Lyme the First Epidemic of Climate Change Reviews
Can J Infect Dis Med Microbiol. 2018; 2018: 5719081.
"Ticking Bomb": The Impact of Climate change on the Incidence of Lyme Disease
Igor Dumic
1Mayo Dispensary College of Medicine and Scientific discipline, Rochester, MN, USA
iiPartition of Hospital Medicine, Mayo Clinic Wellness System, Eau Claire, WI, United states
Edson Severnini
3Carnegie Mellon University, Heinz College, 4800 Forbes Ave., Pittsburgh, PA, The states
Received 2018 Jul 12; Accustomed 2018 Sep 16.
Abstract
Lyme illness (LD) is the most mutual tick-borne disease in North America. Information technology is caused by Borrelia burgdorferi and transmitted to humans by blacklegged ticks, Ixodes scapularis. The life cycle of the LD vector, I. scapularis, usually takes two to three years to complete and goes through three stages, all of which are dependent on environmental factors. Increases in daily average temperatures, a manifestation of climate modify, might have contributed to an increase in tick abundance via higher rates of tick survival. Additionally, these environmental changes might take contributed to better host availability, which is necessary for tick feeding and life cycle completion. In fact, information technology has been shown that both tick activeness and survival depend on temperature and humidity. In this study, we have examined the human relationship between those climatic variables and the reported incidence of LD in 15 states that contribute to more than than 95% of reported cases within the Unites States. Using fixed effects assay for a console of 468 U.Southward. counties from those high-incidence states with annual data bachelor for the period 2000–2016, we accept found sizable impacts of temperature on the incidence of LD. Those impacts tin exist described approximately by an inverted U-shaped relationship, consistent with patterns of tick survival and host-seeking behavior. Assuming a 2°C increase in almanac boilerplate temperature—in line with mid-century (2036–2065) projections from the latest U.Southward. National Climate Assessment (NCA4)—we have predicted that the number of LD cases in the United states of america will increment by over 20 percent in the coming decades. These findings may help improving preparedness and response by clinicians, public health professionals, and policy makers, also as raising public awareness of the importance of beingness cautious when engaging in outdoor activities.
i. Introduction
Lyme disease (LD) is the near common reportable vector-borne zoonosis in the United States, and its incidence has sharply increased over the terminal decade. The causative pathogen, spirochete B. burgdorferi, is transmitted to humans by a tick vector. The primary vector of LD is I. scapularis in the northeastern and midwestern Unites States, and Ixodes pacificus in the Pacific Northwest [1].
The kickoff evidence of Lyme disease dates back to 1883, when a German physician described acrodermatitis chronica atrophycans, which was after recognized every bit the late dermatological manifestation of LD [two, 3]. After on, other seemingly unassociated manifestations were reported such as erythema chronicum migrans in 1913 by Lipschutz [4]. In 1930 Hellstrom associated neurological symptoms with dermatologic manifestations of the affliction [five]. Nevertheless, it was non until 1976, when an outbreak of juvenile arthritis and peel rash occurred in Connecticut'south city of Lyme, that LD was described [six]. Several years later on, in 1982, the American entomologist Willy Burgdorfer described the causative agent of LD, a spirochete, named afterwards him B. burgdorferi [7].
If not treated, LD progresses through 3 stages. The first phase—early on localized disease—manifests by erythema migrans, which is an erythematous macule or papule that occurs i to two weeks following the tick bite and subsequently enlarges [one]. This rash tin can be uniformly erythematous or might have central clearing ("bull's centre") with a median diameter around fifteen cm [viii]. Left untreated, B. burgdorferi disseminates from the site of the bite, and the disease progresses to the early disseminated stage. In this phase, which occurs three to v weeks post-obit the initial bite, multiple (secondary) erythema migrans occur. These lesions tend to be similar to the main erythema migrans merely are unremarkably smaller [8]. Cardiac and neurologic manifestations are also seen in the early disseminated stage, with atrioventricular eye block beingness the virtually common cardiac manifestation. Peripheral nervus palsy (specially facial nervus) and meningitis are the virtually mutual neurological manifestations of this stage of LD. In the United States, the most common manifestation of the tardily disseminated phase of LD is Lyme arthritis [1, 8]. Lyme arthritis is usually mono or oligoarticular, affects big joints (knee, most ordinarily), and occurs weeks to months later on the bite. Dissimilar in Europe, neurological manifestations of late LD are rare in the United States [8].
