scholarly journals Identifying and interpreting subgroups in health care utilization data with count mixture regression models

2019 ◽  
Vol 38 (22) ◽  
pp. 4423-4435 ◽  
Author(s):  
Christoph F. Kurz ◽  
Laura A. Hatfield
Keyword(s):  
Health Care ◽  
Health Care Utilization ◽  
Regression Models ◽  
Care Utilization ◽  
Mixture Regression ◽  
Utilization Data ◽  
Mixture Regression Models

scholarly journals Health status and health care utilization after discharge from geriatric in-hospital stay – description of a register-based study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
E. Rydwik ◽  
R. Lindqvist ◽  
C. Willers ◽  
L. Carlsson ◽  
G. H. Nilsson ◽  
...  
Keyword(s):  
Primary Care ◽  
Health Care ◽  
Health Status ◽  
Health Care Utilization ◽  
Care Utilization ◽  
Future Studies ◽  
Healthcare Data ◽  
Utilization Data ◽  
Hospital Stays ◽  
Socioeconomic Data

Abstract Background This study is the first part of a register-based research program with the overall aim to increase the knowledge of the health status among geriatric patients and to identify risk factors for readmission in this population. The aim of this study was two-fold: 1) to evaluate the validity of the study cohorts in terms of health care utilization in relation to regional cohorts; 2) to describe the study cohorts in terms of health status and health care utilization after discharge. Methods The project consist of two cohorts with data from patient records of geriatric in-hospital stays, health care utilization data from Stockholm Regional Healthcare Data Warehouse 6 months after discharge, socioeconomic data from Statistics Sweden. The 2012 cohort include 6710 patients and the 2016 cohort, 8091 patients; 64% are women, mean age is 84 (SD 8). Results Mean days to first visit in primary care was 12 (23) and 10 (19) in the 2012 and 2016 cohort, respectively. Readmissions to hospital was 38% in 2012 and 39% in 2016. The validity of the study cohorts was evaluated by comparing them with regional cohorts. The study cohorts were comparable in most cases but there were some significant differences between the study cohorts and the regional cohorts, especially regarding amount and type of primary care. Conclusion The study cohorts seem valid in terms of health care utilization compared to the regional cohorts regarding hospital care, but less so regarding primary care. This will be considered in the analyses and when interpreting data in future studies based on these study cohorts. Future studies will explore factors associated with health status and re-admissions in a population with multi-morbidity and disability.

scholarly journals Health Status and Health Care Utilization of US Adults Under Probation: 2015–2018

2020 ◽  
Vol 110 (9) ◽  
pp. 1411-1417
Author(s):  
Laura Hawks ◽  
Emily A. Wang ◽  
Benjamin Howell ◽  
Steffie Woolhandler ◽  
David U. Himmelstein ◽  
...  
Keyword(s):  
Health Care ◽  
Substance Use ◽  
Health Care Utilization ◽  
Regression Models ◽  
Outpatient Visit ◽  
Population Based ◽  
Care Utilization ◽  
Logistic Regression Models ◽  
Race Ethnicity ◽  
Use Of Health Services

Objectives. To compare the health and health care utilization of persons on and not on probation nationally. Methods. Using the National Survey of Drug Use and Health, a population-based sample of US adults, we compared physical, mental, and substance use disorders and the use of health services of persons (aged 18–49 years) on and not on probation using logistic regression models controlling for age, race/ethnicity, gender, poverty, and insurance status. Results. Those on probation were more likely to have a physical condition (adjusted odds ratio [AOR] = 1.3; 95% confidence interval [CI] = 1.2, 1.4), mental illness (AOR = 2.4; 95% CI = 2.1, 2.8), or substance use disorder (AOR = 4.2; 95% CI = 3.8, 4.5). They were less likely to attend an outpatient visit (AOR = 0.8; 95% CI = 0.7, 0.9) but more likely to have an emergency department visit (AOR = 1.8; 95% CI = 1.6, 2.0) or hospitalization (AOR = 1.7; 95% CI = 1.5, 1.9). Conclusions. Persons on probation have an increased burden of disease and receive less outpatient care but more acute services than persons not on probation. Public Health Implications. Efforts to address the health needs of those with criminal justice involvement should include those on probation.

scholarly journals Validation of Diagnostic Groups Based on Health Care Utilization Data Should Adjust for Sampling Strategy

Medical Care ◽  
2017 ◽  
Vol 55 (8) ◽  
pp. e59-e67 ◽  
Author(s):  
Geneviève Cadieux ◽  
Robyn Tamblyn ◽  
David L. Buckeridge ◽  
Nandini Dendukuri
Keyword(s):  
Health Care ◽  
Health Care Utilization ◽  
Sampling Strategy ◽  
Care Utilization ◽  
Utilization Data

External adjustment for unmeasured confounders improved drug–outcome association estimates based on health care utilization data

2012 ◽  
Vol 65 (11) ◽  
pp. 1190-1199 ◽  
Author(s):  
Giovanni Corrao ◽  
Federica Nicotra ◽  
Andrea Parodi ◽  
Antonella Zambon ◽  
Davide Soranna ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Utilization ◽  
Care Utilization ◽  
External Adjustment ◽  
Utilization Data ◽  
Unmeasured Confounders

scholarly journals Estimation in the zero-inflated bivariate Poisson model, with an application to health-care utilization data

2021 ◽  
Vol 16 (2) ◽  
pp. 2767-2788
Author(s):  
Konan Jean Geoffroy Kouakou ◽  
Ouagninia Hili ◽  
Jean-Etienne Ouindllassida Dupuy
Keyword(s):  
Health Care ◽  
Health Care Utilization ◽  
Asymptotic Properties ◽  
Poisson Model ◽  
Care Utilization ◽  
Likelihood Estimator ◽  
Health Care Demand ◽  
Excess Zeros ◽  
Finite Samples ◽  
Utilization Data

Data on the demand for medical care is usually measured by a number of different counts. These count data are most often correlated and subject to high proportions of zeros. However, excess zeros and the dependence between these data can jointly affect several utilization measures.In this paper, the zero-inflated bivariate Poisson regression model (ZIBP) was used to analyze health-care utilization data. First, the asymptotic properties of the maximum likelihood estimator (MLE) of this model were investigated theoretically. Then, a simulation study is conducted to evaluate the behaviour of the estimator in finite samples. Finally, an application of the ZIBP model to health care demand data is provided by way of illustration.

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