Question:
You are given the full text of a scientific research paper.

    Summarize the paper's key **research findings** in the style of a policy brief. Your response should:
    - Focus on the major empirical or model-based results
    - Present findings that are policy-relevant and supported by the study
    - Use concise, non-technical language with essential technical terms as needed
    - Avoid quoting directly or summarizing minor details

    Write a short, clear paragraph appropriate for the “Scientific Research Findings” section of a policy brief.

    Scientific Research Paper:
    Articles
https://doi.org/10.1038/s41560-020-0600-2
1School of Public Health, University of California, Berkeley, CA, USA. 2Columbia University Mailman School of Public Health, New York, NY, USA.
3Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA. 4Department of Statistics and Data Sciences and
Department of Women’s Health, University of Texas, Austin, TX, USA. 5Department of Environmental and Radiological Sciences, Colorado State University,
Fort Collins, CO, USA. 6Propeller Health, San Francisco, CA, USA. 7Louisville Metro Department of Public Health and Wellness, Louisville, KY, USA.
8Christina Lee Brown Environme Institute, University of Louisville, Louisville, KY, USA. 9Louisville Metro Office of Civic Innovation, Louisville, KY, USA.
10Family Allergy & Asthma, Louisville, KY, USA. ✉e-mail: jac2250@cumc.columbia.edu
C
oal-fired power plants provide a large amount of electric-
ity worldwide. In 2015, they produced six trillion megawatt
(MW) hours and 25% of the global supply1. Simultaneously,
coal-fired power plants emit air pollution, which included 63% of
the US economy-wide sulfur dioxide (SO2) emissions in 20142, as
well as nitrogen oxides (NOx), PM2.5 (particulate matter less than
2.5 μm in diameter) and PM10 (particulate matter between 2.5 and
10 μm in diameter), mercury, acid gases, polycyclic aromatic hydro-
carbons and volatile organic compounds3. Such pollutants are asso-
ciated with increased asthma symptoms, emergency room visits
(ERVs), hospitalizations and mortality4–8.
Among populations that live near coal-fired power plants and
fossil fuel refineries, some, though not all9,10, epidemiological stud-
ies found a relationship between higher SO2 levels and uncontrolled
asthma11, respiratory symptoms12–15 and respiratory-related hospi-
talizations16. Residential proximity to such facilities alone, without
assessed air quality, was also identified as a risk factor for asthma
exacerbation17–19.
Prior scholarship related to asthma and coal-fired power plant
exposures often consisted of observational and cross-sectional
studies that considered single air pollutants (usually SO2) and
hospitalizations, pulmonary function or symptoms alone. Studies
of symptoms lacked objective measures and usually relied on par-
ticipant diaries. Some studies overcame a portion of these limita-
tions; for example, Smargiassi et al.16 used a case-crossover design to
evaluate the relationship between SO2 concentrations and ERVs and
hospitalizations among young children who lived near a refinery.
To build on these prior studies, we incorporate improved exposure
and outcome assessment and capitalize on recent abrupt changes in
coal-fired power plant emissions.
Between 2000 and 2015 in the United States, 49.5 GW of capacity
from coal-fired generators retired at 146 coal-fired power plants1,20
and many generating units installed flue-gas desulfurization systems
(alternatively, SO2 emission ‘controls’) to comply with regulations
from the US Environmental Protection Agency (USEPA) and indi-
vidual states, which include the Acid Rain Program, the Clean Air
Interstate Rule and Mercury and Air Toxics Standards2. The discrete
nature of these energy transitions and the ensuing abrupt changes in
emissions present circumstances for a ‘natural experiment’ to study
related changes in health in the time frame and population exposed
to emissions from the coal-fired power plants21,22. The key feature is
that the transition-induced change in exposure occurs for reasons
unrelated to a health investigation and produce exposure changes
that more closely resemble an experiment than a typical observa-
tional study. Such a natural experiment supports the study of the
influence of coal-fired power plant emissions on asthma outcomes
more directly, without relying on exposure–response functions esti-
mated with different populations across time and space with vary-
ing levels of pollution exposure23. Previous studies framed in this
manner used a steel mill closure24, a ban on coal sales25, the Olympic
Games26,27 and the Nitrogen Oxides Budget Program28 to study the
relationship between air pollution and respiratory-related medica-
tion expenditures, hospitalizations and death.
We used retirements and SO2 emissions control installations
at four coal-fired power plant facilities near Louisville, Kentucky,
between 2013 and 2016 to frame a natural experiment, which
Improved asthma outcomes observed in the
vicinity of coal power plant retirement, retrofit
and conversion to natural gas
Joan A. Casey   1,2 ✉, Jason G. Su1, Lucas R. F. Henneman   3, Corwin Zigler4, Andreas M. Neophytou1,5,
Ralph Catalano   1, Rahul Gondalia6, Yu-Ting Chen7, Leanne Kaye6, Sarah S. Moyer7, Veronica Combs8,
Grace Simrall9, Ted Smith8, James Sublett10 and Meredith A. Barrett6
Coal-fired power plants release substantial air pollution, which included over 60% of US sulfur dioxide emissions in 2014. Such
air pollution may exacerbate asthma, but direct studies of the health impacts linked to power plant air pollution are rare. Here
we take advantage of a natural experiment in Louisville, Kentucky, where one coal-fired power plant was retired and converted
to natural gas, and three others installed SO2 emission control systems between 2013 and 2016. Dispersion modelling indicated
that exposure to SO2 emissions from these power plants decreased after the energy transitions. We used several analysis strate-
gies, which include difference-in-differences, first-difference and interrupted time-series modelling to show that the emissions
control installations and plant retirements are associated with a reduced asthma disease burden related to hospitalizations and
emergency room visits at the ZIP-code level, and to individual-level medication use as measured by digital medication sensors.
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generated abrupt changes in air pollution exposures that varied
in extent across the Louisville area. The circumstances of these
energy transitions and their resemblance to an experiment that
(approximately) randomizes exposure changes motivates a vari-
ety of analysis approaches that each mitigate some of the common
threats to observational studies. Our approaches include compar-
ing asthma-related end-points in areas more and less exposed to
changes in air pollution and individuals before and after the dis-
crete transition events to evaluate impacts on the asthma-related
end-points. Kentucky has historically ranked among the top five
in the United States for SO2, NO2 and PM10 emissions from power
generation29. We confirmed air-quality improvements after the
power plant energy transitions using a longitudinal coal-fired
power plant emission exposure model30. Analyses that leveraged
different elements of spatial and temporal variability in exposure
demonstrated that the coal-fired power plant energy transitions
were associated with reductions in asthma-related hospitalizations
and ERVs at the ZIP-code level and asthma symptoms measured
at the individual level using digital medication sensors within the
Louisville metropolitan area.
Impact of retirement, retrofit and conversion on emissions
Four coal-fired power plants—Cane Run, Clifty Creek, Mill
Creek and Rockport—with emissions that impacted air quality in
Jefferson County, Kentucky (which fully contains Louisville), either
retired or installed SO2 controls between 2012 and 2016 (Fig. 1). We
quantified monthly ZIP-code variability in SO2 emissions exposure
from these plants using the HYSPLIT Average Dispersion (HyADS)
model, which aggregates the results from 146,000 HYSPLIT runs
per power plant30. During the study period, the quarterly median
unitless HyADS exposure was 6,553 (interquartile range (IQR),
2,283 and 9,702; maximum, 31,781). Power plant energy transitions
resulted in declining levels over time (Fig. 2 and Supplementary
Figs. 1 and 2). Data from the Louisville Metro Air Pollution Control
District confirmed that retirements at Cane Run and scrubbers at
Mill Creek reduced the annual SO2 emissions by 9.6 and 12.9 mil-
lion kg, respectively (Supplementary Fig. 3). Comparing years pre-
and post-control installations, SO2 emissions also declined at Clifty
Creek (−90%) and Rockport (–50%). These four facilities contrib-
uted 36, 30 and 16% of the total average ZIP-code HyADS exposure
in Louisville in 2012, 2014 and 2016, respectively. The transitions
at the four facilities contributed to an overall decline in coal-fired
power plant emissions exposures in Jefferson County.
HyADS emissions exposures peaked annually during the third
quarter (April–June, Fig. 2). In a companion analysis, we found that
meteorological variability contributed more than emissions reduc-
tions to changing HyADS in 2012, 2013 and 201431. Table 1 shows
that, accounting for seasonality and meteorological factors, the
average level of HyADS exposure decreased substantially after three
of the four energy transitions, with a 55% decline from baseline after
the second quarter of 2015 (Q2-2015).
Observed changes in ZIP-code-level asthma outcomes
The median (IQR) quarterly asthma hospitalizations/ERV counts
across the 35 ZIP codes in Jefferson County between 2012 and 2016
was 16 (range 9–31), and the counts declined county-wide over time
(Fig. 3 and Supplementary Table 1). Between 2012 and 2016 the rates
of uninsured and unemployed individuals also fell. Quarter 4 typi-
cally exhibited hospitalization and ERV values higher than those of
other quarters after adjusting for ZIP code and annual specific means.
Three of the four energy transitions were associated with reduc-
tions in ZIP-code-level asthma hospitalizations and ERVs (Table 2),
which correspond to the three transitions associated with reduced
HyADS (Table 1). The largest reduction in risk came after the
Q2-2015 transitions, relative risk (RR) = 0.81; 95% confidence inter-
val (CI), 0.70, 0.92. We therefore focused the next stage of analysis
on the Q2-2015 transitions at Cane Run, Mill Creek and Rockport.
During the time surrounding the Q2-2015 transition (Q2-2014 to
Q2-2016), the average quarterly ZIP code HyADS reduction was
25,281 (standard deviation (s.d.) = 3,638).
In a difference-in-differences framework, we categorized ZIP
codes based on their pre-period HyADS exposure (high versus low).
The results indicated that the Q2-2015 energy transition reduced