The major reservoirs for B. burgdorferi are birds and small mammals such every bit mice and chipmunks [one, 9]. While deer are not competent hosts for B. burgdorferi, they are essential for the I. scapularis life cycle. The tick I. scapularis has three stages of development: larva, nymph, and developed tick [i]. In North America, the life bicycle of I. scapularis takes approximately ii years to complete [nine]. Egg laying ordinarily begins in May; hence, larvae are the nigh abundant during the summertime. These larvae feed on small mammals such every bit the white footed mouse during summer, at which bespeak transmission of B. burgdorferi occurs. Equally the wintertime approaches, the tick larvae enter a dormant stage in which they stay throughout the winter. In the offset of the spring of the second twelvemonth, the larvae that survived the winter mold into the side by side stage of tick evolution—nymph. During the leap/summer of the 2d year those nymphs seek suitable hosts for feeding, including humans. Following a bloody meal, the nymphs mold into adults. If an adult tick survives the winter, it will seek another host (commonly a large mammal such as deer) on which it will feed and be able to lay eggs. At that point the two year life wheel is completed [eight, 9].
Nymphs are usually responsible for the majority of the infection manual to humans. They are abundant during the spring and summertime months when humans' outdoor activities are at the peak. Their pocket-size size (only few millimeters in diameter unlike common dog ticks) and the secretion of bradykininases (enzymes that break bradykinins—enzymes of inflammation) contribute to the fact that the bulk of patients do non remember the tick bite [8, 10]. The gamble of infection manual from the infected tick depends on the duration of feeding. The ticks are most likely to transmit infection subsequently a prolonged period of feeding, such every bit 36 hours or more. Yet, infection can be transmitted even after as fiddling as 24 hours of feeding [10].
At that place is a growing body of evidence showing that climate alter may affect the incidence and prevalence of certain vector-borne diseases such as malaria, dengue, Due west Nile fever, and LD. Different weather, which defines a condition of the temper over a short period of time, climate represents atmospheric "behavior" over a relatively long menstruation of time [eleven]. Climate change, therefore, refers to changes in long-term averages of daily weather including temperature, humidity, air pressure, and precipitation. The incidence of tick-born zoonoses such every bit LD is particularly probable to exist affected by climate change because ticks spend the majority of their life cycle outside the host in an environment where temperature and humidity directly affect their evolution, activity, survival, and host-seeking behavior [12]. The number of annually reported cases of LD in the United states of america has sharply increased over the final iii decades, from about x,000 in 1991 to most 28,000 annually in recent years [13]. Not just did the incidence of the disease increase, but also its geographical distribution. While climate change might significantly contribute to the emergence of new infections, it is interesting to contrast this modify in the incidence of tick-borne diseases in the United states with the changes happening in Europe. There, during the 9 years prior to 2015, the growth of the cases of louse-born relapsing fever (due to B. recurrentis) has been associated to the increment in refugees [14–16]. Furthermore, in the last few decades, newly recognized tick-borne rickettsioses have been shown to exist present. R. conorii sub sp. Israelensis has been detected in homo cases in Sicily and Sardinia in Italia and in different regions of Portugal [17].
This emergence of Lyme disease in the Us is at least partially attributed to climatic change [12]. Nonetheless, the magnitude of bear upon is still unclear. In this study, we investigate the event of climatic variables on the incidence of LD in xv U.S. states with the highest incidence of the affliction. Those states contribute to 95 percent of reported cases.
2. Materials and Methods
We merged two types of information to bear the fixed effects analysis in this study: almanac canton-level epidemiological data on LD cases from the Centers for Affliction Control and Prevention (CDC) and meteorological information from the National Oceanic and Atmospheric Administration (NOAA). Both databases are publicly available.
2.1. Epidemiological Data
LD cases have been voluntarily reported to the CDC since 1991 by land and territorial wellness departments as part of the National Notifiable Illness Surveillance System (NNDSS). The annual county-level number of cases for the period 2000–2016 is publicly bachelor at http://.cdc.gov/lyme/stats/ and is the principal input for our analysis. A total of 482,297 cases were reported during that flow (run into the evolution of the number of cases in annual maps elaborated by the CDC, too available at http://.cdc.gov/lyme/stats/). Until 2007, a case of LD was defined as either (1) a physician-diagnosed erythema migrans rash of more than v cm in bore or (2) at least one objective tardily manifestation (i.east., musculoskeletal, cardiovascular, or neurologic) with laboratory bear witness of infection with B. burgdorferi (CDC 1997). The national surveillance case definition was revised in 2008 to include probable cases. Country or local health departments are responsible for ensuring that cases reported to the CDC see the case definition, and state wellness officials have used various methods to ascertain cases including provider-initiated passive surveillance, laboratory-based surveillance, and enhanced or active surveillance [18]. Over 95 percentage of LD cases in the The states occurred in 15 states during our study flow, primarily in the Northeast and Upper Midwest (Connecticut, Delaware, Maine, Maryland, Massachusetts, Minnesota, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, Virginia, West Virginia, and Wisconsin). These are the "high-incidence states," where the average incidence was at least 10 confirmed cases per 100,000 persons in the previous 3 reporting years (run into http://.cdc.gov/lyme/stats/tables.html). We focus our analysis on counties from those states and nowadays results for the incidence of LD—cases per 100,000 population—including all cases reported during our period of analysis. Because the case definition changed in 2008, we as well provide estimates based on the LD incidence reported before and after 2008. Annual population data used to calculate the LD incidence is publicly available from the U.S. Bureau of Economic Analysis (http://.bea.gov/itable/index_regional.cfm).
