Author ORCID Identifier

0000-0002-2126-2398

Date of Award

Summer 8-9-2022

Degree Type

Thesis

Degree Name

Master of Public Health (MPH)

Department

Public Health

First Advisor

Brian Barger

Second Advisor

Terri Pigott

Abstract

INTRODUCTION: Early identification and early childhood intervention are known to improve cognitive, social, and adaptive functioning and improve the quality of life of children diagnosed with autism spectrum disorders. Since early intervention and treatment success depends on early identification, early childhood screening with high-quality screening tools is critical. Recovering missing data and adjusting the data for epidemiologically reported prevalence can provide more accurate estimates of missed cases when studies report results without follow-up data to verify missed cases.

AIM: The purpose of this study was to conduct an umbrella review and meta-analysis of diagnostic accuracy (MADA) of autism spectrum disorder (ASD) using prior published meta-analyses data to evaluate the effect of three data missing data and prevalence adjustment methods on pooled diagnostic accuracy metrics.

METHODS: This study selected a final sample size of 28 previously reviewed population-level studies for inclusion that solely focused on children ages 6 to 72 months (total screened = 205,934). The three data adjustments included: recovering missing or unreported data using standard diagnostic accuracy formulas; epidemiologically adjusting the recovered data using reported ASD prevalence by year and nation of origin; epidemiologically adjusting the recovered data using CDC yearly reported ASD prevalence. The study calculated pooled sensitivity, specificity, and a pooled diagnostic odds ratio for each data adjustment approach using a bivariate random effects Reitsma model. The bivariate analysis also applied metaregression models to simultaneously compare the pooled sensitivity and false positive rate for each data adjustment.

RESULTS: Sensitivity systematically decreased for each data source in both the univariate and bivariate analysis. Sensitivity increased missing data was recovered for both the univariate and bivariate analysis (0.732 and 0.759, respectively). Sensitivity decreased after the national yearly prevalence adjustment (0.628 and .652, respectively) and the CDC yearly prevalence adjustment (0.422 and .384). The current results suggest that recovering missing data and adjusting for national prevalence may be an appropriate data adjustment method to obtain unbiased assessments of diagnostic accuracy and help detect screening tools weaker than those promoted from cross-sectional studies.

File Upload Confirmation

1

Share

COinS