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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+language:
+  - en
+tags:
+  - substandard-falsified-medicines
+  - pharmacovigilance
+  - adverse-drug-reactions
+  - drug-safety
+  - VigiFlow
+  - synthetic
+  - sub-saharan-africa
+pretty_name: Pharmacovigilance & Adverse Drug Reactions (SSA)
+size_categories:
+  - 10K<n<100K
+configs:
+  - config_name: tertiary_hospital_pv
+    data_files: data/pv_tertiary_hospital.csv
+    default: true
+  - config_name: community_primary_care
+    data_files: data/pv_community_primary.csv
+  - config_name: mass_treatment_campaign
+    data_files: data/pv_mass_treatment.csv
+---
+
+# Pharmacovigilance & Adverse Drug Reactions in Sub-Saharan Africa
+
+## Abstract
+
+Synthetic dataset modelling ADR occurrence, reporting cascades, PV system capacity, and medication errors across three healthcare settings in SSA. Massive underreporting exists — <10% of ADRs reported in most SSA countries. Inadequate training and deficient reporting culture are key barriers.
+
+## Parameterization Evidence
+
+| Parameter | Value | Source | Year |
+| --- | --- | --- | --- |
+| ADR reporting patterns in Africa | Reporting | PMC4796322 | 2016 |
+| PV systems for medication error reporting | Systems | PLOS ONE | 2022 |
+| ADR report completeness; VigiFlow system | Quality | PMC9900287 | 2023 |
+| West African PV: inadequate training, poor culture | Barriers | CJGH | 2025 |
+
+## Validation
+
+![Validation Report](validation_report.png)
+
+## Usage
+
+```python
+from datasets import load_dataset
+ds = load_dataset("electricsheepafrica/pharmacovigilance-adr", "tertiary_hospital_pv")
+```
+
+## References
+
+1. PMC4796322. ADR reporting in Africa. 2016.
+2. PLOS ONE. PV systems for medication errors in Africa. 2022.
+3. PMC9900287. ADR report completeness in South Africa. 2023.
+4. CJGH. PV systems in West African countries. 2025.
+
+## Citation
+
+```bibtex
+@dataset{electricsheepafrica_pharmacovigilance_adr_2025,
+  title={Pharmacovigilance and Adverse Drug Reactions in Sub-Saharan Africa},
+  author={Electric Sheep Africa},
+  year={2025},
+  publisher={HuggingFace},
+  url={https://huggingface.co/datasets/electricsheepafrica/pharmacovigilance-adr}
+}
+```
+
+## License
+
+CC-BY-4.0

