Abstract P118: County-Level Disparities in the Availability of Farmers Markets in the United States: An Ecological Study

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Chelsea Singleton ◽  
Olivia Affuso ◽  
Bisakha Sen
Keyword(s):  
At Risk ◽  
Household Income ◽  
Ecological Study ◽  
Negative Association ◽  
Farmers Markets ◽  
Grocery Stores ◽  
County Level ◽  
Median Household Income ◽  
Recreation Centers ◽  
Per Capita

Introduction: Farmers markets (FM) have been hypothesized to be a potential community-level obesity prevention strategy for populations at risk for chronic diseases because they provide a mechanism for communities to purchase healthy locally grown produce. This study aimed to identify county-level factors associated with FM availability in an effort to determine if disparities in availability exist in the US. Hypothesis: Increased FM availability will be associated with higher median household income, lower % minority residents, lower % obese residents and a higher number of grocery stores and recreation centers per 100,000 residents. Methods: An ecological study was conducted using 2009 data from the USDA Food Environment Atlas on 3,135 US counties. Crude and multivariable adjusted logistic regression models where used to determine associations between having at least one FM available and county-level variables such as % African American (AA) residents, % Hispanic residents, median household income, % WIC participants, % adults obese, % adults with diabetes, per capita grocery stores, per capita supercenters and per capita recreation centers. All analyses were stratified by metro county status and adjusted to address data clustering at the state-level. Results: There were 1,088 and 2,047 counties labeled metro and non-metro respectively. Metro Results : Median household income (p = 0.002) and per capita recreation centers (p < 0.0001) were positively associated with FM availability while % WIC residents (p = 0.008), per capita grocery stores (p = 0.02) and % adults with diabetes (p < 0.0001) showed a negative association. Non-Metro Results: Median household income (p < 0.0001), per capita recreation centers (p < 0.0001) and per capita supercenters (p < 0.0001) were positively associated with FM availability while % WIC residents (p = 0.02), per capita grocery stores (p < 0.0001) and % adults with diabetes (p < 0.03) showed a negative association. The % AA residents appeared to be negatively associated with FM availability but did not achieve statistical significance. County obesity prevalence was not associated with FM availability in both metro and non-metro counties. Conclusion: Results showed that counties with more recreation centers and a higher median household income have increased FM availability while counties with more WIC participants and residents with diabetes have less availability. More information on the association between FM access, diet and obesity in at risk populations should be collected at the individual level.

Surgical density and its effect on esophageal cancer (EC) and gastric (GC) mortality.

2011 ◽  
Vol 29 (4_suppl) ◽  
pp. 16-16
Author(s):  
M. Y. Ho ◽  
J. S. Albarrak ◽  
W. Y. Cheung
Keyword(s):  
Household Income ◽  
Population Level ◽  
Multimodality Treatment ◽  
General Surgeon ◽  
County Level ◽  
Median Household Income ◽  
Hospital Beds ◽  
Beta Coefficient ◽  
General Surgeons ◽  
The Impact

16 Background: Surgical resection plays an integral role in the multimodality treatment of patients with EC or GC. The distribution of thoracic and general surgeons at the county level varies widely across the US. The impact of the allocation of these surgeons on cancer outcomes is unclear. Our aims were to 1) examine the effect of surgeon density on EC or GC mortality, 2) compare the relative roles of thoracic and general surgeons on EC and GC outcomes and 3) determine other county characteristics associated with cancer mortality. Methods: Using county-level data from the Area Resources File, U.S. Census and National Cancer Institute, we constructed regression models to explore the effect of thoracic and general surgeon density on EC and GC mortality, respectively. Multivariate analyses controlled for incidence rate, county demographics (population aged 65+, proportion eligible for Medicare, education attainment, metropolitan vs. rural), socioeconomic factors (median household income) and healthcare resources (number of general practitioners, number of hospital beds). Results: In total, 332 and 402 counties were identified for EC and GC, respectively: mean EC/GC incidence = 5.29/6.83; mean EC/GC mortality=4.70/3.92; 91% were metropolitan and 9% were rural; mean thoracic and general surgeon densities were 10 and 63 per 100,000 people, respectively. When compared to counties with no thoracic surgeons, those with at least 1 thoracic surgeon had reduced EC mortality (beta coefficient -0.031). For GC, counties with 1 or more general surgeons also had decreased number of deaths (beta coefficient -0.095) when compared with those without any surgeons. While increasing the density of surgeons beyond 10 only yielded minimal improvements in EC mortality, it resulted in significant further reductions in GC mortality. Other county characteristics, such as increased number of hospital beds and higher median household income, were correlated with improved outcomes. Conclusions: Mortality from GC appears to be more susceptible to the benefits of increased surgeon density. For EC, a strategic policy of allocating health resources and distributing the workforce across counties will be best able to optimize outcomes at the population-level. No significant financial relationships to disclose.

