Entire United States Research Articles

Effects of Purchase Restrictions on Smoking During Pregnancy: An Analysis of U.S. Birth Records.

In December 2019, the minimum legal sales age (MLSA) for tobacco products in the United States was raised from 18 to 21 years as a measure of preventing smoking initiation. Smoking during pregnancy, which causes maternal, fetal, and infant morbidity and mortality, is a major public health concern. We first study the effects of purchase restrictions on smoking during different stages of pregnancy covering the entire United States and using the records of all live births during 2014-2018 by birthing people aged 18-21 years. Our identification strategy utilizes the variation in the MLSA of tobacco products over time and across counties in the United States during 2014-2018 using a two-way fixed effects model, modified to accommodate a partially observable treatment indicator. The results show that 37.4% of potential smokers in our study population would have been prevented from smoking before pregnancy, 50.5% during the first trimester, 53.4% during the second trimester, and 14.8% during the third trimester if the law prevented selling tobacco products to them in the locality they live. A purchase restriction is estimated to cause the smoking intensity of those who smoke, the intensive margin, to drop by 5.4% before pregnancy, 10.4% during the first trimester, 14.8% during the second trimester, and 5.2% during the third trimester. Our results show that purchase restrictions are highly effective in preventing smoking among birthing people in the 18-21 years age group. The effect on smoking prevalence during the first and second trimesters is more pronounced. Many countries around the world have recently tightened age-based purchase restriction policies as a tobacco control measure. We first study the effects of purchase restrictions on smoking during different stages of pregnancy using the birth records of all pregnancies in the United States by birthing people aged 18-21 years during 2014-2018. The study utilizes the variation in the MLSA of tobacco products over time and across counties during that period. Our results show that purchase restrictions are highly effective in preventing smoking among birthing people in the 18-21 years age group.

Associations of four indexes of social determinants of health and two community typologies with new onset type 2 diabetes across a diverse geography in Pennsylvania.

Evaluation of geographic disparities in type 2 diabetes (T2D) onset requires multidimensional approaches at a relevant spatial scale to characterize community types and features that could influence this health outcome. Using Geisinger electronic health records (2008-2016), we conducted a nested case-control study of new onset T2D in a 37-county area of Pennsylvania. The study included 15,888 incident T2D cases and 79,435 controls without diabetes, frequency-matched 1:5 on age, sex, and year of diagnosis or encounter. We characterized patients' residential census tracts by four dimensions of social determinants of health (SDOH) and into a 7-category SDOH census tract typology previously generated for the entire United States by dimension reduction techniques. Finally, because the SDOH census tract typology classified 83% of the study region's census tracts into two heterogeneous categories, termed rural affordable-like and suburban affluent-like, to further delineate geographies relevant to T2D, we subdivided these two typology categories by administrative community types (U.S. Census Bureau minor civil divisions of township, borough, city). We used generalized estimating equations to examine associations of 1) four SDOH indexes, 2) SDOH census tract typology, and 3) modified typology, with odds of new onset T2D, controlling for individual-level confounding variables. Two SDOH dimensions, higher socioeconomic advantage and higher mobility (tracts with fewer seniors and disabled adults) were independently associated with lower odds of T2D. Compared to rural affordable-like as the reference group, residence in tracts categorized as extreme poverty (odds ratio [95% confidence interval] = 1.11 [1.02, 1.21]) or multilingual working (1.07 [1.03, 1.23]) were associated with higher odds of new onset T2D. Suburban affluent-like was associated with lower odds of T2D (0.92 [0.87, 0.97]). With the modified typology, the strongest association (1.37 [1.15, 1.63]) was observed in cities in the suburban affluent-like category (vs. rural affordable-like-township), followed by cities in the rural affordable-like category (1.20 [1.05, 1.36]). We conclude that in evaluating geographic disparities in T2D onset, it is beneficial to conduct simultaneous evaluation of SDOH in multiple dimensions. Associations with the modified typology showed the importance of incorporating governmentally, behaviorally, and experientially relevant community definitions when evaluating geographic health disparities.

