Modelling zero-truncated overdispersed antenatal health care count data of women in Bangladesh

Autoři: Zakir Hossain aff001;  Rozina Akter aff001;  Nasrin Sultana aff002;  Enamul Kabir aff003
Působiště autorů: Department of Statistics, University of Dhaka, Dhaka, Bangladesh aff001;  Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, United States of America aff002;  School of Sciences, University of Southern Queensland, Toowoomba, Australia aff003
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227824


Overdispersion in count data analysis is very common in many practical fields of health sciences. Ignorance of the presence of overdispersion in such data analysis may cause misleading inferences and thus lead to incorrect interpretations of the results. Researchers should account for the consequences of overdispersion and need to select the correct choice of models for the analysis of such data. In this paper, Generalized Linear Models (GLMs) are applied in modelling and analysis of antenatal care (ANC) count data extracted from the Bangladesh Demographic and Health Survey (BDHS) 2014. Pearson chi-square and different score tests are used to investigate the effect of overdispersion in the analysis. Overdispersion is found to be significant in the antenatal health care count data and so appropriate modelling is used to produce valid inferences for the regression parameters. The zero-truncated negative binomial regression (0-NBR) is found to be the best choice for analysing such data while excluding zero counts. Study findings reveal that place of residence, order of birth, exposure to mass media, wealth index and education of mother have significant impacts on the ANC status of women during pregnancy in Bangladesh.

Klíčová slova:

Antenatal care – Bangladesh – Health care providers – Labor and delivery – Obstetrics and gynecology – Pregnancy – Pregnancy complications – Statistical dispersion


1. Hilbe JM. Negative Binomial Regression. Cambridge University Press: 2nd edition.; 2011.

2. Kiser H, Hossain MA. Estimation of number of ever born children using zero truncated count model: evidence from Bangladesh Demographic and Health Survey. Health Information Science and Systems. 2019; 7(3): 30588293

3. Islam M, Sultana N. Risk factors for pregnancy related complications among urban slum and non-slum women in Bangladesh. BMC Pregnancy and Childbirth. 2019; 19(235):

4. Latif AHMM, Hossain MZ, Islam MA. Model Selection Using Modified Akaike’s Information Criterion: An Application to Maternal Morbidity Data. Austrian Journal of Statistics. 2008; 37(2): 175–184:

5. Chowdhury RI, Islam MA, Chakraborty N, Akhter HH. Determinants of Antenatal Morbidity: A Multivariate Analysis. World health & population. 2007; 9(3): 9–18:

6. Mohammad KA, Zahura FT, Rahman MM. Importance of maternal education on antenatal care visits in Bangladesh. Bangladesh Journal of Scientific Research. 2017; 30(1&2): 23–33.

7. WHO. World Health Organisation. Maternal mortality. 2019:, Accessed 2019-07-23.

8. BMMS. Bangladesh Maternal Mortality and Health Care Survey (2016). National Institute of Population Research and Training (NIPORT), International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b), and MEASURE Evaluation. 2017. Preliminary Report. Dhaka, Bangladesh, and Chapel Hill, NC, USA: NIPORT, icddr,b, and MEASURE Evaluation.

9. WHO. World Health Organisation: Sustainable Development Goals (SDG) 3. Ensure healthy lives and promote wellbeing for all at all ages. 2018:, Accessed 2019-07-23.

10. Titaley CR, Hunter CL, Heywood P, Dibley MJ. Why don’t some women attend antenatal and postnatal care services?: a qualitative study of community members’ perspectives in Garut, Sukabumi and Ciamis districts of West Java Province, Indonesia. BMC pregnancy and childbirth. 2010; 10(61):

11. Pandit RD. Role of Antenatal Care in Reducing Maternal Mortality. Asia-Oceania Journal of Obstetrics and Gynaecology. 1992; 18(1): 1–6: 1627055

12. WHO. World Health Organisation. Standards for Maternal and Neonatal Care. 2007: Accessed 2019-07-23.

13. BDHS. Bangladesh Demographic and Health Survey (BDHS) 2014. NIPORT, Mitra and Associates; Dhaka Bangladesh, The DHS Program, ICF International; Rockville, Maryland, USA.

14. Nisar N, White F. Factors affecting utilization of antenatal care among reproductive age group women (15-49 years) in an urban squatter settlement of Karachi. Journal of Pakistan Medical Association. 2003; 53(2): 47–53:

15. Jayaraman A, Chandrasekhar S, Gebreselassie T. Factors Affecting Maternal Health Care Seeking Behavior in Rwanda. DHS Working Papers; USAID. 2008; 59:

16. Navaneetham K, Dharmalingam A. Utilization of maternal health care services in Southern India. Social Science & Medicine. 2002; 55(10): 1849–1869:

17. Rahman KMM. Determinants of Maternal Health Care Utilization in Bangladesh. Research Journal of Applied Sciences. 2009; 4(3): 113–119: Determinants of Maternal Health Care Utilization in Bangladesh.

18. Becker S, Peters DH, Gray RH, Gultiano C, Black RE. The determinants of use of maternal and child health services in Metro Cebu, the Philippines. Health Transitional Review. 1993; 3(1): 77–89:

19. Chakraborty N, Islam MA, Chowdhury RI, Bari W, Akhter HH. Determinants of the use of maternal health services in rural Bangladesh. Health Promotion International. 2003; 18(4): 327–337: 14695364

20. Yebyo H, Alemayehu M, Kahsay A. Why Do Women Deliver at Home? Multilevel Modeling of Ethiopian National Demographic and Health Survey Data. PLoS One. 2015; 10(4): 25874886

21. Stroup WW. Generalized Linear Mixed Models: Modern Concepts, Methods and Applications. CRC press: 2012.

22. Dean C, Lawless JF. Tests for detecting overdispersion in Poisson regression models. Journal of the American Statistical Association. 1989; 84(406):467–472. doi: 10.1080/01621459.1989.10478792

23. Cameron AC, Trivedi PK. Regression-based tests for overdispersion in the Poisson model. Journal of Econometrics. 1990; 46(3): 347–364. doi: 10.1016/0304-4076(90)90014-K

24. Winkelmann R. Econometric Analysis of Count Data. New York: 5th edition.; 2008.

25. McCullagh P, Nelder JA. Generalized Linear Models. London: 2nd edition.; 1989.

26. Dobson AJ. An Introduction to Generalized Linear Models. New York: 3rd edition.; 2008.

27. Akaike H. Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Information Theory. Akademia Kiado Budapest. 1973; 267–281.

Článek vyšel v časopise


2020 Číslo 1