medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Springing Forward and Falling Back on Health:
The Effect of Daylight Saving Time on
Acute Myocardial Infarction∗
Shinsuke Tanaka†

Hideto Koizumi‡

July 2022

Abstract
We examine the effects of Daylight Saving Time (DST) on the incidence of acute
myocardial infarction (AMI) over three distinct time frames: short, medium, and longrun. By exploiting the unique circumstances in Indiana, our findings highlight substantial short-term costs of increased AMI admissions at the spring transition by 27.2%,
which last for approximately two weeks, are not displaced by counteractive reductions
during the DST period, and are incurred at each transition over the years studied
without adaptation. Together, in the context of current policy debates, these findings
support terminating time adjustments yet provide little evidence to support permanent
DST.

Keywords: daylight saving time, acute myocardial infarction, energy policy
JEL Codes: I18, K32, Q48∗

We thank Hunt Allcott, Nathan Barker, Michael Boozer, Ellen Degnan, Laura Gee, Hugo Gerard, Meredith
Houck, Michael Klein, Kazuki Motohashi, Muthoni Ngatia, Pace Phillips, Masayuki Sawada, Matthew D.
Shapiro, Glynis Startz, Adam Storeygard, Tavneet Suri, Andreas Tiemann, Kosuke Uetake, Matthew White,
Karen Zhang, Nicolas R. Ziebarth, and seminar participants at the 2015 ASSA-AEA, 6th Biennial Conference of the American Society of Health Economists, Osaka University, Tsukuba University, and Universityof Tokyo, and 2020 AERE-EEA for helpful comments. We also gratefully acknowledge the Indiana State
Department of Health for generously providing data and Michele Starkey for her assistance with the data.
Research results and conclusions expressed in this study are those of authors and do not necessarily reflect
the views of the Indiana State Department of Health. All remaining errors are our own.
†
Corresponding author. Tufts University. Email: Shinsuke.Tanaka@tufts.edu.
‡
Hitotsubashi University. Email: koizumi@iir.hit-u.ac.jp.

NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
All rights reserved. No reuse allowed without permission.

I.

Introduction

As of this writing, the U.S. Congress is currently considering legislation to make Daylight
Saving Time (DST) permanent starting in 2023. DST is a common energy policy around theglobe. Approximately one quarter of the world’s population is exposed to the semiannual
ritual of adjusting the clock by an hour forward in spring (commonly known as “springing
forward”) and by an hour backward in autumn (“falling back”). Its practice, however, hasalmost always been fraught with controversy and unpopularity.1 Recent compelling evidence
has shown that the policy’s original intention to conserve energy by utilizing daylight insummer has backfired in that DST actually increases energy consumption.2 In addition,
sizable evidence has substantiated adverse consequences on various economic activities aswell as on the population where DST is practiced.3
But perhaps the most troubling concern over the practice of DST is that the seeminglysmall yet abrupt one-hour change in how clocks are set can cause substantial anomalies tohuman health. When announcing the abolition of DST in Russia, former Russian President
Medvedev succinctly summed up these anomalies by saying “Our biorhythms are damaged”(NEWSRT 2011). However, despite the widely accepted perceptions that DST leads to “fallback” in health outcomes, the direct linkage between the two has remained thin and inconclusive to date (Manfredini et al. 2019). Further, existing studies regarding the effectsof DST on AMI exclusively focus on the transitional periods, whereas two important gaps
remain in our understanding of i) the extent of the overall effects of DST during the DST
period relative to the short-term effects of time adjustments and ii) whether people newly
subject to DST adapt to the short-term effects of time adjustments at each DST transition
over a period of years.
In this paper, we attempt to answer these long-standing questions by examining a relationship between DST and acute myocardial infarction (AMI), or heart attack, one of the
primary health concerns associated with DST.4 Sleep deprivation (at the spring transition)
1

Many countries, such as Russia, Egypt, and Ukraine, have recently abolished DST, and many others,
including the EU and U.S., are currently considering terminating its practice. In contrast, Japan is probably the only major economy moving in the other direction, as the government recently announced it wasconsidering reinstating DST.
2
For example, Kotchen and Grant (2011), Kellogg and Wolff (2008), and Sexton and Beatty (2014) findincreased electricity usage, whereas others find otherwise (Aries and Newsham 2008; Hill et al. 2010).
3
For instance, studies find that DST affects traffic accidents (Coren 1996; Hicks et al. 1998; Vincent 1998;
Coate and Markowitz 2004; Barger et al. 2005; Huang and Levinson 2010; Smith 2016, Bünnings and Schiele2021), returns in the financial markets (Kamstra et al. 2000; Pinegar 2002; Müller et al. 2009), suicide rates(Berk et al. 2008), criminal activity (Doleac and Sanders 2015), SAT scores (Gaski and Sagarin 2011), andindividual well-being (Kountouris and Remoundou 2014).
4
Cardiovascular disease is the leading cause of morbidity and mortality in the U.S. and around the world;in the US., the estimated annual incidence of heart attack was 605,000 new cases and 200,000 recurrent

1medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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and disrupted biorhythms (present at both transitions) are two physiological and pathological mechanisms through which DST can trigger AMI. We test whether DST increases theincidence of AMI over three distinct time frames: i) at the transitions in the short run; ii)
during the overall DST period in the medium run; and iii) at each transition over a periodof years in the long run.
To analyze the effects of DST in three temporal dimensions, we take advantage of thediscrete nature of DST transitions and historical disputes over the adoption of DST in Indiana, which exempted a large majority of counties from adopting DST until 2006, resulting
in county-date-year level variation in the practice of DST.5 This gives us three main advantages over the existing literature. First, to the best of our knowledge, this is the first
paper to estimate the causal impact of DST on AMI by explicitly controlling for the seasonality effects at the transitional periods. Existing studies typically follow the time-series
patterns of outcomes in days shifting to and from DST. These estimations are in principle
similar to the regression discontinuity (RD) design, which compares mean outcomes acrossthe discrete change in time on a certain day. An important concern with this and similar approaches that predominate in the relevant literature is that unobserved seasonality
effects may bias the estimated treatment effects. Because daylight is known to serve as azeitgeber (timekeeper) that can exactly synchronize or otherwise reset the biological clock
within the human body, misalignment between the biological clock and the external environment at the seasonal transitions, even without DST transitions, is likely to cause disruption
in biorhythms and increase the incidence of AMI.6 Our quasi-experimental research designoffers a rare opportunity to address this concern by adopting the regression discontinuity
difference-in-differences (RDDD) approach that differences out seasonality effects by using
observations over the DST transitions when DST was not in effect.
Second, existing evidence regarding the relationship between DST and AMI exclusively
focuses on the transitional periods. However, the short-term effects alone potentially distortthe overall cost-benefit analysis of DST when the medium-term benefits are not accountedfor. For example, the increased AMI cases at the transitions may reflect temporal displacement if the time change hastens heart attacks that would have occurred later even without
the time change. Alternatively, the overall effects of DST on AMI may be beneficial if people
utilize the longer daylight hours to engage in more physical activities. Our research designcases between 2005 and 2014, whereas treating heart attacks is one of the most expensive treatments in U.S.
(AHA, 2021).
5
Kotchen and Grant (2011) exploit the same setting to examine the relationship between DST and energy
consumption in Indiana.
6
Indeed, seasonal variation in AMI occurrence with preponderance in winter is well known in the medical
literature (Marchant et al. 1993; Spencer et al. 1998).

2

medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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provides a rare context whereby we can address important gaps that remain in our understanding of i) the duration of the effects of the time adjustments, and ii) the extent of theoverall effects of DST during the DST period.
Third, we present the first findings regarding whether those newly subject to DST continue to experience the short-term effects at each DST transition over a period of years, andthus whether there is adaptation to the effects of time change. For example, people newly
subject to DST may begin to adjust their behaviors, e.g., by going to bed earlier on thenight before the spring transition, thereby inflating the short-term effects in the first year ofpracticing DST while gradually or quickly lessening those effects over subsequent years.
Using administrative data on all AMI cases in Indiana in 2002–2012, our findings withrespect to each of the three dimensions are as follows. First, the spring transition to DST
increased AMI admissions by approximately 27.2%, whereas the smaller in magnitude, yetstatistically significant, increase in AMI admissions after the autumn transition is likely toreflect the effects of seasonality, given the similar, often greater, increase in AMI admissions
at the autumn transition, even when DST was not in effect. Second, while the effects of DST
on AMI last for approximately two weeks after the spring transition, we find no evidenceof temporal displacement by counteractive reductions in AMI admissions later, resulting inoverall increased AMI admissions. Third, we find little evidence of adaptation over years;
while the effect appears to be strongest in the first year in which populations are subject
to the spring transition, we find no qualitatively or statistically significant decline in theeffects of the spring transition over our study period. Together, these findings hold important
policy implications in the context of current policy debates, providing strong support for
terminating time adjustments yet no evidence to support permanent DST in terms of healthbenefits.

II.

BackgroundA.

Medical Linkage between DST and AMI7

Two channels explain the physiological and pathological mechanisms through which DST
triggers AMI. First, DST directly disrupts circadian rhythms, an intrinsic 24-hour clock thatsynchronizes with external environmental factors such as daylight duration. An increasing
number of studies in the medical literature document the close relationship between circadianrhythmicity and the cardiovascular system. Second, DST, directly and indirectly throughdisrupted circadian rhythm, can harm the quality and duration of sleep, which triggers
7

More detailed and extensive literature is described in Online Appendix A.

3

medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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cardiovascular disease. Using data from the American Time Use Survey, Barnes and Wagner(2009) show that the average sleep duration is 40 minutes shorter on the night of the springtransition, whereas no more sleep is gained at the autumn transition despite an extra houron the clock.8
Although these studies illustrate plausible channels through which DST may cause AMI,
the medical literature documenting the direct association between the two is relatively scarceand, when it does exist, has generated little consensus, as summarized in the recent metaanalysis study by Manfredini et al. (2019).9 Importantly, existing studies regarding the effectsof DST on AMI exclusively focus on the transitional periods, e.g., only the first week after
the time adjustment, whereas our study addresses important gaps in our understanding of:i) the duration of the effects of the time adjustments, ii) the extent of the overall effects of
DST during the DST period relative to the short-term effects, and iii) whether people newly
subject to DST adapt to the short-term effects of time adjustments at each DST transition
over a period of years.

B.

Historical Debates over DST in Indiana10

Internal disputes over the practice of DST rendered Indiana a special case with multiple timezones and exemption from DST. As a result, during the period between 1991 and 2005, 15
out of 92 counties observed DST, while another 77 counties did not (Online Appendix FigureB1). In 2005, the Indiana state legislature passed a law that required all counties to observe
DST starting in 2006.11 For the purpose of identifying the effect of DST on health, it is worth
noting that the variation in time zone and related DST observance status in Indiana is basedon the “convenience of commerce,” as evaluated by the Department of Transportation, andthus unrelated to other factors such as health concerns.

III.

