Datasets:

electricsheepafrica
/

africa-synth-malaria-paediatric-anaemia-all

id int64	sex string	age_months int64	region_type string	ses_quintile int64	hemoglobin_gdl float64	anaemia_severity string	ferritin_ngml float64	iron_deficiency int64	malaria_rdt int64	weight_kg float64	height_cm float64	muac_cm float64	wasting_muac string	stunted int64	severely_stunted int64	underweight int64	dietary_diversity_score int64	breastfeeding_status string	deworming_last_6mo int64
1	F	44	rural	1	10.4	mild	159.2	0	1	14.4	96.8	15.3	Normal	0	0	0	4	partial	1
2	M	44	rural	2	10.2	mild	177.7	0	1	12.7	97.6	14.2	Normal	0	0	0	3	none	0
3	F	17	urban	4	10.3	mild	17.4	0	0	9	75	14.2	Normal	0	0	0	5	partial	0
4	F	8	urban	3	9.5	moderate	1.9	1	0	7.6	66.9	13.8	Normal	0	0	0	7	partial	0
5	M	22	rural	1	10.3	mild	29.7	0	0	12.3	79.4	15.7	Normal	1	0	0	1	none	1
6	F	14	rural	5	10.5	mild	5.5	1	0	7.2	76.4	13.2	Normal	0	0	1	2	none	1
7	F	27	rural	1	12.4	none	24.4	0	0	9.4	90.5	14.1	Normal	0	0	1	1	none	0
8	F	58	rural	1	9.4	moderate	7.5	1	1	18.8	97.4	15.2	Normal	1	0	0	3	none	0
9	M	16	rural	2	11.6	none	44.9	0	0	9.8	79.1	15.4	Normal	0	0	0	0	partial	1
10	M	23	urban	1	7.9	moderate	271.8	0	1	9.1	84.2	14.5	Normal	0	0	1	1	none	1
11	M	44	rural	3	11.1	none	198.3	0	1	14.6	103.4	15	Normal	0	0	0	1	partial	0
12	F	33	rural	3	11.4	none	64.9	0	0	12.4	88.2	15.1	Normal	0	0	0	6	none	0
13	F	8	rural	5	7.4	moderate	323.7	0	1	7.3	65.7	13.5	Normal	0	0	0	3	partial	0
14	F	15	rural	1	11.5	none	1	1	0	9.9	77.7	15	Normal	0	0	0	1	partial	0
15	M	56	urban	2	11.8	none	51.8	0	0	13.8	102.9	14.6	Normal	0	0	1	2	none	0
16	M	11	urban	4	11.7	none	4.7	1	0	9.3	72.1	14.9	Normal	0	0	0	5	partial	0
17	F	40	rural	4	10.8	mild	26.8	0	0	13.1	92.8	14.7	Normal	0	0	0	3	none	1
18	M	48	rural	2	13	none	84.2	0	0	15.8	102.9	15.9	Normal	0	0	0	4	none	0
19	F	20	rural	5	10.6	mild	4.9	1	0	11.6	84.5	16.4	Normal	0	0	0	5	partial	0
20	F	44	rural	5	10.4	mild	31.2	0	0	16.3	96.3	16.3	Normal	0	0	0	7	none	0
21	F	15	rural	4	12	none	105.3	0	0	11.6	76.1	16	Normal	0	0	0	7	partial	0
22	M	21	rural	4	11.7	none	30	0	0	8.7	85.9	14.7	Normal	0	0	1	4	partial	1
23	F	23	urban	3	9.2	moderate	31.2	0	0	11.6	84.2	15.9	Normal	0	0	0	3	none	1
24	F	59	urban	3	12.6	none	75	0	0	18.7	106.1	16.9	Normal	0	0	0	3	none	1
25	F	17	rural	3	12.5	none	233.5	0	1	9.3	82.4	14.9	Normal	0	0	0	3	none	0
26	M	13	urban	5	11.5	none	15.3	0	0	9.6	72.7	15.4	Normal	0	0	0	4	none	0
27	M	55	urban	1	12.3	none	26.7	0	0	19.5	103.3	17.3	Normal	0	0	0	3	none	0
28	M	15	urban	1	9	moderate	1.4	1	0	9.5	74.2	15.6	Normal	0	0	0	1	none	0
29	M	53	rural	3	10.5	mild	203	0	1	18.7	99.9	16.3	Normal	0	0	0	2	none	0
