Measurement and effects of home care policy on health care use

Thesis

Reference

Measurement and effects of home care policy on health care use

GONCALVES, Judite

Abstract

This dissertation focuses on the measurement of regional home care policy and its effects on health care utilization. It brings three main contributions. First, it provides causal evidence on the effects of home care policy generosity on hospitalizations, doctor visits, and nursing home use. Variations in generosity across the Swiss cantons and over time are used to identify the effects of canton home care policy. The endogeneity of that policy is addressed by using instrumental variables. Second, comprehensive measures of regional home care policy are developed. Two dimensions of generosity are measured as latent variables using factor analysis: the Participation and Intensity dimensions (i.e. how many persons have access to home care services and how much care is provided to home care users). Third, it proposes a bias correction to the OLS estimator for linear regression with factor scores (i.e. estimated values of latent variables such as home care policy).

GONCALVES, Judite. Measurement and effects of home care policy on health care use . Thèse de doctorat : Univ. Genève, 2015, no. GSEM 19

URN : urn:nbn:ch:unige-800316

DOI : 10.13097/archive-ouverte/unige:80031

Available at:

http://archive-ouverte.unige.ch/unige:80031

Disclaimer: layout of this document may differ from the published version.

Measurement and effects of home care policy on health care use

by

Judite Gonc ¸alves

A dissertation submitted to the

Geneva School of Economics and Management, University of Geneva, Switzerland,

in fulfillment of the requirements for the degree of PhD in Econometrics

Members of the committee:

Prof. France Weaver, Adviser, University of Geneva Prof. Jaya Krishnakumar, Chair, University of Geneva

Prof. Eva Cantoni, University of Geneva Prof. Tamara Konetzka, University of Chicago

Dissertation No. 019 Geneva, December 2015

Para os meus pais

Abstract

This dissertation focuses on the measurement of regional home care policy generosity —i.e.

commitment to home care services— and its effects on health care utilization. The main contribution of the first study and part of the third study is to provide causal evidence on the effects of home care policy generosity on hospitalizations, doctor visits, and nursing home use. Variations in generosity across the Swiss cantons and over time are used to identify the effects of canton home care policy. The endogeneity of that policy is addressed by using instrumental variables. The instruments used in the first and third studies are the introduction of patient cost-sharing for home care in some cantons in 2011 and the proportion of women in the cantons’ legislative assemblies, respectively. The first study finds that expanding home care policy generosity increases the individual likelihoods of having a hospitalization, any doctor visit, or a general practitioner visit. In addition, it reduces lengths of stay up to 30 days; there is no effect on the number of doctor visits.

The third study finds that canton-level nursing home use rates are reduced by home care policy generosity.

The second study’s main contribution is to develop comprehensive measures of regional home care policy generosity. Two dimensions of generosity are measured as latent variables, using factor analysis. They are the Participation and Intensity dimensions

—i.e. how many persons have access to home care services and the quantity of services that is provided to home care users. Exploratory and confirmatory factor analyses are conducted, using home care data for Medicaid in the US and Spitex in Switzerland. In the US, most states have become more generous on both dimensions since the late 1990s.

In contrast, most Swiss cantons increased generosity in the Participation dimension and decreased generosity in the Intensity dimension.

The third study deals with bias in linear regression with factor scores —i.e. estimated

values of latent variables such as home care policy. Its main contribution is to provide a bias correction to the OLS estimator —i.e. OLS-corrected estimator. The OLS, OLS-corrected, and 2SLS estimators are compared using simulated data. The consistent OLS-corrected and 2SLS estimators are two options to deal with measurement error bias from using factor scores. OLS-corrected offers the advantage of not requiring instruments.

However, only the 2SLS estimator can deal with both measurement error and endogeneity from simultaneity or unobserved heterogeneity.

R´ esum´ e

Cette th`ese porte sur les mesures de la g´en´erosit´e de la politique r´egionale des services d’aide et de soins `a domicile et leurs effets sur le recours aux services de sant´e. La contribution principale de la premi`ere ´etude et d’une partie de la troisi`eme

´

etude est d’estimer empiriquement les effets causals de cette g´en´erosit´e sur le recours aux hospitalisations, aux visites chez le m´edecin en cabinet, et aux ´etablissements m´edico-sociaux et pour personnes ˆag´ees. La variation dans le temps de cette g´en´erosit´e entre cantons en Suisse est utilis´ee pour identifier les effets de la politique des services d’aide et de soins `a domicile. L’endog´en´eit´e de cette politique est corrig´ee en utilisant des variables instrumentales. Les instruments utilis´es dans les premi`eres et troisi`emes ´etudes sont respectivement l’introduction de contributions des patients pour les soins `a domicile dans certains cantons en 2011 et la proportion de femmes dans les parlements cantonaux.

La premi`ere ´etude trouve que la g´en´erosit´e de la politique des services `a domicile augmente les probabilit´es individuelles d’ˆetre hospitalis´e, d’avoir une consultation chez un m´edecin, ou plus sp´ecifiquement chez un m´edecin g´en´eraliste. Pour les dur´ees de s´ejour en hˆopital, un effet de r´eduction est obtenu pour les dur´ees inf´erieures `a 30 jours. L’effet sur le nombre de consultations chez un m´edecin n’est pas statistiquement significatif. La troisi`eme

´

etude obtient un effet de r´eduction sur le taux cantonal de recours aux ´etablissements m´edico-sociaux et pour personnes ˆag´ees.

La contribution principale de la deuxi`eme ´etude est d’estimer des mesures globales de la g´en´erosit´e de la politique des services d’aide et de soins `a domicile. Deux dimensions distinctes de la g´en´erosit´e des cantons, mesur´ees en tant que variables latentes, sont obtenues en se basant sur des analyses factorielles. Les deux dimensions sont la ‘Participation’ et l’‘Intensit´e’, qui correspondent `a la probabilit´e d’acc`es aux services `a domicile par les r´esidents d’un canton et `a la quantit´e de services fournie aux

utilisateurs d’aide et de soins `a domicile. Des mod`eles d’analyse factorielle exploratoire et confirmatoire sont estim´es, en utilisant les donn´ees Medicaid aux ´Etats-Unis et Spitex en Suisse. Depuis 1999 aux ´Etats-Unis, la plupart des ´Etats sont devenus plus g´en´ereux dans les deux dimensions. En revanche depuis 1997 en Suisse, dans la plupart des cantons, la g´en´erosit´e de la Participation a augment´e et celle de l’Intensit´e a diminu´e.

La troisi`eme ´etude porte sur le biais dans la r´egression lin´eaire r´esultant de l’utilisation de factor scores. Ces derniers correspondent aux valeurs estim´ees des variables latentes comme la politique des services d’aide et de soins `a domicile. La contribution principale est de fournir une correction du biais de l’estimateur des moindres carr´es ordinaires (MCO):

estimateur MCO-corrig´e. Les estimateurs MCO, MCO-corrig´e, et doubles moindres carr´es (DMC) sont compar´es en utilisant des donn´ees simul´ees. Les estimateurs MCO-corrig´e et DMC, tous les deux convergents, sont deux alternatives pour traiter l’erreur de mesure due `a l’utilisation de factor scores. Le MCO-corrig´e offre l’avantage de ne pas requ´erir d’instruments. Toutefois, seul l’estimateur DMC permet de traiter `a la fois de l’erreur de mesure et d’autres sources d’endog´en´eit´e.

