Applied Managerial Decisions

RESEARCH ARTICLE

Applying Multivariate Clustering Techniques to Health Data: The 4 Types of Healthcare Utilization in the Paris Metropolitan Area Thomas Lefèvre1,2*, Claire Rondet1,3, Isabelle Parizot4, Pierre Chauvin1,2

1. Inserm, UMRS 1136, Pierre Louis Institute of Epidemiology and Public Health, Department of Social Epidemiology, Paris, France, 2. Sorbonne Universités, UPMC Univ Paris 06, UMRS 1136, Paris, France, 3. Sorbonne Universités, UPMC Univ Paris 06, Faculty of Medicine Pierre and Marie Curie, Department of general practice, Paris, France, 4. CNRS, UMR 8997, Centre Maurice Halbwachs, Research group on social inequalities, Paris, France

*[email protected]

Abstract

Background: Cost containment policies and the need to satisfy patients’ health

needs and care expectations provide major challenges to healthcare systems.

Identification of homogeneous groups in terms of healthcare utilisation could lead to

a better understanding of how to adjust healthcare provision to society and patient

needs.

Methods: This study used data from the third wave of the SIRS cohort study, a

representative, population-based, socio-epidemiological study set up in 2005 in the

Paris metropolitan area, France. The data were analysed using a cross-sectional

design. In 2010, 3000 individuals were interviewed in their homes. Non-

conventional multivariate clustering techniques were used to determine

homogeneous user groups in data. Multinomial models assessed a wide range of

potential associations between user characteristics and their pattern of healthcare

utilisation.

Results: We identified four distinct patterns of healthcare use. Patterns of

consumption and the socio-demographic characteristics of users differed

qualitatively and quantitatively between these four profiles. Extensive and intensive

use by older, wealthier and unhealthier people contrasted with narrow and

parsimonious use by younger, socially deprived people and immigrants. Rare,

intermittent use by young healthy men contrasted with regular targeted use by

healthy and wealthy women.

Conclusion: The use of an original technique of massive multivariate analysis

allowed us to characterise different types of healthcare users, both in terms of

OPEN ACCESS

Citation: Lefèvre T, Rondet C, Parizot I, Chauvin P (2014) Applying Multivariate Clustering Techniques to Health Data: The 4 Types of Healthcare Utilization in the Paris Metropolitan Area. PLoS ONE 9(12): e115064. doi:10.1371/ journal.pone.0115064

Editor: Kimon Divaris, UNC School of Dentistry, University of North Carolina-Chapel Hill, United States of America

Received: May 15, 2014

Accepted: November 11, 2014

Published: December 15, 2014

Copyright: � 2014 Lefèvre et al. This is an open- access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and repro- duction in any medium, provided the original author and source are credited.

Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All relevant data have been deposited to Dryad (DOI:10.5061/dryad.9v79s).

Funding: Funding for this study was provided by the Institute for Public Health Research (IReSP) and the Interministerial Committee for Urban Affairs. 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.

PLOS ONE | DOI:10.1371/journal.pone.0115064 December 15, 2014 1 / 20

resource utilisation and socio-demographic variables. This method would merit

replication in different populations and healthcare systems.

Introduction

In the European context of cost-containment policies and the post-2008 economic

and financial crisis [1], cost optimisation and, in some countries, cost reduction

of public expenditure has become unavoidable and the healthcare system is no

exception. For this reason, the healthcare system may need to be adapted to cost-

containment goals while at the same time meeting patients’ needs and

expectations as closely as possible. This requires, among other issues, accurate

characterisation of healthcare resource utilisation by the user population, as well

as identification of determinants of use.

Many studies have previously addressed the use of the healthcare system (either

individual services, or globally) by the general population or by specific

population subgroups. For example, several studies have examined healthcare

system utilisation from a systemic point of view or from a decision-making

approach [2–4], or by subgroups of the population, such as cancer survivors [5],

migrants [6, 7], or the underserved and low-income people [8–10]. In addition,

determinants of utilisation of specific healthcare services have been investigated,

including mental healthcare services [11], emergency care units [12], primary care

resources [13], dental care [14] and specialist consultations [15]. Associations

between health insurance and healthcare research have also been regularly

documented [16].

It has been suggested that healthcare systems themselves could not be analysed

through a classical reductionist approach but should be considered as complex

systems [17] which require analysis with non-conventional techniques. In

particular, it could be interesting to identify distinct groups of patients which

would exhibit different homogeneous patterns of resource utilisation. If such

groups can be identified, then factors associated with each utilisation profile can

be examined using conventional approaches [18–20].

Identifying such utilisation patterns requires the use of particular multivariate

techniques, which are capable of taking into account a vast amount and variety of

variables simultaneously, documented from the largest population possible. These

techniques, particularly clustering techniques, have been applied and validated in

a wide range of areas of medicine, including genetics [21–23], imaging [24–26],

clinical medicine [27, 28] and public health [29].

In this study, we aimed to identify and characterise distinct profiles of users of

the French healthcare system in an urban environment, through analysis of data

from a representative, population-based study in the Paris metropolitan area,

using clustering techniques.

