Statistical analysis
REGRESSION
Baseline characteristics were summarised as mean ± SD for continuous variables and the number of subjects and percentages for binary and categorical variables. LAZ, weight‐for‐length Outcome variables included the number of times an infant consumed egg (chicken or duck), dairy (milk or yogurt), meat (flesh meat or liver from poultry, goats, sheep, cows or water buffalo), or fish (dried or fresh) in the last 7 days. To generate each outcome variable, the frequencies of consumption for the relevant food items were summed at each timepoint. Exposure variables included the number of animals or fishponds owned by a household. For livestock ownership, animal types were grouped together based on the animal product for which they were commonly raised, thereby creating three variables: (1) poultry (chicken and duck), (2) dairy‐producing animals (goats, sheep, cows and water buffalo), and (3) meat‐producing animals (poultry, goats, sheep, cows and water buffalo).We explored the distributions of each exposure variable to categorise them into levels of ownership. The cut‐offs for low, medium and high animal ownership were based on the 25th, 50th and 75th percentiles of the distributions of each variable, respectively, among households owning any animals. For fishponds, the cut‐off for a medium level of ownership was ≤75th percentile of the distribution and >75th percentile for a high level of ownership, among households with any fishponds. Poultry ownership was categorised as none, 1–3 poultry (low), 4–10 poultry (medium), or ≥11 poultry (high); ownership of dairy‐producing animals as none, 1 animal (low), 2–3 animals (medium), or ≥4 animals (high); ownership of meat‐producing animals as none, 1–4 animals (low), 5–11 animals (medium), or ≥12 animals (high); and fishpond ownership as none, 1 pond (medium), or ≥2 ponds (high). We stratified the number of animals/fishponds owned by season (i.e., monsoon, winter, summer) and gender to explore variations in ownership. Responses from the eight‐item maternal decision‐making questionnaire were used to generate a score ranging from 0 to 16, with higher values indicating greater decision‐making power. After exploring the distribution, the score was dichotomised around the median to categorise respondents as having either ‘low’ or ‘high’ decision‐making power.Trends on average consumption and variance were examined for each outcome variable to inform model assumptions. Four separate regression models were developed to assess associations between poultry ownership and frequency of infants' egg intake, ownership of dairy‐producing animals and dairy intake, ownership of meat‐producing animals and meat intake, and fishpond ownership and fish intake. Each model was specified as a negative binomial regression model with random intercepts for infant and cluster and adjusted for infant age and sex, maternal age, LSI, and season (Diggle et al., We explored reasons for missingness and implemented multiple imputations by chained equations (