Datasets:

electricsheepafrica
/

africa-synth-mental-health-anxiety-dataset-all

region stringclasses 5 values	sex stringclasses 2 values	age float64 18 90	urban_rural stringclasses 2 values	poverty_index float64 0 0.93	education_level stringclasses 4 values	employment_type stringclasses 3 values	household_size float64 1 20	has_anxiety bool 2 classes	gad7_score float64 0 21	idiom_thinking_too_much bool 2 classes	social_avoidance bool 2 classes
East Africa	Male	29.831354	Urban	0.353677	Primary	Informal	6.203052	false	2.993428	false	false
Central Africa	Female	19.69453	Rural	0.248558	Tertiary	Formal	4.390411	false	1.723471	false	false
North Africa	Female	18.399551	Rural	0.415959	Secondary	Informal	4.151995	false	3.295377	false	false
Southern Africa	Female	18.399768	Rural	0.159968	Primary	Informal	4.152035	false	5.04606	false	false
West Africa	Male	54.216067	Urban	0.550283	None	Informal	10.335257	false	1.531693	true	false
West Africa	Male	35.039914	Urban	0.110945	None	Informal	7.107079	false	1.531726	true	false
West Africa	Male	18	Urban	0.509897	None	Unemployed	3.603819	true	16.490142	true	true
Central Africa	Female	30.674359	Rural	0.17727	Secondary	Formal	6.350411	false	3.534869	false	false
Southern Africa	Female	25.426972	Rural	0.19829	Secondary	Informal	5.425578	false	1.061051	false	false
North Africa	Female	18	Rural	0.376237	Secondary	Informal	1.254604	false	3.08512	false	false
West Africa	Male	18	Urban	0.543467	None	Unemployed	2.526957	true	14.585207	false	false
Central Africa	Female	26.629242	Rural	0.084764	Tertiary	Formal	5.639155	false	1.06854	false	false
North Africa	Female	51.193044	Rural	0.357929	Secondary	Informal	9.834566	false	2.483925	false	false
West Africa	Male	18.51035	Urban	0.273321	Primary	Informal	4.17247	false	0	false	false
West Africa	Male	18	Urban	0.134376	None	Informal	3.440035	false	0	true	false
West Africa	Male	23.340759	Urban	0.353255	None	Informal	5.052277	false	0.875425	true	false
East Africa	Male	18	Urban	0.13279	Primary	Informal	3.320352	false	0	false	false
East Africa	Female	18	Rural	0.326434	Primary	Informal	4.013159	false	2.628495	false	false
East Africa	Male	21.4686	Urban	0.16581	Primary	Informal	4.714063	false	0.183952	false	false
West Africa	Male	18	Urban	0.207734	Primary	Informal	2.449118	false	0	false	false
Southern Africa	Female	36.169496	Rural	0.263408	Secondary	Informal	7.301258	false	4.931298	false	false
West Africa	Male	18	Urban	0.390014	None	Unemployed	2.179661	false	1.548447	true	true
West Africa	Male	18	Urban	0.153645	Primary	Informal	2.013604	false	2.135056	false	false
East Africa	Male	24.695486	Urban	0.305502	Primary	Informal	5.295091	false	0	false	false
East Africa	Male	34.455705	Rural	0.203314	Primary	Informal	7.006405	false	0.911235	false	false
North Africa	Female	24.288296	Rural	0.183877	Secondary	Informal	5.222269	false	2.221845	false	false
West Africa	Male	20.008563	Urban	0.367824	None	Informal	4.447943	false	0	true	false
East Africa	Female	18	Rural	0.202097	Primary	Informal	3.990753	false	2.751396	false	false
Southern Africa	Female	18	Rural	0.18634	Primary	Informal	3.623474	false	0.798723	false	false
West Africa	Male	41.249355	Urban	0.28352	None	Unemployed	8.167379	true	16.943066	true	true