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Methods

This work is based on the SIRS cohort study that received legal authorization from

two French national authorities for non-biomedical research: the Comité

consultatif sur le traitement de l’information en matière de recherche dans le

domaine de la santé (CCTIRS) and the Commission nationale de l’informatique

et des libertés (CNIL) [30]. The participants provide their verbal informed

consent. Written consent was not necessary because this survey did not fall into

the category of biomedical research (as defined by French law).

This study represents a cross-sectional analysis of data collected in the SIRS

cohort study in 2010 among a representative sample of 3,000 French-speaking

adults in the Paris metropolitan area (Paris and its suburbs, a region with a

population of 6.5 million).

The SIRS cohort

The SIRS cohort was constituted in 2005 using a 3-level random sampling

method. In a first step, 50 census blocks (with about 2000 inhabitants each) were

randomly selected using a stratification based on socioeconomic status and

whether they qualified or not for ‘‘underprivileged urban area’’ according to the

central government list. In the next step, 60 households were randomly chosen

from a complete list of households within each selected census block. In the final

step, one adult was randomly selected from each household by the birthday

method. The refusal rate among the newly contacted people was 29%. The

methodology of the SIRS study and detailed characteristics of the study

population have been described previously elsewhere, for example in [31].

Characterisation of healthcare utilisation

A comprehensive, detailed profile of the French healthcare system is provided in

reference [32]. Interviewees were asked in detail about their own use of healthcare

services during the twelve months preceding the interview. All responses were

codes as categorical variables and all reference periods were the last twelve

months. Resource use was grouped into categories as detailed below. Unless

otherwise specified, all consultation frequencies fell into one of five categories

(none, only once, only twice, 3–5 times, or $6 times).

Primary care: French people seeking healthcare may consult a general

practitioner (GP), whether as an end in itself or as an entry point to specialists

(the French system has adopted this gate-keeping model since 2004 [32]). Patients

need to respect this procedure so that they can be reimbursed. Exceptions are

made for four kinds of specialists who can be consulted directly, namely

gynaecologists (who are mainly community-based in France), ophthalmologists,

paediatricians and psychiatrists; these four specialities will henceforward be

referred to as direct access specialists (DAS). We used six variables to characterise

primary care utilisation, namely date of the latest dental consultation (4

categories: less than 2 years, between 2 and 3 years, more than 3 years, never),

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having declared a referring GP (yes/no), frequency of GP consultation, frequency

of DAS consultation, undergoing a medical check-up in a dedicated Social

security centre (yes/no), frequency of requests for medical advice from friends and

relatives (4 categories: none, 1 or 2, 3 to 10, more than 10 times).

Indirect access to a specialist (IAS): IAS concerns all other specialists except

DAS. The patient may access to them only when referred by their GP (or from

their own initiative but at full cost). A single variable was documented, the

frequency of IAS consultations.

Paramedical or alternative care: two variables were considered: having

consulted an acupuncturist or an osteopath (yes/no) and having consulted for

non-conventional or alternative healthcare (yes/no). Traditional Chinese

medicine fell into the latter category.

Site of healthcare consumption: in France, healthcare can be delivered in three

principal settings: public hospitals or clinics, private hospitals or clinics, and

community settings. Since the place of consultation was systematically

documented for each medical consultation over the previous twelve months, six

distinct variables were considered: having consulted (at least once) a GP in a

public hospital or clinic, a private hospital or clinic, or in a community setting,

and having consulted (at least once) a specialist in a public hospital or clinic, a

private hospital or clinic, or in a community setting.

Emergency care: two variables documented healthcare utilisation in emergency

situations, depending on the place where care was delivered; these were the

frequency of home visits for emergency reasons and the frequency of consultations

in an emergency unit.

Population characteristics and factors associated with healthcare

utilisation

Five dimensions were explored as possibly associated with healthcare utilisation,

all of them made up of three items (except for the socioeconomic status, with four

items).

Demographics: age (5 categories: 18–29, 30–44, 45–59, 60–74, and 75 years old

or more), gender, origin (distinguishing, as previously reported [33, 34], between

French people born to two French parents, French people born to at least one

foreign parent, and foreign immigrants).

Socioeconomic status: education level (none or primary/secondary/tertiary),

employment status (employed, unemployed, inactive or retired), monthly

household income per consumption unit (in quintiles, and computed as the total

household income divided by the number of consumption units [adult: 1; child

$14 years: 0.5; child ,14 years: 0.3]), according to the usual OECD-modified

scale recommended by Eurostat, and health insurance status (full coverage by the

statutory health insurance – SHI, SHI plus a voluntary health insurance - VHI, full

coverage by a special insurance for the poor, partial coverage by the SHI only, and

no insurance at all).

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Stance regarding health and medicine: general attitude toward medical

consultation (people were asked if they generally consult a doctor as a last resort,

or as soon as they are not feeling well), having a relative or a friend suffering from

a severe condition, and having medical professionals among relatives.

Social integration: feeling of isolation (very isolated, rather isolated, rather

supported, very supported), level of social support (low, medium, high) and

frequency of social contacts (quartiles), both as described in [34].

