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# Short term exposure to fine particulate matter and hospital admission risks and costs in the Medicare population: time stratified, case crossover study

BMJ 2019; 367 (Published 27 November 2019) Cite this as: BMJ 2019;367:l6258
1. Yaguang Wei, doctoral student1,
2. Yan Wang, doctoral student1 2,
3. Qian Di, assistant professor3,
4. Christine Choirat, chief innovation officer4,
5. Yun Wang, senior research scientist2,
6. Petros Koutrakis, professor1,
7. Antonella Zanobetti, principal research scientist1,
8. Francesca Dominici, Clarence James Gamble professor of biostatistics, population, and data science2,
9. Joel D Schwartz, professor1
1. 1Department of Environmental Health, Harvard T H Chan School of Public Health, Boston, MA, USA
2. 2Department of Biostatistics, Harvard T H Chan School of Public Health, Boston, MA 02115, USA
3. 3Research Center for Public Health, School of Medicine, Tsinghua University, Beijing, China
4. 4Swiss Data Science Centre (ETH Zürich and EPFL), Zürich, Switzerland
• Accepted 16 October 2019

## Abstract

Objective To assess risks and costs of hospital admission associated with short term exposure to fine particulate matter with diameter less than 2.5 µm (PM2.5) for 214 mutually exclusive disease groups.

Design Time stratified, case crossover analyses with conditional logistic regressions adjusted for non-linear confounding effects of meteorological variables.

Setting Medicare inpatient hospital claims in the United States, 2000-12 (n=95 277 169).

Participants All Medicare fee-for-service beneficiaries aged 65 or older admitted to hospital.

Main outcome measures Risk of hospital admission, number of admissions, days in hospital, inpatient and post-acute care costs, and value of statistical life (that is, the economic value used to measure the cost of avoiding a death) due to the lives lost at discharge for 214 disease groups.

Results Positive associations between short term exposure to PM2.5 and risk of hospital admission were found for several prevalent but rarely studied diseases, such as septicemia, fluid and electrolyte disorders, and acute and unspecified renal failure. Positive associations were also found between risk of hospital admission and cardiovascular and respiratory diseases, Parkinson’s disease, diabetes, phlebitis, thrombophlebitis, and thromboembolism, confirming previously published results. These associations remained consistent when restricted to days with a daily PM2.5 concentration below the WHO air quality guideline for the 24 hour average exposure to PM2.5. For the rarely studied diseases, each 1 µg/m3 increase in short term PM2.5 was associated with an annual increase of 2050 hospital admissions (95% confidence interval 1914 to 2187 admissions), 12 216 days in hospital (11 358 to 13 075), US$31m (£24m, €28m;$29m to $34m) in inpatient and post-acute care costs, and$2.5bn ($2.0bn to$2.9bn) in value of statistical life. For diseases with a previously known association, each 1 µg/m3 increase in short term exposure to PM2.5 was associated with an annual increase of 3642 hospital admissions (3434 to 3851), 20 098 days in hospital (18 950 to 21 247), $69m ($65m to $73m) in inpatient and post-acute care costs, and$4.1bn ($3.5bn to$4.7bn) in value of statistical life.

Conclusions New causes and previously identified causes of hospital admission associated with short term exposure to PM2.5 were found. These associations remained even at a daily PM2.5 concentration below the WHO 24 hour guideline. Substantial economic costs were linked to a small increase in short term PM2.5.

## Introduction

Particulate matter (PM) is a mixture of solid and liquid particles with aerodynamic diameter smaller than 2.5 µm (PM2.5) and between 2.5 and 10 µm (PM10-2.5), respectively known as fine and coarse particles. PM2.5 particles are smaller than PM10-2.5 particles but have a larger surface area to volume ratio. Thus PM2.5 can carry more toxic pollutants and pass through the lung into the bloodstream.123 In 2005, the World Health Organization set the air quality guideline for 24 hour average exposure to PM2.5 at 25 µg/m3.4 The guideline is being reviewed and expected to be published in 2020. Scientific evidence supporting update of the guideline is subject to an unprecedented level of scrutiny.5

