---
license: cc-by-4.0
task_categories:
- tabular-classification
language:
- en
tags:
- synthetic
- healthcare
- adolescent-health
- sexual-reproductive-health
- contraception
- hiv
- sti
- pregnancy
- gender-based-violence
- sub-saharan-africa
- lmic
pretty_name: Adolescent Sexual & Reproductive Health Dataset (Contraception, STI,
  Pregnancy, GBV)
size_categories:
- 10K<n<100K
configs:
- config_name: youth_friendly_clinic
  data_files: data/adolescent_srh_youth_friendly_clinic.csv
- config_name: public_health_facility
  data_files: data/adolescent_srh_public_health_facility.csv
  default: true
- config_name: rural_limited_access
  data_files: data/adolescent_srh_rural_limited_access.csv
data_type: synthetic
---

> ⚠️ **Synthetic dataset** — Parameterized from published SSA literature, not real observations. Not suitable for empirical analysis or policy inference.

# Adolescent Sexual & Reproductive Health Dataset

## Abstract

This dataset provides **30,000 synthetic records** (10,000 per scenario) of adolescents and young people aged 10–24 years accessing SRH services in sub-Saharan African settings. Each record contains 35 variables including demographics, sexual activity, contraceptive use and method, pregnancy history, antenatal care, HIV testing, STI screening, GBV and depression screening, and service satisfaction. Three scenarios range from youth-friendly clinics (34% mCPR, 4% pregnancy) to rural limited-access settings (8% mCPR, 13% pregnancy, 60% unmet need).

## 1. Introduction

Sub-Saharan Africa has the world's highest adolescent birth rate (101/1000 girls aged 15–19) and 21 million adolescent pregnancies annually in developing regions (WHO 2023). Unmet need for modern contraception among sexually active 15–19 year olds reaches 60% in SSA (Darroch et al., Guttmacher 2016). UNAIDS reports 4,000 new HIV infections per week among 15–24 year old women in SSA. Gender-based violence affects ~30% of women (Palermo et al., 2019).

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

## 2. Methodology

### 2.1 Epidemiological Parameterization

| Parameter | Value | Source |
| --- | --- | --- |
| Adolescent birth rate (SSA) | 101/1000 | WHO 2023 |
| mCPR 15–19 year olds (SSA) | 8–15% | Chandra-Mouli et al., J Adolesc Health 2015 |
| Unmet need contraception (15–19) | ~60% | Darroch et al., Guttmacher 2016 |
| HIV incidence (15–24 F, SSA) | 4,000/week | UNAIDS 2023 |
| GBV prevalence (women SSA) | ~30% | Palermo et al., 2019 |
| Median age first sex (SSA) | 16–17 years | DHS/MICS compilations |
| ANC 4+ visits (adolescents) | 50–60% | DHS/MICS |

### 2.2 Scenario Design

| Scenario | Setting | mCPR | Pregnancy Rate | HIV Testing | Unmet Need |
| --- | --- | --- | --- | --- | --- |
| Youth-friendly clinic | Dedicated YF services | 34% | 4% | 70% | 20% |
| Public health facility | General facility | 18% | 6% | 51% | 40% |
| Rural limited access | Rural, minimal SRH | 8% | 13% | 26% | 60% |

## 3. Schema

| Column | Type | Description |
| --- | --- | --- |
| id | int | Unique identifier |
| age_years | int | Age (10–24) |
| sex | categorical | F / M |
| education_level | categorical | none / primary / lower_secondary / upper_secondary / tertiary |
| marital_status | categorical | never_married / married / cohabiting / divorced_separated |
| sexually_active | binary | Currently sexually active |
| age_at_first_sex | int | Age at sexual debut (-1 if not applicable) |
| number_sexual_partners_12mo | int | Partners in past 12 months |
| using_modern_contraceptive | binary | Using modern contraceptive method |
| contraceptive_method | categorical | injectable / implant / oral_pill / male_condom / iud / female_condom / none |
| condom_last_sex | binary | Condom used at last sexual intercourse |
| unmet_need_contraception | binary | Unmet need for contraception |
| currently_pregnant | binary | Currently pregnant (females) |
| ever_pregnant | binary | Ever been pregnant |
| number_pregnancies | int | Total pregnancies |
| pregnancy_outcome_last | categorical | live_birth / miscarriage / stillbirth / abortion / not_applicable |
| anc_visits | int | ANC visits during pregnancy |
| anc_4plus | binary | 4 or more ANC visits |
| delivery_location | categorical | health_facility / home / tba / not_applicable |
| hiv_tested_12mo | binary | HIV tested in past 12 months |
| hiv_status | categorical | positive / negative / unknown |
| sti_screened | binary | Screened for STIs |
| sti_positive | binary | STI detected |
| sti_type | categorical | chlamydia / gonorrhoea / syphilis / trichomoniasis / hpv / none |
| gbv_screened | binary | Screened for gender-based violence |
| gbv_positive | binary | GBV screen positive |
| depression_screened | binary | Screened for depression |
| depression_positive | binary | Depression screen positive |
| peer_support_accessed | binary | Accessed peer support services |
| received_srh_counselling | binary | Received SRH counselling |
| referred_to_higher_level | binary | Referred to higher-level facility |
| satisfaction | categorical | very_satisfied / satisfied / neutral / dissatisfied |

## 4. Validation

<p align="center">
  <img src="validation_report.png" alt="Validation Report" width="100%">
</p>

Key validation checks:

- **mCPR gradient**: Youth clinic 34% → public 18% → rural 8% ✓
- **Unmet need inverse**: 20% → 40% → 60% ✓
- **Pregnancy rate**: Increases with lower contraceptive access ✓
- **HIV testing**: Highest in youth-friendly clinic (70%) ✓
- **GBV prevalence**: Highest in rural (45%) ✓

## 5. Usage

```python
from datasets import load_dataset
dataset = load_dataset("electricsheepafrica/adolescent-srh", "public_health_facility")
df = dataset["train"].to_pandas()
```

```bash
python generate_dataset.py --all-scenarios --n 10000 --seed 42
```

## 6. Limitations

- **Synthetic**: Not derived from real patient records.
- **No longitudinal follow-up**: Single service encounter; no tracking over time.
- **Simplified STI model**: No syndromic management pathway or treatment outcomes.
- **No partner data**: Partner characteristics not modelled.
- **No cultural context**: Traditional practices and beliefs simplified.
- **Binary sex**: Non-binary gender identities not modelled.

## 7. References

1. WHO (2023). Adolescent health fact sheet.
2. Darroch JE, et al. (2016). Adding it up: costs of meeting contraceptive needs. Guttmacher Institute.
3. DHS/MICS compilations. Adolescent birth rates and SRH indicators.
4. Chandra-Mouli V, et al. (2015). Contraception for adolescents in SSA. *J Adolesc Health*, 56(1):S47–S58.
5. UNAIDS (2023). HIV among adolescent girls and young women.
6. WHO (2017). Global accelerated action for adolescents (AA-HA!).
7. Neal S, et al. (2020). Adolescent pregnancy outcomes in SSA. *Lancet Glob Health*.
8. Palermo T, et al. (2019). GBV among adolescents. *J Adolesc Health*.

## Citation

```bibtex
@dataset{esa_adolescent_srh_2025,
  title={Adolescent Sexual and Reproductive Health Dataset},
  author={Electric Sheep Africa},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/electricsheepafrica/adolescent-srh}
}
```

## License

[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)