Emily K Johnson, Dillon Sylte, Sandra S Chaves, You Li, Cedric Mahe, Harish Nair, John Paget, Tayma van Pomeren, Ting Shi, Cecile Viboud, Spencer L James
{"title":"Hospital utilization rates for influenza and RSV: a novel approach and critical assessment.","authors":"Emily K Johnson, Dillon Sylte, Sandra S Chaves, You Li, Cedric Mahe, Harish Nair, John Paget, Tayma van Pomeren, Ting Shi, Cecile Viboud, Spencer L James","doi":"10.1186/s12963-021-00252-5","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Influenza and respiratory syncytial virus (RSV) contribute significantly to the burden of acute lower respiratory infection (ALRI) inpatient care, but heterogeneous coding practices and availability of inpatient data make it difficult to estimate global hospital utilization for either disease based on coded diagnoses alone.</p><p><strong>Methods: </strong>This study estimates rates of influenza and RSV hospitalization by calculating the proportion of ALRI due to influenza and RSV and applying this proportion to inpatient admissions with ALRI coded as primary diagnosis. Proportions of ALRI attributed to influenza and RSV were extracted from a meta-analysis of 360 total sources describing inpatient hospital admissions which were input to a Bayesian mixed effects model over age with random effects over location. Results of this model were applied to inpatient admission datasets for 44 countries to produce rates of hospital utilization for influenza and RSV respectively, and rates were compared to raw coded admissions for each disease.</p><p><strong>Results: </strong>For most age groups, these methods estimated a higher national admission rate than the rate of directly coded influenza or RSV admissions in the same inpatient sources. In many inpatient sources, International Classification of Disease (ICD) coding detail was insufficient to estimate RSV burden directly. The influenza inpatient burden estimates in older adults appear to be substantially underestimated using this method on primary diagnoses alone. Application of the mixed effects model reduced heterogeneity between countries in influenza and RSV which was biased by coding practices and between-country variation.</p><p><strong>Conclusions: </strong>This new method presents the opportunity of estimating hospital utilization rates for influenza and RSV using a wide range of clinical databases. Estimates generally seem promising for influenza and RSV associated hospitalization, but influenza estimates from primary diagnosis seem highly underestimated among older adults. Considerable heterogeneity remains between countries in ALRI coding (i.e., primary vs non-primary cause), and in the age profile of proportion positive for influenza and RSV across studies. While this analysis is interesting because of its wide data utilization and applicability in locations without laboratory-confirmed admission data, understanding the sources of variability and data quality will be essential in future applications of these methods.</p>","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2021-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s12963-021-00252-5","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Population Health Metrics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12963-021-00252-5","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 4
Abstract
Background: Influenza and respiratory syncytial virus (RSV) contribute significantly to the burden of acute lower respiratory infection (ALRI) inpatient care, but heterogeneous coding practices and availability of inpatient data make it difficult to estimate global hospital utilization for either disease based on coded diagnoses alone.
Methods: This study estimates rates of influenza and RSV hospitalization by calculating the proportion of ALRI due to influenza and RSV and applying this proportion to inpatient admissions with ALRI coded as primary diagnosis. Proportions of ALRI attributed to influenza and RSV were extracted from a meta-analysis of 360 total sources describing inpatient hospital admissions which were input to a Bayesian mixed effects model over age with random effects over location. Results of this model were applied to inpatient admission datasets for 44 countries to produce rates of hospital utilization for influenza and RSV respectively, and rates were compared to raw coded admissions for each disease.
Results: For most age groups, these methods estimated a higher national admission rate than the rate of directly coded influenza or RSV admissions in the same inpatient sources. In many inpatient sources, International Classification of Disease (ICD) coding detail was insufficient to estimate RSV burden directly. The influenza inpatient burden estimates in older adults appear to be substantially underestimated using this method on primary diagnoses alone. Application of the mixed effects model reduced heterogeneity between countries in influenza and RSV which was biased by coding practices and between-country variation.
Conclusions: This new method presents the opportunity of estimating hospital utilization rates for influenza and RSV using a wide range of clinical databases. Estimates generally seem promising for influenza and RSV associated hospitalization, but influenza estimates from primary diagnosis seem highly underestimated among older adults. Considerable heterogeneity remains between countries in ALRI coding (i.e., primary vs non-primary cause), and in the age profile of proportion positive for influenza and RSV across studies. While this analysis is interesting because of its wide data utilization and applicability in locations without laboratory-confirmed admission data, understanding the sources of variability and data quality will be essential in future applications of these methods.
期刊介绍:
Population Health Metrics aims to advance the science of population health assessment, and welcomes papers relating to concepts, methods, ethics, applications, and summary measures of population health. The journal provides a unique platform for population health researchers to share their findings with the global community. We seek research that addresses the communication of population health measures and policy implications to stakeholders; this includes papers related to burden estimation and risk assessment, and research addressing population health across the full range of development. Population Health Metrics covers a broad range of topics encompassing health state measurement and valuation, summary measures of population health, descriptive epidemiology at the population level, burden of disease and injury analysis, disease and risk factor modeling for populations, and comparative assessment of risks to health at the population level. The journal is also interested in how to use and communicate indicators of population health to reduce disease burden, and the approaches for translating from indicators of population health to health-advancing actions. As a cross-cutting topic of importance, we are particularly interested in inequalities in population health and their measurement.