scholarly journals Characterizing Health Care Delays and Interruptions in the United States During the COVID-19 Pandemic: Internet-Based, Cross-sectional Survey Study (Preprint)

2020 ◽  
Author(s):  
Elizabeth Lerner Papautsky ◽  
Dylan R Rice ◽  
Hana Ghoneima ◽  
Anna Laura W McKowen ◽  
Nicholas Anderson ◽  
...  
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
General Health ◽  
Regression Models ◽  
Statistical Tests ◽  
Diagnostic Testing ◽  
The United States ◽  
Multiple Logistic Regression ◽  
Convenience Sample ◽  
Logistic Regression Models

BACKGROUND The COVID-19 pandemic has broader geographic spread and potentially longer lasting effects than those of previous disasters. Necessary preventive precautions for the transmission of COVID-19 has resulted in delays for in-person health care services, especially at the outset of the pandemic. OBJECTIVE Among a US sample, we examined the rates of delays (defined as cancellations and postponements) in health care at the outset of the pandemic and characterized the reasons for such delays. METHODS As part of an internet-based survey that was distributed on social media in April 2020, we asked a US–based convenience sample of 2570 participants about delays in their health care resulting from the COVID-19 pandemic. Participant demographics and self-reported worries about general health and the COVID-19 pandemic were explored as potent determinants of health care delays. In addition to all delays, we focused on the following three main types of delays, which were the primary outcomes in this study: dental, preventive, and diagnostic care delays. For each outcome, we used bivariate statistical tests (<i>t</i> tests and chi-square tests) and multiple logistic regression models to determine which factors were associated with health care delays. RESULTS The top reported barrier to receiving health care was the fear of SARS-CoV-2 infection (126/374, 33.6%). Almost half (1227/2570, 47.7%) of the participants reported experiencing health care delays. Among those who experienced health care delays and further clarified the type of delay they experienced (921/1227, 75.1%), the top three reported types of care that were affected by delays included dental (351/921, 38.1%), preventive (269/921, 29.2%), and diagnostic (151/921, 16.4%) care. The logistic regression models showed that age (<i>P</i>&lt;.001), gender identity (<i>P</i>&lt;.001), education (<i>P</i>=.007), and self-reported worry about general health (<i>P</i>&lt;.001) were significantly associated with experiencing health care delays. Self-reported worry about general health was negatively related to experiencing delays in dental care. However, this predictor was positively associated with delays in diagnostic testing based on the logistic regression model. Additionally, age was positively associated with delays in diagnostic testing. No factors remained significant in the multiple logistic regression for delays in preventive care, and although there was trend between race and delays (people of color experienced fewer delays than White participants), it was not significant (<i>P</i>=.06). CONCLUSIONS The lessons learned from the initial surge of COVID-19 cases can inform systemic mitigation strategies for potential future disruptions. This study addresses the demand side of health care delays by exploring the determinants of such delays. More research on health care delays during the pandemic is needed, including research on their short- and long-term impacts on patient-level outcomes such as mortality, morbidity, mental health, people’s quality of life, and the experience of pain.

scholarly journals Catastrophic Household Expenditure Associated with Out-of-Pocket Healthcare Payments in Spain

2021 ◽  
Vol 18 (3) ◽  
pp. 932
Author(s):  
Samuel López-López ◽  
Raúl del Pozo-Rubio ◽  
Marta Ortega-Ortega ◽  
Francisco Escribano-Sotos
Keyword(s):  
Health Care ◽  
Logistic Regression ◽  
Regression Models ◽  
Financial Risk ◽  
Household Expenditure ◽  
Financial Protection ◽  
Logistic Regression Models ◽  
Ordered Logistic Regression ◽  
Out Of Pocket Payments ◽  
The Subject

