Deaths ascribed to non-communicable diseases among rural kenyan adults are proportionately increasing: Evidence from a health and demographic surveillance system, 2003-2010

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Study Justification:
– Non-communicable diseases (NCDs) are a leading cause of death globally.
– Monitoring systems in low and middle-income countries (LMICs) need to be strengthened to accurately attribute the burden of NCDs.
– Health and demographic surveillance systems (HDSS) can contribute to understanding the impact of NCDs in LMICs.
Highlights:
– Between 2003 and 2010, 37% of deaths in rural Kenyan adults were attributed to NCDs.
– The proportion of deaths from NCDs increased from 35% in 2003 to 45% in 2010.
– Cancers and cardiovascular diseases were the main causes of NCD deaths.
– NCD mortality rates decreased overall, but cancer-specific mortality rates increased.
– NCD mortality rates were higher among males than females.
Recommendations:
– Strengthen monitoring systems to accurately track NCD burden in rural areas.
– Increase awareness and prevention efforts for NCDs, particularly cancers and cardiovascular diseases.
– Improve access to healthcare services for NCDs, especially for older adults.
– Implement targeted interventions to reduce NCD mortality rates among males.
Key Role Players:
– Researchers and scientists to conduct further studies and analysis.
– Health professionals and policymakers to implement recommendations.
– Community leaders and organizations to raise awareness and promote prevention.
Cost Items for Planning Recommendations:
– Research and data collection expenses.
– Training and capacity building for healthcare professionals.
– Healthcare infrastructure and equipment.
– Awareness campaigns and education materials.
– Monitoring and evaluation of interventions.

The strength of evidence for this abstract is 8 out of 10.
The evidence in the abstract is strong because it is based on data from a health and demographic surveillance system (HDSS) in rural western Kenya. The study includes a large sample size of 15,228 deaths in adults aged 15 years and older over a period of 8 years. The cause of death was attributed using the InterVA-4 methodology, a probabilistic model for interpreting cause of death from verbal autopsy data. The abstract provides detailed information on the methods and findings, including the proportionate mortality from non-communicable diseases (NCDs) and the specific causes of NCD deaths. The abstract also mentions the approval of the study protocol and consent procedures by the Ethical Review Committee of the Kenyan Medical Research Institute and the Centers for Disease Control and Prevention Institutional Review Board. To improve the evidence, the abstract could provide more information on the representativeness of the study population and the generalizability of the findings to other settings. Additionally, it would be helpful to include information on any limitations of the study and potential sources of bias.

Background: Non-communicable diseases (NCDs) result in more deaths globally than other causes. Monitoring systems require strengthening to attribute the NCD burden and deaths in low and middle-income countries (LMICs). Data from health and demographic surveillance systems (HDSS) can contribute towards this goal. Methods and Findings: Between 2003 and 2010, 15,228 deaths in adults aged 15 years (y) and older were identified retrospectively using the HDSS census and verbal autopsy in rural western Kenya, attributed into broad categories using InterVA-4 computer algorithms; 37% were ascribed to NCDs, 60% to communicable diseases (CDs), 3% to injuries, and ,1% maternal causes. Median age at death for NCDs was 66y and 71y for females and males, respectively, with 43% (39% male, 48% female) of NCD deaths occurring prematurely among adults aged below 65y. NCD deaths were mainly attributed to cancers (35%) and cardiovascular diseases (CVDs; 29%). The proportionate mortality from NCDs rose from 35% in 2003 to 45% in 2010 (x2 linear trend 93.4; p,0.001). While overall annual mortality rates (MRs) for NCDs fell, cancer-specific MRs rose from 200 to 262 per 100,000 population, mainly due to increasing deaths in adults aged 65y and older, and to respiratory neoplasms in all age groups. The substantial fall in CD MRs resulted in similar MRs for CDs and NCDs among all adult females by 2010. NCD MRs for adults aged 15y to ,65y fell from 409 to 183 per 100,000 among females and from 517 to 283 per 100,000 population among males. NCD MRs were higher among males than females aged both below, and at or above, 65y.

