The main finding of our study demonstrates place of residence exerting a clear impact on hospital readmission rates. People living in agglomerations and in urbanized regions had a significantly higher risk for earlier hospital readmission as compared to people living in a rural environment. Notably urban environment seems to be an independent risk factor for hospital readmission as this effect could be found after correcting for a large number of patient-related, disease-related, treatment related and socio-economic factors (see Table 2). Thus our data clearly support our first hypothesis that urban environment has an influence on the course of psychiatric disorders in general. This fits with earlier data that have shown a clear trend towards higher prevalence rates for psychiatric disorders with increasing urbanization(9). Greenspace in rural counties might be the pivotal protective agens to ameliorate course of mental diseases, as its effect on mental health has been shown by ecological (35) studies and by studies on publicly accessible greenspace in urban living areas (36).
Our second research question was, whether the impact of urbanicity on hospital readmission rates depends on the diagnosis. In addition to the overall effect of urbanicity on readmission risk we found an interaction between urbanicity and both F1 as well as F3 diagnosis. In patients with co-morbid substance abuse an urban environment leads to a particular high increase of the hospital readmission risk whereas the effect of urbanicity on the contrary is less pronounced in patients with affective disorders. Our finding of a particular relevance of urban environment for the course of addictive disorders should be interpreted with caution, as our sample did not include patients with addictive disorders as the primary complaint. Nevertheless our findings provide support for the relevance of environmental factors in addictive disorders (37).
The finding of a lower relevance of urbanicity on hospital readmission rates in patients with affective disorders fits with earlier reports of a lower impact of urbanicity on affective symptoms as compared to psychotic symptoms (38, 39). However, notably, even if the effect of urbanicity was less pronounced in patients with affective disorders, it was still detectable, fitting with previous findings of increased prevalence rates of depression and anxiety disorders in urban environments (24) (40–42).
Our third research question was, whether specific economic, geographic and/or demographic characteristics of a patient’s living community beyond the number of inhabitants can be identified as risk factors for readmission.
“Distance to the hospital” has been known for more than 50 years to influence the hospital admission rate in mental disorders (43, 44) or service utilization in outpatient facilities (45). We could confirm in our dataset that lower travelling distances between residence and hospital are associated with a higher readmission risk. Though we had no exact geo-positioning data on patients’ living places, and therefore this variable might be subject to diminished reliability due to measurement error, its effect seems stable. Notably, as “distance to the hospital” was included in our final model we can be sure that the observed effect of urbanicity is corrected for this effect.
We also included “Change of residence” in our model, as change of residence belongs to stressful live events which are known to increase the risk for the development of depressive disorders (46). Accordingly, we found a generally increased risk for hospital readmissions after changes of residence. Interestingly there was an interaction between “change of residence” and co-morbid addiction (F1 diagnosis), indicating that in patients with co-morbid addiction the effect of change of residence on hospital readmission risk was less pronounced as compared to patients without co-morbid addiction. Thus, a change in the social environment might be beneficial for people suffering from substance abuse by creating a distance to peer pressure of addictive behaviour.
Beyond “distance to the hospital” and “change of residence” we could not identify any further association between geospatial variables and hospital readmission risk. Population density within a patient’s living county, proportion of non-german citizens living in the county, local population growth tendency, and local unemployment rate exerted no independent effect on rehospitalisation risk neither as direct main effect, nor in interaction with sex or diagnosis. However, we cannot definitively exclude an effect of these parameters, as the lack of an effect could be on the one hand due to a selection effect of the analysed sample. Within the investigated area, there were no large differences in population density or population growth, unemployment rates were low and there were no high proportions of non-german citizens (historical situation prior to the so-called “refugee crisis” in 2015). As in other studies evaluation the impact of spatial variables on health outcomes, the classification of a living place according to strict administrative boundaries has been critized (Cummins et al., 2007). Using the classification system of the official german authorities to characterize patients’ counties of origin partially avoids a bias of neglecting autocorrelative spatial interdependencies between counties. But the classification system might already include the above mentioned spatial risk factors, though the Federal Institute for Research on Building, Urban Affairs and Spatial Development constructed it for socioeconomic, not for epidemiological reasons.
