Trends in Causes of Adult Deaths among the Urban Poor: Evidence from Nairobi Urban Health and Demographic Surveillance System, 2003-012

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• 作者：Blessing Mberu ; Marylene Wamukoya ; Samuel Oti…
• 关键词：Adult mortality ; Cause ; specific mortality ; Urban poor ; Verbal autopsy ; Informal settlements ; Nairobi ; Kenya
• 刊名：Journal of Urban Health
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：92
• 期：3
• 页码：422-445
• 全文大小：493 KB
• 参考文献：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.
  2.Med PS. Measuring mortality in developing countries. PLoS Med. 2006; 3(2): e55-.View Article
  3.Lopez AD, Mathers CD. Measuring the global burden of disease and epidemiological transitions: 2002-030. Ann Trop Med Parasitol. 2006; 100(5-): 481-9.PubMed View Article
  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-.PubMed Central PubMed
  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 View Article
  7.United Nations Development Programme. Beyond scarcity: power, poverty and the global water crisis. New York, NY: United Nations Development Programme; 2006.
  8.Satterthwaite D. Health in urban slums depends on better local data. Manchester, United Kingdom: 11th International Conference on Urban Health; 2014.
  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): 145-0.PubMed
  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): 349-8.PubMed View Article
  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): 1041-1.PubMed View Article
  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-.PubMed Central PubMed View Article
  16.Soleman N, Chandramohan D, Shibuya K. Verbal autopsy: current practices and challenges. Bull World Health Organ. 2006; 84(3): 239-5.PubMed Central PubMed View Article
  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-.
  18.Byass P, et al. Refining a probabilistic model for interpreting verbal autopsy data. Scand J Public Health. 2006; 34(1): 26-1.PubMed Central PubMed View Article
  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): 204-0.PubMed Central PubMed View Article
  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.-186/-741-7015-12-5 .
  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): 547-3.PubMed Central PubMed
  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.
  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): 205-3.PubMed View Article
  26.Gould WTS. African mortality and the new ‘urban penalty- Health Place. 1998; 4(2): 171-1.PubMed View Article
  27.Kenya National Bureau of Statisitics (KNBS) and ICF Macro. Kenya demographic and health survey 2008-9. Calverton, Maryland: KNBS and ICF Macro; 2010.
  28.Warner DF, Hayward MD. Early-life origins of the race gap in men’s mortality. J Health Soc Behav. 2006; 47(3): 209-6.PubMed
• 作者单位：Blessing Mberu (1)
  Marylene Wamukoya (1)
  Samuel Oti (1)
  Catherine Kyobutungi (1)
  
  1. African Population and Health Research Center, APHRC Campus, Kirawa Road, off Peponi Road,, 10787-00100,, Nairobi, Kenya
  
• 刊物主题：Public Health; Health Informatics; Epidemiology;
• 出版者：Springer US
• ISSN：1468-2869

文摘

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 15-9 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.