ArticleScientific reports2016
Spatial distribution of psychotic disorders in an urban area of France: an ecological study.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Social Fragmentation and Schizophrenia: A Systematic Review.The Journal of clinical psychiatry · 2021Pooled it
- Impacts of Social Environments on Neighborhood Depression Incidence: Fully Accounting for Spatial Effects.International journal of environmental research and public health · 2026Article
- Urbanicity and psychotic disorders: Facts and hypotheses.Dialogues in clinical neuroscience · 2023Review
- Relationship between incidence and prevalence in psychotic disorders: An incidence-prevalence-mortality model.International journal of methods in psychiatric research · 2018Article
- Neighbourhood deprivation is positively associated with detection of the ultra-high risk (UHR) state for psychosis in South East London.Schizophrenia research · 2018Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Previous analyses of neighbourhood variations of non-affective psychotic disorders (NAPD) have focused mainly on incidence. However, prevalence studies provide important insights on factors associated with disease evolution as well as for healthcare resource allocation. This study aimed to investigate the distribution of prevalent NAPD cases in an urban area in France. The number of cases in each neighbourhood was modelled as a function of potential confounders and ecological variables, namely: migrant density, economic deprivation and social fragmentation. This was modelled using statistical models of increasing complexity: frequentist models (using Poisson and negative binomial regressions), and several Bayesian models. For each model, assumptions validity were checked and compared as to how this fitted to the data, in order to test for possible spatial variation in prevalence. Data showed significant overdispersion (invalidating the Poisson regression model) and residual autocorrelation (suggesting the need to use Bayesian models). The best Bayesian model was Leroux's model (i.e. a model with both strong correlation between neighbouring areas and weaker correlation between areas further apart), with economic deprivation as an explanatory variable (OR = 1.13, 95% CI [1.02-1.25]). In comparison with frequentist methods, the Bayesian model showed a better fit. The number of cases showed non-random spatial distribution and was linked to economic deprivation.
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