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Objective: The purpose of the study was to examine the relationship between alcohol environments and problem drinking, including excessive alcohol consumption, heavy episodic drinking, driving after drinking, and riding with a drinking driver. Method: We merged geo-coded individual-level data from the California Health Interview Survey and Los Angeles County Health Survey with alcohol license data from the California Department of Alcoholic Beverage Control, distinguishing off-sale retails from on-sale establishments and, among onsales, eating places from bars and taverns as well as minor-unrestricted establishments from minor-restricted establishments (i.e., youth below age 21 not allowed on business premises). The primary explanatory variable was alcohol outlets within various distances from an individual's residence or census tract. Multivariate logistic regression and simulation were run for men and women separately. Results: On-sale establishments, particularly minor-restricted establishments, were significantly associated with excessive alcohol consumption and heavy episodic drinking, after controlling for individual and neighborhood sociodemographics. The effect was limited to outlets located within proximity, roughly 1 mile from residential homes. Off-sale retails were not found to be related to problem drinking. If the number of minor-restricted establishments increases from median to 90th percentile of their distribution, heavy episodic drinking would increase from 11.1% to 14.3% among women and from 19.6% to 22.0% among men. Conclusions: Certain types of alcohol retailers in neighborhoods were associated with problem drinking. Moratorium of new licenses based on number of licenses per capita at county level is not effective because only a subgroup of licenses matters, and alcohol is more available in terms of distance, travel time, or search costs in densely populated cities. (J. Stud Alcohol Drugs 68: 923-933, 2007), STATE AND LOCAL GOVERNMENTS often regulate alcohol outlets by placing limits on the number of available licenses, typically in relation to population. California imposes a moratorium on the issuance of [...]

Objective: This study had two purposes: (1) to characterize the density of liquor stores and bars that individuals face according to race, economic status, and age in the urban United States and (2) to assess alternative measures of retailer density based on the road network and population. Method: We used census data on business counts and sociodemographic characteristics to compute the densities facing individuals in 9,361 urban zip codes. Results: Blacks face higher densities of liquor stores than do whites. The density of liquor stores is greater among nonwhites in lower-income areas than among whites in lower- and higher-income areas and nonwhites in higher-income areas. Nonwhite youths face higher densities of liquor stores than white youths. The density of liquor stores and bars is lower in higher-income areas, especially for nonwhites. Conclusions: Mismatches between alcohol demand and the supply of liquor stores within urban neighborhoods constitute an environmental injustice for minorities and lower-income persons, with potential adverse consequences for drinking behavior and other social ills. Our results for bars are sensitive to the measure of outlet density as well as population density. Although neither measure is clearly superior, a measure that accounts for roadway miles may reflect proximity to alcohol retailers and thus serve as a useful refinement to the per-capita measure. If so, alcohol policy might also focus on density per roadway mile. Further research on the existence, causes, and consequences of environmental injustice in alcohol retailing is warranted. (J. Stud Alcohol Drugs 68: 48-55, 2007), DISPARITIES IN HEALTH RISKS across racial/ethnic and income groups have been documented for toxic waste sites, air pollution, and industrial sites (Brown, 1995; Environmental Protection Agency, 1992). In response, a [...]

In this analysis we create census-tract level indices of area deprivation for the US that parallel similar indices developed in Britain, and we determine whether these indices are related to physical and mental health outcomes. Data for the indices come from the 2000 Census Summary File Tapes and the 2001 Zip Code Business Patterns Files. These indices are then linked by census tract to cross-sectional data from the HealthCare for Communities (HCC) study, and equations are estimated using multi-level models with census-tract random effects. We find that area-level deprivation predicts poor mental and physical health outcomes, but different components of deprivation have different effects. When we measure deprivation using three factor scores that emerged from our analysis (rather than combining all measures of deprivation into a single index), we find that access to services has a more pronounced association with physical health, whereas racial composition and local language barriers are more strongly correlated with mental health. These findings suggest that grouping all variables into a single index may mask important heterogeneity in the ways in which area characteristics affect health outcomes. Keywords: Neighborhoods; Community; Deprivation; Deprivation indices; USA

