The forthcoming Public Health Twitter Journal Club will be discussing this paper byWilkinson and Pickett
Join us on Twitter at 8.00pm UK time (BST) on Sunday 8th April, using the hashtag #PHTwitJC. All are welcome.
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An ecological study design in epidemiology uses the group as a unit of analysis, rather than the individual. Ecological studies are useful for identifying new hypotheses for further exploration, and they tend to be comparatively quick, easy and inexpensive to conduct.
However ecological study designs are seen as inferior to other designs (such as cohort, case-control and trials) because they are susceptible to the ecological fallacy. The ecological fallacy is an error of interpretation of statistical data where aggregate group characteristics are assumed to apply to all individuals within the group.
This study investigated whether the processes associated between income inequality and population health are related to those responsible for the socioeconomic gradient in health, and whether health disparities are smaller when income differences are narrower.
The authors employed multilevel models in a regression analysis of 10 age- and cause-specific US county mortality rates on county median household incomes and on state income inequality. They assessed whether mortality rates more closely related to county income were also more closely related to state income inequality.
It was reported that mortality rates more strongly associated with county income were more strongly associated with state income inequality: across all mortality rates, r=−0.81; P=.004. Overall greater equality usually benefited both wealthier and poorer US counties.
The authors concluded that
“although mortality rates with steep socioeconomic gradients were more sensitive to income distribution than were rates with flatter gradients, narrower income differences benefit people in both wealthy and poor areas and may, paradoxically, do little to reduce health disparities.”
Questions for Journal Club:
- Were the aims of the study clear?
- Were the parameters used clear, relevant and valid?
- Are the findings justifiable considering the information inputted ?
- Could anything else explain the results (chance, bias, confounders?)
- What implications do the findings have for public health practice & policy?