IDEAS home Printed from https://ideas.repec.org/a/sae/jodepp/v6y2021i2p210-230.html
   My bibliography  Save this article

Determinants of Antenatal Care Utilisation in India: A Count Data Modelling Approach

Author

Listed:
  • Arvind Kumar Yadav
  • Susanta Nag
  • Pabitra Kumar Jena
  • Kirtti Ranjan Paltasingh

Abstract

The article explores the micro-level factors (social, economic and demographic) that determine the utilisation of antenatal care (ANC) services in India using the Bayesian count data regression model. The primary purpose is to rectify the methodological loopholes in the existing literature using a count data regression model that overcomes the problems of overdispersion in the data. Using data from ‘National Family Health Survey’ (NFHS) data on women of reproductive age (15–49 years), we find that about 33% of pregnant women have not availed ANC during their pregnancy. The factors such as women’s education and partner/husband’s education, children’s birth order, household income, availability of basic amenities, like clean drinking water, media exposure, holding of bank accounts and use of mobile phones are statistically significant and positively affect ANC utilisation. Therefore, the study calls for prioritisation of and special attention to uneducated or less educated rural pregnant women. They should be incentivised adequately to utilise ANC services, which may drastically reduce inadequacy in ANC utilisation and improve mothers’ health before and after delivery. Awareness camps should be organised in every village in rural areas about pregnancy-related complications and the benefits of ANC check-ups. Massive infrastructure in the form of primary health centres or community health centres is the need of the hour in rural India.

