I am interested in looking at
Outcome- adolescent sexual reproductive health outcomes ---binary outcome – positive vs negative
Exposure: household level food insecurity status as measured by the household food insecurity assessment scale (HFIAS)
The life course model that is likely most appropriate is the cumulative model. Although by itself adolescence is both a sensitive and critical period, I believe that sexual reproductive health outcomes among adolescents are secondary to a gradual accumulation of promotive and risk factors right from in utero, through early childhood, pre-adolescence and during the adolescent period in this case- here food insecurity. In my study, food insecurity is a measure of personalized poverty.
My research question: Does household level food insecurity exposure over the life course have a cumulative effect on adolescent SRH outcomes.
Data collection and definition
I would need to measure exposure and health outcomes at 3 time points,
- Time point 1: 0- 5 years
- Time point 2: very young adolescents (VYAs) 10-14 years
- Time point 3: adolescents proper 15-19 years
Exposure: Household food insecurity (HFI) is measured using the Household food insecurity assessment score (HFIAS) which divides food insecurity into 3 tertiles of 1-3 = low HFI (0), 4-6 = moderate HFI (1) and 7-9 = severe HFI (2)
Data analysis: A combination of the 3 levels of HFI (0,1,2) at 3-time points would result in 27 different life course trajectories. Life course trajectory 1, for example, would be represented by HFIS exposure 000, implying low HFIS at all three time points in the life course. Trajectory 27 would be represented by exposure 222, implying high HFIS at all three time points.
In order to represent cumulative HFIS exposure over the life course, I would create a summary score by summing up the exposure level at the three time points for each trajectory. Potentially the cumulative scores could look as follows
Summary score 0: 000
Summary score 1: 001, 010, and 100.
Summary score 2: 002, 011, 020, 101, 110, and 200.
Summary score 3: 012, 021, 102, 111, 120, 201, and 210.
Summary score 4: 022, 112, 121, 202, 211, and 220.
Summary score 5: 122, 212, and 221.
Summary score 6: 222
It is also possible for me to examine the accumulation hypothesis for a given level of household food insecurity (HFI) by doing a trend analysis. Thinking about it this way- there are nine possible ways of getting to each of the three levels of HFI.
Low HFI- the different trajectories are: 000, 010, 100, 110, 020, 200, 120, 210, and 220
Moderate HFI at time point 3: 001, 011, 101, 111, 021, 201, 121, 211, and 221
Severe HFI at time point 3: 002, 012, 102, 112, 022, 202, 122, 212, and 222
For this trend analysis: I would do a Logistic regression, with the category having the lowest cumulative score for each level HFI as the reference group and determine the odds of negative SRH outcomes.
Data sources/ sets
Realistically, without setting up a new study, the International Epidemiology Databases to Evaluate AIDS (IeDEA) is a database that provides a rich resource for globally diverse HIV/AIDS data on infants, children, adolescents, and pregnant women. It is the lowest hanging fruit that would enable to look at this question (may need to tweak the measurement of my exposure)
This data base contains longitudinal data from pregnancy, HIV outcome for the infant. Infants who test HIV positive are followed up prospectively for life beginning from the year 2006
Infants who are HIV –exposed are followed up prospectively upto 18 months.
The database has got rich programmatic level data for individual adolescents including the very young adolescents and the older adolescents.
For the Kenya data, there is baseline household socio-economic demographic data but this is not collected beyond this.
Concerns about my tests:
These are mainly do with the nature of my data.
The limitations of this data set are that it is limited largely HIV-infectedted infants and adolescents and so the findings may not be easily generalizable to adolescents who are HIV negative.
The data – does not also look at household food insecurity but rather measures levels of malnutrition using height for weight z scores, BMI and Mid upper arm circumference. This is measured atleast once every 1,2, or 3 months. While important, may not be able to tell us the HFIs but can serve as a proxy. Also the measurement is not standardized across different facilities and so there may be errors arising from measurement due to instrumentation
Other challenges with this data is incompleteness of data, children, adolescents who are lost to follow up, high mortality rate and issues around structural validity of the data (I am thinking here – maturation)
From an analytical perspective, this database only provides observational data and so the issue of being able to infer causality from this data would have to be looked at carefully.