Improving program targeting to combat early-life mortality by identifying high-risk births: an application to India

Background

It is widely recognized that there are multiple risk factors for early-life mortality. In practice most interventions to curb early-life mortality target births based on a single risk factor, such as poverty. However, most premature deaths are not from the targeted group. Thus interventions target many births that are at not at high risk and miss many births at high risk.

Methods

Using data from the second wave of Demographic and Health Surveys from India and a hierarchical Bayesian model, we estimate infant mortality risk for 73.320 infants in India as a function of 4 risk factors. We show how this information can be used to improve program targeting. We compare our novel approach against common programs that target groups based on a single risk factor.

Results

A conventional approach that targets mothers in the lowest quintile of income correctly identifies only 30% of infant deaths. By contrast, using four risk factors simultaneously we identify a group of births of the same size that includes 57% of all deaths. Using the 2012 census to translate these percentages into numbers, there were 25.642.200 births in 2012 and 4.4% died before the age of one. Our approach correctly identifies 643.106 of 1.128.257 infant deaths while poverty only identifies 338.477 infant deaths.

Conclusion

Our approach considerably improves program targeting by identifying more infant deaths than the usual approach that targets births based on a single risk factor. This leads to more efficient program targeting. This is particularly useful in developing countries, where resources are lacking and needs are high.

     

Sociodemographic and Lifestyle Factors,and Health Conditions of Dominican Adults Living in Puerto Rico

Dominicans are the largest migrant community in Puerto Rico, yet understudied. We compared risk factors and health conditions of Dominicans versus Puerto Ricans (PRs). Cross-sectional survey of Dominicans (n?=?55) and PRs (n?=?310) aged 30–75 years, assessed with validated questionnaires and standardized anthropometric measurements. Significantly, more Dominicans than PRs had attained <8th grade education (37.7 vs. 8.0%), reported household income ≤$10,000 (76.1 vs. 56.9%), lacked health insurance (19.6 vs. 5.5%), and reported food insecurity (24.5 vs. 12.1%). They spent fewer hours/day watching television (2.9 vs. 3.8), and were less likely to smoke (7.6 vs. 19.6%). Medically-diagnosed depression was lower among Dominicans than PRs (9.6 vs. 23.0%); questionnaire-based high depressive symptomatology was similar (47.9 vs. 52.8%). Dominicans living in Puerto Rico had more socioeconomic risk factors but healthier lifestyle behaviors and lower prevalence of medically-diagnosed depression than PRs. Tailored approaches are needed to ameliorate disparities in each ethnic group.     

Comparison of Health and Risk Factors of Older,Working-age Australians,Italians and Italian-born Migrants to Australia,with Data from an Italian (PASSI), and an Australian (SAMSS) Risk Factor Surveillance System

Italian-born migrants (post-WWII) are the largest non-English-speaking background migrant group in South Australia. A cross-sectional, inter-country comparison using independent samples (40–69 years of age) from two (one in Australia, one in Italy) similar risk factor and chronic disease surveillance systems. None of the three groups (Italians, Australian-born and Italian-born Australians) had definitively worse health although the Italians had high rates for four of the seven risk factors reported (current high blood pressure, current high cholesterol, current smoking, eating less than five fruit and/or vegetables per day) than Australian-born and Italian-born Australians. Italian-born Australians had higher rates for insufficient physical activity, overweight/obese, poor self-reported health and diabetes. Australian respondents were more likely to report having two or more drinks of alcohol per day. Issues facing an ageing population require appropriate health care needs and an assessment of structural or cultural barriers to health services.