Understanding public perspectives on fracking in the United States using social media big data

ABSTRACT People’s attitudes towards hydraulic fracturing (fracking) can be shaped by socio-demographics, economic development, social equity and politics, environmental impacts, and fracking-related information. Existing research typically conducts surveys and interviews to study public attitudes towards fracking among a small group of individuals in a specific geographic area, where limited samples may introduce bias. Here, we compiled geo-referenced social media big data from Twitter during 2018–2019 for the entire United States to present a more holistic picture of people’s attitudes towards fracking. We used a multiscale geographically weighted regression (MGWR) to investigate county-level relationships between the aforementioned factors and percentages of negative tweets concerning fracking. Results indicate spatial heterogeneity and varying scales of those associations. Counties with higher median household income, larger African American populations, and/or lower educational level are less likely to oppose fracking, and these associations show global stationarity in all contiguous US counties. Eastern and Central US counties with higher unemployment rates, counties east of the Great Plains with less fracking sites nearby, and Western and Gulf Coast region counties with higher health insurance enrolments are more likely to oppose fracking activities. These three variables show clear East-West geographical divides in influencing public perspective on fracking. In counties across the southern Great Plains, negative attitudes towards fracking are less often vocalized on Twitter as the share of Republican voters increases. These findings have implications for both predicting public perspectives and needed policy adjustments. The methodology can also be conveniently applied to investigate public perspectives on other controversial topics.

Optimization of United States Residential Real Estate Investment through Geospatial Analysis and Market Timing.

In this study, we examined the long-term spatial patterns and trends of home values within the United States from 1996 to 2019. The Zillow Home Value Index (ZHVI) data were obtained from the Zillow’s housing data portal. The overall trends indicated both coasts of the United States having the highest concentration of hotspots. The ideal time to sell varied seasonally across the country. We used geo-statistical analysis techniques, such as cluster analysis and emerging hot spot analysis (EHSA), to reveal the average and long-term trends in spatial patterns of home values. Washington was the only state with sporadic hot spots. The Western United States showed consecutive hotspots, most notably in California, but in surrounding states as well. The Northeast, Mid-Atlantic, and Florida were dominated by oscillating hotspots. Texas and the Southeast display new hotspots that signal change, indicative of southward migration of population. Finally, the Midwest has been a historical cold spot, but Cleveland has improved to a diminishing cold spot, pointing to the revitalization of the region. The seasonal level analysis displayed southern and coastal states as those benefiting most from winter home sales, and the colder northern states captured the most value and sold quicker in the summer months. This intuitive analysis of the country’s variation in ideal sale month reveals a local view of when to sell rather than a generalized view of the entire United States.

Minimum wages and restaurant employment for teens and adults in metropolitan and nonmetropolitan areas

AbstractThis study estimates effects of minimum wages on individual restaurant employment using the 2005–2019 Current Population Survey (CPS) and a two‐way fixed effects regression model. I examine effects for teens and adults with less than an associate's degree for the entire United States and by metropolitan area status. The results indicate that minimum wages on average decrease restaurant employment for teens and increase restaurant employment for these adults, suggesting that minimum wages induce labor‐labor substitution. However, this pattern is driven by metropolitan areas residents. The estimated coefficient for minimum wages on teen restaurant employment in nonmetropolitan areas is not statistically significant.

Leveraging ecocomDP as a Flexible Intermediate Data Pattern to Expose NEON Biodiversity Data in GBIF

The Environmental Data Initiative (EDI) and the National Ecological Observatory Network (NEON) have been developing a flexible intermediate data design pattern for ecological community data called “ecocomDP”, which is intended to promote FAIR data principles. Specifically, this effort will enhance the discoverability of and access to biodiversity data from NEON and EDI data holdings, including data from the United States Long Term Ecological Research (USLTER) program (O'Brien et al. 2021). The ecocomDP data model is applied in the ecocomDP R (programming language) library, which provides tools for independent researchers to format their data following the ecocomDP standard, as well as tools to search and visualize data from NEON and EDI data holdings in their R environment. The flexibility of the ecocomDP data model allows for much of the ancillary data associated with observation events to be preserved. Here we describe a modular workflow that is under development to expose ecocomDP-formatted data packages in the Global Biodiversity Information Facility (GBIF) data portal (Fig. 1). Specifically, we highlight an effort to apply this workflow to create a pipeline to convert and submit NEON biodiversity data products to GBIF. EDI now has more than 70 data packages reformatted to the ecocomDP model, and has nearly finished developing a conversion of that intermediate format to a Darwin Core Archive (DwC-A, event core) format (Wieczorek et al. 2012) for submission to GBIF. This workflow takes advantage of EDI’s dataset subscription service, which triggers creation of an updated DwC-A when an original dataset is revised. Because ecocomDP provides a standardized input to this submission process, any data package in the ecocomDP format can be exposed in GBIF through this workflow. Thus, we are working to leverage the EDI-managed conversion and submission process to expose NEON data in GBIF, which is possible because of the existing mappings of NEON data products to ecocomDP (Li et al. 2022). This will include data products representing terrestrial and aquatic organisms (Table 1) from all NEON sites, spanning the entire United States. The overall goal of this effort is to provide an automated, modular workflow with complete provenance to submit NEON and EDI datasets to GBIF, built in such a way that datasets can be properly updated as new samples are collected and the data are published. The development of such a submission pipeline will provide a standardized process to expose biodiversity data from two continental scale networks, NEON and the U.S. National Science Foundation's Long-term Ecological Research network in GBIF. Further, the modularity of the workflow will allow independent researchers to adapt tools developed in this effort for their data archiving and publishing needs.

