Kalseth et al. BMC Psychiatry (2016) 16:376
DOI 10.1186/s12888-016-1099-8
RESEARCH ARTICLE
Open Access
Psychiatric readmissions and their
association with environmental and health
system characteristics: a systematic review
of the literature
Jorid Kalseth1* , Eva Lassemo1, Kristian Wahlbeck2, Peija Haaramo2 and Jon Magnussen3
Abstract
Background: Psychiatric readmissions have been studied at length. However, knowledge about how
environmental and health system characteristics affect readmission rates is scarce. This paper systemically reviews
and discusses the impact of health and social systems as well as environmental characteristics for readmission after
discharge from inpatient care for patients with a psychiatric diagnosis.
Methods: Comprehensive literature searches were conducted in the electronic bibliographic databases Ovid Medline,
PsycINFO, ProQuest Health Management and OpenGrey. In addition, Google Scholar was utilised. Relevant publications
published between January 1990 and June 2014 were included. No restrictions regarding language or publication
status were imposed. A qualitative synthesis of the included studies was performed. Variables describing system and
environmental characteristics were grouped into three groups: those capturing regulation, financing system and
governance; those capturing capacity, organisation and structure; and those capturing environmental variables.
Results: Of the 734 unique articles identified in the original search, 35 were included in the study. There is a limited
number of studies on psychiatric readmissions and their association with environmental and health system
characteristics. Even though the review reveals an extensive list of characteristics studied, most characteristics appear in
a very limited number of articles. The most frequently studied characteristics are related to location (local area, district/
region/country). In most cases area differences were found, providing strong indication that the risk of readmission not
only relates to patient characteristics but also to system and/or environmental factors that vary between areas. The
literature also points in the direction of a negative association of institutional length of stay and community aftercare
with readmission for psychiatric patients.
Conclusion: This review shows that analyses of system level variables are scarce. Furthermore they differ with respect
to purpose, choice of system characteristics and the way these characteristics are measured. The lack of studies looking
at the relationship between readmissions and provider payment models is striking. Without the link to provider
payment models and other health system characteristics related to regulation, financing system and governance
structure it becomes more difficult to draw policy implications from these analyses.
Keywords: Psychiatry, Readmission, Rehospitalisation, Recidivism, Health care system, Environment, Systematic review
* Correspondence: jorid.kalseth@sintef.no
1
SINTEF Technology and Society, Health Research, P.O. Box 4760
SluppenNO-7465 Trondheim, Norway
Full list of author information is available at the end of the article
© The Author(s). 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Kalseth et al. BMC Psychiatry (2016) 16:376
Background
Repeated hospitalisations for patients with psychiatric disorders may reflect the type of illness, but also environmental factors and underlying inefficiencies in pre- and postdischarge treatment [1]. Readmissions may be disruptive to
patients and their families, and represent a strain on limited
health care resources. Readmissions may be avoided by providing adequate treatment during the index hospital stay,
combined with an adequate discharge and transition plan
and a follow-up regime that allows the patient to remain in
the community after discharge. Thus, the probability for readmission will depend on individual characteristics of the
patient, but also on factors in the patient’s environment, on
the way services are delivered, and on the organisation,
governance and financing of health care.
Readmission rates are used as an indicator of quality
of hospital care [2, 3], and several strategies have been
developed to reduce readmission rates [4]. These include
enhanced patient education, more post-discharge followup care, and increased coordination with outpatient
providers [5]. There is considerable variation in rates of
30-day unplanned readmissions between different highlevel income countries [6]. The readmission rate for
schizophrenia varied from 5 to 20 % among 20 countries
reporting data for the year 2011.
Previous reviews of factors that affect the probability of
a patient being readmitted to psychiatric inpatient care indicate that studies of the impact of health care system are
scarce. Also, previous reviews have typically identified and
discussed system characteristics as pre- or post-discharge
service use of psychiatric patients [1, 7, 8]. However, in
many instances there is not a clear cut separation between
patient characteristics and systems characteristics. Service
use as a patient level predictor (pre- or post-discharge) is
likely to reflect case-mix characteristics as well as, and
sometimes rather than, system characteristics. For instance, length of index hospitalisation may be an indicator
of patient needs due to severity of illness as well as reflecting system level differences in capacity, structure or treatment. Likewise, number of outpatient follow-up visits may
reflect patients’ needs and also be a system characteristic.
Patient level predictors may confound or mask system effects. For example, outpatient follow-up may be associated
with an increased likelihood of readmission when measured at patient level (follow-up visits are an indicator of
severity), whereas as a system characteristic availability of
aftercare may be associated with a reduced likelihood of
readmission (preventive measure). To identify system effects using variables characterising service use at patient
level, such as length of stay or receiving aftercare visits,
will be particularly difficult in analysis of readmission risks
based on data for patients treated within broadly speaking
the same health care system [9]. Variables measured at a
higher than patient level on the other hand, capture effects
Page 2 of 9
of characteristics of health care system and other context
factors on readmission risks.
European countries differ in how health care is regulated, financed, governed, organised and delivered. Thus,
across Europe we observe a multitude of different
financial, organisational and institutional mental health
care models [6, 10]. Furthermore, local environmental or
context factors vary, which may influence both the “demand”- and “supply” side in health care delivery. For policy makers, knowledge about the relationship between
environmental and system characteristics and the
probability for readmissions is of value. Hence a review of
aggregate system characteristics is warranted.
