README.md

metadata

language:
  - en
tags:
  - missing-data
  - lost-to-follow-up
  - retention
  - breast-cancer
  - sub-saharan-africa
  - health-systems
license: cc-by-nc-4.0
pretty_name: SSA Breast Missing Data Patterns (Retention & Incomplete Tests)
task_categories:
  - other
size_categories:
  - 1K<n<10K

SSA Breast Missing Data Patterns (Synthetic)

Dataset summary

This module provides a synthetic missing-data sandbox for oncology care in African healthcare contexts, focusing on:

• Realistic loss-to-follow-up (LTFU) and retention patterns over 0–24 months.
• Incomplete diagnostic and laboratory test results (ordered vs completed vs available in records).
• Non-random missingness driven by facility type, distance, socioeconomic status (SES), and insurance.

The dataset is anchored in published evidence from:

• The ABC-DO sub-Saharan African breast cancer cohort (low LTFU with active tracing).
• Meta-analyses of HIV ART retention (60–70% retained at 2–3 years in routine care).
• Surveys of breast cancer pathology services and management (AORTIC, BMC Health Serv Res, JCO GO).
• Real-world challenges in SSA breast cancer care (BMJ Open 2021).

All records are fully synthetic and intended for methods development and teaching on missing data and retention, not for inference on real facilities.

Cohort design

Sample size and populations

• Total N (baseline patients): 6,000.
• Populations:
  • SSA_West: 1,500
  • SSA_East: 1,500
  • SSA_Central: 1,000
  • SSA_Southern: 1,000
  • AAW (African American women): 1,000 (reference/high-resource context)

Key baseline variables

• sex: predominantly Female (~96%), with a small proportion of Male to allow mixed-sex analyses.
• age_years: 18–90 (mean ~52, SD ~10).
• facility_type:
  • Tertiary_urban
  • Regional_hospital
  • District_hospital
• urban_rural:
  • Urban, Periurban, Rural.
• distance_km from facility:
  • Drawn from normal distributions by urban_rural (e.g., Urban mean ~5 km, Rural mean ~60 km).
• ses (socioeconomic status): Low, Middle, High with higher Low fractions in SSA cohorts.
• insurance_status: None, National_insurance, Private.

These variables drive missingness mechanisms (higher LTFU and test missingness with longer distance, low SES, and lack of insurance).

Follow-up and retention

Patients are scheduled for visits at months:

• visit_month: [0, 3, 6, 9, 12, 18, 24].

Retention targets (probability of still in care at 12 and 24 months) vary by facility type, loosely anchored by ABC-DO and HIV ART literature:

• Tertiary_urban: 12m ≈ 0.85, 24m ≈ 0.75.
• Regional_hospital: 12m ≈ 0.75, 24m ≈ 0.60.
• District_hospital: 12m ≈ 0.65, 24m ≈ 0.50.

In the generator, a per-interval dropout probability is derived from these targets. This dropout risk is then modulated by:

• Distance: higher risk for patients living >75 km from the facility.
• SES: higher risk for Low vs High SES.
• Insurance: higher risk for None vs National_insurance or Private.

At each scheduled month, patients either:

• Remain in care and have a visit record (visit_attended True/False).
• Drop out (no further visits recorded).

Retention at 12 and 24 months is validated against the configuration.

Test ordering and result availability (missingness)

Baseline tests

For each patient at baseline, the following are simulated with facility-specific probabilities:

• baseline_pathology_ordered
• baseline_pathology_result_available (e.g., ER/PR/HER2 receptors)
• baseline_cbc_ordered
• baseline_cbc_result_available
• baseline_imaging_ordered (staging imaging)
• baseline_imaging_result_available

Approximate patterns by facility type:

• Pathology receptors:

  • High ordering and completion in Tertiary_urban (~90%+ completed).
  • Lower completion in Regional_hospital (~65–70%).
  • Substantial missingness in District_hospital (~40–50% completed).

• CBC and imaging:

  • More widely available, but still with gradients by facility and context.

Follow-up labs (CBC)

At each attended follow-up visit (months >0):

• cbc_ordered is drawn from facility-specific probabilities.
• cbc_result_available is drawn conditional on ordering and reflects:
  • Higher completion in tertiary centres.
  • Lower completion in district hospitals.

