{"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":null,"pages":null},"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}
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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.
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