Skip to main content

Trends in Causes of Adult Deaths among the Urban Poor: Evidence from Nairobi Urban Health and Demographic Surveillance System, 2003–2012

Journal of Urban Health volume 92, pages 422–445 (2015)Cite this article

Abstract

What kills people around the world and how it varies from place to place and over time is critical in mapping the global burden of disease and therefore, a relevant public health question, especially in developing countries. While more than two thirds of deaths worldwide are in developing countries, little is known about the causes of death in these nations. In many instances, vital registration systems are nonexistent or at best rudimentary, and even when deaths are registered, data on the cause of death in particular local contexts, which is an important step toward improving context-specific public health, are lacking. In this paper, we examine the trends in the causes of death among the urban poor in two informal settlements in Nairobi by applying the InterVA-4 software to verbal autopsy data. We examine cause of death data from 2646 verbal autopsies of deaths that occurred in the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) between 1 January 2003 and 31 December 2012 among residents aged 15 years and above. The data is entered into the InterVA-4 computer program, which assigns cause of death using probabilistic modeling. The results are presented as annualized trends from 2003 to 2012 and disaggregated by gender and age. Over the 10-year period, the three major causes of death are tuberculosis (TB), injuries, and HIV/AIDS, accounting for 26.9, 20.9, and 17.3 % of all deaths, respectively. In 2003, HIV/AIDS was the highest cause of death followed by TB and then injuries. However, by 2012, TB and injuries had overtaken HIV/AIDS as the major causes of death. When this is examined by gender, HIV/AIDS was consistently higher for women than men across all the years generally by a ratio of 2 to 1. In terms of TB, it was more evenly distributed across the years for both males and females. We find that there is significant gender variation in deaths linked to injuries, with male deaths being higher than female deaths by a ratio of about 4 to 1. We also find a fifteen percentage point increase in the incidences of male deaths due to injuries between 2003 and 2012. For women, the corresponding deaths due to injuries remain fairly stable throughout the period. We find cardiovascular diseases as a significant cause of death over the period, with overall mortality increasing steadily from 1.6 % in 2003 to 8.1 % in 2012, and peaking at 13.7 % in 2005 and at 12.0 % in 2009. These deaths were consistently higher among women. We identified substantial variations in causes of death by age, with TB, HIV/AIDS, and CVD deaths lowest among younger residents and increasing with age, while injury-related deaths are highest among the youngest adults 15–19 and steadily declined with age. Also, deaths related to neoplasms and respiratory tract infections (RTIs) were prominent among older adults 50 years and above, especially since 2005. Emerging at this stage is evidence that HIV/AIDS, TB, injuries, and cardiovascular disease are linked to approximately 73 % of all adult deaths among the urban poor in Nairobi slums of Korogocho and Viwandani in the last 10 years. While mortality related to HIV/AIDS is generally declining, we see an increasing proportion of deaths due to TB, injuries, and cardiovascular diseases. In sum, substantial epidemiological transition is ongoing in this local context, with deaths linked to communicable diseases declining from 66 % in 2003 to 53 % in 2012, while deaths due to noncommunicable causes experienced a four-fold increase from 5 % in 2003 to 21.3 % in 2012, together with another two-fold increase in deaths due to external causes (injuries) from 11 % in 2003 to 22 % in 2012. It is important to also underscore the gender dimensions of the epidemiological transition clearly visible in the mix. Finally, the elevated levels of disadvantage of slum dwellers in our analysis relative to other population subgroups in Kenya continue to demonstrate appreciable deterioration of key urban health and social indicators, highlighting the need for a deliberate strategic focus on the health needs of the urban poor in policy and program efforts toward achieving international goals and national health and development targets.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

FIG. 1
FIG. 2
FIG. 3
FIG. 4
FIG. 5
FIG. 6
FIG. 7
FIG. 8
FIG. 9

References

  1. Rao C, Lopez AD, Hemed Y. Chapter 5: disease and mortality in Sub-Saharan Africa. In: Jamison DT, ed. Causes of death. 2nd ed. Washington (DC): World Bank; 2006.