The HDSS study site is located in a rural part of Siaya County, in western Kenya [20], [27]. The area consists of 385 villages spread over a 700 km2 area along the shores of Lake Victoria, with a population in 2010 of 224,500 adults in aged 15 years (y) and above. The population, mainly subsistence farmers, are almost exclusively members of the Luo ethnic group and traditionally polygynous, have been described in detail elsewhere [20], [28]. The HDSS is a population-based system with GPS locational data, that longitudinally records demographic (births, deaths, pregnancies, and in- and out-migrations) information [20]. Household census among the population takes place tri-annually, in January-March, May-August, and October-December, by field staff who visit all households in the study site. Verbal autopsy is conducted in subpopulations covered by HDSS. All deaths in residents, defined as having resided in the area for at least four consecutive months, are identified by local village reporters through ongoing local monitoring, and are validated during the tri-annual census. At least one month following death, and within four months to reduce recall bias, an interviewer returns to the home and records events surrounding the death, using standardized WHO verbal autopsy (VA) questionnaires endorsed by the INDEPTH Network [20], [29], [30], with spouses or another close relative of the deceased. Resident identification numbers allow linkage of each death with HDSS data. In this paper, cause of death was attributed using the InterVA-4 methodology, a new public-domain probabilistic model for interpreting cause of death from VA data [31], [32]. This methodology attributes cause of death compatible with the International Classification of Diseases 10 (ICD-10) categorised into 62 overall groups through a computer simulated algorithm. ICD-10 codes and the respective VA coding and disease categories are listed in Table S1. Indicators required to run the InterVA-4 Model were extracted from VA data and entered into the model to generate cause of death. The model produces a maximum of three probable causes of death and their corresponding likelihoods. In this paper, analyses focus on primary cause of deaths since only 10% received a secondary and <1% received a tertiary diagnosis. The model has a built-in facility to adjust for the prevalence of malaria and HIV/AIDS. Before running, the model was set high for both the diseases. Previous studies in our surveillance area have reported the prevalence of malaria and HIV/AIDs at 33% and 14% respectively [20], [21], [25]. The HDSS protocol and consent procedures, including surveillance and VA, are approved by the Ethical Review Committee of the Kenyan Medical Research Institute (#1801) and by the Centers for Disease Control and Prevention Institutional Review Board (#3308). Following cultural customs, compound heads provide written informed consent for all compound members to participate in HDSS activities. Individuals can refuse to participate at any time. All HDSS census and VA data are maintained on a secure server accessed by authorized researchers only. Named data are securely stored in a MS-SQL database and only authorized data personnel have access rights. Datasets analysed by scientists are stripped of names to protect identity. For this evaluation, adults were defined as persons aged 15 years and older. Data were extracted from the HDSS database for all adult deaths in residents, generated from the adult VA questionnaire, between January 2003 and December 2010. Primary cause was derived from aggregated ICD-10 codes generated by the InterVA-4 algorithms [31] (Table S1). Median age of death is presented with interquartile ranges (IQR), for grouped causes of NCD deaths. Analyses are stratified by sex, and into two age groups, using 65 years as the break-point (i.e. below; 15y to <65y, and at or above; ≥65y), to investigate trends in the causes and proportion of premature (aged below 65y) NCD deaths. Descriptive data include deaths in Karemo villages captured 2008–2010, but time trends on the absolute number of deaths and mortality rates 2003–2010 exclude these villages. Mortality rates per 100,000 population for CDs and NCDs, and for main NCD causes (as aggregated through ICD-10 codes; Table S1), were estimated by year and age category using mid-year population-point estimates generated from the HDSS census. The age-sex structure of the adult population was examined per year to clarify if relative proportions changed over time. The highly stable population profile precluded the need to make temporal adjustments to the denominator for analysis of rates. Key social and demographic characteristics generated from questioning the compound head during HDSS census surveys were examined, to compare differences among deaths from NCD and CD, and by sex. This included marital status (ever married, married at time of death, divorced), education (completed primary, secondary school), and socio-economic status (SES). SES quintiles were based on multiple correspondence analysis (MCA) generated from biennial surveys on wealth indicators, reported elsewhere [20], [26]. In this paper we collapsed the five SES quintiles into two, portraying poorest (lowest two quintiles) and less poor (highest three quintiles). Analyses were conducted using SPSS for Windows (Release v21.0; IBM, Endicott, NY, USA), and EpiInfo Stat Calc (v7; CDC Atlanta, USA). Chi-squared (χ2) test for linear trend (LT) determined the significance of changing rates by sex and age over time (2003 to 2010). Pearson's χ2 test was used to determine differences between groups. Mantel Haenszel Relative Risks (RR), with Taylor Series 95% confidence intervals (CI), were used to compare annual mortality rates between sexes. We stratified RR analyses for mortality rates by age groups, sex, and year generating a summary χ2, with a Mantel Haenszel weighted RR (MHRR) and Greenlands-Robins 95% CI. Significance was set at 5% or less.

Based on the provided information, here are some potential innovations that could improve access to maternal health:

1. Mobile health (mHealth) applications: Develop mobile applications that provide information and resources related to maternal health, including prenatal care, nutrition, and postnatal care. These apps can be easily accessible to women in rural areas with limited access to healthcare facilities.

2. Telemedicine: Implement telemedicine services that allow pregnant women to consult with healthcare professionals remotely. This can help address the issue of limited healthcare facilities in rural areas by providing access to medical expertise and advice.

3. Community health workers: Train and deploy community health workers in rural areas to provide maternal health education, prenatal care, and postnatal support. These workers can bridge the gap between healthcare facilities and remote communities, ensuring that women receive the necessary care and information.

4. Maternal health clinics: Establish dedicated maternal health clinics in rural areas to provide comprehensive prenatal and postnatal care. These clinics can be equipped with necessary medical equipment and staffed by trained healthcare professionals to ensure safe deliveries and postpartum care.

5. Transportation services: Improve transportation infrastructure and services in rural areas to ensure that pregnant women can easily access healthcare facilities. This can include providing ambulances or transportation vouchers to pregnant women in need.