scholarly journals County-Level Social Distancing and Policy Impact in the United States: A Dynamical Systems Model

10.2196/23902 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e23902
Author(s):  
Kevin L McKee ◽  
Ian C Crandell ◽  
Alexandra L Hanlon
Keyword(s):  
United States ◽  
Differential Equation ◽  
Population Density ◽  
Household Income ◽  
The United States ◽  
Policy Impact ◽  
County Level ◽  
Median Household Income ◽  
Social Distancing

Background Social distancing and public policy have been crucial for minimizing the spread of SARS-CoV-2 in the United States. Publicly available, county-level time series data on mobility are derived from individual devices with global positioning systems, providing a variety of indices of social distancing behavior per day. Such indices allow a fine-grained approach to modeling public behavior during the pandemic. Previous studies of social distancing and policy have not accounted for the occurrence of pre-policy social distancing and other dynamics reflected in the long-term trajectories of public mobility data. Objective We propose a differential equation state-space model of county-level social distancing that accounts for distancing behavior leading up to the first official policies, equilibrium dynamics reflected in the long-term trajectories of mobility, and the specific impacts of four kinds of policy. The model is fit to each US county individually, producing a nationwide data set of novel estimated mobility indices. Methods A differential equation model was fit to three indicators of mobility for each of 3054 counties, with T=100 occasions per county of the following: distance traveled, visitations to key sites, and the log number of interpersonal encounters. The indicators were highly correlated and assumed to share common underlying latent trajectory, dynamics, and responses to policy. Maximum likelihood estimation with the Kalman-Bucy filter was used to estimate the model parameters. Bivariate distributional plots and descriptive statistics were used to examine the resulting county-level parameter estimates. The association of chronology with policy impact was also considered. Results Mobility dynamics show moderate correlations with two census covariates: population density (Spearman r ranging from 0.11 to 0.31) and median household income (Spearman r ranging from –0.03 to 0.39). Stay-at-home order effects were negatively correlated with both (r=–0.37 and r=–0.38, respectively), while the effects of the ban on all gatherings were positively correlated with both (r=0.51, r=0.39). Chronological ordering of policies was a moderate to strong determinant of their effect per county (Spearman r ranging from –0.12 to –0.56), with earlier policies accounting for most of the change in mobility, and later policies having little or no additional effect. Conclusions Chronological ordering, population density, and median household income were all associated with policy impact. The stay-at-home order and the ban on gatherings had the largest impacts on mobility on average. The model is implemented in a graphical online app for exploring county-level statistics and running counterfactual simulations. Future studies can incorporate the model-derived indices of social distancing and policy impacts as important social determinants of COVID-19 health outcomes.

scholarly journals Lithuania in the Context of Sustainable Development

2014 ◽  
Vol 53 (1) ◽  
pp. 24-33
Author(s):  
Beatričė Leiputė
Keyword(s):  
At Risk ◽  
Sustainable Development ◽  
Social Exclusion ◽  
Gini Coefficient ◽  
Household Income ◽  
Lorenz Curve ◽  
Average Effect ◽  
Random Effects Models ◽  
Fixed And Random Effects ◽  
Per Capita

