Association between spatial access and hospitalization for ambulatory care sensitive conditions: A retrospective cohort study using claims data.

Background: Hospitalization for ambulatory care sensitive conditions (ACSCs) is potentially preventable with timely and effective primary care but may increase owing to poor access. Spatial access inequalities exist between Japan and other countries. This retrospective cohort study examined the association between admission for ACSC and spatial accessibility to primary care. Methods: We used claims data and spatial data of 50-74 years-old beneficiaries of the National Health Insurance program in a large city in Japan from April 2013-March 2014 and followed them until March 2015. We used a multilevel Poisson regression model to assess the association between the number of ACSC admissions, the distance to the nearest clinic, and the number of physicians in a given area, adjusting for age, gender, comorbidities, number of visits, and household income. Results: Among 126,666 eligible beneficiaries (mean age 65.8 years, 54% were women), 1,793 (1.4%) were hospitalized for ACSCs. The ACSC admission rate was significantly higher in those with a distance to the nearest clinic of >1 km than in those with <0.3 km (incident rate ratio [IRR] 1.32, 95% confidence interval [CI] 1.03-1.69). In the stratified analyses, a longer distance to the nearest clinic was associated with higher ACSC admission rates among women (≥0.3 km and <0.5 km: IRR 1.48, 95% CI 1.01-2.17; ≥0.5 km and <1 km: IRR 1.74, 95% CI 1.19-2.56; >1 km: IRR 1.98, 95% CI 1.29-3.03, respectively) and those aged ≥65 years (≥0.3 km and <0.5 km: IRR 1.38, 95% CI 1.07-1.79; ≥0.5 km and <1 km: IRR 1.38, 95% CI 1.06-1.80; >1 km: IRR 1.48, 95% CI 1.10-1.98). Conclusion: Unfavorable spatial access was associated with ACSC admissions, particularly among women and older adults.

Oxidative stress caused by TiO2 nanoparticles under UV irradiation is due to UV irradiation not through nanoparticles.

Currently, nanoparticles are used in various commercial products. One of the most common nanoparticles is titanium dioxide (TiO2). It has a catalytic activity and UV absorption, and generates reactive oxygen species (ROS). This catalytic activity of TiO2 nanoparticles was believed to be capable of killing a wide range of microorganisms. In the environment, the unique properties of TiO2 nanoparticles can be maintained; therefore, the increasing use of TiO2 nanoparticles is raising concerns about their environmental risks. Thus, assessment of the biological and ecological effects of TiO2-NOAAs is necessary. In this study, we assessed the effect of TiO2-NOAAs for S. cerevisiae using DNA microarray. To compare yeast cells under various conditions, six treatment conditions were prepared (1. adsorbed fraction to TiO2-NOAA under UV; 2. non-adsorbed fraction to TiO2-NOAA under UV; 3. adsorbed fraction to TiO2-NOAA without UV; 4. non-adsorbed fraction to TiO2-NOAA without UV; 5. under UV; and 6. untreated control). The result of the DNA microarray analysis, suggested that yeast cells that are adsorbed by TiO2-NOAA under UV irradiation suffer oxidative stress and this stress response was similar to that by only UV irradiation. We concluded that the effect of TiO2-NOAAs on yeast cells under UV irradiation is not caused by TiO2-NOAA but UV irradiation.

The effect of titanium dioxide (TiO2) nano-objects, and their aggregates and agglomerates greater than 100nm (NOAA) on microbes under UV irradiation.

Today, nanoparticles are used in many products. One of the most common nanoparticles is titanium dioxide (TiO2). These particles generate reactive oxygen species (ROS) upon UV irradiation. Although nanoparticles are very useful in many products, there are concerns about their biological and ecological effects when released into the environment. Thus, it was assessed that the effect of TiO2 nano-objects, and their aggregates and agglomerates greater than 100nm (NOAA) on microbes under UV irradiation by using Escherichia coli and Saccharomyces cerevisiae. ROS generation was evaluated by adding TiO2 nanoparticles and methylene blue to distilled water. We also assessed growth inhibition by adding TiO2 nanoparticles and microbes in minimal agar medium. Moreover, microbial inactivation was assessed by adding TiO2 nanoparticles and microbes to PBS. Upon UV irradiation, TiO2-NOAAs decomposed methylene blue and generated ROS. TiO2-NOAAs also decomposed methylene blue in minimal agar medium under UV irradiation; however, they did not inhibit microbial growth. Surprisingly, TiO2-NOAAs in the medium protect microbes from UV irradiation as colony formation was observed only near TiO2-NOAAs. In PBS, TiO2-NOAAs did not inactivate microbes but instead protected microbes from lethal UV irradiation. These results suggest that the amount of ROS generated by TiO2-NOAAs is not enough to inactivate microbes. In fact, our results suggest that TiO2-NOAAs may protect microbes from UV irradiations.