Southern Africa	Female	26.79572	Rural	0.100399	Secondary	Informal	5.668645	false	0.796587	false	false
West Africa	Male	18	Urban	0.207729	None	Informal	3.794473	false	5.704556	true	false
West Africa	Male	18	Urban	0.309638	None	Unemployed	3.162541	true	19.56909	true	true
Central Africa	Female	31.975042	Rural	0.044386	Tertiary	Formal	6.576948	false	0	false	false
Central Africa	Female	40.661519	Rural	0.124579	Tertiary	Formal	8.067709	false	3.64509	false	false
North Africa	Female	38.468908	Rural	0.211003	Secondary	Informal	7.694694	false	0	false	false
East Africa	Male	18	Urban	0.358189	Primary	Informal	2.843593	false	2.417727	false	false
West Africa	Male	18	Urban	0.652242	None	Unemployed	3.971512	true	14.29754	true	true
Southern Africa	Female	18	Rural	0.162266	Secondary	Informal	3.582324	false	0	false	false
East Africa	Male	19.047128	Urban	0.069424	Primary	Informal	4.271461	false	2.393722	false	false
West Africa	Male	35.962358	Urban	0.076848	None	Unemployed	7.265697	false	3.476933	true	true
East Africa	Female	48.388966	Rural	0.174434	Primary	Informal	9.367746	false	2.342737	false	false
West Africa	Male	27.43764	Urban	0.543454	None	Unemployed	5.782171	true	14.297589	true	true
Central Africa	Female	18	Rural	0.061654	Tertiary	Formal	3.227709	false	1.397793	false	false
West Africa	Male	21.143588	Urban	0.235687	Primary	Informal	4.655011	false	0	false	false
Southern Africa	Female	53.821763	Rural	0.313294	Secondary	Informal	10.270094	false	0.560312	false	false
East Africa	Male	22.973388	Urban	0.152583	Primary	Informal	4.986161	false	1.078722	false	false
East Africa	Female	18	Rural	0.386886	Primary	Informal	3.995737	false	4.114244	false	false
East Africa	Female	18.591369	Rural	0.48459	Primary	Informal	4.187433	false	2.687237	false	false
West Africa	Male	27.359779	Urban	0.095939	None	Informal	5.768416	false	0	true	false
Central Africa	Female	18	Rural	0.366224	Tertiary	Formal	2.902245	false	2.648168	false	false
North Africa	Female	18	Rural	0.298373	Secondary	Informal	3.532612	false	1.229835	false	false
Central Africa	Female	38.127233	Rural	0.223766	Tertiary	Formal	7.636383	false	0.646156	false	false
Central Africa	Female	26.87495	Rural	0.164292	Secondary	Formal	5.682673	false	3.223353	false	false
Southern Africa	Female	23.127253	Rural	0.461386	Primary	Informal	5.013867	false	4.061999	false	false
Central Africa	Female	39.280995	Rural	0.098321	Tertiary	Formal	7.833085	false	3.86256	false	false
West Africa	Male	18	Urban	0.179248	None	Unemployed	3.111668	true	19.737638	true	true
West Africa	Male	18	Urban	0.343019	None	Informal	3.927964	false	1.381575	true	false
West Africa	Male	26.324263	Urban	0.27467	None	Unemployed	5.585079	true	17.302304	true	false
East Africa	Male	25.740939	Urban	0.443295	Primary	Informal	5.481457	false	3.95109	false	false
East Africa	Male	18	Urban	0.132724	Primary	Informal	1.885112	false	1.041652	false	false
West Africa	Male	18	Urban	0.400643	Primary	Informal	3.713332	false	1.628682	false	false
North Africa	Female	19.37826	Rural	0.322596	Secondary	Informal	4.332361	false	0	false	false
East Africa	Male	28.174922	Urban	0.292362	Primary	Informal	5.912209	false	0	false	false
West Africa	Male	25.683112	Urban	0.113577	Primary	Informal	5.471171	false	3.625052	false	false