Perceived health: as measured by the Minimum European Health Module

[35, 36] that assembles the global perceived health status (good, average, bad), the

global activity limitation indicator (presence of a long-standing activity limitation

in the previous six months), and the presence of a chronic or long standing health

problems over the twelve months.

These dimensions are similar to those identified by Anderson in the late 60’s

[37, 38]. In his Behavioral Model of Health Service Use (BMHSU), this author

distinguished between three classes of factors: predisposing factors (such as age,

gender, education, occupation, social relationships, attitudes and knowledge

related to health services and professionals), enabling factors (such as income or

health insurance), and need factors (such as perceived health status or functional

disability). According to this model, Andersen suggested that the respective roles

of these factors may provide clues for measuring equity in service use. For

example, if the main drivers of health service use are need factors, access can be

considered equitable. Conversely, if the main drivers are constituted of social

factors, beliefs and enabling factors, access can be considered as not equitable.

Clustering methods

The use of clustering techniques available for health scientists has been described

previously [39–41]. Clustering techniques require the determination of some

data-specific parameters, such as the number of groups to be retrieved. In order to

identify the different types of healthcare system utilisation, we used the

partitioning around medoı̈ds (PAM) algorithm with the Euclidean distance as a

reference analysis and applied it to the healthcare system utilisation variables [40].

A resampling-based scheme and cluster-robustness approach [42] was used to

determine the key parameters of the algorithm and in particular the number of

clusters. Other clustering methods or sets of parameters were further used for

sensitivity analyses. The PAM algorithm was run with alternative distance or

similarity measure (Manhattan distance and Gower measure [40]). A fuzzy-logic

version of PAM, the FANNY algorithm [40], was also applied to the data, with

several values for the fuzziness parameter. All analysis was conducted on R 2.13.1

(R Foundation for Statistical Computing, 2012), with the clusterCons package.

Statistical analyses

We accounted for the three-level sampling design of the SIRS cohort by using the

survey command options of the STATA IC 10 software (STATA Corp, 2007) for

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descriptive statistics and multinomial models. Classical ratio tests (chi-square or

exact Fisher) were used to compare population characteristics according to their

type of care utilisation.

We used multinomial regression models to investigate significant associations

between variables of each of the five dimensions studied, and the types of

healthcare utilisation as categorical outcomes. Adjusted odd ratios (OR) are

reported with a p value for linear trend. Statistical significance was assessed at a

bilateral p value,0.05.

Results

Cluster identification

The optimal number of individual clusters that would account for the data was

four. This was the value at which mean cluster robustness was maximal and the

range of robustness values narrowest (Fig. 1). A cluster robustness of unity

indicates that no member of a given cluster was likely to be assigned to another

cluster when the algorithm was reiterated and also directly reflects the stability of

the cluster. In our analysis, mean robustness for the four-cluster model was high

.0.99, indicating that very few individuals could not be assigned unequivocally to

a given cluster.

Sensitivity analyses

No qualitative differences in cluster distribution or robustness were identified by

reiterating the algorithm with alternative distance or similarity measures.

Qualitatitively identical findings were obtained in all models (results not shown).

The four types of healthcare utilisation

The four clusters identified in our data were associated with four distinct types of

healthcare utilisation, accounting for 30.0% (Type 1), 21.0% (Type 2), 25.7%

(Type 3) and 23.3% (Type 4) of the study population. Table 1 shows the

contribution of each variable of healthcare utilisation to each Type.

Type 1 represents the largest users of primary care. These individuals used all

available resources, including GPs, social security centres for check-ups and

medical advice from relatives, and used these resources extensively. For example,

71.6% had consulted their GP three times or more in the past twelve months and

also consulted DAS extensively (50.7% with at least one visit). They were also the

most frequent users of IAS, with 100% having consulted at least one IAS in the last

12 months and 69.5% consulted more than two times. They were also the largest

users of paramedical or alternative care. Type 1 individuals consulted in all

settings (principally in public hospitals for specialists and in community care for

GPs) and were the largest users of the private sector. Finally, they were also the

principal users of emergency care, both in the home (12.9%) and in emergency

units (22.5%).

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Type 2 was the mirror image of Type 1. Together with Type 3, individuals in

Type 2 were the least frequent users of primary care. In Type 2, 28.5% of

individuals had no referring GP and only 12.3% had consulted a DAS

(furthermore, only once in most cases). Although 18.9% had consulted an IAS in

the previous year, but only 4.9% consulted more than twice. These individuals

rarely used paramedical or alternative resources. Whatever the setting, Type 2

users had the lowest rate of healthcare utilisation, and this was especially true for

private hospitals and clinics. Type 2 individuals rarely required emergency care in

the home (3.5%) or in emergency units (9.8%) and, when they did, they usually

(9.7%) consulted only once and hardly ever more than twice (0.1%).