Predicting flood damage using the flood peak ratio and Giovanni Flooded Fraction.

A spatially-resolved understanding of the intensity of a flood hazard is required for accurate predictions of infrastructure reliability and losses in the aftermath. Currently, researchers who wish to predict flood losses or infrastructure reliability following a flood usually rely on computationally intensive hydrodynamic modeling or on flood hazard maps (e.g., the 100-year floodplain) to build a spatially-resolved understanding of the flood’s intensity. However, both have specific limitations. The former requires both subject matter expertise to create the models and significant computation time, while the latter is a static metric that provides no variation among specific events. The objective of this work is to develop an integrated data-driven approach to rapidly predict flood damages using two emerging flood intensity heuristics, namely the Flood Peak Ratio (FPR) and NASA’s Giovanni Flooded Fraction (GFF). This study uses data on flood claims from the National Flood Insurance Program (NFIP) to proxy flood damage, along with other well-established flood exposure variables, such as regional slope and population. The approach uses statistical learning methods to generate predictive models at two spatial levels: nationwide and statewide for the entire contiguous United States. A variable importance analysis demonstrates the significance of FPR and GFF data in predicting flood damage. In addition, the model performance at the state-level was higher than the nationwide level analysis, indicating the effectiveness of both FPR and GFF models at the regional level. A data-driven approach to predict flood damage using the FPR and GFF data offer promise considering their relative simplicity, their reliance on publicly accessible data, and their comparatively fast computational speed.

Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data

CONTEXTMapping crop types from satellite images is a promising application in agricultural systems. However, it is a challenge to automate in-season crop type mapping over a large area because of the insufficiency of ground truth and issues of scalability, reusability, and accessibility of the classification model. This study introduces a framework for automatic crop type mapping using spatiotemporal crop information and Sentinel-2 data based on Google Earth Engine (GEE). The main advantage of the framework is using the trusted pixels extracted from the historical Cropland Data Layer (CDL) to replace ground truth and label training samples in satellite images. OBJECTIVEThis paper will achieve three objectives: (1) assessing spatiotemporal crop information derived from the historical crop cover maps; (2) mapping crop cover, mainly crop fields without regular historical crop rotation patterns, from remote sensing data using supervised learning classification and validating mapping results; and (3) automating in-season crop mapping and exploring the scalability of the framework. METHODSThe proposed crop mapping workflow consists of four stages. The data preparation stage preprocesses CDL and Sentinel-2 data into the required structure. The spatiotemporal crop information sampling stage extracts trusted pixels from the historical CDL time series and labels Sentinel-2 data. Then a crop type classification model can be trained using the supervised learning classifier in the model training stage. In the mapping/validation stage, an in-season crop cover map over the full Sentinel-2 tile will be produced using the trained model and the classification performance will be validated using CDL or other ground truth data. RESULTS AND CONCLUSIONSWe systematically perform a group of experiments for in-season mapping of five major crop types (corn, cotton, rice, soybeans, and soybeans-wheat double cropping) over the Mississippi Delta region. The result indicates that the crop cover map of the study area is expected to reach 80%–90% agreement with CDL within the growing season. To further facilitate the use of the framework, we also develop a GEE-enabled online prototype, In-season Crop Mapping Kit, and explore its scalability over agricultural fields in various ecoregions including California, Idaho, Kansas, and Illinois. SIGNIFICANCEThe mapping-without-ground-truth approach described in this paper can significantly reduce ground truthing process and save substantial resource needs and labor costs, which is applicable to the production of in-season CDL-like data for the entire United States. The findings and outputs will benefit the agriculture community and other agricultural sectors ranging from government, academia, and companies.