The objective of the present study was to systematically review what type of aggregate system level and environmental characteristics have been studied and
examine their association with readmission after discharge from psychiatric in-patient hospital care.
Methods
The Comparative Effectiveness Research on Psychiatric
Hospitalisation by Record Linkage of Large Administrative
Data Sets (CEPHOS-LINK) is a European research project
investigating psychiatric services across six countries,
namely Austria, Finland, Italy, Norway, Romania, and
Slovenia, carried out from 2014 to 2017. CEPHOS-LINK
aims to compare different types of health service interventions in terms of differences in readmission outcomes in
adult patients, who have been discharged from hospital
with a psychiatric diagnosis. This will be achieved utilizing
state-of-the-art approaches using large ‘real world’ electronic databases, allowing for specific comparisons and
detailed analysis to be made. The present study belongs to
a series of systematic reviews from the CEPHOS-LINK
project on predictors of readmission after discharge from
psychiatric or general in-patient health care for patients
with a psychiatric diagnosis. The first steps in the literature search process and the initial screening for eligibility
were performed in collaboration between participants in
the CEPHOS-LINK project.
Searches
Studies of predictors of psychiatric readmission were
identified through comprehensive literature searches
conducted in the electronic bibliographic databases Ovid
Medline, PsycINFO, ProQuest Health Management,
OpenGrey and Google Scholar. Additionally, the reference
lists of all included articles were screened for additional
papers to be included. Searches were performed using
combinations of terms (used as MeSH terms or free text,
depending on the database) describing mental health services and readmission. For more detailed description of
the search terms, please see Additional file 1.
Kalseth et al. BMC Psychiatry (2016) 16:376
Page 3 of 9
Types of studies included
Environmental and system characteristics
Studies on readmissions after discharge from psychiatric
in-patient treatment that include environmental and/or
health care system variables were included. To separate
patient level pre-discharge and post-discharge variables
from the system/environmental variables, and to be eligible for inclusion in the present study, the variables
should, be measured at an aggregate level i.e. above
patient level, and should be characteristics not of the patient, but of the provider or environment of the patient.
There are grey zones between aggregate system and/or
environmental variables and patient characteristics, for
example when system level characteristics are measured
at the patient level. In such cases the studies were included. Relevant publications from January 1990 through
June 2014 were included. No diagnostic, time frame, language or publication status restrictions were applied.
Only quantitative study designs were included (observational and interventional studies). Studies not covering
the issue of readmission were excluded. Admissions to
day hospitals or community programs were not considered as readmissions. Studies only reported in abstract
format with no access to full text (e.g. conference abstracts) were excluded. Papers not including original
data, such as editorials, letters to the Editor, commentaries, reviews and meta-analyses were excluded.
Environmental and system level variables can be defined
and operationalised in different ways. For clarity, we make
distinction between the following types of variables:
The first category of readmission predictors describes
regulation, financing system, and governance structures.
These variables describe features of the healthcare
system along dimensions such as legal and other types
of regulation, funding (e.g. tax vs. social insurance),
provider payment systems, degree of decentralisation,
the extent of financial integration between hospitals and
primary (community) care providers, etc.
The second category of readmission predictors describes the capacity, organisation and structure of the
healthcare providers. Furthermore, they may in general
terms describe the composition and structure of the delivery of health and social services within geographical
areas (region or community) or within specific provider
types (hospital or community providers). Capacity variables can be measured directly in the form of resources
(spending, bed rates, staffing rates) or indirectly (e.g. institutional length of stay). Hospital and/or community
treatment policies, processes and procedures, treatment
orientation and philosophy can be viewed as organisational variables. These may also describe more narrow
aspects of the organisation of services, and thus
characterize specific interventions. Typical structural
variables are size (of hospital or community services),
the scope of services provided within a specific setting
(e.g. teaching and research in addition to patient treatment), and the case-mix of the hospital. Also variables
capturing aftercare are included in this category.
The third category capture what can be denoted as
environmental variables, i.e. variables that describe the
surroundings of the patient. Environmental variables are
measured at community level and can be further classified into geographical variables, including variables describing the geographical distribution of population and
service (such as area of location of hospital or of residence of patients, population density etc.); demographics
(composition of population in terms of age, gender etc.);
and socio-economic variables (community income, education level, unemployment rates etc).
Types of participants
Only studies examining adult populations (mean age ≥
18 years) were included in the review. Only studies on
patient populations having been admitted to in-patient
health care were included.
Selection of studies
Studies were initially selected for inclusion by two pairs
of researchers independently [EL and VD, LS and RS].
Conflicting decisions were resolved by discussion. Fulltexts were screened, if necessary, to establish eligibility.
Next, all candidate studies were screened once more for
eligibility by two researchers [EL, JM]. Full texts of
potentially relevant studies were assessed for eligibility
by at least two of the authors [EL, JK, JM]. Discrepancies
were resolved by discussion by all three authors, until
consensus on inclusion, or exclusion, was reached.
Data extraction
Available structured data on variables associated with readmission were extracted from the included studies, and
entered into an evidence evaluation table by two pairs of
researchers [EL, JM; EL, JK]. Key characteristics of the
studies selected for the systematic review are presented
in Additional file 2. A qualitative synthesis of the included studies was performed.