Acknowledgements

The PhD has been the longest experience of my life and an enriching one, at different levels. Many people impacted my life over these four years, more than can be mentioned in a page. Here, I name those who possibly impacted me the most.

First and foremost I thank my adviser, Professor France Weaver, for the opportunity to work on this and other projects, her thoughtful guidance, as well as her friendship. A special thank you goes to Professor Jaya Krishnakumar, for encouraging me to work on the topic of the third chapter of this dissertation and carefully advising me through it. I sincerely thank Professors Eva Cantoni and Tamara Konetzka for their precious time and comments.

I am grateful to Professor Milad Zarin, who encouraged me to apply to the PhD. My gratitude also goes to the people at the Swiss School of Public Health Plus for the enriching activities and companionship, namely Dominique Actis-Datta, Professor J¨urgen Maurer, the lecturers of the PhD courses, my fellow students, and seminar participants. I thank the University of Geneva and the Swiss National Science Foundation for providing me with the resources to conduct this research. I appreciate the support of the administrative staff at the department and thank my colleagues for brightening my days at the University, in particular Setareh, Elena, Lili, Laura, and Virginie. I warmly thank all my friends for being there, specially Tilmann, Nuno, and Kyle for also reading my papers.

Un ´enorme MERCI `a toute la famille Waeber, qui ont toujours ´et´e l`a pour moi, notamment Francis et Fran¸coise, Sophie, Patrik, Ga¨el et Killian, Marc, Sonia, Noah et Mandy, G´eraldine et Gabriel, Monique, Rose et Georges, et Marie-Th´er`ese. Finalmente, aos meus pais, irm˜as, e av´os, o maior OBRIGADA pelo amor e apoio incondicionais.

Dedico esta tese aos meus pais. I dedicate this dissertation to my parents. Je d´edie cette th`ese `a mes parents.

Contents

General introduction 1

1 Effects of formal home care on hospitalizations and doctor visits 5

1.1 Introduction . . . 6

1.2 Home care in Switzerland . . . 9

1.3 Home care, hospitalizations, and doctor visits . . . 11

1.4 Methods . . . 13

1.5 Data . . . 15

1.5.1 Dependent variables . . . 16

1.5.2 Home care use . . . 16

1.5.3 Instrumental variable . . . 16

1.5.4 Individual-level explanatory variables . . . 18

1.6 Results . . . 19

1.6.1 Summary statistics . . . 19

1.6.2 Specification tests . . . 19

1.6.3 Effects of home care on hospitalizations and doctor visits . . . 21

1.6.4 Heterogeneous effects . . . 22

1.6.5 Sensitivity checks . . . 24

1.7 Discussion . . . 25

1.A Additional figures and tables . . . 29

2 Measuring state Medicaid home care Participation and Intensity comprehensively as latent variables 43 2.1 Introduction . . . 44

2.1.1 New contribution . . . 46

2.2 Conceptual framework . . . 47

2.3 Methods . . . 48

2.4 Results . . . 50

2.5 Discussion and conclusions . . . 60

2.A Model of canton home care Participation and Intensity in Switzerland . . . 64

3 Dealing with bias in linear regression with factor scores: OLS-corrected and 2SLS estimators 73 3.1 Introduction . . . 74

3.2 Model specification . . . 77

3.2.1 OLS estimation and a bias correction . . . 78

3.2.2 2SLS estimation . . . 80

3.3 Simulation study . . . 80

3.3.1 Results . . . 82

3.4 Empirical study . . . 87

3.4.1 Background . . . 88

3.4.2 Data . . . 94

3.4.3 Results . . . 96

3.5 Discussion . . . 102

3.A Proof of inconsistency of OLS . . . 106

3.B Proof of inconsistency of OLS with additional sources of endogeneity . . . 110

3.C Proof of consistency of 2SLS . . . 112

3.D Simulation study: additional figures and tables . . . 114

3.E Empirical study: additional figures and tables . . . 122

Concluding remarks 127

References 130

List of Figures

1.5.1 Home care hours per capita in cantons with and without patient

contributions . . . 18

1.A.1 Hospital beds per 100 population in cantons with and without patient contributions . . . 29

1.A.2 Doctors per 100 population in cantons with and without patient contributions . . . 30

1.A.3 Nursing home beds per 65+ population in cantons with and without patient contributions . . . 31

1.A.4 Hospital beds per 100 population by canton . . . 39

1.A.5 Doctors per 100 population by canton . . . 40

1.A.6 Nursing home beds per 65+ population by canton . . . 41

2.2.1 Model of state Medicaid home care Participation and Intensity . . . 48

2.4.2 Model of state Medicaid home care Participation and Intensity: confirmatory factor analysis . . . 53

2.4.3 Maps of generosity in state Medicaid home care Participation in 1999 and 2012 . . . 55

2.4.4 Maps of generosity in state Medicaid home care Intensity in 1999 and 2012 57 2.A.1 Model of canton home care Participation and Intensity: confirmatory factor analysis . . . 66

2.A.2 Maps of generosity in canton home care Participation in 1997 and 2012 . . 68

2.A.3 Maps of generosity in canton home care Intensity in 1997 and 2012 . . . . 69

3.3.1 Simulation results for the case with no additional sources of endogeneity besides measurement error, with observed covariates . . . 84

3.3.2 Simulation results for the case with additional endogeneity from simultaneity and unobserved heterogeneity (cov(z, µ) = 0,cov(f, z) = 1) . 86 3.4.3 Canton nursing home use rates in 1997 and 2012 . . . 92 3.4.4 Canton home care policy generosity in 1997 and 2012 . . . 93 3.5.5 How to estimate linear regression with latent explanatory variables . . . . 105 3.D.1 Simulation results for the case with no additional sources of endogeneity

besides measurement error, without observed covariates . . . 115 3.D.2 Simulation results for the case with additional endogeneity from

simultaneity and unobserved heterogeneity (cov(z, µ) = 0.2,cov(f, z) = 1) 116 3.D.3 Simulation results for the case with additional endogeneity from

simultaneity and unobserved heterogeneity (cov(z, µ) = 0.4,cov(f, z) = 1) 117 3.D.4 Simulation results for the case with additional endogeneity from

simultaneity and unobserved heterogeneity (cov(z, µ) = 0,cov(f, z) = 0.6) 118 3.D.5 Simulation results for the case with additional endogeneity from

simultaneity and unobserved heterogeneity (cov(z, µ) = 0,cov(f, z) = 0.2) 119 3.E.1 Annual growth rate in number of nursing home beds by canton . . . 122

