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Count data regression modeling: an application to spontaneous abortion.

Prashant Verma1,2, Prafulla Kumar Swain3, Kaushalendra Kumar Singh1

  • 1Department of Statistics, Banaras Hindu University, Varanasi, India.

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|July 10, 2020
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Summary
This summary is machine-generated.

The Zero-Inflated Negative Binomial (ZINB) model best predicts spontaneous abortions in Indian women. Key factors include education, antenatal care, and family size, guiding public health policies.

Keywords:
Count data; spontaneous abortion; Poisson modelNegative binomial modelRegressionZero hurdle negative binomialZero-inflated negative binomial

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Area of Science:

  • Biostatistics
  • Public Health
  • Demography

Background:

  • Abortion-related complications cause approximately 20,000 female deaths annually in India.
  • Count data modeling often faces a prevalence of zero counts, necessitating specialized statistical approaches.
  • Understanding spontaneous abortion rates is crucial for maternal health initiatives in India.

Purpose of the Study:

  • To estimate spontaneous abortion counts using various regression models in Punjab and northern Indian states.
  • To identify significant determinants associated with the number of spontaneous abortions.
  • To validate the best predictive model using national survey data.

Main Methods:

  • Utilized DLHS-4 survey data (2012-13) of 27,173 married women in Punjab for model training.
  • Applied and compared multiple count regression models to predict spontaneous abortion frequency.
  • Validated the optimal model with NFHS-4 data (2015-16) from other northern Indian states.

Main Results:

  • The Zero-Inflated Negative Binomial (ZINB) model demonstrated superior predictive accuracy for spontaneous abortions.
  • Significant factors influencing spontaneous abortions include total children born, antenatal care (ANC) place, residence, education, and economic status.
  • Statistical comparisons confirmed the ZINB model's effectiveness over other estimation methods.

Conclusions:

  • The ZINB model is recommended for policymakers to accurately predict spontaneous abortion numbers.
  • Promoting female education and institutional antenatal care are key recommendations for reducing spontaneous abortions.
  • Limiting family size is also suggested as a factor to consider in public health strategies.