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

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.

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Associations between county-level general surgeon (GS) and gastroenterologist (GA) density and outcomes for hepatobiliary cancer (HBC).

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.4_suppl.292 ◽

2012 ◽

Vol 30 (4_suppl) ◽

pp. 292-292

Author(s):

Trevor C. Tsang ◽

Winson Y. Cheung

Keyword(s):

Level Density ◽

Population Level ◽

General Surgeon ◽

County Level ◽

Physician Density ◽

Logistic Regression Models ◽

Hepatobiliary Cancer ◽

Incidence And Mortality ◽

Beta Coefficient ◽

The Impact

292 Background: Surgical resection is the mainstay of treatment for early, localized HBC. Prior studies consistently show an association between procedure volumes and cancer outcomes, but the impact of surgeon and physician density is unclear. Our aims were to 1) examine the effects of GS and GA density on HBC mortality and 2) compare the relative importance of GS versus GA density on HBC outcomes. Methods: Using county-level data from the Area Resource File, US Census, and National Cancer Institute, we developed both multivariate linear and logistic regression models to determine the effect of GS and GA density on overall HBC mortality between 2002 and 2006, while controlling for cancer incidence, county demographics and socioeconomic factors. Results: In total, 793 counties were analyzed: mean HBC incidence and mortality were 5.89 and 5.34 per 100,000 persons, respectively; 77% were metropolitan; mean GS and GA densities were 10.6 and 3.5 per 100,000 people, respectively. When compared to counties with no GS, those with at least one had a statistically significant decrease in HBC-specific mortality (beta coefficient -.115; p=.001). In contrast, when compared to counties with no GA, those with at least one showed a trend towards lower mortality (beta coefficient -.0677; p=.065). Increasing the county-level density of GS and GA improved outcomes, but increases beyond 10 GS or 4 GA per 100,000 people did not continue to result in significant reductions in HBC mortality; rather, these showed an increase in HBC mortality. Conclusions: Reductions in HBC mortality are more strongly influenced by increasing GS than GA density. There appears to be a ceiling effect at which point increasing GS and GA density does not appear to result in improvements in HBC outcomes. A strategy of allocating healthcare resources and distributing the workforce across counties will optimize outcomes at the population-level.

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Correlating Geographic Location with Incidence of Motor Vehicle–Induced Pedestrian Injury

The American Surgeon ◽

10.1177/000313481808400664 ◽

2018 ◽

Vol 84 (6) ◽

pp. 1049-1053 ◽

Cited By ~ 1

Author(s):

Neal Bhutiani ◽

Keith R. Miller ◽

Matthew V. Benns ◽

Nicholas A. Nash ◽

Glen A. Franklin ◽

...

Keyword(s):

Household Income ◽

Motor Vehicle ◽

Motor Vehicle Accident ◽

Multivariable Analysis ◽

Geographic Location ◽

Motor Vehicles ◽

Median Household Income ◽

Risk Of Death ◽

Pedestrian Injury ◽

The Impact

To date, no studies have examined the relationship between geographic and socioeconomic factors and the frequency of pedestrians sustaining traumatic injuries from a motor vehicle. The objective of this study was to analyze the impact of location on the frequency of pedestrian injury by motor vehicle. The University of Louisville Trauma Registry was queried for patients who had been struck by a motor vehicle from 2010 to 2015. Demographic and injury information as well as outcome measures were evaluated to identify those impacting risk of pedestrian versus motor vehicle accidents. Number of incidents was correlated with lower median household income. There was also a moderate correlation between the number of incidents and population density. Multivariable analysis demonstrated a significant association between increased median household income and distance from downtown Louisville and decreased risk of death following pedestrian versus motor vehicle accident. Incidence of pedestrian injury by motor vehicles is influenced by regional socioeconomic status. Efforts to decrease the frequency of these events should include further investigation into the mechanisms underpinning this relationship.

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

JMIR Public Health and Surveillance ◽

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.

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Poverty In The United States: A Continuing And Growing Problem

Journal of Business & Economics Research (JBER) ◽

10.19030/jber.v5i12.2610 ◽

2011 ◽

Vol 5 (12) ◽

Author(s):

Berch Haroian ◽

Elizabeth C. Ekmekjian ◽

Elias C. Grivoyannis

Keyword(s):

Household Income ◽

Insurance Coverage ◽

Poverty Line ◽

Health Insurance Coverage ◽

The United States ◽

Poverty Rate ◽

Median Household Income ◽

The Past ◽

Area Of Interest ◽

The Impact

In recent years, the ability to deal with the problem of poverty in the US, in light of the new “Federalism,” is an area of interest to scholars. The poverty rate over the past 50 years has fluctuated from a high of 22.4% in 1959 to a low of 11.1% in 1973. Under George Bush’s presidency, we again see an increase in the poverty rate to 12.7% in 2004. This paper provides an overview of poverty data for the 21st century, by region, race and age.  A discussion and comparison of median household income follows. Facts and figures are then provided/compared, tying in health care issues to income levels and citizenship/ethnicity. A brief introduction of the various attempts over the past years by the federal government to reduce the proportion of the American population that falls below the poverty line follows.  This section merely provides a listing of programs designed to satisfy social and equity considerations.  This paper does not provide the reader with the impact of these programs on the economy; a brief mention is provided to generate further thought and discussion.  The paper concludes with a summary of key elements of the above issues. The sole purpose is to provide an overview of historical data as concerns poverty, median household income and health insurance coverage. The ability to deal with the problem of poverty in the U S, is left for another paper.

