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DEVELOPMENT OF A NEW RISK SCREENING TOOL OF READMISSIONS
IN THE SPANISH HEALTHCARE SYSTEM
Stream 11. Long-term Care Policies in Europe
Doñate-Martínez, A. 1
Zafra, E. 2
1
Polibienestar Research Institute – University of Valencia.
Edificio Institutos de Investigación c/ Serpis nº29, 2ª planta. 46022 Valencia (Spain)
Phone: +34 961624512
[email protected]
2
Valencian Health Agency, Regional Ministry of Health, Generalitat Valenciana
(Spain)
Abstract
Due to the current population ageing and its increasing perspective in the future, it is necessary to develop
and establish resources of health and social systems aimed to improve the management of elderly patients
and to strengthen the sustainability of Welfare systems. This study proposes a useful mechanism to
improve the continuity and efficiency of care provided to elders through the development of a new tool to
detect elderly patients at risk of future and potentially preventable hospital readmissions. In this sense, the
present study is based on data and results obtained in a previous research carried out in a sample of the
elderly (+65) from the Valencian Community (Spain) through the collection of variables from the two
instruments employed: Probability of Repeated Admissions – Pra – and The Community Assessment
Risk Screen – CARS. Several statistical analyses have been done (correlations and regression) to obtain
variables and factors that could be included in a new screening tool addressed to identify patients at risk
of future readmissions within the Spanish Healthcare System. The main results obtained show that the
variables statistically and significantly associated with the predictive variable ‘future hospital admissions’
were: sex, self-assessment health status, prescribed medications, hospital admissions in the previous 12
months, hospital admissions or emergency department visits in the previous 6 months and the diagnosis
of coronary disease.
Keywords
Readmissions – Elderly – Health and social care – Screening tools – Risk – Chronic
Patients
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INTRODUCTION:
The current study is framed within the Social Sustainability Theory developed
by Polibienestar, which consists of a joint reorganization of the social services and the
health system as a holistic model providing an answer to the necessities of people that
require long-term care to increase their well-being and quality of life (Garcés, Carretero
& Ródenas, 2012; Garcés & Ródenas, 2012). Population ageing in Spain and all around
Europe has caused a notable increase on the demand of health and social services by
elderly people, as this kind of patients presents, usually, chronic diseases and
comorbidities that require long-term care and/or repeated use of care services
(Dobrzanska & Newell, 2006). One of the indicators that reflects this increased
consumption and use of resources is the hospital admissions (Commission
Communication, 2009; Landi et al., 2004). For example, in Spain, it is worth
mentioning the notable increase of elderly people on hospital admissions in the last two
decades. Thus, in 1990 2,5 out of every 10 discharges were concerned to elders (65
years and over), in comparison to 4 out of every 10 discharges in 2010 (INE, 2011).
The term readmission has been defined as a repeated hospitalization within 1
month (e.g. Ashton et al., 1997), 2 months (e.g. Wilkins & Beckett, 1992) or 12 months
(e.g. Kelly et al., 1992) of discharge; and according to several data, about one third of
them occur within a month of discharge, half of them within 90 days and 80% within a
year (Corrigan & Kazandjian, 1991; Henderson et al., 1993).
The causes of readmissions may be inferred from differences in their rates
among various patient populations and according to demographic, social, and diseaserelated characteristics (Benbassat & Taragin, 2000). There is a wide literature focused in
the research on indicators or characteristics strongly related to readmissions. Thus,
rehospitalizations are associated with different kind of risk factors, as for example,
demographic (e.g. Allaudeen et al., 2011; Kirby et al., 2010), social (e.g. Berkman et al.,
1991; Landi et al., 2004), clinical (e.g. Dobrzanska & Newell, 2006; Hasan et al., 2010)
or related to the use of previous use of health resources (e.g. Howell et al., 2009; Martín
et al., 2011). The relevance of studying these indicators is based on the possibility to
define them and, thus, the posterior identification of patients at high risk to design
targeted interventions aimed, finally, to avoid repeated admissions.
In the Spanish Healthcare System, the number of hospital discharges since 1990
to 2010 have increased around 23,89% (Instituto de Información Sanitaria, 2012). To
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decrease this incidence it would be necessary to invest resources in improving health
services, in linking health and social care and in a preventive approach of chronic
diseases (Garcés & Ródenas, 2012).