List of Tables

1.6.1 Summary statistics . . . 20

1.6.2 Marginal effects of home care on hospitalizations and doctor visits . . . . 22

1.6.3 Marginal effects of home care by age groups and informal care availability 23 1.A.1 Specification tests . . . 32

1.A.2 First-stage regressions . . . 33

1.A.3 Two-part models for the German-speaking cantons . . . 34

1.A.4 Two-part models for the national sample . . . 36

1.A.5 Sensitivity checks . . . 38

2.4.1 Summary statistics of available indicators of state Medicaid home care Participation and Intensity . . . 51

2.4.2 Exploratory factor analysis results (unstructured model): factor loadings and dimensions . . . 52

2.4.3 Estimated values of Medicaid home care Participation and Intensity . . . 54

2.4.4 Comparison of state rankings based on the Participation latent dimension and observed indicators (2012^a) . . . 58

2.4.5 Comparison of state rankings based on the Intensity latent dimension and observed indicators (2012^a) . . . 59

2.5.6 States’ generosity in Medicaid home care policy Participation and Intensity in 2012 and participation in main ACA HCBS-promoting programs . . . 62

2.A.1 Summary statistics of indicators of canton home care Participation and Intensity . . . 65

2.A.2 Estimated values of home care Participation and Intensity . . . 67

2.A.3 Comparison of canton rankings based on the Participation latent dimension and observed indicators (2010^a) . . . 70 2.A.4 Comparison of canton rankings based on the Intensity latent dimension

and observed indicators (2010^a) . . . 71 3.4.1 Summary statistics (N = 378) . . . 97 3.4.2 Measurement model of canton home care policy generosity: confirmatory

factor analysis . . . 98 3.4.3 Effect of canton home care policy generosity on nursing home use rates . 99 3.D.1 Mean bias and MSE of the different estimators for the case with

no additional sources of endogeneity besides measurement error, with observed covariates . . . 114 3.D.2 Mean bias and MSE of the different estimators for the case with additional

endogeneity from simultaneity or unobserved heterogeneity (cov(z, µ) = {0,0.2,0.4}, cov(f, z) = 1) . . . 120 3.D.3 Mean bias and MSE of the different estimators for the case with additional

endogeneity from simultaneity or unobserved heterogeneity (cov(z, µ) = 0, cov(f, z) = {0.2,0.6,1}) . . . 121 3.E.1 Home care policy generosity and nursing home use rates in 1997 and 2012,

and estimated effects of home care policy generosity on nursing home use rates by canton . . . 123 3.E.2 Results of alternative specifications (2SLS) . . . 124 3.E.3 Results of using observed home care indicators . . . 125

General introduction

Health care expenditures are expected to rise substantially over the next decades in most OECD countries, in part as a result of population aging. For example, total health care expenditures in Switzerland are forecasted to rise from 11.3% of GDP in 2009 to 15.8% in 2060 (Colombier, 2012). Some policy makers view home care as a way to limit the growth in health care expenditures, by meeting increasing demand and substituting partly for more costly forms of care —e.g. nursing home care, hospital care. Home care is also seen as a way to improve well-being, as many people prefer receiving care in their homes (WHO, 2008). In the OECD, home care expenditures increased by about 5% annually between 2000 and 2011 (OECD, 2013). Here, home care refers to formal care, provided by paid professionals, in the patients’ homes. Whether expanding home care policy generosity —i.e. commitment to home care services— reduces the utilization of other health care services is an empirical question. An important part of answering that question is measuring home care policy appropriately. This work has two global research questions. The first one is ‘how to measure regional home care policy?” In this dissertation, a region can be a Swiss canton or a US state. The second question is

“what are the effects of regional home care policy on health care use?” The three studies presented in Chapters 1-3 of this dissertation answer different parts of these questions.

Regional home care policy is challenging to measure empirically because it has many different components. One or two indicators may not capture this concept comprehensively. In such context, there are two options to measure home care policy.

The first option is to select observed indicators as proxies. Alternatively, regional home care policy may be modeled as a latent variable. Prior literature has relied on the first alternative. The second one has not been considered so far. Hospital, doctor, and nursing home care are three important types of health care that can be impacted by regional

home care policy. The relationship between regional home care policy generosity and the utilization of health care services has received little attention in Europe. That policy is likely to be endogenous. Overall in Europe and abroad, few studies address this issue by using statistical methods that allow the identification of causal effects. In this dissertation, the two strategies to measure regional home care policy are applied: observed indicators and latent variables. In addition, the causal effects of regional home care policy generosity on individual-level use of hospital and doctor care as well as canton-level nursing home use are assessed.

The first study estimates the effects of canton home care policy generosity on hospitalizations and doctor visits, using Swiss data (Chapter 1). Besides adding to the limited evidence in Europe, this study has three main contributions. First, both acute and post-acute care are considered, by assessing effects on different lengths of stay and types of doctor visits (general practitioner and specialist visits). Second, the analysis includes the entire adult population. Third, heterogeneous effects by age groups and informal care availability are evaluated. Here, canton home care policy generosity is captured by an observed indicator of aggregate home care use: home care hours per capita provided in a given canton and year. Thus, in this study the term ‘home care use’ is used instead of ‘home care policy’. Two-part generalized linear models estimate the effects of home care hours per capita on the individual likelihood of hospitalization and length of stay, conditional on hospitalization; similarly for doctor visits. To address endogeneity, the models include canton and time fixed-effects and home care hours per capita is instrumented by the introduction of cost-sharing for home care in some cantons in 2011. The main findings are that home care increases the likelihoods of having a hospitalization, any doctor visit, or a general practitioner visit, and reduces lengths of stay up to 30 days; there is no statistically significant effect on the number of doctor visits. These findings are driven by the effects on persons 65 years and older. Overall, all the effects are small.

The second study is devoted to the first research question (Chapter 2). Regions can increase home care policy generosity in two distinct ways. They can expand access to home

care services and/or provide higher levels of care to home care users. These two dimensions of regional home care policy generosity are referred to as Participation and Intensity dimensions. The main contribution of this study is to measure these two dimensions comprehensively as latent variables, using exploratory and confirmatory factor analyses.

The main analyses use Medicaid home care data for the US, 1999-2012. In Appendix 2.A, those analyses are reproduced using Swiss home care data, from 1997 to 2012. The two measures are used to describe the trends in home care Participation and Intensity across US states and Swiss cantons over time. Since 1999, most US states have become more generous in both dimensions of Medicaid home care policy. In Switzerland since 1997, most cantons have increased generosity in the Participation dimension of canton home care policy and decreased generosity in the Intensity dimension. The rankings of states and cantons by their generosity in the two dimensions are also compared with those resulting from using observed indicators. Such comparison shows the importance of measuring the two home care policy dimensions comprehensively as latent variables. Canton home care Participation, as measured in this study, is a direct input to the empirical analysis in the third study.