Data

The primary data for our analysis comes from nonpublic administrative records from Indiana
Hospital Discharge Data (Indiana Department of Health 2012) that cover all AMI cases inIndiana from 2002 through 2012.12 The data are collected and managed by the Indiana
8

In contrast, Jin and Ziebarth (2020) find increased sleep time at the autumn transition using the Behavioral Risk Factor Surveillance System in 2013–2016.
9
Online Appendix Table A1 summarizes the existing studies.
10
More details are presented in Online Appendix B.
11
In conjunction with DST shifts, several counties were approved to change the time zones, which often
negated the time change.
12
Our sample includes hospital admissions with the diagnosis code of 410 based on ICD-9. The data also
report the subcategories at the 5-digit level, indicating that 94.1% of the cases in our data constitute initial

4medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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State Department of Health13 , and by special arrangement we obtained access to patientlevel observations. The data contains detailed information on patients, such as age, gender,
county of hospital, admitted date, discharge date, type of insurance a patient possesses,
and whether s/he is an inpatient or outpatient, total medical charges before insurance isapplied, and length of hospital stay for inpatients. In total, we observe close to 185,000
hospital admissions which constitute one of the most comprehensive datasets ever applied
to this topic. For the main analysis, we focus on hospital admissions on weekdays because
AMI admissions are known to be distinctively lower on weekends (Witte et al. 2005); wethen attest the robustness of the findings by including weekends. Online Appendix Table
C1 presents the descriptive statistics for the sample used in the analysis: the working-age
population aged between 20 and 64.

IV.
A.

Econometric Framework
Regression Discontinuity (RD) Methods

To estimate the effect of DST on AMI at the transitional period, we employ the regression
discontinuity (RD) design by including a flexible function of the running variable, f(·);
Ycdt = α + βP ostT ransdt + f (Daysdt ) + εcdt ,(1)where Ycdt is the outcome of interest in county c on day d of the year t, P ostT rans is adummy variable for observations after the transition to or from DST, and Days denotes thenumber of days from the DST transition, normalized to zero on the first day of the transition,and thus P ostT rans = 1(Days ≥ 0). Our main outcome is AMI admission rates per one
million working-age population.14 The parameter of interest, β, measures a discontinuouschange in the outcome at the DST transition date, after flexibly controlling for the trends
leading to and following to the transition.
We follow Imbens and Lemieux (2008) to use the uniform weight and specify the function
episodes. Since our data are discharge records, the diagnosis codes reflect the actual discharge diagnoses
rather than an, often tentative, admission diagnosis. Also, since our data are discharge records, our sample
does not include fatal cases. We discuss that including fatal cases does not alter our conclusions in Online
Appendix D.6.
13
The credibility and completeness of the data are ensured by the Indiana State Department of Health:“IHA [Indiana Hospital Association] processes the records for accuracy, consistency and completeness, andrequests resubmissions as necessary” (Indiana Department of Health, 2012).
14
We present analogous evidence using medical charges and length of hospital stay in Online Appendix
D.1, for whom DST appears to have little effect, suggesting that the DST-induced AMI are as severe asothers.

5

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to be a linear model whose slope is allowed to vary before and after the threshold:f (Daysdt ) = γ1 Daysdt + γ2 Daysdt × P ostT ransdt .

(2)

We further extend the model by including a full set of control variables;
Ycdt = α + βP ostT ransdt + γ1 Daysdt + γ2 Daysdt × P ostT ransdt(3)+ µc + τt + DayOf W eekdt + θW eathercdt + εcdt ,where county fixed effects µc control for permanent county-level characteristics associatedwith variation in AMI incidence, year fixed effects τt absorb overall time trends in AMI
incidence common across counties within a year, DayOf W eek is a set of day-of-week fixed
effects, and W eathercdt controls for weather variability by including bins of every 3-degree
Celsius interval and total precipitation at the county-daily level.15 The regression is weighted
by the denominator of the outcome, e.g., the working-age population, as counties with larger
populations provide more precise estimates in computing averages.16 The standard errors
are clustered at the county level to correct heteroskedasticity in the error term and allow
for correlation between observations within cluster. The selection of bandwidth provokes the
known tradeoff between efficiency and bias. We begin with 4 weeks before and after the DSTtransition and test the robustness to various other bandwidths.17
The causal inference of Equation (3) is warranted if the conditional expectation function,
E[ε|Days], is locally smooth over the DST transitions. That is, no factor affecting AMI otherthan DST exhibits a discontinuity at the DST transitions. However, such an assumption
should not be taken for granted because the well-known circadian effect during the seasonaltransitions is likely to confound the estimates. For example, changes in zeitgeber, such as
the length of daytime and exposure to greater evening ambient light, disturbs an organism’s
biological rhythm (Durgan and Young 2012; Burgess 2013) even in the absence of DST. Since
15

We obtained daily precipitation and temperature information, important factors that change on a daily
basis and are likely to be associated with AMI, from NOAA Global Historical Climatology Network Daily(GHCN-D) (NOAA, 2022). In practice, we follow Smith (2016) by first demeaning the outcome by thesecontrols to account for persistent and long-term effects by using all observations throughout the year. Yet,running Equation (3) with controls to account for the effects of these controls specifically around the DSTtransitions produces the same results.
16
The population data come from the Surveillance, Epidemiology, and End Results Program at National
Institute of Health, Single Year of Age County Population Estimates (SEER, 2019).
17
The initial selection of bandwidth at four weeks is set to the one that allows a reasonable comparison
with the weekly aggregated analysis (e.g., a multiple of seven days) and is deliberately close to those provided
by optimal bandwidth selectors proposed by Ludwig and Miller (2007) (29 days), Imbens and Kalyanaraman(2012) (23 days), and Calonico et al. (2014) (18 days). See Online Appendix D.3 for the estimates basedon optimal bandwidths selected by these methods as well as additional bandwidths, which give consistentestimates.

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medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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the timing of DST is by design commensurate with seasonal transitions, due to its original
intention to utilize longer daylight, the estimated DST effect is likely to spuriously pick up
such seasonal effect.

B.

Regression Discontinuity Difference-in-Differences (RDDD)
Methods

One of our main innovations over previous studies is that we can directly observe and control
for seasonality effects, if any, based on the unique experiences in Indiana. Unlike most countries and those U.S. states that either have or have not observed DST over a long period oftime, our research context offers a rare opportunity to directly test the identification assumption by applying the same model to observations when DST was not practiced. Because there
was indeed no time change in those counties, the effect of DST must be zero. Any significant
estimates would invalidate the RD assumption by indicating that there is a discontinuous
trend in the outcome variable around the threshold for reasons other than DST. Further, we
can estimate the true DST effect by differencing out the seasonality effect, if any, by using
all observations regardless of the practice of DST and estimating the regression discontinuity
difference-in-differences (RDDD) model:
Ycdt = α + βP ostT ransdt × Obdct + γ1 P ostT ransdt × Daysdt + γ2 Daysdt × Obdct+ γ3 P ostT ransdt × Daysdt × Obdct + γ4 P ostT ransdt + γ5 Daysdt + γ6 Obdct(4)+ µc + τt + DayOf W eekdt + θW eathercdt + εcdt ,where the additionally introduced variable, Obd, is a dummy variable taking the value of one
if the county observed DST in that year for that season.18 The basic intuition behind thismodel is that, to the extent there is an underlying discontinuity in AMI trend at transition
dates even in the absence of transitions, we difference it out from the estimated treatment
effect by using observations when DST was not practiced. The true effect of time change
at the transition date, net of the seasonality effect, is captured by the coefficient β. The
standard errors are clustered at the county level.
Because we directly control for discontinuities in the underlying conditional function
at the threshold, we no longer require the usual continuity assumption of the conditional
expectation function. Rather, the model assumption is that any changes in the outcome
at the DST transition when DST is not practiced provide a counterfactual for what would
18

Note that while the day-of-week effects are absorbed in the RDDD analysis, to be consistent throughout
the paper, we first demean the outcome by the county effects, year effects, day-of-week effects, and weather
effects.

7

medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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have happened in the absence of DST for observations when DST was practiced. Thus, theidentification assumption is that E[ε|Days, Obd = 1] = E[ε|Days, Obd = 0] holds at least
locally across the DST transitions. With the model that includes the county-fixed effects,
the estimated effect utilizes only the variation in DST practice by treatment counties where
DST became in effect from 2006, and the counterfactual is provided by the same set ofcounties when DST was not in effect before 2006. We attest the validity of such identification
assumption by applying the same RDDD model to the pre-reform period; if the assumption isvalid, there should not be any effect on outcomes at the DST transitions in years before DST
was adopted. The potential confounding variables in RDDD are those that discontinuously
change at the DST transition only among the treatment counties and in the post-reformperiod. We are not aware of any variables that meet these conditions.
Further, we extend the model to explore the effects at every DST transition over the years
studied to yield an inference regarding adaptation to the time adjustments among people
who are newly subject to DST. In particular, we run the event-study RDDD model adapted
to estimate the effects of the time adjustments in each year:
Ycdt = α +2012
X

βT P ostT ransdt × Y earT + γ1 P ostT ransdt × Daysdt + γ2 Daysdt × Obdct

T =2002+ γ3 P ostT ransdt × Daysdt × Obdct + γ4 P ostT ransdt + γ5 Daysdt + γ6 Obdct+ µc + τt + DayOf W eekdt + θW eathercdt + εcdt ,(5)where Y earT is an indicator variable for each year between 2002 and 2012. The omitted
group is 2005, the year before the policy reform.19 Each βT indicates the effects of time
adjustment in year T relative to that in 2005, and we expect the coefficients to be zero
during the pre-reform period as a test of our identification assumption that within-county
variation in AMI admissions at DST transitions over years is similar during the pre-reformperiod, whereas the coefficients during the post-reform period infer the rates of adaptation
to the effects of time adjustments at every transition over a period of years.

C.

Difference-in-differences (DD) Methods

While the RD and RDDD analyses provided above are useful in estimating the causal effectof DST on AMI at the transitions, it is of limited use in estimating the medium-term effects
19

For ease of interpretation, we remove counties that the time adjustment was offset in 2006 by switching
their time zones, yet including these counties has virtually no impact on the results due to their small sample
size.

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medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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of the time adjustments. In particular, we explore i) the duration of the effects of the time
adjustments, and ii) the extent of the overall effects of DST during the DST period relative
to the short-term adverse effects. An empirical investigation of these questions has been
impeded by an empirical challenge to construct valid control observations during the DST
period for what would happen in the treatment observations without a treatment. Our
research design provides a rare context whereby we can estimate such medium-term effects
by utilizing the unique policy change in Indiana.
To investigate the first question, we employ the DD model that explores the lasting effectsof DST on AMI over several weeks after the transitions. In particular, using days surrounding
the time adjustments, we estimate:
Ycdt = α +3Xβw W kT ransdt × Obdct +

w=−3

3
X

γw W kT ransdt + δObdct

w=−3

(6)

+ µc + τt + νm + DayOf W eekdy + θW eathercdt + εcdt ,where W kT rans is a dummy variable for each week after the time transition, normalized to
be zero in the week of the transition, and νm is the month fixed effects. The weeks after thetransitions are considered as the treatment group, whereas those before are the control group,and Obd is regarded as an indicator for the post-reform period. The parameters of interests
are βw ’s, which estimate the effects on AMI admissions in each week over the transitional
period in years when DST was in effect relative to years when DST was not in effect. Thus, we
expect the coefficients to be zero for weeks before the time transition, whereas the coefficients
after the time transition indicate the lasting effects of the time adjustments. The regressions
are weighted by the working-age population at the county level. The omitted group is theweek before the time transition, e.g., w = −1.
Further, to investigate the second question, we compare the changes in AMI admission
rates from pre- to post-reform periods among treatment counties where DST was not in effectbefore 2006 with those changes in control counties where DST was in effect throughout the
study period using observations during the DST period:
Ycdt = α + βT reatc × P ostRef ormt + µc + τt + νm + DayOf W eekdt + θW eathercdt + εcdt , (7)where T reat is an indicator variable taking the value of one for treatment counties where
DST was not in effect before 2006, P ostRef orm is an indicator variable taking the valueof one for the post-reform period, e.g., post-2006 for most counties,20 while other variables
20

Since the number of counties that started practicing DST in 2007 is small, our results are not affectedeven when we consider a staggered adoption design (de Chaisemartin and D’Haultfœuille 2020), since a9

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are similarly defined as above. The coefficient of interest, β, measures the average effects ofswitching from Standard Time to DST over all days during the DST period, which captures
the net effects of the spring transition and potential medium-term benefits of longer daylight.
The regressions are weighted by the working-age population at the county level. All standard
errors are clustered at the county level.
The model assumption of Equation 7 is that the treatment counties have similar trends
to the control counties in the absence of treatment, e.g., the parallel trend assumption.
We will present evidence to support such assumption by showing that the outcomes evolved
similarly between the treatment and control observations during the pre-reform period. If any
systematic differences in characteristics confounded our estimates, e.g., an age composition orunderlying health behaviors, we would observe differential patterns in the pre-reform period
as well. The potential confounding variables in DD are those that changed systematically
only in the treatment counties in the same year as the policy changed. We are not aware ofany variables that meet such condition.

