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The United States must be in a strong position to prevent, detect, and respond to diseases threats to ensure the health of the American people. A reader asks: I noticed that the W.H.O. overruled the C.D.C. and issued What's the difference between the W.H.O. and the C.D.C., and which. As millions of students across the United States head back to school, Centers for Disease Control and Prevention (CDC) today released new.
For example, positive emotions are heritable to some degree heritability estimates range from 0. Longitudinal studies have found that well-being is sensitive to life events e. Some personality factors that are strongly associated with well-being include optimism, extroversion, and self-esteem.
While genetic factors and personality factors are important determinants of well-being, they are beyond the realm of public policy goals. Age and Gender Depending on which types of measures are used e.
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In general, men and women have similar levels of well-being, but this pattern changes with age,63 and has changed over time. In general, associations between income and well-being usually measured in terms of life satisfaction are stronger for those at lower economic levels, but studies also have found effects for those at higher income levels.
Countries differ substantially in their levels of well-being. Traditionally, health-related quality of life has been linked to patient outcomes, and has generally focused on deficits in functioning e. In contrast, well-being focuses on assets in functioning, including positive emotions and psychological resources e.
Some researchers have drawn from both perspectives to measure physical and mental well-being for clinical and economic studies.
Subjective well-being typically refers to self-reports contrasted with objective indicators of well-being. This composite score enabled us to test the effects of our 3 predictor variables at once, which was important for this sample.
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Race was represented as percentage of the adult population that is black or African American and ethnicity was represented by percentage of the adult population that is Hispanic or Latino. Median income for the city was obtained from Data USA Finally, education was represented as the percentage of the adult population with a college degree, according to the ACS.
City-wide median income and education level were converted to Z scores and averaged together to create a socioeconomic status SES variable.
Assessing the Relationship Between a Composite Score of Urban Park Quality and Health
Prevalence of smoking and obesity were expressed as percentage of the adult population in the city who smoke and meet criteria for obesity, respectively.
Analysis First, descriptive statistics were calculated for sociodemographic characteristics, city population, smoking and obesity prevalence, ParkScore, physical health, and leisure-time physical inactivity Table 1. Pearson correlations were run to examine the strength of the relationships between outcome and predictor variables.
ParkScore as a predictor of physical inactivity and perceived health was then tested using 2 weighted least squares regression models, controlling for city-wide SES, race, ethnicity, smoking rates, and obesity levels. The health outcome variables are already adjusted for age, eliminating the need to control for age with an additional variable; information on age-adjustment procedures is also available Analytic weights were applied to both models to account for variation in the precision of estimates eg, larger cities construct estimates from larger samples than do smaller cities.
Weights were calculated by using the inverse of the standard error of the confidence intervals for estimates of physical inactivity and physical health. Results Sample characteristics Of the cities from the CDC data set and cities from the TPL data set, 98 overlapped, and of those, 59 had ParkScores, providing a sample of 59 cities.
Those 59 cities represent 31 states and the District of Columbia. Table 1 shows full descriptive statistics. Correlations were strong between predictor and outcome variables. Regression models assessed the associations between ParkScore and physical inactivity and physical health. Top Discussion Our results illustrate the potential contribution of a quality city park system to physical activity.
We found that in cities with robust park systems as determined by their ParkScoresresidents were more engaged in physical activity. For example, residents from cities with higher ParkScores were less likely to be physically inactive, even while controlling for other lifestyle factors, such as SES, race, ethnicity, and obesity.
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These results are consistent with prior research that looked at park acreage and its impact on obesity and physical activity 11and our study shows the additional impact of 2 other domains of park capacity, park access and investment, as part of the ParkScore although the individual contributions of these factors were not assessed in this study.
These results have implications for city governments, park agencies, and park nonprofit organizations. According to our model, if a city increases its ParkScore by 10 points out of a possible points while holding all else constant, the percentage of the population getting no leisure-time physical activity could decrease by 0. At a population level, this effect could be quite noticeable.
For example, if Atlanta — a city with a population of— increased its ParkScore of 44 points to 54 points, 2, additional people could engage in leisure-time physical activity. Although this study was cross-sectional and therefore did not look at increases directly, it is possible that enhancements made to proximity, acreage, and funding could provide physical activity benefits across these cities.
Limitations We acknowledge several study limitations. First, our results represent a snapshot in time; all data are fromand therefore causality cannot be determined. The way physical health was measured in this study may limit its usefulness.
The criteria for determining physical health are restrictive, and the range in values within the sample was small compared with other variables in the models. Additionally, physical health may take longer to achieve and be more resistant to change than physical activity.
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A longitudinal study may be better able to capture the possible effects of park system quality and physical activity on physical health. Finally, a sample size of 59 cities is relatively small. Despite these limitations, our findings have implications for future research that integrates park capacity data with health data. More effort could be devoted to connecting secondary parks, recreation, and health data, especially from this type of paired data set Given that city-level health data are now available, fulfilling prior promises to connect physical activity and health at more precise levels 27their use could be expanded.
For researchers, the development of the CDC city-level data set is significant, because of its potential to be matched with city-level park excellence data, allowing for a more direct comparison between park metrics and health outcomes. Future work in this area is encouraged and could become part of a wider research agenda. For instance, in addition to physically active use of parks, social indicators related to park use should not be forgotten in this research agenda.