ii.two. Meteorological Data
For meteorological data, we used daily measurements of maximum and minimum temperature as well as total precipitation from NOAA, publicly bachelor at http://.ncdc.noaa.gov/cdo-spider web/datasets. This dataset provides detailed weather measurements at over 20,000 weather condition stations across the state. Daily average temperature was calculated equally the arithmetic average of daily maximum and minimum temperatures, in degree Celsius (°C). Annual boilerplate temperature for the period 2000–2016 was obtained by averaging all daily observations throughout the yr. For counties with no weather condition stations, we imputed annual average temperature past computing a weighted average of that variable from the counties within 50 miles of the original county centroid using inverse altitude weights. With measures of annual county-level average temperatures in hand, indicator variables for bins of almanac boilerplate temperature were generated straightforwardly. Each indicator variable takes the value one if the annual average temperature for a county is in the prespecified range, and zero otherwise. We created indicators for the post-obit ranges: below 5, 5–seven, 7–ix, 9–11, 11–13, 13–15, and above 15°C. The shares of observations in each bin are reported in Table 1. Almanac total precipitation for the period 2000–2016 was obtained by summing all daily precipitation for a county, in centimeters (cm). Imputation for counties with no weather stations was done as described for temperature. Indicator variables for bins of almanac full atmospheric precipitation were generated in a fashion similar to temperature for the post-obit ranges: below seventy, 70–120, 120–170, 170–220, 220–270, and above 270 cm. Again, the shares of observations in each bin are reported in Table one. The average annual total precipitation for the counties in our sample is approximately 174 cm. For reference, the average annual rainfall is 20 cm in Phoenix (Arizona), 87 cm in Madison (Wisconsin), 120 cm in Providence (Rhode Island), 157 cm in Miami (Florida), and 300 cm in Mt. Rainier (Washington).
Table 1
Summary statistics from our sample.
| Variable | Obs. | Hateful | Std. dev. | Min. | Max. |
|---|---|---|---|---|---|
| Incidence of Lyme disease | 7,956 | 41.75 | 76.24 | 0 | 1581.15 |
| Avg. temp.: below 5°C | vii,956 | 0.07 | 0.25 | 0 | i |
| Avg. temp.: 5–7°C | seven,956 | 0.sixteen | 0.36 | 0 | 1 |
| Avg. temp.: vii–ix°C | 7,956 | 0.26 | 0.44 | 0 | 1 |
| Avg. temp.: 9–xi°C | 7,956 | 0.19 | 0.39 | 0 | 1 |
| Avg. temp.: xi–13°C | 7,956 | 0.18 | 0.38 | 0 | 1 |
| Avg. temp.: 13–15°C | 7,956 | 0.eleven | 0.31 | 0 | 1 |
| Avg. temp.: above fifteen°C | seven,956 | 0.03 | 0.18 | 0 | ane |
| Total prcp.: beneath 70 cm | vii,956 | 0.05 | 0.22 | 0 | ane |
| Total prcp.: seventy–120 cm | 7,956 | 0.twenty | 0.twoscore | 0 | one |
| Total prcp.: 120–170 cm | 7,956 | 0.27 | 0.44 | 0 | one |
| Full prcp.: 170–220 cm | 7,956 | 0.22 | 0.42 | 0 | one |
| Total prcp.: 220–270 cm | 7,956 | 0.15 | 0.35 | 0 | one |
| Total prcp.: above 270 cm | seven,956 | 0.10 | 0.31 | 0 | 1 |
| Connecticut | 7,956 | 0.01 | 0.12 | 0 | 1 |
| Delaware | 7,956 | 0.01 | 0.08 | 0 | 1 |
| Maine | seven,956 | 0.03 | 0.18 | 0 | one |
| Maryland | 7,956 | 0.04 | 0.19 | 0 | 1 |
| Massachusetts | seven,956 | 0.03 | 0.17 | 0 | one |
| Minnesota | 7,956 | 0.17 | 0.37 | 0 | 1 |
| New Hampshire | vii,956 | 0.02 | 0.14 | 0 | ane |
| New Bailiwick of jersey | 7,956 | 0.04 | 0.xx | 0 | 1 |
| New York | 7,956 | 0.12 | 0.32 | 0 | one |
| Pennsylvania | 7,956 | 0.12 | 0.33 | 0 | i |
| Rhode Island | 7,956 | 0.01 | 0.09 | 0 | 1 |
| Vermont | seven,956 | 0.03 | 0.17 | 0 | 1 |
| Virginia | vii,956 | 0.12 | 0.33 | 0 | 1 |
| Due west Virginia | 7,956 | 0.10 | 0.29 | 0 | 1 |
| Wisconsin | 7,956 | 0.15 | 0.35 | 0 | one |
Once we merged the information of LD cases with climatic variables, our sample contained a balanced panel of 468 U.South. counties over the period 2000–2016. Figure 1 displays the counties in our sample in the map of the United States, with colour code based on the incidence of LD. Tabular array 1 reports the summary statistics of our sample. Observe that most of the counties used in our sample come up from Minnesota, Wisconsin, New York, Pennsylvania, Virginia, and W Virginia. Too, observe that the temperature bins with the highest shares of observations cover the range 7–13°C and the precipitation bins with the largest shares comprehend the range 70–220 cm.