Results
Comprehensive literature searches returned 1 018 records,
of which 734 were unique, the process is depicted in the
flow-chart of Fig. 1. Of the 734 unique records identified
in the original CEPHOS-LINK search, 155 were identified
as potentially describing system level or environmental readmission predictors. Following additional screening, 50
articles were included for full text assessment of eligibility.
Review of full texts left 33 articles for inclusion. Upon
checking the references of these, two more were added.
Kalseth et al. BMC Psychiatry (2016) 16:376
Page 4 of 9
environmental and/or system characteristics described
above, and also according to level of measurement:
physician level, hospital level and community level.
1018 Records identified
284 Duplicates removed
Regulation, financing system, and governance structures
Finally, 35 articles were included in the literature review.
An overview of the included articles is provided in
Additional file 2.
Three studies investigated the impact of system characteristics falling within this category [11–13]. The studies
are heterogeneous both in terms of system (Israel vs.
US), and in terms of variables studied (legislation, carveout and utilisation management programs respectively).
Grinshpoon et al. [11] found that passing of rehabilitation legislation in Israel aiming at establishing infrastructure in the community for the rehabilitation of patients
with severe mental disorders was associated with increased survival in the community. The two US studies
looked at different aspects of health insurance policies
targeted towards cost-containment and/or quality improvement likely to affect outcomes in mental health
care. Utilisation Management programs potentially restrict access to services and treatment by imposing preadmission certification, concurrent review, and case
management. The program was found, by limiting length
of stay, to increase the risk for early readmission for psychiatric patients [13]. Behavioural health carve-outs means
that mental health and substance abuse benefits are separated from general medical benefits. Carve-out was not
found to affect risk of readmission for major depressive disorder [12]. Although these studies illustrate the possible
relevance of aggregate level system characteristics, it is difficult to draw conclusions based on isolated studies.
Type and level of analysis
Capacity, organization and structure
Eight of the included articles were studies of interventions. Of these, three were randomized controlled trials
and five were case-control type studies. Five studies
could be characterised as “natural experiments” and 22
were other types of observational studies. Of the latter,
six used aggregated readmission rates either at the program/hospital level, or small area (administrative) level.
The remaining 16 observational studies used patient
level data, and (in all but one) multivariate analytical
methods, i.e. controlling for possible confounders. The
follow-up period included both short (up to 30 days),
medium (30 + days-1 year), and long (>1 year) follow-up
times. The interpretation of results differs for patient
level analyses and analyses of aggregated readmission
rates at hospital or area level. Patient level analyses separate patient level (“need”) effects and “pure” system or
environmental level effects. In aggregated readmission
rates analyses, the results will comprise both aggregated
patient level effects and system/environmental effects.
The system and environmental characteristics identified
in each study are summarised in Additional file 2. Characteristics are put in one of the three broad categories for
The majority of the studies (69 %) included in this review
falls into this category. They can further be divided into
studies capturing potential hospital size effects [14–17],
capacity variables [18–23], resource availability/utilisation/ quality [14, 17, 24, 25], length of stay [14, 16–18, 20],
case-mix [14, 20], treatment policies or orientation [14, 16,
17, 26], hospital type [24, 25, 27, 28] and community
aftercare [14, 16, 17, 29–36].
Hospital size is measured differently across studies
(e.g. number of beds, staff, patient volume). With the exception of a study by Lee and Lin [15] which found a
positive association between patient volume and readmission rates, none of the studies were able to establish any association between size and readmission rates.
Capacity is also captured through a number of different variables, i.e. beds -, staff -, spending (hospital and/
or community) -, or patient volume per capita. Again, in
the majority of studies (4 out of 6) there was no association between capacity and readmission rates. There are
however, some exceptions. Øiesvold et al. [18] found
differences in time to readmission between areas with
different bed rates, with areas with an intermediate level
734 Records screened for inclusion in
CEPHOS-LINK study
579 Records not relevant for
this review removed
155 Records screened for inclusion in
system review
42 Exclusionsfor not meeting
system characteristic review
criteria
63 Exclusions for not meeting
other review criteria
50 Full-text articles assessed for
eligibility
5 Exclusions for not meeting
system characteristic review
criteria
2 Records identified
through reference lists
12 Exclusions for not meeting
other review criteria
35 Studies included in qualitative
synthesis
Fig. 1 The article selection: CEPHOS-LINK Literature Review Flow Diagram
Kalseth et al. BMC Psychiatry (2016) 16:376
of beds per population associated with the highest readmission risk. Wan and Ozcan [20] found both spending at area level and per capita patient volume to be
positively associated with higher readmission rates, however Turner and Wan [19] did not find any association
between readmission rates and mental health spending
per population.
Resource availability/utilisation/quality can be captured by staff-patient ratios and staff composition. Although results are conflicting, Heggestad [24] found that
accessibility to therapists was negatively associated with
readmission rates, and Lin and Lee [25] found that patients treated by psychiatrists with the highest caseloads
had the highest likelihood of being readmitted. Others
[14, 17] did not find any association between overall
staff-patient ratio and readmission rates, suggesting that
availability of specialist may be more important than
overall staffing ratios.
Hospital length of stay is consistently found to be
negatively related to (risk/rate of ) readmission when
measured both at hospital level or community level
[14, 16–18, 20]. Heggestad [24] also found that high
patient turnover (annual discharges/beds) increased
the likelihood of readmissions.
