Socioeconomic disadvantage and its implications for population health planning of obesity and overweight, using cross-sectional data from general practices from a regional catchment in Australia

Abstract

ObjectivesTo identify smaller geographic and region-specific evidence to inform population health planning for overweight and obesity.DesignCross-sectional secondary analysis of data.SettingPrimary healthcare—17 general practices located in the Illawarra-Shoalhaven region of New South Wales (NSW).ParticipantsA subset (n=36 674) of the Sentinel Practices Data Sourcing project adult persons data set (n=118 794) that included information on disease status of all adult patients who had height and weight measurements recorded in their electronic health records and had visited the included general practices within the Illawarra-Shoalhaven region of NSW between September 2011 and September 2013.Main outcome measuresAge-adjusted odds ratio (aOR) of overweight and obesity was determined for high and low levels of socioeconomic disadvantage based on Socio-Economic Indexes for Areas (SEIFA)—Index of Relative Socio-Economic Disadvantage (IRSD) scores of patients' residential statistical local area.ResultsIn men, overweight was lowest in areas of highest socioeconomic disadvantage (aOR=0.910; 95% CI 0.830 to 0.998; p<0.001); but no statistically significant association with socioeconomic score was found for women. Overall obesity was associated with high socioeconomic disadvantage (aOR=1.292; 95% CI 1.210 to 1.379; p<0.001).ConclusionsThis type of data analysis reveals multiple layers of evidence that should be assessed for population health approaches to curb the epidemic of obesity and overweight. It strongly highlights the need for preventive health initiatives to be specific to gender and socioeconomic attributes of the target population.

Changes in body mass index in long-term survivors of childhood acute lymphoblastic leukemia treated without cranial radiation and with reduced glucocorticoid therapy

“…The results of our study were consistent with previous research highlighting the influence of a younger age at diagnosis and family SES on increases in BMI z‐score. With regard to sex, male survivors of ALL had a significantly higher average BMI z‐score compared with females at diagnosis.…”

“…Each patient was assigned an index of relative socioeconomic status disadvantage percentile score (IRSD%) by home postal code using the Socio‐Economic Indexes for Areas . The IRSD is a validated indicator providing a socioeconomic status (SES) score based on the economic and social conditions of households within a geographical area in Australia and has been used by previous studies . This score incorporates several measures indicative of SES, including, but not limited to, the percentage of people aged 15 years and over who have no educational attainment or who are unemployed and the percentage of families with children less than 15 years of age whose parents are unemployed.…”

“…Ethnic diversity could not be adjusted for in our anlayses as ethnicity data were not collected as part of the current treatment protocol. While SES data were not available on an individual level, a commonly used standardized summary measure was utilized that incorporated several variables indicative of social economic disadvantage . Although this study had significant patient numbers from diagnosis to the end of treatment, it is limited in that 31.6% and 45.4% of patients had not yet reached 5 and 7 years from diagnosis at the time of data collection.…”

“…While SES data were not available on an individual level, a commonly used standardized summary measure was utilized that incorporated several variables indicative of social economic disadvantage. 14…”

“…13 The IRSD is a validated indicator providing a socioeconomic status (SES) score based on the economic and social conditions of households within a geographical area in Australia 13 and has been used by previous studies. 14…”