A decreased impact of air pollution on hospital pneumonia visits during COVID-19 outbreak in northeastern Thailand

Background

The coronavirus disease 2019 (COVID-19) pandemic had effects on changes in people, society, and pollutant sources. This was a unique research opportunity to assess the effects on the risk of pneumonia resulted from the changes in air pollution and personal hygiene regarding city lockdown.

Methods

This study, we estimated time-series relative risks (RRs) of pneumonia (n=94,288) associated with PM10, PM2.5, NO2, and O3 in Khon Kaen province and its vicinity, using Poison regression with generalized additive model and compared air pollutant-associated risk of pneumonia before vs. during the COVID-19 outbreak [2018-2021].

Results

During the COVID-19 period, pneumonia cases, PM2.5, PM10, and NO2 levels were lower than those before the COVID-19 but the O3 level was significantly higher. The single-pollutant analyses showed that the increase in PM10, PM2.5, and NO2 were significantly associated with pneumonia risks at single-day lag 0 in the earlier two years (2018-2019). For multi-pollutant analyses, there were higher RRs in PM2.5 at lag 0 [RR =1.078, 95% confidence interval (CI): 1.004 to 1.157], lag 4 (RR =1.054, 95% CI: 1.011 to 1.098) and lag 5 (RR =1.090, 95% CI: 1.021 to 1.165) and for all cumulative-day lags, greatest was at lag 0-5 (RR =1.314, 95% CI: 1.200 to 1.439) before the COVID-19 period while there were lower pneumonia RRs of a 10-µg/m3 increase in PM2.5 at single-day lag 1 (RR =1.064, 95% CI: 1.002 to 1.130) and for all cumulative-day lags, greatest was at lag 0-5 (RR =1.201, 95% CI: 1.073 to 1.344) during the COVID-19 outbreak. Multi-pollutant of NO2 significantly increased pneumonia risk in cumulative day exposure before the COVID-19 outbreak at lag 0-3 (RR =1.050, 95% CI: 1.001 to 1.100). It was significantly greater than that risk during the outbreak.

Conclusions

This study revealed that the lockdown measures to control COVID-19 were effective in improving air quality and lowering associated pneumonia risk. These findings would help raise awareness about measures and policies to preserve the air quality to increase respiratory health benefits.

Shifts in competitive structures can drive variation in species' phenology

For many species, a well documented response to anthropogenic climate change is a shift in various aspects of its life history, including its timing or phenology. Often, these phenological shifts are associated with changes in abiotic factors used as proxies for resource availability or other suitable conditions. Resource availability, however, can also be impacted by competition, but the impact of competition on phenology is less studied than abiotic drivers. We fit generalized additive models (GAMs) to a long-term experimental dataset on small mammals monitored in the southwestern United States and show that altered competitive landscapes can drive shifts in breeding timing and prevalence, and that, relative to a dominant competitor, other species exhibit less specific responses to environmental factors. These results suggest that plasticity of phenological responses, which is often described in the context of annual variation in abiotic factors, can occur in response to biotic context as well. Variation in phenological responses under different biotic conditions shown here further demonstrates that a more nuanced understanding of shifting biotic interactions is useful to better understand and predict biodiversity patterns in a changing world.

Sleep Duration and Metabolic Syndrome among Early Adolescents-A Cross-Sectional Study in Khartoum State, Sudan

Numerous studies have reported that sleep disorders are linked to poor health outcomes. However, studies on these associations in children and adolescents in an African context are limited. The aim of the present study was to analyze the relationship between sleep duration and the presence of metabolic syndrome among early adolescents in Sudan. Methods: A cross-sectional study was conducted on participants aged 10-15 years in Khartoum State, Sudan. Metabolic syndrome (MetS) was diagnosed by increased waist circumference and the presence of two or more metabolic abnormalities (triglycerides [TG], high-density lipoproteins [HDL-C], blood pressure [BP], and fasting plasma glucose [FBG]). Short sleep duration was defined based on National Sleep Foundation (NSF) classification. Data were collected by physical examinations, biochemical analyses, and self-developed standardized questionnaires. Data were analyzed with IBM SPSS Statistics Version 24. A generalized additive model (GAM) was used for the smoothing function between sleep duration and MetS. p < 0.05 was considered as significant. Results: The prevalence of MetS and short sleep among early adolescents aged 10-15 years in Sudan was 2.3% and 55.0%, respectively. A higher prevalence of short sleep was found among overweight and obese participants (p < 0.05). The prevalence of MetS among short sleepers was 2.8%. Binary logistic regression analysis showed that male short sleepers had higher odds of having MetS compared to female short sleepers. The relationship between short sleep and low HDL-C in boys and between short sleep and high TG in girls was statistically significant. The highest risk of MetS was observed at less than 6.5 h of sleep per night. Conclusions: Short sleep duration was significantly associated with overweight/obesity in the total population and with low HDL-C in boys and high triglycerides in girls. A nonlinear curve pattern was observed between sleep duration and prevalence of MetS. Longitudinal studies are needed to further determine the causal relationship between sleep habits and MetS and its components.

