Coccidioidomycosis, or valley fever, is caused by inhalation
of spores from Coccidioides immitis and Coccidioides
posadasii. These dimorphic soil fungi are endemic to the
deserts of the southwestern United States, Mexico, and elsewhere
in Central and South America (Fisher et al. 2002; Kolivras
et al. 2001). Although approximately 60% of people infected
with the disease are asymptomatic, others experience mild influenza-like
symptoms, and a small percentage experience severe effects
and sometimes death resulting from dissemination of the disease
to other parts of the body (Kolivras et al. 2001). Those at
greatest risk for coccidioidomycosis infection include immunocompromised
patients, young children, the elderly, and members of several
ethnic minorities in the United States (Kolivras et al. 2001;
Pappagianis 1988). In Arizona alone, > 2,000 cases per year
have been reported (Komatsu et al. 2003), and the incidence
of coccidioidomycosis is greater than that for other emerging
infectious diseases in the region such as West Nile virus [Centers
for Disease Control and Prevention (CDC) 2004a]. The number
of Arizona cases is likely to exceed 3,000 by the end of 2004
(CDC 2004b).
Environmental conditions appear to have an important impact
on coccidioidomycosis incidence. The current Arizona coccidioidomycosis
epidemic has been linked to climate conditions (Kolivras and
Comrie 2003; Komatsu et al. 2003; Park et al. 2005), whereas
California experienced an epidemic in the 1990s that was possibly
linked to drought conditions (Jinadu 1995). Initial links between
climate conditions and the disease were identified several
decades ago (Hugenholtz 1957; Maddy 1965). It is only recently
that further details on climate and coccidioidomycosis have
been published (Kolivras and Comrie 2003; Komatsu et al. 2003).
These studies identified associations linking climate and other
factors to seasonal patterns of coccidioidomycosis and to interannual
variability and trends in the disease. Significant variables
included drought indices, lagged precipitation, temperature,
wind speed, and dust during the preceding 1 or more years.
The relationships to coccidioidomycosis were quite complex,
however, perhaps because of disease data issues outlined below.
In this article I aim to identify simple and robust relationships
linking climatic controls to seasonal timing and outbreaks
of the disease, which until now have remained elusive and poorly
understood. Important public health opportunities exist if
environmental factors controlling coccidioidomycosis outbreaks
and trends can be better comprehended, including the timing
and degree of mitigation efforts such as soil treatment and
the development of an advance warning system for public health
management.
Part of the reason for the current state of knowledge has
been the lack of high-
quality disease data series. In fact, a major challenge to understanding more
about the links between climate and infectious disease continues to be the
difficulty in obtaining regular time series of disease data (National Research
Council 2001). This is especially true for coccidioidomycosis with respect
to data on Coccidioides in the soil or atmosphere. The current
environmental detection method using laboratory mice is expensive and time-consuming,
and although there is ongoing research into more rapid detection techniques
(e.g., using polymerase chain reaction analysis to detect the fungus in soil
samples), it will be several years before time series of such data become available.
In the absence of suitable data on the environmental variability of the fungus
itself, there is a need to exploit epidemiologic data in different ways to
better identify the role of environmental controlling factors such as climate.
Thus, for now, disease incidence data offer the best (and only) available multiyear
time series for comparison with climatic conditions.
The use of human disease data to study potential relationships
to climate conditions introduces numerous methodologic and
analytical issues related to collection and reporting. Incidence
data do not provide a homogeneous time series because of changes
in reporting requirements, changes in population demographics,
and the introduction of new diagnostic tests. In addition,
the reported data necessarily contain imprecise estimations
of disease onset dates because of various factors including
patient recall, incorrect or delayed diagnoses caused by displacement
of diagnoses during the respiratory disease season, and the
variability in disease incubation and onset of symptoms from
case to case.