Characteristics capturing institutional case-mix were
found to be associated with readmission rates in studies
at aggregated small area or program level [14, 20]. However, case-mix may vary across several dimensions, i.e.
diagnosis or form of admission (voluntary, compulsory),
and it is difficult to draw conclusion from the results of
these limited analyses.
Characteristics capturing treatment policies, process/
procedure and treatment orientation are viewed as system variables by way of not being patient level (individual characteristics) variables. However as treatment
policies as well as procedures vary, these studies are difficult to summarize. As an example Moos et al. [17] and
Peterson et al. [14] report correlations/associations between program level readmission rates and different
treatment process and/or orientation variables for patients with substance abuse disorders. However, the recent large scale patient level study of Mark et al. [16] did
not find any effect of hospital-level procedure variables
when controlling for other influential variables.
Niehaus et al. [26] studied the effect of the implementation of the policy of early (“crisis”) discharge as a response to pressure on bed capacity. The results showed
a far higher risk of readmission for patients discharged
due to bed pressure than patients discharged as usual.
Some studies included variables describing hospital type.
There is, however, no common international typology of
hospital types, and again the results of these studies are
likely to be context specific. Studies that include hospital
type have looked at degree of specialisation, ownership
Page 5 of 9
and type of hospital wards. A series of studies from
Taiwan reported associations with degree of specialisation
[25, 27, 28] and hospital ownership [25, 27]. Public
hospitals were found to have higher readmission rates
than private for-profit and non-profit hospitals. Heggestad
[24] did not find differences in risk of readmission between patients discharged from wards in general hospitals
and psychiatric hospitals.
Several studies include the type and extent of community aftercare. Generally, the results depend on the type
of care, but suggest overall that providing aftercare
follow-up visits [14, 16, 17], or designing specific aftercare programmes in the communities [29–32, 35], may
reduce readmission rates.
Environment
Studies including environmental variables comprise geographical characteristics [12, 13, 21, 23, 25, 27, 28, 37–44],
demographic composition [19–21, 23], and socioeconomic
variables [19–21, 23, 44, 45].
Geographical characteristics such as population and
service location were included in several studies. The
most commonly used – and the simplest –are area variables or area comparisons [12, 13, 23, 25, 27, 28, 37–42].
In the majority of these studies (10 out of 12) significant
area differences in readmission rates were found. Studies
including area/region/country variables, or comparing different areas, may capture system level differences related
both to regulation, financing and governance, capacity, organisation and structure, as well as environmental factors,
but do not directly investigate specific system variables.
However, some studies that include area level variables
provide an explicit motivation/discussion that relates to
health care system differences. Sytema [38, 39] compares
readmission between areas at a different stage of deinstitutionalisation by way of comparing Victoria (Australia) and
Groningen (The Netherlands) [38] and also including
Verona (Italy) [39]. Differences are interpreted as being the
result of capacity and/or organisational variables, however
without these variables explicitly included in the analysis.
Lower readmission rates in urban regions were found
in two studies [21, 28]. Rüesch et al. [21] also found a
positive association between readmission rates and
population density, i.e. combined, the results of the two
variables seem to capture non-linearity in the effect of
density. Other studies, however, do not find any association between population density [23, 43] or distance to
services [44] and readmission rates.
Variables characterising the demographic composition
of communities like age composition [20], gender composition [21], ethnicity [19, 20, 23], foreign born population [21, 23] were also identified. High share of foreign
born were found to be positively associated with hospital
readmissions in both studies. The other variables were
Kalseth et al. BMC Psychiatry (2016) 16:376
not significantly associated with readmission risk/rate in
any of the studies.
Socio-economic characteristics of the community have
also been investigated in the included studies. Three
studies included income level. The two studies involving
area level readmission rate [20, 21] did not find any effect of community income (measured as median income/average taxable income in the area), while Prince
et al. [23] found the risk of readmission to be lower for
elderly patients living in high income communities than
in low income communities (four categories (quartiles)
of median community income). Education level was included in three studies. Stahler et al. [44] found lower
likelihood of being readmitted for patients residing in
communities with a higher educational attainment. The
two studies performed at area level found either an inverse bivariate effect or did not find any effect of this
variable [19, 21]. Other socio-economic variables studied
were unemployment [19, 45], poverty [19, 20], deprivation
and social class level [19, 21, 45], family structure and
crowded household [19, 21], and a range of substance
abuse specific variables in Stahler et al. [44]. Most often
no significant associations with readmission rates were
found. The exception being Turner and Wan [19] finding
lower readmission rates in areas with the lowest socioeconomic status (composite measure of unemployment,
crowed household, low education, and poverty) and finding the share of female-headed households to be positively
associated with readmission rates.
Discussion
This review identified 35 studies including one or more
system and/or environmental characteristics. The vast majority of studies look at variables characterising capacity,
organisation and structure (24 studies), and variables describing the environment (17 studies). Only three studies
included characteristics within the category of regulation,
financing system and governance structure.
The most frequent type of variable studied was related
to location of services or patients (local area, district, region, country). In most cases area differences were found.
Hence, there is a strong indication that the risk of readmission not only relates to patient characteristics but
also to system and/or environmental factors that varies
between areas. However, simply adding location to the
analysis will help adjust for factors that differ systematically between geographical areas, but without detailed
information about factors such as capacity, governance
structures or treatment profiles, and environmental characteristics, there is little policy relevant information from
these variables.