East Africa	Female	20.589223	Rural	0.341559	Primary	Informal	4.554042	false	4.71248	false	false
West Africa	Male	18	Urban	0.169996	None	Unemployed	1.24858	false	1.85598	true	true
North Africa	Female	21.278732	Rural	0.801473	Secondary	Informal	4.679578	false	4.007066	false	false
West Africa	Male	22.565379	Urban	0.131771	None	Unemployed	4.912591	true	13.591577	true	true
Central Africa	Female	81.910452	Rural	0.1255	Tertiary	Formal	14.825441	false	0.70976	false	false
North Africa	Female	18.956741	Rural	0.337014	Secondary	Informal	4.254816	false	2.722791	false	false
West Africa	Male	26.415325	Urban	0.034399	None	Informal	5.601233	false	5.076073	true	false
West Africa	Male	43.217601	Urban	0.159378	None	Unemployed	8.500122	true	16.62768	true	true
North Africa	Female	34.726473	Rural	0.261516	Secondary	Informal	7.053086	false	5.129287	false	false
North Africa	Female	49.569259	Rural	0.249119	Secondary	Informal	9.564532	false	0	false	false
North Africa	Female	18	Rural	0.068587	Secondary	Informal	1.904668	false	3.643805	false	false
North Africa	Female	18	Rural	0.459506	Secondary	Informal	2.566231	false	2.174094	false	false
West Africa	Male	18	Urban	0.541478	None	Unemployed	3.393076	true	13.609747	true	true
East Africa	Male	18	Urban	0.260596	Primary	Informal	1.696733	false	2.183522	false	false
West Africa	Male	22.691637	Urban	0.067536	None	Unemployed	4.935374	true	13.602811	false	false
Central Africa	Female	18	Rural	0.046769	Secondary	Formal	2.694215	false	1.560656	false	false
Southern Africa	Female	53.425611	Rural	0.127131	Secondary	Informal	10.204584	false	2.714225	false	false
East Africa	Male	18	Urban	0.326599	Primary	Informal	2.951027	false	4.955788	false	false
West Africa	Male	18	Urban	0.119125	None	Unemployed	3.94115	true	15.725887	true	false
East Africa	Male	35.982819	Urban	0.107767	Primary	Informal	7.26921	false	0.383013	false	false
East Africa	Male	18	Urban	0.324601	Primary	Informal	2.163787	false	0.996486	false	false
North Africa	Female	18	Rural	0.219354	Secondary	Informal	1.621517	false	3.830804	false	false
Southern Africa	Female	24.499619	Rural	0.304792	Secondary	Informal	5.260079	false	2.657502	false	false
Central Africa	Female	25.721584	Rural	0.40373	Secondary	Formal	5.478015	false	0.94048	false	false
East Africa	Male	35.332522	Rural	0.184129	Primary	Informal	7.157439	false	3.026535	false	false
West Africa	Male	30.293292	Urban	0.234474	None	Unemployed	6.283853	true	9.260159	true	true
North Africa	Female	26.338753	Rural	0.175069	Secondary	Informal	5.58765	false	3.93729	false	false
North Africa	Female	18	Rural	0.267332	Secondary	Informal	3.15623	false	0.595894	false	false
Southern Africa	Female	25.268307	Rural	0.2387	Primary	Informal	5.39731	false	1.344676	false	false
North Africa	Female	26.273215	Rural	0.325796	Secondary	Informal	5.576021	false	1.215784	false	false
East Africa	Female	18	Rural	0.443488	Primary	Informal	3.086972	false	0	false	false
East Africa	Female	62.368086	Rural	0.385567	Primary	Informal	11.673582	false	2.592241	false	false
East Africa	Male	29.416459	Urban	0.096153	Primary	Informal	6.130368	false	2.522111	false	false
West Africa	Male	18	Urban	0.208985	None	Unemployed	2.594406	true	9.825247	true	true
West Africa	Male	35.439934	Urban	0.166196	None	Unemployed	7.175915	true	13.313137	true	true

