Global understanding of the epidemiological landscape of non-affective psychotic disorders (NAPD) is predominantly based on studies from high-income countries. We sought to systematically review and meta-analyse all incidence studies conducted in low and middle-income countries (LMICs).We systematically searched four databases using terms for NAPD, incidence and LMICs. Citations were eligible for inclusion if: published between 1 January 1960 and 31 May 2022; wholly or partially conducted in an LMIC, and; containing data on NAPD incidence in the general adult population. Two independent raters assessed study quality according to previously published criteria. We conducted a narrative synthesis and random-effects meta-analyses where sufficient studies were available (N ≥ 5).We retrieved 11 421 records, of which 23 citations met inclusion criteria from 18 unique studies across 19 settings in 10 LMICs. Median study quality was 4 out of 7 (interquartile range: 3-6). The crude incidence of NAPD varied around 4.2 times, from 10.0 per 100,000 person-years (95% confidence interval [CI] 8.7-11.4) in Brazil to 42.0 (95%CI 32.2-54.8) in India, with marked heterogeneity in methodologies and rates. Our 60-year review highlights the dearth of robust evidence on the incidence of psychotic disorders in LMICs.Without reliable, contemporary estimates of this fundamental cornerstone of population health, it is impossible to understand the true burden, distribution or causes of psychotic disorders in over 87% of the world's population. A new, more equitable global mental health evidence base for NAPD is now urgently required. 

/pdf/the-incidence-of-non-affective-psychotic-disorders-in-low-s11uh0h1.pdf

The incidence of non-affective psychotic disorders in low and middle-income countries: a systematic review and meta-analysis

Abstract Ethics in biomedical research in mental health plays a central role. Historically the principles of autonomy, beneficence, non-maleficence and justice have been violated in individuals with mental disorders. This article aims to analyse the perceptions of individuals involved in research ethics advocacy in Aranzazu-Colombia, from the Declaration of the Town Square to current research practices and community empowerment initiatives. For this, a qualitative study was conducted in the municipality of Aranzazu as part of a Community-Based Rehabilitation strategy in Mental Health. Two focus groups were conducted with a total of 30 participants, including community representatives, institutional members, and healthcare personnel. The themes that emerged were ‘The urgency of speaking as one’, ‘Our research questions’ and ‘Human rights for recovery’. The process of adoption of community protection measures that guided the investigative processes with the town of Aranzazu is presented. A discussion is raised around the instrumentalization of people in the investigation and the claim of human rights by communities, as a mechanism to strengthen community recovery actions. It is concluded that mental health research should help to affirm dignity, reduce stigma, improve access to psychosocial recovery and achieve real participation in building the well-being of communities. 

Declaration of the Town Square: The Urgency of Speaking as One

Investigations using Electronic Health Records (EHR) databases could enable accurate delineation of psychiatric disease trajectories at an unprecedented scale. Using EHR from a single institution (Clinica San Juan de Dios in Manizales, Colombia), we characterize diagnostic trajectories of >22,000 (ages 4-90, 60% female) individuals treated for severe mental illness (SMI), including schizophrenia (SCZ), bipolar disorder (BD), and severe or recurrent major depressive disorder (MDD). We extracted diagnostic codes, clinical notes, and healthcare use data collected since 2005. Using a subsample of 105 SMI patients, we assessed diagnostic reliability, comparing EHR to clinical chart review. EHR diagnostic codes showed very good agreement with chart review diagnoses (Cohens kappa 0.78). Using 3,600 annotated sentences from 2,788 patients, we developed a pipeline for extracting clinical features from the electronic text, which showed high agreement with gold-standard annotations (average F1 0.88). Factors associated with diagnostic instability, defined as changes in diagnosis between successive visits, were identified using mixed-effect logistic regression models. Of SMI patients with >3 visits (n=12,962), 64% had multiple EHR diagnoses; diagnostic switches (19%), comorbidities (30%), and both (15%). While some diagnostic switches are common, such as the switch from MDD to BD (observed in 22% of BD patients), trajectories are highly heterogeneous, with rare trajectories (occurring in <1% of patients) making up the majority (58% of all patients). Predictors of diagnostic instability include time since initial visit (OR 0.56 by visit number, p-value 2e-66), previous diagnostic change (OR= 4.02, p-value 3e-250) and NLP-derived descriptions of delusions (OR 1.50, p-values 2e-18). Our results underline the importance of considering longitudinal rather than cross-sectional diagnoses in psychiatric research and show how high-quality EHR data can contribute to global efforts to understand disease trajectories.