    Google Scholar 

  2. Med PS. Measuring mortality in developing countries. PLoS Med. 2006; 3(2): e55–6.

    Article  Google Scholar 

  3. Lopez AD, Mathers CD. Measuring the global burden of disease and epidemiological transitions: 2002–2030. Ann Trop Med Parasitol. 2006; 100(5–6): 481–99.

    CAS  PubMed  Article  Google Scholar 

  4. Mathers CD, et al. Counting the dead and what they died from: an assessment of the global status of cause of death data. Bull World Health Organ. 2005; 83(3): 171–7.

    PubMed Central  PubMed  Google Scholar 

  5. World Health Organization. World health statistics 2007. Geneva, Switzerland: World Health Organization; 2007

  6. Attaran A. An immeasurable crisis? A criticism of the millennium development goals and why they cannot be measured. PLoS Med. 2005; 2(10): e318.

    PubMed Central  PubMed  Article  Google Scholar 

  7. United Nations Development Programme. Beyond scarcity: power, poverty and the global water crisis. New York, NY: United Nations Development Programme; 2006.

    Google Scholar 

  8. Satterthwaite D. Health in urban slums depends on better local data. Manchester, United Kingdom: 11th International Conference on Urban Health; 2014.

    Google Scholar 

  9. Oti SO, Kyobutungi C. Verbal autopsy interpretation: a comparative analysis of the InterVA model versus physician review in determining causes of death in the Nairobi DSS. Popul Health Metrics. 2010;8(21).

  10. Byass P, et al. The role of demographic surveillance systems (DSS) in assessing the health of communities: an example from rural Ethiopia. Public Health. 2002; 116(3): 145–50.

    CAS  PubMed  Google Scholar 

  11. de Savigny D, Kasale H, Mbuya C, Reid G. Fixing health systems (In-Focus). Ottawa, Ontario: International Development Research Centre; 2008.

  12. Korenromp EL, et al. Measurement of trends in childhood malaria mortality in Africa: an assessment of progress toward targets based on verbal autopsy. Lancet Infect Dis. 2003; 3(6): 349–58.

    PubMed  Article  Google Scholar 

  13. Morris SS, Black RE, Tomaskovic L. Predicting the distribution of under-five deaths by cause in countries without adequate vital registration systems. Int J Epidemiol. 2003; 32(6): 1041–51.

    PubMed  Article  Google Scholar 

  14. Murray CJL, Lopez AD. The global burden of disease: a comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020. In: Murray CJL, Lopez AD, editors. Global Burden of Disease and Injury Series. The Harvard School of Public Health on behalf of the World Health Organization and the World Bank; 1996.

  15. Baiden F, et al. Setting international standards for verbal autopsy. Bull World Health Organ. 2007; 85(8): 570–1.

    PubMed Central  PubMed  Article  Google Scholar 

  16. Soleman N, Chandramohan D, Shibuya K. Verbal autopsy: current practices and challenges. Bull World Health Organ. 2006; 84(3): 239–45.

    PubMed Central  PubMed  Article  Google Scholar 

  17. Dao PB, Huong L, Van Minh H. A probabilistic approach to interpreting verbal autopsies: methodology and preliminary validation in Vietnam. Scand J Public Health. 2003; 31(62): 32–7.

    Google Scholar 

  18. Byass P, et al. Refining a probabilistic model for interpreting verbal autopsy data. Scand J Public Health. 2006; 34(1): 26–31.

    PubMed Central  PubMed  Article  Google Scholar 

  19. Fantahun M, et al. Assessing a new approach to verbal autopsy interpretation in a rural Ethiopian community: the InterVA model. Bull World Health Organ. 2006; 84(3): 204–10.

    PubMed Central  PubMed  Article  Google Scholar 

  20. Murray CJL, et al. Using verbal autopsy to measure causes of death: the comparative performance of existing methods. BMC Med. 2014; 12: 5. doi:10.1186/1741-7015-12-5.

  21. Tensou B, et al. Evaluating the InterVA model for determining AIDS mortality from verbal autopsies in the adult population of Addis Ababa. Trop Med Int Health. 2010; 15(5): 547–53.