Healthcare resource utilisation by Type 3 users was closer to that observed in

Type 2 than that in Types 1 or 4. Type 3 individuals were characterised by

extensive recourse to GPs, with 20.3% of users have consulted more than six times

in the last twelve months. In contrast, they rarely consulted DAS (6.4%) or IAS

(only 21.6% of them had consulted an IAS and never more than once). They

seldom used paramedical or alternative care (9.4% and 3.3% respectively). When

consulting GPs, they were more likely to consult in community care, and had little

use for the public sector. Nonetheless, Type 3 users constituted the second most

frequent user of emergency resources, especially in emergency units (19.3%

consulted at least once in emergency units).

Type 4 shared similarities with Type 1, in that it was constituted by people who

were heavy users of the healthcare system. Type 4 did not present a particularly

Fig. 1. Cluster robustness according to the number of assumed clusters in the data. Mean cluster robustness, together with minimum and maximum values, is presented as a function of the number of searched clusters.

doi:10.1371/journal.pone.0115064.g001

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Table 1. Resource utilisation by the four types of healthcare utilisation in the Paris metropolitan area, 2010.

Type 1 Type 2 Type 3 Type 4 All types p

(30.0%) (21.0%) (25.7%) (23.3%) N53006

Primary care

Date of the last dentist consultation ,0.001

,2 yr 84.3 68.1 72.9 87.0 78.2

2–3 yr 4.0 9.8 9.2 4.3 6.8

.3 yr 7.8 16.8 13.1 6.2 10.9

never 3.9 5.3 4.8 2.5 4.1

Having a referring GP ,0.001

96.0 71.5 93.2 90.6 88.1

Frequency of consultation with a GP

,0.001

0 2.4 60.4 - 6.7 16.9

1 9.0 39.6 - 23.7 17.7

2 17.0 - 31.8 23.9 18.1

3–5 38.6 - 47.9 34.6 30.5

6+ 33.0 - 20.3 11.1 16.8

Frequency of consultation with a DAS

,0.001

0 49.3 87.7 93.6 - 57.8

1 22.3 12.2 6.4 40.2 20.2

2 11.4 0.1 - 25.1 9.1

3–5 9.4 - - 16.3 6.5

6+ 7.6 - - 18.3 6.4

Has had a medical check-up in a dedicated centre

0.127

7.0 5.1 5.7 3.6 5.4

Frequency of requests for medical advice from relatives

0.0057

0 78.1 85.3 84.4 78.3 81.4

1–2 14.2 10.7 12.3 14.7 13.0

3–10 6.1 3.8 3.4 6.3 4.9

11+ 1.6 0.2 - 0.7 0.6

Paramedical and alternative care

Has consulted an acupuncturist/osteopath

,0.001

22.7 6.4 9.4 17.1 17.1

Has consulted for non-conventional/alternative healthcare

,0.001

7.2 2.1 3.3 6.9 4.9

Indirect access specialists

Frequency of consultation with an IAS

,0.001

0 - 81.1 78.4 68.0 54.8

1 1.3 12.2 21.6 28.6 15.3

2 29.3 1.8 - 3.3 9.4

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high rate of GP consultations (being the third-most frequent user) but had the

highest use of DAS (100%). Type 4 individuals were also the second most frequent

users who referred to relatives for medical advice, and who consulted for

paramedical or alternative care. Type 4 was also the second highest user of IAS,

although the frequency of consultation was relatively low (21.6% had consulted

once and none consulted more than once). Individuals in Type 4 used all settings

when consulting specialists (public and private hospitals, as well as community

care). For emergency care, Type 4 presented the lowest level of use compared to

the other Types, with 17.0% having consulted in emergency units, 13.0% only

once, and 1.6% between three and five times (second user in that case).

Factors associated with healthcare utilization

Univariate associations between independent variables and each of the four

profiles of healthcare utilisation are presented in Table 2. Only three variables

were not associated with significant differences between profiles, namely having

Table 1. Cont.

Type 1 Type 2 Type 3 Type 4 All types p

(30.0%) (21.0%) (25.7%) (23.3%) N53006

3–5 40.8 - - 0.1 12.6

6+ 28.7 - - - 0.8

Site of healthcare consumption

Public hospital or clinic-GP 12.3 3.1 14.1 7.6 9.4 ,0.001

Public hospital or clinic-specialist 42.5 5.5 7.2 19.9 19.6 ,0.001

Private hospital or clinic-GP 14.2 1.8 10.5 6.3 8.4 ,0.001

Private hospital or clinic-specialist 21.1 1.5 3.5 9.3 9.3 ,0.001

Ambulatory settings-GP 91.0 34.9 90.9 88.6 76.8 ,0.001

Ambulatory settings-specialist 83.0 22.3 16.3 88.8 53.4 ,0.001

Emergency care

Frequency of home visits 0.002

0 - 81.1 78.4 68.0 54.8

1 1.3 12.2 21.6 28.6 15.3

2 29.3 1.8 - 3.3 9.4

3–5 40.8 - - 0.1

6+ 28.7 - - - 0.8

Frequency of consultations in emergency units

,0.001

0 77.5 89.8 80.7 83.0 82.6

1 15.9 9.7 15.7 13.0 13.7

2 4.0 0.1 2.2 2.1 2.3

3–5 2.2 - 1.2 1.6 1.3

6+ 0.4 - 0.2 0.3 0.2

GP: General Practitioner. DAS: Direct Access Specialist. IAS: Indirect Access Specialist. All results are expressed in percentages.

doi:10.1371/journal.pone.0115064.t001

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Table 2. Population characteristics of the four types of healthcare utilisation in the Paris metropolitan area, 2010.

