uptake of health interventions in rural poor villages.

SUBMITTER: Chami GF

PROVIDER: S-EPMC4114748 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Social network analysis predicts health behaviours and self-reported health in African villages.

Chami Goylette F GF Ahnert Sebastian E SE Voors Maarten J MJ Kontoleon Andreas A AA

PloS one 20140729 7

The provision of healthcare in rural African communities is a highly complex and largely unsolved problem. Two main difficulties are the identification of individuals that are most likely affected by disease and the prediction of responses to health interventions. Social networks have been shown to capture health outcomes in a variety of contexts. Yet, it is an open question as to what extent social network analysis can identify and distinguish among households that are most likely to report poo ...[more]

PMID: 25072820

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Project description:BACKGROUND:There has been a growing interest in understanding the effects of social networks on health-related behaviour, with a particular backdrop being the emerging prominence of complexity or systems science in public health. Social network interventions specifically use or alter the characteristics of social networks to generate, accelerate, or maintain health behaviours. We conducted a systematic review and meta-analysis to investigate health behaviour outcomes of social network interventions. METHODS AND FINDINGS:We searched eight databases and two trial registries from 1990 to May 28, 2019, for English-language reports of randomised controlled trials (RCTs) and before-and-after studies investigating social network interventions for health behaviours and outcomes. Trials that did not specifically use social networks or that did not include a comparator group were excluded. We screened studies and extracted data from published reports independently. The primary outcome of health behaviours or outcomes at ?6 months was assessed by random-effects meta-analysis. Secondary outcomes included those measures at >6-12 months and >12 months. This study is registered with the International Prospective Register of Systematic Reviews, PROSPERO: CRD42015023541. We identified 26,503 reports; after exclusion, 37 studies, conducted between 1996 and 2018 from 11 countries, were eligible for analysis, with a total of 53,891 participants (mean age 32.4 years [SD 12.7]; 45.5% females). A range of study designs were included: 27 used RCT/cluster RCT designs, and 10 used other study designs. Eligible studies addressed a variety of health outcomes, in particular sexual health and substance use. Social network interventions showed a significant intervention effect compared with comparator groups for sexual health outcomes. The pooled odds ratio (OR) was 1.46 (95% confidence interval [CI] 1.01-2.11; I2 = 76%) for sexual health outcomes at ?6 months and OR 1.51 (95% CI 1.27-1.81; I2 = 40%) for sexual health outcomes at >6-12 months. Intervention effects for drug risk outcomes at each time point were not significant. There were also significant intervention effects for some other health outcomes including alcohol misuse, well-being, change in haemoglobin A1c (HbA1c), and smoking cessation. Because of clinical and measurement heterogeneity, it was not appropriate to pool data on these other behaviours in a meta-analysis. For sexual health outcomes, prespecified subgroup analyses were significant for intervention approach (p < 0.001), mean age of participants (p = 0.002), and intervention length (p = 0.05). Overall, 22 of the 37 studies demonstrated a high risk of bias, as measured by the Cochrane Risk of Bias tool. The main study limitations identified were the inclusion of studies of variable quality; difficulty in isolating the effects of specific social network intervention components on health outcomes, as interventions included other active components; and reliance on self-reported outcomes, which have inherent recall and desirability biases. CONCLUSIONS:Our findings suggest that social network interventions can be effective in the short term (<6 months) and longer term (>6 months) for sexual health outcomes. Intervention effects for drug risk outcomes at each time point were not significant. There were also significant intervention effects for some other health outcomes including alcohol misuse, well-being, change in HbA1c, and smoking cessation.

Project description:BackgroundWomen and men share similar health challenges yet women report poorer health. The study investigates the social determinants of self-reported health in women and men, and male-female differences in health.MethodsData on 103154 men and 125728 women were analysed from 57 countries in the World Health Survey 2002-2004. Item Response Theory was used to construct a composite measure of health. Associations between health and determinants were assessed using multivariate linear regression. Blinder-Oaxaca decomposition partitioned the inequality in health between women and men into an "explained" component that arises because men and women differ in social and economic characteristics, and an "unexplained" component due to the differential effects of these characteristics. Decomposition was repeated for 18 countries in the World Health Organization (WHO) African region and 19 countries in the WHO European region.ResultsWomen's health was significantly lower than men's. Health was associated with education, household economic status, employment, and marital status after controlling for age. In the pooled analysis decomposition showed that 30% of the inequality was "explained", of which almost 75% came from employment, education, marital status. The differential effects of being in paid employment increased the inequality. When countries in Africa and Europe were compared, the "explained" component (31% and 39% respectively) was largely attributed to the social determinants in the African countries and to women's longevity in the European countries. Being in paid employment had a greater positive effect on the health of males in both regions.ConclusionsWays in which age and the social determinants contribute to the poorer health status of women compared with men varies between groups of countries. This study highlights the need for action to address social structures, institutional discrimination and harmful gender norms and roles that differently influence health with ageing.

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Social network analysis predicts health behaviours and self-reported health in African villages.

Publications

Social network analysis predicts health behaviours and self-reported health in African villages.

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