The Topography of Subnational Inequality

AbstractIn this study, I examine inequality divergence, or the inequality of inequalities across local labor markets. While divergence trends of central tendencies such as per capita income have been well documented, less is known about the descriptive trends or contributing mechanisms for inequality itself. In this study, I construct wage inequality measures in 722 commuting zones covering the entire contiguous United States across 22 waves of Census and American Community Survey data from 1940 to 2019 to assess the historical trends of inequality divergence. I synthesize counterfactual and variance decomposition techniques to develop main conclusions. Results show two eras, one of convergence between 1950 and 1990, and divergence between 1990 and 2019, resulting in rates of divergence today as high as any time in the past 80 years. With a brief exception from 1990 to 2007, most changes occurred through the geographical mobility of wages across labor markets. In the 1950–1990 period, this primarily occurred through equalization of wages across regions and within the US South, while the takeoff of highly urban and unequal labor markets made a novel contribution to the 1990–2019 era. In total, results point to the fundamental importance of what I term the topography of inequality, or the contours of inequality levels across spatial locations and the change in the size and location of peaks and valleys across time, for understanding the full nature of long run inequality change.

Statistics Show Value in Old‐School Crop Condition Reports

Statistics Show Value in Old‐School Crop Condition Reports

A Verdict Overturned: Are We at Our Wit's End?

Despite the 2008 Mental Health Parity and Addiction Equity Act, access to psychotherapy through health insurance is quite limited. The 2019 landmark verdict in Wit v. United Behavioral Health offered hope of change. This column describes the recent overturning of the verdict in Wit v. United Behavioral Health, efforts by the Plaintiffs' attorneys to pursue a review by the entire 29-member United States Court of Appeals for the Ninth Circuit of the puzzling decision apparently based on a misunderstanding of the arguments of the case, and steps forward to implement the mental health parity law.

Spatial Distribution of Hateful Tweets Against Asians and Asian Americans During the COVID-19 Pandemic, November 2019 to May 2020.

Objectives. To illustrate the spatiotemporal distribution of geolocated tweets that contain anti-Asian hate language in the contiguous United States during the early phase of the COVID-19 pandemic. Methods. We used a data set of geolocated tweets that match with keywords reflecting COVID-19 and anti-Asian hate and identified geographical clusters using the space-time scan statistic with Bernoulli model. Results. Anti-Asian hate language surged between January and March 2020. We found clusters of hate across the contiguous United States. The strongest cluster consisted of a single county (Ross County, Ohio), where the proportion of hateful tweets was 312.13 times higher than for the rest of the country. Conclusions. Anti-Asian hate on Twitter exhibits a significantly clustered spatiotemporal distribution. Clusters vary in size, duration, strength, and location and are scattered across the entire contiguous United States. Public Health Implications. Our results can inform decision-makers in public health and safety for allocating resources for place-based preparedness and response for pandemic-induced racism as a public health threat. (Am J Public Health. 2022;112(4):646-649. https://doi.org/10.2105/AJPH.2021.306653.

Observed Changes in the Frequency, Intensity, and Spatial Patterns of Nine Natural Hazards in the United States from 2000 to 2019.

There is increasing evidence from across the globe that climate change results in changes in the frequency, location, and impact of natural hazards. Much of this evidence is conceptual, inferential, or simply assumed. To provide objective support to confirm these hypotheses, we constructed county-level time-series datasets (2000-2019) for nine natural hazards for the entire United States. Hazards considered for this study included hurricanes, tropical storms, landslides, wildfires, earthquakes, drought, inland flooding, coastal flooding, and tornadoes. Geospatial analysis techniques were used to calculate the percentage (range: 0-100) of land area in each county exposed to each natural hazard for all the years that hazard data were available. The best available data were acquired from publicly accessible sources. Cumulative distribution functions were calculated for each hazard in five-year intervals to test for statistically significant changes in distribution patterns across the five-year time periods using the Kolmogorov-Smirnov test. There were significant changes in hurricanes, tropical storms, and drought over the two decades; changes in tornadoes, landslides, and wildfires were not significant in terms of frequency, likely due to the site-specific nature of their occurrences. The intensity and spatial distribution and an emerging hot spot and spatial trend analyses and an emerging hot spot and spatial trend analyses were also completed (except for flooding events and earthquakes due to insufficient data). All datasets provide empirical support for earlier inferences concerning the connections between the hazards and climate change. Analyses showed apparent changes in the frequency and intensity of hurricanes, tropical storms, and drought-related to climate change factors. Internal and coastal flooding also demonstrated these connections, although the length of the dataset did not permit significant testing but shows significant hot spots and trending locations. Tornadoes, landslides, and wildfires showed significant hot spots and trending locations, but the specific locational nature of the data did not show significant changes in frequency. Earthquakes showed no significant changes over the time period.