If these data issues can be dealt with at least partially,
the research challenge in using human incidence data is to
understand the second- or third-order connections between the
soil fungus and reported cases of the disease. There are essentially
two hypothesized parts to the role of climate (Kolivras and
Comrie 2003) that need to be evaluated. First, existing Coccidioides spores
present in dry soil need increased soil moisture (via precipitation)
to grow the fungus, followed by a dry period during which fungal
hyphae desiccate and form spores. Second, wind or other disturbance
is required to disperse the spores for inhalation by a host.
The relative roles of these climate factors in the seasonality
and outbreaks of coccidioidomycosis are not clearly understood.
My principal goals in this article are therefore to analyze
the postulated climate and dust relationships to fungal growth
and dispersion and evaluate their respective roles.
Two subquestions are also considered. First, southern Arizona
has a bimodal annual precipitation pattern with one peak in
summer and one in winter (Sheppard et al. 2002), but county-level
coccidioidomycosis reports in the past have not clearly reflected
an associated bimodal coccidioidomycosis pattern (Kolivras
and Comrie 2003). Yet early work and a study using student
health service data have noted such a pattern (Hugenholtz 1957;
Kerrick et al. 1985). Thus, in this article I examine whether
recent county-level reports can shed light on the existence
of a bimodal incidence pattern in reported data. Second, in
evaluating climatic controls on coccidioidomycosis, the critical
date is the date of exposure (spore inhalation) rather than
the case report date. A method is required that incorporates
this lag as well as the changes in coccidioidomycosis reporting
characteristics over time. This article presents such an adaptive
data-oriented method for estimating date of exposure.
Tucson and the surrounding areas of Pima County in Arizona
are highly endemic for coccidioidomycosis (Kolivras et al.
2001). Pima County coccidioidomycosis case data were obtained
from the Arizona Department of Health Services (Phoenix, Arizona)
for the period 1992-2003. Reporting was voluntary at the beginning
of this period (Ampel et al. 1998), although the data continuity
and quality are good relative to previous decades (Kolivras
and Comrie 2003). The disease became nationally notifiable
in 1995 and reporting by laboratories became mandatory at the
state level in 1997 (Komatsu et al. 2003). Although the number
of reported cases initially appeared to increase as a result,
this effect appears to have been minor because incidence continued
to grow in an ongoing epidemic (Komatsu et al. 2003).
Pima County annual mid-year population data were obtained
from the U.S. Census Bureau (2004). Environmental data were
obtained for the greater Tucson urban area, which contains > 90%
of the county population. Both precipitation and dust are good
potential predictors of coccidioidomycosis (Kolivras and Comrie
2003; Komatsu et al. 2003). Monthly precipitation data for
all five available sites in the Tucson area were obtained from
the Western Regional Climate Center (2004) for 1988-2003. In
conjunction with the incidence data, the precipitation data
enable evaluation of hypothesized soil-moisture-fungal-growth
relationships. Ambient concentrations of atmospheric particulate
matter with a diameter < 10 µm (PM10) were
obtained from the Pima County Department of Environmental Quality
(2004) for the five stations with data from 1991-2003. The
PM10 data are a direct measure of airborne dust,
and because this size threshold includes the typical spore
size, these data should be proportionally related to the hypothesized
windblown spore concentrations. Precipitation and PM10 values
were averaged across sites to provide a single time series
of the areawide mean for each.
With regard to analyzing the hypothesized climatic controls
on coccidioidomycosis, the most relevant information to extract
from the incidence data is the date that each patient most
likely inhaled the fungal spore (i.e., exposure date). The
coccidioidomycosis incidence data include three possibly useful
dates to approximate exposure date: estimated date of onset
of symptoms (“onset date”), diagnosis date, and
report date (although many cases do not have all three dates
recorded). Onset date is potentially the most useful of the
three, but it is only available for about one-third of the
cases, and that proportion varies considerably over time. Ideally,
the onset date accounts for some of the variable lag between
exposure and reporting; although it is imprecise, it is likely
the most accurate index of exposure date. Conversely, the diagnosis
date is more precise but the exposure-to-diagnosis lag, which
varies from case to case and is longer than the exposure-to-onset
lag, has to be estimated in some way. Diagnosis dates are available
for most cases. Report dates are, de facto, available for all
cases, but they are the most lagged in time from the exposure
date; exposure-to-report lags therefore display the greatest
variability and are least likely to provide useful links to
climate.