30	F	40	urban	3	10.1	mild	6.7	1	1	13.8	89.8	15.2	Normal	1	0	0	2	none	1
31	F	37	urban	1	10.5	mild	136.9	0	1	14.1	91.5	16.1	Normal	0	0	0	3	none	1
32	F	46	rural	4	11.3	none	32.1	0	0	16.8	100.2	16.2	Normal	0	0	0	7	none	1
33	M	44	rural	1	11.1	none	93.3	0	0	13.4	88.6	15	Normal	1	1	0	1	none	1
34	M	12	urban	2	7.6	moderate	28	0	0	9.7	72.8	15.7	Normal	0	0	0	2	partial	1
35	M	59	rural	3	12.3	none	56.6	0	0	16.6	106.1	15.1	Normal	0	0	0	4	none	0
36	M	56	rural	1	5.8	severe	154.9	0	1	19	102.5	17.3	Normal	0	0	0	2	none	0
37	M	54	rural	4	11.4	none	1	1	0	16.2	97	15.1	Normal	1	0	0	2	none	1
38	M	39	rural	3	12.2	none	35.9	0	0	13.9	99.6	15.2	Normal	0	0	0	1	none	1
39	M	34	urban	3	12.8	none	36.4	0	0	12.3	96.6	14.2	Normal	0	0	0	6	none	0
40	F	34	urban	3	8.5	moderate	4.2	1	0	12.9	95.3	14.9	Normal	0	0	0	4	none	0
41	M	7	rural	4	12.5	none	78.6	0	0	8.7	66.6	14.8	Normal	0	0	0	7	partial	0
42	F	32	urban	4	8.4	moderate	6.4	1	0	12.8	90.6	15.4	Normal	0	0	0	0	partial	1
43	F	39	rural	4	8.1	moderate	8.1	1	0	17.4	91.3	16.7	Normal	0	0	0	7	none	0
44	M	41	urban	2	11.5	none	296.5	0	1	14.1	93.2	15.6	Normal	0	0	0	2	none	0
45	F	24	rural	4	10.4	mild	46.1	0	0	10.1	80.6	14.7	Normal	1	0	0	3	none	0
46	F	31	rural	1	10.6	mild	22.8	0	0	12.3	90.1	14.5	Normal	0	0	0	4	none	0
47	M	40	rural	1	11.8	none	39.1	0	0	15.2	99.8	16.5	Normal	0	0	0	1	none	1
48	M	29	urban	5	10.4	mild	12.3	0	0	10.7	93.5	14.8	Normal	0	0	0	3	none	0
49	F	57	urban	1	12.9	none	76.5	0	0	19.5	104.2	15.9	Normal	0	0	0	1	none	0
50	M	43	rural	1	10.2	mild	265.1	0	1	14.4	94.5	16.3	Normal	0	0	0	3	none	0
51	M	6	urban	2	8.8	moderate	4.9	1	1	9.2	65.6	14.7	Normal	0	0	0	1	exclusive	0
52	M	7	rural	5	10.7	mild	5.8	1	1	9.2	69.7	14.3	Normal	0	0	0	5	partial	0
53	F	43	rural	5	10.7	mild	1.5	1	1	15.5	102.3	15.1	Normal	0	0	0	5	none	0
54	F	28	urban	4	11.7	none	31.5	0	0	12.3	92.5	14.4	Normal	0	0	0	5	none	0
55	F	50	rural	1	11.9	none	263.9	0	1	14	105.2	14.6	Normal	0	0	0	0	none	0
56	F	15	urban	4	10.5	mild	3.6	1	0	8.7	80	14.1	Normal	0	0	0	3	none	0
57	M	7	rural	5	12.7	none	56.4	0	0	7.2	68.2	12.9	Normal	0	0	0	7	partial	0
58	F	27	rural	2	10.2	mild	8	1	1	10.2	87.6	14.2	Normal	0	0	0	2	none	0
59	M	45	rural	2	13	none	65.1	0	0	16	103.4	16.8	Normal	0	0	0	2	none	0
60	M	58	rural	5	6.8	severe	203.9	0	1	17.7	106.8	15.7	Normal	0	0	0	5	none	1
61	F	53	rural	1	10.3	mild	8.6	1	0	13	104.6	14.2	Normal	0	0	1	3	none	0
62	M	32	urban	5	10.5	mild	4.8	1	0	12.3	88.9	15.4	Normal	0	0	0	3	partial	0
63	F	49	urban	3	10.4	mild	11.4	1	1	13.9	104.7	16	Normal	0	0	0	1	none	0
64	F	10	urban	4	7.9	moderate	22.9	0	0	9.9	68.9	15.5	Normal	0	0	0	7	none	0