Analysis of diagnosis instability in electronic health records reveals diverse disease trajectories of severe mental illness

Bipolar Disorder (BD) is a severe and chronic disorder characterized by recurrent episodes of depression, mania, and/or hypomania. Most BD patients initially present with depressive symptoms, resulting in a delayed diagnosis of BD and poor clinical outcomes. This study leverages electronic health record (EHR) data from the Clinica San Juan de Dios Manizales in Colombia to identify features predictive of the transition from Major Depressive Disorder (MDD) to BD. Analyzing EHR data from 13,607 patients diagnosed with MDD over 15 years, we identified 1,610 cases of conversion to BD. Using a multivariate Cox regression model, we identified severity of the initial MDD episode, the presence of psychosis and hospitalization at first episode, family history of mood or psychotic disorders, female gender to be predictive of the conversion to BD. Additionally, we observed associations with medication classes (prescriptions of mood stabilizers, antipsychotics, and antidepressants) and clinical features (delusions, suicide attempt, suicidal ideation, use of marijuana and alcohol use/abuse) derived from natural language processing (NLP) of clinical notes. Together, these risk factors predicted BD conversion within five years of the initial MDD diagnosis, with a recall of 72% and a precision of 38%. Our study confirms many previously identified risk factors identified through registry-based studies (such as female gender and psychotic depression at the index MDD episode), and identifies novel ones (specifically, suicidal ideation and suicide attempt extracted from clinical notes). These results simultaneously demonstrate the validity of using EHR data for predicting BD conversion as well as underscore its potential for the identification of novel risk factors and improving early diagnosis.

http://medrxiv.org/cgi/content/short/2023.09.28.23296092v1?rss=1

Predicting diagnostic conversion from major depressive disorder to bipolar disorder: an EHR based study from Colombia

Abstract Purpose Relatively little epidemiological evidence on psychosis from diverse settings in the Global South exists, where many people with untreated psychosis seek help outside of formal health service settings. Here, we report a preliminary mapping study of formal and informal community resources within a catchment area in South Africa that established an infrastructure that could be used to detect a representative sample of individuals with untreated psychosis. Methods PSYMAP-ZN is a 3-year study of incidence, clinical presentation and associated risk factors for untreated psychosis in Msunduzi Municipality in South Africa. We conducted a preliminary mapping study of the region in which we aimed to document all potential providers of care (gatekeepers) in both formal (health services) and informal (folk) sectors, with the purpose of enrolling them in a collaborative case surveillance system. We drew on official sources, local knowledge from key stakeholders and utilised snowballing techniques. Results We established a surveillance system which included (a) all secondary mental health and primary care services (b) the majority of informal providers (including traditional health practitioners, religious institutions) and (c) a wide range of key informants. Conclusion Expanding the global knowledge base on psychosis to diverse settings in the Global South requires a surveillance and case-detection method that includes (in addition to formal health settings) informal settings and local key informant knowledge in the community. This preliminary ‘mapping’ process established a platform for the ongoing PSYMAP study of untreated psychosis in South Africa. 

Searching for people with psychosis in the global south: mapping and establishing a case surveillance system in South Africa (PSYMAP-ZN study)

Background: The identification of geographic variation in incidence can be an important step in the delineation of disease risk factors, but has mostly been undertaken in upper-income countries. Here, we use Electronic Health Records (EHR) from a middle-income country, Colombia, to characterize geographic variation in major mental disorders. Method: We leveraged geolocated EHRs of 16,295 patients at a psychiatric hospital serving the entire state of Caldas, all of whom received a primary diagnosis of bipolar disorder, schizophrenia, or major depressive disorder at their first visit. To identify the relationship between travel time and incidence of mental illness we used a zero-inflated negative binomial regression model. We used spatial scan statistics to identify clusters of patients, stratified by diagnosis and severity: mild (outpatients) or severe (inpatients). Results: We observed a significant association between incidence and travel time for outpatients (N = 11,077, relative risk (RR) = 0.80, 95% confidence interval (0.71, 0.89)), but not inpatients (N = 5,218). We found seven clusters of severe mental illness: the cluster with the most extreme overrepresentation of bipolar disorder (RR = 5.83, p < 0.001) has an average annual incidence of 8.7 inpatients per 10,000 residents, among the highest frequencies worldwide. Conclusions: The hospital database reflects the geographic distribution of severe, but not mild, mental illness within Caldas. Each hotspot is a candidate location for further research to identify genetic or environmental risk factors for severe mental illness. Our analyses highlight how existing infrastructure from middle-income countries can be extraordinary resources for population studies.

https://www.medrxiv.org/content/medrxiv/early/2022/03/24/2022.03.23.22272776.full.pdf

Geospatial analysis reveals distinct hotspots of severe mental illness

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Analysis of diagnosis instability in electronic health records reveals diverse disease trajectories of severe mental illness

Geospatial analysis reveals distinct hotspots of severe mental illness