Antonello Maruotti, Pierfrancesco Alaimo Di Loro, Cathleen Johnson
{"title":"Beyond catastrophic payments: modeling household health expenditure shares with endogenous selection","authors":"Antonello Maruotti, Pierfrancesco Alaimo Di Loro, Cathleen Johnson","doi":"10.1007/s10182-024-00519-w","DOIUrl":null,"url":null,"abstract":"<div><p>The primary purpose of this paper is to assess households’ burden due to out-of-pocket healthcare expenditures. These payments are modeled on a representative sample of 25668 Italian households as the fraction of out-of-pocket healthcare expenditures over the households’ capacity to pay. For this purpose, we propose extending the analysis of the so-called catastrophic payments by looking at the entire distribution of this ratio. We introduce a novel finite mixture regression able to capture different levels of heterogeneity in the data. By using such a model specification, the fairness of the Italian National Health Service and its determinants are investigated.</p></div>","PeriodicalId":55446,"journal":{"name":"Asta-Advances in Statistical Analysis","volume":"109 2","pages":"363 - 386"},"PeriodicalIF":1.4000,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asta-Advances in Statistical Analysis","FirstCategoryId":"100","ListUrlMain":"https://link.springer.com/article/10.1007/s10182-024-00519-w","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"STATISTICS & PROBABILITY","Score":null,"Total":0}
引用次数: 0
Abstract
The primary purpose of this paper is to assess households’ burden due to out-of-pocket healthcare expenditures. These payments are modeled on a representative sample of 25668 Italian households as the fraction of out-of-pocket healthcare expenditures over the households’ capacity to pay. For this purpose, we propose extending the analysis of the so-called catastrophic payments by looking at the entire distribution of this ratio. We introduce a novel finite mixture regression able to capture different levels of heterogeneity in the data. By using such a model specification, the fairness of the Italian National Health Service and its determinants are investigated.
期刊介绍:
AStA - Advances in Statistical Analysis, a journal of the German Statistical Society, is published quarterly and presents original contributions on statistical methods and applications and review articles.