Data quality assessment and subsampling strategies to correct distributional bias in prevalence studies.
BMC Med Res Methodol
; 21(1): 90, 2021 04 30.
Article
em En
| MEDLINE
| ID: mdl-33931025
ABSTRACT
BACKGROUND:
Healthcare-associated infections (HAIs) represent a major Public Health issue. Hospital-based prevalence studies are a common tool of HAI surveillance, but data quality problems and non-representativeness can undermine their reliability.METHODS:
This study proposes three algorithms that, given a convenience sample and variables relevant for the outcome of the study, select a subsample with specific distributional characteristics, boosting either representativeness (Probability and Distance procedures) or risk factors' balance (Uniformity procedure). A "Quality Score" (QS) was also developed to grade sampled units according to data completeness and reliability. The methodologies were evaluated through bootstrapping on a convenience sample of 135 hospitals collected during the 2016 Italian Point Prevalence Survey (PPS) on HAIs.RESULTS:
The QS highlighted wide variations in data quality among hospitals (median QS 52.9 points, range 7.98-628, lower meaning better quality), with most problems ascribable to ward and hospital-related data reporting. Both Distance and Probability procedures produced subsamples with lower distributional bias (Log-likelihood score increased from 7.3 to 29 points). The Uniformity procedure increased the homogeneity of the sample characteristics (e.g., - 58.4% in geographical variability). The procedures selected hospitals with higher data quality, especially the Probability procedure (lower QS in 100% of bootstrap simulations). The Distance procedure produced lower HAI prevalence estimates (6.98% compared to 7.44% in the convenience sample), more in line with the European median.CONCLUSIONS:
The QS and the subsampling procedures proposed in this study could represent effective tools to improve the quality of prevalence studies, decreasing the biases that can arise due to non-probabilistic sample collection.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Infecção Hospitalar
/
Confiabilidade dos Dados
Tipo de estudo:
Observational_studies
/
Prevalence_studies
/
Risk_factors_studies
Limite:
Humans
País/Região como assunto:
Europa
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article