Geographic Heterogeneity in Otolaryngology Medicare New Patient Visits

2020 ◽  
Vol 162 (6) ◽  
pp. 860-866
Author(s):  
Kevin Hur ◽  
Joseph Gibbons ◽  
Brian Karl Finch
Keyword(s):  
Spatial Variation ◽  
Geographically Weighted Regression ◽  
Negative Binomial ◽  
Sociodemographic Factors ◽  
Weighted Regression ◽  
Cross Sectional ◽  
Commute Time ◽  
Medicare Patients ◽  
Work Commute ◽  
Sectional Analysis

Objective To analyze the spatial variation of sociodemographic factors associated with the geographic distribution of new patient visits to otolaryngologists. Study Design Retrospective cross-sectional analysis. Setting United States. Subject and Methods Medicare new patient visits pooled from 2012 to 2016 to otolaryngology providers were obtained from the Centers for Medicare and Medicaid Services, and county-level sociodemographic data were obtained from the 2012-2016 American Community Survey. The mean number of new patient visits per otolaryngology provider by county was calculated. The spatial variation was analyzed with negative binomial and geographically weighted regression. Predictors included various neighborhood characteristics. Results There were 7,199,129 Medicare new patient visits to otolaryngology providers from 2012 to 2016. A 41.7-fold difference in new patient evaluation rates was observed across US counties (range, 11-458.8 per otolaryngology provider). On multivariable regression analysis, median age, sex, work commute time, percentage insured, and the advantage index of a county were predictors for the rate of new patient visits to otolaryngology providers. However, geographically weighted regression demonstrated that the association of a county’s disadvantage index, advantage index, percentage insured, and work commute times with new patient visits per provider varied across space. Conclusions There are wide geographic differences in the number of new Medicare patients seen by otolaryngologists, and the influence of county sociodemographic factors varied regionally. Further research to analyze the variations in practice patterns of otolaryngologists is warranted to predict future public health needs.

Modeling the Spatial Variation in U.S. Airfares Utilizing Geographically Weighted Regression

2014 ◽  
Vol 5 (4) ◽  
pp. 54-71
Author(s):  
Hilton A. Cordoba ◽  
Russell L. Ivy
Keyword(s):  
Spatial Variation ◽  
Influencing Factors ◽  
Geographically Weighted Regression ◽  
Regression Models ◽  
Yield Management ◽  
Weighted Regression ◽  
Local Models ◽  
Multivariate Approach ◽  
Global Models ◽  
Economic Changes

Modeling airline fares is quite challenging due to the constantly changing fare structure of the airlines in response to competitors, yield management principles, and a variety of political and economic changes, and has become more complex since deregulation. This paper attempts to add to the literature by providing a more in-depth look at fare structure using a multivariate approach. A total 6,200 routes between 80 primary U.S. airports are analyzed using linear and geographically weighted regression models. The results from the global models reinforce some of the expectations mentioned in the literature, while the local models provide an opportunity to analyze the spatial variation of influencing factors and predictability.

scholarly journals Cross-sectional analysis of the prevalence and predictors of statin utilisation in Ireland with a focus on primary prevention of cardiovascular disease

BMJ Open ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. e018524 ◽  
Author(s):  
Paula Byrne ◽  
John Cullinan ◽  
Catríona Murphy ◽  
Susan M Smith
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
Primary Prevention ◽  
Sociodemographic Factors ◽  
Cross Sectional ◽  
Factors Associated ◽  
History Of ◽  
Base Category ◽  
Sectional Analysis ◽  
Prevention Of Cardiovascular Disease

ObjectiveTo describe the prevalence of statin utilisation by people aged over 50 years in Ireland and the factors associated with the likelihood of using a statin, focusing particularly on those using statins for primary prevention of cardiovascular disease (CVD).MethodsThis is a cross-sectional analysis of cardiovascular risk and sociodemographic factors associated with statin utilisation from wave 1 of The Irish Longitudinal Study on Ageing. A hierarchy of indications for statin utilisation, consisting of eight mutually exclusive levels of CVD-related diagnoses, was created. Participants were assigned one level of indication. The prevalence of statin utilisation was calculated. The likelihood that an individual was using a statin was estimated using a multivariable logistic regression model, controlling for cardiovascular risk and sociodemographic factors.ResultsIn this nationally representative sample (n=5618) of community-dwelling participants aged 50 years and over, 1715 (30.5%) were taking statins. Of these, 65.0% (57.3% of men and 72.7% of women) were doing so for the primary prevention of CVD. Thus, almost two-thirds of those taking statins did so for primary prevention and there was a notable difference between women and men in this regard. We also found that statin utilisation was highest among those with a prior history of CVD and was significantly associated with age (compared with the base category 50–64 years; 65–74 years OR 1.38 (95% CI 1.16 to 1.65); 75+ OR 1.33 (95% CI 1.04 to 1.69)), living with a spouse or partner (compared with the base category living alone; OR 1.35 (95% CI 1.10 to 1.65)), polypharmacy (OR 1.74 (95% CI 1.39 to 2.19)) and frequency of general practitioner visits (compared with the base category 0 visits per year; 1–2 visits OR 2.46 (95% CI 1.80 to 3.35); 3–4 visits OR 3.24 (95% CI 2.34 to 4.47); 5–6 visits OR 2.98 (95% CI 2.08 to 4.26); 7+ visits OR 2.51 (95% CI 1.73 to 3.63)), even after controlling for clinical need. There was no association between using statins and gender, education, income, social class, health insurance status, location or Systematic Coronary Risk Evaluation (SCORE) risk in the multivariable analysis.ConclusionStatin utilisation among those with no history of CVD accounted for almost two-thirds of all statin use, in part reflecting the high proportion of the population with no history of CVD, although utilisation rates were highest among those with a history of CVD.

