Mortality Trends from 2003 to 2009 among Adolescents and Young Adults in Rural Western Kenya Using a Health and Demographic Surveillance System

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Study Justification:
– Targeted global efforts to improve survival of young adults require information on mortality trends.
– Health and demographic surveillance systems (HDSS) provide valuable data for understanding mortality patterns.
– This study aimed to explore changing trends in deaths among adolescents and young adults in rural western Kenya using a HDSS.
Highlights:
– In 2003, all-cause mortality rates among females aged 15-19 years and 20-24 years were 403 and 1,613 per 100,000 population, respectively.
– Communicable disease (CD) mortality rates were significantly higher among females compared to males in the 15-24 age group.
– By 2009, there was a significant reduction in all-cause mortality rates among young adult females (53%) and adolescent females (61.5%).
– CD mortality rates among males and non-communicable disease (NCD) mortality rates in both genders did not show significant reductions.
– Injuries became the top cause of death among males by 2009, equaling HIV.
Recommendations:
– Strengthen public health programs and target strategies to reach both males and females.
– Promote initiatives to reduce the mortality burden from both communicable and non-communicable diseases.
Key Role Players:
– Researchers and scientists
– Health policymakers and government officials
– Healthcare providers and community health workers
– Non-governmental organizations (NGOs) and international agencies
– Community leaders and influencers
Cost Items for Planning Recommendations:
– Funding for research and data collection
– Training and capacity building for healthcare providers and community health workers
– Implementation of targeted public health programs
– Development and dissemination of educational materials and campaigns
– Provision of healthcare services and interventions
– Monitoring and evaluation of program effectiveness

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study utilized a health and demographic surveillance system (HDSS) to explore changing trends in deaths among adolescents and young adults in rural western Kenya. The study used census and verbal autopsy data to generate mortality rates by age, gender, and cause of death. The study found significant reductions in mortality rates among adolescent and young adult females, but rates remain alarmingly high. The study also highlighted the need to strengthen programs and target strategies to reach both males and females, and to promote non-communicable disease (NCD) initiatives. To improve the strength of the evidence, the study could have included a larger sample size and conducted a more in-depth analysis of the factors contributing to the mortality trends.

Background: Targeted global efforts to improve survival of young adults need information on mortality trends; contributions from health and demographic surveillance system (HDSS) are required. Methods and Findings: This study aimed to explore changing trends in deaths among adolescents (15-19 years) and young adults (20-24 years), using census and verbal autopsy data in rural western Kenya using a HDSS. Mid-year population estimates were used to generate all-cause mortality rates per 100,000 population by age and gender, by communicable (CD) and non-communicable disease (NCD) causes. Linear trends from 2003 to 2009 were examined. In 2003, all-cause mortality rates of adolescents and young adults were 403 and 1,613 per 100,000 population, respectively, among females; and 217 and 716 per 100,000, respectively, among males. CD mortality rates among females and males 15-24 years were 500 and 191 per 100,000 (relative risk [RR] 2.6; 95% confidence intervals [CI] 1.7-4.0; p<0.001). NCD mortality rates in same aged females and males were similar (141 and 128 per 100,000, respectively; p = 0.76). By 2009, young adult female all-cause mortality rates fell 53% (χ2 for linear trend 30.4; p<0.001) and 61.5% among adolescent females (χ2 for linear trend 11.9; p<0.001). No significant CD mortality reductions occurred among males or for NCD mortality in either gender. By 2009, all-cause, CD, and NCD mortality rates were not significantly different between males and females, and among males, injuries equalled HIV as the top cause of death. Conclusions: This study found significant reductions in adolescent and young adult female mortality rates, evidencing the effects of targeted public health programmes, however, all-cause and CD mortality rates among females remain alarmingly high. These data underscore the need to strengthen programmes and target strategies to reach both males and females, and to promote NCD as well as CD initiatives to reduce the mortality burden amongst both gender.

