Trends and seasonal variation of hospitalization and mortality of interstitial lung disease in the United States from 2006 to 2016

Abstract

Background: In the recent years, the overall trends in hospital admission and mortality of interstitial lung disease (ILD) are unknown. In addition, there was some evidence that interstitial lung disease death rate highest in the winter but this finding was only available in one study. This study will investigate the trend and seasonal variations in hospital admission and mortality rates of ILD from 2006 to 2016. Method: From the Nationwide Inpatient Sample database, we collected all cases with the International Classification of Diseases (ICD)-9 or ICD-10 codes of ILD excluding identifiable external causes (drug, organic or inorganic dusts) from 2006 to 2016. Hospitalization rates of each year were calculated based on U.S Census population data. Monthly hospitalization and in-hospital mortality rates were analyzed by seasonal and trend decomposition. Subgroups of idiopathic interstitial fibrosis (IPF), acute respiratory failure (ARF), pneumonia were analyzed. Results: From 2006 to 2016, all-cause hospital admission rate of patients with interstitial lung disease (ILD) and IPFonly subgroup declined but their overall mortality remained unchanged (except IPF subgroup and acute respiratory failure subgroup). Acute respiratory failure related admission account for 23% of all causes and pneumonia 17.6%. Mortality of ILD in general and subgroup of ILD with ARF was highest in winter, up to 8.13% ± 0.60 and 26.3% ± 10.2% respectively. The seasonal variations of hospital admission and mortality of ILD in general was not changed when infectious pneumonia cases were ruled out. All cause admission rates were highest in months from January to April. Subgroup analysis also showed seasonal variations with highest hospitalization rates for all subgroups (IPF, ARF, pneumonia) in the months from December to April (winter to early Spring). Conclusion: From 2006 to 2016, admission rates of ILD of all causes and IPF subgroup declined but in-hospital mortality of ILD of all causes remained unchanged. Mortality of IPF subgroup and acute respiratory failure subgroup trended down. All-cause hospital admissions and mortality of ILD have a strong seasonal variation. Hospitalization rates for all subgroups (IPF, ARF, pneumonia) were highest in the months from December to April.

In-hospital mortality trends among patients with idiopathic pulmonary fibrosis in the United States between 2013-2017: a comparison of academic and non-academic programs

“…These findings stand in contrast to those published using the online CDC national death certificate database, which showed IPF-related mortality to be increasing over this timeframe [ 31 , 32 ]. Besides, others reported decline in IPF all-cause mortality and hospitalizations using NIS dataset [ 33 ]. With different case finding methodologies employed by each study, these observations highlight the difficulties with capturing accurate IPF data using claims databases.…”

Diagnostic Coding Intensity among a Pneumonia Inpatient Cohort Using a Risk-Adjustment Model and Claims Data: A U.S. Population-Based Study

“…There was a stark increase in ECI from August until October, followed by a sudden decline in the latter part of the year. Since winter months see a higher hospitalization rate [ 19 ], and another study has shown a similar pattern in admission months for patients with pneumonia across hospitals [ 20 ], then a possible reason for under-coding could be the increased workload on hospital staff, resulting in exhaustion, under-documentation, and under-coding.…”

In-hospital mortality trends among patients with idiopathic pulmonary fibrosis in the United States between 2013-2017: a comparison of academic and non-academic programs.

“…[31,32] Besides, others reported decline in IPF all-cause mortality and hospitalizations using NIS dataset. [33] With different case nding methodologies employed by each study, these observations highlight the di culties with capturing accurate IPF data using claims databases.…”