asthma hospitalizations and ERVs by an additional 2.8 visits per ZIP
code per quarter in areas with high pre-transition exposure relative to
areas with a lower pre-transition exposure (Fig. 4). When we specified
pre-transition HyADS as a continuous variable, results were similar
when converted into a comparable scale (−0.4 (95% CI: −0.2, −0.7))
asthma hospitalizations and ERVs per ZIP code per 1,000-unit higher
pre-period HyADS exposure; Supplementary Fig. 4).
With a first-difference linear regression model, we found that a
1,000-unit ZIP-code-level reduction in HyADS exposure from the
Rockport
99 million kg
100
105
110
2012 HyADS, 1,000s
115
2012 total SO2 emissions (million kg)
10
3
30
30
60 km
Clifty Creek
96 million kg
Cane Run
6 million kg
Mill Creek
28 million kg
0
1,000
Indiana
Kentucky
Fig. 1 | Power plant locations, emissions and exposure. The locations of the four coal-fired power plant facilities, their total SO2 emissions (larger grey
circles indicate higher emissions) and their total HyADS exposures at the ZCTA level within Jefferson County in 2012. Unit and facility data downloaded
from the USEPA Air Markets Program50.
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year prior to the year after the Q2-2015 energy transitions resulted
in, on average, 2.2 fewer asthma hospitalizations and ERVs (95%
CI: −4.5, 0.2) per ZIP code per year and a first-difference model
that specified categories of ΔHyADS showed the largest effect for
the highest ΔHyADS category (Supplementary Fig. 4). Inferences
remained stable in sensitivity analyses using baseline popula-
tion weights instead of adjusting models for baseline population
(Supplementary Table 2).
Observed changes in individual-level asthma symptoms
We identified 207 study participants in the AIR Louisville pro-
gramme who were under observation during the year prior to and
the year after the 8 June 2016 Mill Creek power plant scrubber instal-
lation. Most participants were female (67%), of white race/ethnicity
(63%) and middle-aged (average age of 45 years) (Supplementary
Table 3). Participants’ median monthly HyADS exposure was
1,915 (IQR: 1,172, 3,050) (Supplementary Fig. 5). Participants had
a daily mean of one short-acting beta agonist (SABA) inhaler use
(s.d. = 1.5) and a median of no SABA uses (IQR = 0, 1). From visual
inspection, daily rescue inhaler use was more prevalent and variable
earlier in the study period, probably driven by the smaller number
of enrolled participants (Supplementary Fig. 6) and later trended
downward (Supplementary Fig. 7).
In a within-person conditional quasi-Poisson model, we
observed a reduced monthly average daily SABA use associated
with a reduced monthly HyADS exposure (RR = 0.94, 95% CI: 0.89,
0.98, for each 1,000-unit decrease in HyADS).
As the June 2016 Mill Creek scrubber installation resulted in
relatively uniform reductions in HyADS exposure across Jefferson
county (Fig. 5b), we used an interrupted time-series framework.
We identified a level shift in SABA use (at the time of scrubber
installation) and a possible slope change (decreasing trend in
SABA use) (Fig. 6). The scrubber installation was associated with
a 17% reduction in monthly average daily SABA use (RR = 0.83,
95% CI: 0.69, 1.00) and a 2% reduction (95% CI: −5%, 1%) for each
month thereafter.
In two sensitivity analyses, we evaluated the change in odds of
having any daily SABA use (monthly average of ≥1 use day–1 ver-
sus <1 use day–1) and high daily SABA use (monthly average of
≥4 uses day–1 versus <4 uses day–1) at the time of scrubber instal-
lation. We found an apparent larger immediate effect on higher
monthly average daily SABA use (32% reduction (odds ratio = 0.68,
95% CI: 0.45, 1.02]) and a trend in reduced monthly average daily use
of ≥1 uses (17% reduction each month after the scrubber installation
(odds ratio = 0.83, 95% CI: 0.71, 0.97]) (Supplementary Table 4).
Discussion
The top four coal-fired power plants in terms of emissions that
affect air quality in Louisville in 2012 either retired or installed SO2
emission controls between 2012 and 2016. The resulting air-qual-
ity improvements translated into reductions in both acute asthma
outcomes—measured by quarterly ZIP-code-level asthma-related
Quarter 3
2012
0
1 × 104
2 × 104
a
b
Unit ID
983-1
983-2
983-3
983-4
1,363-4
1,363-5
1,363-6
1,364-1
1,364-2
1,364-3
1,364-4
6,166-MB1
6,166-MB2
983-5
983-6
–1,250
–1,000
–750
–500
–250
0
2013
2014
2015
2016
2017
2015–2014
Mean reduction = 60%
Mean reduction = 86%
HyADS absolute change for selected units
HyADS, unitless
Mean reduction = 90%
Mean reduction = 76%
2015–2014
2016–2015
2016–2015
Quarter 4
Quarter 1
Quarter 2
Fig. 2 | Quarterly mean ZIP code-level HyADS exposure in Jefferson County. a, From 2012 to 2017 stratified by coal-fired power plant unit. Clifty Creek,
983; Cane Run, 1,363; Mill Creek, 1,364; Rockport, 6,166. b, Absolute change in quarterly ZIP code tabulation area-level HyADS and mean percentage
reduction across the two stated years in the stated quarter.
Table 1 | Coal-fired power plant events and HyADS emissions
exposures
Coal-fired power plant transition date
%Δ HyADS (95% CI)
 Q2-2013
11 (6, 16)
 Q4-2014
−27 (−31, −23)
 Q2-2015
−55 (−61, −47)
 Q2-2016
−29 (−33, −24)
Association between coal-fired power plant events and ZIP-code-level HyADS emissions exposures
in the 35 Jefferson County ZIP codes with a population greater than 5. %Δ was calculated relative
to countywide average HyADS level in 2012 (11,392 units). Estimates provided from an ordinary
least squares linear regression model adjusted for quarterly mean temperature, wind speed,
relative humidity and atmospheric pressure, and quarter and ZIP code. Includes Liang and Zeger
cluster-robust standard errors. Q1, quarter 1 (Jan–Mar); Q2, quarter 2 (Apr–June); Q3, quarter 3
(July–Sept); and Q4, quarter 4 (Oct–Dec).
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hospitalizations and ERVs—and daily symptoms, as measured by
sensor-collected SABA use. In the spring of 2015, coal-fired power
plant units retired at Cane Run and SO2 controls were installed
at Mill Creek and Rockport. These energy transitions resulted in
approximately three fewer hospitalizations and ERVs per ZIP code
per quarter in the following year, which translates into nearly 400
(−2.8 per ZIP quarter × 4 quarters × 35 ZIPs) avoided hospitaliza-
tions and ERVs in Jefferson County annually. At the individual level,
the Mill Creek SO2 scrubber installed in June 2016 was associated
with immediate reductions in SABA use and a marginally declining
trend in use through May 2017.
Uniquely, this study found that exposure reductions to coal-fired
power plant emissions were consistently associated with a reduced
asthma morbidity at two spatial and temporal resolutions: monthly
average daily SABA use within individuals and acute quarterly
ERVs and hospitalizations at the ZIP-code level. Acute outcomes,
such as hospitalizations, represent an important, yet more infre-
quent, severe and costly32 signal of asthma morbidity. SABA use can
represent the daily burden of disease and the future risk of adverse
outcomes33. Our analyses used digital medication sensors to objec-
tively track the time and location of SABA use and avoid the poten-
tial recall bias associated with the diaries used in previous studies of
short-term air pollution levels and asthma15,34,35.
In 2014, US power plants accounted for 64% of SO2, 14% of NOx
and 7% of PM2.5 economy-wide emissions, of which coal power
comprised 98% of SO2 and >82% of both NOx and PM2.5 power
plant emissions2. Exposures to these air pollutants, even at relatively
low levels, are associated with asthma morbidity7,8,10,36. Four main
mechanisms—oxidative stress, airway remodelling, inflammation
or immunological response and an enhanced response to inhaled
allergens37—likely explain how air pollution contributes to asthma
onset and exacerbation. Laboratory studies of SO2 exposure among
asthmatics have demonstrated bronchoconstriction in humans38
and airway inflammatory and immune responses in rats39.
In a 2017 report for the US Department of Energy, Massetti et al.
characterized SO2 as the main source of air-pollution-related eco-
nomic damage associated with coal-fired power plants due to its
high volume of emissions and because SO2 is a PM2.5 precursor2.
Among populations that live close to coal-fired power plants, SO2 is
associated with wheeze and asthma prevalence13,40, asthma attacks
and ERVs12,15,16, although it is likely that other pollutants play a
role. Coal electricity generation contributes to anthropogenic PM2.5
levels (which account for 1–3% of all asthma-related ERVs in the
Americas41) and NO2 exposures (which may cause 19% of asthma
incidence in high-income countries42). Among children who lived
near Israel’s 2580 MW Orot Rabin coal-fired plant, a combined NOx
and SO2 exposure was more strongly associated with a reduced pul-
monary function test performance than either pollutant alone14.
Several studies characterized exposure via residential proximity
to coal-fired power plants, a metric that incorporates, albeit crudely,
1,000
a
b
750
500
250
0
Q1-2012
Q2-2012
Q3-2012
Q4-2012
Q1-2013
Q2-2013
Q3-2013
Q4-2013
Q1-2014
Q2-2014
Q3-2014
Q4-2014
Q1-2015
Q2-2015
Q3-2015
Q4-2015
Q1-2016
Q2-2016
Q3-2016
Q4-2016
Count
Indicates the quarter-year
in which the SO2 controls were
installed: 3 units in Q1-2013, 3
in Q2-2013–2, 1 in Q4-2014, 3 in
Q2-2015, 1 in Q2-2016 (n = 1)
and Q2-2015 when 2 coal-fired
units were retired.
0
20
40
60
80
Q1-2012
Q2-2012
Q3-2012
Q4-2012
Q1-2013
Q2-2013
Q3-2013
Q4-2013
Q1-2014
Q2-2014
Q3-2014
Q4-2014
Q1-2015
Q2-2015
Q3-2015
Q4-2015
Q1-2016
Q2-2016
Q3-2016
Q4-2016
Quarterly Louisville-wide
asthma hospitalizations and ERVs, N
Fig. 3 | Quarterly ZIP-code-level counts of asthma hospitalizations and
ERVs in Jefferson County. a, Countywide from 2012 to 2016. b, By ZCTA
from 2012 to 2016. Data provided by the Louisville Metro Department of
Public Health and Wellness.
Table 2 | Relative risk of ZIP-code-level asthma hospitalization
and ERV
Coal-fired power plant transition date
RR (95% CI)
 Q2-2013
1.08 (0.96, 1.19)
 Q4-2014
0.90 (0.75, 1.06)
 Q2-2015
0.81 (0.70, 0.92)
 Q2-2016
0.91 (0.80, 1.03)
These estimates of relative risk are related to each coal-fired power plant event in the 35 Jefferson
ZIP codes with a population greater than 5. They are provided from a quasi-Poisson regression
model with a ZIP-code-level annual population offset and adjusted for annual percentage non-
Hispanic black individuals, unemployed individuals, individuals living below the federal poverty
threshold, quarterly mean temperature, wind speed, relative humidity and atmospheric pressure,
and year, quarter and ZIP code. Includes Liang and Zeger cluster-robust standard errors.
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multiple coal-related pollutants17–19. The continuous and quantita-
tive HyADS metric improves the exposure characterization around
