The National Psychiatric Morbidity Survey of Pakistan (2022): Prevalence, socio-demographic and disability correlates
Bibliographic record
Abstract
Abstract Background National psychiatric morbidity surveys have shown a wide range of prevalence of psychiatric disorders across different countries. Pakistan with its sociocultural and ethnic diversity, has the fifth largest population in the world. There was no prior high-quality nationally representative data on the prevalence of psychiatric disorders and their socio-demographic correlates for Pakistan. To fill this gap in the planning of mental health services, the Pakistan Psychiatric Society conducted the National Psychiatric Morbidity Survey (NPMS) of Pakistan, in the years 2019-2022. Aim To estimate the prevalence and socio-demographic correlates of psychiatric morbidity in a representative sample of Pakistan. Methods The cross-sectional NPMS collected data from the four provinces of Pakistan. After selection through a three-stage, stratified, random cluster sampling technique we interviewed 17,773 adults above the age of 18. We used the MINI International Neuropsychiatric Interview (MINI Version 7.0.2) to evaluate psychiatric morbidity. Current and lifetime precise and weighted prevalence is reported according to ICD-10 (International Classification of Disease-10 th version). We used multivariate logistic regression to investigate the association between the risk of psychiatric illness and sociodemographic variables. National Bio-ethic Committee of Pakistan granted approval of survey. Results The lifetime and current weighted prevalence of all psychiatric disorder is 37.91% (95% Confidence Interval (CI) =37.22-38.59) and 32.28% (95% CI=31.62-32.94) respectively. The weighted prevalence of common psychiatric disorders in Pakistan included Mood Disorders (F30-F39; 19.62%), Neurotic and Stress-related Disorders (F40 F48; 24.81%), Psychotic Disorders (F20-F29; 4.52%) and Mental and Behavioural Problems due to Psychoactive Substance use (F10-F19; 0.85%). The psychiatric disorders had an association with age, female gender, urban living, lower income and being divorced. Among participants, 6.17% acknowledged suicidality in the past month, while 1.05% acknowledged a lifetime suicide attempt. Conclusion The NPMS is the first nationally representative study of psychiatric morbidities in Pakistan. The data from this survey can be utilized for designing and implementing mental health services and support programmes in the country.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".