The empirical part of the third study answers the remainder of the second research question (Chapter 3). It assesses the effect of canton home care policy generosity on nursing home use in Switzerland. Nursing home use is analyzed using a different strategy from hospitalizations and doctor visits, because the necessary individual-level data on nursing home utilization are not available in Switzerland. The novelty and contribution of this empirical analysis is to account for the fact that canton home care policy is difficult to capture comprehensively using one or two observed indicators, by measuring it as a latent variable. The analysis is conducted entirely at the canton level. The causal effect of canton home care Participation on canton-level rates of nursing home use is estimated by 2SLS. Canton home care Participation is instrumented by the proportion of seats in the cantons’ legislative assemblies occupied by women. Increasing generosity in the Participation dimension of canton home care policy reduces nursing home use rates.

To use the Participation latent dimension as an explanatory variable in the regression,

its values have to be estimated from factor analysis —i.e. factor scores. The use of factor scores, as if they were observed variables, causes all regression coefficients to be biased and inconsistent, due to measurement error. Focusing on linear regression, the third study also brings three theoretical contributions: it specifies the bias of the OLS estimator, derives a correction to that bias (i.e. OLS-corrected estimator), and formulates the 2SLS estimator. The OLS, OLS-corrected, and 2SLS estimators are compared using simulated data. The impact of weak or invalid instruments on consistency of 2SLS is also assessed. The consistent OLS-corrected and 2SLS estimators are two options to deal with measurement error bias in regression with factor scores. OLS-corrected offers the advantage of not requiring instruments. However, if there is additional endogeneity from simultaneity or unobserved heterogeneity, only the 2SLS estimator can deal with both issues. The empirical analysis of the effect of canton home care policy generosity on nursing home use is an illustration of the latter case.

To sum up, the three studies are linked in two main ways. First, the research question

“what are the effects of regional home care policy on health care use?” is answered in the first and third studies. These studies assess the effects of canton home care policy generosity on hospitalizations, doctor visits, and nursing home use in Switzerland. Second, regional home care policy is measured in two distinct ways (first research question). In the first study, it is proxied by an observed indicator. In the second and third studies, it is modeled using latent variables. In addition, the third study analyzes the problems with using factor scores in regression from a methodological perspective. The Concluding remarks chapter discusses the pros and cons of the methods used in this dissertation. It concludes with some policy implications of the empirical findings.

Chapter 1

Effects of formal home care on hospitalizations and doctor visits

Abstract

This study estimates the effects of formal home care, provided by paid professionals, on hospitalizations and doctor visits. We look at different lengths-of-stay (LOS) and types of doctor visits —general practitioners (GP) and specialists— and investigate heterogeneous effects by age groups and informal care availability. Two-part generalized linear models are estimated, using data from Switzerland. In this federal country, home care policy is decentralized into 26 cantons. The endogeneity of home care is addressed by using an instrumental variable strategy combined with canton and time fixed-effects. We instrument canton-level home care use with the introduction of patient cost-sharing for home care in some cantons in 2011. Overall, home care significantly increases the likelihoods of having a hospitalization, any doctor visit, or a GP visit. In addition, home care significantly reduces LOS up to 30 days, but has no effect on the number of doctor visits. These results are driven by the effects on persons 65 years and older.

All the effects are small, suggesting that the potential of formal home care to limit the growth in inpatient care and doctor visits may be limited.

Keywords: Home care; Hospitalizations; Doctor visits; Instrumental variable JEL classification: C26; H75; I11; I18

This paper is co-authored with France Weaver. We are grateful to Tamara Konetzka for her suggestions and support. We appreciate the comments of Courtney Van Houtven, Will Manning, Eva Cantoni, Jaya Krishnakumar, Peter Zweifel, and participants at the 5^thAmerican Society of Health Economists Conference, the Health Economics Workshop at the University of Chicago, and the Swiss School of Public Health+ PhD Seminar. We thank Flavia Lazzeri, from the Swiss Federal Statistical Office, for data support, Silvia Marti Lavanchy and Walter Zecca, from the Swiss and Geneva’s Home Care Associations, for answering questions about home care policy in Switzerland.

1.1 Introduction

With population aging, the number of people who need health care is increasing in most OECD countries. In Switzerland, the 65+ are expected to represent at least 28% of the population by 2060 (FSO, 2010). Aging presents a key concern because health care costs are rising and financed by a decreasing share of the population. New ways to provide appropriate care at affordable costs for society are needed. One solution may be to provide care in peoples’ homes —i.e. home care. Often, patients prefer receiving care at home (Costa-Font et al., 2009; Guo et al., 2015a). Moreover, for some medical conditions or functional limitations, home care may be less costly than nursing home or inpatient care, for example (Frick et al., 2009; Kok et al., 2015). The potential of home care to limit the growth in health care costs depends in part on whether it can substitute for some of these more costly forms of care. To inform policy, the effects of home care on utilization of other health care services need to be assessed; in particular hospitalizations and doctor visits because they represent the largest share of health care expenditures in many countries —e.g. more than 50% in Switzerland (OECD, 2014). In this study, we investigate whether formal home care (i.e. paid professional care provided at home) complements or substitutes for hospital and doctor care, using Swiss data.

Prior literature has mainly focused on the effects of home care policy on long-term care use —i.e. home care itself, nursing home care, and informal care (Golberstein et al., 2009;

Guo et al., 2015b; Hoerger et al., 1996; Meijer et al., 2015; Pezzin et al., 1996; Pezzin and Kasper, 2002; Rice et al., 2009; Stabile et al., 2006). The impact of home care on the use of (post-)acute care, such as hospitalizations and doctor visits, has received less attention, particularly in Europe. Using data at the local level in England, one study finds a negative effect of home care on emergency hospital readmission rates (Fernandez and Forder, 2008). In that study, home care is captured by home care hours per individual over 65 and its endogeneity is addressed with instrumental variables (IV). Overall in Europe, the effects of home care tend to be analyzed via randomized controlled trials (RCT). For example, two RCTs conducted in Switzerland and Denmark find no significant effects of home preventive visits on hospitalizations (Stuck et al., 2000; Vass et al., 2008) and a

positive effect on the number of doctor visits (Stuck et al., 2000).

Most studies on the effects of home care on acute care use come from the US. Most of them focus either on Medicare or Medicaid, hence considering specific types of home care and populations —i.e. the 65+ for Medicare and the low-income for Medicaid. The Channeling Demonstration, conducted in ten states in the early 1980s, is one of the first studies on the effects of expanded home care on health care use among frail elderly.

Neither hospitalizations nor doctor visits were significantly impacted (Kemper, 1988). As with RCTs, the findings of the Channeling have high internal validity (because it is a demonstration), but are difficult to generalize to other populations and contexts.