coal-fired power plants and attempts to isolate the effect of collective
coal emissions on health. However, the temporal resolution of our
data and the HyADS metric make it difficult to compare our find-
ings, in magnitude, to those of prior research that reports the rela-
tionships between coal-fired power plant exposure and increased
diary-reported respiratory symptoms12,13,40 and SABA use15, as well
as ERVs and hospitalizations16,19.
Epidemiologists cannot randomize individuals to differing lev-
els of air pollution, which can make causal inference challenging.
Quasi-experimental designs23 that leverage the circumstances of nat-
ural experiments provide one alternative method that, when paired
with the appropriate analytical approaches, can mimic pseudo-ran-
domization of individuals and populations to varying levels of envi-
ronmental exposures21,22. Compared to traditional observational
studies, quasi-experimental techniques are better equipped to han-
dle unobserved differences in populations that might otherwise
confound exposure–outcome associations43. A few prior studies
of asthma exacerbations used quasi-experimental designs24,26–28,
for example, monitoring changes in asthma-related hospitaliza-
tions and acute care visits before, during and after an Olympic
Games that reduced local air pollution26,27. Environmental expo-
sures tend to follow a social gradient44 and co-occurring factors,
such as high body mass index, smoking, low socio-economic sta-
tus and area-level racial segregation are risk factors for asthma45.
Therefore, an abrupt change in an environmental exposure
improves our ability to disentangle the social and environmental
determinants of health.
To our knowledge, no study has focused on installations for the
control of SO2 emissions or on power plant retirements to assess
the potential asthma-related health benefits. We examined multi-
ple interventions across several years and tracked the intervention
impacts at several phases (that is, intervention, emissions, exposure
and health outcome). An important analytical decision in quasi-
experimental studies is the choice of control group. Our difference-
in-differences analysis compared changes between ZIP codes that
were initially most exposed to the power plants that generated the
transition and control ZIP codes that were comparatively less subject
to exposure from these plants. In our first-difference analysis, we
used ZIP codes that experienced less reduction in HyADS exposure
during the study period to control for time trends in hospitaliza-
tions and ERVs and other factors over the study period, and thereby
minimized the threat of confounding due to such factors. Both
designs control for the variation in observed and unobserved fixed
characteristics of place46. At the individual level, we implemented
a within-person analysis, which regards each person as his or her
own control, to reduce the threat of bias associated with confound-
ing variables related to differences across people. This case time-
series analysis only controlled for the overall linear temporal trends.
Individual- and ZIP-code-level analyses consistently supported the
notion that the energy transitions improved asthma outcomes.
This study has limitations. The AIR Louisville cohort partici-
pants were enrolled through a number of channels and did not have
standardized clinical oversight. We lacked access to individual-level
data on participants (for example, healthcare utilization, socio-eco-
nomic status, smoking or tobacco-smoke exposure), although most
of these factors are considered time-invariant and controlled for by
Q1-2012
10
Average ZIP-code asthma count
20
High
Low
Unadjusted
Adjusted
30
a
b
0
–2
–4
–6
–8
∆ quarterly asthma hospitalizations/ERVs
Q2-2012
Q3-2012
Q4-2012
Q1-2013
Q2-2013
Q3-2013
Q4-2013
Q1-2014
Q2-2014
Q3-2014
Q4-2014
Q1-2015
Q2-2015
Q3-2015
Q4-2015
Q1-2016
Q2-2016
Q3-2016
Q4-2016
HyADS group
Retrofits and
retirements
Fig. 4 | Spring 2015 coal-fired power plant events and counts of ZIP-code-level asthma hospitalization and ERVs. a, Trends in count of ZIP-code-level
asthma hospitalization and ERVs from Q1-2012 to Q4-2016. The dashed line notes Q2-2015, where the transition took place late in the quarter. Prior
to Q2-2015, trends in ZIP-code-level counts of asthma events appear parallel, which provides evidence that we meet the parallel trends assumption of
difference-in-differences analysis. b, Difference-in-differences results with 95% CI from an ordinary least squares model (equation (3)) with a binary
specification of pre-period HyADS (high (≥32,500) versus low (<32,500)). The model was adjusted for annual total population, percentage non-Hispanic
black individuals, unemployed individuals, individuals living below the federal poverty threshold, quarterly mean temperature, wind speed, relative
humidity and atmospheric pressure, and included fixed effects for year, quarter and ZIP code. Liang and Zeger cluster-robust standard errors were used.
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the study design. Time-varying factors, however, both at the indi-
vidual and area level could have confounded our results. A prior
study found that the digital health intervention (that is, Propeller
inhaler system) may have helped reduce SABA use, with the larg-
est improvements during the first few months47. If enrolment in the
programme coincided with plant changes, this could have resulted
in overestimated effects. However, our within-person interrupted
time-series design suggested an abrupt change in SABA use at the
time of a SO2-control installation and a continued decline thereaf-
ter. We assumed a linear trend in outcome, although changes may
have followed a non-linear pattern. Although the results in Fig. 6
suggest that the largest change in SABA use corresponds to the larg-
est drop in HyADS in Q2-2015, we lacked the individual-level data
in the pre-period to assess this statistically. At the ZIP-code level,
due to data limitations, we could not consider asthma-related hospi-
talizations and ERVs separately, nor could we track multiple events
within individuals. Finally, we were not able to assess cumulative
impacts; for example, the relationship between chronic coal-fired
power-plant-related exposures and asthma prevalence or the co-
occurrence of asthma and chronic disease. As the evidence is mixed
Mean reduction = 58%
Mean reduction = 32%
Mean reduction = 82%
Mean reduction = 82%
Mean reduction = 92%
Mean reduction = 88%
Mean reduction = 100%
Mean reduction = 84%
Mean reduction = 88%
Mill Creek unit 3 HyADS absolute change
–6,000
–4,000
–2,000
0
January
February
Unit ID
Mill creek
scrubber
installed
1,364-1
6,000
a
b
4,000
2,000
Coal-fired power plant exposure
0
June 2015
September 2015
December 2015
March 2016
June 2016
September 2016
December 2016
March 2017
June 2017
1,364-2
1,364-3
1,364-4
March
April
May
June
July
August
September
October
November
December
Mean reduction = 88%
Mean reduction = 96%
Mean reduction = 58%
Fig. 5 | Monthly mean ZIP-code-level HyADS exposure from the Mill Creek power plant in Jefferson County, 2015–2017. a, Stratified by Mill
Creek power plant unit. b, Average change in monthly mean ZIP code tabulation area-level coal-fired power plant air pollution exposure (HyADS)
between 2015 and 2016.
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regarding the association between long-term air pollution exposure
and asthma10,48, this presents an area for future research.
Our study focused specifically on the impact of four coal-fired
power plants, but took place against the backdrop of changing emis-
sions at other power plants and reductions in vehicle emissions. We
did not explicitly include air pollutants that might affect asthmat-
ics, for example, SO2, PM2.5, NO2 or ozone, but HyADS does repre-
sent an index of the mixture of pollutants emitted by power plants.
Changes in air quality unrelated to power plants but that trended
in time were controlled for in our models. Although the HyADS
model provided a summary of coal-fired power-plant-specific
exposure, it did so at the monthly level, which limited our ability
to assess the relationship between short-term exposure fluctuations
and SABA use.
In conclusion, this study showed reductions in coal-fired power
plant air pollution exposure after retirements and SO2 control
installations near Louisville. These reductions appear to translate
into substantially fewer asthma-related ERVs and hospitalizations,
as well as fewer average daily SABA uses. Given that 20.4 million
adults, or about 9% of the population, suffered from current asthma
in 201649, the shift in US energy trends away from coal-fired elec-
tricity generation may reduce asthma morbidity below otherwise
expected levels. Future research should evaluate this potential
impact so that plant controls and retirement sites can be phased to
affect neighbourhoods and schools at the highest risk for asthma.
Methods
The natural experiment. The circumstances that support this natural experiment
arose in Jefferson County, which fully contains Louisville, a city that covers
842 km2 and had a population of 600,000 people in 2010. We hypothesized
that (1) coal-fired power plant emissions and subsequent population exposures
dropped after the coal-fired power plant energy transitions and (2) the lower
exposures resulting from energy transitions translated into fewer healthcare
utilization events and symptoms. Our analyses used two health outcomes (acute
asthma-related healthcare utilization and asthma symptoms) at two spatial scales
(ZIP-code and individual level) and employed a measure of coal-fired power plant
emission exposure.
Exposure to power plant emissions. From the USEPA Air Markets Program50,
we downloaded data on power plant facilities (which typically comprise multiple
generating units) nationwide with at least one unit using coal as its primary
fuel between 2008 and 2017. These data included power plant latitude and
longitude, number of units and fuel type by unit, monthly SO2 emissions, SO2
control type and installation date, and retirement date. We obtained further
information on the timing of the Jefferson County coal-fired power plant control
installations, retirements and SO2 emissions from the Louisville Metro Air
Pollution Control District.
We estimated the exposure to power plant emissions over time using the
recently developed HyADS model30. HyADS uses the HYSPLIT air parcel
trajectory model51 to quantify the extent to which any power plant influences air
quality across US ZIP code tabulation areas (ZCTAs), which we mapped to ZIP
codes using a crosswalk (https://www.udsmapper.org/zcta-crosswalk.cfm) (see
Supplementary Note 1 and Henneman et al.30 for details). This method produces
a unitless measure of emissions influence that is highly correlated with related
measured and modelled exposure metrics for coal emissions exposure30,52. HyADS
has previously been applied to estimate US-wide health benefits achieved through