6. Health education programs: Implement health education programs that specifically target maternal health in rural communities. These programs can provide information on prenatal care, nutrition, family planning, and safe delivery practices.

7. Maternal health incentives: Introduce incentives for pregnant women to seek timely and regular prenatal care. This can include providing financial incentives or rewards for attending prenatal check-ups and following recommended care guidelines.

8. Maternal health awareness campaigns: Conduct awareness campaigns to educate communities about the importance of maternal health and the available resources. These campaigns can help reduce stigma, increase awareness, and encourage women to seek appropriate care.

9. Partnerships with local organizations: Collaborate with local organizations, such as NGOs and community-based groups, to improve access to maternal health services. These partnerships can leverage existing networks and resources to reach women in remote areas.

10. Data-driven approaches: Utilize data from health and demographic surveillance systems to identify areas with high maternal mortality rates and target interventions accordingly. This can help prioritize resources and interventions where they are most needed.
AI Innovations Description
Based on the information provided, the recommendation to improve access to maternal health in rural Kenya could be to strengthen the health and demographic surveillance system (HDSS) in the area. This could involve the following steps:

1. Enhance data collection: Ensure that the HDSS collects comprehensive and accurate data on maternal health, including information on pregnancies, births, and maternal deaths. This can be done through regular household census and verbal autopsy interviews.

2. Improve cause of death attribution: Utilize advanced computer algorithms, such as the InterVA-4 methodology, to accurately attribute causes of maternal deaths. This will help identify the specific factors contributing to maternal mortality and guide targeted interventions.

3. Increase coverage: Expand the HDSS coverage to include a larger population and more villages in the rural area. This will provide a more representative sample and allow for a better understanding of the maternal health situation in the region.

4. Strengthen community engagement: Involve local village reporters and community members in the HDSS activities. This can help increase awareness about maternal health issues, encourage reporting of maternal deaths, and facilitate the implementation of interventions at the community level.

5. Enhance data security and privacy: Ensure that the HDSS data is securely stored and accessible only to authorized personnel. This will protect the privacy of individuals and maintain the confidentiality of the collected information.

By implementing these recommendations, the HDSS can serve as a valuable tool for monitoring and improving maternal health in rural Kenya. The data collected can inform evidence-based interventions and policies to reduce maternal mortality and improve access to maternal healthcare services.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthening Health and Demographic Surveillance Systems (HDSS): Investing in the improvement and expansion of HDSS can provide valuable data on maternal health outcomes, including causes of maternal deaths and trends over time. This data can inform targeted interventions and policies to address the specific needs of the population.

2. Enhancing Verbal Autopsy (VA) Methodology: Improving the accuracy and efficiency of VA questionnaires and data collection processes can help gather more reliable information on the causes of maternal deaths. This can aid in identifying preventable factors and designing appropriate interventions.

3. Increasing Awareness and Education: Implementing community-based education programs to raise awareness about maternal health, including the importance of antenatal care, skilled birth attendance, and postnatal care, can help improve access to these services. This can be done through community health workers, local leaders, and mass media campaigns.

4. Strengthening Health Systems: Investing in the infrastructure, equipment, and human resources necessary for providing quality maternal health services is crucial. This includes ensuring the availability of skilled healthcare providers, essential medicines, and functioning referral systems.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Baseline Data Collection: Gather data on the current status of maternal health indicators, such as maternal mortality ratio, antenatal care coverage, skilled birth attendance, and postnatal care utilization. This data can be obtained from existing health information systems, surveys, and research studies.

2. Define Simulation Parameters: Determine the specific indicators and outcomes that will be simulated, such as changes in maternal mortality ratio, increase in antenatal care coverage, or reduction in delays in accessing emergency obstetric care. Set realistic targets for improvement based on evidence and expert recommendations.

3. Model Development: Develop a simulation model that incorporates the recommended interventions and their potential impact on the selected indicators. This can be done using mathematical modeling techniques, such as system dynamics or agent-based modeling, which simulate the interactions between different factors and their effects on the outcomes of interest.

4. Data Input and Validation: Input the baseline data into the simulation model and validate its accuracy by comparing the simulated results with the actual data. Adjust the model parameters as needed to ensure a good fit between the simulated and observed outcomes.

5. Scenario Testing: Run the simulation model with different scenarios that represent the implementation of the recommended interventions. This can include variations in the coverage and effectiveness of the interventions, as well as different implementation timelines.

6. Impact Assessment: Analyze the simulated results to assess the potential impact of the recommended interventions on the selected indicators. This can include estimating the reduction in maternal mortality, increase in antenatal care coverage, or improvement in other relevant outcomes.

7. Sensitivity Analysis: Conduct sensitivity analysis to explore the robustness of the simulation results to variations in the model parameters and assumptions. This can help identify the key factors that influence the outcomes and assess the uncertainty associated with the simulation results.

8. Policy Recommendations: Based on the simulation results, provide evidence-based policy recommendations on the most effective interventions and strategies to improve access to maternal health. Consider the feasibility, cost-effectiveness, and scalability of the recommended interventions in the local context.

It is important to note that the methodology described above is a general framework and can be adapted and customized based on the specific context and data availability.

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