Evaluating Safety Effectiveness of Truck Climbing Lanes using Cross-Sectional Analysis and Propensity Score Models

2019 ◽  
Vol 2673 (7) ◽  
pp. 662-672 ◽  
Author(s):  
Muhammad Tahmidul Haq ◽  
Milan Zlatkovic ◽  
Khaled Ksaibati
Keyword(s):  
Propensity Score ◽  
Traffic Safety ◽  
Propensity Scores ◽  
Negative Binomial ◽  
Panel Count Data ◽  
Cross Sectional ◽  
Crash Data ◽  
Cross Sectional Analysis ◽  
Safety Effectiveness ◽  
Sectional Analysis

The State of Wyoming is characterized by heavy truck traffic flow, especially along Interstate 80 (I-80). A large portion of I-80 in Wyoming goes through mountainous and rolling terrain, resulting in significant vertical grades. About 9% of I-80 in each direction is within vertical grades of more than 3%, with certain sections reaching grades of close to 7%. Currently, there are 14 miles of climbing lanes in both directions. This study investigates the effects of climbing lanes on traffic safety using sections of I-80 in Wyoming. Cross-sectional analysis and propensity score methods were applied to evaluate the safety effectiveness and calibrate the Crash Modification Factor (CMF) and Relative Risk (RR) for climbing lanes. Data were collected from different sources and Wyoming-specific safety performance functions were developed using crash data from 2008 to 2016 for total crashes and truck-related crashes. All the segments were selected from I-80 in Wyoming with climbing lanes as treatment sites, and segments with similar geometrical characteristics without climbing lanes as comparison sites. Aggregated data were used to develop Negative Binomial and Zero-Inflated Negative Binomial models for performing cross-sectional analysis as they were found to fit better for the crash data. On the other hand, panel count data were used to conduct a propensity scores-potential outcomes framework. The CMFs and RR for climbing lanes from both analyses were found to be effective in reducing total and truck-related crashes. This is a first study that develops CMFs for climbing lanes in Wyoming.

scholarly journals Spatial Variations in Fertility of South Korea: A Geographically Weighted Regression Approach

2019 ◽  
Vol 8 (6) ◽  
pp. 262 ◽  
Author(s):  
Myunggu Jung ◽  
Woorim Ko ◽  
Yeohee Choi ◽  
Youngtae Cho
Keyword(s):  
South Korea ◽  
Geographically Weighted Regression ◽  
Population Policy ◽  
Local Population ◽  
Sociodemographic Factors ◽  
Ordinary Least Squares ◽  
Spatial Variations ◽  
Low Fertility ◽  
Weighted Regression

South Korea has witnessed a remarkable decline in birth rates in the last few decades. Although there has been a large volume of literature exploring the determinants of low fertility in South Korea, studies on spatial variations in fertility are scarce. This study compares the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to investigate the potential role of the spatially heterogeneous response of the total fertility rate (TFR) to sociodemographic factors. The study finds that the relationships between sociodemographic factors and TFRs in South Korea vary across 252 sub-administrative areas in terms of both magnitude and direction. This study therefore demonstrates the value of using spatial analysis for providing evidence-based local-population policy options in pursuit of a fertility rebound in South Korea.

scholarly journals Prevalence and Risk Factors of Food Insecurity among Mexican University Students’ Households

Nutrients ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 3426
Author(s):  
Pablo Alejandro Nava-Amante ◽  
Alejandra Betancourt-Núñez ◽  
Barbara Vizmanos ◽  
Miguel Amaury Salas-García ◽  
María Fernanda Bernal-Orozco ◽  
...  
Keyword(s):  
Risk Factors ◽  
University Students ◽  
Food Insecurity ◽  
Low Socioeconomic Status ◽  
Sociodemographic Factors ◽  
Cross Sectional ◽  
Factors Affecting ◽  
Low Socioeconomic ◽  
Indigenous Group ◽  
Sectional Analysis