generate_dataset.py

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+"""Generate synthetic pharmacovigilance & adverse drug reaction dataset for SSA.
+
+Research-based parameterization:
+- PMC4796322: ADR reporting in Africa; targeted spontaneous reporting
+  and cohort event monitoring could strengthen PV.
+- PLOS ONE (2022): PV systems for medication error reporting in Africa.
+- PMC9900287: ADR report completeness in South Africa; VigiFlow system.
+- CJGH: West African PV systems; inadequate training, deficient
+  reporting culture, underdeveloped regulatory frameworks.
+- SSA context: Massive underreporting; <10% of ADRs reported in most
+  countries; limited PV centres; few trained personnel.
+"""
+
+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 = {
+    "tertiary_hospital_pv": {
+        "setting_probs": {"tertiary_hospital": 0.40, "regional_hospital": 0.30,
+                          "teaching_hospital": 0.20, "specialist_clinic": 0.10},
+        "drug_class_probs": {"antiretroviral": 0.20, "antibiotic": 0.15,
+                             "antimalarial": 0.12, "NSAID_analgesic": 0.12,
+                             "antiepileptic": 0.08, "antihypertensive": 0.08,
+                             "anti_TB": 0.08, "chemotherapy": 0.07,
+                             "antidiabetic": 0.05, "other": 0.05},
+        "adr_reporting_rate": 0.15,
+        "serious_adr_pct": 0.25,
+        "medication_error_pct": 0.08,
+        "vigiflow_reporting": 0.30,
+        "pv_trained_staff_pct": 0.20,
+    },
+    "community_primary_care": {
+        "setting_probs": {"health_centre": 0.35, "district_hospital": 0.25,
+                          "community_pharmacy": 0.25, "private_clinic": 0.15},
+        "drug_class_probs": {"antibiotic": 0.25, "antimalarial": 0.20,
+                             "NSAID_analgesic": 0.15, "antihypertensive": 0.10,
+                             "contraceptive": 0.08, "antiretroviral": 0.08,
+                             "antidiabetic": 0.05, "supplement": 0.05,
+                             "other": 0.04},
+        "adr_reporting_rate": 0.03,
+        "serious_adr_pct": 0.15,
+        "medication_error_pct": 0.12,
+        "vigiflow_reporting": 0.05,
+        "pv_trained_staff_pct": 0.05,
+    },
+    "mass_treatment_campaign": {
+        "setting_probs": {"community_outreach": 0.35, "school_campaign": 0.20,
+                          "health_post": 0.25, "mobile_clinic": 0.20},
+        "drug_class_probs": {"ivermectin_MDA": 0.25, "praziquantel_MDA": 0.20,
+                             "albendazole_MDA": 0.15, "azithromycin_MDA": 0.10,
+                             "antimalarial_SMC": 0.10, "vaccine": 0.10,
+                             "DEC_MDA": 0.05, "other": 0.05},
+        "adr_reporting_rate": 0.08,
+        "serious_adr_pct": 0.05,
+        "medication_error_pct": 0.06,
+        "vigiflow_reporting": 0.10,
+        "pv_trained_staff_pct": 0.08,
+    },
+}
+
+SCENARIO_FILES = {
+    "tertiary_hospital_pv": "pv_tertiary_hospital.csv",
+    "community_primary_care": "pv_community_primary.csv",
+    "mass_treatment_campaign": "pv_mass_treatment.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(30, 18), 0, 80))
+        sex = rng.choice(["male", "female"], p=[0.45, 0.55])
+        is_child = int(age < 15)
+        pregnant = int(sex == "female" and 15 <= age <= 45 and rng.random() < 0.08)
+
+        drug_class = _choice(rng, params["drug_class_probs"])
+        polypharmacy = int(rng.random() < 0.25)
+        num_medicines = int(np.clip(rng.poisson(2 if polypharmacy else 1), 1, 10))
+
+        # ADR occurrence
+        adr_occurred = int(rng.random() < 0.10)
+        adr_type = rng.choice(["skin_rash", "GI_disturbance", "hepatotoxicity",
+                                "nephrotoxicity", "haematological", "neurological",
+                                "anaphylaxis", "Stevens_Johnson", "other"],
+                               p=[0.25, 0.20, 0.10, 0.08, 0.08, 0.08, 0.03, 0.02, 0.16]) if adr_occurred else "none"
+        adr_severity = rng.choice(["mild", "moderate", "severe", "fatal"],
+                                   p=[0.50, 0.30, 0.15, 0.05]) if adr_occurred else "none"
+        serious_adr = int(adr_occurred and adr_severity in ("severe", "fatal"))
+        hospitalisation_due_adr = int(serious_adr and rng.random() < 0.50)
+        death_due_adr = int(adr_severity == "fatal")
+
+        # Causality
+        causality = rng.choice(["certain", "probable", "possible", "unlikely",
+                                 "unassessable", "not_assessed"],
+                                p=[0.05, 0.20, 0.30, 0.10, 0.10, 0.25]) if adr_occurred else "not_applicable"
+
+        # Medication error
+        medication_error = int(rng.random() < params["medication_error_pct"])
+        error_type = rng.choice(["wrong_dose", "wrong_drug", "wrong_route",
+                                  "omission", "wrong_frequency", "dispensing_error"],
+                                 p=[0.30, 0.10, 0.05, 0.25, 0.15, 0.15]) if medication_error else "none"
+
+        # Reporting (massive underreporting in SSA)
+        adr_reported = int(adr_occurred and rng.random() < params["adr_reporting_rate"])
+        reported_to_nmra = int(adr_reported and rng.random() < 0.40)
+        vigiflow_submitted = int(reported_to_nmra and rng.random() < params["vigiflow_reporting"])
+        report_complete = int(adr_reported and rng.random() < 0.35)
+        time_to_report_days = int(np.clip(rng.exponential(30), 1, 365)) if adr_reported else 0
+
+        # Reporter
+        reporter_type = rng.choice(["physician", "pharmacist", "nurse", "patient", "other"],
+                                    p=[0.30, 0.25, 0.25, 0.10, 0.10]) if adr_reported else "none"
+
+        # PV system capacity
+        pv_focal_person = int(rng.random() < params["pv_trained_staff_pct"])
+        adr_form_available = int(rng.random() < 0.30)
+        pv_training_received = int(rng.random() < params["pv_trained_staff_pct"])
+        knows_reporting_process = int(rng.random() < 0.15)
+
+        # Signal detection
+        signal_detected = int(adr_reported and rng.random() < 0.05)
+        regulatory_action = int(signal_detected and rng.random() < 0.20)
+
+        record = {
+            "record_id": f"{name[:3].upper()}-{idx:05d}",
+            "scenario": name,
+            "year": year,
+            "setting": setting,
+            "age": age,
+            "sex": sex,
+            "is_child": is_child,
+            "drug_class": drug_class,
+            "polypharmacy": polypharmacy,
+            "num_medicines": num_medicines,
+            "adr_occurred": adr_occurred,
+            "adr_type": adr_type,
+            "adr_severity": adr_severity,
+            "serious_adr": serious_adr,
+            "hospitalisation_due_adr": hospitalisation_due_adr,
+            "death_due_adr": death_due_adr,
+            "causality": causality,
+            "medication_error": medication_error,
+            "error_type": error_type,
+            "adr_reported": adr_reported,
+            "reported_to_nmra": reported_to_nmra,
+            "vigiflow_submitted": vigiflow_submitted,
+            "report_complete": report_complete,
+            "time_to_report_days": time_to_report_days,
+            "reporter_type": reporter_type,
+            "pv_focal_person": pv_focal_person,
+            "adr_form_available": adr_form_available,
+            "pv_training_received": pv_training_received,
+            "knows_reporting_process": knows_reporting_process,
+            "signal_detected": signal_detected,
+            "regulatory_action": regulatory_action,
+        }
+        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()