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Local Economic Impacts of Investments in Community Technology Centers: An Empirical Investigation

The Journal of Community Informatics ◽

10.15353/joci.v13i1.3297 ◽

2017 ◽

Vol 13 (1) ◽

Author(s):

Krishna Jayakar ◽

Jenna Grzeslo

Keyword(s):

Digital Divide ◽

Household Income ◽

Empirical Investigation ◽

Economic Benefits ◽

Economic Consequences ◽

County Level ◽

Technology Centers ◽

Community Technology ◽

Community Technology Centers ◽

The Impact

Although community technology centers (CTCs) provide a host of services that may have economic consequences, few studies have attempted to empirically assess the impact of CTCs on local communities. Controlling for broadband availability and demographics, this study found that there is a small but positive and significant impact of CTC quality on median household income, at the county level. In addition to bridging the digital divide for disadvantaged populations, our research shows that there are also quantifiable economic benefits from CTC investments.

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

Circulation ◽

10.1161/circ.129.suppl_1.p118 ◽

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.

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RACE INDEPENDENTLY PREDICTS UNSUCCESSFUL HEALING OF OSTEOCHONDRITIS DISSECANS IN THE PEDIATRIC KNEE

Orthopaedic Journal of Sports Medicine ◽

10.1177/2325967119s00035 ◽

2019 ◽

Vol 7 (3_suppl) ◽

pp. 2325967119S0003

Author(s):

Jigar S. Gandhi ◽

Theodore J. Ganley

Keyword(s):

Osteochondritis Dissecans ◽

Household Income ◽

Univariate Analysis ◽

Sports Participation ◽

Black Children ◽

Multivariate Model ◽

Insurance Status ◽

Median Household Income ◽

The Impact

BACKGROUND: Previous studies have reported disparities in medical and surgical care along the lines of race and socioeconomic status. The purpose of this study is to evaluate the impact of these factors on successful or unsuccessful healing of juvenile osteochondritis dissecans (OCD) lesions in the knee. METHODS: We retrospectively reviewed patients younger than 18 years that were treated for a knee OCD lesion at our urban, tertiary children’s hospital between 2006 and 2017. Demographic data included patient-reported race, median household income for the patient’s zip code, and insurance status. We also collected information regarding clinical history, imaging, treatment course, and post-treatment outcomes. The primary outcome of interest was healing of the OCD lesion based on radiographic and clinical examination. Univariate analysis was followed by purposeful entry multivariate regression to control for confounders. RESULTS: A total of 205 children with mean follow-up of 15.8 ± 6.5 months were included in the analysis. The mean age was 12.4 ± 2.8 years and 145 (71%) were male. At their most recent follow-up, 28 subjects (13.7%) did not show radiographic or clinical evidence of healing. In univariate analysis, non-healing lesions were found in 25% of black children compared to 9.4% of white children (p=0.02). There was no difference in insurance status or median household income between patients who successfully and unsuccessfully healed their OCD lesion. After controlling for age, sex, sports participation, lesion size and stability, skeletal maturity, and operative vs. non-operative treatment in a multivariate model, black children had 6.7 times higher odds of unsuccessful healing compared to their white counterparts (95% CI 1.1, 41.7; p=0.04). CONCLUSION: In this study, black children with OCD of the knee were less likely to heal than white patients even when accounting for socioeconomic and other factors in a multivariate model.

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The Impact of Socioeconomic Status on Voice Outcomes in Patients With Spasmodic Dysphonia Treated With Botulinum Toxin Injections

Annals of Otology Rhinology & Laryngology ◽

10.1177/0003489418823013 ◽

2019 ◽

Vol 128 (4) ◽

pp. 316-322 ◽

Cited By ~ 2

Author(s):

Dianne Valenzuela ◽

Joel Singer ◽

Terry Lee ◽

Amanda Hu

Keyword(s):

Socioeconomic Status ◽

Botulinum Toxin ◽

Household Income ◽

Spasmodic Dysphonia ◽

Median Household Income ◽

Botulinum Toxin Injections ◽

The Impact ◽

The Voice ◽

Professional Voice ◽

Validated Instrument

Objectives: To determine the impact of socioeconomic status (SES) on voice outcomes for spasmodic dysphonia (SD) patients treated with botulinum toxin injections. Methods: This was a prospective cross-sectional study in a tertiary care, academic voice clinic in Canada. Adult SD patients returning to the voice clinic for their botulinum toxin injections were recruited from October 2017 to April 2018. Patients completed a questionnaire on demographic data, the Hollingshead Four-Factor Index for socioeconomic status (validated instrument based on education, occupation, gender, and marital status), and the Voice-Handicap Index 10 (VHI-10) (validated instrument on self-reported vocal handicap). Primary outcome was the association between VHI-10 and Hollingshead Index. Secondary variables were median household income by postal code, duration of disease, gender, age, and professional voice user. Descriptive statistics and multiple linear regression were conducted. Results: One hundred and one patients (age = 62.8 ± 13.7 years, 20.8% male) were recruited with VHI-10 of 22.1 ± 8.1 (out of 40) and Hollingshead Index of 46.3 ± 11.7 (range, 8-66). Median household income was $75 875 ± $16 393, which was above the Canadian average of $70 336. About 91.1% were Caucasian, 54.4% had university degree, 86.1% spoke English, and 43.5% were employed. In multiple linear regression, there was mild to moderate negative correlation (r = −.292, P = .004) between VHI-10 and Hollingshead Index when controlling for disease duration, age, gender, and professional voice use. Conclusion: SD patients treated with botulinum toxin were mostly affluent, Caucasian, well educated, and English speakers. Lower self-perceived vocal handicap was associated with higher socioeconomic status.

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

BMJ Open ◽

10.1136/bmjopen-2019-029373 ◽

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.

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