Association between ambient air pollution and daily hospital visits for cardiovascular diseases in Wuhan, China: a time-series analysis based on medical insurance data

Although evidence showed the adverse effects of air pollution on cardiovascular disease (CVDs), few studies were based on medically insured populations. We applied a generalized additive Poisson model (GAM) to estimate the short-term effects of ambient air pollution on a group of medically insured population in Wuhan, China. We extracted daily air pollution data, meteorological data, and daily hospital visits for CVDs. We found that the ambient air pollutants sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ground-level ozone (O₃) particulate matter (PM) with an aerodynamic diameter ≤10 μm (PM₁₀), and those ≤2.5 μm (PM_2.5) all increased the risk of daily hospital visits for CVDs. We also found that the effect of air pollution on daily hospital visits for CVDs is greater in the cold season than in the warm season. Our findings can be used as evidence that supports the formulation of policies for air pollution and CVDs.

The association between air pollutants and hospitalizations for asthma: an evidence from Chengdu, China

Background

Exposure to air pollutants has been linked to the exacerbation of asthma, and it has become a risk to human welfare and health throughout the globe. Previous works did not achieve a systematic study by considering interactive effects of age, gender, and meteorological factors on the exposure-effect relationship between air pollutants and asthma. We aimed to quantitatively investigate the effects of air pollutants on hospitalizations for asthma in Chengdu, China.

Methods

This is a retrospective and population-based study. Data of asthma hospitalization records for residents, the average daily concentrations of air pollutants including SO₂, CO, NO₂, O₃, PM10, and PM2.5, and meteorological variables from 1 January 2014 to 31 December 2014 in Chengdu, China, were obtained from several government departments. A generalized additive model (GAM) was used to analyze the exposure-effect relationship between air pollutants and daily asthma hospitalizations after controlling the long-time seasonal trend, "day-of-week (DOW)" effect, holiday effect, and confounding meteorological factors.

Results

A total of 7,503 hospitalizations were assessed. Significant associations between hospitalizations and air pollutants were found. The relative risk (RR) for hospitalizations for every 10 µg/m³ increase in PM2.5 and PM10 for the male group were 1.0121 [95% confidence interval (CI): 1.0012-1.0232] and 1.0075 (95% CI: 1.0001-1.015), respectively. The elderly (≥65 years old) tended to have a higher RR (1.0022; 95% CI: 1.0001-1.0043) for each 10 mg/m³ increase in CO than the other age groups. All pollutants had slightly protective effects on the younger age group (≤14 years old). O₃ had more significant effects in cold season, whereas SO₂ impacted more significantly in warm seasons, particularly for females and adults (14-65 years old).

Conclusions

Adverse effects of ambient concentrations of air pollutants on hospitalizations for asthma are evident, especially in specific population groups. Male patients were more susceptible to PM2.5 and PM10, and the elderly were more sensitive to CO. The effects of O₃ in China were significant in the cold season, whereas SO₂ impacted more significantly in the warm season particularly on females and adults. The study would be meaningful for asthma intervention and corresponding healthcare resource management.

Effects of ambient particulate exposure on blood lipid levels in hypertension inpatients

Background

With modernization development, multiple studies of atmospheric particulate matter exposure conducted in China have confirmed adverse cardiovascular health effects. However, there are few studies on the effect of particulate matter on blood lipid levels in patients with cardiovascular disease, especially in southern China. The purpose of this study was to investigate the association between short- and long-term exposure to ambient particulate matter and the levels of blood lipid markers in hypertension inpatients in Ganzhou, China.

