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Pediatric Quality Measures in Vision Screening and Follow-Up
Alaa A Ahmed1, Genie Han1, Megan E Collins2
1Evidence-Based Practice Center, Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland.
Insights
Pediatric vision screening and follow-up quality measures show promise in reliability and validity, but feasibility remains a challenge. Future research should focus on practical, standardized measures and integrated data systems for better child vision care.
Area of Science:
- Ophthalmology
- Pediatric Healthcare Quality
- Health Services Research
Background:
- Effective early detection and management of childhood vision impairments are critical for long-term visual health and development.
- Quality measures are essential for standardizing and improving the delivery of pediatric vision care.
Purpose of the Study:
- To systematically review and summarize existing pediatric quality measures for vision screening and follow-up care in the United States.
- To identify the strengths and limitations of current quality measures in pediatric vision health.
Main Methods:
- A comprehensive literature search was conducted across multiple databases (PubMed, Embase, CINAHL, Cochrane Central, PsychINFO) from 2009 to December 2024.
- Gray literature from governmental and nongovernmental organizations was also included.
- Information was extracted by two independent reviewers using Distiller SR and Excel.
Main Results:
- Fifty vision screening and 34 follow-up quality measures were identified, primarily targeting early childhood in primary care.
- While many measures demonstrated evidence of reliability, validity, and usability, feasibility was a significant concern for several.
- Barriers to implementation included data infrastructure, collection issues, and lack of measure standardization; evidence on demographic variations and outcomes was limited.
Conclusions:
- Numerous pediatric vision quality measures possess good reliability, validity, and usability, yet their practical feasibility requires further attention.
- Future research and development should concentrate on creating feasible, standardized follow-up measures.
- Integration of data systems and rigorous evaluation of measure alignment and outcomes are recommended to enhance pediatric vision care quality.
Context:
Timely detection and management of vision problems is essential in children for optimal outcomes.
Objective:
To summarize pediatric quality measures for vision screening and follow-up in the United States.
Data Sources:
PubMed, Embase, CINAHL, Cochrane Central, and PsychINFO (2009-December 2024), plus gray literature from governmental and nongovernmental sources.
Study Selection:
Relevant systematic reviews, original studies, and gray literature.
Data Extraction:
Two reviewers extracted information using Distiller SR and Excel.
Results:
We identified 50 vision screening and 34 follow-up measures, mostly focused on early childhood in primary care settings. Sources included 6 systematic reviews (7 screening and 10 follow-up measures), 12 studies (36 screening and 24 follow-up), and 11 gray literature (8 screening and 2 follow-up). We found evidence of reliability, validity, usability, and feasibility for 21, 32, 21, and 28 screening measures and for 5, 21, 11, and 9 follow-up measures, respectively; 3 screening and 9 follow-up measures had evidence against feasibility. Several sources showed improvement with the use of specific tools or implementation practices. Reports described differences by population, state, institution, or provider. Barriers were related to data infrastructure, collection, and measure standardization. Evidence was limited on demographic variations, use and alignment across care levels, and associated outcomes.
Limitations:
Exclusion of non-US sources and studies before 2009.
Conclusions:
Many pediatric vision screening and follow-up measures have evidence of reliability, validity, and usability, but feasibility is a concern. Research should prioritize feasible, standardized follow-up measures; integrated data systems; and evaluation, alignment, and stratification of measure outcomes.
