| Literature DB >> 30231887 |
Evelyn Kabia1, Rahab Mbau2, Kelly W Muraya3, Rosemary Morgan4, Sassy Molyneux5,6, Edwine Barasa2,6.
Abstract
BACKGROUND: Health inequity has mainly been linked to differences in economic status, with the poor facing greater challenges accessing healthcare than the less poor. To extend financial coverage to the poor and vulnerable, Kenya has therefore implemented several pro-poor health policy reforms. However, other social determinants of health such as gender and disability also influence health status and access to care. This study employed an intersectional approach to explore how gender disability and poverty interact to influence how poor women in Kenya benefit from pro-poor financing policies that target them.Entities:
Keywords: Disability; Gender; Intersectionality; Kenya; Poverty; Pro-poor
Mesh:
Year: 2018 PMID: 30231887 PMCID: PMC6146517 DOI: 10.1186/s12939-018-0853-6
Source DB: PubMed Journal: Int J Equity Health ISSN: 1475-9276
County demographic and health indicators
| Indicator | County A (Urban) | County B (Rural) | Country |
|---|---|---|---|
| Population 2015/2016 [ | |||
| Total | 4,463,000 | 985,000 | 45,371,000 |
| Male | 2,237,000 (50.1%) | 466,000 (47.3%) | 22,393,000 (49.4%) |
| Female | 2,226,000 (49.9 %) | 519,000 (52.7%) | 22,977,000 (50.6%) |
| Population with any disability | 1.2% | 5.3% | 2.8% |
| Morbidity | 19.2% | 33.2% | 21.5% |
| Poverty rate | 16.7% | 33.8% | 36.1% |
| Home deliveries for under 5 | 8.8% | 13% | 31.3% |
| HDSS | |||
| HDSS residents | 63,639 [ | 255,000 [ | 824,595 [ |
| Health facilities in 2015 [ | |||
| Public | 161 | 123 | 4,929 |
| Nongovernmental | 118 | 7 | 347 |
| Faith-based | 100 | 16 | 1,081 |
| Private | 543 | 28 | 3,797 |
| Health Financing | |||
| Total government health spending (per capita, KES) (2015) [ | 1,745 | 1,495 | 1,585 |
| Health insurance coverage (2015/2016) [ | 40.7% | 7.6% | 19.0% |
Participants socio-demographic profile
| Participant | Age | Type of disability | Highest level of education | Marital status | Source of income | Residence | Participant description |
|---|---|---|---|---|---|---|---|
| 1 | 70 | mobility impaired | none | single | small-scale trading | urban | HDSS resident |
| 2 | 32 | mobility impaired | primary | single | laundering | urban | HDSS resident |
| 3 | 30 | mobility impaired | pre-school | married | none | urban | HDSS resident |
| 4 | 35 | visually impaired | secondary | separated | community health volunteer | urban | HISP beneficiary |
| 5 | 60 | mobility impaired | primary | divorced | small-scale trading | urban | HISP beneficiary |
| 6 | 57 | mobility impaired | primary | widowed | subsistence farming | rural | HDSS resident |
| 7 | 48 | mobility impaired | primary | married | subsistence farming | rural | HISP beneficiary |
| 8 | 24 | mobility impaired | none | married | small-scale trading | rural | HDSS resident |
| 9 | 77 | visually impaired | none | widowed | government cash transfer | rural | HISP beneficiary |
| 10 | 81 | visually impaired | none | widowed | government cash transfer | rural | HISP beneficiary |
| 11 | 58 | mobility impaired | primary | single | small-scale trading | rural | HISP beneficiary |
Fig. 1Conceptual framework
Distribution of interviews per county
| Data collection method | County A | County B | Total | ||
|---|---|---|---|---|---|
| HDSS | HISP | HDSS | HISP | ||
| FGDs | 2 | 2 | 2 | 2 | 8 |
| IDIs | 8 | 7 | 7 | 8 | 30 |