Estimation of the Distribution of Duration of Breastfeeding from Cross-Sectional Data: Some Methodological Issue.
,
Abstract
Background: Duration of breastfeeding is an important health indicator of mother and child. There are various indirect epidemiological methods available to estimate the duration of breastfeeding from cross sectional data.
Objective: To estimate the distribution of duration of breastfeeding at national level cross sectional data and compare various available technique. The impact of the sampling frame (ascertain of the individual understudy) is also evaluated.
Method: National Family Health Survey (NFHS-IV) data is used. Duration of breastfeeding of only those children who were born before 60 months from survey date were included in the study. The technique of Current Status Data, Life Table Analysis, and Kaplan Meier (KM) estimator is applied to assess the distribution of duration of breastfeeding.
Result: The mean estimate is 32.84, 33.14 and 33.64 months by Kaplan Maier Estimator, Current Status Data and Life Table Analysis respectively. The Current Status and Life Table method are better than Kaplan Meier Estimator as it is doesn’t based on recall data and heaping present in the data.
Conclusion: One must be very cautions while estimating the various epidemiological parameters from cross section data set. The assumptions of the methodology as per data available should be evaluate. If such data is not available, the available methodology may be modified. Regression analysis based on Current Status data technique may be used to assess the impact of various clinical and epidemiological factors (such as nutrition of mother, health status of mother etc.) on duration of breastfeeding.
2. Breastfeeding [Internet]. [cited 2020 Nov 7]. Available from: https://www.who.int/news-room/facts-in- pictures/detail/breastfeeding
3. Ma S. Additive risk model with case-cohort sampled current status data. Stat Pap. 2007;48(4):595–608.
4. Jewell NP, Van Der Laan M, Lei X. Bivariate current status data with univariate monitoring times. Biometrika. 2005;92(4):847–62.
5. Smith DP. Regression analysis of “ current status ” life tables on duration of breastfeeding in Sri Lanka. Soc Biol. 1985;32(April 2015):90–101.
6. Agampodi SB, Agampodi TC, Kankanamge U, Piyaseeli D. Breastfeeding practices in a public health field practice area in Sri Lanka : a survival analysis. Int Breastfeed. 2007;2(1):13.
7. Augusto J, Carrazedo DA, Antonio F, Colugnati B. Exclusive breastfeeding duration and determinants among Brazilian children under two years of age Duração e determinantes do aleitamento materno exclusivo entre crianças brasileiras menores de dois anos. Rev Nutr. 2013;26(3):259–69.
8. Kasahun AW, Wako WG, Gebere MW, Neima GH. Predictors of exclusive breastfeeding duration among 6 – 12 month aged children in gurage zone , South Ethiopia : a survival analysis. Int Breastfeed J. 2017;12(20):1–9.
9. Rahman S, Rahman A. Prevalence of undernutrition in Bangladeshi children. J Biosoc Sci. 2020;52(4):596–609.
10. Robert E, Coppieters Y, Swennen B, Dramaix M. Breastfeeding Duration : A Survival Analysis — Data from a Regional Immunization Survey. Biomed Res Int. 2014;2014.
11. Abada TSJ, Trovato F, Lalu N. Determinants of breastfeeding in the Philippines : a survival analysis. Soc Sci Med. 2001;52(1):71–81.
12. Giashuddin MS, Kabir M. Duration of breast-feeding in Bangladesh. Indian J Med Res. 2004;119(June):267–72.
13. Taylor P, Grummer-strawn LM, Grummer-strawn LM. Regression Analysis of Current-Status Data : An Application to Breast-Feeding Regression Analysis of Current-Status Data : An Application to Breast-Feeding. J Am Stat Assoc. 1993;88(42):758–65.
14. Chakrabarty TK. Deconvolving Kernel Density Estimation of Right Censored Duration Data with Recall Errors. Am J Appl Math Stat. 2016;(January 2014).
15. Suchard MA. Sex, lies and self-reported counts: Bayesian mixture models for heaping in longitudinal count data via birth–death processes. Ann Appl Stat. 2015;9(2):572–96.
16. Diamond ID, Mcdonald JW. Proportional hazards models for current status data : application to the study of differentials in age at weaning in pakistan. Demography. 1986;23(4).
17. Vanderhoeft C. Accelerated failure time models: an application to cur&ent status breast-feeding data from pakistan 1. Genus. 1982;38(1):135–57.
18. Trussell J, Grummer-strawn L. Trends and Differentials in Breastfeeding Behaviour: Evidence from the WFS and DHS*. Popul Stud (NY). 1992;46(2):285–307.
19. Sheps MC. Distribution of Birth Intervals to the Sampling. Theor Popul Biol. 1972;1(1972):1–26.
20. Sheps MC, Menken JA, Ridley JC, Lingner JW. Truncation Effect in Closed and Open Birth Interval Data. J Am Stat Assoc. 1972;65(330):678–93.
21. Wolfers D. Determinants of birth intervals and their means. Popul Stud (NY). 1968;22(2):253–62.
22. Chiang long chin. Constructing Current Life Tables. J Am Stat Assoc. 1972;67(339):538–41.
23. Wegman EJ, Wright IW. Splines in statistics. J Am Stat Assoc. 1983;78(382):351–65.
24. Rao CR. Handbook of Statistics [Internet]. Handbook o. 29, editor. elsevier; 2009. Available from:
www.elsevier.com/books/sample-surveys-design-methods-and-applications/pfeffermann/978-0-444-53124-7
25. Klein JP. Handbook of Survival Analysis. Handbook of Survival Analysis. 2016.
| Files | ||
| Issue | Vol 6 No 4 (2020) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/jbe.v6i4.5686 | |
| Keywords | ||
| Breastfeeding epidemiological life table Kaplan-Meier estimator Sampling frame. | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