The goal of this paper is to analyze the tendencies of social exclusion and inequality in Lithuania and in the context of Europe. By using statistical methods such as Gini coefficient and Lorenz curve, firstly we analyze the inequality of average county disposable household income per month. In the second part of the study, we analyze an indicator of people at-risk-of poverty or social exclusion in 26 different countries of Europe. In this section, we want to test the fixed and random effects models on our data. Based on them, the average effect of the at-risk-of poverty or social exclusion indicator can be measured. Based on real expenditure per capita analysis, Lithuania can be classified to a group of post-Soviet countries, where tendencies of poverty or social exclusion risk are similar.

Examining Associations among Obesity and Per Capita Farmers' Markets, Grocery Stores/Supermarkets, and Supercenters in US Counties

2011 ◽  
Vol 111 (4) ◽  
pp. 567-572 ◽  
Author(s):  
Stephanie B. Jilcott ◽  
Thomas Keyserling ◽  
Thomas Crawford ◽  
Jared T. McGuirt ◽  
Alice S. Ammerman
Keyword(s):  
Farmers Markets ◽  
Grocery Stores ◽  
Per Capita ◽  
Us Counties

scholarly journals Decomposition of the US black/white inequality in premature mortality, 2010–2015: an observational study

BMJ Open ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e029373
Author(s):  
Mathew V Kiang ◽  
Nancy Krieger ◽  
Caroline O Buckee ◽  
Jukka Pekka Onnela ◽  
Jarvis T Chen
Keyword(s):  
Health Policy ◽  
Geographic Variation ◽  
Risk Ratio ◽  
Household Income ◽  
Spatial Models ◽  
Premature Mortality ◽  
County Level ◽  
Median Household Income ◽  
Relative Risks ◽  
The Us

ObjectiveDecompose the US black/white inequality in premature mortality into shared and group-specific risks to better inform health policy.SettingAll 50 US states and the District of Columbia, 2010 to 2015.ParticipantsA total of 2.85 million non-Hispanic white and 762 639 non-Hispanic black US-resident decedents.Primary and secondary outcome measuresThe race-specific county-level relative risks for US blacks and whites, separately, and the risk ratio between groups.ResultsThere is substantial geographic variation in premature mortality for both groups and the risk ratio between groups. After adjusting for median household income, county-level relative risks ranged from 0.46 to 2.04 (median: 1.03) for whites and from 0.31 to 3.28 (median: 1.15) for blacks. County-level risk ratios (black/white) ranged from 0.33 to 4.56 (median: 1.09). Half of the geographic variation in white premature mortality was shared with blacks, while only 15% of the geographic variation in black premature mortality was shared with whites. Non-Hispanic blacks experience substantial geographic variation in premature mortality that is not shared with whites. Moreover, black-specific geographic variation was not accounted for by median household income.ConclusionUnderstanding geographic variation in mortality is crucial to informing health policy; however, estimating mortality is difficult at small spatial scales or for small subpopulations. Bayesian joint spatial models ameliorate many of these issues and can provide a nuanced decomposition of risk. Using premature mortality as an example application, we show that Bayesian joint spatial models are a powerful tool as researchers grapple with disentangling neighbourhood contextual effects and sociodemographic compositional effects of an area when evaluating health outcomes. Further research is necessary in fully understanding when and how these models can be applied in an epidemiological setting.

scholarly journals Distribution of Patients at Risk for Complications Related to COVID-19 in the United States: Model Development Study (Preprint)

2020 ◽  
Author(s):  
Renae Smith-Ray ◽  
Erin E Roberts ◽  
Devonee E Littleton ◽  
Tanya Singh ◽  
Thomas Sandberg ◽  
...  
Keyword(s):  
United States ◽  
At Risk ◽  
High Risk ◽  
Risk Model ◽  
The United States ◽  
Systems Science ◽  
Chronic Obstructive ◽  
Grocery Stores ◽  
County Level ◽  
Urban Density

