The independent associations of recorded crime and perceived safety with physical health in a nationally representative cross-sectional survey of men and women in New Zealand

Objectives: We investigated associations of officially recorded crime and perceived neighbourhood safety with physical health, evaluating potential effect modification by gender. Setting: Nationally representative population-based survey in New Zealand. Participants: Individual-level data from 6995 New Zealand General Social Survey (2010–2011) participants with complete data on physical health status, perceived neighbourhood safety, sociodemographic characteristics and smoking. Crime rate for each participant's home census was estimated based on data from the New Zealand Police (2008–2010). Primary outcome measure: The Transformed Physical Composite Score from the SF-12, a physical health summary score based on self-report ranging from 0 to 100. Results: We used cluster robust multivariable regression models to examine the associations among neighbourhood crime rates, perceived neighbourhood safety and the physical health summary score. Crime rates predicted adults’ perception that it was unsafe to walk in their neighbourhood at night: for each additional crime per 100 000 residents adults were 1.9% more likely to perceive their neighbourhood as unsafe (95% CI 1.2% to 2.5%). While relatively uncommon, the rate of crime with a weapon strongly predicted perceived safety: for each additional crime per 100 000 residents in this category, adults were 12.9% more likely to report the neighbourhood as unsafe (95% CI 8.8% to 17.0%). Police-recorded violent and night crime rates were associated with worse physical health among women: for each additional crime per 100 000 residents in these category women had a 0.3 point lower physical health score (95% CIs −0.6 to −0.1 for violent crime and −0.5 to −0.1 for crime at night, gender interaction p values 0.08 and 0.01, respectively). Perceiving the neighbourhood as unsafe was independently associated with 1.0 point lower physical health score (95% CI −1.5 to −0.5). Conclusions: Gender may modify the associations of officially recorded crime rates with physical health. Perceived neighbourhood safety was independently associated with physical health

Cause and context: place-based approaches to investigate how environments affect mental health

Objectives Our surroundings affect our mood, our recovery from stress, our behavior, and, ultimately, our mental health. Understanding how our surroundings influence mental health is central to creating healthy cities. However, the traditional observational methods now dominant in the psychiatric epidemiology literature are not sufficient to advance such an understanding. In this essay we consider potential alternative strategies, such as randomizing people to places, randomizing places to change, or harnessing natural experiments that mimic randomized experiments. Methods We discuss the strengths and weaknesses of these methodological approaches with respect to (1) defining the most relevant scale and characteristics of context, (2) disentangling the effects of context from the effects of individuals’ preferences and prior health, and (3) generalizing causal effects beyond the study setting. Results Promising alternative strategies include creating many small-scale randomized place-based trials, using the deployment of place-based changes over time as natural experiments, and using fluctuations in the changes in our surroundings in combination with emerging data collection technologies to better understand how surroundings influence mood, behavior, and mental health. Conclusions Improving existing research strategies will require interdisciplinary partnerships between those specialized in mental health, those advancing new methods for place effects on health, and those who seek to optimize the design of local environments

Residential Relocation by Older Adults in Response to Incident Cardiovascular Health Events: A Case-Crossover Analysis

We use a case-crossover analysis to explore the association between incident cardiovascular events and residential relocation to a new home address. Methods. We conducted an ambidirectional case-crossover analysis to explore the association between incident cardiovascular events and residential relocation to a new address using data from the Cardiovascular Health Study (CHS), a community-based prospective cohort study of 5,888 older adults from four U.S. sites beginning in 1989. Relocation was assessed twice a year during follow-up. Event occurrences were classified as present or absent for the period preceding the first reported move, as compared with an equal length of time immediately prior to and following this period. Results. Older adults (65+) that experience incident cardiovascular disease had an increased probability of reporting a change of residence during the following year (OR 1.6, 95% confidence interval (CI) = 1.2–2.1). Clinical conditions associated with relocation included stroke (OR: 2.0, 95% CI: 1.2–3.3), angina (OR: 1.6, 95% CI: 1.0–2.6), and congestive heart failure (OR: 1.5, 95% CI: 1.0–2.1). Conclusions. Major incident cardiovascular disease may increase the probability of residential relocation in older adults. Case-crossover analyses represent an opportunity to investigate triggering events, but finer temporal resolution would be crucial for future research on residential relocations