Map of the Us with highlighted counties from our Sample. Note: this map displays our sample of 468 counties in the xv states considered by CDC as usa with the highest incidence of LD (over 95 percentage of all cases in the United states). Darker blue colors represent higher incidence of the illness—cases per 100,000 population.
2.3. Empirical Strategy
Using standard longitudinal or stock-still furnishings methods [19–21], the typical panel regression model to examine the impact of climatic variables C—in our case, annual average temperature and annual full atmospheric precipitation—on an outcome of interest y—in our case, the incidence of LD (cases per 100,000 population)—takes the form
(1)
where i indexes different geographic areas (in our instance, counties), t indexes time (in our case, years), and s indexes a larger geographical area (in our instance, states) [22]. The additional explanatory variables will exist explained beneath, but the error process ε is typically modeled using robust standard errors, allowing for arbitrary correlation over time and infinite in the covariance matrix past clustering at the county level.
Noting that C varies plausibly randomly over time—i.e., "conditions" draws from the county "climate" distribution—this approach resembles an experimental design and, therefore, β identifies the causal effect of atmospheric condition shocks on the incidence of LD [22]. The fixed effects for county, μ i , absorb fixed spatial characteristics, whether observed or unobserved, disentangling the shock from many possible sources of omitted variable bias, such as geographic features (e.g., elevation and ruggedness) and canton baseline economical characteristics (e.g., Gross domestic product, population, and number of infirmary beds and number of physicians per 100,000 population) that are likely to be correlated to climatic variables. Fourth dimension-fixed effects, θ t , further neutralize whatsoever common trends and thus aid ensure that the relationships of interest are identified from idiosyncratic local shocks. State-specific fourth dimension trends, λ due south f(t), are added to allow for differential trends in subsamples of the data, controlling for a number of observed and unobserved factors affecting the upshot of interest that vary over time at the land level, such as state health expenditures and state public awareness campaigns regarding the incidence of detail diseases. In our preferred specification of Equation (i), f(t) is a quadratic function of time, that is, it includes state-specific quadratic time trends, as volition be explained in more details in Results and Discussion.
Information technology is imperative to explicate the choice of temperature and precipitation as our climatic variables. One can assume that the incidence of LD might be related to tick activity. In fact, laboratory studies indicate that temperature determines whether or not, and to what extent, I. scapularis can move to seek hosts, whereas humidity determines how loftier ticks quest above footing level, where their resource for rehydration exists, and for how long they can remain actively host-seeking earlier retreating to rehydrate [23–25]. We use precipitation instead of the ideal measure of relative humidity because the latter is only available for half of our canton-yr observations. Nevertheless, in unreported analysis bachelor upon request, we find similar results when using the subsample with information on relative humidity. As well the biological furnishings of climate on tick vector affluence and activeness, at that place may be behavioral impacts of climate on human exposure to ticks. Previous studies have found that individuals spend more fourth dimension outdoors as temperature rises, up to a point where being outside becomes undesirable due to the excessive heat [26, 27]. Additionally, individuals may engage in adaptive responses to avoid exposure to ticks such equally the employ of deer-baiting devices to kill ticks [28].
A fundamental issue in Equation (1) is regarding the functional form of C. Following previous studies [29–32], nosotros use indicator variables for bins of almanac average temperature and for bins of annual total atmospheric precipitation. These bins are listed in Table 1 and were described in the information section. Thus, the only functional form brake is that the impact of the annual average temperature on the incidence of LD is constant within 2°C intervals. The choice of narrow temperature bins represents an try to allow the data, rather than parametric assumptions, to determine the incidence-temperature human relationship, while too obtaining estimates that are precise enough that they have empirical content [29–32]. This degree of flexibility and liberty from parametric assumptions is only feasible considering we are using sixteen years of data from a large area of the Usa. Similarly, nosotros utilize simple indicator variables for precipitation based on annual rainfall in county i in twelvemontht. Each indicator corresponds to a l-cm bin, ranging from less than 70 cm to more 270 cm.
