Health Care Reform

RESEARCH ARTICLE

Associations of lack of voluntary private

insurance and out-of-pocket expenditures

with health inequalities. Evidence from an

international longitudinal survey in countries

with universal health coverage

Stéphanie BaggioID 1,2*, Marc Dupuis3, Hans Wolff1, Patrick Bodenmann4

1 Division of Prison Health, Geneva University Hospitals and University of Geneva, Geneva, Switzerland,

2 Life Course and Social Inequality Research Centre, University of Lausanne, Lausanne, Switzerland,

3 Institute of Psychology, University of Lausanne, Lausanne, Switzerland, 4 Vulnerable Population Center,

Department of Ambulatory Care and Community Medicine, University of Lausanne & Lausanne University

Hospital, Lausanne, Switzerland

* [email protected]

Abstract

Objectives

In countries with universal health coverage (UHC), national public health insurances cover

70% of health expenditures on average, but health care user fees and out-of-pocket expen-

ditures have been neglected in empirical patient-centered health inequality research. This

study is the first to investigate how health care-related factors are associated with health sta-

tus among middle-aged and elderly people—vulnerable groups for the burden of illness—in

countries with UHC.

Design

Longitudinal observational cohort study.

Setting

Population-based cohort Survey of Health, Ageing and Retirement in Europe (SHARE) in

twelve countries with UHC.

Participants

Non-institutionalized people aged 50 and older (n = 29,260). Two subsamples were also

used: participants without global activity limitation at baseline (n = 16,879) and participants

without depression at baseline (n = 21,178).

Main outcome measures

Risk of death, risk of global activity limitations, and risk of depression. We used mixed-

effects Cox proportional hazards regressions to estimate hazard ratios (HR) for all-cause

mortality, physical limitations, and depression.

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OPEN ACCESS

Citation: Baggio S, Dupuis M, Wolff H, Bodenmann

P (2018) Associations of lack of voluntary private

insurance and out-of-pocket expenditures with

health inequalities. Evidence from an international

longitudinal survey in countries with universal

health coverage. PLoS ONE 13(10): e0204666.

https://doi.org/10.1371/journal.pone.0204666

Editor: Mary Bowen, West Chester University of

Pennsylvania, UNITED STATES

Received: March 30, 2018

Accepted: September 12, 2018

Published: October 9, 2018

Copyright: © 2018 Baggio et al. This is an open access article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

Data Availability Statement: Data of the SHARE

study are available from http://www.share-project.

org/data-access.html third party data (conditions of

use: http://www.share-project.org/data-access/

share-conditions-of-use.html). The authors did not

have any special access privileges and others

would be able to access these data in the same

manner as the authors.

Funding: The SHARE data collection has been

primarily funded by the European Commission

Results

Having a voluntary private insurance to cover health expenses not included in the public

health care system (44.1% of the total sample) was a protective factor for all outcomes

(HR�0.91), controlling for a large range of socio-economic variables. On the contrary, hav-

ing out-of-pocket expenditures (62.4%) was a risk factor (HR�1.12).

Conclusions

UHC systems are not free from health inequalities: there is a potential effect of lack of volun-

tary private insurance and out-of-pocket expenditures on mortality and health. Health care-

related factors should be at focus in future researches designed to understand and address

health inequalities. Reducing out-of-pocket expenditures and developing voluntary private

insurance may protect against premature illness and death.

Introduction

Universal health coverage (UHC) is a major health and political concern worldwide. It has

been described as the best way to achieve health equity [1]. Health inequalities can be defined

as differences in health status (e.g., mortality, mobility, body mass index) between individuals

and groups (e.g., racial/ethnic disparities, gender disparities, income) [2]. One of most impor-

tant health inequality is the inequitable access to health care [3]. During the past three years,

several countries have successfully switched to a publicly financed health system [4]. However,

even in countries with UHC, health inequalities remain [4, 5]. Indeed, public health insurance

is always partial [6], even if it lowers the risk of catastrophic health care expenses and improve

the access to health care [7]. For example, in most countries of the Organization for Economic

Co-operation and Development, national public health insurances cover an average of 70% of

health expenditures.