    PubMed Central  PubMed  Google Scholar 

  22. Kenya National Bureau of Statistics & Ministry of Planning National Development and Vision 2030. Kenya population and housing census 2009. Nairobi: Kenya National Bureau of Statistics; 2009.

  23. United Nations Children’s Fund. The state of the world’s children 2012: children in an urban world. New York, NY: UNICEF; 2012.

    Google Scholar 

  24. African Population and Health Research Center (APHRC). Population and health dynamics in Nairobi’s informal settlements. Nairobi, Kenya: African Population and Health Research Center; 2002.

  25. Fotso JC. Urban–rural differentials in child malnutrition: trends and socioeconomic correlates in sub-Saharan Africa. Health Place. 2007; 13(1): 205–23.

    PubMed  Article  Google Scholar 

  26. Gould WTS. African mortality and the new ‘urban penalty’. Health Place. 1998; 4(2): 171–81.

    CAS  PubMed  Article  Google Scholar 

  27. Kenya National Bureau of Statisitics (KNBS) and ICF Macro. Kenya demographic and health survey 2008–09. Calverton, Maryland: KNBS and ICF Macro; 2010.

    Google Scholar 

  28. Warner DF, Hayward MD. Early-life origins of the race gap in men’s mortality. J Health Soc Behav. 2006; 47(3): 209–26.

    PubMed  Article  Google Scholar 

  29. Hayward MD, Gorman BK. The long arm of childhood: the influence of early-life social conditions on men’s mortality. Demography. 2004; 41(1): 87–107.

    PubMed  Article  Google Scholar 

  30. World Health Organization. The second decade: improving adolescent health and development. Geneva, Switzerland: World Health Organization; 2001.

  31. Amuyunzu-Nyamongo M, Taffa N. The triad of poverty, environment and child health in Nairobi informal settlements. J Health Popul Dev Countries. 2004; 1–14.

  32. Taffa N, Chepngeno G, Amuyunzu-Nyamongo M. Child morbidity and healthcare utilization in the slums of Nairobi, Kenya. J Trop Pediatr. 2005; 51(5): 279–84.

    CAS  PubMed  Article  Google Scholar 

  33. Kyobutungi C, et al. The burden of disease profile of residents of Nairobi’s slums: results from a demographic surveillance system. Popul Health Metrics. 2008; 6(1).

  34. World Health Organization. The world health report 2004 - changing history. Geneva: World Health Organization; 2004.

  35. Emina J, et al. Monitoring of health and demographic outcomes in poor urban settlements: evidence from the Nairobi urban health and demographic surveillance system. J U Health: Bulletin N Y Acad Med. 2011; 88(Suppl 2): S200–18.

  36. Bauni E, et al. Validating physician-certified verbal autopsy and probabilistic modeling (InterVA) approaches to verbal autopsy interpretation using hospital causes of adult deaths. Popul Health Metrics. 2011; 9: 49. doi:10.1186/1478-7954-9-49.

  37. Fottrell E, et al. Probabilistic methods for verbal autopsy interpretation: InterVA robustness in relation to variations in a priori probabilities. PLoS ONE. 2011; 6(11): e27200.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  38. Mwanyangala MA, et al. Verbal autopsy completion rate and factors associated with undetermined cause of death in a rural resource-poor setting of Tanzania. Popul Health Metrics. 2011; 9: 41. doi:10.1186/1478-7954-9-41.

  39. Ramroth H, et al. Cause of death distribution with InterVA and physician coding in a rural area of Burkina Faso. Tropical Med Int Health. 2012; 17(7): 904–13.

    Article  Google Scholar 

  40. Vergnano S, et al. Adaptation of a probabilistic method (InterVA) of verbal autopsy to improve the interpretation of cause of stillbirth and neonatal death in Malawi, Nepal, and Zimbabwe. Popul Health Metrics. 2011; 9: 48. doi:10.1186/1478-7954-9-48.

  41. Byass P. InterVA-4 user guide. 2012 [cited November 29 2013]; Available from: https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=2&cad=rja&ved=0CDAQFjAB&url=http%3A%2F%2Fwww.globalhealthaction.net%2Findex.php%2Fgha%2Farticle%2FdownloadSuppFile%2F19281%2F6616&ei=yTSYUuuaHeTNygO_9IH4CA&usg=AFQjCNHwhHNsOiv6Qg9WxtQ3K8XaXm5tgg.