Previous studies have primarily focused on either long term or short term exposure to PM2.5.678910111213141516 In the Global Burden of Disease Study 2017, long term exposure to PM2.5 was assessed extensively and considered to be a leading risk of global disease burden from lower respiratory infections, ischemic heart disease, chronic obstructive pulmonary disease, lung cancer, and diabetes mellitus.17 Short term exposure to PM2.5 is also associated with adverse health effects, including cardiovascular and respiratory diseases, diabetes, neurological diseases, and deep vein thrombosis, among others.910111213141516 However, a comprehensive analysis investigating associations between short term exposure to PM2.5 and all possible diseases is lacking. Such an investigation is needed to discover associations between short term exposure to PM2.5 and other prevalent but rarely studied diseases, and to thoroughly evaluate the corresponding healthcare costs. Cost-benefit analyses considering only cardiovascular and respiratory diseases could underestimate the costs of the impact of PM2.5 on health if other diseases are not negligible.

We analyzed 95 277 169 Medicare inpatient claims of all fee-for-service beneficiaries in the United States during 2000-12. We classified all plausible causes of hospital admission into 214 mutually exclusive disease groups to estimate the increased risk of admission and the corresponding costs associated with 1 µg/m3 increase in short term exposure to PM2.5 for each disease group.

## Methods

### Medical data

We obtained Medicare inpatient claims from January 1, 2000 to December 31, 2012 from the Medicare Provider Analysis and Review (MEDPAR) file. From each claim, we extracted the following details:

• Principal discharge ICD-9 (international classification of diseases, 9th revision) code

• Number of days in hospital

• Unfavorable discharge destinations (that is, death at discharge, discharge to skilled nursing facilities, or home healthcare services)

• Diagnosis related group (DRG) price (payment due to healthcare providers determined by DRG, if there are no deductibles, co-insurance, primary payers, or outliers)

• DRG outlier amount (additional amount approved because an outlier exceeded the DRG price)

### Comparison with other studies

We confirmed previously published results suggesting positive associations between cardiovascular and respiratory diseases and short term exposure to PM2.5, including those by Bell et al,12 Dominici et al,9 Kloog et al,15 and Zanobetti et al.11 We also found positive associations of PM2.5 exposure with Parkinson’s disease and diabetes mellitus with complications, which is consistent with findings by Kloog et al14 and Zanobetti et al.1116 We identified multiple prevalent but rarely studied causes of hospital admissions that had significant associations with short term exposure to PM2.5. Our understanding, however, of the varied disordered physiological processes induced by PM2.5 for the newly identified disease groups is incomplete. Thus the associations and the corresponding hospital admissions and costs should be interpreted cautiously. This lack of supporting evidence requires further epidemiological studies and investigations into possible underlying mechanisms.

A number of explanations are possible for the negative associations between influenza and aneurysms and an increase in short term exposure to PM2.5. Short term exposure to PM2.5 might increase risk of mortality and admission to hospital for other diseases, thus reducing the number of subjects at risk for admission due to influenza or aneurysms25; influenza or aneurysms might trigger hospital admission due to diseases that are positively associated with short term PM2.5, making themselves less likely to be the principal discharge diagnosis owing to the coexisting conditions.

### Strengths and limitations

Our comprehensive analysis of all disease groups has advantages. Firstly, it avoided selective presentation of only positive findings. Secondly, it allowed us to identify new causes of hospital admission that have never been studied. Thirdly, it confirmed the causes of admissions previously detected, thus demonstrating consistency of the study findings. Fourthly, the negative outcome control made it possible to evaluate residual confounding for estimated associations. Finally, the implementation of the Bonferroni correction minimized the chance of falsely identifying significant associations.