reduced coal-fired power plant SO2 emissions53.
We ran HyADS for each of the over 1,009 US coal-fired power plant units in
operation (located at 478 facilities) nationwide in 2012, 2014 and 2016. In 2012,
we ranked each unit’s exposure contribution in each Louisville ZIP code and
identified the four facilities that had the highest influence on Louisville ZIP codes
(Supplementary Fig. 8). These four facilities subsequently underwent retirement
or control installations before 2017. We ran HyADS for each facility’s units for
years 2012–2017 to estimate the ZIP-code-level monthly (used in individual-
level analyses) and quarterly (as the average of monthly, used in ZIP-code-level
analyses) influence of each unit’s emissions on Louisville air quality over time
(Supplementary Data).
Emissions exposures for the four plants of interest. The four highest-ranked
facilities by HyADS impact in 2012 included Cane Run, a 943 MW plant located
14 km southwest of downtown Louisville; Clifty Creek, a 1,303 MW plant located
about 75 km northeast of Louisville that had the greatest impact of any coal-fired
power plant in the country on east Jefferson County; Mill Creek, a 1,717 MW plant
located 25 km southwest of downtown Louisville that had the greatest impact of
any coal-fired power plant in the country on west Jefferson County and Rockport,
a 2,600 MW plant located about 100 km west of Louisville.
Clifty Creek installed wet limestone scrubbers for units 1–3 on 20 March 2013
and for units 4–6 on 15 May 2013. The Mill Creek plant installed wet limestone
scrubbers for unit 4 on 9 December 2014, for units 1 and 2 on 27 May 2015 and for
unit 3 on 8 June 201654. Cane Run retired its three active units in May 2015, after
which a 650 MW natural gas combined cycle plant began operating at the site.
Rockport installed dry sodium scrubbers on its two coal units in April 2015.
Area-level confounders of asthma symptoms. We assembled several indicators
of community socio-economic status and demographic composition at the ZIP-
code level from the US American Community Survey (ACS)55. These are variables
potentially associated with coal-fired power plant exposure that might also predict
asthma exacerbations56 and included total population, number of non-Hispanic
black individuals, number of individuals without health insurance coverage,
number of individuals 16 years and older that were unemployed, number of
individuals 25 years and older without a high school diploma or equivalent and
number of individuals with income in the previous 12 months below the federal
poverty level. To create a time-varying dataset, we linked multiple 5-yr surveys
because annual ZCTA-level estimates were not available. For example, we used
the 2008–2012 ACS to estimate ZCTA characteristics in 2012 and the 2009–2013
ACS for 2013, which we then assigned to ZIP codes using a crosswalk. Finally, we
downloaded ZIP-code-level meteorological data on ambient temperature, relative
humidity, windspeed and atmospheric pressure from the USEPA57.
Jefferson County quarterly ZIP-code-level asthma data. The Louisville Metro
Department of Public Health and Wellness provided quarterly combined counts
of asthma-related hospitalizations and ERVs among all ages for the years 2012–
2016 for all Louisville ZIP codes, which we restricted to the 35 ZIP codes with
population greater than 5 in 2012. Hospitalizations and ERVs were considered
asthma related if the following International Classification of Diseases 9 or 10
diagnosis codes were present in the first through to the third diagnosis positions:
493.XX or J45.X. To present health data spatially, we used a crosswalk to map ZIP
codes to ZCTAs.
Participant recruitment for individual-level asthma data. Data for the
individual-level analyses came from the AIR Louisville pilot (2012–2014) and
full programme (2015–2017)47. Eligible participants (n = 1,021 from pilot and full
programme) were enrolled from 2012 to 2016 through multiple channels, which
included employer partner wellness programmes, clinics, community events and
social media campaigns. Inclusion criteria consisted of a self-reported diagnosis
of asthma, a current prescription for a compatible inhaled medication for asthma
and a home or work address in Jefferson County. Additional details on the study
are given elsewhere47. Participants all agreed to and signed Propeller Health’s
Terms of Service, which stated that their data may be used in an aggregated
RR = 0.83,
95% CI:
0.69, 1.00
RR = 0.98,
95% CI: 0.95, 1.01
Pre
May 2015
1
2
Monthly average daily SABA use
August 2015
November 2015
February 2016
May 2016
August 2016
November 2016
February 2017
May 2017
Post
Mill Creek
scrubber
installed
Fig. 6 | Monthly average daily SABA use before and after the June 2016
Mill Creek SO2 scrubber installation. Orange represents the period prior to
the scrubber installation and navy blue that after it. Points are the monthly
average daily predicted SABA use from the adjusted model. Relative risks
(RR) are from a conditional Poisson case interrupted time-series model,
which should be interpreted within individual. The model was adjusted for
temperature, humidity, windspeed, ambient pollen (grass, tree and weed),
mould counts and long-term and seasonal trends. Lagged residuals were
used to account for autocorrelation.
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fashion for public-health-oriented analyses. We also received a waiver of consent
and exemption (PRH1-17-508) from the Copernicus Group Independent Review
Board. The protocol was additionally approved by the University of California
Berkeley Committee for Protection of Human Subjects.
We included participants in the analyses if they had inhaler-use data
collected in the year before and the year after the coal-fired power plant energy
transitions. We focused on the 8 June 2016 SO2 scrubber installation for Mill
Creek Unit 3, because this date supported the largest participation cohort for the
individual-level analyses (N = 207, 20.3% of the entire AIR Louisville cohort)
(Supplementary Fig. 9).
Digital health platform. All participants received digital sensors (Propeller Health,
Madison) to attach to their inhaled SABA (that is, ‘rescue’) medications. The sensor
and platform comprised a US Food and Drug Administration-cleared digital health
intervention that combined inhaler sensors, mobile apps, web-based dashboards
and predictive analytics for patients and clinicians47,58,59. The sensor objectively
monitored the use of inhalers, capturing the date, time of day and number of
actuations, and wirelessly transmitted these data back to secure servers through
a smartphone application or hub base station. Actuations that occurred within
2 min were grouped into a single-use event, and therefore each use could represent
multiple actuations. The sensors regularly transmitted medication use data back
to the server (‘sync’) through the smartphone or hub, and also transmitted a
‘heartbeat’ signal, which reported sensor battery life, confirmed no actuations
had occurred since the previous sync and recorded the participant’s GPS (global
positioning system) location from which we assigned HyADS exposure estimates.
Heartbeats occurred approximately every 3 h .
The majority (98%) of participants entered the cohort in 2015 or 2016 with a
mean (standard deviation) duration of follow-up of 602 (321) days from first to last
day under observation. We used the monthly average count of SABA use events per
person per day as our outcome of interest.
Individual meteorological data and plant-specific exposures. Over 85% of
SABA-use events and heartbeats had a recorded location. For those SABA events
without a GPS location (about 6%), we retro-filled the location information with
the most recent recorded location within 24 h before or after the index event. If
no location was available, we assigned the participant’s home address location.
At the time and place of inhaler use or sensor heartbeat, we assigned hourly
meteorological data, which included temperature, wind speed, atmospheric
pressure and relative humidity from the National Oceanic and Atmospheric
Administration for the years 2008–201660. These data were averaged first to daily
and then to monthly means. A specialty clinic, Family Allergy & Asthma, provided
daily counts of grass, weed and tree pollen, as well as of mould spores from their
National Allergy Bureau-certified pollen-counting station in Louisville. We
assigned each SABA use or heartbeat event daily pollen or mould counts based
on the event date, with events that occurred on the same day assigned the same
pollen or mould counts. For analyses, we took the monthly mean of the pollen or
mould count. We assigned monthly HyADS exposures based on the ZIP code in
which the participant spent the most time that month. We linked a measure of
community social vulnerability in 2014 from the US Centers for Disease Control
and Prevention to each individual’s census tract of residence61.
Statistical analysis. We conducted ZIP-code and individual-level analyses to test
the hypothesis that coal-fired power plant energy transitions altered air quality
and thereby asthma morbidity among populations living nearby. In Supplementary
Table 5, we provide a roadmap for all the analyses, which includes questions,
datasets, analyses and locations of results. We performed analyses using R
Statistical Software version 3.5.1 (R Core Team (2018) https://www.R-project.org/).
Individual-level analyses were conducted using the gnm package (https://cran.
rproject.org/package=gnm). All the tests were two-sided.
ZIP-code level HyADS analysis. We first tested whether the energy transitions
were associated with reduced HyADS exposures throughout Louisville. To do so,
we used linear regression to fit the equation:
HyADSpqr ¼ β0 þ β1Transition1pqr þ β2Transition2pqr þ β3Transition3pqr
þβ4Transition4pqr þ λpqr þ Pp þ Qq þ εpqr
ð1Þ
HyADSpqr is the level of HyADS in ZIP code p, quarter q, and year r.
Transition1pqr–Transition4pqr are indicator variables equal to 1 if the energy
transition has occurred and 0 otherwise. For example, Transition1pqr equals 1 after
Q2-2013 and 0 beforehand, Transition2pqr equals 1 after Q4-2014 and 0 beforehand.
Pp and Qq are indicator variables for the ZIP code and quarter of year, respectively.
λpqr is a set of quarterly meteorological variables that include temperature, wind
speed, relative humidity and atmospheric pressure. Coefficients β1–β4 can be
interpreted as the change in HyADS associated with their respective energy
transition; negative values indicate decreased HyADS post-transition.
ZIP-code-level quasi-Poisson asthma analysis. We then used three approaches
to estimate the relationship between energy transitions and ZIP-code-level asthma
hospitalizations and ERV counts. The first was a generalized linear model with a
quasi-Poisson distribution, which accommodates overdispersion, and a log link:
log E Asthmapqr

