共 6 条
Survived infancy but still vulnerable: spatial-temporal trends and risk factors for child mortality in rural South Africa (Agincourt), 1992-2007
被引:28
|作者:
Sartorius, Benn
[1
]
Kahn, Kathleen
[1
,2
,3
]
Collinson, Mark A.
[1
,2
,3
]
Vounatsou, Penelope
[4
,5
]
Tollman, Stephen M.
[1
,2
,3
]
机构:
[1] Univ Witwatersrand, MRC Wits Rural Publ Hlth & Hlth Transit Res Unit, Sch Publ Hlth, Fac Hlth Sci, Johannesburg, South Africa
[2] Umea Univ, Ctr Global Hlth Res Epidemiol & Global Hlth, Umea, Sweden
[3] INDEPTH Network, Accra, Ghana
[4] Swiss Trop & Publ Hlth Inst, Dept Epidemiol & Publ Hlth, CH-4002 Basel, Switzerland
[5] Univ Basel, CH-4003 Basel, Switzerland
基金:
新加坡国家研究基金会;
英国惠康基金;
美国安德鲁·梅隆基金会;
瑞士国家科学基金会;
英国医学研究理事会;
关键词:
Bayesian inference;
autoregressive;
geostatistical data;
child mortality;
kriging;
survival;
Bernoulli or logistic;
spatio-temporal model;
health and demographic surveillance;
mortality;
South Africa;
RANDOMIZED-TRIAL;
HEALTH;
TRANSMISSION;
MALARIA;
MODEL;
DETERMINANTS;
PATTERNS;
EQUITY;
WOMEN;
AREA;
D O I:
10.4081/gh.2011.181
中图分类号:
R19 [保健组织与事业(卫生事业管理)];
学科分类号:
摘要:
Targeting of health interventions to poor children at highest risk of mortality are promising approaches for enhancing equity. Methods have emerged to accurately quantify excess risk and identify space-time disparities. This provides useful and detailed information for guiding policy. A spatio-temporal analysis was performed to identify risk factors associated with child (1-4 years) mortality in the Agincourt sub-district, South Africa, to assess temporal changes in child mortality patterns within the study site between 1992 and 2007, and to produce all-cause and cause-specific mortality maps to identify high risk areas. Demographic, maternal, paternal and fertility-related factors, household mortality experience, distance to health care facility and socio-economic status were among the examined risk factors. The analysis was carried out by fitting a Bayesian discrete time Bernoulli survival geostatistical model using Markov chain Monte Carlo simulation. Bayesian kriging was used to produce mortality risk maps. Significant temporal increase in child mortality was observed due to the HIV epidemic. A distinct spatial risk pattern was observed with higher risk areas being concentrated in poorer settlements on the eastern part of the study area, largely inhabited by former Mozambican refugees. The major risk factors for childhood mortality, following multivariate adjustment, were mother's death (especially when due to HIV and tuberculosis), greater number of children under 5 years living in the same household and winter season. This study demonstrates the use of Bayesian geostatistical models for accurately quantifying risk factors and producing maps of child mortality risk in a health and demographic surveillance system. According to the space-time analysis, the southeast and upper central regions of the site appear to have the highest mortality risk. The results inform policies to address health inequalities in the Agincourt sub-district and to improve access to health services. Targeted efforts to prevent vertical transmission of HIV in specific settings need to be undertaken as well as ensuring the survival of the mother and father in childhood.
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页码:285 / 295
页数:11
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