Wealth inequalities and other social determinants are well-known causes of health inequali-

ties [8, 9], but health care-related factors may also be a cause of these health inequalities. A

recent editorial suggested that health care user fees should be abolished because they create

health care inequalities [1] and out-of-pocket payments are associated with an unmet need for

health care [10]. However, few empirical studies investigated these potential factors of health

inequalities. A Spanish study reported that women with voluntary private health insurance

were more likely to use preventive health services than women who did not [11]. Associations

of out-of-pocket expenditures with health inequalities have been studied in middle and low-

income countries, where out-of-pocket expenditures correspond to a decrease in health care

use [12]. Overall, research regarding the association between health care related factors and

health inequalities in countries with UHC remains scarce. Such investigations are needed to

highlight the potential effect of health care users fees on health inequalities.

Health inequalities persist in middle- and old-age with the accumulation of lifelong socio-

economic difficulties and vulnerability (i.e., the fragility of people’s lives and the incapacity to

protect their own interests) [13] and the greater need for health care compared with other

stages of life. Middle-aged and elderly people are one of the most vulnerable groups for health

care burden [10], but they have been neglected in empirical patient-centered health inequality

research [14]. In an ageing world with a rising proportion of elderly people, this question is

particularly salient [15].

Health care related factors and health inequalities

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through FP5 (QLK6-CT-2001-00360), FP6

(SHARE-I3: RII-CT-2006-062193, COMPARE:

CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-

2006-028812) and FP7 (SHARE-PREP: N˚211909,

SHARE-LEAP: N˚227822, SHARE M4: N˚261982).

Additional funding from the German Ministry of

Education and Research, the Max Planck Society

for the Advancement of Science, the U.S. National

Institute on Aging (U01_AG09740-13S2,

P01_AG005842, P01_AG08291, P30_AG12815,

R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11,

OGHA_04-064, HHSN271201300071C) and from

various national funding sources is gratefully

acknowledged (see www.share-project.org). The

funders had no role in study design, data collection

and analysis, decision to publish, or preparation of

the manuscript.

Competing interests: The authors have declared

that no competing interests exist.

Thus, this study aimed to investigate how health inequalities among middle-aged and

elderly people persisted in countries with UHC. The study assessed the potential effect of the

lack of voluntary private insurance and out-of-pocket expenditures on mortality and indica-

tors of physical and mental health (physical limitations and depression) of middle-aged and

elderly people in countries with UHC.

Materials and methods

Study design

This study is a sub-analysis of data collected in the population-based cohort Survey of Health,

Ageing and Retirement in Europe (SHARE) [16–18]. It focuses on non-institutionalized peo-

ple aged 50 and older. The data analyzed include eleven European countries (Austria, Belgium,

Denmark, Germany, Greece, Italy, France, Netherland, Spain, Sweden, and Switzerland), plus

Israel. These countries were covered from the first wave in 2004 and in the following five

waves until 2015, except Greece, which was covered in the three first waves (ending in 2009)

and in the sixth wave (2015). A probability sample was used in countries in which registers of

individuals were available (in most countries, there were stratified by age). In three countries

in which no register was available (Austria, Greece, and Switzerland), pre-screening in the

field were used to identify eligible sample participants. Participants were eligible for the study

inclusion of they were born in 1954 or earlier, spoke the official language of the country, did

not live abroad or in an institution (prison or institutions for elderly). Data were collected in

2004 using a computer-assisted personal interview program with an additional self-completed

pencil and paper questionnaire for sensitive questions. The SHARE study was reviewed and

approved by the Ethics Committee of the University of Mannheim (waves 1 to 4) and the Eth-

ics Council of the Max Planck Society (waves 4 to 6).