V.

Results

A.

Short-term Effects at the Cutoffs

We begin with the graphical illustration of how DST transitions affected AMI admission
rates.21 Figure 1 presents the trends of AMI admission rates for eight weeks before and after
the DST transition. The y-axis measures the admission rates, residualized by the day-of-weekeffects, county fixed effects, year fixed effects, and temperature and precipitation effects, andx-axis measures the number of days after the transition, normalized to zero on the date of
the DST transition. Panel A uses observations surrounding the spring transition when DST
was in effect. We observe that while AMI admissions fall in the month before the springtransition, a sharp and discontinuous increase in AMI admissions is observed exactly at the
beginning of DST. To test if seasonality effects can explain the increased AMI around thistiming, Panel B plots an analogous figure using only the observations when DST was not in
effect before 2006. It shows that there is no such discontinuity around the threshold for theseobservations; otherwise the trend appears to be similar to counties that observed DST.
We now turn to the distinct observations for the autumn transition. On the one hand,
Panel C illustrates a similar increase in AMI admissions at the DST transition for counties that practiced DST. On the other hand, Panel D highlights that AMI admissions also
negative weight is not detected, or when we drop those counties and keep the same year of the policy reform.
21
Online Appendix D.1 provides additional figures for other dependent variables (e.g., total charges andlength of hospital stay).

10

medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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increased in counties that did not practice DST. Because there was no time shift for theseobservations, such an increase in AMI cannot be explained by DST and highlights the seasonality effect at the timing of the DST transition that triggers AMI even without DST.
The evidence above that placebo effects are present suggests that it is important toadequately control for the seasonality effect by constructing the appropriate counterfactualin order to estimate the true effect of DST, which is achieved by RDDD. Table 1 presents
evidence based on the RDDD model as specified by Equation (4) using all samples, including
both counties that did and did not practice DST, in which observations when DST was notpracticed allow us to adequately control for the seasonality effects.22
The findings are consistent with what is expected from the graphical results, suggestingthat AMI cases increased after the spring transition but not after the autumn transition.
Importantly, Column (3) controls for the county-fixed effects, and therefore counterfactual isconstructed from their own counties when DST was not practiced before 2006. Overall, thepreferred model in Column (3) suggests that AMI admission rates increased by 1.507 per
one million working-age population (incidence ratio (IR) = 1.272, 95%CI [1.076 1.467], p =0.007, n = 40,480) by moving into DST. In contrast, AMI admission rates decreased by 0.533
per one million working-age population (IR = 0.901 95%CI [0.68, 1.122], p=0.376, n=38,824)
by moving out of DST, although the point estimate is not statistically significantly different
from zero. Column (4) presents the consistent results by the week-level observations.23
Online Appendix D.3 attests the robustness of the RDDD results to various other specifications. Briefly summarizing the results, we find that the findings above are robust to: i)using the count model, such as poisson and negative binomial models, ii) using various optimum bandwidth selections, iii) including weekends, iv) using polynomial running variables,v) using alternative control groups, and vi) accounting for commuting effects. Further, thepermutation test that assigns the placebo DST transition on each date outside the DSTtransitional period confirms that our estimated effect is clearly an outlier and has an implied
p-value of 0.0068 (Online Appendix D.4). In addition, Online Appendix D.5 explores potential heterogeneities in the effects of DST on AMI across various dimensions by applying themain RDDD analysis to a set of subsamples. Briefly summarizing the results, while we findthat the effects are relatively similar across various subgroups, if anything, the effects arestronger for women relative to men, people aged above 50 relative to below 50, and thosewith private insurance relative to those with Medicaid. Last, Online Appendix D.6 showsthat including fatal incidences does not alter our conclusions.
22

Online Appendix D.1 presents analogous results using the RD model. Essentially, the estimated effects
based on the RDDD analysis closely mimic the differences in the point estimates between Panel A and B in
the RD results for each transitional period.
23
More results by the weekly-level analysis are presented in Online Appendix 2.

11

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B.

Medium-term Effects after Time Adjustments

This section explores the medium-run effects of time adjustments.24 Figure 2 presents theestimated coefficients based on Equation (6). We find that the coefficients are zeros in weeks
leading to the time adjustments, confirming the identification assumption that the AMI
admissions trended similarly before the spring transition between the pre- and post-reformperiods. In contrast, consistent with the RDDD results, we find that the week after the springtransition experiences a substantial increase in AMI admissions. The size of the effect in thefirst week is similar to that predicted by RDDD analysis, suggesting that the impacts at the
spring transition remain of similar magnitude throughout the first week (Online Appendix
Table E1). More importantly, the results highlight slightly lower, yet similar, magnitudes ofthe effects in the second week after the spring transition. The point estimate halves in thethird week and diminishes further to virtually zero in the subsequent week. These results
suggest strongly that the effects of the spring transition last for two weeks after the time
adjustment. Figure 1 also illustrates a consistent pattern of a discontinuous jump in AMI
admissions at the spring transition that appears to persist for two weeks before reversing toessentially the same seasonal path observed before the transition.
Figure 3 presents the results from the event-study analysis using Equation (A.2) in Online
Appendix E based on the DD model of Equation (7) that explores the overall effects of time
adjustments during the DST or Standard Time period. Panel A presents the results during
the DST period. We observe that AMI admission rates are virtually zero up to the year oftreatment, ensuring that that there was no obvious pre-existing differential trend between
the treatment and control counties; if any, AMI admissions were declining in the treatment
counties. Yet, there is an evident increase in the first year subject to the spring transition,
and we cannot reject that the effects are similar thereafter. Overall, the introduction of DST
resulted in an increased daily AMI admission rates by 0.479 per one million working-age
population (IR = 1.076, 95%CI [1.064 1.240], p = 0.090, n = 159,160) (Online Appendix
Table E2). In contrast, consistent with the RDDD results, Panel B shows that AMI admission
rates do not change after the autumn transition during the Standard Time (Online Appendix
Table E2). These results suggest that the overall AMI admission rates increased during the
DST period after the policy reform, and there is no evidence of any potential benefits of thelonger daylight hours during the DST period that offset the short-term costs.25
24

We present the estimates based on the DD analyses in Online Appendix E.
While the precise mechanism to explain the results is unclear, both the American Academy of Sleep
Medicine and the Society for Research on Biological Rhythms made recommendations in favor of a permanent shift to Standard Time due primarily to less morning light exposure during DST (Rishi et al. 2020;
Roenneberg et al. 2019).
25

12

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C.

Long-term Effects via Adaptation

Last, we are interested in exploring the effects of DST transitions at each transition over a
period of years. In particular, we investigate whether people newly subject to DST adapt tothe effects of time adjustments over time.
Figure 4 plots the estimated effects of the spring transition in each year based on Equation(5). The estimated coefficients are small and not statistically significantly different from zero
over years before the policy reform, ensuring that there is no discontinuous change in AMI
admissions at the spring transition when DST was not in effect. As aforementioned, thisevidence supports the identification assumption of RDDD in that within-county variation in
AMI admissions at the DST transitions across years is similar in the pre-reform period. In
contrast, large and significant effects are observed starting in 2006. Notably, the estimatedcoefficient is the largest in 2006, and the estimated effects are high and consistent over thesubsequent years studied.
Overall, we find little evidence of adaptation; while the effect appears to be strongest
in the first year subject to the spring transition, these results indicate no qualitatively or
statistically significant decline in the effects of the spring transition at each transition over
our study period.26

VI.

Conclusions

This paper presents empirical evidence regarding the effects of DST transitions on AMI
admissions. We offer three main findings. First, DST increases AMI admissions only at
the spring transition. While there appears to be a small increase in AMI occurring at theautumn transition, this increase becomes negligible after differencing out the seasonality
effect. Second, while we find that the effects of DST last for approximately two weeks after
the spring transition, we find no counteractive reduction in AMI admissions following thespring transition during the DST period, documenting no evidence of overall net benefits oflonger daylight during the DST period. Last, such adverse impacts on health are high andpersistent at each spring transition over a period of years without evidence of adaptation.
These findings hold important policy implications in the context of current policy debatesby providing strong support for terminating the time change yet providing no evidence ofnet health benefits by permanent DST. The overall cost-benefit analysis of permanent DST
needs to be weighted against benefits of longer daylight in other dimensions.
26
Online Appendix F shows that the autumn transition has no impact on the AMI incidence in any
particular year.

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medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
All rights reserved. No reuse allowed without permission.

FiguresFigure 1: AMI Admissions around DST

Notes: These figures plot the daily average AMI admission rates per one million working-age
population among the working population aged between 20 and 64, weighted by the working population at the county level, residualized by the day-of-week effects, county fixed effects, year fixed
effects, and temperature and precipitation effects. The lines indicate smoothed local polynomial
trends with the bandwidth of seven days. Panels A and C include observations when the respective
DST was practiced, whereas Panels B and D include observations when the respective DST was
not practiced.

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Figure 2: Lasting Effects of the Spring Transition

Notes: This figure presents the effects of the spring transition on the AMI admission rates per
one million working-age population over weeks before and after the time adjustment, normalized
to zero in the week of the transition, based on Equation (6). The regressions are weighted by
the working-age population at the county level. The vertical lines indicate the 95% confidence
interval. The underlying estimates are presented in Online Appendix E.

19

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Figure 3: Effects of Time Adjustments during DST and Standard Time Periods
Panel A: DST Period

Panel B: Standard Time Period

Notes: This figure presents the effects of the time adjustments on AMI admission rates per one
million working-age population during the DST period in Panel A and during the Standard Time
period in Panel B. Each coefficient is estimated using the event-study model based on the DD
model of Equation (7):Ycdt = α +6Xβt T reatc × Y earT + µc + τt + DayOf W eekdt + θW eathercdt + εcdt ,T =−4

where the additional variable of Y eartT is an indicator variable for each number of years fromthe first year of practicing time adjustment, e.g., T = 0 in 2006 for most treatment counties. The
omitted group is the year before the treatment starts, e.g., T = −1. The outcomes are the daily
AMI admission rates per one million working-age population. The regressions are weighted by
the working-age population at the county level. The vertical lines indicate the 95% confidence
interval.

20

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Figure 4: Effects of Spring Transition over Years

Notes: This figure presents the effects of the spring transition on the AMI admission rates per
one million working-age population in each year based on the RDDD model specified by Equation(5). The regressions are weighted by the working-age population at the county level. The verticallines indicate the 95% confidence interval.

