Explore the vast range of titles available.

Background Area-level measures are often used to approximate socioeconomic status (SES) when individual-level data are not available. However, no national studies have examined the validity of these measures in approximating individual-level SES. Methods Data came from ~ 3,471,000 participants in the Mortality Disparities in American Communities study, which links data from 2008 American Community Survey to National Death Index (through 2015). We calculated correlations, specificity, sensitivity, and odds ratios to summarize the concordance between individual-, census tract-, and county-level SES indicators (e.g., household income, college degree, unemployment). We estimated the association between each SES measure and mortality to illustrate the implications of misclassification for estimates of the SES-mortality association. Results Participants with high individual-level SES were more likely than other participants to live in high-SES areas. For example, individuals with high household incomes were more likely to live in census tracts ( r = 0.232; odds ratio [OR] = 2.284) or counties ( r = 0.157; OR = 1.325) whose median household income was above the US median. Across indicators, mortality was higher among low-SES groups (all p < .0001). Compared to county-level, census tract-level measures more closely approximated individual-level associations with mortality. Conclusions Moderate agreement emerged among binary indicators of SES across individual, census tract, and county levels, with increased precision for census tract compared to county measures when approximating individual-level values. When area level measures were used as proxies for individual SES, the SES-mortality associations were systematically underestimated. Studies using area-level SES proxies should use caution when selecting, analyzing, and interpreting associations with health outcomes.

Son preference is culturally rooted across generations in India. While the social and economic implications of son preference are widely acknowledged, there is little evidence on spatial heterogeneity, especially at the district level. To derive estimates of son preference for the 640 districts of India and examine spatial heterogeneity in son preference across the districts of India. We apply model-based Small-Area Estimation (SAE) techniques, linking data from the 2015-2016 Indian National Family Health Survey and the 2011 Indian Population and Housing Census to generate district-level estimates of son preference. The diagnostic measures confirm that the model-based estimates are robust enough to provide reliable estimates of son preference at the district level. Son preference is highest in the districts across northern and central Indian states, followed by districts in Gujarat and Maharashtra, and lowest in the southern districts in Telangana, Andhra Pradesh, Kerala, and Tamil Nadu. There is considerable heterogeneity in son preference across Indian districts, often masked by state-level average estimates. Our findings warrant urgent policy interventions targeting specific districts in India to tackle the ongoing son-preference attitudes and practices.

A variation of Shannon’s relative entropy statistic is presented as a measure of configurational entropy for variables known to exhibit spatial autocorrelation using census tract data for the city of Birmingham, Alabama. Standardized and non-standardized configurational entropy indices (CEIs) are introduced to measure the amount of spatial order in a geographic distribution. As the degree of spatial autocorrelation increases and the amount of entropy or uncertainty decreases, the CEIs produce values that diverge from Shannon’s statistic, which tends to overstate the degree of disorder or uncertainty in the presence of spatial autocorrelation. The CEIs incorporate a spatial covariance approach to estimating spatial order based on connectivity and the differencing of values for adjacent areal units. While Shannon’s entropy statistic is insensitive to spatial arrangement, the CEIs provide a scale-targeted quantification of the amount of inherent spatial order in a distribution as defined by the connective structure of the areal units and the degree to which a variable is spatially autocorrelated. The amount of spatial order, as manifested within an autocorrelated pattern at a given geographic scale and for a given connectivity structure, is directly proportional to the difference between Shannon’s measure and the CEIs.

The humble census risks becoming a casualty of the rush to embrace big data. But it has the potential to save lives. The humble census risks becoming a casualty of the rush to embrace big data. But it has the potential to save lives. A baby is weighed as part of a nutrition project in Zinder, Niger.