A total of 31,161 participants were included at baseline in 2004. The average response rate

was 61.6%, ranging from 38.8% in Switzerland to 81.0% in France. The attrition rate from the

beginning of the study until the end was quite low (6.5% of the baseline sample). Some partici-

pants were excluded in subsequent waves (in total 54.4% of the baseline sample) because: 1)

their household was not part of the wave, 2) they were not listed as members of households, or

3) they were dead. In the sixth wave in 2015, 11,446 participants were included in the study

(among participants included in the first wave). For analyses on mortality, we used the whole

sample. For analyses on physical limitations, we included participants without physical health

problems at baseline (n = 17,918). For analyses on depression, we included participants with-

out depression at baseline (n = 22,153).

Measures

Health status was assessed using three outcomes: mortality, physical limitations, and

depression.

Mortality. Participants’ ages and years of death (all-cause mortality) were recorded from

the beginning of the study and until the sixth wave.

Physical limitations. We used the Global Activity Limitation Indicator (GALI), to assess

long-term health-related disability (binary outcome coded “not limited” versus “limited”).

This measure underlies the European indicator Healthy Life Years and is widely used to com-

pare population health across different countries [19].

Depression. Mental health was measured using EURO-D, a 12-item self-reported ques-

tionnaire for depression (cut-off score � 4) [20].

Physical limitations and depression were completed at baseline (2004), in the second wave

(2006), in the fourth wave (2008), in the fifth wave (2013), and in the sixth wave (2015).

Health care related factors and health inequalities

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Risk factors for health inequalities were assessed at baseline and included demographics

and socioeconomic status, health behaviors, and health care factors.

Health care-related factors. We measured two individual-level factors related to health

care: having or not a voluntary private health insurance and out-of-pocket expenditures dur-

ing the previous twelve months (recoded as: 0€, 1 to 199€, and 200€ or more –200€ being the median for participants having out-of-pocket expenditures). It included hospital (inpatient

and outpatient) care, all kinds of consultations with health professionals (including dentists,

exams, and therapies but not alternative medicines), drugs (excluding self-medication and

drugs not prescribed), and day car/nursing care/home-based care.

Demographics and socioeconomic status. These individual-level variables included age,

gender, marital status (single, divorced, or widowed versus being in couple or married), and

income (using four categories corresponding to quartiles).

Health behaviors. Lifetime smoking (ever smoked daily, “yes/no”) and sport activity

(“never or hardly ever” versus “yes”) were assessed. Participants were also asked whether they

had or not forgone care because of financial problems during the previous twelve months for

all kind of health care: surgery, care from a general practitioner, care from a specialist physi-

cian, drugs, dental care, hospital (inpatient) rehabilitation, ambulatory (outpatient) rehabilita-

tion, aids and appliances, care in a nursing home, home care, paid home care, and any other

care.

Type of UHC. At the system-level, the type of UHC was recorded for each country. There

were three different models: 1) insurance mandate: The government mandates all citizens to

purchase insurance; 2) two-tier: The government mandates minimum insurance coverage and

allows purchasing additional voluntary insurance; and 3) single-payer: The government pro-

vides insurance for all residents and pays for all expenses.

Type of voluntary private insurance. At the system-level there are also differences

between different types of voluntary private insurance. We can distinguish between three

main types: duplicate, supplementary, and complementary coverage [21–23]. The duplicate

system covers services already included in the mandatory health insurance. It increases users’

choice and improve access to health services. The supplementary system provides a coverage

for health services not included in the mandatory health insurance. Finally, the complemen-

tary system provides a coverage for health services that are not totally covered by the manda-

tory health insurance and covers the residual costs. Some countries have a combination of

these different types, as shown in Table 1.

