Association of environmental and socioeconomic indicators with serious mental illness diagnoses identified from general practitioner practice data in England: A spatial Bayesian modelling study

Area-level factors associated with variation in involuntary psychiatric hospitalisation across England: a cross-sectional, ecological study

PURPOSE

Involuntary hospitalisations for mental health care are rising in many high income countries, including England. Looking at variation between areas can help us understand why rates are rising and how this might be reversed. This cross-sectional, ecological study aimed to better understand variation in involuntary hospitalisations across England.

METHOD

The unit of analysis was Clinical Commissioning Groups (CCGs), NHS bodies responsible for delivering healthcare to local areas in England. 205 CCGs were included in the analysis. Demographic, clinical, and socioeconomic variables at CCG-level were extracted from national, open access data bases. The outcome variable was the rate of involuntary hospitalisation for psychiatric care under the 1983 Mental Health Act in 2021/22.

RESULTS

There was a four-fold difference between the CCGs with the highest and lowest involuntary hospitalisations. In an adjusted analysis, CCGs with a higher percentage of severe mental illness in the population, higher percentage of male population, and higher community and outpatient mental health care use showed a higher rate of involuntary hospitalisation. Depression, urbanicity, deprivation, ethnicity, and age were not strongly associated with involuntary hospitalisation after adjustment. These variables explained 10.68% of the variation in involuntary hospitalisations across CCGs.

CONCLUSION

Some demographic and clinical variables explained variation in involuntary hospitalisation between areas in England, however, most of the variance was unexplained. Complex relationships between urbanicity, deprivation, age, and ethnicity need to be further explored. The role of other influences, such as variation in service organisation or clinical practice, also need to be better understood.

Urbanization and psychosis: an update of recent evidence

PURPOSE OF REVIEW

Urbanization, a complex global phenomenon, has a significant bearing on schizophrenia/psychosis burden through various socioeconomic and environmental factors. This review focuses on recent evidence (2019-2023) linking urbanization, schizophrenia, and the role of green space.

RECENT FINDINGS

This review analyzed 43 articles that examined the correlation between urban birth or upbringing, urban living (urbanicity), and various schizophrenia/psychosis-related outcomes such as incidence, psychotic experiences, etc. The studies showed differing results across geographical locations. Socioeconomic factors like area deprivation, migrant status (ethnic density) and social fragmentation were independently associated with the risk of schizophrenia/psychosis irrespective of urbanicity. More recently, environmental factors such as green space reduction and air pollution have been explored in urban living conditions and were positively associated with an increased risk of schizophrenia/psychosis.

SUMMARY

There is a need for further investigation in low and middle-income countries. The impact of urbanization-related factors and green space on the risk of schizophrenia/psychosis calls for appropriate governmental commitments toward structured and healthy urban planning.

Psychosis prevalence in London neighbourhoods; A case study in spatial confounding

Analysis of impacts of neighbourhood risk factors on mental health outcomes frequently adopts a disease mapping approach, with unknown neighbourhood influences summarised by random effects. However, such effects may show confounding with observed predictors, especially when such predictors have a clear spatial pattern. Here, the standard disease mapping model is compared to methods which account and adjust for spatial confounding in an analysis of psychosis prevalence in London neighbourhoods. Established area risk factors such as area deprivation, non-white ethnicity, greenspace access and social fragmentation are considered as influences on psychosis. The results show evidence of spatial confounding in the standard disease mapping model. Impacts expected on substantive grounds and available evidence are either nullified or reversed in direction. It is argued that the potential for spatial confounding to affect inferences about geographic disease patterns and risk factors should be routinely considered in ecological studies of health based on disease mapping.

Inequalities in cancer screening participation between adults with and without severe mental illness: results from a cross-sectional analysis of primary care data on English Screening Programmes

BACKGROUND

People with severe mental illness (SMI) are 2.5 times more likely to die prematurely from cancer in England. Lower participation in screening may be a contributing factor.

METHODS

Clinical Practice Research Datalink data for 1.71 million, 1.34 million and 2.50 million adults were assessed (using multivariate logistic regression) for possible associations between SMI and participation in bowel, breast and cervical screening, respectively.

RESULTS

Screening participation was lower among adults with SMI, than without, for bowel (42.11% vs. 58.89%), breast (48.33% vs. 60.44%) and cervical screening (64.15% vs. 69.72%; all p < 0.001). Participation was lowest in those with schizophrenia (bowel, breast, cervical: 33.50%, 42.02%, 54.88%), then other psychoses (41.97%, 45.57%, 61.98%), then bipolar disorder (49.94%, 54.35%, 69.69%; all p-values < 0.001, except cervical screening in bipolar disorder; p-value > 0.05). Participation was lowest among people with SMI who live in the most deprived quintile of areas (bowel, breast, cervical: 36.17%, 40.23%, 61.47%), or are of a Black ethnicity (34.68%, 38.68%, 64.80%). Higher levels of deprivation and diversity, associated with SMI, did not explain the lower participation in screening.

CONCLUSIONS

In England, participation in cancer screening is low among people with SMI. Support should be targeted to ethnically diverse and socioeconomically deprived areas, where SMI prevalence is greatest.

Effects of social support on suicide-related behaviors in patients with severe mental illness: A systematic review and meta-analysis

OBJECTIVE

Patients with severe mental illness have a high risk of suicide and frequently living eliminated from mainstream society, but the effects of social support on suicide-related behaviors among this crowd is inconclusive. The present study aimed to explore such effects among patients with severe mental illness.

METHODS

We implemented a meta-analysis and a qualitative analysis of relevant studies published before February 6, 2023. Correlation coefficients (r) and 95 % CI were selected as the effect size index in meta-analysis. Studies that did not report correlation coefficients were employed for qualitative analysis.

RESULTS

Out of 4241 identified studies, 16 were identified in this review (6 for meta-analysis, 10 for qualitative analysis). The meta-analysis presented that the pooled correlation coefficients (r) were - 0.163 (95%CI = -0.243, -0.080, P < 0.001), suggesting a negative correlation between social support and suicidal ideation. The subgroup analysis showed that this effect works in all bipolar disorder, major depression, and schizophrenia. Concerning qualitative analysis, social support presented positive effects on reducing suicidal ideation, suicide attempts, and suicide death. The effects were consistently reported in female patients. However, there existed some unaffected results in males.

LIMITATIONS

The included studies were from middle- and high-income countries and used inconsistent measurement tools, our results may have some bias.

CONCLUSIONS

The effects of social support in reducing suicide-related behaviors were positive, but it showed better effects in both female patients and adults. Males and adolescents deserve more attention. Future research needs to pay more attention to the implementation methods and effects of personalized social support.