65	F	36	rural	2	11.5	none	77.9	0	0	10.3	97.4	14.3	Normal	0	0	1	4	none	0
66	F	55	rural	4	11.3	none	76	0	0	20.3	103	17.7	Normal	0	0	0	0	none	0
67	F	27	rural	5	9.5	moderate	39.6	0	0	12.7	84.8	15.8	Normal	0	0	0	6	none	1
68	M	57	rural	3	11.3	none	17.1	0	0	12.3	97.1	13.2	Normal	1	0	1	4	none	0
69	M	18	rural	2	7.1	moderate	1	1	1	9.6	79.4	14.3	Normal	0	0	0	0	none	0
70	M	12	urban	1	11.2	none	24.7	0	0	10	71	15.8	Normal	0	0	0	6	partial	0
71	M	34	urban	5	10.8	mild	1	1	0	12.3	88.9	14.7	Normal	0	0	0	7	none	0
72	M	54	rural	5	11.9	none	40.1	0	0	16.3	105.9	14.9	Normal	0	0	0	2	none	1
73	F	22	urban	4	10.4	mild	4	1	0	14	77.8	16.4	Normal	1	0	0	2	none	0
74	M	48	urban	1	12.7	none	38.6	0	0	15.2	105.7	15.8	Normal	0	0	0	4	none	1
75	M	15	urban	5	5.7	severe	6.4	1	0	8.5	84.4	14.7	Normal	0	0	0	1	none	0
76	M	46	rural	3	11.9	none	36.9	0	0	17.3	97.9	16.4	Normal	0	0	0	3	none	0
77	M	36	urban	2	10.3	mild	36.2	0	0	11.5	97.7	13.5	Normal	0	0	0	6	none	1
78	F	15	rural	2	8.6	moderate	2.2	1	0	10.3	72	15.5	Normal	1	0	0	3	partial	0
79	F	44	urban	5	11.3	none	259.2	0	1	15.9	99.3	15.2	Normal	0	0	0	7	none	0
80	F	44	urban	4	10.3	mild	39.8	0	0	17.7	98	17	Normal	0	0	0	6	none	1
81	F	33	rural	3	12.6	none	24.2	0	0	13.8	102.6	15.8	Normal	0	0	0	6	partial	1
82	M	18	rural	4	12	none	57.4	0	0	10.4	79.9	14.8	Normal	0	0	0	7	none	0
83	F	13	rural	5	10.3	mild	6	1	0	9	75.2	14.1	Normal	0	0	0	5	none	0
84	M	57	urban	4	9.1	moderate	13.2	0	0	21.1	103.4	18.1	Normal	0	0	0	3	none	0
85	M	41	rural	2	12.8	none	71.7	0	1	14.9	95.8	15.9	Normal	0	0	0	3	none	1
86	M	49	rural	1	12	none	38.8	0	0	15.7	100.7	15.1	Normal	0	0	0	2	none	0
87	F	43	rural	1	12.8	none	69.3	0	0	14.6	96.5	16.3	Normal	0	0	0	3	partial	1
88	M	50	urban	2	11.3	none	75.2	0	0	17.1	99.2	16.2	Normal	0	0	0	2	none	1
89	M	15	rural	4	11.8	none	33.4	0	0	9.6	78.7	14.7	Normal	0	0	0	6	partial	1
90	M	32	urban	1	12.7	none	33.5	0	0	11.8	88.9	15.4	Normal	0	0	0	3	none	0
91	M	41	rural	1	6.7	severe	200.1	0	1	13.4	94.6	15.5	Normal	0	0	0	0	none	0
92	F	11	rural	5	12	none	54.9	0	0	7.9	77.6	14.5	Normal	0	0	0	4	none	0
93	M	14	rural	3	12.8	none	42	0	0	9.7	76.5	15.3	Normal	0	0	0	5	none	0
94	M	40	urban	1	12.2	none	44	0	0	13.6	100	14.6	Normal	0	0	0	3	none	1
95	M	52	urban	5	12.8	none	46.4	0	0	16.2	102.5	15.1	Normal	0	0	0	2	none	0
96	F	41	urban	5	9.4	moderate	167.6	0	1	13.8	98	15	Normal	0	0	0	1	none	0
97	M	8	rural	3	10.5	mild	10.2	1	0	8.6	68.2	13.4	Normal	0	0	0	3	partial	0
98	M	57	urban	1	7.8	moderate	26.1	0	0	15	93.4	15.4	Normal	1	1	0	3	none	0
99	M	13	urban	4	11.4	none	19.3	0	0	11.6	75.6	16.3	Normal	0	0	0	5	none	1
100	F	18	rural	5	10.6	mild	15.9	0	0	11.7	76.1	15.8	Normal	0	0	0	4	none	0