The Impact of Atrial Fibrillation on Hospitalization Outcomes of Endovascular Repair of Abdominal Aortic Aneurysm

ObjectiveWe aimed to study the impact of atrial fibrillation (AF) on hospitalization outcomes of abdominal aortic artery (AAA) endovascular aneurysm repair (EVAR) at a large-scale sample size representative of the entire United States (U.S.) population. MethodsWe included all adults who were hospitalized in the U.S. for AAA repair with EVAR between 2016 and 2017 using the International Classification of Diseases—10th Revision, Clinical procedural diagnosis codes. The primary outcome was all-cause in-hospital mortality. Secondary outcomes were stroke, myocardial infarction, respiratory failure, acute kidney injury (AKI), bleeding event, vascular complications, aortic dissection, length of stay (LOS), and hospitalization cost. Hospitalization outcomes were modeled using logistic regression for binary outcomes and Poisson or log-gamma regression for count or right-skewed numeric outcomes, respectively. ResultsWe included a total of 39,330 records for patients with AAA who underwent EVAR. There were 5.940 patients with AF. On multivariable analysis, EVAR patients with AF had significantly higher odds for mortality with adjusted OR 2.06 (95%CI: 1.09–3.91). They also had significantly higher odds for AKI (A-OR: 1.79 (95%CI: 1.38–2.32), p < 0.001), acute myocardial infarction (A-OR: 2.72 (95%CI: 1.39–5.32), p = 0.004), post procedural bleedings (A-OR: 1.51 (95%CI: 1.20–1.89), p < 0.001), LOS (1.35 (95%CI: 1.24–1.47) p < 0.001) and higher cost (A-OR: 1.06 (9% CI, 1.03–1.09) p < 0.001). There was no significant difference in risk of stroke, vascular complications, and aortic dissection. ConclusionAF is an important independent predictor for mortality and adverse outcomes in patients with AAA undergoing EVAR.

Comprehensive Radar Data for the Contiguous United States: Multi-Year Reanalysis of Remotely Sensed Storms

Abstract The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) dataset blends radar data from the WSR-88D network and Near-Storm Environmental (NSE) model analyses using the Multi-Radar Multi-Sensor (MRMS) framework. The MYRORSS dataset uses the WSR-88D archive starting in 1998–2011, processing all valid single-radar volumes to produce a seamless three-dimensional reflectivity volume over the entire contiguous United States with an approximate 5-min update frequency. The three-dimensional grid has an approximate 1 km × 1 km horizontal dimension and is on a stretched vertical grid that extends to 20 km MSL with a maximal vertical spacing of 1 km. Several reflectivity-derived, severe-storm-related products are also produced, which leverage the ability to merge the MRMS and NSE data. Two Doppler velocity-derived azimuthal shear layer maximum products are produced at a higher horizontal resolution of approximately 0.5 km × 0.5 km. The initial period of record for the dataset is 1998–2011. The dataset underwent intensive manual quality control to ensure that all available and valid data were included while excluding highly problematic radar volumes that were a negligible percentage of the overall dataset, but which caused large data errors in some cases. This dataset has applications toward radar-based climatologies, postevent analysis, machine learning applications, model verification, and warning improvements. Details of the manual quality control process are included and examples of some of these applications are presented.

OPTN/SRTR 2020 Annual Data Report: COVID

OPTN/SRTR 2020 Annual Data Report: COVID

The Jangling Discords of Lynching Memorialization in Jackson, Tennessee: Dreaming the Rhetoric of the South

ABSTRACT Current debates about the appropriateness of Civil War symbolism and toppling Confederate statues raise important questions about how to communicate the history of race relations in the South, and by extension the entire United States. As we confront our painful past and debate who and what to commemorate, we are (re)writing history and (re)creating realities in small towns and big cities through the Southern United States. This narrative ethnography traces the development and placement of a monument to lynching victims in a mid-size Southern city, revealing complex connections and divisions across geographic, temporal, and racial borders.

The Rise and Fall of Bone Morphogenetic Protein 2 Throughout the United States.