Census tracts are often used to investigate area-based correlates of a variety of health outcomes. This approach has been shown to be valuable in understanding the ways that health is shaped by place and to design appropriate interventions that account for community-level processes. Following this line of inquiry, it is common in the study of pedestrian injuries to aggregate the point level locations of these injuries to the census tracts in which they occur. Such aggregation enables investigation of the relationships between a range of socioeconomic variables and areas of notably high or low incidence. This study reports on the spatial distribution of child pedestrian injuries in a mid-sized U.S. city over a three-year period. Utilizing a combination of geospatial approaches, Near Analysis, Kernel Density Estimation, and Local Moran's I, enables identification, visualization, and quantification of close proximity between incidents and tract boundaries. Specifically, results reveal that nearly half of the 100 incidents occur within roads that are also census tract boundaries. Results also uncover incidents that occur on tract boundaries, not merely near them. This geographic pattern raises the question of the utility of associating area-based census data from any one tract to the injuries occurring in these border zones. Furthermore, using a standard spatial join technique in a Geographic Information System (GIS), these points located on the border are counted as falling into census tracts on both sides of the boundary, which introduces uncertainty in any subsequent analysis. Therefore, two additional approaches of aggregating points to polygons were tested in this study. Results differ with each approach, but without any alert of such differences to the GIS user. This finding raises a fundamental concern about techniques through which points are aggregated to polygons in any study using point level incidents and their surrounding census tract socioeconomic data to understand health and place. This study concludes with a suggested protocol to test for this source of uncertainty in analysis and an approach that may remove it.

Objective: To estimate variation between small areas in the levels of walking, cycling, jogging, and swimming and overall physical activity and the importance of area level socioeconomic disadvantage in predicting physical activity participation. Methods: All census collector districts (CCDs) in the 20 innermost local government areas in metropolitan Melbourne, Australia, were identified and ranked by the percentage of low income households (<$400/week) living in the CCD. Fifty CCDs were randomly selected from the least, middle, and most disadvantaged septiles of the ranked CCDs and 2349 residents (58.7% participation rate) participated in a cross sectional postal survey about physical activity. Multilevel logistic regression (adjusted for extrabinomial variation) was used to estimate area level variation in walking, cycling, jogging, and swimming and in overall physical activity participation, and the importance of area level socioeconomic disadvantage in predicting physical activity participation. Results: There were significant variations between CCDs in all activities and in overall physical participation in age and sex adjusted models; however, after adjustment for individual SES (income, occupation, education) and area level socioeconomic disadvantage, significant differences remained only for walking (p = 0.004), cycling (p = 0.003), and swimming (p = 0.024). Living in the most socioeconomically disadvantaged areas was associated with a decreased likelihood of jogging and of having overall physical activity levels that were sufficiently active for health; these effects remained after adjustment for individual socioeconomic status (sufficiently active: OR 0.70, 95% CI 0.55 to 0.90 and jogging: OR = 0.69, 95% CI 0.51 to 0.94). Conclusion: These research findings support the need to focus on improving local environments to increase physical activity participation.

Study objective: To examine the association between area and individual level socioeconomic status (SES) and food purchasing behaviour. Design: The sample comprised 1000 households and 50 small areas. Data were collected by face to face interview (66.4% response rate). SES was measured using a composite area index of disadvantage (mean 1026.8, SD = 95.2) and household income. Purchasing behaviour was scored as continuous indices ranging from 0 to 100 for three food types: fruits (mean 50.5, SD = 17.8), vegetables (61.8, 15.2), and grocery items (51.4, 17.6), with higher scores indicating purchasing patterns more consistent with dietary guideline recommendations. Setting: Brisbane, Australia, 2000. Participants: Persons responsible for their household’s food purchasing. Main results: Controlling for age, gender, and household income, a two standard deviation increase on the area SES measure was associated with a 2.01 unit increase on the fruit purchasing index (95% CI −0.49 to 4.50). The corresponding associations for vegetables and grocery foods were 0.60 (−1.36 to 2.56) and 0.94 (−1.35 to 3.23). Before controlling for household income, significant area level differences were found for each food, suggesting that clustering of household income within areas (a composition effect) accounted for the purchasing variability between them. Conclusions: Living in a socioeconomically advantaged area was associated with a tendency to purchase healthier food, however, the association was small in magnitude and the 95% CI for area SES included the null. Although urban areas in Brisbane are differentiated on the basis of their socioeconomic characteristics, it seems unlikely that where you live shapes your procurement of food over and above your personal characteristics.

Billard discusses the history of the census, how it is organized and what the information means. The 1787 Constitution of the US mandates that starting in 1790, a census must be conducted every 10 years.