Exploration of the various lags and dates indicated no consistent
bias or pattern that could be satisfactorily corrected via
simple adjustments, such as an overall mean onset-to-diagnosis
delay. Instead, the mean onset-to-diagnosis and onset-to-report
lag times were calculated for each individual month in the
record (rather than averaged across the entire time series).
These temporally adaptive empirical lags were smoothed with
a 3-month moving average, centered on the middle month, and
then used to estimate exposure dates. For cases with an onset
date, the exposure date was estimated to be 14 days earlier
to allow for the incubation period (Kolivras and Comrie 2003);
for cases without an onset date but with a diagnosis date,
the exposure date was estimated to occur earlier by the number
of days for that month-specific onset-to-diagnosis lag plus
14 days; for cases with only a report date, the exposure date
was estimated to occur earlier by the number of days for that
month-specific onset-to-report lag plus 14 days. For example,
a case reported on 24 November 2003 might have a diagnosis
date of 24 July 2003 and no onset date. Based on the mean of
other reports with onset dates in November 2003 (actually the
October through December 2003 mean), the onset-diagnosis lag
is 10 days, so this case would be estimated to have had an
onset date of July 14, and thus an estimated exposure 14 days
before, on 30 June.
There were 3,283 cases in the data set; 3,181 of these had
diagnosis dates, but only 1,089 had onset dates. The proportion
of the latter each month and the length of lag for either varied
inconsistently over time, necessitating this set of temporally
adaptive adjustments. Onset-diagnosis lags had a mean of 12.6,
a median of 11.5, a standard deviation of 5.9, a minimum of
2, and a maximum of 32 days; onset-report lags had respective
values of 43.0, 44.0, 19.1, 8, and 99 days. Monthly case totals
based on estimated exposure were computed and converted to
incidence rates per 100,000 of population using linearly interpolated
monthly population estimates.
To analyze the lagged relationships and the relative climatologic
significance of different times of year, the data were grouped
into seasons. Seasonal analyses are advantageous for several
reasons: a) they are a useful way of dividing the year
into alternating wet and dry periods, b) they facilitate
identification of recurring times of the year that are important, c)
seasonal aggregation avoids the monthly variability that characterizes
the region and leads to overly complex analyses, and d)
it is analytically and conceptually simpler to compute and
understand seasonal lag relationships. In the southwestern
United States, seasons are defined principally by precipitation
rather than the thermally based spring, summer, fall, and winter
sequence typical of middle-latitude locations (Sheppard et
al. 2002). Seasonal groupings are widely used for similar kinds
of climate analyses (Crimmins and Comrie 2004). Seasons were
defined by monthly sequences that captured the predominant
seasonal maxima and minima for each variable.
Stepwise regression of the 1992-2003 seasonal data was used
to model coccidioidomycosis rates from concurrent PM10 (hypothetically
related to spore dispersion and therefore exposure) and concurrent
and lagged antecedent precipitation (hypothetically related
to fungal growth). Previous work has shown that the relevant
climate conditions may be different for each coccidioidomycosis
season (Kolivras and Comrie 2003), and therefore each season
was modeled separately. Models were cross-validated on independent
data points using a leave-one-out jackknife method. Because
coccidioidomycosis reporting before 1997 may not have been
consistent, the same modeling analysis was run on a subset
of the data covering just the improved reporting period from
1997 through 2003 for confirmatory purposes.
Application of the estimated exposure date methodology resulted
in a time series of coccidioidomycosis incidence, as defined
above. An annual plot shows the epidemic in recent years, which
coincides with an ongoing regional drought as well as variability
in PM
10 (Figure 1). The 2003 decrease may end up
being less pronounced after some reports recorded later in
2004 (unavailable at the time these study data were acquired)
are estimated to have been 2003 exposures. Analysis of similar
data for the Phoenix area attributed the increase in coccidioidomycosis
to climate-related factors (Komatsu et al. 2003).
