electricsheepafrica/hiv-art-treatment-cascade

HIV/ART Treatment Cascade Dataset

Abstract

This dataset provides 30,000 synthetic records (10,000 per scenario) of people living with HIV (PLHIV) aged 15–65 years across the treatment cascade in sub-Saharan African ART programmes. Each record contains 20 variables spanning cascade stage (undiagnosed / diagnosed not on ART / on ART), ART regimen and line, CD4 count at diagnosis and current, viral load, adherence category, WHO clinical stage, TB co-infection, pregnancy/PMTCT status, VL monitoring, and outcome (alive in care / LTFU / transferred / died). Three programme-performance scenarios range from high-performing (59% population-level VL suppression, 4.2% mortality) to low-performing (25% suppression, 12.1% mortality), reflecting real-world disparities across sub-Saharan Africa.

1. Introduction

Sub-Saharan Africa bears the largest HIV burden globally, with 25.6 million people on ART (UNAIDS 2023). The UNAIDS 90-90-90 targets aim for 90% diagnosed, 90% of diagnosed on ART, and 90% of those on ART virally suppressed. A systematic review of 92 studies (Hakim et al., JIAS 2020) found substantial heterogeneity: youth aged 15–24 achieved only 60-49-81 across the cascade, while men achieved 66-72-85 compared to women at 79-76-89. Dolutegravir-based regimens have improved retention and suppression rates (Dzangare et al., 2023).

This dataset is entirely synthetic. It must not be used for clinical decision-making.

2. Methodology

2.1 Epidemiological Parameterization

Parameter	Value	Source
PLHIV on ART in E/S Africa	25.6 million	UNAIDS 2023
Cascade (youth 15–24)	60-49-81	Hakim et al., JIAS 2020 (92 studies)
Cascade (men)	66-72-85	Hakim et al., 2020
Cascade (women)	79-76-89	Hakim et al., 2020
CD4 at ART initiation (SSA)	Median 200–350; late presenters 30–50%	IeDEA Consortium
CD4 recovery year 1	~120 cells/µL/yr	IeDEA
VL suppression on ART	85–92%	Bock et al., Lancet HIV 2022
12-month retention	75–85% (good programmes)	Onoya et al., 2021
DTG vs EFV retention	aRR 1.09 (95% CI 1.04–1.14)	Dzangare et al., 2023
PMTCT ART coverage	82% globally	UNAIDS 2022
TB co-infection (HIV+)	8–18% depending on setting	WHO TB/HIV 2022
Pre-treatment NNRTI resistance	10–15%	Gupta et al., Lancet ID 2018

2.2 Scenario Design

Scenario	Setting	Diagnosed	On ART	VL Suppressed	Pop VL Supp	Deaths
High performing	Botswana, Rwanda	90%	89%	92%	59.3%	4.2%
Moderate performing	Kenya, Tanzania	80%	78%	88%	43.2%	7.1%
Low performing	Nigeria, DRC	65%	62%	80%	25.3%	12.1%

3. Schema

Column	Type	Description
id	int	Unique identifier
age_years	int	Age (15–65)
sex	categorical	M / F
cascade_stage	categorical	undiagnosed / diagnosed_not_on_ART / on_ART
years_on_art	float	Duration on ART (years; 0 if not on ART)
art_regimen	categorical	TDF/3TC/DTG, TDF/3TC/EFV, AZT/3TC/ATV-r, DRV-r/DTG/AZT/3TC, none
regimen_line	int	0=none, 1=first-line, 2=second-line, 3=third-line
ever_switched_regimen	binary	Ever switched ART regimen
cd4_at_diagnosis	int	CD4 count at HIV diagnosis (cells/µL)
cd4_current	int	Current CD4 count (cells/µL)
adherence_category	categorical	good / moderate / poor / unknown
adherence_pct	int	Self-reported adherence (%)
viral_load_copies_ml	int	Most recent viral load (copies/mL)
vl_suppressed	binary	VL <1000 copies/mL (WHO threshold)
vl_test_done_12mo	binary	VL test done in last 12 months
who_clinical_stage	int	WHO clinical stage (1–4)
tb_coinfection	binary	Active TB co-infection
pregnant	binary	Currently pregnant (females only)
on_pmtct	binary	Receiving PMTCT services
outcome	categorical	alive_in_care / ltfu / transferred / died / undiagnosed / pre_art_care

4. Validation

Key validation checks:

• Cascade monotonicity: On-ART proportion decreases with programme weakness ✓
• VL suppression by adherence: Good 90%+ → moderate ~65% → poor ~25% ✓
• CD4 recovery: On-ART CD4 higher than not-on-ART ✓
• Age-sex gap: Men and youth have lower cascade achievement ✓
• DTG dominance: First-line DTG-based regimen most common ✓
• Population VL suppression: 59% → 43% → 25% across scenarios ✓

5. Usage

from datasets import load_dataset
dataset = load_dataset("electricsheepafrica/hiv-art-treatment-cascade", "moderate_performing")
df = dataset["train"].to_pandas()

import pandas as pd
df = pd.read_csv("data/hiv_art_moderate_performing.csv")

Regenerate:

python generate_dataset.py --scenario low_performing --n 20000 --seed 123
python generate_dataset.py --all-scenarios --n 10000 --seed 42

6. Limitations

• Synthetic: Not derived from real patient records.
• Cross-sectional: Single time-point snapshot; no longitudinal CD4/VL trajectories.
• No resistance genotyping: Drug resistance mutations not modelled.
• Simplified adherence: Three-category model; real adherence is continuous and time-varying.
• No geographic specificity: Parameters are regional averages, not country-specific.
• No opportunistic infections: WHO staging simplified to CD4-driven; no specific OI modelling.
• No paediatric HIV: Only adults ≥15 years; PMTCT modelled for pregnant women only.

7. References

1. UNAIDS (2023). Global AIDS Update. 25.6M on ART in E/S Africa.
2. Hakim J, et al. (2020). Sociodemographic heterogeneity across HIV treatment cascade in SSA. JIAS, 23(2):e25470. PMID: 32153117
3. WHO (2019). Updated recommendations on first-line and second-line ART.
4. IeDEA Consortium. CD4 at ART initiation in SSA.
5. Bock P, et al. (2022). Viral load monitoring cascade in SSA. Lancet HIV.
6. Onoya D, et al. (2021). 12-month retention in care in SSA ART programmes.
7. WHO (2007/2014). WHO clinical staging of HIV/AIDS.
8. Gupta RK, et al. (2018). HIV drug resistance in SSA. Lancet Infect Dis, 18(12):1378–1390.
9. Dzangare J, et al. (2023). Implementation of DTG-based first-line regimens. Lancet HIV. PMID: 37001536
10. Shroufi A, et al. (2021). TLD as second-line. JAIDS. PMID: 33973876
11. UNAIDS (2022). PMTCT: 82% coverage globally.

Citation

@dataset{esa_hiv_art_cascade_2025,
  title={HIV/ART Treatment Cascade Dataset},
  author={Electric Sheep Africa},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/electricsheepafrica/hiv-art-treatment-cascade}
}

License

CC-BY-4.0