Methods

Data on admission lipid index testing for hypertension inpatients which were divided into those with and without arteriosclerosis disease were extracted from the hospital's big data center from January 1, 2016 to December 31, 2020, and air pollution and meteorology data were acquired from the China urban air quality real time release platform from January 1, 2015 to December 31, 2020 and climatic data center from January 1, 2016 to December 31, 2020, with data integrated according to patient admission dates. A semi-parametric generalized additive model (GAM) was established to calculate the association between ambient particulate matter and blood lipid markers in hypertension inpatients with different exposure time in 1 year.

Results

Long-term exposure to particulate matter was associated with increased Lp(a) in three kinds of people, and with increased TC and decreased HDL-C in total hypertension and hypertension with arteriosclerosis. But particulate matter was associated with increased HDL-C for hypertension inpatients without arteriosclerosis, at the time of exposure in the present study. It is speculated that hypertension inpatients without arteriosclerosis has better statement than hypertension inpatients with arteriosclerosis on human lipid metabolism.

Conclusion

Long-term exposure to ambient particulate matter is associated with adverse lipid profile changes in hypertension inpatients, especially those with arteriosclerosis. Ambient particulate matter may increase the risk of arteriosclerotic events in hypertensive patients.

Generalized models for quantifying laterality using functional transcranial Doppler ultrasound

We consider how analysis of brain lateralization using functional transcranial Doppler ultrasound (fTCD) data can be brought in line with modern statistical methods typically used in functional magnetic resonance imaging (fMRI). Conventionally, a laterality index is computed in fTCD from the difference between the averages of each hemisphere's signal within a period of interest (POI) over a series of trials. We demonstrate use of generalized linear models (GLMs) and generalized additive models (GAM) to analyze data from individual participants in three published studies (N = 154, 73 and 31), and compare this with results from the conventional POI averaging approach, and with laterality assessed using fMRI (N = 31). The GLM approach was based on classic fMRI analysis that includes a hemodynamic response function as a predictor; the GAM approach estimated the response function from the data, including a term for time relative to epoch start (simple GAM), plus a categorical index corresponding to individual epochs (complex GAM). Individual estimates of the fTCD laterality index are similar across all methods, but error of measurement is lowest using complex GAM. Reliable identification of cases of bilateral language appears to be more accurate with complex GAM. We also show that the GAM-based approach can be used to efficiently analyze more complex designs that incorporate interactions between tasks.

Use of Generalized Weighted Quantile Sum Regressions of Tumor Necrosis Factor Alpha and Kidney Function to Explore Joint Effects of Multiple Metals in Blood

Exposure to heavy metals could lead to adverse health effects by oxidative reactions or inflammation. Some essential elements are known as reactors of anti-inflammatory enzymes or coenzymes. The relationship between tumor necrosis factor alpha (TNF-α) and heavy metal exposures was reported. However, the interaction between toxic metals and essential elements in the inflammatory response remains unclear. This study aimed to explore the association between arsenic (As), cadmium (Cd), lead (Pb), cobalt (Co), copper (Cu), selenium (Se), and zinc (Zn) in blood and TNF-α as well as kidney function. We enrolled 421 workers and measured the levels of these seven metals/metalloids and TNF-α in blood; kidney function was calculated by CKD-EPI equation. We applied weighted quantile sum (WQS) regression and group WQS regression to assess the effects of metal/metalloid mixtures to TNF-α and kidney function. We also approached the relationship between metals/metalloids and TNF-α by generalized additive models (GAM). The relationship of the exposure−response curve between Pb level and TNF-α in serum was found significantly non-linear after adjusting covariates (p < 0.001). Within the multiple-metal model, Pb, As, and Zn were associated with increased TNF-α levels with effects dedicated to the mixture of 50%, 31%, and 15%, respectively. Grouped WQS revealed that the essential metal group showed a significantly negative association with TNF-α and kidney function. The toxic metal group found significantly positive associations with TNF-α, serum creatinine, and WBC but not for eGFR. These results suggested Pb, As, Zn, Se, and mixtures may act on TNF-α even through interactive mechanisms. Our findings offer insights into what primary components of metal mixtures affect inflammation and kidney function during co-exposure to metals; however, the mechanisms still need further research.