Average monthly coccidioidomycosis rates based on estimated
exposure dates display obvious seasonal behavior (Figure 2),
but with greater clarity than in previous studies. A bimodal
pattern with peaks in June-July and October-November is apparent,
along with relatively lower incidence in August-September and
February-March. PM10 concentrations follow an inverse
relationship with soil moisture, falling during wet periods
and rising during dry periods (Figure 2). Monthly coccidioidomycosis
rates are largely consistent with the hypothesis of increased
dust exposure leading to increased disease incidence. On the
average at least, the less dusty months of the year coincide
with lower coccidioidomycosis exposure rates, and elevated
rates coincide with or follow the dustier months. Although
it is tempting to draw a similar first-order inverse connection
between precipitation and incidence at the overall mean monthly
level, it is important to recall that this is likely valid
for the immediate dust-inhibiting role of rainfall (precipitation
has a strong negative correlation with dust) but not likely
for its antecedent fungal growth and desiccation role. Thus,
although a wet-dry precipitation sequence occurs during the
several months before each of the annual coccidioidomycosis
peaks on average, closer examination shows that the amount
of precipitation and the matching responses as well as the
time lags for each are inconsistent. This underlines the importance
of investigating the role of antecedent moisture at time scales
longer than a season or year.
The monthly averages presented in Figure 2 enabled the definition
of seasonal groupings centered on the periods of maxima and
minima. Coccidioidomycosis seasons for estimated exposure dates
consist of a winter decrease that occurs January through April,
a foresummer peak that is seen May through July, a monsoon
decrease that takes place in August and September, and a fall
peak that is experienced October through December. The same
seasons were used for monthly PM10 concentrations
because they had similar periods of maxima and minima, and
because they needed to match the coccidioidomycosis seasons
for analysis. For precipitation, the winter peak occurs between
December and March, followed by the driest time of the year
during the arid foresummer from April through June. The monsoon
is the most distinctive aspect of the region’s climate,
bringing rainfall during July, August, and September, after
which conditions become dryer in a brief fall during October
and November (Crimmins and Comrie 2004). Because precipitation
is hypothesized to affect fungal growth months or years before
the exposure date, it is not necessary to have precipitation
seasons exactly match the monthly groupings for the other variables.
Thus, for example, it is more meaningful to use July through
September for monsoon precipitation and relate that seasonal
peak to coccidioidomycosis in subsequent seasons. For simplicity,
the names of the seasons are kept the same across all variables.
Adjusted R2 values for the four seasonal
models and standardized (β)
coefficients for the variables found to be significant in each
model are shown in Table 1. All four models explained significantly
high to very high proportions of the variance in coccidioidomycosis
rates. It is notable that the strongest relationships do not
occur simply in a wet-dry sequence in the season immediately
before a rise in coccidioidomycosis rates. A remarkable result
is the positive role of precipitation during the arid foresummer
for coccidioidomycosis occurring in all subsequent seasons
up to 2 years later. One implication is that precipitation
during this hottest and driest part of the year (April through
June), as opposed to other wetter seasons, is most favorable
for Coccidioides growth in the environment. This is
typically a time of soil desiccation and vegetation dormancy,
so the ability to grow opportunistically in the foresummer
may be a competitive advantage of Coccidioides over
other soil biota. A second implication is that fungal spores
produced after a wet period in the foresummer may accumulate
in the soil and remain viable for several years. Consistent
with this hypothesis, monsoonal precipitation does not appear
in any model within a 3-year lag, and in only one at 4 years.
Ambient dust levels, as an index of potential spore dispersion,
are positively associated with concurrent coccidioidomycosis
rates in winter and the foresummer. Dust is not a useful predictor
of coccidioidomycosis rates during the monsoon or the fall.
Yet wetter conditions in fall appear to decrease concurrent
coccidioidomycosis rates and in the winter immediately after,
presumably via dispersion inhibition due to greater soil moisture.
The analysis was repeated on the more reliable 1997-2003
data period to check for consistency. This step reduced the
modeled n from 12 to 7, which decreased statistical
reliability, and therefore detailed results are not shown.