Retrospective Database Study. Investigate utilization of bone morphogenetic protein (BMP-2) between 2004 and 2014. The utilization, particularly off-label utilization, of BMP-2 has been controversial and debated in the literature. Given the concerns regarding cancer and potential complications, the risk benefit profile of BMP must be weighed with each surgical case. The debate regarding the costs and potential side effects of BMP-2 compared with autologous iliac crest bone harvest has continued. The National Inpatient Sample (NIS) database was queried for the use of BMP-2 (ICD-9-CM 84.52) between 2004 and 2014 across 44 states. The NIS database represents a 20% sample of discharges, weighted to provide national estimates. BMP-2 utilization rates in spine surgery fusion procedures were calculated as a fraction of the total number of thoracic, lumbar, and sacral spinal fusion surgeries performed each year. Between 2004 and 2014, BMP-2 was utilized in 927,275 spinal fusion surgeries. In 2004, BMP-2 was utilized in 28.3% of all cases (N=48,613). The relative use of BMP-2 in spine fusion surgeries peaked in 2008 at 47.0% (N=112,180). Since then, it has continued to steadily decline with an endpoint of 23.6% of cases in 2014 (N=60,863). Throughout the United States, the utilization of BMP-2 in thoracolumbar fusion surgeries increased from 28.3% to 47.0% between 2004 and 2008. However, from 2008 to 2014, the utilization of BMP-2 in thoracolumbar spine fusion surgeries decreased significantly from 47.0% to 23.4%. While this study provides information on the utilization of BMP-2 for the entire United States over an 11-year period, further research is needed to the determine the factors affecting these trends.

A Review of Racial Disparities in Infant Mortality in the US.

Racial disparities in infant mortality have persisted, despite the overall decline in the United States’ overall infant mortality rate (IMR). The overall IMR of the entire United States (5.58 per 1000 live births) population masks significant disparities by race and ethnicity: the non-Hispanic Black population experienced an IMR of 10.8 followed by people from Native Hawaiian or Other Pacific Islander populations at 9.4 and American Indians at 8.2. The non-Hispanic White and Asian populations in the United States have the lowest IMR at 4.6 and 3.6, respectively, as of 2018. A variety of factors that characterize minority populations, including experiences of racial discrimination, low income and education levels, poor residential environments, lack of medical insurance, and treatment at low-quality hospitals, demonstrate strong correlations with high infant mortality rates. Identifying, acknowledging, and addressing these disparities must be performed before engaging in strategies to mitigate them. Social determinants of health play a major role in health disparities, including in infant mortality. The study and implementation of programs to address neighborhood factors, education, healthcare access and quality, economic stability, and other personal and societal contexts will help us work towards a common goal of achieving health equity, regardless of racial/ethnic background.

Influence of urban expansion on Lyme disease risk: A case study in the U.S. I-95 Northeastern corridor

Currently Lyme disease (LD) is the most common vector-borne disease in the United States. Understanding the potential effects of urban expansion on LD risk is an emerging global health concern. The U.S. Northeastern corridor has experienced a spatio-temporal increase in Lyme disease (LD) and rapid urban expansion over the past decades. The effects of LULCC factors on LD incidence thus were investigated based on county unit through a geographical detector and statistical methods along the Northeastern I-95 corridor during the year of 2008–2018. Analyses indicated that the Northeastern I-95 corridor was characterized by both higher urbanization and increasing LD cases. About 28% of the study area is covered by urban land, which is more than nine times that of the U.S. average. And LD incidence in the study area was calculated up to 39.1 cases per 100,000 population, nearly five times higher than that of the entire United States (8.3 cases according to CDC). In addition, the similar spatial distribution and spatial pattern between LD cases increase and urban expansion were confirmed. The statistical results showed that their similarity in spatial trend was up to 84.9%. Meanwhile, the correlation coefficients also verified that urban expansion has a significant positive correlation with LD incidence (r = 0.511) at the 99% confidential level. Geographical detector analysis further proved that urban expansion is the dominant factor for LD cases increase (P = 0.21). Moreover, the geographic location where the county is located is also a crucial factor for LD cases' impact (P = 0.15). Regional analysis revealed that LD risk appeared in urbanizing areas rather than urbanized and rural areas. These results suggested that rapidly urbanizing areas may become LD hotspots. Our studies improve understanding of LD occurrence in the context of urbanization and provide a more precise public health message to mitigate the tick-borne disease.