In a previous analysis of the sample we could identify multiple clinical and demographic variables that contributed to the readmission risk (27). In comparison to that analysis we now differentiated the urbanicity level in up to 9 categories and added multiple additional geospatial and socioeconomic factors in the model. The fact that the previously identified risk factors remained essentially unaltered by these changes, suggests that there exists no major interaction between the newly added geospatial and socioeconomic data and the already previously included clinical and demographic data.
Taken together our analysis clearly indicates an effect of the living environment on the course of psychiatric diseases: more rural living environments have a clear protective effect on readmissions: Whereas in metropolitan regions of our sample it takes about one year, till 50% of the discharged patients have been readmitted to the psychiatric clinic, this takes 6 years in rural regions. It should be kept in mind, that this effect might vary in other countries and regions, when e.g. regional distinctions are sharper vs. more attenuated, or distances are considerably larger, or accessibility of outpatient care (47) is different from our bavarian situation.
Beside the relative large sample size and the long observation period for the analysis of recurrent events, our study has the strength that not only patients with schizophrenia or affective disorders were analysed, but also patients with neurotic, stress-related and somatoform disorders (F4) and behavioural syndromes (F5). Moreover, the data analysed come from a hospital, which is the exclusive provider of in-patient treatment for a catchment area of about 800’000 inhabitants with a low rate of population movement. Thus, no selection bias due to different provider profiles seems probable. Nevertheless, our study clearly is not free of limitations.
First, 2.2% of all patients (n = 405) had to be precluded from the analysis because of implausible data (e.g. overlapping inpatient treatment episodes). Second, for each patient in our dataset the duration of their last “Time in community” (TIC)-episode is censored. Moreover, some patients might have moved outside the catchment area (and been treated elsewhere) or might have deliberately chosen another hospital for further inpatient treatment. This artificially prolongs the measured duration of their last TIC-episode in our data set. Especially, if the patient has deceased after their last discharge, an inflated estimate of the impact of age on TIC cannot be excluded because the probability of death increases also with age. Third, biased estimates due to “informative censoring” (48, 49) on missing predictor variables might be possible (50). Though we (51) have shown for the first years of our observation period that missing values in the standardized documentation system in our hospital were not hampering the statistical analysis of essential epidemiological results, there is no similar analysis available for the years since 2000. Consequently, a rate of 6.8% of all patients with lacking or implausible data should be kept in mind while interpreting the substantive results.
Fourth, as our study period started already in 1996, there was no standardized tool for mapping mental health care provision in our region available (e.g.(52, 53)). Therefore we had no reliable information on spatial provision of outpatient care in our catchment area, and it might be difficult to compare our results straightforward to later studies on the impact of service provision on course of illness. Finally, the inherent limitation of the observational design with data stemming from a single provider has to be considered while interpreting our results.
Even when all these limitations are taken into account, our data are of high relevance:
It has been well known for many years, that urbanicity represents a risk factor for the development of psychosis (5) and other mental disorders (9). Here we demonstrate that the living environment (metropolitan versus rural) has also a significant impact on the course of psychiatric disorders.
The reasons that account for this effect, remain largely speculative at the moment. Gene – environment interactions might play a role: Recently it has been demonstrated, that the genetic risk sore and urbanicity (place of birth) interact in their effect on treatment resistance among patients with schizophrenia (19).
It remains also speculative whether the effect of urbanicity can be further disentangled. Our approach, which focussed primarily on socioeconomic factors that are presumably related to urban living such as unemployment rate, proportion of immigrants, economic climate etc. did not reveal any additional explanations which aspects of urbanicity might play a role.
However there exist many other aspects that are related to urbanicity and that might be potentially relevant such as social isolation, noise exposition or air pollution. For air pollution for example very recently a significant association with an increased risk of psychiatric disorders has been shown (54).
Some aspects of urban living might also be particularly burdensome for patients with psychiatric disorders. For patients with psychosis it has been shown that they perceive the city and the urban environment as more stressful, leading to a high rate of city avoidance (55). Thus further research is needed to identify which factors related to urbanicity might contribute to the increased risk of hospital readmissions. This is of utmost importance for drawing further practical conclusions from these data.
Such practical conclusions could consist in the modification of specific influencing factors, e.g. air pollution or high noise levels, which would require appropriate political decisions.
An alternative or complementary approach could consist in the development and evaluation of targeted interventions for patients to cope better with urban environment. But it has already been postulated that the design of such public-health-oriented interventions should be holistically designed and integrate various health risks from a population perspective (56).