Informing Policy from Prices: An Overview of California Nursing Homes

University of Toronto Journal of Public Health Pub Date : 2021-09-05 DOI:10.33137/utjph.v2i2.36806

Karen El Hajj

{"title":"Informing Policy from Prices: An Overview of California Nursing Homes","authors":"Karen El Hajj","doi":"10.33137/utjph.v2i2.36806","DOIUrl":null,"url":null,"abstract":"Introduction: The rising cost of healthcare along with the aging demographic requires the attention of policy makers. The United States’ nursing home industry is costly to older adults, requiring many to resort to government funded Medicare to offset these costs. This study aims to understand determinants of nursing home prices in the state of California. Variables included in the analysis are selected based on previous literature on the costs of nursing homes in the US. \nMethods: The data were analyzed using a multi-variable regression analysis. The analysis sample included 1,121 nursing homes across California, using facility level and governmental data that is publically available for the years of 2016-2017. Data collected included financial indicators (net income), ownership (for-profit, non-profit) represented as a dummy variable, occupancy rates, reimbursement rates (Medicare & Medicaid), staffing, quality and competition variables such as nursing homes per county. \nResults: The regression analysis indicated that ownership type (for-profit), competition and occupancy rates have a negative significant effect on nursing home prices. Whereas, reimbursement rates of both Medicare and Medicaid, home income and staffing levels have a positive significant effect, driving further nursing home prices. \nConclusion: The study aimed to understand the relevant variables that influence nursing home prices in the state of Califronia. The regression analysis yielded significant results for various factors including reimbursement rates, occupancy rates and the number of nursing homes per county. However, a notable limitation to the study is the inability to generalize these factors to the rest of the US due to state specific health policies. Determinants such as reimbursement rates and nursing homes per county vary by governmental decisions, therefore, a comprehensive policy tool could be designed to alter nursing home costs through state health policies.","PeriodicalId":265882,"journal":{"name":"University of Toronto Journal of Public Health","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"University of Toronto Journal of Public Health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.33137/utjph.v2i2.36806","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 0

Abstract

Introduction: The rising cost of healthcare along with the aging demographic requires the attention of policy makers. The United States’ nursing home industry is costly to older adults, requiring many to resort to government funded Medicare to offset these costs. This study aims to understand determinants of nursing home prices in the state of California. Variables included in the analysis are selected based on previous literature on the costs of nursing homes in the US. Methods: The data were analyzed using a multi-variable regression analysis. The analysis sample included 1,121 nursing homes across California, using facility level and governmental data that is publically available for the years of 2016-2017. Data collected included financial indicators (net income), ownership (for-profit, non-profit) represented as a dummy variable, occupancy rates, reimbursement rates (Medicare & Medicaid), staffing, quality and competition variables such as nursing homes per county. Results: The regression analysis indicated that ownership type (for-profit), competition and occupancy rates have a negative significant effect on nursing home prices. Whereas, reimbursement rates of both Medicare and Medicaid, home income and staffing levels have a positive significant effect, driving further nursing home prices. Conclusion: The study aimed to understand the relevant variables that influence nursing home prices in the state of Califronia. The regression analysis yielded significant results for various factors including reimbursement rates, occupancy rates and the number of nursing homes per county. However, a notable limitation to the study is the inability to generalize these factors to the rest of the US due to state specific health policies. Determinants such as reimbursement rates and nursing homes per county vary by governmental decisions, therefore, a comprehensive policy tool could be designed to alter nursing home costs through state health policies.

查看原文本刊更多论文

从价格向政策提供信息:加州养老院概述

引言:随着人口老龄化，医疗保健成本的上升需要政策制定者的关注。美国的养老院行业对老年人来说是昂贵的，要求许多人求助于政府资助的医疗保险来抵消这些成本。本研究旨在了解加州养老院价格的决定因素。分析中包含的变量是根据以前关于美国养老院成本的文献选择的。方法:采用多变量回归分析。分析样本包括加利福尼亚州的1121家养老院，使用了2016-2017年公开提供的设施级别和政府数据。收集到的数据包括财务指标(净收入)、所有权(营利和非营利)(以虚拟变量表示)、入住率、报销率(医疗保险和医疗补助)、人员配备、质量和竞争变量(如每个县的养老院)。结果:回归分析表明，所有权类型(营利性)、竞争和入住率对养老院价格有显著的负向影响。然而，医疗保险和医疗补助的报销率、家庭收入和人员配备水平对养老院价格有显著的正向影响。结论:本研究旨在了解影响加州养老院价格的相关变量。回归分析结果表明，各因素包括报销率、入住率和每个县的养老院数量。然而，该研究的一个显著局限性是，由于各州具体的卫生政策，无法将这些因素推广到美国其他地区。每个县的报销率和养老院等决定因素因政府的决定而异，因此，可以设计一种综合政策工具，通过国家卫生政策来改变养老院的成本。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

University of Toronto Journal of Public Health

自引率

0.00%

发文量