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By Jean-Christophe Fotso, Alex Ezeh, Nyovani Madise, Abdhalah Ziraba and Reuben Ogollah

What does access to health care among the urban poor mean? Factors associated with use of “appropriate” maternal health services in the slum settlements of Nairobi, Kenya. By Jean-Christophe Fotso, Alex Ezeh, Nyovani Madise, Abdhalah Ziraba and Reuben Ogollah INDEPTH Network AGM

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By Jean-Christophe Fotso, Alex Ezeh, Nyovani Madise, Abdhalah Ziraba and Reuben Ogollah

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  1. What does access to health care among the urban poor mean?Factors associated with use of “appropriate” maternal health services in the slum settlements of Nairobi, Kenya By Jean-Christophe Fotso, Alex Ezeh, Nyovani Madise, Abdhalah Ziraba and Reuben Ogollah INDEPTH Network AGM Nairobi, September 3-7, 2007

  2. Background: Maternal Deaths and Mortality Ratio(Deaths per 100,000 live births) 1,200 247,000 deaths 11,000 deaths 1,000 800 Maternal Mortality Ratio (per 100,000) 600 529,000 deaths 527,000 deaths 400 200 0 World Developing Sub-Saharan Kenya countries Africa Source: WHO/UNICEF/UNFPA, 2004

  3. Background (Ct’d) • Kenya resolved to reduce MMR by 3/4 by 2015. • Kenya NRHSDS (1997-2010) • Safe motherhood and child survival • Key pillars include clean and safe delivery. • Urbanization, poverty and health inequities in SSA • More than 50% of SSA population will be living in urban areas by 2030. • About 7 out 10 inhabitants of Nairobi live in slums. • Growing inequities between the poor and the non-poor. • MDG-5: Attention to the growing urban poor populations in SSA.

  4. Background (Ct’d) • In the slums co-exist: • Private, sub-standard and often unlicensed clinics, with • Well equipped public, religious or large NGO facilities, generally in the outskirts of the slums. • Preferable to deliver at home or at TBA’s? • Misleading not to treat the two categories of HFs separately.

  5. Objectives • Improve understanding of maternity health seeking behaviors in resource-deprived urban settings • Identify the factors which influence the choice of place of delivery among the urban poor; • Distinction between sub-standard and “appropriate” health facilities; • Formulate recommendations aimed at improving maternal health.

  6. Data and Methods • Data from a DSS-nested MHP • 1,927 who had pregnancy outcomes in 2004-2005 • 25 HFs providing obstetric care • Dependent variable: Place of delivery • Public/religious/large NGO HF: coded 2 • Private, sub-standard HF: coded 1 • Not HF (home, TBA …): coded 0 • Covariates • Socioeconomic variables • Biodemographic and health-related covariates • Slum residence (Korogocho, Viwandani) • Methods • Descriptive analysis • Ordered logistic - Partial proportional odds models

  7. Health facility deliveries in Kenya 90 78 80 70 70 60 48 50 Percentage of women (%) 40 33 30 20 10 0 Nairobi slums (1) Nairobi slums (2) Nairobi Rural Kenya (1): All types of HFs; (2): Appropriate HFs

  8. Multivariate results: Socioeconomic & Residence †p<0.10; *p<0.05; **p<0.01

  9. Multivariate results: Biodemo and health-related †p<0.10; *p<0.05; **p<0.01

  10. Multivariate results: Interactions

  11. Recommendations • Provision of health services to the urban poor: • Registration of private facilities and clinics – minimum criteria • Provision of public health services in/near the slums • Improvement of the quality of care (delays, attitude of staff) in public HFs • Health education campaigns • Antenatal care attendance • Advice/counseling on delivery and postnatal care, and other pregnancy-related issues • Target groups: • Poorest, not educated, not working women • Higher parity women • Access to FP and RH services

  12. Acknowledgements: • The World Bank • The Wellcome Trust Thank you

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