Another important methodological decision to brand when implementing console regression models concerns the inclusion of other time-varying observables, Z it . Including Z it may absorb balance variation, hence producing more precise estimates. Nevertheless, adding more than controls will non necessarily produce an estimate of β that is closer to the true β. If the Z's are themselves an upshot of C, which may well be the case for controls such as Gross domestic product, institutional measures, and population, including them will induce an "overcontrolling trouble" (in the language of the model, if Z is in fact Z(C), and then Equation (one) would instead be written as y = f(C), Z(C)) and estimating an equation that included both Z and C would non capture the true cyberspace effect of C on y (again, see Dell et al. [22]). For example, suppose that poorer counties in the United States tend to exist both hot and have depression-quality institutions. If hot climates were to crusade depression-quality institutions, which in turn cause depression income, and then controlling for institutions in Equation (ane) tin have the issue of partially eliminating the explanatory ability of climate, even if climate is the underlying fundamental cause. Therefore, if the incidence of LD is the issue of interest, for instance, then controlling for changes in wellness personnel or infrastructure would exist problematic if the climatic variables influence those changes, directly or indirectly. Our preferred specification of Equation (1) does not include additional fourth dimension-varying explanatory variables, but nosotros also report dissever estimates for counties above and beneath the U.Due south. median per capita income. This variable should reflect patterns of development across the nation.
iii. Results and Discussion
Tick-borne diseases are an important public health business and the incidence of these infections is increasing in the Unites States and worldwide [33]. Complex interactions between humans and climatic change are contributing to the emergence of new diseases and the spread of already known ones to regions where they were unable to exist earlier. Environmental factors such as temperature and humidity take been shown to influence tick affluence, availability of hosts, their survival, and affliction transmission. LD is a classic example of linkage between environmental factors and affliction occurrence and spread (the U.S. Environmental Protection Agency (EPA) is actually using the number of LD cases as a climate change indicator (http://.epa.gov/climate-indicators/climate-change-indicators-lyme-disease)). For a region to exist suitable for LD occurrence and transmission, the climate needs to allow the survival of both ticks and mammalian hosts necessary for completion of tick life bicycle [25]. The emergence of LD in the northeast of the Unites States in 1970 was thought to exist due to the expansion of the tick population associated with reforestation and expansion of the key host for tick life cycle—deer [34]. However, a contempo study from Canada demonstrated the expansion of I. scapularis population despite deforestation [35]. Previous studies, both empirical and simulation-based, have demonstrated that a warming climate has a positive effect on the expansion of the tick population through an increase in tick survival and improved access to hosts necessary for feeding [36, 37]. Our study aimed to decide the influence of temperature and humidity on the incidence of LD within fifteen U.Southward. states that account for the majority of reported cases.
Our estimated impacts of climatic variables on the incidence of LD—cases per 100,000 population—are reported on Table 2. Recollect that the main sample contains only counties from those 15 U.South. states with the highest incidence of LD cases according to CDC. In column one, we controlled for observed and unobserved time-varying factors affecting all sample counties equally in each year such as macroeconomic conditions and changes in health law and health expenditure at the federal level, and for observed and unobserved time-invariant factors affecting each county over the sample period such as canton geographical features and historical (baseline) health infrastructure. In column 2, nosotros added country-specific linear time tendency to command for observed and unobserved changes in state variables affecting the health outcomes such every bit expansion of Medicaid, campaigns to enhance awareness of salubrious behaviors, etc. For our preferred specification in column three, nosotros allowed those land-specific time trends to opposite direction over time by calculation quadratic terms. For instance, nosotros are decision-making for observed and unobserved increases in country wellness expenditures in a number of years equally well as decreases later, or decreases in funds for campaigns raising awareness of LD, and increases in funding once more than cases are confirmed. Column three is our preferred specification not merely because the increment in the R-squared relative to previous columns indicates an comeback in the goodness-of-fit of our econometric model, but also considering information technology takes into account of import controls. In fact, the similarity in the increment in the R-squared and in the adapted R-squared indicates that the additional explanatory variables are indeed relevant to explicate the incidence of LD. Otherwise, the adjusted R-squared would have penalized our column-3 econometric specification. Both the R-squared and the adapted R-squared reveal that our model explains over seventy per centum of the variation in the incidence of LD in the U.s.a. over the period 2000–2016.
Table 2
The impacts of temperature and precipitation on the incidence of LD.
