Data source and selection
This cross-sectional study used data from Riskesdas 2010. The Riskesdas is a national basic health survey that has been conducted in 2007, 2010 and 2013. It focused on health indicators mandated by the Millenium Development Goals. Riskesdas was conducted between May and August 2010 covering 33 provinces and 441 districts in Indonesia. The survey was a community based survey using a sample of 70.000 household. Primary data cleaning and analysis were conducted by the National Institute of Health Research and Development Ministry of Health of the Republic of Indonesia.
The data in Riskesdas were collected through stratified random sampling. Census Block (CB) was selected from each district/municipality in proportion to the population size. There were 2800 CBs that were selected randomly from the universe of CBs, with 25 households again randomly selected from each selected CB. The Riskesdas survey contains information for a total of 251,388 individuals from 70,000 households [2].
In this analysis, we included data from subjects aged 12–23 months. We excluded subjects who did not have information on birth weight, history of feeding practices (breast feeding and complementary food), as well as history of neonatal illness since birth. There were 3024 subjects involved in the analysis.
Information about the subjects was collected by interviewing their mother. Some information, for example the LBW was taken from growth monitoring cards. In Indonesia, infants and toddlers are regularly weighed once a month as part of a child growth and nutritional status monitoring program. The data are recorded in a nationally standardized monitoring card.
Anthropometric measurements were taken by enumerators using standardized body length with the precision of 0.1 cm. Enumerators were trained not only for technical interviews, but also to take the anthropometric measurement. Records of the subjects’ birth weight was taken either from the monitoring card, or the book that is provided by the health ministry to every pregnant woman in Indonesia for self-monitoring [2].
The enumerators were fresh graduates with health-related background such as nurse, midwife, nutritionist and public health. In addition to their education background, they were well trained in class as well as in field trials.
Study variables
Infants’ demographic characteristics (i.e., gender, status of LBW and history of neonatal illness); food intake history (i.e., whether the colostrum was taken, whether the baby had breast-feeding initiated within 1 h of birth, exclusive breast feeding, received pre-lacteal food, whether the infant was weaned at 1 year of age and received complementary feeding more than 6 months); the exposure to health program/services (i.e., routinely weighed for growth monitoring, complete immunization, and vitamin A supplementation), and the socioeconomic status of the household. LBW status was defined as a birth weight of less than 2500 g. Neonatal illness was defined as whether or not the infants were sick due to any causes up to 28 days after birth.
Colostrum is defined as the thick yellowish substance secreted from the mammary glands after giving birth that is high in protein, fat-soluble, vitamins, minerals and antibodies that can protect the baby from illness [6]. Breastfeeding initiation is defined as the baby being breast-fed less than one hour after birth as recommended by the WHO. Exclusive breast-feeding was defined as the 6 month period after birth during which the child receives no other food or drink, not even water, except breast milk (including milk expressed or from a wet nurse), but allows the infant to receive ORS, drops and syrups (vitamins, minerals and medicines). The cut-off point for age of weaning was defined as 12 months based on the median of the data.
Pre-lacteal food is any food except mother’s milk provided to a newborn before initiating breastfeeding. Complementary feeding refers to the process starting when breast milk alone is no longer sufficient to meet the nutritional requirements of infants, and therefore other foods and liquids are needed, along with breast milk [7].
Complete immunization is defined as having received all immunizations as provided by the programs at 1 years of age, namely Hepatitis B (first dose) at the age of 0–7 days; BCG at the age of one month; Hepatitis B (second dose), DPT (first dose) and Polio (first dose) at the age of 2 months; Hepatitis B (third dose), DPT (second dose) and Polio (second dose) at the age of 3 months; DPT (third dose) and Polio (third dose) at the age of 4 months, and finally Polio (fourth dose) and Measles at the age of 9 months. Infants were categorized as receiving vitamin A supplementation if they receive vitamin A supplementation in the last 6 months prior to the interview.
Socio economic status was assessed using the yearly household expenditures. Quintiles were used to categorize the socio-economic status into five different categories. The two lowest quintiles were categorized as the poor; while the highest quintiles were categorized as the very rich.
Stunting as the outcome was provided in the data set. Calculation of the Z-scores were based on body length by age and converting a child’s variables of identification number, gender, age (in months), and body length using WHO anthropometry soft-ware (WHO AnthroII.PC2007). Stunting was defined as a Z-score less than minus two standard deviations (<−2SD) from the median [8].
Statistical analysis
Data analysis was undertaken using SPSS version 13.0. Normality of the distribution of numerical variables was tested using the Kolmogorov-Smirnov test. Since the distribution was not normal, a binary binomial categorical variable was constructed by classifying as stunted a child with a height for age Z-score less than −2 standard deviations (HAZ < −2 SD) and as not stunted if HAZ ≥ −2 SD. Univariate analysis were used to understand the distribution of the variables. Cross-tabulations were done to analyze the association (Chi-square tests) between stunted and all independent variables, simultaneously with the odd ratios. Finally, multiple logistic regressions were run step by step by selecting the final list of variables used among all candidates based on whether they were significant. All variables with p <0.20 were included in the multiple logistics regression using forward selection.