BACKGROUND Coronavirus disease (COVID-19) has spread exponentially across the United States. Older adults with underlying health conditions are at an especially high risk of developing life-threatening complications if infected. Most intensive care unit (ICU) admissions and non-ICU hospitalizations have been among patients with at least one underlying health condition. OBJECTIVE The aim of this study was to develop a model to estimate the risk status of the patients of a nationwide pharmacy chain in the United States, and to identify the geographic distribution of patients who have the highest risk of severe COVID-19 complications. METHODS A risk model was developed using a training test split approach to identify patients who are at high risk of developing serious complications from COVID-19. Adult patients (aged ≥18 years) were identified from the Walgreens pharmacy electronic data warehouse. Patients were considered eligible to contribute data to the model if they had at least one prescription filled at a Walgreens location between October 27, 2019, and March 25, 2020. Risk parameters included age, whether the patient is being treated for a serious or chronic condition, and urban density classification. Parameters were differentially weighted based on their association with severe complications, as reported in earlier cases. An at-risk rate per 1000 people was calculated at the county level, and ArcMap was used to depict the rate of patients at high risk for severe complications from COVID-19. Real-time COVID-19 cases captured by the Johns Hopkins University Center for Systems Science and Engineering (CSSE) were layered in the risk map to show where cases exist relative to the high-risk populations. RESULTS Of the 30,100,826 adults included in this study, the average age is 50 years, 15% have at least one specialty medication, and the average patient has 2 to 3 comorbidities. Nearly 28% of patients have the greatest risk score, and an additional 34.64% of patients are considered high-risk, with scores ranging from 8 to 10. Age accounts for 53% of a patient’s total risk, followed by the number of comorbidities (29%); inferred chronic obstructive pulmonary disease, hypertension, or diabetes (15%); and urban density classification (5%). CONCLUSIONS This risk model utilizes data from approximately 10% of the US population. Currently, this is the most comprehensive US model to estimate and depict the county-level prognosis of COVID-19 infection. This study shows that there are counties across the United States whose residents are at high risk of developing severe complications from COVID-19. Our county-level risk estimates may be used alongside other data sets to improve the accuracy of anticipated health care resource needs. The interactive map can also aid in proactive planning and preparations among employers that are deemed critical, such as pharmacies and grocery stores, to prevent the spread of COVID-19 within their facilities.

scholarly journals County-Level Social Distancing and Policy Impact in the United States: A Dynamical Systems Model (Preprint)

2020 ◽  
Author(s):  
Kevin L McKee ◽  
Ian C Crandell ◽  
Alexandra L Hanlon
Keyword(s):  
United States ◽  
Differential Equation ◽  
Population Density ◽  
Household Income ◽  
The United States ◽  
Policy Impact ◽  
County Level ◽  
Median Household Income ◽  
Social Distancing

BACKGROUND Social distancing and public policy have been crucial for minimizing the spread of SARS-CoV-2 in the United States. Publicly available, county-level time series data on mobility are derived from individual devices with global positioning systems, providing a variety of indices of social distancing behavior per day. Such indices allow a fine-grained approach to modeling public behavior during the pandemic. Previous studies of social distancing and policy have not accounted for the occurrence of pre-policy social distancing and other dynamics reflected in the long-term trajectories of public mobility data. OBJECTIVE We propose a differential equation state-space model of county-level social distancing that accounts for distancing behavior leading up to the first official policies, equilibrium dynamics reflected in the long-term trajectories of mobility, and the specific impacts of four kinds of policy. The model is fit to each US county individually, producing a nationwide data set of novel estimated mobility indices. METHODS A differential equation model was fit to three indicators of mobility for each of 3054 counties, with T=100 occasions per county of the following: distance traveled, visitations to key sites, and the log number of interpersonal encounters. The indicators were highly correlated and assumed to share common underlying latent trajectory, dynamics, and responses to policy. Maximum likelihood estimation with the Kalman-Bucy filter was used to estimate the model parameters. Bivariate distributional plots and descriptive statistics were used to examine the resulting county-level parameter estimates. The association of chronology with policy impact was also considered. RESULTS Mobility dynamics show moderate correlations with two census covariates: population density (Spearman <i>r</i> ranging from 0.11 to 0.31) and median household income (Spearman <i>r</i> ranging from –0.03 to 0.39). Stay-at-home order effects were negatively correlated with both (<i>r</i>=–0.37 and <i>r</i>=–0.38, respectively), while the effects of the ban on all gatherings were positively correlated with both (<i>r</i>=0.51, <i>r</i>=0.39). Chronological ordering of policies was a moderate to strong determinant of their effect per county (Spearman r ranging from –0.12 to –0.56), with earlier policies accounting for most of the change in mobility, and later policies having little or no additional effect. CONCLUSIONS Chronological ordering, population density, and median household income were all associated with policy impact. The stay-at-home order and the ban on gatherings had the largest impacts on mobility on average. The model is implemented in a graphical online app for exploring county-level statistics and running counterfactual simulations. Future studies can incorporate the model-derived indices of social distancing and policy impacts as important social determinants of COVID-19 health outcomes.