| Main results | |||
|---|---|---|---|
| Dep. var.: LD incidence | (one) | (ii) | (three) |
| Avg. temp.: below 5°C | 7.9101 (6.1820) | 4.9673 (5.9583) | i.6156 (5.7073) |
| Avg. temp.: five–7°C | 17.2713 ∗∗∗ (5.6208) | xiii.3615 ∗∗ (5.4461) | 10.7294 ∗∗ (v.0919) |
| Avg. temp.: vii–9°C | 21.3359 ∗∗∗ (v.2593) | 16.2189 ∗∗∗ (v.2152) | 15.1306 ∗∗∗ (iv.8862) |
| Avg. temp.: 9–11°C | 19.9290 ∗∗∗ (four.3244) | 15.4690 ∗∗∗ (four.5382) | xiv.4033 ∗∗∗ (four.2444) |
| Avg. temp.: 11–xiii°C | nine.7629 ∗∗∗ (2.9059) | half-dozen.5636 ∗∗ (3.1989) | 5.3232 ∗ (2.9025) |
| Avg. temp.: 13–xv°C | 6.6229 ∗∗∗ (2.0306) | 4.7761 ∗∗ (ii.1972) | 3.8847 ∗∗ (1.9730) |
| Reference: above fifteen°C | 0 | 0 | 0 |
| Total prcp.: below 70 cm | 13.0452 ∗∗ (five.4358) | thirteen.5556 ∗∗∗ (four.5898) | 4.6664 (four.5738) |
| Full prcp.: 70–120 cm | 10.7113 ∗∗ (4.7829) | 11.8325 ∗∗∗ (4.0619) | 4.2597 (3.9230) |
| Total prcp.: 120–170 cm | 8.1580 ∗∗ (four.1505) | 10.4118 ∗∗∗ (3.6474) | 5.3288 (3.4688) |
| Full prcp.: 170–220 cm | iii.7551 (iii.1761) | six.3591 ∗∗ (2.8603) | iii.6003 (2.6573) |
| Total prcp.: 220–270 cm | one.8596 (2.8608) | 2.3633 (2.8913) | 1.2022 (ii.8126) |
| Reference: to a higher place 270 cm | 0 | 0 | 0 |
| | |||
| Year stock-still effects | Yes | Yes | Yes |
| Canton fixed effects | Yes | Yeah | Yep |
| Linear trend past state | Yes | Yeah | |
| Quadratic tendency by state | Yeah | ||
| | |||
| Observations | 7,956 | 7,956 | 7,956 |
| R ii | 0.6771 | 0.7068 | 0.7226 |
| Adapted R 2 | 0.656 | 0.687 | 0.703 |
We now draw the results from our preferred specification (Tabular array two, cavalcade 3). Relative to counties with annual average temperature higher up 15°C, counties with almanac average temperature below v°C accept ane.half-dozen additional cases of LD per 100,000 population, simply this estimate is non statistically significant (non distinguishable from zero, or alternatively not distinguishable from the reference group). That judge jumped to 10.vii cases per 100,000 population for counties with annual average temperature between 5 and 7°C, and to 15.1 for counties with annual average temperature betwixt 7 and 9°C. And so, it stabilized for counties with annual boilerplate temperature betwixt ix and 11°C—xiv.four cases per 100,000 population—but dropped to v.3 and three.9 for counties with annual average temperature betwixt 11 and 13°C, and 13 and 15°C, respectively. Nosotros display these estimates more clearly in Figure 2, where we can see the approximately concave or inverted-U shape of the incidence of LD response to temperature.
The impact of temperature on the incidence of LD. Note: this figure presents the estimated impacts of temperature on the incidence of LD—cases per 100,000 population—reported in the last cavalcade of Table 2 and represented here past blue squares. The vertical dashed lines around each blue square stand for the 95 percent confidence interval.
Three features of these results are worth discussing. Get-go, the abrupt increase in the number of LD cases per 100,000 population happens in the 5–7 and 7–nine°C annual boilerplate temperature bins. This might exist associated with tick activity (Schulze et al. provided suggestive evidence that atmospheric precipitation and temperature played a limited function in predicting the abundance of I. scapularis nymphs at an LD-endemic surface area over the period 1998–2005. Thus, we focus on tick action in agreement our findings, as in Burtis et al.). Indeed, Duffy and Campbell [38] used flagging samples of adults I. scapularis through the winter to infer a minimum temperature threshold for activeness of approximately 4°C. Clark [23] used laboratory experiments to determine a minimum temperature threshold for activity by developed I. scapularis of 9–11°C, but some individual nymphs were capable of movement and coordinated motility at much lower temperatures, 4.ii and 6.3°C, respectively. (Notice that, by definition, almanac boilerplate temperature includes a range of observed temperatures throughout the year. Therefore, our temperature bins do not have to match precisely the thresholds highlighted in those studies. Information technology is remarkable, however, that the correspondence is approximately accurate.) 2d, the similarity of estimates for the ranges of annual average temperature xi–13°C, 13–15°C, and above fifteen°C. In fact, Vail and Smith [24] found no significant difference in the mean altitude moved or time spent in questing posture for I. scapularis nymphs held at 10 versus fifteen or 20°C. Tertiary, the concave or inverted-U shape of the LD incidence response to temperature. Vail and Smith [24, 39] and Ogden et al. [25] constitute laboratory and field evidence that tick survival and action peaked at sure temperatures, and then decreased, with peak temperatures varying considerably depending on the outcome of interest. The inverted U-shaped human relationship between temperature and LD incidence is too consistent with the pattern of human exposure to ticks. In fact, temperature has been shown to have nonlinear impacts on the time adults and children classify to outdoor activities. As temperature rises, individuals engage in more outdoor activities, but after information technology reaches a maximum tolerable temperature, they decrease the fourth dimension spent outdoors [26, 27]. Similarly, equally temperature rises, lodge may engage in adaptive actions to avoid exposure to ticks such every bit the deployment of deer-baiting devices called four-posters to kill ticks [28]. Feeder stations that resemble 4-poster beds lure deers with corn. Rollers soaked with the pesticide permethrin rub the animals' necks as they swallow the corn, killing ticks. As an example, this year, dozens of those devices were installed in Shelter and Burn Islands, NY, as function of a $1.2 million tick-removal effort.