The Medicare studies look at changes in the reimbursement of home health (HH) providers in 1997 and 2000. The 1997 change lowered both average and marginal payments to HH providers. The 2000 change raised average but further reduced marginal payments (Huckfeldt et al., 2014). These changes led to a decrease in HH utilization and are used to see whether lower HH use impacted the likelihood and costs of post-acute inpatient care and inpatient care expenditures. The likelihood of post-acute inpatient care significantly increased after the 1997 change, but decreased after the 2000 change (Buntin et al., 2009). The findings of McKnight (2006), for inpatient care expenditures, and Huckfeldt et al. (2014), for post-acute inpatient care costs, are not statistically significant. The results of these three studies have a potentially causal interpretation because they rely on differences-in-differences models. However, two of the studies lack generalizability because they only consider post-acute inpatient care and patients with stroke, hip fracture, or joint replacement (Buntin et al., 2009; Huckfeldt et al., 2014).

One descriptive study, which considers all Medicaid home- and community-based services (HCBS) programs, finds that states with above-median HCBS expenditures tend to have lower rates of avoidable hospitalizations (Konetzka et al., 2012). Other Medicaid studies looking at specific state programs are inconclusive. D’Souza et al. (2009) and Xu et al. (2010) find a negative association between home care and hospitalization rates. Two other studies find no significant effects of home care on inpatient and doctor expenditures (Felix et al., 2011; Shapiro et al., 2011). They tackle the endogeneity of home care by

using matched control and treatment groups. A significant positive effect on inpatient care expenditures is found in one of the five programs analyzed in Shapiro et al. (2011).

Overall, most studies find non-significant effects of home care on hospital and doctor care utilization. Evidence outside the US is limited. Furthermore, most studies consider particular types of home care or inpatient care (post-acute care) and look at specific populations. This limits the generalizability of their findings. Thus, based on the existing evidence, it is not possible to conclude on the substitutability or complementarity between home care and hospital or doctor care.

Besides adding to the limited evidence on the effects of formal home care in Europe, this study has three key contributions. First, we analyze both acute and post-acute care, by looking at different lengths of stay (LOS) and doctor visits —i.e. general practitioner (GP) and specialist visits. Second, we look at the entire adult population. Third, we investigate heterogeneous effects by age groups and availability of informal care within the household. Separating the elderly from younger adults is a way of focusing on long-term home care. Looking at those with and without informal care available in their household informs on the way formal and informal home care may interact. These groups deserve attention, as the numbers of elderly persons and one-person households, i.e. no informal care available at home, are increasing in most Western countries.

Switzerland is a federal country where health policy is decentralized in 26 cantons.

Decentralization results in variations in home care use across cantons and over time, which are used to identify the effects of home care (Section 1.2). Two-part generalized linear models are estimated to determine the effects of home care hours per capita provided in a given canton on the individual likelihood of hospitalization and LOS, conditional on hospitalization; similarly for doctor visits. The endogeneity of home care is addressed through the use of an IV, canton and time fixed-effects. We instrument home care use with the introduction of patient contributions to home care (i.e. cost-sharing) in some cantons in 2011. We conduct separate analyses for the German-speaking cantons. We perform various robustness checks and estimate the effects of home care on dentist and optician visits as falsification tests.

1.2 Home care in Switzerland

In Switzerland, health policy is decentralized in the 26 cantons. Broadly speaking, the federal government defines the general principles of health policy and the cantons and municipalities (the smallest administrative units) regulate and finance health care. Compulsory health insurance (CHI) means that every resident is covered for hospital-based inpatient and outpatient care, doctor visits, and medical care provided in nursing homes or at home.

The decentralization of health policy drives variation in home care use across cantons.

Cantons influence home care use through the regulation of supply and subsidies to home care providers and users (see below). Only public and private non-for-profit home care providers are entitled to public subsidies; they represent the majority of the home care market (about 85% of the patients). Overall, there is large heterogeneity across cantons in their choices regarding home care policy. Canton-level home care use can be viewed as a summary measure of the generosity of canton home care policy and is relevant for funding decisions and policy making. In 2012, the canton of Schwyz provided 0.9 hours of home care per capita and Jura 3.2 hours. Cantons also differ in their growth rates of home care use over time. From 1997 to 2012, home care hours per capita decreased by 20% in Geneva and increased by 398% in Ticino. Nationwide, home care expenditures have grown by 92%. In 2011, home care amounted to 2.7% of total health expenditures, the 8^th largest proportion among OECD countries (OECD, 2014).

Switzerland has two main linguistic regions: nineteen German-speaking cantons and seven Latin cantons (French- or Italian-speaking). The two regions differ in their home care policies. For example in the Latin cantons, home care supply tends to be centralized, whereas in the German-speaking cantons, there are usually many small providers operating locally. Public subsidies to providers represent larger portions of providers’ revenues in the Latin cantons. In the German-speaking cantons, typically smaller portions of the populations use home care, and each user receives on average more hours of home care than users in Latin cantons. There are also differences between the two regions in their nursing home sectors. For instance, nursing home use tends to be

higher in the German-speaking cantons and residents are in better health, on average.

In Switzerland, any sick or disabled person is eligible for home care. There are no age or financial eligibility criteria, though the 65+ represent more than 70% of home care users. The local home care provider assesses the level of care required by each patient.

Many home care services are covered by CHI and require a doctor referral. These include help with the activities of daily living (ADL), such as bathing and transferring, as well as medical treatments, e.g. intravenous administration of medicine and monitoring of glucose levels. Some home care services are not covered by CHI; they are mostly help with the instrumental activities of daily living (IADL), e.g. shopping and housekeeping.

Non-covered services are usually paid out-of-pocket or through complementary insurances.

Public subsidies provided directly to the patients may be used to pay for non-covered home care services and their price may depend on the patient’s income or wealth (Weaver, 2011).

In 2012, about 2.2% of the population received CHI-covered home care services, and 1.3%

received non-covered services.

In this study, we focus on home care services that are reimbursed by CHI for three reasons. First, CHI-covered services are the most likely to impact hospitalizations and doctor visits, because they include home care services that are medically-related (Section 2.2). Second, these services have a relatively clear mode of financing (covered by CHI and the cantons/municipalities). The financing of non-covered services is more complex, with virtually a different system in each canton. This makes it hard to identify what drives variation in the use of those services. Thus, it is also difficult to find a valid instrument for non-covered home care, which is the third reason for focusing on CHI-covered home care (Section 1.5.3).

Until 2010, the prices of CHI-covered home care resulted from negotiations between the home care providers in each canton and the association that represents the health insurers. These services were billed either based on the number of minutes or on a flat fee, depending on the canton. Since 2011, the federal government fixes the prices of CHI-covered services and these are billed per interval of 5 minutes. The health insurers reimburse either the patient or the provider directly. Because the prices are inferior to the

costs, the difference is covered by the cantons and municipalities (varies across cantons, but up to a maximum of about 40% of the costs). Since 2011, the cantons/municipalities can decide if they fully cover the remainder of the costs or if patients must contribute and by how much (up to a federally-set limit of 15.95 CHF per day). Nationally in 2012, patient contributions covered about 5% of the costs. The cantons’ decisions to introduce or not patient contributions may reflect various factors, e.g. beliefs about whether there should be some market mechanism, the promotion of ambulatory versus stationary care, or the sustainability of public finances.