Household food insecurity (FI) remains a major public health challenge worldwide. Data about perceived FI and its risk factors in Mexican university students are lacking. We aimed to assess FI’s prevalence and factors affecting it among university students’ households in Mexico. This cross-sectional analysis involved 7671 university students’ households using the 2018 Mexican National of Household Income and Expenditure Survey data. Variables analyzed included sociodemographic characteristics, and the 12-item validated Mexican Scale for Food Security (EMSA). Multivariable logistic regression modelling was performed to identify FI risk factors. The overall household FI prevalence was 30.8%. According to FI severity, prevalence rates were 16.3% for mild-FI, 8.8% for moderate-FI, and 5.7% for severe-FI. Low socioeconomic status (OR = 2.72; 95%CI: 2.09–3.54), low education level of household’s head (OR = 2.36; 95%CI: 1.90–2.94), self-ascription to an indigenous group (OR = 1.59; 95%CI: 1.41–1.79), attending public university (OR = 1.27; 95%CI: 1.13–1.43), female-headed household (OR = 1.26; 95%CI: 1.13–1.40), having worked recently (OR = 1.19; 95%CI: 1.07–1.33), and being in second year of studies (OR = 1.17; 95%CI: 1.03–1.33), were significantly related to FI. Our results confirm that FI is highly prevalent among Mexican university students’ households and that sociodemographic factors are essential in addressing this concern. Findings highlight the need for preventive programs and policies to alleviate FI.

scholarly journals Assessment of the Use of Geographically Weighted Regression for Analysis of Large On-Farm Experiments and Implications for Practical Application

Agronomy ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 1720
Author(s):  
Fiona H. Evans ◽  
Angela Recalde Salas ◽  
Suman Rakshit ◽  
Craig A. Scanlan ◽  
Simon E. Cook
Keyword(s):  
Spatial Variation ◽  
Geographically Weighted Regression ◽  
Large Scale ◽  
Treatment Effects ◽  
Management Practices ◽  
Response To Treatment ◽  
Weighted Regression ◽  
Yield Response ◽  
Practical Application ◽  
On Farm

On-farm experimentation (OFE) is a farmer-centric process that can enhance the adoption of digital agriculture technologies and improve farm profitability and sustainability. Farmers work with consultants or researchers to design and implement experiments using their own machinery to test management practices at the field or farm scale. Analysis of data from OFE is challenging because of the large spatial variation influenced by spatial autocorrelation that is not due to the treatment being tested and is often much larger than treatment effects. In addition, the relationship between treatment and yield response may also vary spatially. We investigate the use of geographically weighted regression (GWR) for analysis of data from large on-farm experiments. GWR estimates local regressions, where data are weighted by distance from the site using a distance-decay kernel. It is a simple approach that can be easily explained to farmers and their agronomic advisors. We use simulated data to test the ability of GWR to separate yield variation due to treatment from any underlying spatial variation in yield that is not due to treatment; show that GWR kernel bandwidth can be based on experimental design to accurately separate the underlying spatial variability from treatment effects; and demonstrate a step-wise model selection approach to determine when the response to treatment is global across the experiment or locally varying. We demonstrate our recommended approach on two large-scale experiments conducted on farms in Western Australia to investigate grain yield response to potassium fertiliser. We discuss the implications of our results for routine practical application to OFE and conclude that GWR has potential for wide application in a semi-automated manner to analyse OFE data, improve farm decision-making, and enhance the adoption of digital technologies.

Maternal Characteristics and Migraine Pharmacotherapy During Pregnancy: Cross-Sectional Analysis of Data from a Large Cohort Study

Cephalalgia ◽  
2009 ◽  
Vol 29 (12) ◽  
pp. 1267-1276 ◽  
Author(s):  
K Nezvalová-Henriksen ◽  
O Spigset ◽  
H Nordeng
Keyword(s):  
Body Mass Index ◽  
Odds Ratio ◽  
Sociodemographic Factors ◽  
Population Based ◽  
Narcotic Analgesics ◽  
Cross Sectional ◽  
Maternal Characteristics ◽  
Factors Associated ◽  
Large Cohort Study ◽  
Sectional Analysis

Little is known about factors associated with migraine pharmacotherapy during pregnancy. Of 60 435 pregnant women in a population-based cohort, 3480 (5.8%) reported having migraine during the first 5 months of pregnancy. Of these, 2525 (72.6%) reported using migraine pharmacotherapy, mostly non-narcotic analgesics (54.1%) and triptans (25.4%). After adjustment for sociodemographic factors and comorbidities in logistic regression analysis, high pregestational body mass index [odds ratio (OR) 1.3, 95% confidence interval (CI) 1.2, 1.4], sleep < 5 h (OR 1.6, 95% CI 1.3, 1.9), being on sick-leave (OR 1.3, 95% CI 1.2, 1.5) and acute back/shoulder/neck pain (OR 0.6, 95% CI 0.6, 0.7) were associated with migraine pharmacotherapy during pregnancy. Many women need drug treatment for migraine during pregnancy, and the choice of pharmacotherapy during this period may be influenced by maternal sociodemographic factors and comorbidities.

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