While temperature is supposed to influence the extent to which I. scapularis can move to seek hosts, humidity is supposed to affect how high ticks quest above ground level, where resources and rehydration are available, and for how long they can remain actively host-seeking [23–25]. In our longitudinal analysis, we included bins of total precipitation during the twelvemonth as a proxy for humidity. As we can see in our preferred specification (column three) in Table 2, in that location was no statistical difference in the incidence of LD between counties with more than 270 cm of total precipitation—the reference group—and counties with less rainfall. This result seems to exist consistent with laboratory evidence provided by Vail and Smith [39] who found no difference in the fourth dimension in questing posture beyond any levels of relative humidity, and no difference in questing peak at levels of relative humidity beneath 100 percent. Berger et al. [40] too found that hateful weekly daytime relative humidity did non significantly predict tick activity in the field. Given such findings, nosotros take focused on the relationship betwixt temperature and LDs cases per 100,000 population in our give-and-take (it is worth mentioning that although our findings are consistent with laboratory and field show on tick activity and survival, and corroborate Subak's [nine] findings of a weak relationship between the incidence of LD and a same-twelvemonth moisture alphabetize for seven northeastern U.S. states during 1993–2001, they are different from the results found by McCabe and Bunnell; using information for seven northeastern U.Due south. states during the 1992–2002 period, those authors institute that late jump/early summertime atmospheric precipitation was a meaning climate cistron affecting the occurrence of LD, but that temperature did non seem to explain the variability in LD reports).
Table 3 reports the impacts of climatic variables on the incidence of LD for counties above and beneath the U.S. median income per capita, and for cases reported before and after 2008. Although richer counties might have more resources to deal with clinical and public health bug, we did not discover any statistical difference betwixt our estimates in richer versus poorer counties. With respect to postal service-2008, when probable cases of LD were included forth with confirmed cases, we noticed a much noisier relationship between temperature and LD incidence. This is not surprising considering attenuation bias from measurement error is usually exacerbated in panel data regressions.
Table 3
The impact of climatic variables by income per capita and before vs. after 2008.
| Heterogeneity | ||||
| Dep. var.: LD incidence | <PCI median | ≥PCI median | <2008 | ≥2008 |
| (1) | (2) | (iii) | (iv) | |
| | ||||
| Avg. temp.: below 5°C | 9.8421 (vii.2752) | 6.5755 (8.5056) | −0.0423 (4.9830) | −6.0330 (6.8374) |
| Avg. temp.: 5–7°C | 16.1789 ∗∗∗ (half dozen.1200) | 12.2321 (7.7779) | v.9658 (three.9566) | 3.3523 (v.7814) |
| Avg. temp.: vii–9°C | 20.0468 ∗∗∗ (vii.2075) | 12.3422 (7.5295) | 6.7271 ∗ (3.9568) | 2.3292 (4.9421) |
| Avg. temp.: ix–xi°C | 17.6905 ∗∗∗ (half dozen.2141) | fifteen.5235 ∗∗ (seven.2675) | v.3618 ∗∗ (2.5961) | 6.0453 (4.2411) |
| Avg. temp.: 11–13°C | 7.5071 ∗∗ (iii.0650) | 8.2686 (5.6081) | 2.7109 (1.8782) | −0.6243 (2.5552) |
| Avg. temp.: 13–xv°C | 6.1809 ∗∗ (two.3800) | 5.3997 (3.3455) | 2.7591 ∗∗ (1.2852) | −0.8361 (two.2234) |
| Reference: above 15°C | 0 | 0 | 0 | 0 |
| Full prcp.: below 70 cm | ane.8066 (seven.2816) | xi.1090 (vii.5837) | −v.7405 (5.5240) | −2.8211 (7.2106) |
| Total prcp.: 70–120 cm | 0.0188 (6.8925) | nine.6526 (vi.5876) | −6.3607 (5.3531) | −3.5179 (6.2751) |
| Total prcp.: 120–170 cm | ane.7949 (vi.0951) | 8.7628 (5.8681) | −2.0996 (4.7471) | −5.4875 (five.2265) |
| Total prcp.: 170–220 cm | −0.4551 (iv.4063) | 8.0211 ∗ (4.4954) | −iii.3351 (4.2346) | −5.8981 (3.8901) |
| Total prcp.: 220–270 cm | −2.3741 (3.0281) | 6.2257 (3.8928) | −1.5489 (ii.9620) | −iv.7632 (three.4002) |
| Reference: above 270 cm | 0 | 0 | 0 | 0 |
| | ||||
| Year fixed furnishings | Yes | Yes | Aye | Yes |
| County fixed effects | Yes | Yes | Yep | Yep |
| Linear trend by land | Aye | Yes | Yes | Yep |
| Quadratic trend by state | Yeah | Yes | Yes | Yeah |
| | ||||
| Observations | 3,978 | three,978 | 3,744 | 4,212 |
| R 2 | 0.7804 | 0.7658 | 0.8565 | 0.8237 |
| Adjusted R 2 | 0.750 | 0.738 | 0.830 | 0.795 |