Type 1 Type 2 Type 3 Type 4 All types p

(30.0%) (21.0%) (25.7%) (23.3%) N53006

Model 1: Demographics

Age (yr.) ,0.001

18–29 11.6 32.1 21.2 23.7 21.8

30–44 22.8 32.0 35.0 39.5 31.9

45–59 25.5 23.4 20.2 23.7 23.3

60–74 24.2 9.3 14.1 9.6 14.7

75+ 15.9 3.3 9.5 3.5 8.3

Gender ,0.001

Male 42.6 64.3 59.6 21.0 47.0

Female 57.4 35.7 40.4 79.0 53.0

Origin 0.006

French, born to French parents

72.2 63.3 61.1 69.1 66.6

French, born to at least one foreign parent

18.3 22.0 23.2 20.1 20.8

Foreigner 9.5 14.7 15.7 10.8 12.6

Model 2: Socioeconomic status

Education level ,0.001

Tertiary 59.3 54.5 48.7 63.1 56.5

Secondary 32.5 38.0 42.3 32.2 36.2

Primary or none 8.2 7.5 9.0 4.7 7.3

Employment status ,0.001

Employed 47.9 61.6 55.5 63.1 56.7

Unemployed 5.7 8.6 9.3 7.1 7.6

Inactive 46.4 29.8 35.2 29.8 35.7

Income (quintiles)

1st 16.5 22.8 24.6 18.9 20.6 ,0.001

2nd 14.2 22.4 23.0 18.5 19.3

3rd 22.3 21.8 19.7 23.2 21.7

4th 20.0 18.2 15.6 17.8 18.0

5th 27.0 14.8 17.1 21.6 20.4

Health insurance status ,0.001

SHI+VHI 94.1 78.5 85.3 89.6 87.1

special insurance for the poor 1.7 4.1 2.1 3.0 2.6

SHI only 3.6 16.6 12.4 6.8 9.7

None 0.6 0.8 0.2 0.6 0.6

Model 3: Stance regarding health and medicine

General attitude toward medical consultation

,0.001

As a last resort 33.0 68.7 38.7 43.2 45.4

As soon as not feeling well

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Table 2. Cont.

Type 1 Type 2 Type 3 Type 4 All types p

(30.0%) (21.0%) (25.7%) (23.3%) N53006

67.0 31.3 61.3 56.8 54.6

Having a relative or a friend suffering from a severe condition

0.002

No 51.5 42.7 41.8 53.0 47.3

Yes 48.5 57.3 58.2 47 52.3

Having medical professionals among relatives

0.215

No 55.7 59.3 61.8 55.8 58.1

Yes 44.3 40.7 38.2 44.2 44.2

Model 4: Social integration

Isolation feeling 0.251

Very supported 29.7 35.2 32.0 34.5 32.7

Rather supported 55.6 54.1 53.7 52.5 54.1

Rather isolated 12.5 9.6 12.1 12.3 11.6

Very isolated 2.2 0.1 0.2 0.7 1.6

Level of social support ,0.001

High 81.9 90.7 87.4 91.3 87.6

Medium 13.8 6.5 9.1 6.2 9.1

Low 4.3 2.8 3.5 2.5 3.3

Frequency of social contacts (quartiles)

0.054

1st 25.0 23.3 22.5 18.7 22.5

2nd 24.2 24.9 27.1 21.1 24.4

3rd 26.3 21.6 22.0 30.4 25.1

4th 24.5 30.2 28.4 29.7 28.0

Model 5: Health status

Perceived health status ,0.001

Good 62.3 92.2 77.4 82.4 78.0

Average 28.5 6.8 20.0 15.2 18.0

Bad 9.2 1.0 2.6 2.4 4.0

Chronic or long standing health problem

,0.001

No 40.2 86.4 64.4 72.2 64.8

Yes 59.8 13.6 35.6 27.8 35.2

Long standing activity limitation

,0.001

No 63.1 94.4 81.1 86.5 80.6

Yes 36.9 5.6 18.9 13.5 19.4

All results are expressed in percentages.

doi:10.1371/journal.pone.0115064.t002

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Table 3. Characteristics associated with the type of healthcare utilisation: multinomial logistic regression model (with Type 4 as reference), Paris metropolitan area, 2010.