Statistical analyses

First, descriptive statistics for individual- and system-level factors were computed. We then

used mixed-effects Cox proportional hazards models to assess the relationship between the

three health outcomes and individual and system-level factors. The country was used as a sec-

ond-order factor in these multilevel models and to take into account that participants are

nested in countries and the corresponding unobserved heterogeneity. Our study did not aim

to compare countries, which would be difficult because of the important differences in public

and private institutional variations in health insurance. Three separate models were developed

for 1) mortality, 2) physical limitation, and 3) depression. The time variables used in these

models were survival time, survival time without physical limitations, and survival time with-

out depression, respectively, from the date of enrollment (2004). Participants who respectively

survived and remained physically and mentally healthy were censored at the end of the study

period (2015) or when they left the survey. Individual- and system-level covariates were

included as predictors in the models. Individual covariates included health care-related factors,

Health care related factors and health inequalities

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demographics, socioeconomic factors, and health behaviors. Since physical limitations and

depression were not measured as continuous scales, we used the Efron method to handle tied

events. This approximation has been demonstrated to perform as well as discrete models,

which are not available for mixed survival models [24]. Countries were considered a second

order level with random intercepts. Hazard ratios (HR) were reported. We used a backward

selection model in order to have parsimonious models. Missing values were list wise excluded

for the three models, except for financial-related variables (income and out-of-pocket expendi-

tures), which had more than 5% of missing values. For these two variables, an additional cate-

gory for participants who did not answer was created. It yielded a total of n = 27,515 (94.0% of

the sample) in mortality model, n = 16,022 (94.9% of the subsample) in physical limitations

model, and n = 20,395 (96.3% of the subsample) in depression model. Analyses were also run

with an additional category for missing values for all variables to test whether results were

changed. Theses analyses yielded similar results to those presented in the study. We also per-

formed sensitivity analyses excluding the three countries in which the sampling frame was dif-

ferent (Austria, Greece, and Switzerland). The results were again similar to those presented

below. A previous study reported that attributing a survival effect in an observational study

should be considered as a tentative [25]. Therefore, we confirmed our results using mixed-

effect logistic regressions, with the health outcome considered as a binary variable (e.g., death/

not death). The results were similar, so we could be confident in the results using survival

times. Analyses were performed using R version 3.4.0 and the package “coxme” version 2.2–5

for mixed-effects Cox models.

Results

In the complete cohort used to assess the risk of mortality, a total of 14.6% of the participants

(n = 4,257) died during the study at, on average, 79.1 years old ± 10.4 years. In the cohort with- out physical limitations at baseline, 37.5% of the participants (n = 6,332) developed global

activity limitations at, on average, 69.2 years old ± 9.5 years. In cohort without depression at baseline, 22.4% of the participants (n = 4,742) were diagnosed with depression by the end of

the study, with a mean age of diagnostic at 70.1 years old ± 10.0 years.

Table 1. Baseline characteristics of countries (system level).

Type of health

insurance

Type of voluntary private

insurance

% private

insurance

% out-of-pocket

expenditures

% forgone care Response rate (%)

Austria IM C + S 25.1 71.1 2.6 55.6

Belgium IM S 75.3 58.2 3.3 39.2

Denmark TT C + S 36.4 77.0 1.5 63.2

France TT C 80.7 23.9 6.3 81.0

Germany IM C + S 13.8 74.6 5.5 63.4

Greece IM D + S 91.3 74.3 6.3 63.1

Israel TT D 48.0 77.5 14.1 60.1

Italy SP C + D 5.6 70.0 5.1 54.5

Netherland TT S 66.8 44.2 2.1 61.6

Spain SP D + S 9.6 34.3 2.9 53.0

Sweden SP C + S 9.3 85.6 2.8 46.9

Switzerland IM S 33.8 80.4 3.7 38.8

IM: insurance mandate, TT: two-tier, SP: single-payer, C: complementary, D: duplicate, S: supplementary.

Baseline data: 2004.

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At baseline, more than half of the participants did not have a voluntary private insurance

to cover expenses not covered by the country’s health insurance (52.1% of the total sample),

and 62.3% reported out-of-pockets expenditures (30.9% reported having paid 200€ or more). Only 4.8% of the participants of the total sample reported to forgo health care for

economic reasons. Other descriptive statistics are reported in Table 2. S1 Table reports the

associations of socio-demographic, health, and health care-related variables with voluntary

private insurance.