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TablesTable 1: The RDDD Estimates of the Effects of DST Transition on AMI Admissions
(1)

(2)

(3)

(4)

Panel A: Spring
PostTrans×Obd. 1.487*** 1.494*** 1.507*** 7.076**(0.546)(0.545)(0.545) (3.453)
Mean
5.548
5.548
5.548
38.467
N
40,48040,48040,4808,096
Panel B: Autumn
PostTrans×Obd.
-0.468-0.512-0.533-1.896(0.599)(0.599)(0.600) (3.219)
Mean
5.393
5.393
5.393
36.811
N
38,82438,82438,824
8,096
Fixed effects
Controls
Level of obs.

N
N
Daily

N
Y
Daily

Y
Y
Daily

Y
Y
WeeklyNotes: This table presents the effects of DST on the daily AMI admission rates per one million working-age
population at the county level in Columns (1)–(3) and the weekly AMI admission rates per one million
working-age population at the county level in Column (4) among the working population aged between 20
and 64 based on the RDDD model of Equation (4). The treatment observations are the observations aroundthe DST transition when DST was observed, whereas the control observations are analogous observations
when DST was not observed. The bandwidth is 4 weeks before and after the transition. The regressions areweighted by the working population at the county level. Fixed effects include year- and county-fixed effects,
and controls include the day-of-week effects, daily mean temperature with bins of every 3-degree Celsius,and daily total amount of precipitation. The mean value of the dependent variable is computed for the
pre-transitional period when DST was practiced, weighted by the working population at the county level.
The standard errors clustered at the county level are reported in the parentheses.
*** p < 0.01 **p < 0.05

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Springing Forward and Falling Back on Health:
The Effect of Daylight Saving Time on
Acute Myocardial Infarction
Shinsuke Tanaka∗

Hideto Koizumi†

Online Appendix∗†

Corresponding Author: Tufts University. Email: Shinsuke.Tanaka@tufts.edu.
Hitotsubashi University. Email: koizumi@iir.hit-u.ac.jp.

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Contents
A Medical Linkage between DST and AMI

3

B Historical Debates over DST in Indiana

8

C Additional Information on Data and Summary Statistics

11

D Short-term Effects at the Cutoffs
1
RD and Additional RDDD results . . . . . . . . . . . . . . . . . . . . . . . .
2
Weekly results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4
Permutation Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5
Heterogeneous Effects of DST . . . . . . . . . . . . . . . . . . . . . . . . . .
6Role of Fatal Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

13
13
21
26
31
33
36

E Medium-term Effects of the Time Adjustments

39

F Long-term Effects of the Time Adjustments

421

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Overview
This Online Appendix provides additional information to what is already presented in themain text as well as additional evidence to attest the robustness of the findings in the main
text to various other specifications and contexts. What follows is a brief summary of what
each appendix presents.
Appendix A presents the detailed literature review regarding the medical linkage between
DST and AMI.
Appendix B presents the detailed historical background over the practice of DST in
Indiana.
Appendix C presents additional information on the data and variable constructions used
for the main analysis. Further, it provides the summary statistics for the sample used in themain analysis.
Appendix D presents additional results on the short-term effects of DST at the transitional periods. Specifically, we present the results based on the RD model, the results
using the weekly observations, the results from the robustness checks, the results from the
permutation test, and the heterogenous treatment effects of DST across various subsamples.
Appendix E presents additional results for the medium-term effects of time adjustments
during the DST and Standard Time periods.
Appendix F presents additional results regarding the long-term effects of time adjustments at each transition over a period of years.

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A.

Medical Linkage between DST and AMI

Figure A1 illustrates several pathological and physiological mechanisms through which DST
triggers AMI. Two channels explain the physiological and pathological mechanisms throughwhich DST triggers AMI.
First, DST directly disrupts circadian rhythms, an intrinsic 24-hour clock that synchronizes with external environmental factors such as daylight duration.1 The 24-hour biologicalrhythm, referred as circadian rhythm, is known to exist in our human body and to consist of
two parts. The central clock is located in the suprachiasmatic nucleus in the hypothalamus.
There also exist peripheral clocks, the biological clocks in each organ including cardiovascular tissues or cells. The medical literature finds that it is essential for cardiovascular healthand disease prevention that all these clocks are tightly synchronized. Daylight is known toplay a key role as a zeitgeber (timekeeper) that can exactly synchronize or otherwise resetthe central clock (see the literature summarized in Berson et al. (2002)).2 Evidence showsthat misalignment between the internal clock and external environment, such as light, caninduce disorders in cardiovascular organ (Smolensky et al. 2007; Takeda and Maemura 2011;
Durgan and Young 2012; Portaluppi et al. 2012; Giuntella and Mazzonna 2019).
Second, DST, directly and indirectly through disrupted circadian rhythm, can also harm
the quality and duration of sleep, and loss of duration and efficiency in sleeping is also known
to contribute to cardiovascular risks.3 Not only can DST directly lead to insufficient sleep
particularly in the timing of the springing forward, but disrupted circadian rhythm due to
DST is also an important factor affecting sleep habits (for example, Monk and Folkard 1976;
Lahti et al. 2006; Kantermann et al. 2007; Harrison 2013; Tonetti et al. 2013), presenting anindirect channel between DST and deprived sleep. The indirect channel makes cardiovascularrisk plausible even after the autumn transition. Using data from the American Time Use
Survey, Barnes and Wagner (2009) show that the average sleep duration is 40 minutes shorteron the night of the spring transition, whereas no more sleep is gained at the autumn transition1

In 2017, the Nobel Prize for medicine was awarded to Jeffrey Hall, Michael Rosbash, and Michael W.
Young for their work on the internal clock. Their work revealed that our biological clock helps regulate
sleeping, feeding, and hormone production, and any misalignment when we experience “jet lag” can havenegative consequences in our health.
2
The peripheral clocks are considered to be similarly affected by external stimuli such as daylight, feedingfasting cycle, or physical activity and/or to be regulated by the central clock, although the exact mechanisms
remain unclear.
3
It is found that 7–9 hours of sleep is ideal for adults, although people in the United States sleep on
average about 6.8 hours a day, and people across the contemporary world in general sleep shorter than
before. Any shorter and longer duration of sleep contributes to the progression of cardiovascular diseases,
including hypertension, coronary artery disease, risk of arrhythmia, stroke, and diabetes mellitus. See theliterature summarized in Cundrle et al. (2014).

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despite an extra hour on the clock.4 This is particularly significant for the large number ofAmericans, and Westerners in general, who suffer from chronic sleep deficiency. Cunningham
et al. (2016) show that about one third of American adults sleep less than 6 hours, an hourless than what is recommended by the American Academy of Sleep Medicine and the Sleep
Research Society (Watson et al. 2015).
Although these studies illustrate plausible channels through which DST could trigger
AMI, the medical literature documenting the direct association between the two is relatively
scarce and, when it does exist, has generated little consensus and sometimes contradictory
results (Manfredini et al. 2019). Table A1 summarize relevant studies. Among them, the mostinfluential study is Janszky and Ljung (2008) who examine the incidences of AMI in Sweden.
They estimate the incidence ratio (IR), as measured by the number of AMI incidences in
the first week following the DST shifts, divided by the mean occurrence of AMI in the two
weeks before and after the transitions. The technique is essentially similar to estimating
differences in means or a regression discontinuity (RD) approach, in which any differences in
AMI occurrence between immediately following the DST transitions and those in the control
period are attributed to the effect of DST.5 In total, they observe 47,812 patients for theperiod of 1987 to 2006. Their results indicate that AMI incidences increased modestly about
5% after the spring transition, whereas there was no statistically significant effect following
the autumn transition. These effects are pronounced more among individuals below 65 years
old relative to older cohorts and among women after the spring transition.
Janszky et al. (2012) conduct a follow-up study based on a smaller sample in Sweden
between 1995 and 2007 but with better information on age and sex, which allows them toidentify risk factors leading to variation in vulnerability to clock adjustments. They findsimilar effect in terms of the incidence ratio after the spring and autumn transitions, yetthey find no statistically significant differences in effect over ages above and below 65 or sex.
Čulić (2013) utilizes detailed information obtained from 2,412 patients at University
Hospital Centre Split in Croatia to identify potential triggers of AMI. The results indicatethat AMI patients increased by about 29% and 44% on the first four working days after
the spring and autumn transitions, respectively. The effects are larger among male after thespring transitions but among female after the autumn transition, and among individuals
employed or engaging in physical activities.
4

Jin and Ziebarth (2020) find a contradictory result of increased sleep time at the autumn transition
using the Behavioral Risk Factor Surveillance System in 2013–2016.
5
The RD normally compares the mean difference between days before and after the DST transitions.
In measuring the IR in this study and others in the medical literature, the comparison group is the meanobservations of several weeks before and after the DST transitions. Although it can account at least partiallyfor time trends over the transitions, its validity depends crucially on the implicit assumption that AMI that
occur in these control weeks after the DST transitions are free from DST effect.

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Given the variation in effects over different demographics, it is likely that the magnitudes
of effects differ across countries. To date, Jiddou et al. (2013) is the only existing study thatprovides evidence in the United States. Based on 935 AMI patients presenting to the emergency centers at the Royal Oak and Troy campuses of the Beaumont Hospitals in Michigan
from October 2006 to April 2012, they find a statistically significant effect (a 17% increasein AMI) only after the spring transition, though the difference in the estimates between thespring and autumn transitions are not statistically significantly different.
Toro et al. (2015) use daily mortality data on AMI in 2007–2012 in Brazilian states thatobserve DST and document an approximately 8.5% increase in the mortality rates after thespring transition and a comparable increase of 6.5% after the autumn transition, while they
do not find any change in other states where DST was not practiced.
Kirchberger et al. (2015) consider 25,499 cases of coronary death and non-fatal AMI
among 25–74 aged population in the region of Augsburg, Germany, between 1985 and 2010.
The study finds no overall impact on AMI risk after the spring or autumn transitions, whereas
the spring transition appears to increase AMI incidence among men.
Sipilä et al. (2016) use nationwide AMI incidence and in-hospital mortality in Finland in
2001–2009 and finds no significant impact after either the spring or autumn transitions.
Jin and Ziebarth (2020) use AMI hospitalization data from Germany and show a decreasein AMI admissions by 3.6% after the autumn transition, while the effects after the springtransition is not reported.

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Figure A1: The Linkage between DST and AMI

Notes: This figure illustrates the mechanisms through which DST can
affect AMI from the medical literature.

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Table A1: Medical Literature on the Linkage between DST and AMI
Results (IR)Authors

SampleSpring

Fall

Risk factors

Janszky and
Ljung (2008)

47,812 patients admitted in Sweden in the first week and two
weeks before and after the shifts
in 1987-2006.

1.051(1.032–1.071)

0.98(0.969–1.002)

Age under
65, women
for spring
and men forautumnJanszky et al.
(2012)

14,521 patients admitted at participating hospitals in Sweden in
the first week and two weeks before and after the shifts in 19952007.

1.039(1.003–1.075)

0.995(0.965–1.026)

No difference
over age andgenderČulić (2013)

2,412 patients hospitalized due
to AMI in 1990-1996 at University Hospital Centre Split in Split,
Croatia

1.29(1.09–1.49)

1.44(1.19–1.69)

Men forspring andwomen forautumnJiddou et al.
(2013)

935 patients to the emergency
centers at Beaumont Hospitals inRoyal Oak and Troy, Michigan.

