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Multilevel Matching in Natural Experimental Studies: Application to Stepping up Counties.

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Area of Science:

  • Public Health
  • Health Services Research
  • Biostatistics

Background:

  • Selecting appropriate control groups is challenging in natural experiments, particularly with clustered or hierarchical data.
  • Lack of treatment randomization necessitates robust statistical methods to control for individual differences.
  • Counties are nested within states, exhibiting clustering on health and social indicators influencing service improvement efforts.

Purpose of the Study:

  • To evaluate the impact of targeted county-level efforts on mental health services and jail utilization.
  • To develop and present methods for identifying matched control counties in hierarchical natural experiments.
  • To account for the nested structure of state and county data in study design.

Main Methods:

  • Utilizing a natural experimental study design with a hierarchical data structure.
  • Employing multivariable models to adjust for observed covariates between treatment and control groups.
  • Applying shrinkage-based LASSO for variable selection and logistic models for analysis.

Main Results:

  • The study presents a blend of probability-based models to identify matched control counties.
  • Methods are designed to account for the hierarchical structure inherent in state and county data.
  • The approach facilitates the comparison of targeted interventions across different county groups.

Conclusions:

  • The proposed methods enhance the rigor of natural experiments involving hierarchical data.
  • Accurate matching of control counties is crucial for evaluating interventions in public health.
  • This framework supports evidence-based decision-making for mental health service improvements and jail reduction strategies.