A few studies included characteristics measuring the effect of size and capacity of service systems. However, the
way these system features were measured varied. Most
Page 6 of 9
often no association with readmission rates were found (8
studies out of 11). Some studies, measuring size or capacity by patient volumes or spending levels, found positive
association with hospital readmission rates. Patient
volumes have been found to be positively associated with
readmission for psychiatric patients when measured both
at physician, hospital and community level [15, 20, 25].
Both patient volumes and spending may capture other
systems features as well as size and capacity. At physician
and hospital level, patient volume also captures case-load
and patient turnover, and at community level it may capture “demand”. Likewise per capita spending on mental
health on an area level may capture service structure; e.g.
a hospital based system could be more expensive than
community based system [46].
Differences in capacity may also indirectly be captured
by other types of variables related to capacity utilisation
and patient turnover. Average length of stay was systematically found to be negatively associated with readmission risk/rates. The interpretation of this result typically
refers to short stays indicating premature discharge [47].
Depending on the heterogeneity of hospital types included in the study, average length of stay could also
capture other factors like different patient populations
and hospital functions. However, the more direct test of
the early discharge hypothesis provided by the study of
the policy of “crisis” discharge in case of bed shortage
supported the hypothesis [26]. Likewise, another “natural
experiment” aimed at cost containment also showed
similar results, i.e. the effect of utilisation management
on shortening length of stay contributed to higher risk
of readmission [13].
Several studies also investigated the effect of, or included characteristics capturing the effect of, the use of
aftercare at system level. Most often positive results were
found, i.e. that higher share of patients receiving aftercare, or specific aftercare or community services interventions, contributed to lowering readmission rates.
The review identifies only a few studies investigating
the level or availability of community resources and no
clear picture on the impact on hospital readmissions
emerge. While e.g. the passing of rehabilitation legislation in Israel was associated with reduced likelihood of
readmission [11], no differences in readmissions were
found for regions in Massachusetts, US, with very different levels of funding for community programs [22]. The
policy relevance of such results is difficult to assess without information on other characteristics describing
structure, capacity and organisation of the health and
social care system.
Environmental characteristics, other than area, have
also been investigated. However, the type of characteristics are scattered and most often no significant effects
are found.
Kalseth et al. BMC Psychiatry (2016) 16:376
Conflicting results for environmental factors such as
variables capturing socio-economic environment i.e. income, education and other measures of socioeconomic
conditions at community level may relate to both how
the variables are measured and what they actually capture. Firstly, conflicting results could of course reflect
deficiencies in the data and measurement of variables,
e.g. if selection of patients or census data are biased in
some way. Secondly, different measures capture different
contextual aspects of living in socio-economic advantageous/disadvantageous neighbourhoods, and both level
and dispersion of community income may matter.
Thirdly, conflicting results could reflect variability in the
level of measurement. A narrow definition of neighbourhood may fail to capture relevant neighbourhood aspects, while if measured at a very aggregate level the
within area variation may be too high and the between
area variation too low to capture any contextual effects.
Further work is needed to understand the right level of
measurement for environmental factors, which could
vary according to study objectives and type of variables.
Finally, the effect of environmental variables may depend
on context. The channels by which the effect of environmental variables operate are complex - from individual
response to physical environment and social norms and
culture to service accessibility and quality, and may
interact with both individual characteristics as well as
other environmental or system variables.
Many of the results found in this review are in line
with previous reviews. Lien [8] concluded that longer
length of stay and follow-up visits after discharge seem
to be important determinants of readmission rates.
These results typically were based on patient level predictors. In an early review of the literature Klinkenberg
and Calsyn [7] found that receipt of aftercare was associated with lower readmission rates. Vigod et al. [48]
reviewed the literature on transitional interventions and
found that in about half of the included studies the
intervention had a statistically significant impact on readmission rates.
The lack of studies looking at the relationship between
readmissions and provider payment models is striking.
Provider payment models inherently send powerful signals to service providers likely to affect the delivery of
care such as patient turnover and length of stay. It is
perhaps not so surprising that there are few studies of
such health systems characteristics since most studies
are single-country analyses and these system characteristics may not vary a lot within a country. Such variables
may also be difficult to study in cross-country analysis,
other than on a speculative basis, unless the number of
countries entering analysis is high enough to control for
other confounding system level or environmental variables. Still, it is somewhat surprising that we were able
Page 7 of 9
to identify only four studies involving patient level crosscountry comparisons of predictors of hospital readmission for patients with a psychiatric diagnosis.
The (possible) effects of regulation, financing system
and governance structure on readmission rates are likely
to go through how the financing, organisation, structure
and capacity of the delivery system are affected, and this
will be captured by studies that include such variables.
Again the type and measurement of variables included
varies. It is also surprising that so few studies include
size and capacity measured by number of beds and bed
rates. Whether this indicates that information on
hospital beds is difficult to collect, either due to lack of
data or to restrictions on access to information on service provider identity, or lack of interest in these types
of variables, is hard to tell. With hospital or catchment
area identification in patient level data, several types of
aggregate system type variables can be constructed, such
as average length of stay and patient case-mix, as well as
different type of environmental variables (geographical,
demographical and socio-economic factors), that typically are easily accessible, and could thus be investigated.