Type 1 Type 2 Type 3 p*

OR [CI 95%] OR [CI 95%] OR [CI 95%]

Model 1: Demographics

Age (yr.) ,0.001

18–29 Ref Ref Ref

30–44 1.18 [0.72–1.93] 0.60 [0.37–0.96] 0.99 [0.67–1.45]

45–59 2.24 [1.35–3.73] 0.77 [0.48–1.31] 1.01 [0.67–1.55]

60–74 5.44 [3.28–9.03] 0.80 [0.48–1.31] 1.87 [1.17–2.97]

75+ 11.14 [5.67–21.89] 1.00 [0.47–2.12] 4.45 [2.49–7.94]

Gender ,0.001

Male Ref Ref Ref

Female 0.32 [0.22–0.46] 0.14 [0.11–0.20] 0.17 [0.11–0.24]

Origin 0.0375

French, born to French parents

Ref Ref Ref

French, born to at least one foreign parent

1.10 [0.79–1.54] 1.32 [0.89–1.95] 1.54 [1.11–2.17]

Foreigner 1.19 [0.72–1.99] 1.67 [1.06–2.61] 1.95 [1.30–2.93]

Model 2: Socioeconomic status

Educational level 0.0119

Tertiary Ref Ref Ref

Secondary 1.23 [0.89–1.69] 1.25 [0.91–1.71] 1.61 [1.24–2.10]

Primary or none 1.94 [1.09–3.48] 1.67 [0.92–3.01] 2.26 [1.39–3.69]

Employment status ,0.001

Employed Ref Ref Ref

Unemployed 1.40 [0.65–3.00] 0.87 [0.43–1.76] 1.20 [0.61–3.36]

Inactive 2.08 [1.59–2.71] 1.01 [0.77–1.34] 1.25 [0.98–1.61]

Income per consumption unit (quintiles)

,0.001

1st Ref Ref Ref

2nd 0.96 [0.61–1.51] 1.11 [0.70–1.76] 1.05 [0.68–1.61]

3rd 1.39 [0.87–2.20] 0.92 [0.53–1.60] 0.82 [0.51–1.29]

4th 1.63 [0.96–2.77] 1.11 [0.62–1.99] 0.91 [0.57–1.47]

5th 1.80 [1.20–2.70] 0.79 [0.51–1.21] 0.90 [0.56–1.43]

Health insurance status 0.0058

SHI+VHI Ref Ref Ref

special insurance for the poor

0.55 [0.24–1.22] 1.45 [0.69–3.05] 0.59 [0.26–1.34]

SHI only 0.63 [0.29–1.37] 2.72 [1.39–5.35] 1.78 [0.89–3.56]

None 1.01 [0.19–5.41] 1.60 [0.41–6.32] 0.28 [0.04–2.03]

Model 3: Stance regarding health and medicine

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Table 3. Cont.

Type 1 Type 2 Type 3 p*

OR [CI 95%] OR [CI 95%] OR [CI 95%]

General attitude toward medical consultation

,0.001

As a last resort Ref Ref Ref

As soon as not feeling well

1.55 [1.19–2.01] 0.33 [0.23–0.47] 1.15 [0.84–1.58]

Having a relative or a friend suffering from a severe condition

0.0058

No Ref Ref Ref

Yes 0.97 [0.71–1.32] 0.62 [0.44–0.85] 0.67 [0.48–0.90]

Having medical professionals among relatives

0.3301

No 0.96 [0.77–1.20] 1.19 [0.90–1.59] 1.20 [0.94–1.55]

Yes Ref Ref Ref

Model 4: Social integration

Isolation feeling 0.1295

Very supported Ref Ref Ref

Rather supported 1.11 [0.81–1.52] 0.94 [0.69–1.29] 1.01 [0.72–1.41]

Rather isolated 0.88 [0.60–1.31] 0.68 [0.43–1.09] 0.90 [0.66–1.22]

Very isolated 2.84 [1.08–7.50] 1.45 [0.46–4.58] 3.06 [1.06–8.84]

Level of social support 0.0384

High Ref Ref Ref

Medium 2.32 [1.40–3.84] 1.03 [0.62–1.72] 1.43 [0.90–2.28]

Low 1.72 [0.84–3.56] 1.11 [0.59–2.11] 1.28 [0.66–2.49]

Frequency of social contacts (quartiles)

0.0370

1st 1.42 [0.96–2.13] 1.28 [0.87–1.90] 1.21 [0.80–1.82]

2nd 1.29 [0.90–1.87] 1.20 [0.87–1.64] 1.32 [0.92–1.89]

3rd 1.03 [0.70–1.53] 0.71 [0.51–1.00] 0.75 [0.46–1.22]

4th Ref Ref Ref

Model 5: Health status

Perceived health status ,0.001

Good Ref Ref Ref

Average 1.40 [0.97–2.03] 0.53 [0.33–0.84] 1.18 [0.84–1.67]

Bad 1.65 [0.71–3.84] 0.75 [0.24–2.36] 0.78 [0.33–1.87]

Chronic or long standing health problem

,0.001

No Ref Ref Ref

Yes 2.91 [2.27–3.71] 0.48 [0.34–0.67] 1.34 [1.02–1.76]

Long standing activity limitation

,0.001

No Ref Ref Ref

Yes 2.24 [1.44–3.48] 0.56 [0.36–0.88] 1.34 [0.90–2.00]

*p value for overall trend. Type 4 is the reference type for the estimation of all the Odd-Ratios.

doi:10.1371/journal.pone.0115064.t003

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medical professionals among relatives, feelings of isolation and frequency of social

contacts.