1.3 Home care, hospitalizations, and doctor visits

We assume that in cantons with greater aggregate home care use, a given individual is more likely to use home care. There is empirical evidence in favor of this assumption (e.g. Muramatsu and Campbell, 2002; Rice et al., 2009; Stabile et al., 2006). There are various channels through which home care use may reduce or increase —i.e. substitute for or complement— hospital and doctor services utilization.

Inpatient care, i.e. care provided in the hospital with overnight stay, can include both acute and post-acute care. Acute care involves medical interventions aimed at restoring a patient’s health; e.g. hip replacement, angioplasty. Post-acute care, associated with the patient’s rehabilitation, is typically less intensive and can take place in diverse settings. Here, we consider both acute and post-acute care provided in the hospital. In Switzerland, a significant part of post-acute care still takes place at the hospital. In general, hospitalization is triggered by a need for acute care, while LOS depends on the severity of the condition, complications, and whether rehabilitation occurs in the hospital or another setting.

Doctor visits include both GP and specialist visits (e.g. orthopedists, cardiologists). In general, a doctor visit is triggered by a need for non-emergency health care. Patients may also seek the doctor in order to obtain a referral to home care (Section 1.2). The number of doctor visits may be associated with both short and long-term care needs. A person may need to see the doctor several times for the follow-up of acute health episodes. Persons

with ongoing illnesses, disabilities, or chronic conditions (e.g. hypertension, diabetes) are likely to see a doctor regularly. Moreover, recipients of CHI-covered home care need to revisit a doctor to obtain an extension of those services.

The likelihood of having a hospitalization or a doctor visit may be reduced by home care use in two main ways. First, home care providers can address some health problems early, before hospitalization or seeing a doctor is required. Because GP services are more general than specialists’, they may be easier to replace with home care. Second, providing patients with appropriate help can avoid hospitalizations or doctor visits associated with events, such as falls or complications related to mismanaged chronic conditions. These two effects may be partially cancelled-out by two other factors. First, home care providers can detect new or previously neglected conditions that require hospital or doctor care.

This effect is likely to be small for hospitalizations and specialist visits, because the typical procedure is to see a GP first, except in emergency or specific cases. Lastly, in Switzerland CHI-covered home care requires a doctor referral prior to use.

LOS may be reduced by home care use if home care providers detect health conditions at an early stage. Consequently, less acute care may be needed. A second way for home care to reduce LOS is by allowing for some post-acute care to be provided at the patient’s home (i.e. earlier discharge). Here, it is important to distinguish between acute (up to 30 days) and non-acute LOS. Both channels for the substitution effect of home care may be limited for non-acute LOS, as persons who have been at the hospital for a long time may be waiting for a nursing home bed; i.e. for them, home care isn’t an alternative. In addition, for very short LOS the substitution effect may also be limited, as there is little care to substitute for.

Home care use may reduce the number of doctor visits, as home care providers can help manage long-term illnesses and chronic conditions, as well as do the follow-up of some acute episodes. This substitution effect may be weaker for specialist care. However, two factors may contribute to a larger number of doctor visits. First, recipients of CHI-covered home care may visit the GP more often to get extensions for those services. Second, if home care substitutes for inpatient care, the number of doctor visits (both GPs and

specialists) may increase to address medical needs once the patient is at home.

Finally, the effects of home care may differ by age group (under 65 or 65 years and older) and informal care availability (whether or not there is another adult in the household). Most long-term home care users are elderly, whereas most post-acute home care cases are younger persons. Formal and informal home care combined may be more effective at providing post-acute care at home, managing conditions, and detecting health problems early. On the other hand, potential informal caregivers may perceive care needs and facilitate hospitalization or a doctor visit. Lastly, relatives may encourage or discourage the use of home care.

To summarize, whether home care substitutes for or complements hospital and doctor care is an empirical question. Home care use may reduce or increase the likelihoods of hospitalization and having a doctor visit. It is hypothesized to reduce LOS, with weaker effects on very short and long LOS. The number of doctor visits may be increased or reduced by home care use and it is unclear which effect might dominate for GPs and specialists. The relative magnitudes of the heterogeneous effects cannot be hypothesized either.

1.4 Methods

For each outcome, we estimate a two-part model (2PM), which deals with dependent variables with many zeros (Duan et al., 1984).¹ For example, the proportions of individuals without hospitalizations or specialist visits are 88% and 68%, respectively. A probit model predicts the likelihood of hospitalization or the likelihood of a doctor visit (equation 1.1).

A generalized linear model (GLM) predicts LOS or the number of doctor visits, conditional on hospitalization or any visit (equation 1.2, McCullagh and Nelder, 1989). The GLM has a log link and a gamma distribution, chosen based on Box-Cox and Park tests (Manning

1In choosing the 2PM over a joint decision model, such as the Tobit model, we assume that the decisions to use any inpatient or doctor care and how much care to use are sequential or independent.

It seems reasonable that first, a person decides whether to go to the hospital or visit a doctor. Second, she decides how long to stay in the hospital or how many more times to visit the doctor based on the professional advice she gets —or that decision is made for her.

and Mullahy, 2001).²

P r[yi,c,t >0] = Φ{α+HCc,tβ+X_i,c,t⁰ η+υc+τt} (1.1) E[y_i,c,t|y_i,c,t >0] = exp{γ+HC_c,tδ+X_i,c,t⁰ λ+ν_c+ι_t} (1.2) In equations 1.1 and 1.2, the subscripts i, c and t denote the individual, canton of residence, and survey year, respectively. Φ represents the standard normal cumulative distribution function. Hospital or doctor utilization, y_i,c,t, is modeled at the individual level, whereas home care use, HC_c,t, is measured at the canton level by the number of home care hours per capita (Sections 1.5.1 and 1.5.2). The coefficients of interest are β and δ. Furthermore, α and γ denote constant terms, X_i,c,t⁰ is a vector of individual-level covariates (Section 1.5.4) with η and λ being the corresponding vectors of coefficients, υ_c and ν_c represent canton fixed-effects, and τ_t and ι_t are time fixed-effects. The overall expected value of yi,c,t is E[yi,c,t] =P r[yi,c,t >0]×E[yi,c,t|yi,c,t >0]. The marginal effects of home care in the combined 2PM are given by equation 1.3.

∂E[y_i,c,t]

∂HCc,t

=P r[y_i,c,t >0]∂E[y_i,c,t|y_i,c,t >0]

∂HCc,t

+E[y_i,c,t|y_i,c,t >0]∂P r[y_i,c,t >0]

∂HCc,t

(1.3) To investigate heterogeneous effects of home care by age group and informal care availability, equations 1.1 and 1.2 are reestimated separately for individuals under 65 (<65) and 65 years or older (65+), as well as individuals with and without informal care available from another adult in the household.

