I have selected data sources and matched them with my context – Africa and in the fields which I am interested in – sexual reproductive adolescent and child health
Data source: Verbal autopsy
Strong research question and why: What are the medical and socioeconomic causes of maternal deaths in Kenya or in an African context
Weak research question and why: What are the clinical practices, biological factors and socio-behavioral factors related to HIV-associated morbidity and mortality in the African context
Alternative design: prospective cohort design e.g. the African Cohort Study (AFRICOS) that enrolls a cohort of HIV-infected persons and follows them. Or use existing databases such as the International Epidemiological Databases to Evaluate AIDS (IeDEA) in East Africa which harmonizes data collected by geographically disparate, but representative, cohorts of persons infected with HIV or at risk for HIV (healthcare system‐based population).
The beauty about this IeDEA cohort is that one can generate inferences about the natural or treated history of HIV, particularly regarding uncommon exposures or outcomes for which large samples would be otherwise needed.
Discussion: Verbal autopsy is problematic with diseases that have few specific symptoms. For example HIV may present in multiple myriad presentations and with varying co-morbidities and for which there may be underlying disease progression factors such as chronic malnutrition that may not be easy to tease apart. On the contrary, for conditions with well defined cause of death lists e.g. maternal mortality, it is easier to collect standardised data with a reasonable degree of reliability.
Data source: health and retirement study (HRS): Prospective cohort data using the longitudinal cohort design such as the health and retirement study – over the years, it has been able to incorporate specific age cohorts such as the children of the depression (born 1924-1930), “war babies” (born 1942-1947), “early boomers” born in 1948-1953
Strong research question and why: What is the impact of early-life adversity (e.g. early childhood malnutrition,) on adult cognitive functioning in Kenya e.g. – can take advantage of the 1983 and 1989 famines in Kenya and look at the famine survivors who were between the ages of 0 and 8 at the time of the famine. I think there is an opportunity for a natural experiment here
Weak research question and why: What are the clinical practices, biological factors and socio-behavioral factors related to cognitive impairment among adults aged >55 years in Kenya
The reason why data from a data source such as the HRS may not be sufficient to answer this question is that because it is cohort based, it may not be able to provide important information for other subpopulations in different cohorts that are not included in the cohorts they are looking at. Or like for this question in Kenya, while we can draw important understanding of etiology course and outcome for this cohort aged 0.8 years at the time of the 1983 and 1989 famines, we cannot provide inferences for other groups outside this time frame
Alternative design: The alternative design here would be to have repeated longitudinal cohort design that is complemented by repeated cross-sectional surveys such as the 10-yearly census or 5-yearly demographic health surveys or 3-yearly multiple indicator cluster surveys
Discussion: Prospective cohort studies can be used to study multiple complex diseases and risk factors simultaneously over an individual's lifetime. Such studies have proved crucial in understanding the etiology, course, and outcome of diseases in other populations and have informed the design of prevention programs.
Unfortunately, longitudinal studies of the same individuals are not sufficient to provide a useful base for analyses of change in the process of aging for other subpopulations in different cohorts.
Data source: ARIC : uses both cohort and community surveillance.
Within my context, I think a similar example to this ARIC is The KEMRI/Centers for Disease Control and Prevention (CDC) Health and Demographic Surveillance System (HDSS) is located in Rarieda, Siaya and Gem Districts (Siaya County), lying northeast of Lake Victoria in Nyanza Province, western Kenya. The KEMRI/CDC HDSS, has approximately 220 000 inhabitants
Strong research question and why:
“What is the impact of malaria transmission reduction activities on the all-cause mortality rate in the local population undergoing community-based malaria control interventions.” Or can narrow it down further to pregnant women
The method is appropriate because
- the HDSS provides demographic and health information (such as population age structure and density, fertility rates, birth and death rates, in- and out-migrations, patterns of health care access and utilization and the local economics of health care) as well as disease- or intervention-specific information.
- Community surveillance data is also linked to health facility surveillance data which similar to the ARIC provides case burden data and allows for cross validation
Weak research question and why
What are the factors associated with lower expectancy among persons in Kenya when Siaya county when compared to the rest of Kenya
The downside of the HDSS is that unlike the ARIC it is not generalizable since it is only located within the Siaya County. While the rationale for hosting the HDSS in Siaya is because Siaya has one of the lowest life expectancies in Kenya, this information is un-informative if it cannot be compared to other regions
Alternative design: It might be necessary to set up a similar HDSS within other areas in the country with higher life expectancies and less morbidity and mortality to allow for comparability