Regarding system-level factors (Table 1), five out of twelve countries had an insurance man-

date system, four a two-tier system, and three a single-payer system. The types of voluntary

private insurances were very different between countries, and even between countries with a

same health insurance system.

Individual-level factors were associated with risk of mortality and risks for physical limita-

tions and depression (Table 3). Controlling for other individual factors, having a voluntary

private insurance was protective (0.87�HR�0.91), whereas having out-of-pocket expendi-

tures was a risk factor (1.14�HR�1.21 when comparing the reference category 0€ with the category expenses � 200€, result marginally significant for risk of depression). However, having no expenses was a risk factor of mortality compared to having expenses between 1

and 199€ (HR = 1.12). It was a protective factor for physical limitations (HR = 0.89) and there was no significant relationship for depression (HR = 0.94). Since we controlled for

demographics and socioeconomic factors (income, gender, age, marital status, and health),

health care factors appeared as independently associated with risks of mortality, physical lim-

itations, and depression.

Age was associated with an increased risk of mortality and risks for physical limitations,

and depression (1.01�HR�1.09). Being male was a risk factor for mortality (HR = 1.61) and

protective against risk of depression (HR = 0.66). There was no significant difference for physi-

cal limitations and this variable was not included in the final model. Being in a couple was pro-

tective against the risk of mortality (HR = 0.88), but there was no significant difference with

single participants for physical limitations and depression, and this variable was excluded from

these models. Participants with higher income were less at risk for all outcomes compared to

participants with lower income (0.81�HR� 0.91, difference only between the first and fourth

quartile for the risk of depression). Having health problems was a risk factor for mortality

(global activity limitation, HR = 1.47; depression, HR = 1.28) and for health outcomes (depres-

sion for physical limitations, HR = 1.05; physical limitation for depression, HR = 1.51). Life-

time smoking was a risk factor only for the risk of mortality (HR = 1.29). Sport was protective

for all outcomes (0.70�HR�0.93). Forgoing care due to cost was also a risk factor for the risks

of physical limitations and depression (HR = 1.21 and 1.22). On the contrary, it was a protec-

tive factor against the risk of mortality (HR = 0.83).

The system-level factors (type of health insurance and type of voluntary private insurance)

were not related to health outcomes and these variables were removed from the final models.

Discussion

The aim of this study was to investigate health inequalities among middle-aged and elderly

people in a large, population-based cohort in countries with UHC. Health care-related factors

were associated with health status, measured using mortality and indicators of physical and

mental health (physical limitations and depression). The preliminary results showed that even

in these countries, financial burden related to health care was likely to occur: 44.1% of the par-

ticipants took a voluntary private health insurance to cover health expenses not included in

the public or compulsory health care system and 62.4% reported out-of-pockets expenditures.

Health care related factors and health inequalities

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Table 2. Baseline characteristics of the study samples (individual level).

Cohort

Complete Physical health Mental health

n = 29260 n = 16879 n = 21178

Socio-demographic variables

Country

Austria 5.2 (1528) 4.6 (783) 5.7 (1204)

Belgium 12.4 (3640) 13.2 (2222) 12.8 (2712)

Denmark 5.5 (1614) 5.1 (867) 6.1 (1300)

France 10.1 (2965) 10.5 (1767) 8.7 (1846)

Germany 10.0 (2930) 8.7 (1475) 11.1 (2343)

Greece 9.1 (2667) 11.0 (1858) 9.1 (1921)

Israel 7.9 (2306) 8.0 (1350) 6.8 (1442)

Italy 8.6 (2506) 8.9 (1497) 7.7 (1640)

Netherland 9.8 (2872) 9.3 (1571) 10.6 (2245)

Spain 7.8 (2284) 7.1 (1198) 6.5 (1382)