  42. Hariri S, McKenna MT. Epidemiology of human immunodeficiency virus in the United States. Clin Microbiol Rev. 2007; 20(3): 478–88. table of contents.

    PubMed Central  PubMed  Article  Google Scholar 

  43. Lemly DC, et al. Race and sex differences in antiretroviral therapy use and mortality among HIV-infected persons in care. J Infect Dis. 2009; 199(7): 991–8.

    PubMed  Article  Google Scholar 

  44. Sackoff JE, et al. Causes of death among persons with AIDS in the era of highly active antiretroviral therapy: New York city. Ann Intern Med. 2006; 145(6): 397–406.

    PubMed  Article  Google Scholar 

  45. Cornell M, et al. Gender differences in survival among adult patients starting antiretroviral therapy in South Africa: a multicentre cohort study. PLoS Med. 2012; 9(9): e1001304.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  46. Klausner JD, et al. Scale-up and continuation of antiretroviral therapy in South African treatment programs, 2005–2009. J Acquir Immune Defic Syndr. 2011; 56(3): 292–5.

    CAS  PubMed  Article  Google Scholar 

  47. Stenehjem E, Shlay JC. Sex-specific differences in treatment outcomes for patients with HIV and AIDS. Expert Rev Pharmacoecon Outcomes Res. 2008; 8(1): 51–63.

    PubMed  Article  Google Scholar 

  48. Stringer JS, et al. Rapid scale-up of antiretroviral therapy at primary care sites in Zambia: feasibility and early outcomes. JAMA. 2006; 296(7): 782–93.

    CAS  PubMed  Article  Google Scholar 

  49. Braitstein P, et al. Gender and the use of antiretroviral treatment in resource-constrained settings: findings from a multicenter collaboration. J Womens Health (Larchmt). 2008; 17(1): 47–55.

    Article  Google Scholar 

  50. Cornell M, et al. Temporal changes in programme outcomes among adult patients initiating antiretroviral therapy across South Africa, 2002–2007. AIDS. 2010; 24(14): 2263–70.

    PubMed Central  PubMed  Article  Google Scholar 

  51. Nglazi MD, et al. Changes in programmatic outcomes during 7 years of scale-up at a community-based antiretroviral treatment service in South Africa. J Acquir Immune Defic Syndr. 2011; 56(1): e1–8.

    PubMed Central  PubMed  Article  Google Scholar 

  52. Ochieng-Ooko V, et al. Influence of gender on loss to follow-up in a large HIV treatment programme in western Kenya. Bull World Health Organ. 2010; 88(9): 681–8.

    PubMed Central  PubMed  Article  Google Scholar 

  53. Kyobutungi C, et al. The burden of disease profile of residents of Nairobi’s slums: results from a demographic surveillance system. Popul Health Metrics. 2008; 6: 1.

    Article  Google Scholar 

  54. Ziraba AK, Kyobutungi C, Zulu EM. Fatal injuries in the slums of Nairobi and their risk factors: results from a matched case–control study. J Urban Health. 2011; 88(Suppl 2): S256–65.

    PubMed  Article  Google Scholar 

  55. Maas AH, Appelman YE. Gender differences in coronary heart disease. Neth Heart J. 2010; 18(12): 598–602.

    PubMed Central  Article  Google Scholar 

  56. Lerner DJ, Kannel WB. Patterns of coronary heart disease morbidity and mortality in the sexes: a 26-year follow-up of the Framingham population. Am Heart J. 1986; 111(2): 383–90.

    CAS  PubMed  Article  Google Scholar 

  57. Mikkola TS, et al. Sex differences in age-related cardiovascular mortality. PLoS ONE. 2013; 8(5): e63347.

    CAS  PubMed Central  PubMed  Article  Google Scholar 

  58. Barrett-Connor E. Sex differences in coronary heart disease. Why are women so superior? The 1995 ancel keys lecture. Circulation. 1997; 95(1): 252–64.

