electricsheepafrica/laboratory-reagent-supply

Laboratory Reagent Supply Dataset

Abstract

This dataset provides 30,000 simulated facility-level observations (10,000 per scenario) of laboratory reagent and consumable availability across three tiers of the laboratory network in sub-Saharan Africa. Each record represents one reagent assessed at one laboratory during one quarterly period. The dataset captures 35+ variables spanning reagent type, department, availability, stockout metrics, equipment functionality, tests missed, sample referral, and supply chain performance. Three scenarios: reference laboratory (80% availability), district laboratory (47%), health centre (22%).

This dataset is entirely simulated. It must not be used for clinical or procurement decisions.

1. Introduction

1.1 The Diagnostic Access Crisis

The Lancet Commission on Diagnostics (2021) estimated that 47% of the global population — predominantly in LMICs — lacks access to essential diagnostics, with only 1% of health spending allocated to diagnostics. In sub-Saharan Africa, laboratory services are among the most neglected components of health systems, despite their critical role in disease surveillance, treatment monitoring, and antimicrobial stewardship.

1.2 Reagent Supply Challenges

Laboratory reagent supply in SSA faces unique challenges: cold chain requirements for many reagents, single-source dependency for proprietary platforms (GeneXpert, Abbott), import clearance delays, and misalignment between equipment procurement and reagent supply contracts. The WHO Service Availability and Readiness Assessment (SARA) consistently documents low laboratory readiness, with reagent stockouts as a primary barrier.

1.3 Molecular Diagnostics

The GeneXpert platform has been widely deployed for TB diagnosis and HIV viral load monitoring through PEPFAR. However, cartridge supply is frequently disrupted, with facilities reporting stockouts of 30-60% for molecular reagents. The African Society for Laboratory Medicine (ASLM) has highlighted the need for integrated reagent supply chain management.

1.4 Rationale

This dataset integrates reagent availability across departments (biochemistry, haematology, microbiology, molecular, serology, POC), equipment functionality, and diagnostic service impact for laboratory supply chain modelling and health systems research.

2. Methodology

2.1 Parameterization

Parameter	Reference Lab	District Lab	Health Centre	Source
Reagent availability	82%	58%	32%	WHO SARA
Molecular availability	70%	40%	2%	PEPFAR lab network
Equipment functional	80%	55%	25%	ASLM assessments
Stocked out 6m	26%	52%	75%	Multi-country data
Expired reagent rate	8%	18%	30%	WHO SARA
Order fill rate	78%	52%	25%	Lab supply data

2.2 Reagent Selection

18 reagents across 9 departments: biochemistry (glucose, chemistry analyzer), haematology (Hb, CBC), urinalysis, blood bank (grouping antisera), serology (RPR, HBV, HCV), molecular (GeneXpert TB/VL/EID), parasitology (Giemsa), microbiology (Gram stain, blood culture, Mueller-Hinton), immunology (CD4), POC (pregnancy test).

2.3 Scenario Design

Scenario A — Reference Laboratory: National/regional reference lab with molecular platforms, culture capability, chemistry analyzers, cold storage, 8+ lab scientists.

Scenario B — District Laboratory: GeneXpert for TB, chemistry analyzer, no culture capability, limited cold storage, 3 staff.

Scenario C — Health Centre Lab: POC tests only (RDTs, dipsticks, Hb), no molecular or culture, no cold storage, 1 lab assistant.

3. Schema

Column	Type	Description
facility_level	categorical	reference_lab / district_lab / health_centre
reagent_name	categorical	18 laboratory reagents
department	categorical	biochemistry / haematology / microbiology / molecular / serology / parasitology / immunology / POC / blood_bank / urinalysis
format	categorical	reagent type format
cold_chain_required	binary	Requires cold storage
available_on_survey_day	binary	Reagent available
stocked_out_in_last_6m	binary	Any stockout in 6 months
stockout_days_last_6m	int	Total stockout days
stockout_cause	categorical	11 root cause categories
equipment_functional	binary	Lab equipment operational
tests_performed_month	int	Tests done this period
tests_not_done_no_reagent	int	Tests not done due to stockout
sample_referred_out	binary	Sample sent to higher lab
turnaround_time_days	float	Result turnaround time
expired_reagent_found	binary	Expired reagent on shelf
months_of_stock	float	Stock in months
order_fill_rate_pct	float	Order fulfilment rate

4. Validation

5. Usage

from datasets import load_dataset

dataset = load_dataset(
    "electricsheepafrica/laboratory-reagent-supply",
    "district_laboratory"
)
df = dataset["train"].to_pandas()

# Which departments are most affected by stockouts?
print(df.groupby('department')['available_on_survey_day'].mean().sort_values())

6. Limitations

• Simulated: Not from real laboratory information systems.
• No platform lock-in: Proprietary reagent-equipment dependencies simplified.
• No quality assurance: External quality assessment participation not modelled.

7. References

1. Lancet Commission on Diagnostics (2021). 47% lack access to diagnostics.
2. WHO SARA. Service Availability and Readiness Assessment. Laboratory indicators.
3. ASLM. African Society for Laboratory Medicine. Lab strengthening frameworks.
4. PEPFAR. Laboratory network and GeneXpert reagent supply data.

Citation

@dataset{esa_lab_reagent_2025,
  title   = {Laboratory Reagent Supply Dataset},
  author  = {{Electric Sheep Africa}},
  year    = {2025},
  publisher = {Hugging Face},
  url     = {https://huggingface.co/datasets/electricsheepafrica/laboratory-reagent-supply},
  note    = {Simulated dataset. Not for clinical or procurement decisions.}
}

License

CC-BY-4.0