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

Synthetic Paediatric Anaemia Screening Dataset (6–59 months)

Abstract

This dataset provides 30,000 synthetic records (10,000 per scenario) of anaemia screening for children aged 6-59 months in LMIC settings. Each record contains 20 variables including haematological biomarkers (hemoglobin, ferritin), malaria RDT status, anthropometry (weight, height, MUAC), dietary diversity, breastfeeding status, and derived nutritional classifications. All distributions are parameterized from WHO anaemia thresholds, the Global Burden of Disease anaemia estimates, WHO Child Growth Standards, and DHS biomarker surveys. Three burden scenarios (low, moderate, high) reflect anaemia prevalence ranging from 30% to 80%.

1. Introduction

Anaemia affects approximately 40% of children aged 6-59 months globally, with prevalence exceeding 60% in parts of Sub-Saharan Africa and South Asia (Stevens et al., 2013). Iron deficiency is the most common cause, but malaria, helminth infections, and chronic inflammation are major contributors in LMIC settings. Despite this burden, open-access individual-level datasets linking haematological biomarkers, malaria status, and nutritional indicators are virtually nonexistent.

This synthetic dataset is designed for:

Training ML models for anaemia severity prediction from clinical and demographic features
Exploring the anaemia-malaria-malnutrition nexus
Benchmarking screening algorithms for community health settings
Educational use in paediatric haematology and global nutrition

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

2. Methodology

2.1 Epidemiological Parameterization

Parameter	Value	Source
Global anaemia prevalence (6-59mo)	~40%	Stevens et al., Lancet Global Health 2013
Severe anaemia (Hb<7)	2-10% by setting	Kassebaum et al., Blood 2014
Iron deficiency anaemia proportion	40-60% of all anaemia	Petry et al., Nutrients 2016
Malaria prevalence (under-5)	5-50% by endemicity	WHO World Malaria Report 2023
Stunting prevalence	18-42%	UNICEF/WHO/World Bank JME 2023

2.2 Scenario Design

Scenario	Any Anaemia	Moderate	Severe	Malaria RDT+	Iron Deficiency
Low burden	30.3%	8.6%	2.0%	7.5%	21.6%
Moderate burden	55.9%	20.1%	4.7%	27.0%	30.0%
High burden	80.3%	34.4%	9.4%	51.5%	35.6%