Our results imply that climate modify will have a sizable bear on on the number of cases of LD in the United States in the coming decades. Using the estimated impacts of temperature on the incidence of the disease in Table two, column 3, and the distribution of county-year observations in each bin of annual boilerplate temperature from Table 1, nosotros have predicted an increase of eight.six cases of LD per 100,000 population per county-year, an increase of almost 21 percent relative to the average incidence of the illness over the period 2000–2016. This was done by assuming a two°C increase in almanac average temperature in the northern area of the Usa by mid-century (2036–2065). (The two°C increment in almanac boilerplate temperature implies that the share of counties in 1 2°C bin in Table 1 would bear witness up in the following 2°C bin. For case, the 26 percent of counties in the 7–9°C temperature bin in Table 1 would show upwardly in the 9–11°C bin. The calculation of the touch on of that increase in annual average temperature and then used the estimates reported in Table 2, column 3, and the shift up of the shares presented in Table one.) This temperature increase is causeless to be an approximation for the change the region we focus on might experience in the future. In fact, it is slightly below the mid-century (2036–2065) projections for the Northeast (two.21°C or 3.98°F) and Midwest (2.34°C or 4.21°F) from the Fourth National Climate Assessment (NCA4) (USGCRP 2017). This is under the more conservative Representative Concentration Pathway (RCP) 4.5, which assumes global annual greenhouse gas emissions peaking around 2040, and so failing. Under the RCP eight.5, which assumes that emissions will continue to ascension throughout the twenty-first century, those mid-century (2036–2065) predicted increases in annual average temperature would be ii.83°C (5.09°F) for the Northeast and ii.94°C (v.29°F) for the Midwest (see definition of the NCA4 regions at scenarios.globalchange.gov/regions_nca4).
Given the increase of 8.6 cases of LD per 100,000 population per county-year associated with a 2°C increase in temperature and the average population for a canton-year in our sample of 149,606 persons, we have predicted an increase in the number of LD cases by approximately 6,040 per year in the counties in our sample (again, they represent over 95 percent of the cases in the unabridged country). Considering the average annual number of LD cases in Us over the period 2000–2016 was 28,370, that amount translates into an increase of roughly 21 percent in the number of LD cases in the coming decades.
4. Conclusion
In this study, nosotros have shown that a sizable increase in the incidence of LD cases in endemic areas of the United States due to climate alter is imminent. These findings should alert public wellness agencies, physicians, and patients. On the one mitt, improve education and increased awareness among patients and physicians is important considering early on recognition and handling are ordinarily highly constructive in preventing debilitating consequences of untreated Lyme disease and the potential post-Lyme syndrome. On the other hand, public health authorities should be alert to work on strategies to limit tick and host population and consequently subtract the incidence of LD not simply in endemic areas, but also in neighboring locations where the illness has simply been sporadically reported, or not reported at all. In fact, climate change may brand those areas suitable for the establishment of tick and host populations.
Our report has a number of limitations. Beginning, because of information limitations, we have used annual information to examine the relationship between the incidence of LD and climatic variables. Thus, we were unable to address the seasonality of LD cases throughout the year, equally highlighted by Moore et al. [41] and the climate modify influences on the almanac onset of LD, as studied by Monaghan et al. [42]. 2d, we take focused our assay on counties from the highest incidence states, regardless of the spatial distribution of blacklegged ticks. Hence, nosotros cannot comment on whether these reported cases were autochthone—most likely the vast majority of the cases—or imported. In an ongoing research projection, we are examining the climate-LD incidence human relationship over U.Southward. counties with established tick population versus counties with ticks reported, only not notwithstanding established. Tertiary, although we have overcome a number of omitted variable bias issues with the fixed effects analysis, we accept not scaled our results relative to areas with few LD cases. In work still in progress, we are using a border approach to compare our estimates for the places with high incidence of LD with estimates for their corresponding neighboring areas.
Data Availability
The data used to support the findings of this study are included inside the article.
Conflicts of Involvement
The authors declare that they accept no conflicts of interest.
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