Analysis of overdispersed antenatal health care count data
Other
Paper/Presentation Title | Analysis of overdispersed antenatal health care count data |
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Presentation Type | Other |
Authors | Hossain, Zakir (Author) and Kabir, Enamul (Author) |
Journal or Proceedings Title | Proceedings of the Melbourne International Business and Social Science Research Conference 2019 |
Number of Pages | 1 |
Year | 2019 |
Place of Publication | Australia |
Conference/Event | Melbourne International Business and Social Science Research Conference 2019 |
Event Details | Melbourne International Business and Social Science Research Conference 2019 Event Date 28 to end of 30 Sep 2019 Event Location Melbourne, Australia |
Abstract | Overdispersion (or greater variability) in count data analysis is very common in many practical fields of health sciences. Ignorance of the presence of overdispersion by the researchers 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. |
Keywords | overdispersion, Pearson chi-square, antenatal care, zero-truncated negative binomial regression and incidence rate ratio |
ANZSRC Field of Research 2020 | 429999. Other health sciences not elsewhere classified |
Public Notes | Oral presentation - Abstract only published in Proceedings. No evidence of copyright restrictions preventing deposit of Accepted Abstract. |
Byline Affiliations | University of Dhaka, Bangladesh |
School of Agricultural, Computational and Environmental Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q55x9/analysis-of-overdispersed-antenatal-health-care-count-data
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