Avoidable morbidity in infants. A classification based on diagnoses in administrative databases

Area of Science:

• Pediatrics
• Public Health
• Epidemiology

Background:

• Infant morbidity presents a significant public health challenge.
• Existing disease classifications may not adequately address preventability for targeted interventions.
• An etiologic framework is crucial for developing effective prevention strategies.

Purpose of the Study:

• To develop a hierarchical classification for avoidable infant morbidity.
• To categorize diseases based on the impact of risk factors and health services.
• To create a framework for policy-oriented research and resource allocation.

Main Methods:

• Experts (16 general pediatricians) rated 346 diagnoses based on 12 attributes.
• Attributes included impact of risk factors (constitutional, environmental) and health services (prevention, treatment).
• Factor analysis was used to derive clusters of diagnoses with similar risk factor profiles.

Main Results:

• A classification of 275 diagnoses was developed, focusing on substantial impact ratings.
• Five mutually exclusive clusters of avoidability were identified: vaccine-preventable, health-care quality indicators, environmental, environmental/constitutional, and constitutional.
• Many diagnoses were influenced by multiple risk factors, highlighting multifactorial etiology.

Conclusions:

• The developed classification aids epidemiologic and health services research focused on prevention.
• Cluster-specific hospitalization rates can inform resource allocation for interventions.
• The classification has potential applications in analyzing morbidity burden using existing ICD-9-CM data.