In Spain is not any validated tool addressed specially to detect potential patients
that can use repeatedly health and social services. So, the aim of this paper is to present
the main variables that could compose a new tool to detect elderly patients at risk of
future and potentially preventable hospital readmissions.
METHODOLOGY:
The target population of this study was patients 65 years or older attended at the
Valencian Healthcare System in one of the followings Health Departments: Arnau de Vilanova
Hospital, Doctor Peset Hospital and Ribera Hospital. Exclusion criteria for participation were
absence of patient data in databases, aged under 65 and exitus.
The total sample recruited was of 432 patients.
The data employed for the analyses carried out are from a previous research in
which it was collected the variables showed on Table 1 from the instruments
Probability of Repeated Admissions – Pra – (Boult et al., 1993) and The Community
Assessment Risk Screen – CARS – (Shelton et al., 2000). The data collection of the
variables that compose the instruments employed was performed through several health
information systems from the Valencian Healthcare System with respect to 2008: 1) Abucasis –
primary care databases; 2) GAIA – with information about prescribed medications; and 3) at
hospitals MDS (Minimum Data Set) that registers the patients’ discharges among other data:
main and secondary diagnostics, clinical and/or surgical procedures, demographical variables
(birthdates and gender), and hospital stay. Moreover, to fill out two items from the Pra
questionnaire (global self-reported health and caregiver availability) it was necessary to contact
by phone with every patient to obtain information related to 2008. Once the information was
collected, we removed any kind of identifying information; preserving only the SIP number
(Population Information System) as a reference number to access medical and admission
histories of patients. Finally, we carried out a search of hospital admissions of each patient in
2009 through the health information system MDS.
Table 1. Variables collected to predict hospital readmissions
Gender
Female
Male
Hospital admissions or ED in past the six
months
Yes
No
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Diagnosis
Diabetes
Heart disease
Myocardial infarction
Stroke
COPD
Cancer
Self-related health status
Very good
Good
Fair
Poor
Hospital admissions in the past year
None
1 time
2-3 times
More than 3 times
Family doctor visits in the past year
None
1 time
2-3 times
4-6 times
More than 6 times
Caregiver availability
Yes
No
Prescript medicaments
5 or more
Less than 5
Source: Polibienestar Research Institute (2011).
Statistical analyses were performed using SPSS 17 software. Analyses consisted of
Student’s t-test of difference between independent sample means or one-way ANOVA test,
Pearson correlation coefficients, Pearson Chi-square tests and binary logistic regression.
RESULTS:
The variables assessed are summarized on Table 2. Moreover, the mean age of the
sample was 74,76 years (± 6,54).
Table 2. Summary of variables
Nº patients (%)
(n=432)
Gender
Female
Male
Diagnosis
Diabetes
Heart disease
Myocardial infarction
Stroke
COPD
Cancer
Self-related health status *
Very good
Good
Fair
Poor
Hospital admissions in the past year
None
1 time
2-3 times
More than 3 times
Hospital admissions or ED in past the six months
Yes
256 (59,26)
176 (40,74)
115 (26,62)
78 (10,06)
4 (0,93)
7 (1,62)
4 (0,93)
65 (15,05)
41 (9,49)
155 (35,88)
138 (31,94)
98 (22,69)
369 (85,42)
50 (11,57)
13 (3,01)
0 (0)
145 (33,56)
4
No
287 (66,44)
Family doctor visits in the past year
None
1 time
2-3 times
4-6 times
More than 6 times
50 (10)
65 (13)
96 (19,2)
63 (12,6)
226 (45,2)
Caregiver availability *
Yes
No
Prescript medicaments
5 or more
Less than 5
401 (92,82)
31 (7,18)
110 (25,46)
322 (74,54)
Source: Polibienestar Research Institute (2011).
The age from patients that were hospitalized in 2009 was statistically significantly
different (higher) than those did not suffered new readmissions (t498= 1,901; p=0,058).
Table 3 shows the results obtained through Chi-Square tests (for categorical variables)
carried.