Re-visiting the relationship between neighbourhood environment and BMI: an instrumental variables approach to correcting for residential selection bias.

BACKGROUND: A burgeoning literature links attributes of neighbourhoods' built environments to residents' physical activity, food and transportation choices, weight, and/or obesity risk. In cross-sectional studies, non-random residential selection impedes researchers' ability to conclude that neighbourhood environments cause these outcomes. METHODS: Cross-sectional data for the current study are based on 14,689 non-Hispanic white women living in Salt Lake County, Utah, USA. Instrumental variables techniques are used to adjust for the possibility that neighbourhoods may affect weight but heavier or lighter women may also choose to live in certain neighbourhoods. All analyses control for the average BMI of siblings and thus familial predisposition for overweight/obesity, which is often an omitted variable in past studies. RESULTS: We find that cross-sectional analyses relating neighbourhood characteristics to BMI understate the strength of the relationship if they do not make statistical adjustments for the decision to live in a walkable neighbourhood. Standard cross-sectional estimation reveals no significant relationship between neighbourhood walkability and BMI. However, the instrumental variables estimates reveal statistically significant effects. CONCLUSIONS: We find evidence that residential selection leads to an understatement of the causal effects of neighbourhood walkability features on BMI. Although caution should be used in generalizing from research done with one demographic group in a single locale, our findings support the contention that public policies designed to alter neighbourhood walkability may moderately affect the BMI of large numbers of individuals.

Mixed land use and obesity: an empirical comparison of alternative land use measures and geographic scales.

Obesity is a growing epidemic in the United States. Walkable neighborhoods, characterized as having the 3Ds of walkability (population Density, land use Diversity, and pedestrian-friendly Design), have been identified as a potentially promising factor to prevent obesity for their residents. Past studies examining the relationship between obesity and walkability vary in geographic scales of neighborhood definitions and methods of measuring the 3Ds. To better understand potential influences of these sometimes arbitrary choices, we test how four types of alternative measures of land use diversity measured at three geographic scales relate to body mass index for 4960 Salt Lake County adults. Generalized estimation equation models demonstrate that optimal diversity measures differed by gender and geographic scale and that integrating walkability measures at different scales improved the overall performance of models.

Effects of neighborhood walkability on healthy weight: assessing selection and causal influences.

Studies that examine the relationship between neighborhood characteristics and weight are limited because residents are not randomly distributed into neighborhoods. If associations are found between neighborhood characteristics and weight in observational studies, one cannot confidently draw conclusions about causality. We use data from the Utah Population Database (UPDB) that contain body mass index (BMI) information from all drivers holding a Utah driver license to undertake a cross-sectional analysis that compares the neighborhood determinants of BMI for youth and young adults. This analysis assumes that youth have little choice in their residential location while young adults have more choice. Our analysis makes use of data on 53,476 males and 47,069 females living in Salt Lake County in 2000. We find evidence of residential selection among both males and females when BMI is the outcome. The evidence is weaker when the outcomes are overweight or obesity. We conclude that studies that ignore the role of residential selection may be overstating the causal influence of neighborhood features in altering residents' BMI.

Running to the store? The relationship between neighborhood environments and the risk of obesity.

