A comparison of statistical methods for modeling count data with an application to hospital length of stay

Background Hospital length of stay (LOS) is a key indicator of hospital care management efficiency, cost of care, and hospital planning. Hospital LOS is often used as a measure of a post-medical procedure outcome, as a guide to the benefit of a treatment of interest, or as an important risk factor for adverse events. Therefore, understanding hospital LOS variability is always an important healthcare focus. Hospital LOS data can be treated as count data, with discrete and non-negative values, typically right skewed, and often exhibiting excessive zeros. In this study, we compared the performance of the Poisson, negative binomial (NB), zero-inflated Poisson (ZIP), and zero-inflated negative binomial (ZINB) regression models using simulated and empirical data. Methods Data were generated under different simulation scenarios with varying sample sizes, proportions of zeros, and levels of overdispersion. Analysis of hospital LOS was conducted using empirical data from the Medical Information Mart for Intensive Care database. Results Results showed that Poisson and ZIP models performed poorly in overdispersed data. ZIP outperformed the rest of the regression models when the overdispersion is due to zero-inflation only. NB and ZINB regression models faced substantial convergence issues when incorrectly used to model equidispersed data. NB model provided the best fit in overdispersed data and outperformed the ZINB model in many simulation scenarios with combinations of zero-inflation and overdispersion, regardless of the sample size. In the empirical data analysis, we demonstrated that fitting incorrect models to overdispersed data leaded to incorrect regression coefficients estimates and overstated significance of some of the predictors. Conclusions Based on this study, we recommend to the researchers that they consider the ZIP models for count data with zero-inflation only and NB models for overdispersed data or data with combinations of zero-inflation and overdispersion. If the researcher believes there are two different data generating mechanisms producing zeros, then the ZINB regression model may provide greater flexibility when modeling the zero-inflation and overdispersion

Understanding the Intention to Use Telehealth Services in Underserved Hispanic Border Communities: Cross-Sectional Study

Background: Despite the United States having one of the leading health care systems in the world, underserved minority communities face significant access challenges. These communities can benefit from telehealth innovations that promise to improve health care access and, consequently, health outcomes. However, little is known about the attitudes toward telehealth in these communities, an essential first step toward effective adoption and use. Objective: The purpose of this study is to assess the factors that shape behavioral intention to use telehealth services in underserved Hispanic communities along the Texas-Mexico border and examine the role of electronic health (eHealth) literacy in telehealth use intention. Methods: We used cross-sectional design to collect data at a community health event along the Texas-Mexico border. The area is characterized by high poverty rates, low educational attainment, and health care access challenges. Trained bilingual students conducted 322 in-person interviews over a 1-week period. The survey instrument assessed sociodemographic information and telehealth-related variables. Attitudes toward telehealth were measured by asking participants to indicate their level of agreement with 9 statements reflecting different aspects of telehealth use. For eHealth literacy, we used the eHealth Literacy Scale (eHEALS), an 8-item scale designed to measure consumer confidence in finding, evaluating, and acting upon eHealth information. To assess the intention to use telehealth, we asked participants about the likelihood that they would use telehealth services if offered by a health care provider. We analyzed data using univariate, multivariate, and mediation statistical models. Results:Participants were primarily Hispanic (310/319, 97.2%) and female (261/322, 81.1%), with an average age of 43 years. Almost three-quarters (219/298) reported annual household incomes below $20,000. Health-wise, 42.2% (136/322) self-rated their health as fair or poor, and 79.7% (255/320) were uninsured. The overwhelming majority (289/319, 90.6%) had never heard of telehealth. Once we defined the term, participants exhibited positive attitudes toward telehealth, and 78.9% (254/322) reported being somewhat likely or very likely to use telehealth services if offered by a health care provider. Based on multivariate proportional odds regression analysis, a 1-point increase in telehealth attitudes reduced the odds of lower versus higher response in the intention to use telehealth services by 23% (OR 0.77, 95% CI 0.73-0.81). Mediation analysis revealed that telehealth attitudes fully mediated the association between eHealth literacy and intention to use telehealth services. For a 1-point increase in eHEALS, the odds of lower telehealth use decreased by a factor of 0.95 (5%; OR 0.95, 95% CI 0.93-0.98; P Conclusions: Telehealth promises to address many of the access challenges facing ethnic and racial minorities, rural communities, and low-income populations. Findings underscore the importance of raising awareness of telehealth and promoting eHealth literacy as a key step in fostering positive attitudes toward telehealth and furthering interest in its use

Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies

The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis

Undiagnosed Diabetes and Pre-Diabetes in Health Disparities

Globally half of all diabetes mellitus is undiagnosed. We sought to determine the extent and characteristics of undiagnosed type 2 diabetes mellitus and pre-diabetes in Mexican Americans residing in the United States. This disadvantaged population with 50% lifetime risk of diabetes is a microcosm of the current pandemic. We accessed baseline data between 2004 and 2014 from 2,838 adults recruited to our Cameron County Hispanic Cohort (CCHC); a two-stage randomly selected \u27Framingham-like\u27 cohort of Mexican Americans on the US Mexico border with severe health disparities. We examined prevalence, risk factors and metabolic health in diagnosed and undiagnosed diabetes and pre-diabetes. Two thirds of this Mexican American population has diabetes or pre-diabetes. Diabetes prevalence was 28.0%, nearly half undiagnosed, and pre-diabetes 31.6%. Mean BMI among those with diabetes was 33.5 kg/m2 compared with 29.0 kg/m2 for those without diabetes. Significant risk factors were low income and educational levels. Most with diabetes had increased waist/hip ratio. Lack of insurance and access to health services played a decisive role in failure to have diabetes diagnosed. Participants with undiagnosed diabetes and pre-diabetes had similar measures of poor metabolic health similar but generally not as severe as those with diagnosed diabetes. More than 50% of a minority Mexican American population in South Texas has diabetes or pre-diabetes and is metabolically unhealthy. Only a third of diabetes cases were diagnosed. Sustained efforts are imperative to identify, diagnose and treat individuals in underserved communities

Metabolic Health Has Greater Impact on Diabetes than Simple Overweight/Obesity in Mexican Americans

To compare the risk for diabetes in each of 4 categories of metabolic health and BMI. Methods. Participants were drawn from the Cameron County Hispanic Cohort, a randomly selected Mexican American cohort in Texas on the US-Mexico border. Subjects were divided into 4 phenotypes according to metabolic health and BMI: metabolically healthy normal weight, metabolically healthy overweight/obese, metabolically unhealthy normal weight, and metabolically unhealthy overweight/obese. Metabolic health was defined as having less than 2 metabolic abnormalities. Overweight/obese status was assessed by BMI higher than 25 kg/m2. Diabetes was defined by the 2010 ADA definition or by being on a diabetic medication. Results. The odds ratio for diabetes risk was 2.25 in the metabolically healthy overweight/obese phenotype (95% CI 1.34, 3.79), 3.78 (1.57, 9.09) in the metabolically unhealthy normal weight phenotype, and 5.39 (3.16, 9.20) in metabolically unhealthy overweight/obese phenotype after adjusting for confounding factors compared with the metabolically healthy normal weight phenotype. Conclusions. Metabolic health had a greater effect on the increased risk for diabetes than overweight/obesity. Greater focus on metabolic health might be a more effective target for prevention and control of diabetes than emphasis on weight loss alone

Comparative analysis of all-terrain vehicles, motorcycle and automobile-related trauma in a rural border community of the USA

Introduction: There is widespread use of all-terrain vehicles (ATVs) in the USA for both work-related and recreational activities. In this study, we aimed to determine the difference in injury severity, Glasgow Coma scales and length of stay between ATV-related injuries and injuries sustained from motorcycles (MOTOs) and automobiles (AUTOs). Methods: We retrospectively analysed ATV, MOTO and AUTO injuries from a Level 2 Trauma Center between 01 January 2015 and 31 August 2020. Proportional odds regression analyses, as well as multivariable regression models, were used to analyse the data. Results: There were significantly more male and paediatric patients that suffered ATV-related injuries compared with MOTO or AUTO injuries. Victims of ATV-related injuries were also more likely to have open fractures. Paediatric patients were less likely to sustain an injury from either AUTO or MOTO accidents compared with ATV accidents. Patients with no drug use during injury and those who used protective equipment such as seat belts and child seats were significantly associated with lower Injury Severity Scores and higher Glasgow Coma Scale scores, indicating less severe injuries. Discussion: Paediatric patients are very likely to suffer sequela and long-term disability due to the severity of ATV-related injuries. Public awareness campaigns to educate our population, especially our youth, about the danger of ATV use are highly needed

