The study was conducted in the Butajera District of southern Ethiopia, which is located approximately 130 km from Addis Ababa (the capital city of Ethiopia) in the Guraghe Zone in the Southern Nations Nationalities and People’s Region (SNNPR). The district houses a Rural Health Program (BRHP) (owned and operated by Addis Ababa University), which is a health and demographic surveillance system (HDSS) with a continuous registration of vital and migratory events among ten selected villages. The studied district was purposely selected for the benefit of a better sampling frame and research infrastructure.
Study design and period
A community-based, cross-sectional study design was employed between November and December 2013. We administered the survey questionnaire twice to the study participants, and the second survey questionnaire was administered to the study participants after 7 days of the first administration. This repeated survey was used to determine the reproducibility of the household food insecurity assessment tool (HFIAS).
Study population and sampling
The study included a total of ten HDSS villages, of which nine were rural and one was urban, and the study population included households residing in these villages.
The sample size for the study was estimated using the formula for a single population proportion. Assuming an 80% prevalence of household food insecurity , a 95% confidence level, a 4% margin of error, and a design effect of 2, the calculated sample size was 768 households. With an expectation of a 5% non-response rate, the final sample size required was approximately 800 households.
The final sample size was allocated to the ten HDSS villages proportionate to the number of households in each village. We then used BRHP data set as a sampling frame and applied a simple random sampling method to select study households within a given village.
The HFIAS is composed of nine items, which are asked with a recall period of 1 month. For each item, there was a follow-up of the frequency of the occurrence question. The tool was also translated into the Amharic (local) language by one of the authors (SHG) and initially reviewed with research assistants who were residents in the study area.
We discussed all nine questions independently with four urban and four rural households in the neighboring villages, basically aiming at whether the questions were clear, easily understandable, and had a minimal amount of multiple interpretations. We read the nine questions to the women, and the responses were recorded. This was followed by a question about how the women understood each question. For example, we asked: “What do you understand when I ask you the question: In the past four weeks, were you or any household member not able to eat the kinds of foods you preferred because of a lack of resources?” We compared their understanding with that of the primary aim of the questions, and when there was a difference between what they understood and what we were actually looking for, a discussion followed on how that question could best be framed to make it clearer and contextually appropriate. Lastly, together with these women, the nine questions were adapted through modification, rephrasing, and adding examples when necessary.
We collected food groups that a household had consumed over the preceding 24 h , with the household food intake structured using the consumption of 12 food groups/item. The food groups included meat, fish, vegetables, fruits, eggs, potatoes, and other roots/tubers, beans, cereals/breads, oil, fat or butter, sugar or honey, as well as other types of foods such as coffee and tea.
Moreover, a range of sociodemographic data about the respondents such as age, education, religion, marital status, and occupation was collected, in addition to household-level data such as ownership and size of land, type of house and construction materials, availability of fixed assets such as radio, television, phone, bed and chair, and other household items, possession of domestic animals, type of water source for drinking and cooking, and availability and type of latrine.
Interviews were conducted by 20 trained and experienced junior nurses who are residents of the local district and had similar data collection experiences. The work was monitored by six supervisors, and interviews were primarily conducted with women in the household, as women are commonly responsible for food preparation in the study area. If women were unavailable, another adult who was present and ate in the household the previous day was asked.
Questionnaires were controlled for completeness and logical errors, and where errors were found, the questionnaires were redone. Consistency checks were done to improve the quality of the data, and inconsistent entries and responses were crosschecked with the questionnaires and corrected accordingly.
The study protocol was approved by institutional review boards from the Addis Ababa University, College of Health Sciences. The study was also approved by the Regional Committee for Medical and Research Ethics, Western Norway (REK Vest). Information on the research objective was read to the participants, verbal informed consent was received, and the privacy and confidentiality of respondents was also maintained.
Data entry and analysis
We used EpiData Version 3.1 for the data entry, and the data was exported to Stata 11.0 (StataCorp, College Station, TX) for cleaning and further analysis.
Household wealth was constructed through a principal component analysis (PCA) of the household-level data described above. The PCA was done independently for urban and rural samples, and the score was then used to assign sampled households into quintiles that indicate poorest, poor, medium, rich, and richest.
The results from HFIAS delineate households across the four levels of food insecurity, including food secure, mild food insecure, moderate food insecure, and severely food insecure. The procedure and steps used to assign households to one of the levels is described elsewhere .
An exploratory analysis was conducted on the nine items, using a Horn’s parallel analysis (PA) to determine the number of factors to retain. PA is a Monte Carlo-based simulation method that compares observed eigenvalues with that obtained from uncorrelated normal variables.
We evaluated the validity of the nine-item food insecurity assessment tool based on the following recommended criteria employed by a few similar studies [9, 15, 16].
The first criterion is the value of the Cronbach’s alpha, which is a measure for internal consistency, approaching 0.85 for the two rounds of surveys. Secondly, we tested for parallelism on HFIAS item response curves across wealth status, which was done by comparing the likelihood of affirmative responses to the nine items across households’ wealth quintiles.
Thirdly, we evaluated the presence of a dose-response relationship between food insecurity level and the previous day consumption of certain food items. We also tested for a dose-response relationship between household wealth status and food insecurity levels and used the extended Mantel-Haenszel chi square for linear trend to check for dose-response relationships.
Additionally, the reproducibility between the first and second HFIAS scores (HFIAS overtime) was estimated by means of a paired t-test.