The five multivariate multinomial models are successively presented in Table 3,

with Type 4 being considered as the reference type. As in the univariate analysis,

many significant associations were observed. For instance, the probability of

belonging to Type 1 increased with age, and women were most likely to belong to

Type 4. Foreigners was more likely to belong to Type 3 (OR51.95, 95% CI5

[1.30–2.93]), as did individuals with a low education level (primary school or

none; OR52.26, 95% CI5 [1.39–3.69]). Inactive or wealthy people were most

likely to belong to Type 1 (OR52.08, 95% CI5 [1.59–2.71], and OR51.80, 95%

CI5 [1.20–2.70], respectively). Individuals with a SHI only had the highest

probability of belonging to Type 2. Referring to their GP for the slightest health

issue was an attitude associated with Type 1 (OR51.55, 95% CI5 [1.19–2.01]),

while the opposite attitude was associated with Type 2. Among the three variables

related to social integration, only the frequency of social contacts tended to be

associated with the type of healthcare utilisation; although the association was not

significant, the point estimate indicated that frequent social contacts were more

characteristic of Type 4 people. In terms of health status, reporting a chronic

condition was significantly associated with Types 1 and 3 (OR52.91, 95% CI5

[2.27–3.71], and OR51.34, 95% CI5 [1.02–1.76], respectively), while reporting a

good health status tended to be more frequent in Type 2 (OR50.53, CI5 [0.33–

0.84]).

Discussion

In this study, we took advantage of a database which was representative of the

general population of French-speaking adults in the Paris metropolitan area.

Because data were recorded from face-to-face interviews, independently from

medical registers or medical consumption records, our sample has the advantage

of taking into account non-users of healthcare. Social and subjective variables are

particularly richly documented in the SIRS cohort, which was originally designed

to study social inequalities in health and access to healthcare. We used an original

and methodologically robust approach to identify homogenous and consistent

types of healthcare system users.

We identified four different types of healthcare user through this approach. The

findings of the cluster analysis exhibited strong robustness in terms of sensitivity

to parameter tuning and of group stability. One type of user (Type 1) typically

consisted of elderly individuals of French origin, who were wealthy but unhealthy,

inactive and socially isolated, and who benefited from a good health insurance and

took advantage of all kinds of healthcare services, which they used extensively.

Type 4 was typically constituted by young, working women of French origin, with

a high educational level, who tended to be wealthy and healthy, socially integrated

and supported and fully insured. These users were the most likely to frequently

consult specialists in the community and to make use of non-conventional care. A

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third type (Type 2) was constituted by young men, frequently foreigners, who

tended to be unemployed and rather poor, healthy but with a mediocre access to

health insurance, and who had the lowest utilisation of healthcare services. The

last type (Type 3) was constituted of a population of diverse ages, often foreigners,

with a poor educational level and low incomes. These users were typically inactive,

with a mediocre health insurance, rather socially isolated and unhealthy, and

principally used GP services or emergency healthcare.

Our study has certain limitations. Firstly, we dealt with declarative data,

without any linkage to medical records or objective measures, so we were unable

to estimate possible reporting and recall biases. It is also specific to the French

healthcare system and we can thus make no direct comparison with or extrapolate

to healthcare systems of other countries, each of which has specific regulation

policies (especially in terms of gate-keeping, extent of public and supplementary

health insurance and out-of-pocket payments) and provision of healthcare

services. For example, if individuals with Type 4 profile present a higher use of

specialists, it is partly both because most of them are women and consult a

gynaecologist every year. In France, gynaecologists represent general ‘‘women’s

health’’ doctors, responsible for all aspects of gynaecological follow-up (including

contraception and cervical smears) and who are directly accessible without going

through a GP. The place of the gynaecologist in the French healthcare system is

atypical and not found in many other countries. Also, despite the existence of a

universal basic health insurance, income and insurance status may still influence

access to healthcare, which could account for the Type 3 profile. Apart from

individuals with the lowest income levels and those suffering from costly chronic

diseases, approximately 30% of health expenses are supported by patients (co-

payment). They may be reimbursed by a voluntary (supplementary) insurance;

but sometimes only partially, according to their contract. In many situations,

people also have to pay upfront and are then reimbursed by the basic public

health insurance. The gate-keeping system can be bypassed, although this incurs a

higher cost or lower reimbursement for patients. In addition, access to IAS and

prescription of paraclinical tests such as imaging or laboratory analyses can be

prescribed without consulting a GP during an emergency unit consultation

instead (with the possibility to get them at the same time rather than in a second

step after the GP consultation).

Moreover, even in France, the Paris metropolitan area region is not

representative of the whole country, being very urbanised, more wealthy on

average and with a higher density of medical provision than the rest of the

country, but also with much more social inequalities and spatial segregation than

other French regions [43].