This study also has some limitations. Firstly, data sources were restricted. Thus we could not fully capture costs after discharge associated with short term exposure to PM2.5, such as drug costs, readmission costs, and outpatient costs, among others. Secondly, the Bonferroni correction for the 214 comparisons performed might have been overly conservative. To reduce the chance of missing true associations for some of the disease groups, we used results from the sensitivity analyses corrected for the false discovery rate. With these corrected results, we further identified stroke and asthma that were significantly associated with exposure to PM2.5. Thirdly, some unmeasured time variant factors might have confounded this study. For example, smoking, alcohol consumption, physical activity, and drug use could trigger hospital admission and could also vary with air pollution levels, which is a concern. Fourthly, the generalizability of results is limited by the characteristics of Medicare population and the study period 2000-12. This restriction meant that we were unable to study whether the associations were consistent in younger populations or in recent years. Fifthly, the diagnostic coding of some diseases may not be accurate, and some were probably classified into miscellaneous disease groups, such as “other diseases”. Finally, the assessment of exposure was subject to measurement error because zip codes (rather than home addresses) were the best geographical unit we could use to match PM2.5 with each beneficiary. A recent methodological study suggests that adjusting for measurement error results in larger effect estimates.43

### Conclusions

Comprehensive analyses provide timely evidence for the revision of WHO air quality guidelines, which is soon to be completed. This study discovered several new causes of hospital admissions associated with short term exposure to PM2.5 and confirmed several already known associations, even at daily PM2.5 concentrations below the current WHO guideline. Economic analysis suggests that even a small increase in short term exposure to PM2.5 is associated with substantial economic effect.

#### What is already known on this topic

• Short term exposure to PM2.5 is associated with increased risk of mortality and hospital admissions due to cardiovascular and respiratory diseases, diabetes mellitus, neurological diseases, and deep vein thrombosis, among others

• Existing evidence for the health effects of short term exposure to PM2.5 was driven by hypotheses about specific disease outcomes that might be affected by the exposure, which could underestimate the potential effects of exposure to PM2.5 if other diseases are not negligible

• Short term exposure to PM2.5 was positively associated with risks of several prevalent but rarely studied causes of hospital admissions, such as septicemia, fluid and electrolyte disorders, acute and unspecified renal failure, and intestinal obstruction without hernia

• When the analysis was restricted to days with a daily PM2.5 concentration below the WHO air quality guideline for the 24 hour average exposure to PM2.5, most newly identified causes of hospital admission and those identified from previously published studies remained positively associated with short term PM2.5 exposure, suggesting that the guideline needs updating

• A small increase in short term PM2.5 was associated with substantial inpatient and post-acute care costs and the economic costs due to lives lost at discharge

## Footnotes

• Contributors: YWe and YaWa are joint first authors and contributed to data preparation, data analysis, data interpretation, development of the Shiny app, and writing of this manuscript. QD and YuWa contributed to data preparation and data analysis. CC contributed to data interpretation and development of the Shiny app. PK, AZ, FD, and JDS contributed to formulation of the idea, study design, data analysis, and review of the manuscript. All authors had full access to all the data in the study. FD is the study guarantor. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

• Funding: This study was supported by National Institute of Health (NIH) grants P30 ES000002, R01 ES024332-01A1, R01 ES026217, R01 ES028033, and R21 ES024012; NIH/National Cancer Institute (NCI) grant R35 CA197449; Health Effects Institute (HEI) grant 4953-RFA14-3/16-4; and United States Environmental Protection Agency (US EPA) grants RD-83587201-0 and RD-83479801. The contents of this publication are solely the responsibility of the grantee and do not necessarily represent the official views of the US EPA. Further, the US EPA does not endorse the purchase of any commercial products or services mentioned in the publication. Research described in this article was conducted under contract to the HEI, an organization jointly funded by the US EPA (Assistance Award No CR-83467701) and certain motor vehicle and engine manufacturers. The contents of this article do not necessarily reflect the views of HEI, or its sponsors, nor do they necessarily reflect the views and policies of the US EPA or motor vehicle and engine manufacturers.

• Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: support from the NIH, NIH/NCI, HEI, and US EPA for the submitted work; no financial relationships with any organizations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.

• Ethical approval: This study was approved by the institutional review board at the Harvard T H Chan School of Public Health and was exempt from informed consent requirements as a study of previously collected administrative data.

• Data sharing: No additional data available.

• The corresponding author affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

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