PFAS Concentrations and Cardiometabolic Traits in Highly Exposed Children and Adolescents

Background: Residents of a large area of north-eastern Italy were exposed for decades to high concentrations of perfluoroalkyl and polyfluoroalkyl substances (PFAS) via drinking water. Despite the large amount of evidence in adults of a positive association between serum PFAS and metabolic outcomes, studies focusing on children and adolescents are limited. We evaluated the associations between serum PFAS concentrations that were quantifiable in at least 40% of samples and lipid profile, blood pressure (BP) and body mass index (BMI) in highly exposed adolescents and children. Methods: A cross-sectional analysis was conducted in 6669 adolescents (14–19 years) and 2693 children (8–11 years) enrolled in the health surveillance program of the Veneto Region. Non-fasting blood samples were obtained and analyzed for perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), perfluorohexanesulfonic acid (PFHxS), perfluorononanoic acid (PFNA), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and triglycerides. Low-density lipoprotein cholesterol (LDL-C) was calculated. Systolic and diastolic BP were measured, and BMI z-score accounting for age and sex was estimated. The associations between ln-transformed PFAS (and categorized into quartiles) and continuous outcomes were assessed using generalized additive models. The weighted quantile sum regression approach was used to assess PFAS-mixture effects for each outcome. Analyses were stratified by gender and adjusted for potential confounders. Results: Among adolescents, significant associations were detected between all investigated PFAS and TC, LDL-C, and to a lesser extent HDL-C. Among children, PFOS and PFNA had significant associations with TC, LDL-C and HDL-C, while PFOA and PFHxS had significant associations with HDL-C only. Higher serum concentrations of PFAS, particularly PFOS, were associated with lower BMI z-score. No statistically significant associations were observed between PFAS concentrations and BP. These results were confirmed by the multi-pollutant analysis. Conclusions: Our study supports a consistent association between PFAS concentration and serum lipids, stronger for PFOS and PFNA and with a greater magnitude among children compared to adolescents, and a negative association of PFAS with BMI.

Short-Term Association of Air Pollutant Levels and Hospital Admissions for Stroke and Effect Modification by Apparent Temperature: Evidence From Shanghai, China

The epidemiological evidence on relationships between air pollution, temperature, and stroke remains inconclusive. Limited evidence is available for the effect modification by apparent temperature, an indicator reflecting reactions to the thermal environment, on short-term associations between air pollution and hospital admissions for stroke. We used a generalized additive model with Poisson regression to estimate the relative risk (RR) of stroke admissions in Shanghai, China, between 2014 and 2016 associated with air pollutants, with subgroup analyses by age, sex, apparent temperature, and season. During the study period, changes in the daily number of stroke admissions per 10 μg/m³ increase in nitrogen dioxide (at lags 0, 1, 0–1, and 0–2) ranged from 1.05 (95% CI: 0.82%, 2.88%) to 2.24% (95% CI: 0.84%, 3.65%). For each 10 μg/m³ increase in sulfur dioxide concentrations at lags 1, 2, 0–1, and 0–2, the RR of daily stroke admissions increased by 3.34 (95% CI: 0.955%, 5.79%), 0.32 (95% CI: −1.97%, 2.67%), 3.33 (95% CI: 0.38%, 6.37%), and 2.86% (95% CI: −0.45%, 6.28%), respectively. The associations of same-day exposure to nitrogen dioxide with stroke admissions remained significant after adjustment for ozone levels. These associations were not modified by sex, age, apparent temperature, or season. More research is warranted to determine whether apparent temperature modifies the associations between air pollution and stroke admissions.

Use of Generalized Additive Model to Detect the Threshold of δ-Aminolevulinic Acid Dehydratase Activity Reduced by Lead Exposure

Background: Lead inhibits the enzymes in heme biosynthesis, mainly reducing δ-aminolevulinic acid dehydratase (ALAD) activity, which could be an available biomarker. The aim of this study was to detect the threshold of δ-aminolevulinic acid dehydratase activity reduced by lead exposure. Methods: We collected data on 121 lead workers and 117 non-exposed workers when annual health examinations were performed. ALAD activity was determined by the standardized method of the European Community. ALAD G177C (rs1800435) genotyping was conducted using the polymerase chain reaction and restricted fragment length polymorphism (PCR-RFLP) method. In order to find a threshold effect, we used generalized additive models (GAMs) and scatter plots with smoothing curves, in addition to multiple regression methods. Results: There were 229 ALAD1-1 homozygotes and 9 ALAD1-2 heterozygotes identified, and no ALAD2-2 homozygotes. Lead workers had significantly lower ALAD activity than non-exposed workers (41.6 ± 22.1 vs. 63.3 ± 14.0 U/L, p < 0.001). The results of multiple regressions showed that the blood lead level (BLL) was an important factor inversely associated with ALAD activity. The possible threshold of BLL affecting ALAD activity was around 5 μg/dL. Conclusions: ALAD activity was inhibited by blood lead at a possible threshold of 5 μg/dL, which suggests that ALAD activity could be used as an indicator for lead exposure regulation.