    CAS  PubMed  Article  Google Scholar 

  59. Wingard DL, Suarez L, Barrett-Connor E. The sex differential in mortality from all causes and ischemic heart disease. Am J Epidemiol. 1983; 117(2): 165–72.

    CAS  PubMed  Google Scholar 

  60. Maas AH, Appelman YE. Gender differences in coronary heart disease. Neth Heart J. 2010; 18(12): 598–603.

    PubMed Central  Article  Google Scholar 

  61. Byass P, et al. InterVA-4 as a public health tool for measuring HIV/AIDS mortality: a validation study from five African countries. Glob Health Action. 2013; 6: 22448.

    PubMed  Google Scholar 

  62. Byass P, et al. Strengthening standardised interpretation of verbal autopsy data: the new InterVA-4 tool. Glob Health Action. 2012; 5: 1–8.

    PubMed  Google Scholar 

  63. Lozano R, et al. Performance of InterVA for assigning causes of death to verbal autopsies: multisite validation study using clinical diagnostic gold standards. Popul Health Metrics. 2011; 9: 50.

    Article  Google Scholar 

  64. Oti SO, Kyobutungi C. Verbal autopsy interpretation: a comparative analysis of the InterVA model versus physician review in determining causes of death in the Nairobi DSS. Popul Health Metrics. 2010; 8: 21.

    Article  Google Scholar 

  65. Oti SO, et al. InterVA versus Spectrum: how comparable are they in estimating AIDS mortality patterns in Nairobi’s informal settlements? Glob Health Action. 2013; 6: 21638.

    PubMed  Google Scholar 

  66. United Nations Human Settlements Programme (UN-HABITAT). The state of African cities 2010: governance, inequality and urban land markets. Nairobi, Kenya: United Nations Human Settlements Programme (UN-HABITAT); 2010.

    Google Scholar 

  67. United Nations Human Settlements Programme (UN-HABITAT). The state of African cities 2008: a framework for addresing urban challenges in Africa. Nairobi, Kenya: United Nations Human Settlements Programme (UN-HABITAT); 2008.

    Google Scholar 

  68. Garenne M. Migration, urbanisation and child health : an African perspective, in Africa on the move : African migration and urbanisation in comparative perspective. Johannesburg, South Africa: Wits University Press; 2006.

    Google Scholar 

  69. Ziraba A, et al. Maternal mortality in the informal settlements of Nairobi city: what do we know? Reprod Health. 2009; 6(1): 6.

    PubMed Central  PubMed  Article  Google Scholar 

  70. The Centers for Disease Control and Prevention in Kenya (CDC-Kenya). CDC in Kenya Factsheet. 2013; Available from: http://www.cdc.gov/globalhealth/countries/kenya/pdf/kenya.pdf. Accessed 17 Sept 2014.

  71. Mberu BU, et al. Bringing sexual and reproductive health in the urban contexts to the forefront of the development agenda: the case for prioritizing the urban poor. Matern Child Health J. 2012; 18: 1572–7.

    Article  Google Scholar 

Download references

Acknowledgments

The authors acknowledge funding support from the following:

• The Bill and Melinda Gates Foundation (Global Health Grant: OPP1021893).

• SIDA (Grant No. 2011–001578), and

• The William and Flora Hewlett Foundation (Grant No. 2012–7612).

Author information

Affiliations

  1. African Population and Health Research Center, APHRC Campus, Kirawa Road, off Peponi Road,, 10787-00100,, Nairobi, Kenya

    Blessing Mberu, Marylene Wamukoya, Samuel Oti & Catherine Kyobutungi

Authors
  1. Blessing Mberu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marylene Wamukoya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Samuel Oti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Catherine Kyobutungi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Blessing Mberu.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mberu, B., Wamukoya, M., Oti, S. et al. Trends in Causes of Adult Deaths among the Urban Poor: Evidence from Nairobi Urban Health and Demographic Surveillance System, 2003–2012. J Urban Health 92, 422–445 (2015). https://doi.org/10.1007/s11524-015-9943-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11524-015-9943-6

Keywords

  • Adult mortality
  • Cause-specific mortality
  • Urban poor
  • Verbal autopsy
  • Informal settlements
  • Nairobi
  • Kenya