ObjectiveImproving diet quality is a key factor for promoting population health. Social norms can support or undermine these efforts. The present study aimed to investigate the relationship between seasonal variation in food purchases and BMI.DesignThe study population comprises members of a South African health promotion programme. Data come from scanner data of food purchases by 400 000 enrolled households at supermarkets and repeated individual surveys (about 500 000 participants) between 2009 and 2013.ResultsMembers in the health promotion programme spent 16·7 % of total food expenditure on nutritionally undesirable foods (sugar-sweetened beverages, candy, ice cream, etc.) and 24·7 % on healthy foods (fruit/vegetables, whole grains, non-fat dairy, etc.). Fruits and vegetables accounted for 13·5 % of purchases (half of all healthy food spending). Yet there were pronounced seasonal variations, with December being the peak month for unhealthy food purchases, which were 40 % higher than in January. This holiday peak was associated with short-term weight gain, but average body mass did not revert to pre-holiday levels. From 2009 to 2013, respondents gained about 0·13 BMI units per year (0·43 kg for men, 0·30 for women). From November to January alone, the increase was 0·1 BMI units for men (0·35 kg) and 0·8 BMI units for women (0·20 kg).ConclusionsPurchases of nutritionally undesirable foods peak in December and are accompanied by weight gain from November to January. Despite weight loss after January, the November to January weight gain accounts for 60–70 % of the annual gain. [ABSTRACT FROM PUBLISHER]

Although the policy debate is only slowly moving away from the focus on individual-level psychological and social factors, the research community has largely recognized that changes in dietary and physical activity patterns are driven by changes in the environment and by the incentives that people face. Many factors have been suggested as causes of the 'obesity epidemic'. Putting a multitude of isolated data points into a coherent picture is a challenging, but necessary, task to assess whether proposed solutions are promising or likely to lead down a blind alley. Conventional wisdom is an unreliable guide and some widely held beliefs are incorrect. Can one distinguish between important and less important behavioural changes and relate them to environmental incentives? People face trade-offs in allocating their scarce resources of time and money to best achieve their goats, including health. Studying what people are doing with their time and money is a good start towards understanding how economic incentives have altered energy intake and energy expenditure in a way that has led to weight gain. A challenging task for policy will be finding the right levers. Both economic and public health/medical perspectives play an important role in the policy process, but often approach policy questions in an incompatible way. Economics and public health perspectives can complement each other, but harnessing any synergy requires an understanding of the other perspective. Arguably the most effective community intervention would be multi-faceted and would include several goals about diet and physical activity. In practice, however, it appears that much more effort is devoted to promoting increased fruit/vegetable consumption, and exhorting individuals to increase physical activity than to environmental intervention that would make it easier for people to reduce energy intake and sedentary entertainment. Politically, it may often be more expedient to promote an increase than a decrease, but it may be far less effective. [ABSTRACT FROM AUTHOR]

Abstract: What are Americans doing with their time and their money and what has changed in recent decades? Do changes suggest interventions that will lead to healthier lifestyles? This paper analyzes several different data sets that reveal some surprising (and some less surprising) insights. The big growth areas, both in terms of expenditure and time allocation, have been leisure time and travel/transportation. Leisure-time industries outpace gross-domestic-product growth for both “active” (sporting goods, dance studios, gyms) and “sedentary” industries (spectator sports, cable TV), although industries associated with more sedentary lifestyles grow the fastest. Overall time spent in productive activities, whether at home or work, has declined by several hours each week for both men and women compared to 40 years ago. Reduced physical activity by itself is not a reason for intervening, as many changes improved overall quality of life (even if not necessarily health-related quality of life). But other trends are more likely to reflect poorly functioning markets, leading to worse economic and health outcomes. Market failures that lead to less physical activity or unhealthy nutrition justify interventions, both from an economic and a public health perspective. [Copyright &y& Elsevier]

Objectives: To analyse the relation between geographical inequalities in income and the prevalence of common chronic medical conditions and mental health disorders, and to compare it with the relation between family income and these health problems. Design: Nationally representative household telephone survey conducted in 1997-8. Setting: 60 metropolitan areas or economic areas of the United States. Participants: 9585 adults who participated in the community tracking study. Main outcome measures: Self report of 17 common chronic medical conditions; current depressive disorder or anxiety disorder assessed by clinical screeners. Results: A strong continuous association was seen between health and education or family income. No relation was found between income inequality and the prevalence of chronic medical problems or depressive disorders and anxiety disorders, either across the whole population or among poorer people. Only self reported overall health, the measure used in previous studies, was significantly correlated with inequality at the population level, but this correlation disappeared after adjustment for individual characteristics. Conclusions: This study provides no evidence for the hypothesis that income inequality is a major risk factor for common disorders of physical or mental health. [ABSTRACT FROM AUTHOR]