1.17(1.00–1.36)

0.99(0.85–1.16)

N.A.

Toro et al.
(2015)

1,995 state-day death cases in
Brazil on the day before and after DST in 2007-2012.

1.085(1.047–1.123)

1.065(1.006–1.124)

N.A.

Kirchberger
et al. (2015)

25,499 coronary deaths and nonfatal AMI cases in the region ofAugsburg, Germany in 1985–2010

1.077(0.981–1.182)

1.025(0.928–1.133)

Men

Sipilä et al.
(2016)

Nationalwide AMI incidence andin-hospital mortality in Finlandin 2001–2009.

0.98(0.94–1.02)

0.99(0.94–1.04)

No difference
over age or
genderJin and
Ziebarth(2020)

336,604 county-day hospitalization cases in Germany in the firstweek and three weeks before andafter DST in 2000-2008.

N.A.

0.964
(0.938–0.990)

N.A.

Notes: This table summarizes the existing studies that examined the effect of DST on AMI. Their results are presented
in incidence rates (IR), which compare the incidence rates of events occurring after the DST transition to those in thecomparison weeks (typically two or three weeks before and after the transition), thereby the value of greater than one
indicates an increased rate, whereas that of lower than one indicates a decreased rate. The respective 95% confidence
intervals are presented below the IR. N.A. means not available.

7

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B.

Historical Debates over DST in Indiana

DST was first introduced in Germany in 1916 and was immediately adopted by many other
European countries. In the U.S., it was first adopted nationwide in 1918 during World War I
for seven months as a means of maximizing use of available sunshine and thereby conserving
coal and energy with the slogan “Set the clock ahead one hour and win the war!”6 Although
DST was immediately repealed by the government after the war due to lack of public support,
it was again adopted and continued for the duration of World War II by President Franklin
Roosevelt in the form of year-round DST, called “War Time.” The Uniform Time Act of1966 marked the first attempt by the federal government to establish standard time withintime zones and to designate dates on which individual states would be required to turn theclock forward in spring and back in autumn.7
Among the historical debates over DST both within and outside the U.S., Indiana presents
a unique scenario. Internal disputes over the practice of DST rendered Indiana a special casewith multiple time zones and exemption from DST under the Uniform Time Act of 1966.8
As a result, 6 northwestern Indiana counties near Chicago and 6 southwestern counties close
to Kentucky and southern Illinois adopted Central Standard Time (CST), while the othercounties adopted Eastern Standard Time (EST).9
The 1972 Uniform Time Act Amendment further permitted multiple time zones and nonuniform practice of DST within states. At that point, 75 Indiana counties in EST decided
not to practice DST, while all 12 counties in CST plus 5 counties in EST10 started observing
DST (Department of Transportation (DOT) 2006). Since then, requests from a few counties
to change their time zones have been made several times in Indiana. The only basis on which
6

The initial idea of DST dates back to Benjamin Franklin in 1789, who proposed that forwarding timeby an hour in summer would more effectively utilize the sunshine and save candles.
7
The initial dates of “spring forward” and “fall back” were established on the last Sunday in April andthe last Sunday in October, respectively. The 1986 amendment of the Uniform Time Act moved forward the
beginning of DST by three weeks. Later, the Energy Policy Act of 2005 further pushed forward the startdate by three weeks to the second Sunday in March and pushed backward the end date by one week to thefirst Sunday in November, effective of 2007.
8
Prior to 1966, Indiana already had internal disputes over the practice of DST between, on the one
hand, urban legislators, who were in favor of Eastern Standard Time (EST), and, on the other, their ruralcounterparts, who were in favor of Central Time Zone (CST). This conflict led Indiana to request the
Secretary of Transportation under the Department of Transportation (DOT) to split the state between timezones. The Uniform Time Act of 1966 exempted states with multiple time zones from the practice of DST,
and Indiana was one of them. The legislation also allowed individual states to opt out as long as they did
so uniformly. For example, Arizona, Hawaii, and Michigan were exempted from observing DST (although
Michigan started observing DST later).
9
The northwestern counties include Jasper, Lake, LaPorte, Newton, Porter, and Starke, and the southwestern counties include Gibson, Pike, Posey, Spencer, Vanderburgh and Warrick.
10
These include Clark, Floyd, Harrison, Dearborn and Ohio counties.

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the DOT was authorized to change time zones was to facilitate or preserve the “convenienceof commerce” (DOT 2006). The DOT accepted a petition from Pike County to move from
CST to EST in 1977 and Starke in 1991, while it denied requests from 5 southwest counties
in 1985 and 2 northwest counties in 1986 to switch to EST. As a result, during the period
between 1991 and 2005, 15 out of 92 counties observed DST, while another 77 counties did
not.11 In 2005, the Indiana state legislature passed a law that required all counties to observe
DST starting in 2006. At the same time, 8 counties petitioned the DOT and were approved
to move from EST to CST when DST began in April 2006, relieving them of the need tomove clocks forward by an hour.12 Later in March 2007, Pulaski County changed its timezone back to EST, resulting in a two-hour shift forward, and in November 2007 Daviess,
Dubois, Knox, Martin, and Pike counties changed their time zone back to EST, negating thetime change when DST ended.
For the purpose of identifying the effect of DST on health, it is worth noting that theseries of federal policies regarding DST resulted in splitting Indiana counties into observing
and not observing DST before 2006. The variation in time zone and related DST observancestatus is based on the “convenience of commerce,” as evaluated by the DOT, and thus
unrelated to other factors such as health concerns.

11

The counties that observed DST up to 2006 include: Clark, Dearborn, Floyd, Gibson, Harrison, Jasper,
Lake, La Porte, Newton, Ohio, Porter, Posey, Spencer, Vanderburgh, Warrick.
12
These counties include Daviess, Dubois, Knox, Martin, Perry, Pike, Pulaski, and Starke.

9medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Figure B1: DST Practice and Time Zone in Indiana

Notes: This map illustrates three types of counties in Indiana as determined by their DST practice and time zone. The counties highlighted by yellow follow the Central Standard Time and practiced DST
throughout our study period. The large majority of counties that arehighlighted by green follow the Eastern Standard Time and did notpractice DST before 2006. The counties with blue follow the EasternStandard Time and practiced DST throughout our study period.

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C.

Additional Information on Data and Summary Statistics

We obtained daily precipitation and temperature information, important factors that change
on a daily basis and are likely to be associated with AMI, from NOAA Global Historical Climatology Network Daily (GHCN-D) (NOAA, 2022). The dataset contains daily precipitationand maximum and minimum temperature at the station level. In total, there are more than
1,760 stations across Indiana. We compute the daily average temperature at the station level
simply by taking an arithmetic average of maximum and minimum temperature of the day.
County level daily temperature and precipitation are then computed by taking an average of
all available readings within a county. For dates without any reading in a county, we use thestate-wide average information as a proxy in order to preserve the number of observations.
There is no day on which none of the stations across the state reports information.
Table C1 describes the mean in the first row, standard deviation in the squared bracket in
the second row, and the number of observations in the third row, for the respective variable.
The sample includes total cases in the second column, observations 4 weeks before andafter the spring transition in the third column, and observations 4 weeks before and after
the autumn transition in the fourth column, regardless of whether DST was observed ornot. It shows that AMI occurred predominantly to older male individuals: the average age ofpatients is 53.1 years old, and 71.1% of incidents occurred to men. The average pre-insurance
medical cost of treatment is $36,099.9, and the average length of stay for inpatients is 4.1
days. 82.3% of AMI admissions are inpatients. 66.5% of the patients have private insurance,
while 13.4% have Medicare, 8.4% have Medicaid, and 7.5% have no insurance. The average
number of AMI admissions at the county-daily level is 5.86 per one million population.

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Table C1: Summary Statistics
Variable
Age

Male (0/1)

Total charge ($)

Hospital stay (days)

Inpatient (0/1)

Medicare (0/1)

Medicaid (0/1)

Govt. insurance (0/1)

Private insurance (0/1)

Self pay (0/1)

AMI admissions ratesAll

SpringAutumn53.07953.12953.283[7.858][7.821][7.800]61,698
9,747
9,119
0.711
0.707
0.705
[0.453][0.455][0.456]
61,6989,747
9,119
36,099.9 36,461.3 36,613.7[31916.4] [32008.8] [32456.1]
60,705
9,603
8,964
4.074
4.064
4.223
[4.084][4.003][4.295]50,053
7,955
7,428
0.823
0.827
0.825
[0.381][0.378][0.380]61,698
9,747
9,119
0.134
0.134
0.137
[0.341][0.341][0.344]
61,697
9,747
9,119
0.084
0.085
0.081
[0.278][0.279][0.272]
61,697
9,747
9,119
0.024
0.023
0.027
[0.153][0.149][0.161]
61,697
9,747
9,119
0.665
0.675
0.659
[0.472][0.468][0.474]
61,697
9,747
9,119
0.075
0.069
0.077
[0.263][0.254][0.267]
61,697
9,747
9,119
5.857
5.880
5.737
[15.027] [15.019] [14.768]
257,232
40,48038,824

Notes: This table presents the summary statistics for the working-age population between 20 and
64 in three periods: throughout the year in the second column, 4 weeks before and after the spring
and autumn DST transitions (regardless of whether it is observed or not) in the third and fourthcolumns, respectively. For each variable, the first row denotes the mean value, the second row in thesquare bracket denotes the standard deviation, and the third is the number of observations. The unitof observations is at the patient level, except AMI admission rates, which is at the county-daily level.
The AMI admissions rates are per one million working-age population.

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D.
1.

Short-term Effects at the Cutoffs
RD and Additional RDDD results

This section provides additional evidence from the main analysis at the daily level.
Table D1 presents the main RD estimates at the spring transition when DST was practiced in Panel A and the placebo evidence when DST was not practiced in Panel B. Consistent
with the graphical evidence in the main text, Panel A documents substantial increases in
AMI admissions around the spring transition. The estimate in Column (1) based on Equation (1) suggests that on average the AMI admissions increased by 1.332 cases per one
million working-age population when DST starts, which represents an approximately 24.0%
increase in the daily AMI admission rates and is statistically significant at the 1 percent
level. Column (2) controls for temperature and precipitation, and Column (3) additionally
removes cross-county and over-time variation in AMI cases by including the county- andyear-fixed effects, but the point estimates are virtually unchanged. Column (4) aggregatesthe observations to the weekly level in an effort to mitigate day-to-day fluctuations in AMI
incidence.The estimated effect is the same as that inferred by the daily analysis, indicating
an approximate 16.4% increase in weekly admission rates of AMI. We also explore the effects
on AMI severity using total medical charges in Column (5) and length of hospital stay for
inpatients in Column (6) at the patient level. We find that the total charges increases by
about 10.1%, whereas the length of hospital stay is unchanged.
Panel B illustrates evidence from placebo tests by exploring discontinuities around theDST transition when DST was not practiced. The evidence supports the identification assumption that the observed increase in AMI incidences is absent when DST was not practiced. The point estimates are substantially lower in magnitude, all negative in sign, and notstatistically significantly different from zero. Moreover, variables that proxy the severity of
AMI cases show no significant discontinuity at the transition either.
Table D2 provides analogous evidence for the autumn transition. We find that the increasein AMI incidence is substantially lower at the autumn transition than at the spring transition.
The point estimate in Column (1) suggests that the AMI admissions increased by 0.499 per
1 million population, which corresponds to an approximate 9.25% increase from the average.
In contrast, the effect on total charges is larger for the autumn transition than the springtransition. The point estimate suggests that the total charges increased by approximately
10.8%, and is statistically significant at the 10% level. The effect on the length of hospital
stay for inpatients remains negligible.13
13