Nonetheless, although the full set of significant variables
differed for each model, the results from the shorter period
showed some similarities with the longer period. Those variables
that were significant in both the full-period and the later-period
models are noted by asterisks in Table 1. Both sets of models
have in common the foresummer precipitation 1 or 2 years before
the predicted coccidioidomycosis season, as well as concurrent
fall precipitation for fall coccidioidomycosis incidence.
The overall time series of observed and predicted seasonal
coccidioidomycosis incidence (for the full period) is shown
in Figure 3. The combined predictions of all four multivariate
seasonal models are in close agreement with observations, with
an overall cross-validated R2 of 0.80, and
a cross-validated mean absolute error of 0.53 cases per 100,000,
or about 19% of the mean incidence. The proportions of model-oriented
(systematic) error versus data-oriented (unsystematic) error
were 14 and 86%, respectively (Comrie 1997), implying that
the model is well specified and that noisy data are responsible
for most of the error. To further isolate the role of the foresummer,
antecedent foresummer precipitation alone was regressed on
coccidioidomycosis incidence in fall, winter, foresummer, and
the monsoon in the relevant period 1.5-2 years later. The resulting
cross-validated R2 between observations and
combined predictions of all four antecedent foresummer-based
models was 0.27.
The development of a method to estimate Coccidioides spore
exposure date from coccidioidomycosis incidence data has enabled
the production of a relatively homogeneous time series. This
approach reveals a strong bimodal seasonality of the disease
in Pima County, Arizona, consistent with earlier findings based
on other data (Hugenholtz 1957; Kerrick et al. 1985), a pattern
that until now was not clearly seen in the regular reported
data. On average, peaks in exposure to the fungal spores occur
in June-July and in October-November, consistent with the drier
and dustier months of the year. Fewer exposures occur in February-March
and August-September, consistent with the timing of the wetter
and less dusty months.
Multivariate models of the incidence data series indicate
that concurrent dispersion conditions are important during
fall (via precipitation) and in winter and the arid foresummer
(via PM10). However, the most striking result of
this study is the dominant role of precipitation during the
normally arid foresummer 1.5-2 years before the season of exposure.
Even when considered alone, April-June precipitation accounts
for more than one-quarter of the overall variance in subsequent
seasonal coccidioidomycosis incidence. When other antecedent
and concurrent seasonal conditions are included as predictors,
the combined seasonal models explain a significant and large
proportion of the variance in coccidioidomycosis incidence.
The model is relatively simple in structure compared with other
studies (Kolivras and Comrie 2003; Komatsu et al. 2003). The
model uses only lagged seasonal precipitation and concurrent
seasonal dust and precipitation, yet it clearly captures both
the seasonality and the trends in the incidence data. The bulk
of the error is associated with noise in the data, so future
improvements to the model are likely to result from improved
data and a longer length of record with a larger model n.
An improved understanding of the climatic factors behind
outbreaks of coccidioidomycosis will enable better timing of
environmental sampling for Coccidioides and any related
mitigation efforts, separation of environmental factors from
population and other factors affecting outbreaks, and the potential
for development of an advance warning system before an outbreak.
The results of this work provide strong support for the two
hypothesized relationships between climate and coccidioidomycosis,
namely, fungal growth in the longer term and spore dispersion
and exposure in the short term. Furthermore, the relative simplicity
and strength of these results relative to earlier studies (Kolivras
and Comrie 2003; Komatsu et al. 2003) lend considerable confidence
to the potential for the development of an operational disease
forecast model. The ability to define a critical event, such
as precipitation during the foresummer, might enable mitigation
procedures immediately after the event as well as provide a
useful public health tool with an 18-month lead time on expected
incidence of coccidioidomycosis. Future work will need to evaluate
how specific these findings are to southern Arizona versus
other regions in which C. posadasii is also endemic,
and whether similar relationships also apply to C. immitis in
California. It will also be valuable to test how a more complex
model (Komatsu et al. 2003) and this simpler model compare
against data from other locations and over time.