[Influencing Factors of Ozone Concentration in Xi'an Based on Generalized Additive Models]

Based on the ozone monitoring data from 2014 to 2018, we presented the variation of ozone concentration in Xi'an and revealed the effects of ozone concentration by meteorological factors based on the generalized additive model (GAM). The results showed that ① with increasing ozone concentration year by year, the assessment standard of ozone pollution was overtaken by three consecutive years since 2016. However, the rising trend was slowed down since 2017 as a result of the strengthened pollution control during summer. ② The monthly curve of ozone concentration was presented as a reversed "V" model with a rising trend accompanying the rising temperature from January to July and a decreasing one during the rest of the year, peaking in July in terms of average monthly ozone concentration. However, this model would turn into an "M" in years with high precipitation when the valley witnessed the highest precipitation in a month. ③ The ozone pollution increased from the year 2014 to 2018 with a stretch-forward ozone polluted time. Furthermore, the rates of ozone non-attainment increased from 1.9% in 2014 to 14% in 2018. In addition, the time ozone pollution emerged advanced from July to May. ④ Based on the GAM model, ozone concentration was non-colinearly related to temperature, air pressure, sunshine duration, and relative humidity. However, the curves of these factors varied considerably, with a positive influence of temperature and sunshine duration and a negative influence of air pressure and relative humidity. The influence of precipitation was mainly witnessed in summers, while no influence of wind was observed. Furthermore, ozone pollution can be easily triggered under the following conditions:temperature>24℃, air pressure <962 hPa, sunshine duration>9 h, and a relative humidity 36%-65% with no rain.

[Spatial distribution and inter-annual variability of spawning grounds of Cynoglossus joyneri in the Yellow Sea coastal waters in summer]

With survey data from three voyages in the summer of 2014, 2016, and 2017, spatial distribution and inter-annual variability of spawning grounds of Cynoglossus joyneri in the coastal waters of Yellow Sea were examined based on Tweedie-generalized additive model (GAM). The results showed that the spawning grounds of C. joyneri mainly distributed in the Haizhou Bay ranging within 34°00'-35°18' N, 119°30'-121°30' E. The other one in Subei Shoal 32°18'-34°00' N, 120°18'-122°00' E. Fish eggs of C. joyneri were few in the southern waters of Shandong Peninsula 34°42'-36°48' N, 119°30'-122°00' E. The distribution of fish eggs was significantly related to the location (i.e., longitude and latitude), depth, and sea surface temperature, and did not relate to the sea surface salinity and surface chlorophyll a. The optimal range of depth, sea surface temperature, sea surface salinity, and surface chlorophyll a were 15-26 m, 29-32 ℃, 22-25 and 0.10-3 mg·m^-3, respectively. There was notable inter-annual variability in the spatial distribution of spawning grounds of C. joyneri and its influencing factors. Overall, the distribution of spawning grounds is relatively stable, but potentially move northward in the year with high sea surface temperature.

Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions

The statistical practice of modeling interaction with two linear main effects and a product term is ubiquitous in the statistical and epidemiological literature. Most data modelers are aware that the misspecification of main effects can potentially cause severe type I error inflation in tests for interactions, leading to spurious detection of interactions. However, modeling practice has not changed. In this article, we focus on the specific situation where the main effects in the model are misspecified as linear terms and characterize its impact on common tests for statistical interaction. We then propose some simple alternatives that fix the issue of potential type I error inflation in testing interaction due to main effect misspecification. We show that when using the sandwich variance estimator for a linear regression model with a quantitative outcome and two independent factors, both the Wald and score tests asymptotically maintain the correct type I error rate. However, if the independence assumption does not hold or the outcome is binary, using the sandwich estimator does not fix the problem. We further demonstrate that flexibly modeling the main effect under a generalized additive model can largely reduce or often remove bias in the estimates and maintain the correct type I error rate for both quantitative and binary outcomes regardless of the independence assumption. We show, under the independence assumption and for a continuous outcome, overfitting and flexibly modeling the main effects does not lead to power loss asymptotically relative to a correctly specified main effect model. Our simulation study further demonstrates the empirical fact that using flexible models for the main effects does not result in a significant loss of power for testing interaction in general. Our results provide an improved understanding of the strengths and limitations for tests of interaction in the presence of main effect misspecification. Using data from a large biobank study "The Michigan Genomics Initiative", we present two examples of interaction analysis in support of our results.

