Policy to Decrease Low Birth Weight in Indonesia: Who Should Be the Target?
Ratna Dwi WulandariAgung Dwi LaksonoRatu MatahariPublished in: Nutrients (2023)
The study aimed to analyze the target of the policy to decrease low birth weight (LBW) in Indonesia. This cross-sectional study used a sample of live births in last five years preceding the survey of birth weight. Data collection took place from July to September 2017. The weighted sample size was 17,848 participants. The variables analyzed included residence, age, marital status, education, employment, parity, and wealth. The study employed binary logistic regression in the final stage to determine the target of policy regarding LBW. The results showed that women in urban areas were 1.200 times more likely to deliver babies with LBW than women in rural areas. All age groups were less likely to deliver babies with LBW than those aged 45-49. The study also found all marital statuses had a lower likelihood of providing babies with LBW than those who had never been in a marriage. Women of all education levels had a greater risk of giving birth to babies with LBW than women with higher education levels. Unemployed women had 1.033 times more chances of delivering babies with LBW than employed women. Primiparous women were 1.132 times more likely to give birth to babies with LBW than multiparous women. Overall, the women in all wealth status categories had a higher probability of delivering babies with LBW than the wealthiest groups. The study concluded that policymakers should target women who live in urban areas, are old, have never been married, have low education, and are unemployed, primiparous, and poor to decrease LBW cases in Indonesia.
Keyphrases
- body mass index
- polycystic ovary syndrome
- gestational age
- pregnancy outcomes
- healthcare
- low birth weight
- birth weight
- preterm birth
- cervical cancer screening
- preterm infants
- public health
- mental health
- insulin resistance
- magnetic resonance imaging
- quality improvement
- pregnant women
- human milk
- artificial intelligence
- ionic liquid