The disparity in the results between total charges and length of stay is not clearly explicable. Although13

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As Figure 1 has already illustrated, Panel B also confirms that the AMI admissions
increased by about 0.829 cases per 1 million population when DST ended, even when DST
was not practiced. The estimated coefficients in Panel B are consistently greater than thosein Panel A across various specifications, suggesting that AMI admissions fell rather thanincreased due to DST relative to what would have happened had DST not been practiced.
In contrast, Column (5) suggests that AMI severity did not increase. If anything, individual
cases became less severe around the end of DST.
Figure D1 presents the analogous figure to Figure 1 in the main text but for the total
charges.
Figure D2 presents the analogous figure to Figure 1 in the main text but for the lengthof hospital stay.
Figure D3 presents the analogous figure to Figure 1 in the main text but including
weekends in the observations.
Table D3 presents additional results using the RDDD analysis on other dependent variables. In particular, we consider total medical charges and the length of hospital stay asproxies for disease severity, following Dranove et al. (2003). We find suggestive evidence thatthe total charges increased after the autumn transition, although the point estimate is notstatistically significant, whereas they clearly did not change after the spring transition in
Column (1). This finding carries two important implications. First, AMI cases induced by
the spring transition are no less severe than typical AMI cases arising from other causes.
Second, although AMI admission rates did not increase after the autumn transition, theseverity of individual cases may have increased. Although the point estimate is not statistically significant, the estimated effect is larger than what is suggested by the simple RD
model (which is statistically significant at the 10% level in Online Appendix Table D2) andeconomically significant at an approximate 17.0% (95%CI [-11.7% 45.7%], p = 0.242, n =8,854) increase. The finding suggests that the number of AMI admissions alone may not
sufficiently capture the overall impacts of DST on health. We do not find any impacts onlength of hospital stay at either spring or autumn transitions in Column (2), suggesting thatsuch increased charges do not reflect longer hospital stays.

in general there is a strong correlation between the two, a large body of evidence has shown that the severityof illness does not explain much variation in the length of hospital stay (Hicks and Kammerling 1993).

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Figure D1: Total Charges around DST

Notes: This figure plots the daily average of (log) total charges among the working population patients
aged between 20 and 64, residualized by the day-of-week effects, county fixed effects, year fixed effects, andtemperature and precipitation effects. The level of observations is at the patient level. The lines indicatesmoothed local polynomial trends with the bandwidth of seven days. Panels A and C include observations
when the respective DST was practiced, whereas Panels B and D include observations when the respective
DST was not practiced.

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Figure D2: Length of Stay around DST

Notes: This figure plots the daily average of (log) total length of hospital stay among the working population
inpatients aged between 20 and 64, residualized by the day-of-week effects, county fixed effects, year fixed
effects, and temperature and precipitation effects. The level of observations is at the patient level. Thelines indicate smoothed local polynomial trends with the bandwidth of seven days. Panels A and C include
observations when the respective DST was practiced, whereas Panels B and D include observations when
the respective DST was not practiced.

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Figure D3: AMI Admission Rates around DST with weekends

Notes: These figures are analogous to Figure 1 in the main text but including weekends in theobservations. The outcome is the daily average AMI admission rates per one million working-age
population among the working population aged between 20 and 64, weighted by the working population at the county level, residualized by the day-of-week effects, county fixed effects, year fixed
effects, and temperature and precipitation effects. The lines indicate smoothed local polynomial
trends with the bandwidth of seven days. Panels A and C include observations when the respective
DST was practiced, whereas Panels B and D include observations when the respective DST was
not practiced.

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Table D1: The RD Estimates of the Effects of Spring Transition on AMI
Admission rates
(1)

(2)

(3)

Total charges Length of stay(4)

Panel A: Main
PostTrans
1.332*** 1.142*** 1.152*** 6.307***(0.343)(0.344)(0.343)(2.093)
Mean
5.548
5.548
5.548
38.467
N
27,84027,84027,8405,568
Panel B: Placebo
PostTrans-0.155-0.352-0.355-0.769(0.508)(0.510)(0.510)(2.975)
Mean
6.392
6.392
6.392
43.331
N
12,64012,64012,6402,528
Fixed effects
Controls
Level of obs.

N
N
Daily

N
Y
Daily

Y
Y
Daily

Y
Y
Weekly(5)(6)

0.101**(0.050)40,416.1
6,603

0.032
(0.033)
4.040
5,322

0.089
(0.095)28,646.7
2,946-0.061(0.061)
4.545
2,426

Y
Y
Individual

YY
IndividualNotes: This table presents the effects of DST on the daily admission rates per one million working-age
population at the county level in Columns (1)–(3), the total weekly admission rates per one million workingage population at the county level in Column (4), the log of total charges at the patient level in Column (5),and the log of total length of stay at hospital in Column (6) among the working population aged between 20
and 64 based on the RD model. The regressions are weighted by the working population at the county level inColumns (1)–(4). Panel A uses the observations around the spring transition with the bandwidth of 4 weeks
when DST was observed, whereas Panel B uses analogous observations when DST was not observed. Fixed
effects include year- and county-fixed effects, and controls include daily mean temperature and total amountof precipitation. The mean value of the dependent variable is computed for the pre-transitional period, andthe one for the admission rates is weighted by the working-age population at the county level. The standard
errors clustered at the county level are reported in the parentheses.
***p < 0.01 **p < 0.05 *p < 0.10

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medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
All rights reserved. No reuse allowed without permission.

Table D2: The RD Estimates of the Effects of Autumn Transition on AMI
Admission rates
(1)

(2)

(3)

Panel A: Main
PostTrans
0.499*
0.300
0.296(0.292) (0.292) (0.292)
Mean
5.393
5.393
5.393
N
26,622 26,622 26,622
Panel B: Placebo
PostTrans
0.967*
0.812
0.829(0.525) (0.526) (0.526)
Mean
6.072
6.072
6.072
N
12,202 12,202 12,202
Fixed effects
Controls
Level of obs.

N
N
Daily

N
Y
Daily

Y
Y
Daily

Total charges Length of stay
(4)

(5)

(6)

3.259*(1.824)36.811
5,592

0.108*(0.061)
39,982.3
6,169

0.032
(0.047)
4.285
5,052

5.155*(2.684)39.5582,504-0.062(0.115)29,256.3
2,685

0.039
(0.058)
4.643
2,210

Y
Y
WeeklyY
Y
Individual

YY
IndividualNotes: This table presents the effects of DST on the daily admission rates per one million working-age
population at the county level in Columns (1)–(3), the total weekly admission rates per one million
working-age population at the county level in Column (4), the log of total charges at the patientlevel in Column (5), and the log of total length of stay at hospital in Column (6) among the working
population aged between 20 and 64 based on the RD model. The regressions are weighted by theworking population at the county level in Columns (1)–(4). Panel A uses the observations aroundthe autumn transition with the bandwidth of 4 weeks when DST was observed, whereas Panel B
uses analogous observations when DST was not observed. Fixed effects include year- and countyfixed effects, and controls include daily mean temperature and total amount of precipitation. The
mean value of the dependent variable is computed for the pre-transitional period, and the one for theadmission rates is weighted by the working-age population at the county level. The standard errors
clustered at the county level are reported in the parentheses.
***p < 0.01 **p < 0.05 *p < 0.10

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medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Table D3: The RDDD Estimates of the Effect of DST Transition on AMI
Total charges Length of stay
(1)

(2)

0.012
(0.105)40,416.1
9,549

0.093
(0.071)
4.040
7,748

Mean
N

0.170
(0.144)39,982.3
8,854-0.007(0.082)
4.285
7,262

Fixed effects
Controls
Level of obs.

Y
Y
Individual

YY
Individual

Panel A: Spring
PostTrans×Obd.
Mean
N
Panel B: Autumn
PostTrans×Obd.

Notes: This table presents the effect of DST on the log of total charges at the patient level in Column (1),and the log of total length of stay at hospital in Column (2) among the working population aged between
20 and 64 based on the RDDD model. The treatment observations are the observations around the DSTtransition when DST was observed, whereas the control observations are analogous observations when DST
was not observed. The bandwidth is 4 weeks before and after the transition. Fixed effects include yearand county-fixed effects, and controls include the day-of-week effects, daily mean temperature with bins ofevery 3-degree Celsius, and daily total amount of precipitation. The mean value of the dependent variable
is computed for the pre-transitional period when DST was practiced. The standard errors clustered at thecounty level are reported in the parentheses.

20medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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2.

Weekly results

This subsection provides additional evidence from the main analysis at the weekly level.
The observations on the number of admissions are aggregated to the weekly level to reducethe day-to-day fluctuations in the number of AMI admissions. Here we consider only AMI
admissions, as it makes no sense to aggregate patient-level total charges or length of hospital
stay to the weekly level. The number of weeks since the DST transition is used as the runningvariable. The sample does not include weekends or holidays to be consistent with the main
analysis. The results are similar to the daily-level of analysis.

21medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Figure D4: Weekly AMI Admissions around DST

Notes: This figure plots the weekly average admission rates per one million working-age population among
the working population aged between 20 and 64, residualized by the day-of-week effects, county fixed effects,
year fixed effects, and temperature and precipitation effects, and weighted by the working-age population
at the county level. The level of observations is at the county-weekly level. The lines indicate smoothed
local polynomial trends with the bandwidth of seven days. Panels A and C include observations when therespective DST was practiced, whereas Panels B and D include observations when the respective DST was
not practiced.

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medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Table D4: The RD Estimates of the Effects of Spring Transition on Weekly AMI Admissions
Admission rates
(1)

(2)

(3)

Panel A: Main
PostTrans
6.173*** 6.429*** 6.307***(2.076)(2.084)(2.093)Mean
38.46738.46738.467
N
5,568
5,568
5,568
Panel B: Placebo
PostTrans-0.665-0.648-0.769(2.944)(2.978)(2.975)Mean
43.33143.33143.331
N
2,528
2,528
2,528
Fixed effects
Controls
Level of obs.

NN
WeeklyN
Y
WeeklyY
Y
WeeklyNotes: This table presents the effects of DST on the weekly admission rates per one million working-age
population at the county level among the working population aged between 20 and 64 based on the RD model.
The regressions are weighted by the working population at the county level. Panel A uses the observations
around the spring transition with the bandwidth of 4 weeks when DST was observed, whereas Panel B uses
analogous observations when DST was not observed. Fixed effects include year- and county-fixed effects,
and controls include weekly mean temperature with binds of every 3-degree Celsius and total amount ofprecipitation. The mean value of the dependent variable is computed for the pre-transitional period weighted
by the working-age population at the county level. The standard errors clustered at the county level arereported in the parentheses.
***p < 0.01 **p < 0.05 *p < 0.10

23

medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Table D5: The RD Estimates of the Effects of Autumn Transition on Weekly AMI Admissions
Admission rates(1)
Panel A: Main
PostTrans
3.282*(1.827)
Mean
36.811N
5,592
Panel B: Placebo
PostTrans
4.761*(2.675)
Mean
39.558
N
2,504

(2)

(3)

3.322*(1.828)36.811
5,592

3.259*(1.824)36.811
5,592

5.145*(2.686)39.5582,504

5.155*(2.684)39.5582,504

Fixed effects
N
N
Y
Controls
NYY
Level of obs. Weekly Weekly Weekly
Notes: This table presents the effects of DST on the weekly admission rates per one million workingage population at the county level among the working population aged between 20 and 64 based on
the RD model. The regressions are weighted by the working population at the county level. Panel A
uses the observations around the autumn transition with the bandwidth of 4 weeks when DST was
observed, whereas Panel B uses analogous observations when DST was not observed. Fixed effects
include year- and county-fixed effects, and controls include weekly mean temperature with binds ofevery 3-degree Celsius and total amount of precipitation. The mean value of the dependent variable
is computed for the pre-transitional period weighted by the working-age population at the county
level. The standard errors clustered at the county level are reported in the parentheses.
***p < 0.01 **p < 0.05 *p < 0.10

24

medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Table D6: The RDDD Estimates of the Effects of DST Transition on Weekly AMI Admissions(1)
Panel A: Spring
PostTrans×Obd.

