Main Findings
The main findings of this study were that a diagnosis of psychosis (F20–29), being male, being unemployed/retired, living in supported accommodation, accommodation with nursing/healthcare or being homeless, and being registered under certain ethnicity categories (particularly Caribbean) was associated with a longer LOS compared to the reference groups. Having a higher number of care coordinators was also associated with a slightly longer LOS. Being a council tenant was associated with a considerably shorter LOS compared to living in privately rented/owned housing, and marital status was not associated with LOS in this sample.
Clinical Factors
The finding that psychosis is associated with a longer LOS replicates previous findings [
8,
9,
11], with a previous paper based on a similar sample as the present study (from 2004 to 2005) finding that having a diagnosis of schizophrenia was significantly associated with longer LOS, however only severity of illness and need for rehousing remained significant in adjusted comparisons [
11]. This may suggest that the effects seen in our sample may be accounted for by illness severity [
9] as opposed to diagnosis alone.
The finding that having a higher number of care coordinators is associated with a slightly longer LOS might indicate that a high turnover of care coordinators is detrimental to the quality of the patient’s care, although it should be noted that the size of the effect was small. A large number of care coordinators may lead to breakdowns in communication and collaboration, leading to communication errors, poor discharge planning or inappropriate treatment, thereby prolonging a patient’s stay in hospital [
25]. Alternatively, patients who stay for a longer period of time may have a higher number of care coordinators as a result of lengthy stays e.g. staff turnover either because of natural change over time, or as a result of their hard-to-manage illness. More research is needed to disentangle these possibilities.
Demographic Factors
Our finding that being without employment is associated with a longer LOS is supported by previous studies [
12,
13]. However, unemployment may simply be a ‘proxy’ for current functional impairment, as measured by scores on the Global Assessment of Functioning (GAF) [
13]. The association between unemployment and longer LOS in the current study may therefore be an indication of poor occupational and social functioning, which is also reflected in need for lengthy hospitalisation. The possibility that lengthy stays in hospital in themselves lead to poor occupational and social functioning should also be considered.
It is interesting to note that a slightly earlier study (from 2007 to 2009) conducted on a similar sample found no effect of unemployment on LOS [
15]. Our sample (7653) was larger than theirs (4485) which may have increased the power necessary to detect an effect in a sample where the majority is unemployed. However, the reason for this discrepancy needs to be explored further, and might again underline that idea that these predictors of LOS are likely to be temporally, as well as locality, specific.
Living in supported accommodation or living in nursing/healthcare were associated with longer LOS. Supporting previous findings by Tulloch et al. [
15], we also found an association between long LOS and homelessness, though the association we found was not as strong. A number of past studies have found an effect of accommodation status on LOS [
11,
14], which may have similar underlying causes such as a high level of current functional impairment.
We expected to find an effect of marital status given the finding that marital status has an effect in adequately powered studies [
9], though in our sample we failed to find an effect. Since it has been suggested that marital status may be another ‘proxy’ for functional impairment, this finding may either suggest that marital status has no effect over and above other variables such as employment and accommodation status. Alternatively, it may reflect the temporal specificity of these results: the changing nature of relationships may mean that the traditional ‘single’, ‘married or cohabiting’ categories are no longer reliable indicators of relationship status or one’s current level of interpersonal functioning.
In contrast to previous studies that have found being female is associated with longer LOS, we found an association between being male and longer LOS. Tulloch et al. [
9] found that the effect of female gender was apparent in samples of over 3000, which suggests that we ought to have found an effect of female gender in the model. London has been found to have a higher rate of admissions for schizophrenia and related psychoses, compared to the rest of the UK [
26], and given that schizophrenia is more common in males [
27,
28] this may have accounted for the association seen in this study.
Ethnicity has been found to have no effect on LOS [
11,
29], though Tulloch et al. [
11] found a significant effect of ethnicity that was found not to be independent of other effects. In the present study, Asian or Asian British, Black or Black British, and Mixed background ethnicity categories were associated with a longer LOS than White or White British recorded ethnicity. The fact that these effects were adjusted for other factors in the model including diagnosis, suggests that this effect is not simply due to an elevated incidence of schizophrenia in the British African population [
30] and in migrant populations [
27,
28].
Limitations of the Study
There was a considerable amount of missing data for employment (34.43%) and accommodation status (39.49%), which we attempted to rectify through the use of multiple imputations (MI). Though MI has been found to be a valid method of dealing with extensive missing data, it’s important to note that that to address this in future research, additional areas and forms within the electronic patient record system will be included in the search criteria.
When considering diagnosis, we only looked at primary diagnosis and did not include secondary diagnosis primarily due to the very high rate of missing and inconsistent secondary diagnostic data in our dataset. Casey [
31] estimates that 30–40% of outpatients and 40–50% of inpatients are now recognised as having a personality disorder, and as comorbid conditions are not often recognised in ordinary practice [
32] we may have overlooked the contribution of personality disorder and other comorbid disorders to LOS.
As suggested by Tulloch et al. [
9], illness severity could relate to LOS because it is related the clinician’s perceived need for hospitalisation, and so in future research this should be included if possible. The observed effects of unemployment, accommodation status and diagnosis of psychosis may be due in part to the patient’s level of illness severity and functional impairment. Measures of current functional impairment such as scores on the GAF would have helped to better understand the effects of these factors and their relationship to the individual’s current functioning.
Future Research
Future research should continue to use large sample sizes to investigate the effects of demographic and clinical variables, in order that there is sufficient power to detect effects. Future research should consider the role of illness severity and level of functioning (e.g. GAF scores) in understanding the role of demographic and clinical factors on LOS. Studies should consider the role of interactions between variables e.g. between gender and diagnosis and the effect of methodology on results e.g. reference groups. Finally, studies should begin to explore the direction of these effects e.g. do a high number of care coordinators lead to lengthy stays or do lengthy stays lead to more care coordinators?; and importantly the mechanisms of these effects e.g. does a high number of care coordinators result in lengthy stays as a result of communication breakdowns? Such investigations would lead the way towards using these findings to directly inform how we care for this population.
Conclusions
Understanding factors associated with lengthy stays in hospital is important in order to better understand psychiatric service use. Using a large sample size we were able to demonstrate the importance of certain demographic and clinical characteristics in predicting LOS, which may have been previously overlooked due to small sample sizes and lack of variance amongst inpatient characteristics [
9,
17]. In addition, our findings concerning ethnicity suggest that certain groups are at a higher risk for lengthy stays, in contrast with previous research in London, which has found no effect of ethnicity. The fact that our results diverge with other studies in some cases underlines the fact that these findings are likely to be location and time specific, which underlines the importance of up-to-date research on this matter. Nevertheless, the large sample size, extended study period and ethnically diverse catchment area may increase the generalisability of our findings to other healthcare settings.
The fact that we were only able to account for 15% of the variance in the model suggests that demographic and clinical factors (at least those used here) cannot completely explain why some individuals are more likely to experience lengthy stays. LOS is likely to be multifactorially determined. Understanding how factors affect LOS is a complex task that is undoubtedly affected by a wide range of factors including setting and time period, length of study period, sample size and composition, method of analysis and definition of lengthy LOS.