¼ log totpoppr

þ β0 þ β1Transition1pqr þ β2Transition2pqr
þβ3Transition3pqr þ β4Transition4pqr þ λpqr þ θpr þ Pp þ Qq þ Rr
ð2Þ
where E(Asthmapqr) represents the expected quarterly asthma hospitalization and
ERV counts, totpoppr is the estimated ZIP-code-level population. θpr is a set of
demographic variables from the ACS that includes percentage non-Hispanic black
individuals, percentage individuals living in poverty and percentage unemployed
individuals. Rr is the year indicator variable. We included population as an offset.
ZIP-code-level difference-in-differences asthma analysis. A second analysis
of the natural experiment posed by the Q2-2015 transitions used a quasi-
experimental difference-in-differences design to evaluate how asthma outcomes
differentially changed in ZIP codes defined as either ‘low’ or ‘high’ HyADS
exposure based on the average pre-period (Q2-2014 to Q2-2015) HyADS exposure.
The post-period spanned the year after Q2-2015, so the analysis covered the time
period that surrounded the installation of the scrubbers on units 1 and 2 of the
Mill Creek plant and both units of the Rockport plant, and the retirement of Cane
Run’s three units. Difference-in-differences analysis is commonly used in studies of
natural experiments and can effectively eliminate both observed and unobserved
confounding variables that do not vary in time46,62. We categorized ZIP codes as
‘low’ when their pre-period average HyADS was <32,500 (near the median) and
‘high’ when their pre-period HyADS was ≥32,500, selecting the cutoff based on
the change’s distribution (Supplementary Fig. 10). By estimating the change in
asthma hospitalization and ERVs from the pre- to the post-period (difference 1)
and subtracting off the difference between the exposed and control exposure ZIP
codes (difference 2), we estimated the effect of the Q2-2015 energy transitions in
Jefferson County. We used the parametric equation:
Asthmapqr ¼
β0 þ β1Hpqr þ β2Cpqr þ β3Hpqr ´ Cpqr þ λpqr þ θpr
þPp þ Qq þ Rr þ εpqr
ð3Þ
where Hpqr is an indicator variable equal to one for ZIP codes with pre-period
HyADS ≥ 32,500 (that is, exposed). The group of ZIP codes with pre-period
HyADS <32,500 served as the control group because these ZIP codes were less
exposed in the pre-period and therefore benefitted less from the Q2-2015 energy
transitions, but experienced similar secular trends as the high pre-period HyADS
group. Cpqr is an indicator variable equal to one when the quarter is after Q2-2015.
β3 represents the difference-in-differences estimate of interest. We also specified
a continuous model by comparing ZIP codes with differing levels of pre-period
HyADS rather than creating a cut point. To do so, we replaced the Hpqr indicator
variable with the pre-period continuous HyADS level. We opted to include the
described set of potential confounding variables in equation (3) based on a priori
hypotheses, causal diagrams63 and the correlation structure of potential covariates
(Supplementary Fig. 11). For example, annual unemployment and uninsured status
had a Spearman correlation of 0.84, so we included only unemployment in our
models to avoid instability in the parameter estimation due to multicollinearity.
Difference-in-difference analysis relies on the parallel trends assumption, that is,
in the absence of intervention, trends in the outcome in the pre-period continue62.
We visually inspected trends over time by low and high pre-Q2-2015 HyADS and
found no evidence of a violation (Fig. 4a).
ZIP-code-level first-difference analysis. In a secondary analysis, we used a
first-difference design64 to evaluate the relationship between the pre-post change
in HyADS and pre-post change in ZIP-code-level hospitalization and ERVs
(Supplementary Note 2). This model took the form:
ΔAsthmapr ¼ β0 þ β1ΔHyADSpr þ Δλpr þ Δθpr þ Δεpr
ð4Þ
where ΔAsthmapr represents the differences between counts of ZIP-code-level
asthma hospitalization and ERVs in the year prior to Q2-2015 and the year after
Q2-2015 and ΔHyADSpr represents the ZIP-code-level difference in HyADS
exposure to the three facilities with energy transitions from the year prior to
Q2-2015 to the year after Q2-2015. We took differences in the meteorological and
demographic variables (Δλpr and Δθpr) by subtracting the average value between
Q2-2014 and Q1-2015 from the average value between Q3-2015 and Q2-2016.
This estimator is unbiased when εpr is independent of ΔHyADSpr, Δλpr and Δθpr.
In a separate model, we allowed for non-linearity in the association between
ΔHyADSpr and ΔAsthmapr by specifying quintiles of ΔHyADSpr. In both the
difference-in-differences and first-difference models, we accounted for correlation
within the ZIP codes using Liang and Zeger cluster-robust standard errors65. We
also completed a sensitivity analysis using baseline population weights rather than
adjusting equations (3) or (4) for estimated total population.
Individual-level case time-series analysis. We used a case time-series analysis that
included 207 participants under observation in the year prior and year after the
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June 2016 Mill Creek scrubber installation. The case time-series design is a within-
person analysis that allows for the control of individual-level confounders similar
to other case-only methods such as case-crossover and self-controlled case-series
designs66,67. Unlike the case-crossover design, the case time-series design does not
rely on the assumptions of risk-set sampling68. It maintains the temporal structure
of the time-series format, which allows for modelling of trends over time. It can
also accommodate count data and account for overdispersion and autocorrelation
in counts within stratum (that is, the individual). The conditional Poisson model
assumes no unmeasured confounding that is not homogeneous within the strata.
We adjusted for measured time-varying meteorological variables and seasonal
trends. The model also assumes no residual autocorrelation of counts (to this end
we adjust for the lagged residuals). We also relaxed the assumption of the Poisson
distribution to allow for overdispersion of counts and assessed the relationship
between exposure to coal-fired power plant emissions and monthly average daily
SABA use with the conditional quasi-Poisson model:
log E Inhaler useim
ð
Þ
½
¼ αi þ β0 þ β1T þ β2HyADSim þ λim þ sm þ rim1
ð5Þ
assuming a quasi-Poisson distribution to account for overdispersion. Inhaler_useim
is the average daily count of SABA uses for individual i in month m (conditional
on the total number of SABA uses for individual i), αi is a parameter for individual
level effects (see equation (6) for more detail), T is the number of days elapsed
since May 2015, HyADSim is the total HyADS exposure in participant i’s ZIP code
in month m, λim is a vector of meteorological variables including natural cubic
splines for temperature, relative humidity, wind speed, atmospheric pressure and
mould counts, and linear terms for ambient pollen concentrations (that is, weed,
tree and grass) and sm is a harmonic term with 2 sine–cosine pairs and a period of
12 months to account for seasonal trends. The model adjusts for autocorrelation
by adding the residuals rim–1 for individual i in month m – 1, which were estimated
as the residuals of a model fitted as in equation (5) without the residual term and
lagged by one month66,69. The parameters αi are not estimated by the model, but are
rather conditioned out, by conditioning on the sum of the total number of inhaler
uses for each individual i, P
i
Inhaler useim
I
, which results in a multinomial model
with the likelihood:
Inhaler useimj P
m
Inhaler useim Multinomial πm
ð
Þ
