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Modified sharp regression discontinuity model to settings with fuzzy variables.

Portia K Mafukidze1, Samuel M Mwalili2, Thomas Mageto2

  • 1Department of Mathematics, The Pan African University, Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya. portiamafukidze@gmail.com.

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|October 18, 2023
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Summary
This summary is machine-generated.

This study introduces a Modified Sharp Regression Discontinuity model to predict alcohol consumption in people with HIV/AIDS. The model effectively uses fuzzy variables, showing counseling improves health outcomes like CD4 counts.

Keywords:
AUDIT scoreCD4 CountsFuzzy variablesPeople Living with HIV and AIDSSharp regression discontinuity modelViral loads

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Area of Science:

  • Causal Inference
  • Statistical Modeling
  • Public Health

Background:

  • Previous research on fuzzy variables in regression discontinuity models was limited, focusing on either dependent or independent variables separately.
  • A gap exists in understanding the interaction between both fuzzy dependent and independent variables in predicting health outcomes.
  • Alcohol consumption is a significant factor influencing the progression of Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS).

Purpose of the Study:

  • To develop and validate a Modified Sharp Regression Discontinuity (RDD) model capable of handling both fuzzy dependent and independent variables.
  • To predict alcohol consumption patterns in People Living with Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS).
  • To address the research gap concerning the simultaneous analysis of fuzzy dependent and independent variables in causal inference.

Main Methods:

  • Development of a novel statistical model, the Modified Sharp Regression Discontinuity (RDD) model.
  • Numerical solution of model equations using parametric estimation techniques.
  • Validation through simulation studies assessing the consistency of the Average Causal Effect estimator.

Main Results:

  • The Modified Sharp RDD model demonstrated probabilistic convergence towards true values with increasing sample size, confirming estimator consistency.
  • Counseling showed a significant average causal effect (approx. 0.199) on Alcohol Use Detective Identification Test (AUDIT) scores within the sharp RDD framework for compliers.
  • Six months of counseling led to decreased AUDIT scores, increased Cluster of Differentiation 4 (CD4) counts, and reduced viral loads in People Living with HIV/AIDS.

Conclusions:

  • The Modified Sharp RDD is a robust methodology for causal inference with fuzzy variables, enhancing Regression Discontinuity Design.
  • The study successfully applied advanced RDD methodology to a real-world health scenario involving uncertain data.
  • Findings highlight the effectiveness of counseling interventions in managing alcohol consumption and improving health outcomes for People Living with HIV/AIDS.