Sweden 10.2 (2996) 9.9 (1665) 11.2 (2376)

Switzerland 3.3 (952) 3.7 (626) 3.6 (767)

Age at entry, mean (standard deviation) 64.6 (10.1) 62.5 (9.1) 63.9 (9.6)

Sex

Female 54.2 (15854) 51.2 (8646) 49.1 (10401)

Male 45.8 (13406) 48.8 (8233) 50.9 (10777)

Marital status

Single/divorce/widow 27.9 (8162) 24.5 (4141) 24.3 (5148)

Married/couple 71.8 (21013) 75.5 (12735) 75.7 (16028)

Did not answer 0.3 (85) <0.01 (3) <0.01 (2)

Income

First quartile 23.0 (6738) 20.8 (3516) 20.3 (4305)

Second quartile 23.0 (6739) 21.8 (3682) 22.7 (4801)

Third quartile 23.0 (6739) 23.9 (4034) 24.7 (5240)

Fourth quartile 23.0 (6738) 25.5 (4297) 25.5 (5390)

Did not answer 8.0 (2306) 8.0 (1350) 6.8 (1442)

Health variables

Depression

No 72.4 (21178) 83.1 (14032) 100 (21178)

Yes 24.9 (7283) 15.5 (2617) -

Did not answer 2.7 (799) 1.4 (230) -

Physical limitations

No 57.7 (16879) 100 (16879) 66.3 (14032)

Yes 41.9 (12246) - 33.7 (7145)

Did not answer 0.5 (135) - <0.01 (1)

Lifetime daily smoking

No 52.8 (15379) 51.7 (8717) 51.0 (10795)

Yes 47.2 (13733) 48.3 (8156) 49.0 (10382)

Sport

No 41.8 (12154) 30.7 (5183) 36.0 (7611)

Yes 58.2 (16949) 69.3 (11682) 64.0 (13561)

Health care-related variables

Forgone care because of cost

(Continued)

Health care related factors and health inequalities

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Health status and voluntary private health insurance

At the individual level, voluntary private health insurance was consistently associated with

survival times of health outcomes. Having a private health insurance was associated with

decreased risk of mortality and decreased risks for physical limitations and depression. Since

the model controlled for income and other socio-demographic factors, having a voluntary pri-

vate insurance was not a proxy for socioeconomic level: people with a similar income having a

voluntary private insurance were more likely to be in good health and have a longer life than

people without voluntary private insurance. However, this effect was of small amplitude. The

main explanation of this association is that there is a causal mechanism between health financ-

ing, health care use, and health status. Having a voluntary private insurance provides an

extended heath care coverage, which is likely to lead to use of preventive care and treatments.

For example, women having a voluntary private insurance are more likely to use preventive

care services [11].

Some countries consider voluntary private insurance as a key element of the health cover-

age system [26], but evaluations of private health insurances contrast. Previous findings

showed that private insurances increase the system costs and enhance individual responsibility

[27]. However, it also faces non-negligible issues: It is likely to exclude low-income people

from access to care, and to increase overall health-related expenditures. From the health

inequalities perspective, voluntary private insurance might be a benefit for middle-aged and

elderly people.

Health status and out-of-pocket expenditures

Out-of-pocket expenditures also appeared to be a factor of health inequality, as having impor-

tant expenditures was a risk for mortality, physical limitations, and depression, with again

small effect sizes. Previous studies in countries with UHC reported that out-of-pocket expendi-

tures are associated with the burden of illness [28] and that elderly people with multiple

chronic conditions are likely to be unable to afford out-of-pocket expenses [29]. However, to

Table 2. (Continued )

Cohort

Complete Physical health Mental health

n = 29260 n = 16879 n = 21178

No 94.5 (27651) 96.6 (16299) 97.1 (20561)

Yes 4.8 (1397) 3.2 (540) 2.9 (607)

Did not answer 0.7 (212) 0.2 (40) <0.01 (10)

