Working Paper: NBER ID: w28708
Authors: Jialin Huang; Jianwei Xing; Eric Zou
Abstract: Many human activities can be strategically timed around forecastable natural hazards to mute their impacts. We study air pollution shock mitigation in a high-stakes healthcare setting: hospital surgery scheduling. Using newly available inpatient surgery records from a major city in China, we track post-surgery survival for over 1 million patients, and document a significant increase of hospital mortality among those who underwent surgeries on days with high particulate matter pollution. This effect has two special features. First, pollution on the surgery day, rather than exposure prior to hospitalization, before or after the surgery, is primarily explanatory of the excess mortality. Second, a small but high-risk group – elderly patients undergoing respiratory or cancer operations – bears a majority of pollution’s damages. Based on these empirical findings, we analyze a model of hospital surgery scheduling. For over a third of the high-risk surgeries, there exists an alternative, lower-pollution day within three days such that moving the surgery may lead to a Pareto improvement in survival.
Keywords: Air Pollution; Surgery Scheduling; Patient Outcomes; Health Economics
JEL Codes: C44; I18; O13; Q53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
Cause | Effect |
---|---|
high levels of particulate matter (PM2.5) pollution on the day of surgery (Q53) | increased susceptibility to infections (I12) |
high levels of particulate matter (PM2.5) pollution on the day of surgery (Q53) | cardiorespiratory complications (I11) |
rescheduling surgeries to lower pollution days (R41) | significant health gains for high-risk group (I14) |
high levels of particulate matter (PM2.5) pollution on the day of surgery (Q53) | hospital mortality (I14) |
high levels of particulate matter (PM2.5) pollution on the day of surgery (Q53) | increased mortality risk for high-risk patients (I12) |