The key identifying assumption in equations 1.1 and 1.2 is that canton-level home care use is uncorrelated with unobservables, such as canton health policy, aggregate needs, and preferences of the population. For example, cantons may coordinate hospital and doctor supply with home care supply and subsidies, taking into consideration indicators of needs and preferences of their populations (Section 1.2). These factors may also influence individual use of hospital and doctor care. Consequently, canton-level unobserved heterogeneity is likely. Moreover, a doctor referral is required prior to the use of CHI-reimbursed home care. Therefore, there is a risk of reversed causality.

2A zero-truncated negative binomial model produces nearly identical results.

Equations 1.1 and 1.2 include canton fixed-effects, which control for time-invariant differences in population preferences, needs, and health policy across cantons. Moreover, time fixed-effects adjust for changes that impact home care use, hospitalizations, and doctor visits in a similar way across cantons over time, such as medical innovation and economic growth. Any remaining endogeneity is controlled for with an IV (Section 1.5.3).

The IV-2PM model is estimated via two-stage residual inclusion (2SRI), which provides consistent estimates when the model is nonlinear (Terza et al., 2008). The standard errors are clustered at the canton level.

1.5 Data

The analyses are conducted on data from the Swiss Health Survey (SHS), which focuses on the 15+ population residing in Switzerland. The SHS is a repeated cross-section conducted every five years since 1992. Phone interviews are conducted with one random member of selected households. The respondent is also invited to answer a written questionnaire.

Because not everyone fills in this supplemental questionnaire, some variables have large proportions of missing values, e.g. specific medical conditions and supplemental health insurance.

This study uses the four waves for which home care data are available: 1997, 2002, 2007, and 2012. We keep individuals aged 20 and older and drop LOS beyond 180 days (eight observations). The main results are based on the German-speaking cantons (19 cantons out of 26), for which our instrument is relevant (Sections 1.5.3 and 1.6.2).

Those cantons include between 37,791 observations for specialist visits and 38,658 for hospitalizations.

Canton-level home care data come from the Home Care Survey, which covers all public and private non-for-profit home care providers. Information on patient contributions in each canton (i.e. the instrument) was provided by the Swiss home care association. Other canton-level variables used in sensitivity checks come from various surveys of the Federal Statistical Office.

1.5.1 Dependent variables

First, we look at the number of days spent in the hospital, which includes at least one night. We consider different LOS to approximate different types of hospitalizations: up to 5, 10, 15, 30, 60, 90, or 180 days (acute hospitalizations are LOS up to 30 days).³ The other dependent variables are the number of doctor visits of any kind, GP, and specialist visits. The recall period is the 12 months preceding the interview.

1.5.2 Home care use

The varying degrees of generosity in home care policy across cantons and over time are captured by aggregate home care use, as this is the result of various canton policies that are difficult to measure (e.g. different regulation of supply and diverse subsidies across cantons; Section 1.2). The most common measure of aggregate home care use is home care expenditures per capita (e.g. Kemper et al., 2008; Muramatsu and Campbell, 2002;

Muramatsu et al., 2007; Pezzin and Kasper, 2002; Stabile et al., 2006). As our focus is on CHI-covered services and we cannot distinguish between expenditures on CHI-covered and non-covered home care, we use hours. CHI-reimbursed home care hours per capita is defined as the number of CHI-reimbursed home care hours provided in a given canton and year divided by the canton’s population in that year. We take the natural logarithm of this ratio (ln) to account for non-linearities and reduce the influence of outliers.

1.5.3 Instrumental variable

We instrument home care use with a binary indicator that captures the introduction of patient contributions for CHI-reimbursed home care in fifteen German-speaking cantons in 2011: Appenzell Innerrhoden, Appenzell Ausserrhoden, Basel-Stadt, Graub¨unden, Luzern, Nidwalden, Obwalden, St. Gallen, Schaffhausen, Solothurn, Schwyz, Thurgau, Uri, Zug, and Z¨urich. Four cantons, representing almost 30% of the sample in 2012, did not introduce patient contributions: Aargau, Bern, Basel-Landschaft, and Glarus.

3The data do not allow us to identify multiple hospitalizations in all waves. LOS includes the total number of days spent in the hospital in the previous 12 months.

Both groups include cantons with different socioeconomic, demographic, and geographic situations and are likely to be comparable. Before 2011, there was no patient cost-sharing for home care in Switzerland. The contribution scheme and amount varies across cantons, but the maximum corresponds to 20% of the price of one hour of home care per day (15.95 CHF). Using variations in the monetary contribution itself did not provide strong enough instruments. A dichotomous measure captures the fact that overall, home care became more costly to the patients in cantons that introduced patient contributions, as before it was fully reimbursed by CHI.

The use of this instrument forms a difference-in-differences first-stage regression. We assume that the cantons that didn’t implement patient contributions form an accurate counterfactual; i.e. they represent what would have occurred in the other cantons, if they hadn’t introduced patient contributions. The German and Latin regions of Switzerland may differ in preferences and health policy dimensions beyond what is captured by the canton fixed-effects. Taking the seven Latin cantons into account would potentially undermine the accuracy of the counterfactual, because only Geneva introduced patient contributions —i.e. all but one Latin canton would be in the control group. Thus, our main analyses focus on the German-speaking cantons, for which the instrument works well. Those cantons represent two thirds of the Swiss population. Figure 1.5.1 shows the evolution of home care hours per capita in the German-speaking cantons that introduced patient contributions in 2011 and those that didn’t (i.e. where home care continued to be fully covered by CHI and public subsidies to providers). Until 2010, the two groups of cantons show increasing trends. After the introduction of patient contributions in 2011, the trends clearly diverge. In sum, for the German-speaking cantons, the instrument is expected to predict home care use —i.e. be relevant.

Besides being relevant, the instrument must be uncorrelated with hospitalizations and doctor visits, except through home care use. The introduction of patient contributions is unlikely to have a direct impact on hospitalizations and doctor visits, but it could have an indirect impact through unobserved health policy. With only one instrument, it is not possible to test the exclusion restriction statistically. However, we look at whether the

Figure 1.5.1. Home care hours per capita in cantons with and without patient contributions

Legend: BLACK: cantons without patient contributions; GRAY: cantons with patient contributions since 2011. Only German-speaking cantons included.

introduction of patient contributions modified the trends in some proxies of health policy that may be related to hospitalizations or doctor visits, such as hospital beds, doctors, and nursing home beds per capita. The absence of changes in the trends of those indicators points towards the validity of our instrument (Figures 1.A.1-1.A.3 in Appendix 1.A).

1.5.4 Individual-level explanatory variables

Individual-level covariates include informal care availability, health status and behaviors, socio-demographics, and pressure to remain at home. Informal care availability is captured by whether the person lives with another adult. This measure excludes potential help from outside the household. In Switzerland, as in most western countries, the main source of informal care is the spouse, who typically lives in the same household, followed by children. Unfortunately, the number of children is not available in the dataset. Health status is measured by self-assessed health, number of symptoms (e.g. back pain, diarrhea), whether the person has ADL limitations, and whether she is unable to walk for at least 200 meters. Health behaviors are being obese, a smoker or ex-smoker, and drinking more than two ‘standard drinks’ (10 grams of alcohol) per day for men and one drink for

women. Socio-demographics include age groups and gender interacted, Swiss nationality, residential area, education, and income. Pressure to remain at home is captured by having children interacted with gender, a full-time or part-time job (Weaver and Weaver, 2014).