Background. The financial effect of households’ out-of-pocket payments (OOP) on access and use of health systems has been extensively studied in the literature, especially in emerging or developing countries. However, it has been the subject of little research in European countries, and is almost nonexistent after the financial crisis of 2008. The aim of the work is to analyze the incidence and intensity of financial catastrophism derived from Spanish households’ out-of-pocket payments associated with health care during the period 2008–2015. Methods. The Household Budget Survey was used and catastrophic measures were estimated, classifying the households into those above the threshold of catastrophe versus below. Three ordered logistic regression models and margins effects were estimated. Results. The results reveal that, in 2008, 4.42% of Spanish households dedicated more than 40% of their income to financing out-of-pocket payments in health, with an average annual gap of EUR 259.84 (DE: EUR 2431.55), which in overall terms amounts to EUR 3939.44 million (0.36% of GDP). Conclusion. The findings of this study reveal the existence of catastrophic households resulting from OOP payments associated with health care in Spain and the need to design financial protection policies against the financial risk derived from facing these types of costs.

scholarly journals Aplicación de modelos de regresión logística en metodología observacional: modalidades de competición en la iniciación al fútbol.

2015 ◽  
Vol 32 (1) ◽  
pp. 288 ◽  
Author(s):  
Daniel Lapresa ◽  
Javier Arana ◽  
M.Teresa Anguera ◽  
J.Ignacio Pérez-Castellanos ◽  
Mario Amatria
Keyword(s):  
Physical Activity ◽  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Multiple Logistic Regression ◽  
Observational Methodology ◽  
Logistic Regression Models ◽  
Game Format ◽  
Simple Logistic

This study shows how simple and multiple logistic regression can be used in observational methodology and more specifically, in the fields of physical activity and sport. We demonstrate this in a study designed to determine whether three-a-side futsal or five-a-side futsal is more suited to the needs and potential of children aged 6-to-8 years. We constructed a multiple logistic regression model to analyze use of space (depth of play) and three simple logistic regression models to determine which game format is more likely to potentiate effective technical and tactical performance.

Correlates of Perceived HIV Prevalence and Associations With HIV Testing Behavior Among Men Who Have Sex With Men in the United States

2014 ◽  
Vol 10 (2) ◽  
pp. 90-99 ◽  
Author(s):  
Darcy White ◽  
Rob Stephenson
Keyword(s):  
United States ◽  
Logistic Regression ◽  
Hiv Testing ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
The United States ◽  
Hiv Prevalence ◽  
Logistic Regression Models ◽  
Sex With Men ◽  
Hiv Testing Behavior

As the rate of HIV infection continues to rise among men who have sex with men (MSM) in the United States, a focus of current prevention efforts is to encourage frequent HIV testing. Although levels of lifetime testing are high, low levels of routine testing among MSM are concerning. Using data from an online sample of 768 MSM, this article explores how perceptions of HIV prevalence are associated with HIV testing behavior. Ordinal logistic regression models were fitted to examine correlates of perceived prevalence, and binary logistic regression models were fitted to assess associations between perceived prevalence and HIV testing. The results indicate that perceptions of higher prevalence among more proximal reference groups such as friends and sex partners are associated with greater odds of HIV testing. Perceptions of HIV prevalence were nonuniform across the sample; these variations point to groups to target with strategic messaging and interventions to increase HIV testing among MSM.

scholarly journals ‘%svy_logistic_regression: A generic SAS® macro for simple and multiple logistic regression and creating quality publication-ready tables using survey or non-survey data

2019 ◽  
Author(s):  
Jacques Muthusi ◽  
Samuel Mwalili ◽  
Peter Young
Keyword(s):  
Logistic Regression ◽  
Survey Data ◽  
Survey Design ◽  
The United States ◽  
Epidemiological Studies ◽  
Biomedical Literature ◽  
Reproducible Research ◽  
Multiple Logistic Regression ◽  
Logistic Regression Models ◽  
Adults And Children

