"""Generate synthetic mental health & psychosocial disability dataset for SSA. Research-based parameterization: - WHO Mental Health Atlas: Treatment gap 76-85% in SSA; <0.1 psychiatrists per 100K; mental health budget <1% of health budget. - Lancet Commission: 1 in 4 people affected by mental disorders; depression, psychosis, epilepsy, substance use most common in SSA. - SSA context: Widespread chaining/confinement; traditional/faith healing common first contact; high stigma; limited community MH. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd SEED = 42 N_PER_SCENARIO = 10_000 YEAR_RANGE = np.arange(2010, 2025) YEAR_WEIGHTS = np.linspace(0.85, 1.3, len(YEAR_RANGE)) YEAR_WEIGHTS = YEAR_WEIGHTS / YEAR_WEIGHTS.sum() SCENARIOS = { "urban_mental_health": { "setting_probs": {"psychiatric_hospital": 0.25, "general_hospital_MH": 0.25, "community_MH_centre": 0.20, "private_practice": 0.30}, "condition_probs": {"depression": 0.25, "psychosis_schizophrenia": 0.20, "bipolar": 0.08, "anxiety": 0.12, "PTSD": 0.08, "substance_use": 0.10, "epilepsy": 0.08, "other": 0.09}, "treatment_access_pct": 0.25, "medication_available_pct": 0.40, "psychosocial_support_pct": 0.15, "psychiatrist_available": 0.20, }, "district_integrated": { "setting_probs": {"district_hospital": 0.30, "health_centre": 0.25, "community_programme": 0.20, "faith_healer": 0.25}, "condition_probs": {"depression": 0.20, "psychosis_schizophrenia": 0.20, "epilepsy": 0.15, "substance_use": 0.12, "anxiety": 0.08, "PTSD": 0.08, "bipolar": 0.07, "other": 0.10}, "treatment_access_pct": 0.10, "medication_available_pct": 0.20, "psychosocial_support_pct": 0.05, "psychiatrist_available": 0.03, }, "rural_traditional": { "setting_probs": {"traditional_healer": 0.30, "faith_healer": 0.25, "home_confinement": 0.20, "health_post": 0.15, "community": 0.10}, "condition_probs": {"psychosis_schizophrenia": 0.25, "epilepsy": 0.20, "depression": 0.15, "substance_use": 0.12, "PTSD": 0.08, "anxiety": 0.05, "bipolar": 0.05, "other": 0.10}, "treatment_access_pct": 0.03, "medication_available_pct": 0.05, "psychosocial_support_pct": 0.02, "psychiatrist_available": 0.005, }, } SCENARIO_FILES = { "urban_mental_health": "mhpd_urban.csv", "district_integrated": "mhpd_district.csv", "rural_traditional": "mhpd_rural.csv", } def _choice(rng, prob_map): keys = list(prob_map.keys()) weights = np.array(list(prob_map.values()), dtype=float) weights = weights / weights.sum() return rng.choice(keys, p=weights) def _simulate_scenario(name, params, seed): rng = np.random.default_rng(seed) records = [] for idx in range(N_PER_SCENARIO): year = int(rng.choice(YEAR_RANGE, p=YEAR_WEIGHTS)) setting = _choice(rng, params["setting_probs"]) age = int(np.clip(rng.normal(32, 14), 10, 75)) sex = rng.choice(["male", "female"], p=[0.45, 0.55]) condition = _choice(rng, params["condition_probs"]) severity = rng.choice(["mild", "moderate", "severe"], p=[0.25, 0.40, 0.35]) duration_years = int(np.clip(rng.exponential(5), 0, 30)) comorbid_physical = int(rng.random() < 0.25) # Treatment treatment_received = int(rng.random() < params["treatment_access_pct"]) medication = int(treatment_received and rng.random() < params["medication_available_pct"]) psychotherapy = int(treatment_received and rng.random() < 0.10) psychosocial_support = int(rng.random() < params["psychosocial_support_pct"]) psychiatrist_seen = int(rng.random() < params["psychiatrist_available"]) nurse_mh_trained = int(treatment_received and rng.random() < 0.15) traditional_healer_consulted = int(rng.random() < 0.50) faith_healer_consulted = int(rng.random() < 0.35) # Human rights chained_confined = int(severity == "severe" and condition in ("psychosis_schizophrenia", "bipolar") and rng.random() < 0.15) involuntary_admission = int(severity == "severe" and treatment_received and rng.random() < 0.10) physical_restraint = int(chained_confined or (involuntary_admission and rng.random() < 0.20)) abuse_experienced = int(rng.random() < 0.12) # Disability & functioning functional_disability = rng.choice(["none", "mild", "moderate", "severe"], p=[0.10, 0.25, 0.35, 0.30]) unable_to_work = int(functional_disability in ("moderate", "severe") and rng.random() < 0.50) social_isolation = int(rng.random() < 0.40) self_care_difficulty = int(severity == "severe" and rng.random() < 0.35) homelessness = int(severity == "severe" and rng.random() < 0.08) # Barriers stigma = int(rng.random() < 0.55) cost_barrier = int(rng.random() < 0.50) no_services = int(rng.random() < 0.45) awareness_barrier = int(rng.random() < 0.40) # Outcomes symptom_improvement = int(treatment_received and rng.random() < 0.40) community_participation = int(rng.random() < (0.35 if symptom_improvement else 0.12)) caregiver_burden = int(severity in ("moderate", "severe") and rng.random() < 0.55) suicide_attempt = int(condition in ("depression", "bipolar", "PTSD") and rng.random() < 0.04) treatment_gap = int(not treatment_received) record = { "record_id": f"{name[:3].upper()}-{idx:05d}", "scenario": name, "year": year, "setting": setting, "age": age, "sex": sex, "condition": condition, "severity": severity, "duration_years": duration_years, "treatment_received": treatment_received, "medication": medication, "psychosocial_support": psychosocial_support, "psychiatrist_seen": psychiatrist_seen, "traditional_healer_consulted": traditional_healer_consulted, "faith_healer_consulted": faith_healer_consulted, "chained_confined": chained_confined, "abuse_experienced": abuse_experienced, "functional_disability": functional_disability, "unable_to_work": unable_to_work, "social_isolation": social_isolation, "homelessness": homelessness, "stigma": stigma, "cost_barrier": cost_barrier, "no_services": no_services, "symptom_improvement": symptom_improvement, "community_participation": community_participation, "caregiver_burden": caregiver_burden, "suicide_attempt": suicide_attempt, "treatment_gap": treatment_gap, } records.append(record) return pd.DataFrame(records) def main(): output_dir = Path("data") output_dir.mkdir(parents=True, exist_ok=True) for idx, (name, params) in enumerate(SCENARIOS.items()): df = _simulate_scenario(name, params, SEED + idx * 211) df.to_csv(output_dir / SCENARIO_FILES[name], index=False) print(f"Saved {name} -> {SCENARIO_FILES[name]}") if __name__ == "__main__": main()