Having a voluntary private insurance

No 52.1 (15243) 48.8 (8174) 51.4 (10895)

Yes 44.1 (12910) 47.8 (8064) 44.9 (9511)

Did not answer 3.8 (1107) 3.8 (641) 3.6 (772)

Having out-of-pocket expenditures

No 27.3 (8002) 31.0 (5224) 27.9 (5898)

1–199€ 56.3 (16460) 56.1 (9466) 58.1 (12297) � 200€ 6.1 (1788) 4.2 (718) 5.0 (1067) Did not answer 10.3 (3010) 8.7 (1471) 9.0 (1916)

Percentage and n under brackets are reported, unless otherwise specified.

Baseline data: 2004.

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our knowledge, no studies highlighted how out-of-pocket expenditures directly affect mortal-

ity and health outcomes among healthy middle-aged and elderly people while taking into

account others well-known confounders such as income or health. Small out-of-pocket expen-

ditures (< 200€) were not consistently associated with premature health problems. Indeed, it was protective against the risk of mortality. It is possible that this variable also captured

whether people used preventive care.

Health status and kind of UHC

Health care factors measured at the system level were not related to health status: single-payer,

two-tier, and insurance mandate systems displayed were not different from one another. Pre-

vious studies reported multiple advantages of a single-payer system compared to a multi-payer

system, such as efficiency in revenue collection, overall cost control, prevention of selection,

and increase in national solidarity [30]. Multi-payer systems have fewer advantages, such as a

greater diversity of insurance products, flexibility, competition between providers, and citi-

zens’ empowerment [30]. These systems were comparable when focusing on health status

among middle-aged and elderly people.

Table 3. Hazard ratios for associations of individual and system-level factors with mortality, physical limitations, and depression, during the follow-up period

2004–2015 in twelve countries.

Mortality Physical limitations Depression

Hazard ratio p-value Hazard ratio p-value Hazard ratio p-value

System-level factors

Type of UHC (ref. single payer)

Insurance mandate - - - - - -

Two-tier - - - - - -

Individual-level factors: health care factors

Voluntary private insurance (ref. no) 0.91 .037 0.90 .002 0.87 < .001

Out-of-pocket expenditures (ref. 1–199€) 0€ 1.12 .023 0.89 .005 0.94 .096 � 200€ 1.21 < .001 1.15 < .001 1.14 .054 Did not answer 0.83 .035 1.01 .910 1.02 .720

Forgone care because of cost (ref. no) 0.83 .020 1.21 .013

Individual-level factors: socio-demographics

Age 1.09 < .001 1.03 < .001 1.01 < .001

Gender (ref. female) 1.61 < .001 - - 0.66 < .001

Marital status (ref. single) 0.88 < .001 - - - -

Income (ref. first quartile)

Second quartile 0.89 .008 0.91 .016 0.97 .460

Third quartile 0.81 < .001 089 .003 0.92 .068

Fourth quartile 0.83 .002 0.86 < .001 0.87 .004

Did not answer 1.18 .650 0.73 .370 1.51 .820

Individual-level factors: health factors

Global activity limitation (ref. no) 1.47 < .001 - - 1.51 < .001

Depression (ref. no) 1.28 < .001 1.36 < .001 - -

Lifetime smoking 1.29 < .001 - - - -

Sport (ref. never/hardly never) 0.70 < .001 0.93 .006 0.87 < .001

Mixed-effects Cox proportional hazards models (Efron method) with backward selection were performed. Only significant variables are included in the final models.

https://doi.org/10.1371/journal.pone.0204666.t003

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Health status and others socio-demographic variables

Finally, this study replicated previous findings regarding associations between individual risk

factors of health inequalities [8]: gender (males being more likely to die earlier than women),

marital status (single participants being more likely to have a premature death than partici-

pants in couple), income (low-income participants being more likely to die prematurely than

high-income participants), and illnesses (participants with physical limitations, depression,

and at-risk health behaviors [smoking and no sport activity] being more likely to die earlier

than participants without physical limitations, depression, and at-risk health behaviors). The

risk factors were almost the same for physical limitations (except gender, marital status, and

smoking) and depression (except marital status and smoking). Forgoing care due to cost

showed inconsistent results. It was a risk factor for health outcomes, with a small effect size.