During the study period, the KEMRI/CDC HDSS study site was located in a rural part of Nyanza Province in western Kenya in Asembo (Rarieda District) and Gem (Yala and Wagai Divisions), Siaya District [19], [20], [22]. The area included 217 villages spread over a 500 km2 area along the shores of Lake Victoria, with a mid-year population of 136,448 in 2003 rising to 146,081 by 2009. Among AYA aged 15–24 years, the gender breakdown is relatively equal, with a mean (annually fluctuating) mid-year annual population of 14,780 males and 14,502 females (total 29,282). The population, mainly subsistence farmers, are almost exclusively members of the Luo ethnic group and have been described in detail elsewhere [19], [22], [23]. Inhabitants live in family compounds comprising one or more (average 2.1) houses surrounded by their land. The society is polygynous with approximately a third of males having more than one wife [23]. The population is impoverished with a mean ‘wealth index’ previously estimated to be $600 to $700 per compound [24]. HIV [5], [25]–[27], TB [9], [28]–[30], malaria [31]–[34], schistosomiasis [35]–[37], and suboptimal water quality, sanitation and hygiene [38]–[41], are leading causes of morbidity and mortality within the study area. The population was registered and households were geo-spatially located during an insecticide treated bednet trial [22]. The HDSS site was registered in 2001 as a member of the INDEPTH Network [19], [20]. A household census is conducted throughout the study area tri-annually to capture births, pregnancies, deaths, in- and out- migration, and economic data. These data provide mid-year population denominators, stratified by age group and gender. All resident deaths reported to field staff during census are followed up with a visit to households to validate deaths and record events surrounding death, using a standardized verbal autopsy (VA) questionnaire. Residents are defined as all persons residing in the study site for 4 months or more, precluding transient residents and visitors. VA is conducted using standardised WHO questionnaires endorsed by INDEPTH, for all deaths occurring in the HDSS [18], [20]. For this analysis, we utilized the adult questionnaire (15 years and above). A previous one year review of deaths examined data from 2003 and describes the VA methodology in detail [42]. Resident identification numbers allow linkage of each death with HDSS data. Parents or spouses are identified as the first respondents. VA interviews are performed, at least one month (average 3 months) after the death to respect the mourning period, while still facilitating recall. Absence of an adult in the home is recorded as a non-VA interview, enabling only verification that death occurred and collection of minimal demographic indices. VA forms are reviewed independently by at least two clinical officers and cause of death assigned. In 2006, “Sample Vital Registration with Verbal Autopsy (SAVVY)” was adopted at the KEMRI/CDC HDSS (and across INDEPTH sites) to strengthen vital event monitoring and measurement. SAVVY constitutes a resource library of best practice to improve the quality of civil registration, harmonized to the WHO International Classification of Disease [43]. This facilitated attribution of the cause of death. Following cultural customs, compound heads provide written consent for all compound members to participate in the HDSS activities. Any individual can refuse to participate at any time. The HDSS protocol and consent procedures, including surveillance and VA activities, were approved by KEMRI (#1801) and CDC Institutional Review Boards (#3308). All HDSS census and VA data are maintained on a secure server with access only by authorized researchers. Named data are securely stored in a MS-SQL database and only authorized data personnel have access rights. Datasets used by scientists for analysis are stripped of names to protect identity. Data were extracted from the HDSS database for all deaths occurring among residents aged between 15 to 24 years of age at the time of death, between January 2003 and December 2009. Data transformation and analyses were conducted using SPSS for Windows (Release v18.0), and EpiInfo Stat Calc (CDC Atlanta, USA). Analyses on proportions and rates per 100,000 population were conducted on all-cause; and grouped into communicable disease (CD), and non-communicable disease (NCD) causes. The category of NCD included injuries, maternal (including septicaemia), cancers and nutritional causes. Mean age of death among all AYA aged 15–24 years is presented with standard deviation (SD), for all-cause mortality by gender, and for key diseases. Analyses are stratified into adolescence (15–19 years old) and young adulthood (20–24 years old). Mortality rates per 100,000 were estimated by year and age category, using mid-year population-point estimates generated from the HDSS census. Dates of death were grouped per year to facilitate calculation of annual mortality rates per age category and by gender. Key social and demographic characteristics generated through the HDSS for analyses here included marital status (ever married; divorced or widowed at time of death), place of death (home or health facility; comprising clinic, hospital, on route to/from health facility); education (attended and completed primary school), and socio-economic status (SES). Routinely collected SES indicators such as occupation of household head, primary source of drinking water, use of cooking fuel, in-house assets (e.g., lantern lamp, sofa, bicycle, radio, TV) and livestock (poultry, pigs, donkey cattle, sheep, goats) [24], were used to calculate an SES index as a weighted average using multiple correspondence analysis [44]. This ranked households into SES quintiles with the first quintile representing the poorest and the fifth representing the least poor; for some analyses this was collapsed into most (1–2) and less (3–5) poor. Analyses to examine trends in mortality rates per 100,000 population over time were conducted for all-cause, CD and NCD sub-strata. Chi-squared (χ2) test for linear trend determined the statistical significance of changing rates by gender over time (2003 to 2009). Differences between groups were determined using Pearson’s χ2 test, and the level of significance was set at 5% or less. Mantel-Haenszel Relative Risks (RR), with Taylor Series 95% confidence intervals (CI), was used to compare mortality rates between genders, by year of death. Unless stated, RR compares female to male rates; where rates are significantly higher for males, reciprocal values are given (RRmale).We stratified RR analyses for mortality rates into the two age groups, by gender, by year for all-cause, CD, and NCD mortality, generating a summary χ2, with a MH weighted RR and Greenlands-Robins 95% CI.

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Based on the provided information, it is difficult to identify specific innovations for improving access to maternal health. The study focuses on mortality trends among adolescents and young adults in rural western Kenya, rather than maternal health specifically. However, some potential recommendations for improving access to maternal health in this context could include:

1. Strengthening public health programs: The study found significant reductions in female mortality rates, indicating the effectiveness of targeted public health programs. These programs could be further strengthened and expanded to specifically address maternal health issues.