Using built environment characteristics to predict walking for exercise

Background: Environments conducive to walking may help people avoid sedentary lifestyles and associated diseases. Recent studies developed walkability models combining several built environment characteristics to optimally predict walking. Developing and testing such models with the same data could lead to overestimating one's ability to predict walking in an independent sample of the population. More accurate estimates of model fit can be obtained by splitting a single study population into training and validation sets (holdout approach) or through developing and evaluating models in different populations. We used these two approaches to test whether built environment characteristics near the home predict walking for exercise. Study participants lived in western Washington State and were adult members of a health maintenance organization. The physical activity data used in this study were collected by telephone interview and were selected for their relevance to cardiovascular disease. In order to limit confounding by prior health conditions, the sample was restricted to participants in good self-reported health and without a documented history of cardiovascular disease. Results: For 1,608 participants meeting the inclusion criteria, the mean age was 64 years, 90 percent were white, 37 percent had a college degree, and 62 percent of participants reported that they walked for exercise. Single built environment characteristics, such as residential density or connectivity, did not significantly predict walking for exercise. Regression models using multiple built environment characteristics to predict walking were not successful at predicting walking for exercise in an independent population sample. In the validation set, none of the logistic models had a C-statistic confidence interval excluding the null value of 0.5, and none of the linear models explained more than one percent of the variance in time spent walking for exercise. We did not detect significant differences in walking for exercise among census areas or postal codes, which were used as proxies for neighborhoods. Conclusion: None of the built environment characteristics significantly predicted walking for exercise, nor did combinations of these characteristics predict walking for exercise when tested using a holdout approach. These results reflect a lack of neighborhood-level variation in walking for exercise for the population studied.University of Washington Royalty Research fund award; by contracts R01-HL043201, R01-HL068639, and T32-HL07902 from the National Heart, Lung, and Blood Institute; and by grant R01-AG09556 from the National Institute on Aging

Association of environmental tobacco smoke exposure in childhood with early emphysema in adulthood among nonsmokers: the MESA-lung study.

Mechanical stress to alveolar walls may cause progressive damage after an early-life insult such as exposure to environmental tobacco smoke (ETS). This hypothesis was examined by using data from the Multi-Ethnic Study of Atherosclerosis (MESA), a population-based cohort aged 45-84 years, free of clinical cardiovascular disease, recruited from 6 US sites in 2000-2002. The MESA-Lung Study assessed a fractal, structural measure of early emphysema ("alpha," lower values indicate more emphysema) and a standard quantitative measure ("percent emphysema") from cardiac computed tomography scans. Childhood ETS exposure was assessed retrospectively as a report of living with one or more regular indoor smokers. Analyses included 1,781 nonsmokers (<100 cigarettes, 20 cigars, or 20 pipefulls in their lifetime and urinary cotinine levels <100 ng/mL); mean age was 61 years (standard deviation, 10), and 65% were women. Childhood ETS exposure from 2 or more smokers (17%) compared with none (52%) was associated with 0.05 lower alpha and 2.8 higher percent emphysema (P for trend = 0.04 and 0.01, respectively) after adjustment for demographic, anthropometric, parental, and participant characteristics, as well as adult exposures (e.g., cumulative residential air pollution exposure, exposure to ETS as an adult). Childhood ETS exposure was associated with detectable differences on computed tomography scans of adult lungs of nonsmokers. PMID: 19942575 [PubMed - indexed for MEDLINE]PMCID: PMC2800303 [Available on 2011/1/1]Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78014/1/Am._J._Epidemiol.-2010-Lovasi-54-62[1].pd

Use of community-level data in the National Children’s Study to establish the representativeness of segment selection in the Queens Vanguard Site

