The data source
In this article, we have used data from a cross-sectional survey that was conducted as part of an evaluation of the Maternal, Infant and Young Child Nutrition (MIYCN) program of BRAC (formerly known as Bangladesh Rural Advancement Committee), an international non-governmental organization based in Bangladesh. Data was collected from April to May 2016.
The study area included nine districts including Barguna, Bogra, Chittagong, Comilla, Cox’s Bazar, Dinajpur, Feni, Jessore, Meherpur of Bangladesh where BRAC implemented its MIYCN program (Fig. 1). BRAC selected these districts considering the availability of BRAC’s program delivery infrastructure, including the availability of its community health workers who have been trained to implement MIYCN interventions at the community level.
The study population included children aged 6-59 months and their caregivers. Caregiver was defined as the child’s birth mother or the person who looks after or cares for the child and feeds the child the most meals most days during the seven days preceding the survey. The inclusion criteria selected caregiver households that had at least one child aged 6–59 months and where the caregivers had resided in that household for at least one year. We excluded households if the caregiver was unable to attend an interview on the day of the survey due to illness or was unable to provide consent to participate in the survey. If the household has more than one eligible child, we randomly selected one child for the survey.
Sample size and sampling
We calculated the sample size for a district-level estimate; we considered a prevalence of 50%, a precision of ±10%, a Zα value of 1.96 and a design effect of 2. Using the standard sample size formula gave the minimum sample size of 192 households per district. Thus, the total sample size was 1,728.
We followed a two-step cluster sampling procedure. In the first step, systematic random sampling was applied to select 16 primary sampling units (PSUs) from the full list of BRAC communities in a district. This procedure ensured an equal chance of being included in the sample, and the resulting sample was close to a uniform spatial sample of the BRAC target areas. In the second step, the survey team verified the population size, approximate total households and PSU boundaries upon arrival at the selected PSU and in consultation with and with the assistance of the local population (President of Union Parishad, member, councillor, school teacher, elderly person and relevant local staff). A physical map-segment sample approach was used to segment the selected community or PSU. Detailed sampling procedures have been reported elsewhere .
Infant morbidity status was the outcome variable of this study. We considered children to have morbidity if their caregivers reported that their child had been ill either from conditions, such as diarrhea (diarrhea with at least 3 loose or watery, bloody stools in the cat or mucous on a 24 h period), fever (with fever), illness with cough and difficulty breathing or rapid breathing, difficult or rapid breathing with stuffy or runny nose in the last 14 days prior to the survey. In addition to caregiver recall, we also collected information on child morbidity from physician prescriptions/medications if the child had received treatment from a physician during the last episode of illness. If caregivers reported any of the illnesses present in their child, or if medical records indicated an episode of illness, then we considered them to have morbidity.
Exposure variable and covariates
Household FI status (categorized as food insecure, food secure) was considered as the main exposure variable in this study. We assessed household FI status based on 9 questions (Supplementary File 1) of the Household Food Insecurity Access Scale (HFIAS) developed by the Food and Nutrition Technical Assistance Group (FANTA) in collaboration with Tufts University and Cornell University. . The answer to each question ranges from 0 to 30. We scored these answers as follows: 0 = 0, 1–2 = 1, 3–10 = 2, and 11–30 = 3. The total score ranged from 0 to 27 for 9 questions. We then ranked the score 0–1 = food secure household and 2–27 = food insecure household. Other exposure variables included toilet availability, categorized as: improved toilet (flush or flush to a sewer system, septic tank, pit latrine, Kumasi ventilated improved pit latrine , pit latrine with slab), unimproved toilet (pit latrine without slab, hanging latrine or defecating in the bush or in a field). We also combined household food security and toilets to see the combined effects in the regression analysis and categorized them as: food secure and improved toilets, food secure and unimproved toilets, poor toilets. food insecure and improved toilets, food insecure and unimproved toilets.
Other covariates included household size (classified as:
The study was conducted in accordance with national/international human ethics guidelines and regulations. Prior to data collection, all participants provided written informed consent. To collect the data, we formed a four-member survey team, including two surveyors, a medical technologist and a supervisor. When recruiting team members, priority was given to members with experience and/or involvement in previous surveys. The supervisor was primarily responsible for selecting study participants, using the mentioned sampling methods, monitoring data collection activities, and ensuring data quality through spot checks and re-interviews.
We measured participants’ level of anxiety and uncertainty regarding household food supply, insufficient food quality and insufficient dietary intake by following the HFIAS which includes a brief survey instrument to assess whether households have experienced problems accessing food in the past 30 days. of investigation. The questionnaire used a nine-item household hunger scale questionnaire [(i) worry about food, (ii) unable to eat preferred foods, (iii) eat just a few kinds of foods, (iv) eat foods they really do not want to eat, (v) eat a smaller meal, (vi) eat fewer meals in a day, (vii) no food of any kind in the household, (viii) go to sleep hungry, and (ix) go a whole day and night without eating)].
Before finalizing the questionnaire, a field test was carried out in a real setting in the unsurveyed areas, and feedback from the field test was incorporated into the final version of the questionnaire. It was then submitted to the icddr,b Institutional Review Board (IRB) for review and approval. A standard operating procedure (SOP) has been developed for investigators. This SOP was a guide for interviewers on how to ask each of the questions to participants. Electronic data collection procedures used an Android-based smartphone survey questionnaire program. To support the Android operating system, the Open Data Kit (ODK) software was used to develop the program. Tabs/smartphones were used and Bengali and English versions of the questionnaire were used in ODK software.
Weighted and cluster-adjusted (PSU) descriptive statistics were estimated and presented as percentages with respective 95% confidence intervals. A bivariate analysis using a chi-square test was performed to measure the association between the outcome variable (morbidity status of children) and the main exposure variables (household IF and sanitation facilities). We performed multivariate logistic regression analysis to measure the association between outcome variables and other independent variables. First, we performed unadjusted logistic regression to find significant variables for the final multivariate regression model; p– a value