Suggested Citation

  • Arvind Kumar Yadav & Susanta Nag & Pabitra Kumar Jena & Kirtti Ranjan Paltasingh, 2021. "Determinants of Antenatal Care Utilisation in India: A Count Data Modelling Approach," Journal of Development Policy and Practice, , vol. 6(2), pages 210-230, July.
    • Handle: RePEc:sae:jodepp:v:6:y:2021:i:2:p:210-230
    DOI: 10.1177/24551333211030349
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/24551333211030349
    Download Restriction: no
    File URL: https://libkey.io/10.1177/24551333211030349?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    2. Habibov, Nazim N., 2011. "On the socio-economic determinants of antenatal care utilization in Azerbaijan: evidence and policy implications for reforms," Health Economics, Policy and Law, Cambridge University Press, vol. 6(2), pages 175-203, April.
    3. Pravin Trivedi & David Zimmer, 2017. "A Note on Identification of Bivariate Copulas for Discrete Count Data," Econometrics, MDPI, vol. 5(1), pages 1-11, February.
    4. Rainer Winkelmann, 2008. "Econometric Analysis of Count Data," Springer Books, Springer, edition 0, number 978-3-540-78389-3, December.
    5. Deb, Partha & Trivedi, Pravin K, 1997. "Demand for Medical Care by the Elderly: A Finite Mixture Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 313-336, May-June.
    6. William H. Greene, 1994. "Accounting for Excess Zeros and Sample Selection in Poisson and Negative Binomial Regression Models," Working Papers 94-10, New York University, Leonard N. Stern School of Business, Department of Economics.
    7. Saleema Razvi & Debashis Chakraborty, 2016. "Does Economic Freedom Influence Major Health Indicators in India? Cross-state Panel Estimation Results," Journal of Development Policy and Practice, , vol. 1(2), pages 203-221, July.
    8. Daniel B. Hall, 2000. "Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study," Biometrics, The International Biometric Society, vol. 56(4), pages 1030-1039, December.
    9. Kevin E. Staub & Rainer Winkelmann, 2013. "Consistent Estimation Of Zero‐Inflated Count Models," Health Economics, John Wiley & Sons, Ltd., vol. 22(6), pages 673-686, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Greene, William, 2007. "Functional Form and Heterogeneity in Models for Count Data," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(2), pages 113-218, August.
    2. Niklas Elert, 2014. "What determines entry? Evidence from Sweden," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 55-92, August.
    3. David Dale & Andrei Sirchenko, 2021. "Estimation of nested and zero-inflated ordered probit models," Stata Journal, StataCorp LP, vol. 21(1), pages 3-38, March.
    4. Llerena, Freddy, 2012. "Determinantes de la fecundidad en el Ecuador [Determinants of fertility in Ecuador]," MPRA Paper 39887, University Library of Munich, Germany, revised Feb 2012.
    5. Gregori Baetschmann & Rainer Winkelmann, 2012. "Modelling zero-inflated count data when exposure varies: with an application to sick leave," ECON - Working Papers 061, Department of Economics - University of Zurich.
    6. William Greene, 2009. "Models for count data with endogenous participation," Empirical Economics, Springer, vol. 36(1), pages 133-173, February.
    7. Cornelia Lawson, 2013. "Academic Inventions Outside the University: Investigating Patent Ownership in the UK," Industry and Innovation, Taylor & Francis Journals, vol. 20(5), pages 385-398, July.
    8. Luiz Paulo Fávero & Joseph F. Hair & Rafael de Freitas Souza & Matheus Albergaria & Talles V. Brugni, 2021. "Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships," Mathematics, MDPI, vol. 9(10), pages 1-28, May.
    9. Christopher J. W. Zorn, 1998. "An Analytic and Empirical Examination of Zero-Inflated and Hurdle Poisson Specifications," Sociological Methods & Research, , vol. 26(3), pages 368-400, February.
    10. Agrawal, Ajay & Cockburn, Iain, 2003. "The anchor tenant hypothesis: exploring the role of large, local, R&D-intensive firms in regional innovation systems," International Journal of Industrial Organization, Elsevier, vol. 21(9), pages 1227-1253, November.
    11. Gamba, Simona & Magazzini, Laura & Pertile, Paolo, 2021. "R&D and market size: Who benefits from orphan drug legislation?," Journal of Health Economics, Elsevier, vol. 80(C).
    12. Payandeh Najafabadi Amir T. & MohammadPour Saeed, 2018. "A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate–Making Systems," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 12(2), pages 1-31, July.
    13. Abbas Moghimbeigi & Mohammed Reza Eshraghian & Kazem Mohammad & Brian Mcardle, 2008. "Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1193-1202.
    14. Nicolas Carayol, 2006. "La production de brevets par les chercheurs et enseignants-chercheurs.. Le cas de l'université Louis Pasteur," Economie & Prévision, La Documentation Française, vol. 0(4), pages 117-134.
    15. Levan Elbakidze & Rodolfo M. Nayga Jr. & Hao Li & Chris McIntosh, 2014. "Value elicitation for multiple quantities of a quasi-public good using open ended choice experiments and uniform price auctions," Agricultural Economics, International Association of Agricultural Economists, vol. 45(2), pages 253-265, March.
    16. Stefano Mainardi, 2003. "Testing convergence in life expectancies: count regression models on panel data," Prague Economic Papers, Prague University of Economics and Business, vol. 2003(4), pages 350-370.
    17. Morescalchi, Andrea & Pammolli, Fabio & Penner, Orion & Petersen, Alexander M. & Riccaboni, Massimo, 2015. "The evolution of networks of innovators within and across borders: Evidence from patent data," Research Policy, Elsevier, vol. 44(3), pages 651-668.
    18. List, John A., 2001. "US county-level determinants of inbound FDI: evidence from a two-step modified count data model," International Journal of Industrial Organization, Elsevier, vol. 19(6), pages 953-973, May.
    19. repec:wsi:acsxxx:v:21:y:2019:i:08:n:s1363919619500142 is not listed on IDEAS
    20. Majo, M.C. & van Soest, A.H.O., 2011. "The Fixed-Effects Zero-Inflated Poisson Model with an Application to Health Care Utilization," Other publications TiSEM 68cf0f9b-fc68-4017-97a9-a, Tilburg University, School of Economics and Management.
    21. Jonas Schreyögg & Markus Grabka, 2010. "Copayments for ambulatory care in Germany: a natural experiment using a difference-in-difference approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(3), pages 331-341, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:jodepp:v:6:y:2021:i:2:p:210-230. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.