AbstractIntroductionReproducible research is increasingly gaining interest in the research community. Automating the production of research manuscript tables from statistical software can help increase the reproducibility of findings. Logistic regression is used in studying disease prevalence and associated factors in epidemiological studies and can be easily performed using widely available software including SAS, SUDAAN, Stata or R. However, output from these software must be processed further to make it readily presentable. There exists a number of procedures developed to organize regression output, though many of them suffer limitations of flexibility, complexity, lack of validation checks for input parameters, as well as inability to incorporate survey design.MethodsWe developed a SAS macro, %svy_logistic_regression, for fitting simple and multiple logistic regression models. The macro also creates quality publication-ready tables using survey or non-survey data which aims to increase transparency of data analyses. It further significantly reduces turn-around time for conducting analysis and preparing output tables while also addressing the limitations of existing procedures.ResultsWe demonstrate the use of the macro in the analysis of the 2013-2014 National Health and Nutrition Examination Survey (NHANES), a complex survey designed to assess the health and nutritional status of adults and children in the United States. The output presented here is directly from the macro and is consistent with how regression results are often presented in the epidemiological and biomedical literature, with unadjusted and adjusted model results presented side by side.ConclusionsThe SAS code presented in this macro is comprehensive, easy to follow, manipulate and to extend to other areas of interest. It can also be incorporated quickly by the statistician for immediate use. It is an especially valuable tool for generating quality, easy to review tables which can be incorporated directly in a publication.

scholarly journals 1400Central but not general obesity is positively associated with risk of hyperhomocysteinemia in middle-aged women

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Yingying Wang
Keyword(s):  
Cardiovascular Disease ◽  
Logistic Regression ◽  
Regression Models ◽  
Central Obesity ◽  
Multiple Logistic Regression ◽  
Obese Women ◽  
General Obesity ◽  
Middle Aged ◽  
Logistic Regression Models ◽  
The Relationship

Abstract Background Obesity and homocysteine (Hcy) are two important risk factors for cardiovascular disease (CVD); however, there were conflicting results for the relationship between them. Our study is to explore the associations of general and central obesity with hyperhomocysteinemia (HHcy) in middle-aged women. Methods The current analysis was based on data from 11007 women aged 40-60 years. Height, weight, and waist circumference (WC) were measured and serum homocysteine was determined. Multiple logistic regression models were used to assess the associations of the risk of hyperhomocysteinemia (HHcy, Hcy&gt;15μmol/L) with BMI and WC. Results 13.71% women had HHcy. The prevalences of BMI-based general obesity and WC-based central obesity were 11.17% and 22.88%, respectively. Compared with non-obese women, the mean serum Hcy concentration was significantly higher in WC-based central obese women (P = 0.002), but not in BMI-based general obese women (P &gt; 0.05). In the multiple logistic regression models, central obesity was positively related to the risk of HHcy (OR = 1.30, 95%CI=1.10 to 1.52), while general obesity was inversely related to the risk of HHcy (OR = 0.82, 95%CI=0.72 to 0.93 and OR = 0.71,95% CI = 0.57 to 0.89). Conclusions Central obesity was positively, while general obesity was negatively related to the risk of HHcy. Menopause showed no effect modification on these associations. Key messages Homocysteine; Central obesity; Menopause; Cardiovascular Disease

Use of Logistic Regression for Developing Risk-adjusted Quality Indicators in Nursing

2010 ◽  
Vol 49 (06) ◽  
pp. 608-612 ◽  
Author(s):  
D. Renner ◽  
B. Fischer ◽  
M. Kutschmann
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Quality Indicators ◽  
Regression Models ◽  
Pressure Ulcers ◽  
Logistic Function ◽  
Multiple Logistic Regression ◽  
Logistic Regression Models ◽  
Joint Influence ◽  
Developed Pressure

Summary Objectives: A low rate of newly developed pressure ulcers is considered as an important quality indicator in nursing. However, the result of a hospital depends not only on the quality of care but on the risk profile of its patients as well. Therefore, based on multiple logistic regression models we describe a method for calculating risk-adjusted quality indicators in nursing. Method: Based on data of 1,009,989 patients from 1747 hospitals in 2009, we developed two multiple logistic regression models to identify and to weigh a possible joint influence of several risk factors on newly developed pressure ulcers. In a further step, we calculated risk-adjusted rates. Results: Factors remaining in the regression models were “micro-movements on admission”, “diabetes mellitus”, “age” and “days on intensive care unit”. Based on the corresponding regression coefficients and the logistic function, the expected rate of newly developed pressure ulcers was calculated for every hospital. Fi nally, expected rates and observed rates both were used to calculate risk-adjusted rates. Conclusion: The simultaneous consideration of relevant risk factors by means of risk- adjusted quality indicators ensures a fair comparison of hospitals.

Sign in / Sign up

Export Citation Format

Share Document