This yields visit-level missingness that depends on both visit attendance and facility/test capacity.

Non-random missingness

Missingness is deliberately not MCAR:

• Baseline pathology results are more often missing in:
  • District_hospital and Regional_hospital than Tertiary_urban.
  • Low SES vs High SES.
• Follow-up CBC results are more often missing among:
  • Patients with long travel distances.
  • Those without insurance.

The validation script checks for higher missingness in Low vs High SES for both baseline pathology and follow-up CBC.

Files and schema

Baseline table

Files:

• missing_data_baseline.parquet
• missing_data_baseline.csv

Columns (per patient):

• Identifiers and demographics:
  • sample_id
  • population
  • region
  • is_SSA
  • sex
  • age_years
• Access and facility characteristics:
  • facility_type
  • urban_rural
  • distance_km
  • ses
  • insurance_status
• Baseline tests (ordered and result availability):
  • baseline_pathology_ordered
  • baseline_pathology_result_available
  • baseline_cbc_ordered
  • baseline_cbc_result_available
  • baseline_imaging_ordered
  • baseline_imaging_result_available

Visit-level table

Files:

• missing_data_visits.parquet
• missing_data_visits.csv

Columns (per scheduled time point while in care):

• Identifiers and baseline covariates:
  • sample_id
  • population
  • facility_type
  • urban_rural
  • ses
  • insurance_status
  • distance_km
• Visit information:
  • visit_month (0, 3, 6, 9, 12, 18, 24)
  • visit_attended (True/False)
• Follow-up CBC:
  • cbc_ordered
  • cbc_result_available

Patients with early loss to follow-up have shorter visit histories, so the visit table is an unbalanced panel that mimics real program data.

Generation

The dataset is generated with:

• missing_data_patterns/scripts/generate_missing_data.py

using configuration:

• missing_data_patterns/configs/missing_data_config.yaml

and literature inventory:

• missing_data_patterns/docs/LITERATURE_INVENTORY.csv

Key steps:

1. Baseline cohort: populations, sex, age, facility type, urban/rural, distance, SES, insurance.
2. Baseline tests: pathology receptors, CBC, and imaging, with ordering/completion probabilities by facility type.
3. Visits and retention: scheduled visits from 0 to 24 months, with facility-specific dropout probabilities tuned to match retention targets and modified by distance/SES/insurance.
4. Follow-up CBC: ordering and result availability for each attended visit, by facility type.

Validation

Validation is performed with:

• missing_data_patterns/scripts/validate_missing_data.py

and summarized in:

• missing_data_patterns/output/validation_report.md

Checks include:

• C01–C02: Baseline sample size and population counts vs configuration.
• C03: Retention at 12 and 24 months by facility vs configured targets.
• C04: Baseline pathology receptor result availability vs expected rates.
• C05: Follow-up CBC result availability vs expected rates for attended visits.
• C06–C07: Non-random missingness by SES (Low vs High) for baseline pathology and follow-up CBC.
• C08: Overall missingness in key baseline and visit variables.

Intended use

This dataset is intended for:

• Developing and benchmarking missing-data methods (imputation, inverse probability weighting, joint models).
• Exploring selection bias introduced by LTFU and incomplete tests.
• Teaching about:
  • How health-system factors (facility, distance, SES, insurance) shape missing data.
  • Differences between MCAR, MAR, and MNAR mechanisms in realistic African oncology settings.

It is not intended for:

• Estimating real-world retention or test completion at specific facilities.
• Evaluating individual centres or countries.
• Clinical decision-making.

Ethical considerations

• All data are synthetic and derived from literature-informed parameter ranges.
• Facility and population labels are generic and must not be interpreted as real institutions.
• The goal is to enable more robust and equitable analyses under realistic data limitations in African healthcare settings.

License

• License: CC BY-NC 4.0.
• Free for non-commercial research, method development, and education with attribution.

Citation

If you use this dataset, please cite:

Electric Sheep Africa. "SSA Breast Missing Data Patterns (Retention & Incomplete Tests, Synthetic)." Hugging Face Datasets.

and relevant literature on retention and pathology services in sub-Saharan Africa (e.g., Foerster et al. 2020, Rosen & Fox 2007, Adesina et al. 2020, Joko-Fru et al. 2021).