2.3 Classification Criteria

Classification	Criteria	Source
No anaemia	Hb ≥ 11.0 g/dL	WHO 2011
Mild anaemia	Hb 10.0-10.9 g/dL	WHO 2011
Moderate anaemia	Hb 7.0-9.9 g/dL	WHO 2011
Severe anaemia	Hb < 7.0 g/dL	WHO 2011
Iron deficiency	Ferritin < 12 ng/mL	WHO 2020
SAM (MUAC)	MUAC < 11.5 cm	WHO/UNICEF
MAM (MUAC)	MUAC 11.5-12.4 cm	WHO/UNICEF

3. Dataset Description

3.1 Schema

Column	Type	Units	Description
id	int	—	Unique identifier
sex	categorical	M/F	Biological sex
age_months	int	months	Age (6-59)
region_type	categorical	—	Urban/rural
ses_quintile	int	1-5	Wealth quintile (1=poorest)
hemoglobin_gdl	float	g/dL	Hemoglobin concentration
anaemia_severity	categorical	—	none/mild/moderate/severe
ferritin_ngml	float	ng/mL	Serum ferritin
iron_deficiency	binary	0/1	Ferritin < 12 ng/mL
malaria_rdt	binary	0/1	Malaria rapid diagnostic test result
weight_kg	float	kg	Body weight
height_cm	float	cm	Standing height/recumbent length
muac_cm	float	cm	Mid-upper arm circumference
wasting_muac	categorical	—	SAM/MAM/Normal
stunted	binary	0/1	Height-for-age z < -2
severely_stunted	binary	0/1	Height-for-age z < -3
underweight	binary	0/1	Weight-for-age z < -2
dietary_diversity_score	int	0-7	Food groups consumed (FAO/WHO)
breastfeeding_status	categorical	—	exclusive/partial/none
deworming_last_6mo	binary	0/1	Received deworming in last 6 months

4. Validation

4.1 Diagnostic Plots

Validation Report

5. Usage

5.1 Loading with HuggingFace `datasets`

from datasets import load_dataset

dataset = load_dataset("electricsheepafrica/synthetic-paediatric-anaemia-screening-WHO-6-59months", "moderate_burden")
df = dataset["train"].to_pandas()

5.2 Regenerating

pip install numpy pandas scipy matplotlib
python generate_dataset.py --all-scenarios --n 10000 --seed 42
python validate_dataset.py

6. Limitations

Synthetic data: Not for clinical use.
Single timepoint: No longitudinal haematological trajectory.
Simplified aetiology: Real anaemia has multiple overlapping causes (hookworm, thalassemia, chronic disease) not fully modelled.
Ferritin in inflammation: Acute phase response adjustment is simplified; real interpretation requires CRP.

7. References

Stevens GA, et al. (2013). Global trends in haemoglobin concentration and anaemia prevalence. Lancet Global Health, 1(1):e16-25.
Kassebaum NJ, et al. (2014). A systematic analysis of global anemia burden. Blood, 123(5):615-624.
WHO (2011). Haemoglobin concentrations for the diagnosis of anaemia. Geneva.
WHO (2006). WHO Child Growth Standards. Geneva.
UNICEF/WHO/World Bank (2023). Joint Malnutrition Estimates.
WHO (2020). WHO guideline on use of ferritin concentrations. Geneva.
Petry N, et al. (2016). The proportion of anemia associated with iron deficiency. Nutrients, 8(11):693.
WHO (2023). World Malaria Report 2023. Geneva.
FAO/WHO (2021). Minimum Dietary Diversity for children 6-23 months.
DHS Program. Biomarker surveys, multiple countries.

Citation

@dataset{esa_anaemia_2025,
  title={Synthetic Paediatric Anaemia Screening Dataset (6-59 months)},
  author={Electric Sheep Africa},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/electricsheepafrica/synthetic-paediatric-anaemia-screening-WHO-6-59months}
}