πm ¼ expðβ0þβ1Tþβ2HyADSimþλimþsmÞ
P
jexpðβ0þβ1Tþβ2HyADSijþλijþsjÞ
ð6Þ
where j ∈ the subset of m with observations for each participant i. Next, we sought
to directly test the relationship between the June 2016 Mill Creek energy transition
and SABA use. Unlike the Q2-2015 energy transitions, the June 2016 Mill Creek
unit 3 scrubber installation resulted in fairly uniform reductions in exposure to
plant emissions across Jefferson County (Fig. 4b). This precluded the assembly of
a control group based on exposure change analogous to that in the ZIP-code-level
difference-in-differences analysis. Instead, we implemented an interrupted time-
series design, which construes each person’s study time during the pre-installation
period as a control for his or her time during the post-installation period. To do so,
we modified equation (5) by replacing HyADSim with a single indicator variable,
cntrlim, of exposure to the SO2 control installation at Mill Creek in June 2016, which
took the value of 1 beginning in May 2016 (Mill Creek unit 3 was turned off in May
for the June control installation) and 0 otherwise. We also added an interaction
between cntrlim and T to estimate trends in changing SABA use over time. A
negative coefficient for the cntrlim variable indicates that the average number of
rescue inhaler uses decreased after the scrubber installation. Likewise, a negative β
on cntrlimT indicates a downward linear trend in rescue inhaler use over time after
the installation.
To assess the sensitivity of the individual-level analysis, we evaluated the
association between the June 2016 Mill Creek event and two different binary
specifications of SABA use: any use and high use. We followed a similar model
specification to that in equation (5), but used a logistic regression model and
substituted for the Inhaler useim variable an any-SABA-use variable (monthly
average of ≥1 use day–1 versus <1 use day–1) and a high-SABA-use variable (monthly
average of ≥4 uses day–1 versus <4 uses day–1).
Reporting summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.
Data availability
The ZIP-code-level asthma hospitalization and ERV data are available from the
authors following the submission of an analysis proposal and written approval
granted by the Louisville Metro Public Health and Wellness. The AIR Louisville
monthly medication use data are considered Protected Health Information under
the Health Insurance Portability and Accountability Act of 1996 (HIPAA) in the
United States, and as such may be accessible from the authors for analysis only
after specific written authorization of access following HIPAA guidelines and
Institutional Review Board approval. We provide Jefferson County ZIP-code-
level monthly HyADS estimates on GitHub at https://github.com/joanacasey/
ky_asthma_coal.
Code availability
An R package is available on GitHub for running the HyADS model (https://github.
com/lhenneman/disperseR). We also provide analysis code on GitHub at https://
github.com/joanacasey/ky_asthma_coal.
Received: 11 May 2019; Accepted: 6 March 2020;
Published online: 13 April 2020
References
	1.	 IEA Statistics (International Energy Agency, 2018); http://www.iea.org/
statistics/
	2.	 Massetti, E. et al. Environmental Quality and the US Power Sector: Air Quality,
Water Quality, Land Use and Environmental Justice (Oak Ridge National
Laboratory, 2017); https://www.energy.gov/sites/prod/files/2017/01/f34/
Environment%20Baseline%20Vol.%202–Environmental%20Quality%20
and%20the%20U.S.%20Power%20Sector–Air%20Quality%2C%20Water%20
Quality%2C%20Land%20Use%2C%20and%20Environmental%20Justice.pdf
	3.	 Toxic Air: The Case for Cleaning Up Coal-Fired Power Plants (American Lung
Association, 2011); http://www.lung.org/assets/documents/healthy-air/
toxic-air-report.pdf
	4.	 Zheng, X.-Y et al. Association between air pollutants and asthma emergency
room visits and hospital admissions in time series studies: a systematic review
and meta-analysis. PLoS ONE 10, e0138146 (2015).
	5.	 Orellano, P., Quaranta, N., Reynoso, J., Balbi, B. & Vasquez, J. Effect of outdoor
air pollution on asthma exacerbations in children and adults: systematic review
and multilevel meta-analysis. PLoS ONE 12, e0174050 (2017).
	6.	 Williams, A. M., Phaneuf, D. J., Barrett, M. A. & Su, J. G. Short-term impact
of PM2.5 on contemporaneous asthma medication use: behavior and the value
of pollution reductions. Proc. Natl Acad. Sci. USA 116, 5246–5253 (2019).
	7.	 Integrated Science Assessment for Oxides of Nitrogen-Health Criteria (US
Environmental Protection Agency, 2016); http://ofmpub.epa.gov/eims/
eimscomm.getfile?p_download_id=526855
	8.	 Integrated Review Plan for the Primary National Ambient Air Quality Standard
for Sulfur Dioxide EPA-452/R-14-007 (US Environmental Protection Agency,
2014); https://www3.epa.gov/ttn/naaqs/standards/so2/
data/20141028so2reviewplan.pdf
	9.	 Ramadour, M. et al. Prevalence of asthma and rhinitis in relation to
long-term exposure to gaseous air pollutants. Allergy 55, 1163–1169 (2000).
	10.	Guarnieri, M. & Balmes, J. R. Outdoor air pollution and asthma. Lancet 383,
1581–1592 (2014).
	11.	Deger, L. et al. Active and uncontrolled asthma among children exposed to
air stack emissions of sulphur dioxide from petroleum refineries in Montreal,
Quebec: a cross-sectional study. Can. Respir. J. 19, 97–102 (2012).
	12.	Charpin, D. et al. Respiratory symptoms and air pollution changes in children:
the Gardanne Coal-Basin Study. Arch. Environ. Health 43, 22–27 (1988).
	13.	Schenker, M. B., Speizer, F. E., Samet, J. M., Gruhl, J. & Batterman, S. Health
effects of air pollution due to coal combustion in the Chestnut Ridge Region
of Pennsylvania: results of cross-sectional analysis in adults. Arch. Environ.
Health 38, 325–330 (1983).
	14.	Dubnov, J. et al. Estimating the effect of air pollution from a coal-fired power
station on the development of children’s pulmonary function. Environ. Res.
103, 87–98 (2007).
	15.	Cohen, A. A., Bromberg, S., Buechley, R. W., Heiderscheit, L. T. & Shy, C. M.
Asthma and air pollution from a coal-fueled power plant. Am. J. Public
Health 62, 1181–1188 (1972).
	16.	Smargiassi, A. et al. Risk of asthmatic episodes in children exposed to sulfur
dioxide stack emissions from a refinery point source in Montreal, Canada.
Environ. Health Perspect. 117, 653 (2009).
	17.	Rodriguez-Villamizar, L. A., Rosychuk, R. J., Osornio-Vargas, A., Villeneuve,
P. J. & Rowe, B. H. Proximity to two main sources of industrial outdoor air
pollution and emergency department visits for childhood asthma in
Edmonton, Canada. Can. J. Public Health 108, e523–e529 (2017).
	18.	Middleton, N., Kolokotroni, O., Lamnisos, D., Koutrakis, P. & Yiallouros, P.
K. Prevalence of asthma and respiratory symptoms in 15–17-year-old
Greek-Cypriots by proximity of their community of residence to power
plants: Cyprus 2006–07. Public Health 128, 288–296 (2014).
	19.	Liu, X., Lessner, L. & Carpenter, D. O. Association between residential
proximity to fuel-fired power plants and hospitalization rate for respiratory
diseases. Environ. Health Perspect. 120, 807–810 (2012).
	20.	Form EIA-860 (US Energy Information Administration, 2019); https://www.
eia.gov/electricity/data/eia860/
	21.	Humphreys, D. K., Panter, J., Sahlqvist, S., Goodman, A. & Ogilvie, D.
Changing the environment to improve population health: a framework for
considering exposure in natural experimental studies. J. Epidemiol.
Community Health 70, 941–946 (2016).
	22.	Rich, D. Q. Accountability studies of air pollution and health effects: lessons
learned and recommendations for future natural experiment opportunities.
Environ. Int. 100, 62–78 (2017).
Nature Energy | VOL 5 | May 2020 | 398–408 | www.nature.com/natureenergy
406

