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Zero adjusted models with applications to analysing helminths count data.

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Zero-inflated and hurdle count models are best for analyzing human helminth infection data with excess zeros. The negative binomial logit hurdle (NBLH) and zero-inflated negative binomial (ZINB) models performed best in this study.

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Classical linear models are often inappropriate for count data in public health due to overdispersion and excess zeros.
  • Zero-adjusted mixture count models, including zero-inflated and hurdle models, are suitable for such data.
  • Human helminth infections, specifically Schistosomiasis haematobium, often present with a high proportion of zero counts, necessitating advanced modeling techniques.

Purpose of the Study:

  • To apply and compare zero-adjusted mixture count models for analyzing risk factors of human helminth infections (S. haematobium).
  • To identify the most appropriate statistical models for count data with excess zeros in epidemiological studies.

Main Methods:

  • Data were collected from a randomized control trial in Malawi and a cross-sectional survey in Zambia.
  • Traditional count models (Poisson, negative binomial) were compared with zero-modified models (zero-inflated Poisson, zero-inflated negative binomial) and hurdle models (Poisson logit hurdle, negative binomial logit hurdle).

Main Results:

  • The negative binomial logit hurdle (NBLH) and zero-inflated negative binomial (ZINB) models demonstrated the best performance based on Akaike information criteria (AIC) in both datasets.
  • These selected models exhibited superior performance in capturing and accounting for zero counts compared to other models evaluated.

Conclusions:

  • Zero-modified NBLH and ZINB models are recommended for analyzing count data with excess zeros in public health and epidemiology.
  • The selection between hurdle and zero-inflated models should align with the specific study objectives and endpoints.