[Relationship between the environmental factors and the CPUE (catch per unit effort) of Pseudocarcharias kamoharai in tropical Atlantic Ocean based on the GAM-Tweedie model]

Crocodile shark (Pseudocarcharias kamoharai) is an important by-catch species of tropical Atlantic tuna longline fishery. The research on the relationship between the CPUE (catch per unit effort) and environmental factors can provide basic data for resource protection and management of this important species. Based on the Tweedie distribution theory, and the data of 1085 croco-dile sharks collected by the national observers of longline fishery from 2009 to 2015 in the tropical Atlantic Ocean (6.38° S-14.92° N, 42.03°-18.53° W), theGAM-Tweedie model was established, and the temporal and spatial effects of CPUE as well as the relationship with environmental factors were analyzed. The results showed the explained cumulative deviance of the crocodile shark's CPUE to be 40.2%. The spatio-temporal factors (year, month, longitude, latitude) all hadsignificant effects (P<0.05) on the crocodile shark CPUE. The environmental factors such as sea surface height, chlorophyll a concentration, weather code, sea surface salinity and sea level pressure all had significant effects on CPUE (P<0.05). However, the Beaufort wind force and lunar calendar did not have significant effects on theCPUE (P>0.05). Significant correlation (P<0.05) was observed at different latitudes and in months between the sea surface temperature and CPUE. The standardized CPUE of the crocodile shark in 2009-2015 showed a fluctuating increase trend.

Association between Pollen Risk Indexes, Air Pollutants, and Allergic Diseases in Korea

OBJECTIVES

This study, different from the past researches, has been conducted in all age groups to understand the association between air pollutants, pollen risk indexes, and outpatients with allergic rhinitis (AR), asthma, and atopic dermatitis (AD).

METHODS

Data on air pollutants, pollen risk indexes, and outpatients with each disease were collected from 2003 through 2011 to verify the association between them. All data are time-series materials that have been observed by time (day) and region, and are in a nonlinear shape. In particular, the total number of outpatients per day is a count data that had a Poisson distribution as the response variable. SAS 9.3 was used to make a statistical model, generalized additive model, with lag effects for the analysis.

RESULTS

For allergic diseases during spring (April-May) and fall (September-October), a significant association was shown between the variables of air pollutants, pollens, and the number of outpatients. Especially, the estimates of NO2 [AR (43.00967 ± 0.11284), asthma (52.01837 ± 0.06452), AD (52.01837 ± 0.06452), p < 0.001] in spring and SO2 [AR (43.00967 ± 0.11284), asthma (52.01837 ± 0.06452), AD (52.01837 ± 0.06452), p < 0.001] in fall were highly significant and showed a positive association with all diseases.

CONCLUSION

Domestically and even internationally, various studies on the allergic diseases are being conducted. However, not many studies related to similar studies. In the need of creating grounds to back up these efforts, additional studies on allergic diseases, as well as researches utilizing pollen data, air pollution data, and claims data provided by the Health Insurance Corporation that has no problem in the representativeness of the data that have close relationships to the allergic disease will be needed.

Evaluation of the performance of smoothing functions in generalized additive models for spatial variation in disease

Generalized additive models (GAMs) with bivariate smoothing functions have been applied to estimate spatial variation in risk for many types of cancers. Only a handful of studies have evaluated the performance of smoothing functions applied in GAMs with regard to different geographical areas of elevated risk and different risk levels. This study evaluates the ability of different smoothing functions to detect overall spatial variation of risk and elevated risk in diverse geographical areas at various risk levels using a simulation study. We created five scenarios with different true risk area shapes (circle, triangle, linear) in a square study region. We applied four different smoothing functions in the GAMs, including two types of thin plate regression splines (TPRS) and two versions of locally weighted scatterplot smoothing (loess). We tested the null hypothesis of constant risk and detected areas of elevated risk using analysis of deviance with permutation methods and assessed the performance of the smoothing methods based on the spatial detection rate, sensitivity, accuracy, precision, power, and false-positive rate. The results showed that all methods had a higher sensitivity and a consistently moderate-to-high accuracy rate when the true disease risk was higher. The models generally performed better in detecting elevated risk areas than detecting overall spatial variation. One of the loess methods had the highest precision in detecting overall spatial variation across scenarios and outperformed the other methods in detecting a linear elevated risk area. The TPRS methods outperformed loess in detecting elevated risk in two circular areas.