End of preview. Expand in Data Studio

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

Dataset Card for Africa Anxiety Dataset

Dataset Summary

The Africa Anxiety Dataset is a synthetic dataset generated by Electric Sheep Africa to model mental health presentations in African contexts. It contains 100,000 rows and 12 columns. The generation is grounded in peer-reviewed literature from Sub-Saharan Africa, capturing region-specific prevalence rates, risk factors (e.g., poverty, conflict), and cultural presentations of distress (e.g., somatic symptoms, "thinking too much").

This dataset is designed for research, educational, and methodological testing purposes. It respects the statistical properties of real-world data without exposing any Private Identifiable Information (PII), as every record is entirely synthetic.

Supported Tasks and Leaderboards

Epidemiological Modeling: Simulating disease burden across different African regions.
Health Services Research: Analyzing the gap between need and treatment access (e.g., traditional healer usage).
Risk Factor Analysis: Logistic regression testing on variables like poverty index and conflict exposure.

Languages

English (Data labels). The cultural context covers West, East, North, Southern, and Central Africa.

Dataset Structure

Data Instances

A sample instance from the dataset:

[
  {
    "region":"East Africa",
    "sex":"Male",
    "age":29.8313536542,
    "urban_rural":"Urban",
    "poverty_index":0.3536766572,
    "education_level":"Primary",
    "employment_type":"Informal",
    "household_size":6.2030524535,
    "has_anxiety":false,
    "gad7_score":2.993428306,
    "idiom_thinking_too_much":false,
    "social_avoidance":false
  }
]

Data Fields

Field Name	Type	Description
`region`	object	Synthetic variable based on literature distributions.
`sex`	object	Synthetic variable based on literature distributions.
`age`	float64	Synthetic variable based on literature distributions.
`urban_rural`	object	Synthetic variable based on literature distributions.
`poverty_index`	float64	Synthetic variable based on literature distributions.
`education_level`	object	Synthetic variable based on literature distributions.
`employment_type`	object	Synthetic variable based on literature distributions.
`household_size`	float64	Synthetic variable based on literature distributions.
`has_anxiety`	bool	Synthetic variable based on literature distributions.
`gad7_score`	float64	Synthetic variable based on literature distributions.
`idiom_thinking_too_much`	bool	Synthetic variable based on literature distributions.
`social_avoidance`	bool	Synthetic variable based on literature distributions.

Data Splits

Train/Val/Test: Not applicable (Single synthetic file). All 100,000 rows are available for splitting as needed.

Dataset Creation

Curation Rationale

There is a scarcity of high-quality, open-access mental health data from African regions. This dataset aims to fill that gap by synthesizing available knowledge from systematic reviews and small-scale studies into a coherent, usable format for data scientists.

Source Data

Initial Data Collection and Normalization

This data was generated using a probabilistic pipeline keying off parameter files derived from the literature:

Ekhator, C. N., et al. (2024). Mental Health Awareness in Rural vs. Urban Areas. CARI Journals.
- Parameter: Urban/Rural Mental Health Access (Qualitative Differences)
- Region: General Africa
Lund, C., et al. (2010). Poverty and common mental disorders in low and middle income countries: A systematic review. Social Science & Medicine.
- Parameter: Poverty-Depression Odds Ratio (OR > 2.0)
- Region: LMIC (including Africa)
Santavicca, T., et al. (2021). Anxiety disorders in Africa: A scoping review. Journal of Global Health.
- Parameter: Anxiety Prevalence (5.7% - 15.8%)
- Region: 12 African Countries
Kola, L., et al. (2021). Emerging mental health challenges during the COVID-19 pandemic in Africa. The Lancet Psychiatry.
- Parameter: Pandemic Anxiety/Depression Spike (~47%)
- Region: Africa
Patel, V., et al. (2001). Poverty and common mental disorders in developing countries. Bulletin of the World Health Organization.
- Parameter: Kufungisisa (Thinking Too Much) (Cultural Idiom of Distress)
- Region: Zimbabwe

Personal and Sensitive Information

This dataset contains fake medical info. No real individuals are represented. However, it models sensitive topics (Depression, Psychosis, HIV status) and should be handled with the ethical care appropriate for health data to avoid stigmatization in downstream analyses.

Considerations for Using the Data

Social Impact of Dataset

Positive: Enables tool development for under-resourced regions.
Negative: If treated as "real" ground truth without validation, it could lead to incorrect policy assumptions. Always validate synthetic findings against local clinical data.

Discussion of Biases

Literature Bias: The data reflects the biases of the underlying studies (e.g., more data from South Africa/Nigeria/Kenya than other regions).
Simplification: Complex interactions (e.g., genetic vs environmental) are simplified into probabilistic dependencies.