It is therefore also surprising that so few studies include
case-mix variables to control for differences in type of
hospital, and also that so few studies have investigated
environmental variables such as distance to nearest inpatient service and other factors likely to affect service
use and aggregate service needs. A caution is also warranted since system level variables need to be carefully
interpreted. Observed differences in readmission rates
related to e.g. hospital ownership could capture other
system characteristics such as payment methods, as well
as other correlated factors such as unobserved differences in patient case-mix, type and accessibility of community after-care etc.
We have included studies that analyse the effects of specific models of care in a community setting. The policy
relevance of local interventions critically depend on how
well they are defined and described as well as whether
they can be applied outside of their specific context. The
main policy implication from these studies is that the way
care is delivered matters. This is hardly surprising, but
serves to underline that while there is a growing literature
that discusses models of care, we still lack knowledge of
the institutional factors that promote good care.
This review provides a systematic attempt to take into
account all literature addressing the impact of system
and environmental variables on hospital readmissions
within mental health, covering publications over a more
than 20 year period and providing an extensive and systemised coverage of different system and environmental
characteristics. Limitations of this systematic review
should also be mentioned. The included studies vary a
lot both in terms of the outcome variable (the length of
Kalseth et al. BMC Psychiatry (2016) 16:376
time to follow-up after discharge), the patient population
studied, number of observations, the type of analysis and
methods used, level of analysis, and the type and number of variables included both at patient level and system
level. Hence, it is not possible to draw strong conclusions on the impact of specific variables.
The interpretation of effects for system level and environmental variables crucially depend both on level of analysis and the control for related factors. The strength of
this review is that it explicitly addresses health system and
environmental factors measured as aggregate level variables. Analysing the impact of health system characteristics using individual level variables involves the possibility
of misinterpreting results, confusing the effect of patient
level service use on readmission risk as an expression of
health system effects where it may capture different patient needs (service use as signal of severity of illness).
Likewise, the effects of aggregate level variables (e.g. community education level) do not apply directly at the individual level since the aggregate variable often measures a
different (group or context) property than the namesake
at the individual level. System or environmental variables
studied in analysis of aggregate readmission rates could
capture both (aggregate of) patient needs and system level
effects. Hence, the ecological inference fallacy may go
both ways [49]. Using patient level data and controlling
for same type of variables both at patient and system
(hospital and/or community) level in a multi-level design
enables separation of effects.
Conclusions
This review identifies gaps in the literature on hospital
readmissions for patients with psychiatric diagnoses. The
included studies provide strong indication of the importance of health system and other context factors. However, studies of system level variables are limited in
numbers and they differ both with respect to purpose,
choice of system characteristics and the way these characteristics are measured. While a number of studies include variables that relate to capacity, organisation and
structure, it becomes more difficult to draw policy implications from these analyses without the link to provider
payment models and other health system characteristics
related to regulation, financing system and governance
structure. Since such variables may not vary much
within a country, cross-country comparative analyses
might contribute to the understanding of the impact of
system characteristics and why we observe differences
between countries in readmission rates for patients with
psychiatric diagnoses. Understanding the link between
health system and environmental variables on the one
hand, and hospital readmissions on the other is important for policy development as well as for planning and
optimising mental health service delivery.
Page 8 of 9
Additional files
Additional file 1: Detailed search strategies. (DOC 47 kb)
Additional file 2: Characteristics and main results of the studies
included in the review. (DOCX 53 kb)
Acknowledgements
This work was conducted as part of the study “The Comparative Effectiveness
Research on Psychiatric Hospitalisation by Record Linkage of Large
Administrative Data Sets (CEPHOS-LINK)”. We are thankful for the work
performed by our European colleagues during the literature search process.
Searches were performed by THL, Finland.
Researcher abbreviations: VD Valeria Donisi, PH Peija Haaramo, JK Jorid
Kalseth, EL Eva Lassemo, JM Jon Magnussen, RS Raluca Sfetcu, LS Liljana
Sprah, KW Kristian Wahlbeck.
Funding
This project has received funding from the European Union’s Seventh framework
Programme for research, technological development and demonstration under
grant agreement no 603264. The sponsor of the study had no role in study design,
data collection, analysis, interpretation, or writing of the report. The corresponding
author had full access to all data in the study and had final responsibility for the
decision to submit for publication.
Availability of data and material
All data generated or analysed during this study are included in this
published article and its supplementary information files.
Authors’ contributions
EL participated in selection of studies to be included, assessed studies in full
text for eligibility, participated in the design of the literature review and helped
draft the manuscript and was involved in revising the manuscript critically for
important intellectual content. JK assessed studies in full text for eligibility,
participated in the design of the literature review and helped draft the
manuscript and was involved in revising the manuscript critically for important
intellectual content. JM assessed studies in full text for eligibility, participated in
the design of the literature review and helped draft the manuscript and was
involved in revising the manuscript critically for important intellectual content.
KW was involved in the conception and design of the study, helped draft the
manuscript and was involved in revising the manuscript critically for important
intellectual content. PH participated in the acquisition of data, helped draft the
manuscript and was involved in revising the manuscript critically for important
intellectual content. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
Not applicable.
Author details
SINTEF Technology and Society, Health Research, P.O. Box 4760
SluppenNO-7465 Trondheim, Norway. 2National Institute for Health and
Welfare (THL), Mental Health Unit, P.O. Box 30FI-00271 Helsinki, Finland.
