Antonius Benny Setyawan, . and Khairil Anwar Notodiputro, . and Indahwati, . (2015) MODELLING CASES OF LOW BIRTH-WEIGHT INFANTS WITH GENERALIZED LINEAR MIXED MODEL. Proceeding of International Conference On Research, Implementation And Education Of Mathematics And Sciences 2015 (ICRIEMS 2015), Yogyakarta State University, 17-19 May 2015. ISSN 978-979-96880-8-8
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Abstract
Low Birth-Weight (LBW) is defined as a birth weight of a live-born infant of less than 2.500 grams regardless of gestational age. The causes of LBW cases can be grouped into two main causes: premature birth and case of small for gestational age (SGA). There are many risk factors that can induce directly or indirectly so that these causes may occur. Case of LBW is associated with infant mortality, infant morbidity, inhibited growth and slow cognitive development, also chronic diseases in later life. To suppress rate of LBW first we must estimate the rate correctly. Data of LBW comes from Indonesian Health and Demographic Survey (IDHS) 2012 which is divided into 3 groups: written (measured accurately), recall (measured inaccurately) and not weighed (not measured). Published national rate of LBW is 7.3% with provincial rates fall between 4.7-15.7 %. The estimation came from only 2 former groups without consideration of assumed difference accuracy on second group. To estimate the difference and the rate of the third group, Generalized Linear Mixed Model (GLMM) is used with live-born infants as observation units because observations from the same sampling unit tends to correlate due to multistage sampling design. The result of the model at α = 0.05 is highly-significant, with fixed effect variables that are statistically significant to the case of LBW are Estimated Size, Preceding Interval, Pregnancy Complication, Mother’s Age, Province and Education. Higher portion of variance component is on the G-side as a result of multistage sampling, with Household level has highest within variance. On the R-side, recall group data has higher variance than written group. It is an indication of lower accuracy of the birth weight data on this group. Based on the model, estimation of LBW rate including not weighed group result 7.96% slightly higher than direct estimate. Keywords: Low Birth-Weight, GLMM, Logistic Regression, IDHS 2012
| Item Type: | Article |
|---|---|
| Subjects: | Prosiding > ICRIEMS 2015 > Mathematics & Mathematics Education |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Pendidikan Matematika > Matematika |
| Depositing User: | Administrator |
| Date Deposited: | 15 Jul 2015 12:31 |
| Last Modified: | 15 Jul 2015 12:31 |
| URI: | http://eprints.uny.ac.id/id/eprint/23630 |
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