6.837*(3.444)(2)(3)

7.077** 7.076**(3.460) (3.453)

Panel B: Autumn
PostTrans×Obd. -1.479(3.217)-1.823(3.224)-1.896(3.219)Fixed effects
Controls
Level of obs.

NY
WeeklyY
Y
WeeklyN
N
WeeklyNotes: This table presents the effects of DST on the weekly admission rates per one million working-age
population at the county level. The bandwidth is 4 weeks. The regressions are weighted by the working
population at the county level. Fixed effects include year- and county-fixed effects, and controls include theday-of-week effects, daily temperature with bins of every 3-degree Celsius, and total amount of precipitation.
The standard errors clustered at the county level are reported in the parentheses.
***p < 0.01 **p < 0.05 *p < 0.1025

medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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3.

Robustness

In this section, we attest the robustness of the RDDD results in the main text to various
other specifications. All results are presented in Table D7, except for alternative bandwidths
whose results are in Table D8.
Count model. We use the count variable, e.g., the number of admissions, instead of admission rates for the measure of AMI admissions. Poisson and negative binomial regressions
are preferred to OLS to model the count data. We first present the results using the poisson
specifications with normalization by including the number of the working-age people as an
exposure variable so as to arrive at population-normalized counts (with its logged variablebeing equal to one). The estimate in the first row based on the poisson regression indicatesstatistically significant increases in AMI admissions at the 1% level. Poisson regressions perform well conditional on equality of the mean and variance. In our data, overdispersion tests
reject equidispersion with a dispersion parameter of approximately 0.53. In this case, poisson
regressions lead to a greater Type I error than should occur, meaning the null hypothesis
would be more likely to be rejected when it is true. Thus, we employ the negative binomial
specifications that perform better when the data are overdispersed. The estimate based on
negative binomial regressions in the second row with normalization suggests that AMI admissions increased by about 30.9% and are statistically significant at the 1% level.14 The
magnitude of the effect is larger yet quantitatively similar to what is suggested by OLS
regressions in the main text.
Alternative bandwidths. We conduct the main analysis using various other bandwidths:
those selected by Ludwig and Miller (2007), Imbens and Kalyanaraman (2012), MSE-optimal
bandwidth selector (Calonico et al. 2014), a half size of MSE, twice larger than MSE, 2 weeks,
and 6 weeks. Since these bandwidth selectors are adapted to the RD analysis rather than
RDDD, we present both results using the same bandwidths selected from the RD analysis
in Table D8. All estimated effects remain in the quantitatively similar range of the main
analysis and, in most cases, are statistically significant.
Including weekends. We include weekends and holidays in the analysis. The average AMI
rate on the first day of spring transition appears to be the same as the day before, suggesting that the effect of DST kicks in from the subsequent Monday (Figure D3). Accordingly,
the point estimate is lower, yet the magnitude remains in the range of the main estimate
and statistically significant at the 5% level in row (3) of Table D7. The evidence that AMI
admissions are less likely to be induced by DST on weekends indicates that sleep deprivation
appears to be an important mechanism triggering AMI. Indeed, the average AMI rates con14

The percentage change in the expected number of AMI admissions is given by exp(β) − 1.

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medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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tinue to be lower on weekends than weekdays, suggesting that the effects of sleep deprivationin the first week entering DST does not extend to the following weekends. The evidence
suggests that sleep deprivation on the day or its accumulation over a few days on weekdays
is likely to induce AMI, whereas extra time to sleep on weekends is likely to mitigate theeffect.
Polynomial running variable. The local linear regressions perform better in the RD contexts than higher order polynomials (Gelman and Imbens 2019). Nonetheless, we use a thirdorder polynomial to allow non-linear trends in the running variable. The point estimate inrow (4) is amplified, although it is less precisely estimated.
Alternative control groups. With the inclusion of the county-fixed effects, the main RDDD
analysis exploits variation in the DST observation status over time within the county, which
essentially utilizes observations in their own counties when DST was not practiced as acounterfactual. With the inclusion of year-fixed effects, which capture the overall time trend,
this assumption seems valid. We still experiment with an alternative construction of thecontrol observations. In particular, using observations only before the policy change in 2006,
we conduct the RDDD analysis without including the county-fixed effects yet with year-fixed
effects. This effectively examines the effect of DST in the counties that had already practiced
DST before the policy change by comparing them with the counties that did not practice
DST in the respective year. An additional benefit in this analysis is that exactly the samedates are used to define the beginning of the spring transition in each year. The estimatedeffects in row (5) remain similar in magnitude.
Commuting effect. It is likely that counties that did not practice DST before the policychange and are adjacent to counties that did practice DST may have been already effectively
affected by DST if people there worked in counties with DST. Thus, we now conduct theanalysis only among the counties that did not practice DST before 2006, and separately forcounties that are and are not adjacent to counties that practiced DST throughout (see theirlocations in Figure D5). With commuting effects present, the main analysis understates thetrue DST effect by differencing out the DST spillover effects. Thus, we expect the effects tobe lower among the adjacent counties than non-adjacent counties. The estimated effects arelarger for adjacent counties, indicating that such commuting effects are negligible.

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medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Figure D5: The Location of Adjacent Counties

Notes: This figure shows the locations of counties that are and are notadjacent to counties that have practiced DST over the study period.

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Table D7: Robustness: The Effect of Spring Transition on AMI Admissions
Specification

Estimates(1) Poisson(2) Neg. bin.
(3) With weekends(4) 3rd order polynomial(5) Alternative control(6) Not adjacent(7) Adjacent0.199***(0.061)0.220***(0.059)1.056**(0.512)2.756*(1.490)1.267(0.852)1.231(0.746)2.142**(1.016)

Notes: This table presents the RDDD estimates of the effects on AMI admission rates at the spring transition
using various alternative specifications. The specifications are from the top row; (1) Poisson regressions areused for the count variable of the number of admissions; (2) Negative binomial regressions are used for thecount variable of the number of admissions; (3) The sample including weekends and holidays is used; (4) 3rd
order polynomial function in the running variable is used; (5) The sample before 2006 is used without thecounty fixed effects; (6) Counties that did not practice DST before 2006 and not adjacent to counties thatpracticed DST throughout the study period are used; (7) Counties that did not practice DST before 2006
and adjacent to counties that practiced DST throughout the study period are used.
***p < 0.01 **p < 0.05 *p < 0.1029

medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Table D8: The Estimates based on Alternative Bandwidths(1)
Panel A: RD
PostTransN
Panel B: RDDD
PostTrans×Obd.
N
Selector
Bandwidth

(2)

(3)

(4)

(5)

(6)

(7)

1.163*** 1.098*** 1.227***(0.354)(0.397)(0.391)
28536
22968
18792

1.220**(0.509)9048

0.891**(0.348)35496

0.877**(0.394)13920

0.789**(0.349)41760

1.353**(0.522)41492

1.268**(0.625)
33396

0.966
(0.745)27324

2.057*(1.234)13156

1.142**(0.484)
51612

1.340
(0.884)20240

0.819*(0.464)
60720

LM
29

IK
23

MSE
18.23

MSE * 1/2 MSE * 2
9.11
36.46

2 weeks 6 weeks

Notes: This table presents the estimated effects of the Spring transition based on the RD analysis in Panel
A and the RDDD analysis in Panel B using alternative bandwidths. The outcome variable is AMI admission
rates per one million working-age population. The level of observation is the daily-county. Column (1) uses
the bandwidth selected by Ludwig and Miller (2007), Column (2) by Imbens and Kalyanaraman (2012),
Column (3) uses MSE-optimum bandwidth selector (Calonico et al. 2014), Column (4) uses a half size ofColumn (3), Column (5) uses twice larger than Column (3), Column (6) uses 2 weeks, and Column (7) uses
6 weeks.
***p < 0.01 **p < 0.05 *p < 0.1030medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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4.

Permutation Test

The key identification assumption for the findings in the main text to offer a causal inferenceis that observations when DST was not practiced adequately capture the unobserved seasonality effect when DST was practiced. To test the validity of the identification assumption andto put the estimated effects into context, we conduct the permutation test. The basic idea
is to assign the placebo DST transition on each date (e.g., 40th day from the actual DST
transition as the placebo cutoff) and conduct the same RDDD analysis based on Equation(4) in the main text, and in doing so, we omit days immediately after the actual DST dates
to avoid capturing the true DST effect. Following Smith (2016), we exclude the first two
weeks after the DST transition as the immediate neighborhood that may be influenced by
the true DST effect (e.g., the first two weeks after the spring transition for the effect on AMI
admissions or the first two weeks after the autumn transition for the effect on total charges)
or any dates that include the 4-week bandwidth in this period (e.g., from 338 days after theactual DST transition to 41 days after the actual DST transition).
Figure D6 plots the distribution of estimated coefficients from the permutation test on the
AMI admissions. The vertical dash line indicates the DST effect estimated from the RDDD
analysis using the actual DST spring transition. Clearly, the estimated discontinuity at thetrue transition is extremely large relative to other effects estimated at placebo cutoffs with the
implied p-value of 0.0068. The evidence that such observed effect at the true DST transition
is a rare event also provides support to the identification assumption that the trends in AMI
admissions are similar on other dates in this time frame between the observations with andwithout DST practice.

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Figure D6: Permutation Test: Distribution of Coefficients on Number of AMI Admissions

Notes: This figure plots the distribution of coefficients estimated from the permutation test on
AMI admission rates per one million working-age population that assigns the placebo DST dateto 296 days (from 42 days to 337 days after the true DST transition) outside the 2-week period
following the spring transition or any dates that include the 4-week bandwidth in this period. The
kernel density is estimated by using Epanechnikov kernel function. The vertical dash line indicatesthe estimated effect in the main RDDD analysis. The implied p-value from this permutation test
is 0.0068.

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5.