requirements.txt

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+numpy>=1.24
+pandas>=2.0
+matplotlib>=3.7

validate_dataset.py

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+"""Validate synthetic pharmacovigilance & adverse drug reaction dataset."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+SCENARIO_FILES = {
+    "tertiary_hospital_pv": "pv_tertiary_hospital.csv",
+    "community_primary_care": "pv_community_primary.csv",
+    "mass_treatment_campaign": "pv_mass_treatment.csv",
+}
+
+COLORS = {"tertiary_hospital_pv": "#e6550d", "community_primary_care": "#756bb1", "mass_treatment_campaign": "#31a354"}
+
+
+def load_data() -> pd.DataFrame:
+    frames = []
+    for scenario, filename in SCENARIO_FILES.items():
+        df = pd.read_csv(Path("data") / filename)
+        frames.append(df)
+    return pd.concat(frames, ignore_index=True)
+
+
+def plot_validation(df: pd.DataFrame, output_path: Path) -> None:
+    fig, axes = plt.subplots(4, 2, figsize=(14, 16))
+    axes = axes.flatten()
+
+    adr_cols = ["adr_occurred", "serious_adr", "hospitalisation_due_adr", "death_due_adr"]
+    adr = df.groupby("scenario")[adr_cols].mean() * 100
+    adr.plot(kind="bar", ax=axes[0])
+    axes[0].set_title("ADR Occurrence & Severity (%)")
+    axes[0].legend(fontsize=6)
+
+    rep_cols = ["adr_reported", "reported_to_nmra", "vigiflow_submitted", "report_complete"]
+    rep = df.groupby("scenario")[rep_cols].mean() * 100
+    rep.plot(kind="bar", ax=axes[1])
+    axes[1].set_title("Reporting Cascade (%)")
+    axes[1].legend(fontsize=6)
+
+    adr_df = df[df["adr_occurred"] == 1]
+    if len(adr_df) > 0:
+        at = adr_df.groupby(["scenario", "adr_type"]).size().groupby(level=0).apply(lambda s: s / s.sum())
+        at.unstack().plot(kind="bar", stacked=True, ax=axes[2])
+    axes[2].set_title("ADR Type Distribution")
+    axes[2].legend(fontsize=4)
+
+    dc = df.groupby(["scenario", "drug_class"]).size().groupby(level=0).apply(lambda s: s / s.sum())
+    dc.unstack().plot(kind="bar", stacked=True, ax=axes[3])
+    axes[3].set_title("Drug Class Distribution")
+    axes[3].legend(fontsize=4)
+
+    cap_cols = ["pv_focal_person", "adr_form_available", "pv_training_received", "knows_reporting_process"]
+    cap = df.groupby("scenario")[cap_cols].mean() * 100
+    cap.plot(kind="bar", ax=axes[4])
+    axes[4].set_title("PV System Capacity (%)")
+    axes[4].legend(fontsize=6)
+
+    err_cols = ["medication_error"]
+    err = df.groupby("scenario")[err_cols].mean() * 100
+    err.plot(kind="bar", ax=axes[5])
+    axes[5].set_title("Medication Error Rate (%)")
+    axes[5].legend(fontsize=7)
+
+    if len(adr_df) > 0:
+        sev = adr_df.groupby(["scenario", "adr_severity"]).size().groupby(level=0).apply(lambda s: s / s.sum())
+        sev.unstack().plot(kind="bar", stacked=True, ax=axes[6])
+    axes[6].set_title("ADR Severity Distribution")
+    axes[6].legend(fontsize=7)
+
+    rep_adr = df[df["adr_reported"] == 1]
+    if len(rep_adr) > 0:
+        rt = rep_adr.groupby(["scenario", "reporter_type"]).size().groupby(level=0).apply(lambda s: s / s.sum())
+        rt.unstack().plot(kind="bar", stacked=True, ax=axes[7])
+    axes[7].set_title("Reporter Type Distribution")
+    axes[7].legend(fontsize=6)
+
+    plt.tight_layout()
+    fig.savefig(output_path, dpi=200)
+    plt.close(fig)
+
+
+def main() -> None:
+    df = load_data()
+    plot_validation(df, Path("validation_report.png"))
+    print("Saved validation_report.png")
+
+
+if __name__ == "__main__":
+    main()