Technically speaking, the robustness and stability of the four clusters could

result, at least in part, from too many constraints in the analysis methods. In other

words, the identified clusters may grossly reflect reality, but lack accuracy. If so,

this is likely to be linked to the intrinsic geometric assumptions underlying the

clustering methods we used. The overall shape of the groups identified cannot

represent complicated configurations, such as reticulated patterns, and the

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clusters generated by the model are spheroid with little scope for interpenetration.

Moreover, we may have encountered a lack of statistical power for some

underrepresented categories such as the utilisation of emergency resources.

From the health inequality point of view, our results help clarify some of the

differences in behaviours with respect to healthcare system use and to

opportunities for healthcare. While it is usually assumed that social inequalities in

access to healthcare mainly stem from economic inequalities [44–46], it would

have been expected that the introduction of universal health insurance in France

in 1999 should have removed such barriers [47]. However, it is clear that this has

not happened systematically [16, 43, 48]. In fact, the social causes and processes

underlying inequalities in access to healthcare are complex, going beyond purely

economical or materialistic factors, and involving different psychosocial and

behavioural factors as well [49, 50]. Our study may help unravel some of this

complexity and diversity. Indeed, we observed several associations between the

type of healthcare resource utilisation and social factors previously found to be

associated with healthcare indicators, such as objective or perceived health status

[45, 51] or, more broadly, health expectations and perceived needs [52], social

capital [53] or social integration [54]. These determinants coexist, but contribute

to different extents to the four types of utilisation. For example, health status,

measured by chronic diseases and functional limitations, was indeed associated

with greater use of the healthcare system, together with higher educational level

and higher income. Stance regarding healthcare was also found to influence extent

of use of the healthcare system and established differences regarding gender and

healthcare use were observed. Among all the variables evaluated, only having

medical professionals among relatives was not significantly associated with

profiles; this may be explained by this question being too general, as it could be

interpreted in a wide variety of different ways with respect to closeness,

confidence, availability and the professional skills involved. With respect to social

integration, the frequency of social contacts appeared to be poorly discriminant.

In this context, we believe that the ‘‘crude’’ frequency of social contacts, without

any further details on their frequency, quality, context or content, is less

meaningful than direct interrogation of global and subjective feelings of isolation.

For the latter, significant differences were found for people reporting a ‘‘very

isolated’’ status in Types 1 and 3 (Table 3). Taken together, these results, which

are consistent with the literature, provide some face validity of our typology.

As mentioned above, this typology can be interpreted according to Andersen’s

BMHSU, in terms of equity in access to healthcare services. When looking at the

variables that discriminated the four types of user best (OR$2 or #0.5), we

observed that the most important predisposing factors for the pattern of

healthcare utilisation were age, gender, origins, educational level, a feeling of

social isolation and the general attitude toward medical consultation (Types 2 and

4 only), whereas the most prominent enabling factors were the feeling of social

isolation and health insurance (Types 2 and 4 only).

Individuals corresponding to the Type 1 profile need to have access to services

due to a higher prevalence of chronic conditions and functional limitations, and

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PLOS ONE | DOI:10.1371/journal.pone.0115064 December 15, 2014 16 / 20

they also use these services, both because they can afford to (they have adequate

financial resources and more time to access services) and because they present

habits and perceptions that make them prone to use them. Our study provides no

information on whether their access to healthcare meet all their needs, but their

pattern of healthcare use does not seem to be explained in terms of predisposing

factors such as gender or educational level. Individuals corresponding to the Type

2 profile, who are in majority young males, have a low level of healthcare

utilisation which reflects their low perceived needs. At the same time, these

individuals are those with the highest proportion of basic insurance status only.

This is an obstacle to access to healthcare which is not very reassuring regarding

equity in access to health services, particularly since young people may

underestimate their real health needs. Individuals corresponding to the Type 3

profile present low rates of use and multiple negative predisposing factors. For

example, they are more likely to be foreigners, with a lower educational level and

low financial resources. On the other hand, they are likely to suffer from chronic

conditions and functional limitations. It is for this profile that the healthcare

system is the most likely to be inequitable. Indeed, these individuals

predominantly use services from GPs and emergency units, and far less often from

specialists. Individuals corresponding to the Type 4 profile have few expressed

needs but use services, especially from GPs and DAS, intensively, preferring

community care. These individuals have the adequate resources to use services,

both economically (income, health insurance) and culturally (female gender,

higher educational level, higher social support). In conclusion, of the four profiles

described, one (Type 3) and possibly two (Type 2) profiles are in a situation where

the French healthcare (and insurance) system is the most likely to be inequitable.

Finally, we demonstrated that the method used was able to reveal stable and

meaningful structures in our data without resorting to the usual reductionism of

classical studies on healthcare utilisation. For this reason, we think that a similar

multivariate clustering method would merit replication in other datasets derived

from other contexts, such as non-urban populations or countries with other

healthcare systems, in order to confirm or refine our findings.

Author Contributions

Conceived and designed the experiments: TL IP PC. Performed the experiments:

TL IP PC. Analyzed the data: TL CR IP PC. Contributed reagents/materials/

analysis tools: TL CR IP PC. Contributed to the writing of the manuscript: TL CR

IP PC.

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