Heterogeneous Effects of DSTThis subsection explores potential heterogeneities in the effects of DST on AMI across various
dimensions by applying the main RDDD analysis to a set of subsamples. We examine here
only the effects on the number of admissions after the spring transition based on the main
results.
On the top row of Figure D7 is the estimate from the main analysis for the purposeof reference. In the second and third rows, we disaggregate the sample into inpatients andoutpatients and find that the main effect is mostly driven by the increase in the number of
inpatients rather than of outpatients. This is consistent with the fact that a large majorityof AMI cases result in hospitalization and with our finding above that AMI cases inducedby the spring transition are as severe as typical AMI cases. Still note that the effect on
outpatients is not negligible. Although the point estimate is smaller, it indicates an increaseof 38.1% from the mean and is statistically significant at the 10% level.
In the following two rows, we explore heterogeneities by gender. While the magnitudes ofthe estimates are similar between men and women, the impact on men is smaller relative tothe mean, and noisily estimated, indicating about an 19.0% increase. On the other hand, AMI
admissions increased significantly among women by about 46.5%. The finding that womenare more adversely affected by the spring transition than are men is consistent with Janszky
and Ljung (2008), although the precise mechanism is unknown. In contrast, Janszky et al.
(2012) find no difference by gender, whereas Čulić (2013) finds impacts on men for the springtransition and women for the autumn transition (although not reported, we find no evidence
of increased AMI admissions among either men or women at the autumn transition).
In the next two rows below, we compare admissions above and below 50 years old, theapproximate mean age of our sample.15 We find that the effects are concentrated on people
above 50 years old, which is consistent with the fact that AMI is more common among
the older population. Although not reported here, we find that DST has no impact on AMI
admissions among seniors, as distinct from older working adults, despite the fact that slightly
more than a half of AMI cases in Indiana during our study period occurred to seniors above
the age of 65 (53.5%). Thus, DST is likely to trigger AMI among the older working population
but not among seniors who are likely to be already retired.
Next, we compare patients by insurance type, i.e., between Medicaid and private insurance. The patients on Medicaid experienced a small and insignificant increase in incidencesof AMI, whereas patients on private insurance experienced a significant increase in such
15

As one can see from Table C1, the average age is 53.1 years old with a relatively low standard deviationof 7.9 years, indicating that there is little dispersion in age. This does not allow us to even look at each
10-year age bin. Thus, we decided to cluster all ages above and below 50, a round number around the mean.

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incidences. Although the precise mechanisms remain unclear, the differential impacts by insurance type may be explained by the fact that people on private insurance are more likely
to be full-time workers engaging in jobs with less flexible work schedules that are not alignedwith their biological rhythms, while those reliant on Medicaid have more opportunity toadjust to the time shift.
Motivated by the evidence above, we also explore counties by employment rates (e.g.,
those above and below the mean employment rates) in the bottom two rows.16 Although
we do not find significant differences here, the point estimate is statistically significant only
for counties above the mean employment rate. The estimated effect for counties with lower
employment rates is slightly larger but is less precisely estimated. The employment statusitself does not indicate the employment sector or whether work is full- or part-time. Unfortunately, we do not have data regarding employment status or work sector in our patient
data to allow further investigation.

16
The employment rate data come from the American Community Survey (United States Census Bureau,2012). We use the data in 2012.

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Figure D7: Heterogeneous Effects on Number of Admissions

Notes: This figure explores heterogeneities in the effect of DST on AMI admission rates per
one million working-age population at the spring transition. The estimated coefficients fromthe RDDD analysis plotted with 95% confidence interval, whose values are provided on theright side. The subsamples are defined at the first column. In particular, “Main” indicatesthe estimate from the main analysis using the working population; “Inpatients” indicates
only inpatients; “Outpatients” indicates only outpatients; “Men” indicates men; “Women”
indicates women; “Age>=50” indicates patients aged 50 and above and below 64, “Age<50”
indicates patients aged below 50 and above 20; “Medicaid” indicates patients on Medicaid;“Private” indicates patients on private insurance; and “Emp high” or “Emp low” indicate
counties above or below the mean employment rate. The mean values presented in the second
column are computed for the pre-transitional period, weighted by the relevant population
at the county level.

35

medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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6.

Role of Fatal Cases

Our analysis considers only non-fatal infarctions since our data come from hospital discharge
and outpatients’ records. In this subsection, we attest that our focus on non-fatal incidences
does not undermine the novel conclusions of the work performed.
First, it is not unusual to focus either on non-fatal or on fatal infarctions in the literatureof DST and AMI. For example, in the literature summarized in Table A1, Jiddou et al. (2013);
Čulić (2013) finds only increased non-fatal admissions but not fatal admissions; Kirchberger
et al. (2015) and Sipilä et al. (2016) find no impact on fatal and non-fatal AMi cases; Toroet al. (2015) focuses only on fatal cases; and neither Janszky and Ljung (2008), Janszky etal. (2012), nor Jin and Ziebarth (2020) states whether the hospital admissions involved fatal
or non-fatal infarctions. Thus, no study has substantiated that considering fatal cases alters
the implications for non-fatal cases.
Second, one may be concerned that fatal cases would truncate the non-fatal observations
in our data, which would alter our conclusions. However, our results indeed have implications
for fatal cases and suggest that such a concern is not well-founded. Unlike the previous studies
on this topic, one of our strengths is to observe hospital medical charges and length of hospital
stay as measures of disease severity. Our results show no impacts on either measure in the
short-term effect. If the marginal cases who would have barely survived with a heart attack
died because the heart attack was intensified by DST, we would expect to see lower disease
severity among people who survived and remained in our data. However, our estimated effecton disease severity is virtually zero, whose evidence is consistent with Jiddou et al. (2013),
Kirchberger et al. (2015), and Sipilä et al. (2016) who find no impact on fatal incidences.
Third, increased fatal cases, if any, would go against finding increased hospital admissions,thus the only result would be in understating the true effects on the number of hospital
admissions. No study has found decreased mortality at the DST transition, and thus lower
mortality would not explain increased hospital discharges. Alternatively, DST may hasten
and weaken a heart attack that would have resulted in a fatal case several days later, yet not
only is there no scientific evidence that hastened heart attack is weakened but also we findno evidence of displacement in hospital admissions, making it unlikely that there is such adisplacement effect for mortality.
Fourth, as direct evidence, we have also investigated the effects of DST on AMI mortality
in the U.S. The data come from the CDC National Vital Statistics System Multiple Causeof Death (NVSS MCOD) (Centers for Disease Control and Prevention 2011), which reports
all AMI fatal cases in 2002–2011 across the U.S. except states that did not practice DST
during the study period (without state identities), e.g., Hawaii, Arizona, Indiana, and Alaska.

36

medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Because the analysis requires restricted-use data of the exact date of occurrence to construct
the daily death counts from AMI, these data were accessed through the Research Data
Center.17 Using the standard RD specification, we estimate:ln M ortdy = β0 + β1 P ostT ransdy + β2 Daysdy + β3 Daysdy × P ostT randy + ε,

(A.1)

where the outcome variable is the logged fatal AMI counts on day d in year y, after being
demeaned by day-of-week and year to eliminate persistent day-of-week effects and year-toyear variation. The other variables are the same as in Equation (1) in the main text. To beconsistent with the main analysis, we use 4 weeks as the bandwidth. The standard errors
are corrected for heteroskedasticity.
Table D9 reports the estimated effects at the spring and autumn transition dates. The
estimated effects are virtually zero, suggesting that the shifts to and from DST do not cause
AMI fatal cases.

17

Analysis of restricted data through the NCHS Research Data Center is approved by the NCHS ERB.
The findings and conclusions in this paper are those of the author(s) and do not necessarily represent theviews of the Research Data Center, the National Center for Health Statistics, or the Centers for Disease
Control and Prevention.

37

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Table D9: The Effect of DST on AMI Mortality
(1)

(2)

SpringAutumnPostTrans -0.00334(0.0199)

0.0179(0.0248)

Notes: This table presents the estimated effects of DST on AMI mortality based on Equation A.1. The levelof observations is at the daily level. The sample size is 400. The bandwidth is 4 weeks before and after the
DST transition. The mean number of daily AMI deaths is 80.84 with the standard deviation of 11.69. Robust
standard errors, corrected for heteroskedasticity, are reported in the parentheses.
Data source: NVSS MCOD

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E.

Medium-term Effects of the Time Adjustments

The event-study model of Equation (7) in the manuscript is:
Ycdt = α +6
X

βT T reatc × Y earT + µc + τt + DayOf W eekdt + θW eathercdt + εcdt , (A.2)

T =−4

where the additional variable of Y eartT is an indicator variable for each number of years
from the first year of practicing time adjustment, e.g., T = 0 in 2006 for most treatment
counties. The omitted group is the year before the treatment starts, e.g., T = −1. The
coefficients of interests are βT ’s, which indicate the changes in the outcome in each year T
from the reference year of T = −1 in the treatment counties relative to those changes in
the control counties. Thus, we expect the coefficients to be zero for T < 0 if we satisfy theparallel trend assumption in the pre-reform period. The standard errors are clustered at thecounty level.
The results from Equation (A.2) are presented in Figure 3 and discussed in the maintext. Importantly, we observe that AMI admission rates are virtually zero up to the yearof treatment, ensuring that there is no pre-existing differential trend between the treatment
and control counties. The admission rates increase in the first year subject to the springtransition and continues to be positive during the post-reform period.
The other two variables regarding the disease severity do not have statistically significant
effects (Online Appendix Table E2).

39

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Table E1: The DD Estimates of the Effects of Time Adjustments on AMI
Variable(1)

Wk(-3) × PostReform-0.617(0.654)Wk(-2) × PostReform 0.257(0.587)
Wk(0) × PostReform
0.967(0.591)
Wk(1) × PostReform
0.888(0.561)
Wk(2) × PostReform
0.511(0.524)
Wk(3) × PostReform

(2)

(3)

-0.620(0.653)0.270(0.588)0.951(0.588)0.872(0.554)0.500(0.524)-0.115(0.600)-0.598(0.659)0.274(0.588)0.960(0.588)0.881(0.554)0.506(0.527)-0.261(0.575)0.274(0.755)5.084
35,4205.084
40,480Wk(4) × PostReform
Mean
N

5.084
30,360Notes: This table presents the estimated coefficients in Figure 2 in the main text
based on Equation (6). All regressions control for the day-of-week effects, daily mean
temperature with bins of every 3-degree Celsius, total amount of precipitation, thecounty fixed effects, the year fixed effects, and the month fixed effects. The level ofobservations is at the county-daily level. The regressions are weighted by the number ofworking-age population. The standard errors clustered at the county level are reported
in the parenthesis. Column (1) considers the sample of 3 weeks before and after thetime transition, and Columns (2) and (3) adds another post-transitional week. The
mean values are calculated in the week before the spring transition in the post-reformperiod.

40

medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Table E2: The DD Estimates of the Effects of Time Adjustments on AMI
Admission rates Total charges Length of stay
Treat × PostReform
Mean
N
Fixed effects
Controls

(1)

(2)

(3)

0.479*(0.278)6.223
159,160-0.112(0.076)27,978.3
37,026-0.033(0.024)4.319
29,929

Y
Y

Y
Y

Y
Y

Notes: This table presents the effects of the time adjustments on AMI based on the DD model
specified by Equation (7) during the DST period. The outcomes are the daily number of hospital
admissions per one million population in Column (1), the (log) of total medical charges in Column(2), and the (log) of length of hospital stay in Column (3). To be consistent throughout the paper,the outcomes are first demeaned by daily average temperature with bins of every 3-degree Celsius,
total daily precipitation, and day-of-week effects to account for persistent and long-term effects
by using all observations throughout the year, whereas including these variables directly into theregressions produces the same results. Treat is a dummy variable for counties that did not practiceDST until 2006. The regressions are weighted by the number of working population in Column(1). All regressions include the county fixed effects, the year fixed effects, the month fixed effects.
The level of observations are at the daily-county level in Column (1) and at the patient level inColumns (2) and (3). The standard errors are clustered at the county level.
*p < 0.10

41medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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F.

Long-term Effects of the Time Adjustments
Figure F1: Effects of Autumn Transition over Years

Notes: This figure presents the effects of the autumn transition on the AMI admission rates per
one million working-age population in each year based on the event-study RDDD model specified
by Equation (5).

42medRxiv preprint doi: https://doi.org/10.1101/2022.07.06.22277274; this version posted July 8, 2022. The copyright holder for this preprint(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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