Articles
NaTUre Energy
	23.	Zigler, C. M. & Dominici, F. Point: clarifying policy evidence with potential-
outcomes thinking—beyond exposure–response estimation in air pollution
epidemiology. Am. J. Epidemiol. 180, 1133–1140 (2014).
	24.	Pope, C. A. 3rd Respiratory disease associated with community air pollution
and a steel mill, Utah Valley. Am. J. Public Health 79, 623–628 (1989).
	25.	Clancy, L., Goodman, P., Sinclair, H. & Dockery, D. W. Effect of air-pollution
control on death rates in Dublin, Ireland: an intervention study. Lancet 360,
1210–1214 (2002).
	26.	Friedman, M. S., Powell, K. E., Hutwagner, L., Graham, L. M. & Teague, W.
G. Impact of changes in transportation and commuting behaviors during the
1996 Summer Olympic Games in Atlanta on air quality and childhood
asthma. J. Am. Med. Assoc. 285, 897–905 (2001).
	27.	Li, Y., Wang, W., Kan, H., Xu, X. & Chen, B. Air quality and outpatient visits
for asthma in adults during the 2008 Summer Olympic Games in Beijing.
Sci. Total. Environ. 408, 1226–1227 (2010).
	28.	Deschenes, O., Greenstone, M. & Shapiro, J. S. Defensive investments and the
demand for air quality: evidence from the NOx budget program. Am. Econ.
Rev. 107, 2958–2989 (2017).
	29.	National Emissions Inventory (US Environmental Protection Agency, 2011);
https://www.epa.gov/air-emissions-inventories/2011-national-emissions-
inventory-nei-data
	30.	Henneman, L. R. F., Choirat, C., Ivey, C. E., Cummiskey, K. & Zigler, C. M.
Characterizing population exposure to coal emissions sources in the United
States using the HyADS model. Atmos. Environ. 203, 271–280 (2019).
	31.	Henneman, L. R. F., Mickley, L. J. & Zigler, C. M. Air pollution accountability
of energy transitions: the relative importance of wind fields and emissions in
exposure changes. Environ. Res. Lett. 14, 115003 (2019).
	32.	Zein, J. G. et al. Impact of age and sex on outcomes and hospital cost of acute
asthma in the United States, 2011-2012. PLoS ONE 11, e0157301 (2016).
	33.	Patel, M. et al. Metrics of salbutamol use as predictors of future adverse
outcomes in asthma. Clin. Exp. Allergy 43, 1144–1151 (2013).
	34.	Prieto-Parra, L. et al. Air pollution, PM2.5 composition, source factors, and
respiratory symptoms in asthmatic and nonasthmatic children in Santiago,
Chile. Environ. Int 101, 190–200 (2017).
	35.	Schildcrout, J. S. et al. Ambient air pollution and asthma exacerbations in
children: an eight-city analysis. Am. J. Epidemiol. 164, 505–517 (2006).
	36.	U.S. Environmental Protection Agency. Federal Registrar. Primary National
Ambient Air Quality Standard for Sulfur Dioxide, Final Rule, 40 CFR Parts 50,
53, and 58., <https://www.govinfo.gov/content/pkg/FR-2010-06-22/
pdf/2010-13947.pdf> (2010).
	37.	Gowers, A. M. et al. Does outdoor air pollution induce new cases of asthma?
Biological plausibility and evidence; a review. Respirology 17, 887–898 (2012).
	38.	Johns, D. O. & Linn, W. S. A review of controlled human SO2 exposure
studies contributing to the US EPA integrated science assessment for sulfur
oxides. Inhal. Toxicol. 23, 33–43 (2011).
	39.	Li, R. et al. Effect of sulfur dioxide on inflammatory and immune regulation
in asthmatic rats. Chemosphere 112, 296–304 (2014).
	40.	Amster, E. D., Haim, M., Dubnov, J. & Broday, D. M. Contribution of
nitrogen oxide and sulfur dioxide exposure from power plant emissions on
respiratory symptom and disease prevalence. Envrion. Pollut. 186,
20–28 (2014).
	41.	Anenberg, S. C. et al. Estimates of the global burden of ambient PM2.5, ozone,
and NO2 on asthma incidence and emergency room visits. Environ. Health
Perspect. 126, 107004 (2018).
	42.	Achakulwisut, P., Brauer, M., Hystad, P. & Anenberg, S. C. Global, national,
and urban burdens of paediatric asthma incidence attributable to ambient
NO2 pollution: estimates from global datasets. Lancet Planet Health 3,
e166–e178 (2019).
	43.	Dominici, F., Greenstone, M. & Sunstein, C. R. Particulate matter matters.
Science 344, 257–259 (2014).
	44.	Cushing, L., Morello-Frosch, R., Wander, M. & Pastor, M. The haves, the
have-nots, and the health of everyone: the relationship between social inequality
and environmental quality. Annu. Rev. Public Health 36, 193–209 (2015).
	45.	Baltrus, P. et al. Individual and county level predictors of asthma related
emergency department visits among children on Medicaid: a multilevel
approach. J. Asthma 54, 53–61 (2017).
	46.	Craig, P., Katikireddi, S. V., Leyland, A. & Popham, F. Natural experiments:
an overview of methods, approaches, and contributions to public health
intervention research. Annu. Rev. Public Health 38, 39–56 (2017).
	47.	Barrett, M. et al. AIR Louisville: addressing asthma with technology,
crowdsourcing, cross-sector collaboration, and policy. Health Affairs 37,
525–534 (2018).
	48.	Anderson, H. R., Favarato, G. & Atkinson, R. W. Long-term exposure to
outdoor air pollution and the prevalence of asthma: meta-analysis of
multi-community prevalence studies. Air Qual. Atmos. Health 6,
57–68 (2013).
	49.	2016 National Health Interview Survey (NHIS) Data (US Centers for
Disease Control & Prevention, 2016); https://www.cdc.gov/asthma/nhis/2016/
table3-1.htm
	50.	Air Markets Program Data (US Environmental Protection Agency, 2018);
https://ampd.epa.gov/ampd/
	51.	Stein, A. et al. NOAA’s HYSPLIT atmospheric transport and dispersion
modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077 (2015).
	52.	Henneman, L. R. F., Dedoussi, I. C., Casey, J. A., Choirat, C. & Zigler, C. M.
Comparisons of simple and complex methods for quantifying exposure to
point source air pollution emissions. J. Expo. Sci. Environ. Epidemiol.
https://doi.org/10.1038/s41370-020-0219-1 (2020).
	53.	Henneman, L. R. F., Choirat, C. & Zigler, C. M. Accountability assessment of
health improvements in the United States associated with reduced coal
emissions between 2005 and 2012. Epidemiology 30, 477–485 (2019).
	54.	Mill Creek Station Wins 2016 Project of the Year Award (Louisville Gas &
Electric, 2017); https://lge-ku.com/newsroom/articles/2017/01/09/mill-creek-
station-wins-2016-project-year-award
	55.	Manson, S., Schroeder, J., Riper, D. V. & Ruggles, S. IPUMS National
Historical Geographic Information System: Version 13.0 (IPUMS, 2018);
https://doi.org/10.18128/D050.V12.0
	56.	Gottlieb, D. J., Beiser, A. S. & O’Connor, G. T. Poverty, race, and medication
use are correlates of asthma hospitalization rates: a small area analysis in
Boston. Chest 108, 28–35 (1995).
	57.	Air Data Pre-generated Data Files (US Environmental Protection Agency)
https://aqs.epa.gov/aqsweb/airdata/download_files.html
	58.	Merchant, R. K., Inamdar, R. & Quade, R. C. Effectiveness of population
health management using the propeller health asthma platform: a
randomized clinical trial. J. Allergy Clin. Immunol. Pract. 4,
455–463 (2016).
	59.	Van Sickle, D., Magzamen, S., Truelove, S. & Morrison, T. Remote monitoring
of inhaled bronchodilator use and weekly feedback about asthma
management: an open-group, short-term pilot study of the impact on asthma
control. PLoS ONE 8, e55335 (2013).
	60.	National Centers for Environmental Information, Weather and Climate Data
(National Oceanic and Atmospheric Administration, accessed 13 February
2017); https://www.ncdc.noaa.gov/orders/qclcd/
	61.	CDC’s Social Vulnerability Index (SVI) (US Centers for Disease Control &
Prevention, 2016); https://svi.cdc.gov/data-and-tools-download.html
	62.	Angrist, J. D. & Pischke, J.-S. Mostly Harmless Econometrics: An Empiricist’s
Companion (Princeton Univ. Press, 2009).
	63.	Glymour, M. & Greenland, S. in Modern Epidemiology 3rd edn (eds
Rothman, K. J., Greenland, S. & Lash, T. L.) 183–209 (Lippincott Williams
and Wilkins, 2008).
	64.	Pope, C. A. 3rd, Ezzati, M. & Dockery, D. W. Fine-particulate air
pollution and life expectancy in the United States. N. Engl. J. Med. 360,
376–386 (2009).
	65.	Abadie, A., Athey, S., Imbens, G. W. & Wooldridge, J. When Should You
Adjust Standard Errors for Clustering? (NBER, 2017); https://www.nber.org/
papers/w24003
	66.	Armstrong, B. G., Gasparrini, A. & Tobias, A. Conditional Poisson models: a
flexible alternative to conditional logistic case cross-over analysis. BMC Med.
Res. Methodol. 14, 122 (2014).
	67.	Bernal, J. L., Cummins, S. & Gasparrini, A. Interrupted time series regression
for the evaluation of public health interventions: a tutorial. Int. J. Epidemiol.
46, 348–355 (2017).
	68.	Petersen, I., Douglas, I. & Whitaker, H. Self controlled case series methods:
an alternative to standard epidemiological study designs. Br. Med. J. 354,
i4515 (2016).
	69.	Brumback, B. A. et al. Transitional regression models, with application to
environmental time series. J. Am. Stat. Assoc. 95, 16–27 (2000).
Acknowledgements
We acknowledge the network of local partners that made the AIR Louisville program
possible, which include the Center for Healthy Air, Water and Soil, Louisville Metro,
the Community Foundation of Louisville and all the AIR Louisville participants.
Partners within the Louisville Metro Government include G. Fischer, the Office of
Civic Innovation, Louisville Metro Department of Public Health and Wellness, the
Office of Sustainability, the Office of Advanced Planning, the Louisville Jefferson
County Information Consortium and Louisville Forward. Specifically, K. Talley,
M. King and R. Hamilton of the Air Pollution Control District provided critical
information and review of this manuscript. We also thank P. Tarini and O. Wójcik
at the Robert Wood Johnson Foundation for helpful guidance throughout the
project. The main funding for the project was provided by the Robert Wood Johnson
Foundation. Support was also provided by the Foundation for a Healthy Kentucky,
Norton Healthcare Foundation, Owsley Brown Charitable Foundation, the American
Lung Association, the National Institute of Environmental Health Sciences
(J.A.C., K99/R00 ES027023; A.M.N, K99/R00 ES027511; C.Z., R01 ES026217)
and the USEPA (C.Z., EPA 83587201). The contents of this work are solely the
responsibility of the grantee and do not necessarily represent the official views
of the USEPA or the Robert Wood Johnson Foundation. Further, the USEPA
does not endorse the purchase of any commercial products or services mentioned
in the publication.
Nature Energy | VOL 5 | May 2020 | 398–408 | www.nature.com/natureenergy
407