scholarly journals Distribution of Patients at Risk for Complications Related to COVID-19 in the United States: Model Development Study

10.2196/19606 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e19606 ◽  
Author(s):  
Renae Smith-Ray ◽  
Erin E Roberts ◽  
Devonee E Littleton ◽  
Tanya Singh ◽  
Thomas Sandberg ◽  
...  
Keyword(s):  
United States ◽  
At Risk ◽  
High Risk ◽  
Risk Model ◽  
The United States ◽  
Systems Science ◽  
Chronic Obstructive ◽  
Grocery Stores ◽  
County Level ◽  
Urban Density

Background Coronavirus disease (COVID-19) has spread exponentially across the United States. Older adults with underlying health conditions are at an especially high risk of developing life-threatening complications if infected. Most intensive care unit (ICU) admissions and non-ICU hospitalizations have been among patients with at least one underlying health condition. Objective The aim of this study was to develop a model to estimate the risk status of the patients of a nationwide pharmacy chain in the United States, and to identify the geographic distribution of patients who have the highest risk of severe COVID-19 complications. Methods A risk model was developed using a training test split approach to identify patients who are at high risk of developing serious complications from COVID-19. Adult patients (aged ≥18 years) were identified from the Walgreens pharmacy electronic data warehouse. Patients were considered eligible to contribute data to the model if they had at least one prescription filled at a Walgreens location between October 27, 2019, and March 25, 2020. Risk parameters included age, whether the patient is being treated for a serious or chronic condition, and urban density classification. Parameters were differentially weighted based on their association with severe complications, as reported in earlier cases. An at-risk rate per 1000 people was calculated at the county level, and ArcMap was used to depict the rate of patients at high risk for severe complications from COVID-19. Real-time COVID-19 cases captured by the Johns Hopkins University Center for Systems Science and Engineering (CSSE) were layered in the risk map to show where cases exist relative to the high-risk populations. Results Of the 30,100,826 adults included in this study, the average age is 50 years, 15% have at least one specialty medication, and the average patient has 2 to 3 comorbidities. Nearly 28% of patients have the greatest risk score, and an additional 34.64% of patients are considered high-risk, with scores ranging from 8 to 10. Age accounts for 53% of a patient’s total risk, followed by the number of comorbidities (29%); inferred chronic obstructive pulmonary disease, hypertension, or diabetes (15%); and urban density classification (5%). Conclusions This risk model utilizes data from approximately 10% of the US population. Currently, this is the most comprehensive US model to estimate and depict the county-level prognosis of COVID-19 infection. This study shows that there are counties across the United States whose residents are at high risk of developing severe complications from COVID-19. Our county-level risk estimates may be used alongside other data sets to improve the accuracy of anticipated health care resource needs. The interactive map can also aid in proactive planning and preparations among employers that are deemed critical, such as pharmacies and grocery stores, to prevent the spread of COVID-19 within their facilities.

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