The WHO Multiple Exposures Multiple Effects (MEME) framework identifies community contextual variables as central to the study of childhood health. Here we identify multiple domains of neighborhood context, and key variables describing the dimensions of these domains, for use in the National Children’s Study (NCS) site in Queens. We test whether the neighborhoods selected for NCS recruitment, are representative of the whole of Queens County, and whether there is sufficient variability across neighborhoods for meaningful studies of contextual variables. Nine domains (demographic, socioeconomic, households, birth rated, transit, playground/greenspace, safety and social disorder, land use, and pollution sources) and 53 indicator measures of the domains were identified. Geographic information systems were used to create community-level indicators for US Census tracts containing the 18 study neighborhoods in Queens selected for recruitment, using US Census, New York City Vital Statistics, and other sources of community-level information. Mean and inter-quartile range values for each indicator were compared for Tracts in recruitment and non-recruitment neighborhoods in Queens. Across the nine domains, except in a very few instances, the NCS segment-containing tracts (N = 43) were not statistically different from those 597 populated tracts in Queens not containing portions of NCS segments; variability in most indicators was comparable in tracts containing and not containing segments. In a diverse urban setting, the NCS segment selection process succeeded in identifying recruitment areas that are, as a whole, representative of Queens County, for a broad range of community-level variables

Disparities in trajectories of changes in the unhealthy food environment in New York City: A latent class growth analysis, 1990-2010.

Disparities in availability of food retailers in the residential environment may help explain racial/ethnic and socio-economic differences in obesity risk. Research is needed that describes whether food environment dynamics may contribute to equalizing conditions across neighborhoods or to amplifying existing inequalities over time. This study improves the understanding of how the BMI-unhealthy food environment has evolved over time in New York City. We use longitudinal census tract-level data from the National Establishment Time-Series (NETS) for New York City in the period 1990-2010 and implement latent class growth analysis (LCGA) to (1) examine trajectories of change in the number of unhealthy food outlets (characterized as selling calorie-dense foods such as pizza and pastries) at the census tract-level, and (2) examine how trajectories are related to socio-demographic characteristics of the census tract. Overall, the number of BMI-unhealthy food outlets increased between 1990 and 2010. We summarized trajectories of evolutions with a 5-class model that indicates a pattern of fanning out, such that census tracts with a higher initial number of BMI-unhealthy food outlets in 1990 experienced a more rapid increase over time. Finally, fully adjusted logistic regression models reveal a greater increase in BMI-unhealthy food outlets in census tracts with: higher baseline population size, lower baseline income, and lower proportion of Black residents. Greater BMI-unhealthy food outlet increases were also noted in the context of census tracts change suggestive of urbanization (increasing population density) or increasing purchasing power (increasing income)

Independent Review Of Social And Population Variation In Mental Health Could Improve Diagnosis In DSM Revisions

At stake in the May 2013 publication of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), are billions of dollars in insurance payments and government resources, as well as the diagnoses and treatment of millions of patients. We argue that the most recent revision process has missed social determinants of mental health disorders and their diagnosis: environmental factors triggering biological responses that manifest themselves in behavior; differing cultural perceptions about what is normal and what is abnormal behavior; and institutional pressures related to such matters as insurance reimbursements, disability benefits, and pharmaceutical marketing. In addition, the experts charged with revising the DSM lack a systematic. way to take population-level variations in diagnoses into account. To address these problems, we propose the creation of an independent research review body that would monitor variations in diagnostic patterns, inform future DSM revisions, identify needed changes in mental health policy and practice, and recommend new avenues of research. Drawing on the best available knowledge, the review body would make possible more precise and equitable psychiatric diagnoses and interventions

Measuring health-relevant businesses over 21 years: refining the National Establishment Time-Series (NETS), a dynamic longitudinal data set

Background The densities of food retailers, alcohol outlets, physical activity facilities, and medical facilities have been associated with diet, physical activity, and management of medical conditions. Most of the research, however, has relied on cross-sectional studies. In this paper, we assess methodological issues raised by a data source that is increasingly used to characterize change in the local business environment: the National Establishment Time Series (NETS) dataset. Discussion Longitudinal data, such as NETS, offer opportunities to assess how differential access to resources impacts population health, to consider correlations among multiple environmental influences across the life course, and to gain a better understanding of their interactions and cumulative health effects. Longitudinal data also introduce new data management, geoprocessing, and business categorization challenges. Examining geocoding accuracy and categorization over 21 years of data in 23 counties surrounding New York City (NY, USA), we find that health-related business environments change considerably over time. We note that re-geocoding data may improve spatial precision, particularly in early years. Our intent with this paper is to make future public health applications of NETS data more efficient, since the size and complexity of the data can be difficult to exploit fully within its 2-year data-licensing period. Further, standardized approaches to NETS and other “big data” will facilitate the veracity and comparability of results across studies