3
Department of Public Health and General Practice, Norwegian University of
Science and Technology, Faculty of Medicine, P.O. Box 8905, MTFSNO-7491
Trondheim, Norway.
1
Received: 21 October 2015 Accepted: 30 October 2016
References
1. Machado V, Leonidas C, Santos MA, Santos MA, Souza J. Psychiatric readmission:
an integrative review of the literature. Int Nurs Rev. 2012;59:447–57.
2. Durbin J, Lin E, Layne C, Teed M. Is readmission a valid indicator of the quality
of inpatient psychiatric care? J Behav Health Serv Res. 2007;34:137–50.
Kalseth et al. BMC Psychiatry (2016) 16:376
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
Rumball-Smith J, Hider P. The validity of readmission rate as a marker of the
quality of hospital care, and a recommendation for its definition. NZ Med J.
2009;122:63–70.
AHRQ. Health Plan and Psychiatric Hospitals Reduce Readmissions by
Reviewing Data and Developing Strategies to Improve Postdischarge Care.
AHRQ Health Care Innovations Exchange. 2009. https://innovations.ahrq.
gov/profiles/health-plan-and-psychiatric-hospitals-reduce-readmissionsreviewing-data-and-developing. Accessed 1 Sept 2015.
Bradley EH, Curry L, Horwitz LI, Sipsma H, Thompson JW, Elma M, et al.
Contemporary evidence about hospital strategies for reducing 30-day
readmissions: a national study. J Am Coll Cardiol. 2012;60:607–14.
OECD. Making Mental Health Count. Paris: Organisation for Economic Cooperation and Development. 2014. http://www.oecd-ilibrary.org/content/
book/9789264208445-en. Accessed 1 Sept 2015.
Klinkenberg WD, Calsyn RJ. Predictors of receipt of aftercare and recidivism among
persons with severe mental illness: A review. Psychiatr Serv. 1996;47:487–96.
Lien L. Are readmission rates influenced by how psychiatric services are
organized? Nord J Psychiatry. 2002;56:23–8.
Frick U, Frick H, Langguth B, Landgrebe M, Hübner-Liebermann B, Hajak G. The
revolving door phenomenon revisited: time to readmission in 17’415 Patients with
37’697 Hospitalisations at a German Psychiatric Hospital. PLoS One. 2013;8:1–9.
Paris V, Devaux M, Wei L. Health Systems Institutional Characteristics: A
Survey of 29 OECD Countries. Paris: OECD Health Working Papers, No. 50,
OECDPublishing; 2010. http://dx.doi.org/10.1787/5kmfxfq9qbnr-en .
Grinshpoon A, Abramowitz MZ, Lerner Y, Zilber N. Re-hospitalization of firstin-life admitted schizophrenic patients before and after rehabilitation
legislation: a comparison of two national cohorts. Soc Psychiatry Psychiatr
Epidemiol. 2007;42:355–9.
Merrick EL. Effects of a behavioral health carve-out on inpatient-related quality
indicators for major depression treatment. Med Care. 1999;37:1023–33.
Wickizer TM, Lessler D. Do treatment restrictions imposed by utilization
management increase the likelihood of readmission for psychiatric patients?
Med Care. 1998;36:844–50.
Peterson KA, Swindle RW, Phibbs CS, Recine B, Moos RH. Determinants of
readmission following inpatient substance abuse treatment: a national
study of VA programs. Med Care. 1994;32:535–50.
Lee HC, Lin HC. Is the volume-outcome relationship sustained in psychiatric
care? Soc Psychiatry Psychiatr Epidemiol. 2007;42:669–72.
Mark T, Tomic KS, Kowlessar N, Chu BC, Vandivort-Warren R, Smith S.
Hospital readmission among medicaid patients with an index
hospitalization for mental and/or substance use disorder. J Behav Health
Serv Res. 2013;40:207–21.
Moos RH, Mertens JR, Brennan PL. Program characteristics and readmission
among older substance abuse patients: comparisons with middle-aged and
younger patients. J Ment Health Adm. 1995;22:332–45.
Oiesvold T, Saarento O, Sytema S, Vinding H, Gostas G, Lonnerberg O, et al.
Predictors for readmission risk of new patients: the Nordic Comparative
Study on Sectorized Psychiatry. Acta Psychiatr Scand. 2000;101:367–73.
Turner JT, Wan TT. Recidivism and mental illness: the role of communities.
Community Ment Health J. 1993;29:3–14.
Wan TT, Ozcan YA. Determinants of psychiatric rehospitalization: a social
area analysis. Community Ment Health J. 1991;27:3–16.
Ruesch P, Meyer PC, Hell D. [Who is rehospitalized in a psychiatric hospital?
Psychiatric hospitalization rates and social indicators in the Zurich canton
(Switzerland)]. Gesundheitswesen. 2000;62:166–71.
Fisher WH, Geller JL, Altaffer F, Bennett MB. The relationship between community
resources and state hospital recidivism. Am J Psychiatry. 1992;149:385–90.
Prince JD, Akincigil A, Kalay E, Walkup JT, Hoover DR, Lucas J, et al.
Psychiatric rehospitalization among elderly persons in the United States.
Psychiatr Serv. 2008;59:1038–45.
Heggestad T. Operating conditions of psychiatric hospitals and early readmission–
effects of high patient turnover. Acta Psychiatr Scand. 2001;103:196–202.