Depression, Obesity, and Metabolic Syndrome: Prevalence and Risks of Comorbidity in a Population-Based Study of Mexican Americans

Introduction: We examined the prevalence of depression, obesity, and metabolic syndrome and associations between them in a population-based representative cohort of Mexican Americans living on the United States-Mexico border. Method: The sample in this cross-sectional analysis consisted of 1,768 Mexican American adults (≥ 18 years of age) assessed between the years 2004 and 2010, with whom we tested our central hypothesis of a significant relationship between obesity and depression. Depression was measured using the Center for Epidemiologic Studies-Depression scale (CES-D) with a cutoff score of ≥ 16 for depression and a cutoff score of ≥ 27 for severe depression. We categorized body mass index (BMI) values as obese (≥ 30kg/m(2)) and later subdivided the obese subjects into obese (30-39 kg/m(2)[inclusive]) and morbidly obese (≥ 40 kg/m(2)). Metabolic syndrome was defined using the American Heart Association definition requiring at least 3 of the following: increased waist circumference, elevated triglycerides, reduced high-density lipoprotein (HDL) cholesterol, elevated blood pressure, and elevated fasting glucose. Weighted data were analyzed to establish prevalence of depression, obesity, and metabolic syndrome. Univariate and multivariable weighted regression models were used to test potential associations between these disorders. Results: Using weighted prevalence, we observed high rates of depression (30%), obesity (52%), and metabolic syndrome (45%). Univariate models revealed female gender (P = .0004), low education (P = .003), low HDL level (P = .009), and increased waist circumference (P = .03) were associated with depression. Female gender (P = .01), low education (P = .003), and morbid obesity (P = .002) were risk factors for severe depression and remained significant in multivariable models. Conclusions: In this large cohort of Mexican Americans, obesity, female gender, and low education were identified risk factors for depression. These indicators may serve as targets for early detection, prevention, and intervention in this population

Social distancing and testing as optimal strategies against the spread of COVID-19 in the Rio Grande Valley of Texas

At the beginning of August 2020, the Rio Grande Valley (RGV) of Texas experienced a rapid increase of coronavirus disease 2019 (abbreviated as COVID-19) cases and deaths. This study aims to determine the optimal levels of effective social distancing and testing to slow the virus spread at the outset of the pandemic. We use an age-stratified eight compartment epidemiological model to depict COVID-19 transmission in the community and within households. With a simulated 120-day outbreak period data we obtain a post 180-days period optimal control strategy solution. Our results show that easing social distancing between adults by the end of the 180-day period requires very strict testing a month later and then daily testing rates of 5% followed by isolation of positive cases. Relaxing social distancing rates in adults from 50% to 25% requires both children and seniors to maintain social distancing rates of 50% for nearly the entire period while maintaining maximum testing rates of children and seniors for 150 of the 180 days considered in this model. Children have higher contact rates which leads to transmission based on our model, emphasizing the need for caution when considering school reopenings

Depression in Mexican Americans with Diagnosed and Undiagnosed Diabetes

Background: Depression and diabetes commonly co-occur; however, the strength of the physiological effects of diabetes as mediating factors towards depression is uncertain. Method: We analyzed extensive clinical, epidemiological and laboratory data from n = 2081 Mexican Americans aged 35-64 years, recruited from the community as part of the Cameron County Hispanic Cohort (CCHC) divided into three groups: Diagnosed (self-reported) diabetes (DD, n = 335), Undiagnosed diabetes (UD, n = 227) and No diabetes (ND, n = 1519). UD participants denied being diagnosed with diabetes, but on testing met the 2010 American Diabetes Association and World Health Organization definitions of diabetes. Depression was measured using the Center for Epidemiological Studies - Depression (CES-D) scale. Weighted data were analyzed using dimensional and categorical outcomes using univariate and multivariate models. Results: The DD group had significantly higher CES-D scores than both the ND and UD (p ⩽ 0.001) groups, whereas the ND and UD groups did not significantly differ from each other. The DD subjects were more likely to meet the CES-D cut-off score for depression compared to both the ND and UD groups (p = 0.001), respectively. The UD group was also less likely to meet the cut-off score for depression than the ND group (p = 0.003). Our main findings remained significant in models that controlled for socio-demographic and clinical confounders. Conclusions: Meeting clinical criteria for diabetes was not sufficient for increased depressive symptoms. Our findings suggest that the \u27knowing that one is ill\u27 is associated with depressive symptoms in diabetic subjects