2. Targeting strategies for both males and females: The study highlights the need to reach both males and females with health interventions. This approach could be applied to maternal health by ensuring that interventions and services are accessible and available to both expectant mothers and fathers, as well as other family members who play a role in maternal health.

3. Promoting non-communicable disease (NCD) initiatives: The study mentions that NCD mortality rates were similar between males and females. Integrating NCD initiatives into maternal health programs could help address underlying health conditions that may impact maternal health outcomes.

4. Improving access to healthcare facilities: The study mentions that deaths occurred both at home and in health facilities. Improving access to healthcare facilities, particularly for expectant mothers, could help ensure timely and appropriate care during pregnancy, childbirth, and postpartum.

5. Enhancing community-based healthcare: Given the rural context of the study area, community-based healthcare approaches could be effective in improving access to maternal health services. This could involve training and empowering community health workers to provide basic maternal health services and referrals.

It is important to note that these recommendations are based on general principles and may need to be tailored to the specific context and needs of the population in rural western Kenya.
AI Innovations Description
The study mentioned in the description focuses on mortality trends among adolescents and young adults in rural western Kenya, specifically in relation to communicable and non-communicable diseases. The findings highlight the need to strengthen public health programs and target strategies to reduce mortality rates among both males and females in the region.

Based on the information provided, a recommendation to improve access to maternal health in this context could be to implement comprehensive reproductive health programs that address the specific needs of adolescent girls and young women. These programs should include:

1. Education and awareness: Implementing comprehensive sexual and reproductive health education programs that provide accurate information on contraception, family planning, and safe motherhood practices. This can help empower young women to make informed decisions about their reproductive health and reduce the risk of unintended pregnancies.

2. Access to healthcare services: Strengthening the healthcare infrastructure in rural areas by increasing the availability and accessibility of maternal health services. This can be achieved by establishing or improving existing health facilities, training healthcare providers, and ensuring the availability of essential maternal health supplies and equipment.

3. Antenatal and postnatal care: Promoting early and regular antenatal care visits for pregnant women, as well as postnatal care for both mothers and newborns. This includes providing essential services such as prenatal screenings, immunizations, and support for breastfeeding and newborn care.

4. Family planning services: Ensuring access to a range of contraceptive methods and family planning services for women and couples, including counseling and provision of contraceptives. This can help prevent unintended pregnancies and reduce the risk of maternal and infant mortality.

5. Community engagement and support: Engaging the community, including religious and traditional leaders, in promoting maternal health and addressing cultural and social barriers that may hinder access to care. This can involve community awareness campaigns, peer education programs, and the involvement of community health workers.

By implementing these recommendations, it is possible to improve access to maternal health services and reduce maternal and infant mortality rates among adolescent girls and young women in rural western Kenya.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Strengthening public health programs: This includes increasing funding and resources for maternal health programs, improving infrastructure and facilities, and expanding the reach of healthcare services to remote areas.

2. Enhancing community engagement: Engaging with local communities and empowering women and families to take an active role in their own healthcare can improve access to maternal health services. This can be done through community health workers, health education programs, and community-based initiatives.

3. Improving transportation and logistics: Ensuring reliable and affordable transportation options for pregnant women can help overcome geographical barriers and improve access to healthcare facilities. This can include providing transportation vouchers, establishing emergency transportation systems, and improving road infrastructure.

4. Increasing availability of skilled healthcare providers: Training and deploying more skilled healthcare providers, such as midwives and obstetricians, in underserved areas can improve access to quality maternal healthcare. This can be done through targeted recruitment and retention strategies, as well as providing incentives for healthcare professionals to work in rural areas.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the indicators: Identify key indicators that measure access to maternal health, such as the number of antenatal care visits, institutional delivery rates, and access to emergency obstetric care.

2. Collect baseline data: Gather data on the current status of these indicators in the target population. This can be done through surveys, interviews, and analysis of existing health records.

3. Develop a simulation model: Create a simulation model that incorporates the various recommendations and their potential impact on the selected indicators. This model should consider factors such as population size, geographical distribution, healthcare infrastructure, and resource allocation.

4. Input data and run simulations: Input the baseline data into the simulation model and run multiple simulations to assess the potential impact of each recommendation on the selected indicators. This can involve adjusting variables such as the number of healthcare providers, availability of transportation, and community engagement levels.

5. Analyze results: Analyze the results of the simulations to determine the potential impact of each recommendation on improving access to maternal health. This can include comparing the simulated outcomes with the baseline data and identifying the most effective strategies.

6. Refine and validate the model: Refine the simulation model based on the analysis of the results and validate it using additional data and feedback from experts in the field. This will ensure the accuracy and reliability of the model for future use.

By following this methodology, policymakers and healthcare providers can gain insights into the potential impact of different recommendations on improving access to maternal health and make informed decisions on implementing the most effective strategies.

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