Childhood cause-specific mortality in rural Western Kenya: application of the InterVA-4 model

Background

Assessing the progress in achieving the United Nation's Millennium Development Goals in terms of population health requires consistent and reliable information on cause-specific mortality, which is often rare in resource-constrained countries. Health and demographic surveillance systems (HDSS) have largely used medical personnel to review and assign likely causes of death based on the information gathered from standardized verbal autopsy (VA) forms. However, this approach is expensive and time consuming, and it may lead to biased results based on the knowledge and experience of individual clinicians. We assessed the cause-specific mortality for children under 5 years old (under-5 deaths) in Siaya County, obtained from a computer-based probabilistic model (InterVA-4).

Design

Successfully completed VA interviews for under-5 deaths conducted between January 2003 and December 2010 in the Kenya Medical Research Institute/US Centers for Disease Control and Prevention HDSS were extracted from the VA database and processed using the InterVA-4 (version 4.02) model for interpretation. Cause-specific mortality fractions were then generated from the causes of death produced by the model.

Results

A total of 84.33% (6,621) childhood deaths had completed VA data during the study period. Children aged 1–4 years constituted 48.53% of all cases, and 42.50% were from infants. A single cause of death was assigned to 89.18% (5,940) of cases, 8.35% (556) of cases were assigned two causes, and 2.10% (140) were assigned ‘indeterminate’ as cause of death by the InterVA-4 model. Overall, malaria (28.20%) was the leading cause of death, followed by acute respiratory infection including pneumonia (25.10%), in under-5 children over the study period. But in the first 5 years of the study period, acute respiratory infection including pneumonia was the main cause of death, followed by malaria. Similar trends were also reported in infants (29 days–11 months) and children aged 1–4 years.

Conclusions

Under-5 cause-specific mortality obtained using the InterVA-4 model is consistent with existing knowledge on the burden of childhood diseases in rural western Kenya.

Pregnancy-related mortality in Africa and Asia: evidence from INDEPTH Health and Demographic Surveillance System sites

Background

Women continue to die in unacceptably large numbers around the world as a result of pregnancy, particularly in sub-Saharan Africa and Asia. Part of the problem is a lack of accurate, population-based information characterising the issues and informing solutions. Population surveillance sites, such as those operated within the INDEPTH Network, have the potential to contribute to bridging the information gaps.

Objective

To describe patterns of pregnancy-related mortality at INDEPTH Network Health and Demographic Surveillance System sites in sub-Saharan Africa and southeast Asia in terms of maternal mortality ratio (MMR) and cause-specific mortality rates.

Design

Data on individual deaths among women of reproductive age (WRA) (15–49) resident in INDEPTH sites were collated into a standardised database using the INDEPTH 2013 population standard, the WHO 2012 verbal autopsy (VA) standard, and the InterVA model for assigning cause of death.

Results

These analyses are based on reports from 14 INDEPTH sites, covering 14,198 deaths among WRA over 2,595,605 person-years observed. MMRs varied between 128 and 461 per 100,000 live births, while maternal mortality rates ranged from 0.11 to 0.74 per 1,000 person-years. Detailed rates per cause are tabulated, including analyses of direct maternal, indirect maternal, and incidental pregnancy-related deaths across the 14 sites.

Conclusions

As expected, these findings confirmed unacceptably high continuing levels of maternal mortality. However, they also demonstrate the effectiveness of INDEPTH sites and of the VA methods applied to arrive at measurements of maternal mortality that are essential for planning effective solutions and monitoring programmatic impacts.