1.6 Results

Descriptive statistics and specification tests for the German-speaking cantons are compared to those for all Swiss cantons (Sections 1.6.1 and 1.6.2). The main results focus on the German-speaking cantons, for which the instrument is sufficiently strong to provide valid results (Sections 1.6.3 and 1.6.4). The full models for the German-speaking cantons as well as the entire country are available in Tables 1.A.3 and 1.A.4 in Appendix 1.A.

1.6.1 Summary statistics

The summary statistics for the German-speaking cantons and all cantons are available in Table 1.6.1. About 12% of the sample residing in German-speaking cantons has a hospitalization. Among hospitalized individuals, average LOS is 9.3 days. Around 77% of the individuals has a doctor visit (GP or specialist), 62% a GP visit, and 32% a specialist visit. On average, there are 4.9 doctor visits, 3.7 GP visits, and also 3.7 specialist visits.

These numbers are similar when looking at the whole country. Home care use varies considerably across the German-speaking cantons over the four years, between 0.3 and 1.6 hours per capita. There is slightly more variation if all cantons are considered.

1.6.2 Specification tests

The specification tests and first-stage regressions for the German-speaking cantons as well as all cantons are available in Tables 1.A.1 and 1.A.2 in Appendix 1.A. The instrument has the expected negative impact on canton-level home care use: patient contributions are associated with a 20% decrease in home care hours per capita in the German-speaking cantons (p≤0.01). The instrument is strong with F-statistics of 12.0-13.9, depending on the sample. When looking at all cantons, the instrument fails to be significant in

Table 1.6.1. Summary statistics

German-speaking cantons All cantons

N = 38,658^a N = 60,524^a

Mean Std. Dev. Mean Std. Dev.

Dependent variables

Any hospitalization 0.12 0.33 0.12 0.32

Length of stay^b 9.29 14.84 9.58 15.37

[1; 180] [1; 180]

Any doctor visit 0.77 0.42 0.78 0.41

Doctor visits^b 4.92 7.45 4.92 7.33

[1; 97] [1; 97]

Any GP visit 0.62 0.48 0.62 0.48

GP visits^b 3.71 5.40 3.63 5.21

[1; 97] [1; 97]

Any specialist visit 0.32 0.47 0.34 0.47

Specialist visits^b 3.73 6.88 3.78 6.99

[1; 97] [1; 97]

Canton-level home care use

Home care hours per capita^b 0.77 0.27 0.87 0.42

[0.26; 1.56] [0.05; 2.57]

Individual-level covariates

Availability of informal care 0.71 0.45 0.71 0.45

Fair health 0.11 0.32 0.12 0.32

Poor health 0.03 0.17 0.03 0.18

Some symptoms 0.34 0.48 0.34 0.47

Many symptoms 0.31 0.46 0.33 0.47

Any ADL limitations 0.01 0.12 0.01 0.12

Not able to walk 200m 0.02 0.14 0.02 0.15

Obese 0.09 0.29 0.09 0.28

(Ex-)smoker 0.51 0.50 0.52 0.50

Excessive alcohol consumption 0.18 0.39 0.21 0.41

Woman 20-39 0.17 0.37 0.17 0.38

Woman 40-64 0.23 0.42 0.24 0.43

Woman 65+ 0.12 0.33 0.12 0.33

Man 40-64 0.22 0.42 0.22 0.41

Man 65+ 0.09 0.29 0.09 0.29

Swiss nationality 0.89 0.32 0.87 0.34

Rural area 0.14 0.35 0.14 0.35

Secondary education 0.64 0.48 0.62 0.48

Tertiary education 0.25 0.44 0.25 0.43

Income in 10k Swiss CHF^b,c 5.37 3.05 5.22 3.06

[0.60; 96.26] [0.60; 96.48]

Woman with children<15 0.13 0.34 0.14 0.35

Man with children <15 0.12 0.33 0.13 0.33

Part-time job 0.24 0.43 0.23 0.42

Full-time job 0.43 0.50 0.43 0.50

aSummary statistics based on all observations used to estimate the likelihood of hospitalization, except for the other dependent variables. Sample sizes for the likelihoods of any doctor, GP, and specialist visit: N = 38,266, N = 38,112, and N = 37,791 (only German-speaking cantons), N = 59,938, N = 59,664, and N = 59,070 (all cantons). ^bContinuous variables with minimum and maximum values in brackets. ^cIncome enters the models in ln form.

most equations and the F-statistics are low (2.9-3.3), indicating a weak instrument. The magnitude of the coefficient on the instrument is slightly larger when considering all cantons, but its standard error increases so much that it loses its significance. Therefore,

we discuss all results for the German-speaking cantons.

Exogeneity is rejected in the likelihood of any doctor visit and likelihood of a GP visit equations only. This is not surprising because a doctor referral, typically from the GP, is required prior to the use of CHI-covered home care (i.e. reversed causality was anticipated). The fact that endogeneity is not detected in the hospitalizations or specialist visits models suggests that the canton fixed-effects effectively capture canton-level unobserved heterogeneity. We interpret the IV probit and IV GLM results whenever exogeneity is rejected in one of the two parts of the 2PM. Therefore, we interpret the IV results for doctor and GP visits and the standard probit and GLM results for hospitalizations and specialist visits.

1.6.3 Effects of home care on hospitalizations and doctor visits

We report average marginal effects.⁴ As the home care indicator is in ln form, Tables 1.6.2 and 1.6.3 show the effects of a 1% change in home care hours per capita on the likelihood of having a hospitalization/doctor visit, LOS (in days), or number of doctor visits. In the German-speaking cantons, expanding home care hours per capita by 1% significantly increases the likelihood of hospitalization by 0.05 percentage points (p≤0.01; Table 1.6.2).

This effect corresponds to a relative increase of 0.42% with respect to the sample’s hospitalization rate (12%). Among individuals with a hospitalization, the effect of home care on all different LOS considered is negative, and significant for acute LOS (LOS≤30).

This negative effect becomes smaller as we consider shorter LOS. A 1% expansion in home care hours per capita reduces all LOS up to 30 days by 0.02 days (p≤0.01), and LOS up to 5 days by less than 0.01 days (p≤0.10).

Home care significantly increases the likelihoods of having a doctor or a GP visit in the German-speaking cantons. A 1% expansion in home care hours per capita increases the likelihood of a doctor visit by 0.09 percentage points (p≤0.05) and the likelihood of a GP visit by 0.08 percentage points (p≤0.01). Considering that the sample’s likelihoods of a doctor or GP visit are 77% and 62%, these effects correspond to relative increases of

4The average marginal effect is the average of all marginal effects calculated for each observation in the sample.