scholarly journals Modeling the Conditional Dependence between Discrete and Continuous Random Variables with Applications in Insurance

Mathematics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 45
Author(s):  
Emilio Gómez-Déniz ◽  
Enrique Calderín-Ojeda
Keyword(s):  
Structural Model ◽  
Medical Expenditure Panel Survey ◽  
Health Care Use ◽  
Medical Expenditure ◽  
Panel Survey ◽  
Conditional Distributions ◽  
Conditional Dependence ◽  
Outpatient Visits ◽  
Conditional Expectations ◽  
The U.S

We jointly model amount of expenditure for outpatient visits and number of outpatient visits by considering both dependence and simultaneity by proposing a bivariate structural model that describes both variables, specified in terms of their conditional distributions. For that reason, we assume that the conditional expectation of expenditure for outpatient visits with respect to the number of outpatient visits and also, the number of outpatient visits expectation with respect to the expenditure for outpatient visits is related by taking a linear relationship for these conditional expectations. Furthermore, one of the conditional distributions obtained in our study is used to derive Bayesian premiums which take into account both the number of claims and the size of the correspondent claims. Our proposal is illustrated with a numerical example based on data of health care use taken from Medical Expenditure Panel Survey (MEPS), conducted by the U.S. Agency of Health Research and Quality.

Fiscal difficulties of cities, the labor market, and health care

2018 ◽  
Vol 18 (04) ◽  
pp. 565-578
Author(s):  
John Hsu ◽  
Joseph Newhouse ◽  
Lindsay Nicole Overhage ◽  
Samuel Zuvekas
Keyword(s):  
Health Care ◽  
Comparison Group ◽  
Medical Expenditure Panel Survey ◽  
Health Care Use ◽  
Medical Expenditure ◽  
Panel Survey ◽  
Public Employment ◽  
Bond Ratings ◽  
Reported Health ◽  
Group Self

AbstractWe investigated labor force and health outcomes in cities experiencing fiscal difficulties to assess how those difficulties might impact their employees. We matched 23 cities with bond downgrades and 31 cities with stable bond ratings to sampling units in the Medical Expenditure Panel Survey. Starting the year before the downgrade and for the four subsequent years, the rate of separation from local public employment fell in the cities with downgrades relative to the comparison group. Self-reported health may have worsened, but there were no statistically significant effects on health care use or spending.

The Effects of Survey Enhancements on the Quality of Reporting in the Medical Expenditure Panel Survey, 2008–2015

2019 ◽  
Vol 8 (3) ◽  
pp. 589-616 ◽  
Author(s):  
Samuel H Zuvekas ◽  
Adam I Biener ◽  
Wendy D Hicks
Keyword(s):  
Health Care ◽  
Data Quality ◽  
Large Scale ◽  
Medical Expenditure Panel Survey ◽  
Lessons Learned ◽  
Health Care Use ◽  
Medical Expenditure ◽  
Panel Survey ◽  
Quality Of Reporting

Abstract It is well established that survey respondents imperfectly recall health care use in surveys. However, careful attention to both survey design and fielding procedures can enhance recall. We examine the effects of a comprehensive, multi-pronged approach to changing field procedures in the Medical Expenditure Panel Survey (MEPS) to improve quality of health care use reporting. Conducted annually since 1996, the MEPS is the leading large-scale nationally representative health survey with detailed individual and household information on health care use and expenditures. These survey enhancements were undertaken in 2013–2014 because of concerns over a drop in the quality of reporting in 2010 that persisted into 2011–2012. The approach combined focused retraining of field supervisors and interviewers, developing quality metrics and reports for ongoing monitoring of interviewers, and revising advanced letters and materials sent to respondents. We seek to determine the extent to which changes in field procedures and trainings improved interviewer and respondent behaviors associated with better reporting, and more importantly, improved reporting accuracy. We use longitudinal MEPS data from 2008 through 2015, combining household reported use with sociodemographic and health status characteristics, and paradata on the characteristics of the interviews and interviewers. We exploit the longitudinal data and timings of major trainings and changes in field procedures in regression models, separating out the effects of the trainings and other fielding changes to the extent possible. We find that the 2013–2014 data quality improvement activities substantially improved reporting quality. Positive interviewer behaviors increased substantially to above pre-2010 levels, and utilization reporting has recovered to above pre-2010 levels, returning MEPS to trend. Importantly, these substantial gains occurred in 2013, prior to extensive in-person training for most of the field force. We examine the lessons learned from this data quality initiative both for the MEPS program and for other large household surveys.

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