Lin H-C, Lee H-C. Psychiatrists’ caseload volume, length of stay and mental
healthcare readmission rates: A three-year population-based study.
Psychiatry Res. 2009;166:15–23.
Niehaus DJ, Koen L, Galal U, Dhansay K, Oosthuizen PP, Emsley RA, et al.
Crisis discharges and readmission risk in acute psychiatric male inpatients.
BMC Psychiatry. 2008;8:44.
Lin HC, Tian WH, Chen CS, Liu TC, Tsai SY, Lee HC. The association between
readmission rates and length of stay for schizophrenia: a 3-year populationbased study. Schizophr Res. 2006;83:211–4.
Page 9 of 9
28. Lin CH, Chen WL, Lin CM, Lee MD, Ko MC, Li CY. Predictors of psychiatric
readmissions in the short- and long-term: a population-based study in
Taiwan. Clin (Sao Paulo). 2010;65:481–9.
29. Gillis LS, Koch A, Joyi M. The value and cost-effectiveness of a home-visiting
programme for psychiatric patients. S Afr Med J. 1990;77:309–10.
30. Reynolds W, Lauder W, Sharkey S, MacIver S, Veitch T, Cameron D. The
effects of a transitional discharge model for psychiatric patients. J Psychiatr
Ment Health Nurs. 2004;11:82–8.
31. Tomita A, Lukens EP, Herman DB. Mediation analysis of critical time
intervention for persons living with serious mental illnesses: Assessing the
role of family relations in reducing psychiatric rehospitalization. Psychiatr
Rehabil J. 2014;37:4–10.
32. Kolbasovsky A. Reducing 30-day inpatient psychiatric recidivism and
associated costs through intensive case management. Prof Case Manag.
2009;14:96–105.
33. Patterson DA, Lee M-S. Intensive case management and rehospitalization: a
survival analysis. Res Soc Work Pract. 1998;8:152–71.
34. Rossler W, Loffler W, Fatkenheuer B, Riecher-Rossler A. Case management
for schizophrenic patients at risk for rehospitalization: a case control study.
Eur Arch Psychiatry Clin Neurosci. 1995;246:29–36.
35. Schmidt-Kraepelin C, Janssen B, Gaebel W. Prevention of rehospitalization in
schizophrenia: results of an integrated care project in Germany. Eur Arch
Psychiatry Clin Neurosci. 2009;259:S205–12.
36. Papageorgiou A, King M, Janmohamed A, Davidson O, Dawson J. Advance
directives for patients compulsorily admitted to hospital with serious mental
illness. Randomised controlled trial. Br J Psychiatry. 2002;181:513–9.
37. Eaton WW, Mortensen PB, Herrman H, Freeman H, Bilker W, Burgess P, et al.
Long-term course of hospitalization for schizophrenia: Part I. Risk for
rehospitalization. Schizophr Bull. 1992;18:217–28.
38. Sytema S, Burgess P. Continuity of care and readmission in two service
systems: a comparative Victorian and Groningen case-register study. Acta
Psychiatr Scand. 1999;100:212–9.
39. Sytema S, Burgess P, Tansella M. Does community care decrease length of
stay and risk of rehospitalization in new patients with schizophrenia
disorders? A comparative case register study in Groningen, The Netherlands;
Victoria, Australia; and South-Verona, Italy. Schizophr Bull. 2002;28:273–81.
40. Zilber N, Hornik-Lurie T, Lerner Y. Predictors of early psychiatric
rehospitalization: a national case register study. Isr J Psychiatry Relat Sci.
2011;48:49–53.
41. Korkeila JA, Lehtinen V, Tuori T, Helenius H. Frequently hospitalised
psychiatric patients: a study of predictive factors. Soc Psychiatry Psychiatr
Epidemiol. 1998;33:528–34.
42. Thornicroft G, Gooch C, Dayson D. The TAPS project. 17: Readmission to
hospital for long term psychiatric patients after discharge to the
community. BMJ. 1992;305:996–8.
43. Husted J, Jorgens A. Population density as a factor in the rehospitalization of
persons with serious and persistent mental illness. Psychiatr Serv. 2000;51:603–5.
44. Stahler GJ, Mennis J, Cotlar R, Baron DA. The influence of neighborhood
environment on treatment continuity and rehospitalization in dually
diagnosed patients discharged for acute inpatient care. Am J Psychiatry.
2009;166:1258–68.
45. Hodgson R, Lewis M, Boardman A. Prediction of readmission to acute
psychiatric units. Soc Psychiatry Psychiatr Epidemiol. 2001;36:304–9.
46. Knapp M, Chisholm D, Astin J, Lelliott P, Audini B. The cost consequences of
changing the hospital–community balance: the mental health residential
care study. Psychol Med. 1997;27:681–92.
47. Capdevielle D, Ritchie K, Villebrun D, Boulenger JP. The long and the short of it:
are shorter periods of hospitalisation beneficial? Br J Psychiatry. 2008;192:164–5.
48. Vigod SN, Kurdyak PA, Dennis C-L, Leszcz T, Taylor VH, Blumberger DM,
et al. Transitional interventions to reduce early psychiatric readmissions in
adults: systematic review. Br J Psychiatry. 2013;202:187–94.
49. Schwartz S. The fallacy of the ecological fallacy: the potential misuse of a
concept and the consequences. Am J Public Health. 1994;84:819–24.