Table 3. Relationship between variables and future hospital readmissions
Chi
p
Self-related health status * Readmissions in 2009
18,81
0,001
Caregiver availability * Readmissions in 2009
0,38
0,54
Family doctor visits in the past year * Readmissions in 2009
3,66
0,301
Prescript medicaments* Readmissions in 2009
8,78
0,003
Hospital admissions in the past year * Readmissions in 2009
12,03
0,002
Hospital admissions or ED in past the six months* Readmissions in 2009
21,84
< 0,001
Diagnosis of diabetes * Readmissions in 2009
2,32
0,13
Diagnosis of heart disease * Readmissions in 2009
7,68
0,006
Diagnosis of myocardial infarction * Readmissions in 2009
0,71
0,40
Diagnosis of stroke * Readmissions in 2009 Readmissions in 2009
0,027
0,87
Diagnosis of COPC * Readmissions in 2009
0
0,98
Diagnosis of cáncer * Readmissions in 2009
1,57
0,21
Source: Polibienestar Research Institute (2012).
Table 4 shows the results obtained through binary logistic regression to study the
variables associated with the occurrence of future hospital readmissions in the sample.
Table 4. Variables that influence on future hospital readmissions
Variables
Hospital admissions or ED in past the six
months
Exp (B)
p
0,969
< 0.001
5
Good self-related health status
3,478
0.002
Male gender
1,688
0.072
5 or more prescript medicaments
0,409
0.09
Source: Polibienestar Research Institute (2012).
DISCUSSION:
In this paper we are searching for specific factors related to socio-demographic,
clinical and related to the use of health resources’ variables in patients, attempting to
develop a new screening tool to detect elderly at risk of future hospital readmissions.
The main results obtained through different statistical analyses showed that the
most influential factors in the predictive variable ‘future hospital admissions’ are the
followings: self-related health status, prescript medicaments, hospital admissions in the
past year, hospital admissions or ED in past the six months, diagnosis of heart disease
and male gender. It is worth mentioning that the variables prescription of medicaments
and age showed a trend to significance (p= 0.05-0.09) so they may be variables to take
into account for further research and analyses. These results are similar to those
obtained in other researches in the Spanish context, in which the variables associated
with higher risk of readmission are based both on information from hospital and
primary care databases (Martín et al., 2010).
So, a higher number of medicaments prescribed (Morrissey et al., 2003), the
diagnosis of heart disease (Allaudeen et al., 2011), as well as a poor self-rated health
(Novotny & Anderson, 2008) implies an increased risk of readmissions. With respect to
variables related to health care, a previous history of hospital admissions and visits to
ED are associated with the probability of being readmitted again (Hasan et al., 2010).
Moreover, it has been observed a less risk of readmission in women (Frankl et al.,
1991).
The relevance of this study consists in it provides guidelines to develop an own
tool adapted to Spanish Healthcare System and validated in a Spanish sample of elderly
patients. For this purpose, it is very useful the availability of data from patients in
informatics databases, as health information systems from public administrations
(Ramalle-Gomara & Gómez-Barragán, 2011). In spite of this approach may have some
limitations related to the lack of codification and record of data, the advances in health
information systems must be taken in consideration as they enable obtaining data from
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patients easily and the integration of several care levels – for example, primary and
hospital care. So, since this approach, the data from this kind of databases, employed
commonly by clinicians, becomes in useful information to develop and implement
several pathways for the provision of specialized health and social care to patients
(Garcés et al., 2011).
The application and study of screening tools, as Pra and CARS, validated in
different Healthcare Systems than Spanish it is a relevant initiative. Researches like the
present are necessary to develop a new instrument based on our own special features
and validated in a Spanish sample, as for healthcare administrations the application of
this kind of tools could be a good strategy to innovate healthcare policies and,
consequently, to optimize the health and social resources (Garcés & Monsonís-Payá,
2012). The introduction of screening tools in health information systems could enable
preventive programs and their link with other health and social resources. So, the
screening tools jointly with methodologies as case-management both could be a relevant
approach to favor the sustainability of European Healthcare Systems according to
comparative studies (Garcés, Ródenas & Hammar, 2012).
Acknowledgements:
The research presented in this paper received financing from the Ministry of
Science and Innovation, through the Spanish National R+D+I Plan (2008–2011)
(Project reference: CSO2009-12086); from the Generalitat Valenciana, project
Prometeo-OpDepTec (Project reference: PROMETEO/2010/065); and from Valencia
Health Agency of Ministry of Health of Valencia 2010. A. Doñate-Martínez is
supported by a predoctoral FPU fellowship of the Spanish Ministry of Education
(AP2010-5354). Special thanks to the Johns Hopkins University: Wesley D. Blaskeslee,
J.D., Executive Director from Johns Hopkins Technology Transfer and Dr. Boult for
facilitating us the use of Pra tool through a Licensee Agreement signed on 8th
September of 2010.
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