Articles
NaTUre Energy
Author contributions
J.A.C., T.S. and M.A.B. secured funding for the study. J.A.C., J.G.S., L.R.F.H., C.Z.,
A.M.N., R.C., S.S.M., Y.-T.C., J.S. and M.A.B. designed the study and provided exposure
and outcome data. J.A.C. and A.M.N. carried out the statistical analyses. J.A.C., J.G.S.,
L.R.F.H., C.Z., A.M.N., R.C., R.G., Y.-T.C., L.K., S.S.M., V.C., G.S., T.S., J.S. and M.A.B.
reviewed and critically revised the manuscript.
Competing interests
M.A.B., R.G. and L.K. are salaried employees of Propeller Health and J.G.S. receives
limited funding from Propeller Health to conduct analyses.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41560-020-0600-2.
Correspondence and requests for materials should be addressed to J.A.C.
Reprints and permissions information is available at www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2020
Nature Energy | VOL 5 | May 2020 | 398–408 | www.nature.com/natureenergy
408

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Corresponding author(s):
Joan A. Casey
Last updated by author(s): Feb 12, 2020
Reporting Summary
Nature Research wishes to improve the reproducibility of the work that we publish. This form provides structure for consistency and transparency
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Statistics
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Our web collection on statistics for biologists contains articles on many of the points above.
Software and code
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Data collection
Asthma data were provided by the Louisville Metro Public Health and Wellness and Propeller Health.
Inputs for the HyADS model were downloaded from US Environmental Protection Agency's Air Markets Program Database.
An R package is available on Github for running the HyADS model (https://github.com/lhenneman/hyspdisp).
Data analysis
We used R statistical software and provide code for the main analyses on GitHub at https://github.com/joanacasey/ky_asthma_coal.
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The ZIP code-level asthma hospitalization/ED visit data are available following approval by the Louisville Metro Public Health and Wellness from the authors.
The AIR Louisville monthly medication use data are considered Protected Health Information (PHI) under the Health Insurance Portability and Accountability Act of
1996 (HIPAA) in the U.S., and as such may be accessible from the authors for analysis only after specific written authorization of access following HIPAA guidelines
and IRB approval.
We provide Jefferson County ZIP code-level monthly HyADS estimates on GitHub at https://github.com/joanacasey/ky_asthma_coal.
Other environmental data are publicly-available for download or available from the authors upon request.

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Field-specific reporting
Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.
Life sciences
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 Ecological, evolutionary & environmental sciences
For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf
Behavioural & social sciences study design
All studies must disclose on these points even when the disclosure is negative.
Study description
This study uses two types of data: ZIP code level asthma hospitalization/emergency department quarterly counts from 2012-2016 and
individual-level daily rescue inhaler use (aggregated to monthly averages) data from the AIR Louisville Cohort.
Data are longitudinal and quantitative.
Research sample
ZIP code-level data: The Louisville Metro Department of Public Health and Wellness provided quarterly ZIP code-level combined counts
of asthma-related hospitalizations and ED visits for the years 2012–2016. Hospitalizations and ED visits were considered asthma-related if
the following International Classification of Diseases 9 (ICD-9) or ICD-10 diagnosis codes were present in diagnosis positions 1–3: 493.XX
or J45.X. The ZIP code-level data includes all asthma-related hospitalizations and ED visits in Jefferson county over the study period. The
median (IQR) ZIP population in 2012 was 21,500 people (13727, 30352).

Individual-level data: Our analysis included 207 participants. The average age was 44.7 years (SD = 17.6) and 67% of participants were
women. We used this study sample because these individuals were under observation during the year before and after the June 2016
Mill Creek sulfur dioxide scrubber installation. Participants were enrolled from 2012 to 2016 through a number of channels, including
employer partner wellness programs, clinics, community events, and social media campaigns. Inclusion criteria consisted of a self-
reported diagnosis of asthma, a current prescription for a compatible inhaled medication for asthma, and living or working in Jefferson
County, Kentucky. No incentives were provided for participation, but participants could benefit from no-cost use of the digital health
intervention and contribute to a community program. The full AIR Louisville cohort contained 1039 individuals, mean age 34.9 years (SD
= 17.6) and also primarily female (n = 659 [63%]). Therefore, our subsample was slightly older but with a similar sex-distribution
compared to the full cohort.
Sampling strategy
ZIP code-level: we obtained all hospitalization/ED visits during the study period.

Individual-level: we used the portion of the existing AIR Louisville cohort that had data overlapping the relevant time period (n = 207).
The original cohort (n = 1039 participants) made efforts to achieve geographic, socioeconomic and demographic representation similar
to that of Jefferson County.
Data collection
ZIP code-level: The Louisville Metro Department of Public Health and Wellness provided the asthma data collected from local hospitals
based on diagnostic codes.

Individual-level: Eligible participants were enrolled in the AIR Louisville pilot program from June 2012–December 2016. Basic
demographic information (e.g., age, sex) was collected. All participants received digital sensors (Propeller Health, Madison, WI) to attach
to their inhaled medications for asthma, including short-acting beta agonist (SABA, i.e., “rescue”) medications. The sensor and platform
comprised a U.S. Food and Drug Administration-cleared digital health intervention that combined inhaler sensors, mobile apps, web-
based dashboards and predictive analytics for patients and clinicians. The sensor monitored the use of inhalers, capturing the date, time
of day, and number of actuations, and wirelessly transmitted these data back to secure servers through a smartphone application or hub
base station. Actuations occurring within 2 minutes were grouped into a single use event, therefore each use could represent multiple
actuations. The sensors regularly transmitted medication use data back to the server (“sync”) through the smartphone or hub, and also
transmitted a “heartbeat” signal, which reported sensor battery life and confirmed no actuations had occurred since the last sync.
Heartbeats occurred approximately every 3 hours. Over 85% of SABA use events and heartbeats had a recorded location. For those SABA
events missing a GPS location (about 6%), we retro-filled location information with the most recent recorded location within 24 hours
before or after the index event. If no location was available, we assigned the participant’s home address location. Researchers involved in
this data collection were not aware of the current study question/hypotheses.
Timing
ZIP code-level: collection of hospitalization/ED visit data spanned January 1, 2012 to December 31, 2016.

Individual-level: Eligible participants were enrolled in the AIR Louisville pilot program from June 2012–December 2016. For the present
analysis, we only included individuals (n = 207 [20% of the original cohort]) that had observations in the year prior (June 2015-June 2015)
and year following (June 2016-June 2017) the June 2016 Mill Creek scrubber installation.
Data exclusions
ZIP code-level: no data were excluded.

Individual-level: we excluded participants from the AIR Louisville cohort who did not have observations in the year before and after the
June 2016 Mill Creek scrubber installation. This was determined a priori.
Non-participation
The AIR Louisville cohort used open enrollment between June 2012-December 2016. For the overall cohort, the median follow-up was
436 days (IQR: 188-628). In our subset (n = 207), median follow-up was 549 days (409-740). 81% of our subset had follow-up through the
last quarter of 2016. Our within-person analysis should help prevent extrapolation beyond the data (since some individuals were lost to
follow-up prior to Q4-2016).

3
nature research  |  reporting summary
October 2018
Randomization
Participants were not randomized.
Reporting for specific materials, systems and methods
We require information from authors about some types of materials, experimental systems and methods used in many studies. Here, indicate whether each material,
system or method listed is relevant to your study. If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.
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Animals and other organisms
Human research participants
Clinical data
Methods
n/a Involved in the study
ChIP-seq
Flow cytometry
MRI-based neuroimaging
Human research participants
Policy information about studies involving human research participants
Population characteristics
See above.
Recruitment
ZIP code-level: no recruitment

Individual-level: Eligible participants were enrolled in the AIR Louisville pilot program from 2012–2016 through multiple
channels, including employer partner wellness programs, clinics, community events, and social media campaigns. Inclusion
criteria consisted of a self-reported diagnosis of asthma, a current prescription for a compatible inhaled medication for asthma,
and a home or work address in Jefferson County, Kentucky. No incentives were provided for participation, but participants could
benefit from no-cost use of the digital health intervention and contribute to a community program. All participants agreed to
and signed Propeller Health’s Terms of Service, which clearly states that their data may be used in an aggregated fashion for
public health-oriented analyses. We also received a waiver of consent from the Copernicus Review Board.

An attempt was made via recruitment to achieve geographic, socioeconomic and demographic representation similar to that of
Jefferson County. Participants who enrolled via social media campaigns may have been self-selecting as more interested in
technology than other enrollees.
Ethics oversight
The protocol was approved by the Copernicus Independent Review Board and the UC Berkeley Committee for Protection of
Human Subjects.
Note that full information on the approval of the study protocol must also be provided in the manuscript.

    Scientific Research Findings:

Answer: Our study took advantage of a natural experiment that occurred between 2013 and 2016, when four coal-fired power plants in the Louisville, Kentucky area retired, transitioned to natural gas or installed SO2emissions controls. We found that these coal-fired power plant changes translated into reduced asthma exacerbations among the local community. Among ZIP codes more exposed to coal-fired power plant emissions, there were three fewer hospitalizations or emergency department visits per ZIP code per quarter in the year following a major transition, which translates into nearly 400 avoided hospitalizations and ER visits each year across Jefferson County, where Louisville is located. At the level of individuals, a 2016 emissions control installation was associated with a 17% reduction in overall counts of rescue inhaler use in the following month, and a 32% reduction in the odds of having high rescue inhaler use (defined as 4 puffs per day on average per month).