We expand the search for modifiable features of neighborhood environments that alter obesity risk in two ways. First, we examine residents' access to neighborhood retail food options in combination with neighborhood features that facilitate physical activity. Second, we evaluate neighborhood features for both low income and non-low income neighborhoods (bottom quartile of median neighborhood income versus the top three quartiles). Our analyses use data from the Utah Population Database merged with U.S. Census data and Dun & Bradstreet business data for Salt Lake County, Utah. Linear regressions for BMI and logistic regressions for the likelihood of being obese are estimated using various measures of the individual's neighborhood food options and walkability features. As expected, walkability indicators of older neighborhoods and neighborhoods where a higher fraction of the population walks to work is related to a lower BMI/obesity risk, although the strength of the effects varies by neighborhood income. Surprisingly, the walkability indicator of neighborhoods with higher intersection density was linked to higher BMI/obesity risk. The expected inverse relationship between the walkability indicator of population density and BMI/obesity risk is found only in low income neighborhoods. We find a strong association between neighborhood retail food options and BMI/obesity risk with the magnitude of the effects again varying by neighborhood income. For individuals living in non-low income neighborhoods, having one or more convenience stores, full-service restaurants, or fast food restaurants is associated with reduced BMI/obesity risk, compared to having no neighborhood food outlets. The presence of at least one healthy grocery option in low income neighborhoods is also associated with a reduction in BMI/obesity risk relative to no food outlets. Finally, multiple food options within a neighborhood reduce BMI/obesity risk, relative to no food options, for individuals living in either low-income or non-low neighborhoods.

Mixed land use and walkability: Variations in land use measures and relationships with BMI, overweight, and obesity.

Few studies compare alternative measures of land use diversity or mix in relationship to body mass index. We compare four types of diversity measures: entropy scores (measures of equal distributions of walkable land use categories), distances to walkable destinations (parks and transit stops), proxy measures of mixed use (walk to work measures and neighborhood housing ages), and land use categories used in entropy scores. Generalized estimating equations, conducted on 5000 randomly chosen licensed drivers aged 25-64 in Salt Lake County, Utah, relate lower BMIs to older neighborhoods, components of a 6-category land use entropy score, and nearby light rail stops. Thus the presence of walkable land uses, rather than their equal mixture, relates to healthy weight.

Walkability and body mass index density, design, and new diversity measures.

BACKGROUND: Rising rates of overweight and obesity in the U.S. have increased interest in community designs that encourage healthy weight. This study relates neighborhood walkability-density, pedestrian-friendly design, and two novel measures of land-use diversity-to residents' excess weight. METHODS: Walkable-environment measures include two established predictors, higher density and pedestrian-friendly design (intersections within 0.25 mile of each address), and two new census-based, land-use diversity measures: the proportion of residents walking to work and the median age of housing. In 2006, weight, height, age, and address data from 453,927 Salt Lake County driver licenses for persons aged 25-64 years were linked to 2000 Census and GIS street-network information that was analyzed in 2007-2008. Linear regressions of BMI and logistic regressions of overweight and obesity include controls for individual-level age and neighborhood-level racial/ethnic composition, median age of residents, and median family income. RESULTS: Increasing levels of walkability decrease the risks of excess weight. Approximately doubling the proportion of neighborhood residents walking to work decreases an individual's risk of obesity by almost 10%. Adding a decade to the average age of neighborhood housing decreases women's risk of obesity by about 8% and men's by 13%. Population density is unrelated to weight in four of six models, and inconsistently related to weight measures in two models. Pedestrian-friendly street networks are unrelated to BMI but related to lower risks of overweight and obesity in three of four models. CONCLUSIONS: Walkability indicators, particularly the two land-use diversity measures, are important predictors of body weight. Driver licenses should be considered as a source of data for community studies of BMI, as they provide extensive coverage at low cost.

Monitoring change in spatial patterns of disease: comparing univariate and multivariate cumulative sum approaches.

Prospective disease surveillance has gained increasing attention, particularly in light of recent concern for quick detection of bioterrorist events. Monitoring of health events has the potential for the detection of such events, but the benefits of surveillance extend much more broadly to the quick detection of change in public health. In this paper, univariate and multivariate cumulative sum methods for disease surveillance are compared. Although the univariate method has been previously used in the context of health surveillance, the multivariate method has not. The univariate approach consists of simultaneously and independently monitoring the disease rate in each region; the multivariate approach accounts explicitly for any covariation between regions. The univariate approaches are limited by their lack of ability to account for the spatial autocorrelation of regional data; the multivariate methods are limited by the difficulty in accurately specifying the multiregional covariance structure. The methods are illustrated using both simulated data and county-level data on breast cancer in the northeastern United States. When the degree of spatial autocorrelation is low, the univariate method is generally better at detecting changes in rates that occur in a small number of regions; the multivariate is better when change occurs in a large number of regions.