This result was in line with previous findings. Cross-sectional studies reported that health care

forgoing due to financial reasons is associated with worse health outcomes [10, 31–34]. It also

showed that forgoing care due to cost was associated with a decreased risk of mortality. Forgo-

ing care is probably not a protective factor for risk of mortality: it is possible that participants

who forwent care were those with minor illnesses, whereas those who did not forgo care had

major health problems, leading to a premature death. Future studies are needed to analyze

type of care participants forgo to achieve a better understanding of this phenomenon.

Limitations

Our study had some limitations. First, we were unable to identify undiagnosed global activity

limitation and depression, which might have led to underestimation of the true effects. Addi-

tionally, we used the EURO-D—a tool designed to assess depression—was used as a proxy for

mental health. Therefore, it excluded other mental health problems. Further studies should

include a larger range of tools to assess mental health problems to provide a more extensive

overview of its association with health status. In addition, it would help to address the unob-

served heterogeneity, i.e., variations dues to omitted variables. Other variables related to physical

health, lifestyles, and anticipation of health care needs should also be included to provide a better

understanding of the associations of health care-related factors with health status. Another limi-

tation was that all questions were self-reported ones, and participants may have under- or over-

reported certain problems. Results should thus be interpreted with caution. Finally, the response

rate was quite low for some countries (especially Switzerland and Belgium). However, we per-

formed analyses excluding these countries, and the findings remained similar.

Conclusion

This population-based cohort from a large range of countries confirmed that health inequali-

ties remained, even in countries with UHC [4, 7], and showed that some of these inequalities

are caused by health care factors. These findings suggest guidance to reform in health care sys-

tems and public health actions to reduce health inequalities from a health care perspective.

Furthermore, middle-aged and elderly people should not be neglected in health inequality

research [14], since they are a vulnerable stage of life for health burden. Health care variables

should be at focus in future researches designed to address health inequalities.

Supporting information

S1 Table. Baseline comparisons between participants with and without voluntary private

insurance.

(DOCX)

Health care related factors and health inequalities

PLOS ONE | https://doi.org/10.1371/journal.pone.0204666 October 9, 2018 10 / 13

Acknowledgments

This paper uses data from SHARE Waves 1, 2, 3 (SHARELIFE), 4, 5 and 6 (DOIs: 10.6103/

SHARE.w1.600, 10.6103/SHARE.w2.600, 10.6103/SHARE.w3.600, 10.6103/SHARE.w4.600,

10.6103/SHARE.w5.600, 10.6103/SHARE.w6.600), see Börsch-Supan et al. [16] for method-

ological details. The SHARE data collection has been primarily funded by the European

Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193,

COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812) and FP7

(SHARE-PREP: N˚211909, SHARE-LEAP: N˚227822, SHARE M4: N˚261982). Additional

funding from the German Ministry of Education and Research, the Max Planck Society for

the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2,

P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01,

IAG_BSR06-11, OGHA_04–064, HHSN271201300071C) and from various national funding

sources is gratefully acknowledged (see www.share-project.org). The funders had no role

in study design, data collection and analysis, decision to publish, or preparation of the

manuscript.

Author Contributions

Conceptualization: Stéphanie Baggio.

Formal analysis: Stéphanie Baggio, Marc Dupuis.

Methodology: Stéphanie Baggio, Marc Dupuis.

Supervision: Hans Wolff, Patrick Bodenmann.

Validation: Hans Wolff, Patrick Bodenmann.

Writing – original draft: Stéphanie Baggio.

Writing – review & editing: Stéphanie Baggio, Marc Dupuis, Hans Wolff, Patrick

Bodenmann.

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