Covariate adjusted weighted normal spatial scan statistics with applications to study geographic clustering of obesity and lung cancer mortality in the United States

Role of geographic characteristics in the spatial cluster detection of cancer: Evidence in South Korea, 1999-2013

BACKGROUND

Identification of high-risk areas of cancer, referred to as spatial clusters, can inform targeted policies for cancer control. Although cancer cluster detection could be affected by various geographic characteristics including sociodemographic and environmental factors which impacts could also vary over time, studies accounting for such influence remain limited. This study aims to assess the role of geographic characteristics in the spatial cluster detection for lung and stomach cancer over an extended period.

METHODS

We obtained sex-specific age-standardized incidence and mortality rates of lung and stomach cancer as well as geographic characteristics across 233 districts in South Korea for three five-year periods between 1999 and 2013. We classified geographic characteristics of each district into four categories: demography, socioeconomic status, behaviors, and physical environments. Specifically, we quantified physical environments using measures of greenness, concentrations of particulate matter and nitrogen dioxide, and air pollution emissions. Finally, we conducted cluster detection analyses using weighted normal spatial scan statistics with the residuals from multiple regression analyses performed with the four progressive sets of geographic attributes.

RESULTS

We found that the size of clusters reduced as we progressively adjusted for geographic covariates. Among the four categories, physical environments had the greatest impact on the reduction or disappearance of clusters particularly for lung cancer consistently over time. Whereas older population affected a decrease of lung cancer clusters in the early period, the contribution of education was large in the recent period. The impact was less clear in stomach cancer than lung cancer.

CONCLUSION

Our findings highlight the importance of geographic characteristics in explaining the existing cancer clusters and identifying new clusters, which jointly provides practical guidance to cancer control.

Role of country of origin and state of residence for dietary change among foreign-born adults in the US

Place of origin and place of current residence may shape migrants' health-related behaviors. Using the nationally-representative US New Immigrant Survey (n = 7930), we examined associations between country of origin, state of residence, and dietary changes among foreign-born adults. 65% of migrants reported dietary change since immigration (mean score = 7.3; range = 1-10); 6% of the variance was explained by country of origin characteristics; 1.6% by US state of residence; 1.4% by their interaction. Country of origin factors, specifically availability of animal source foods and sweets, were associated with dietary change, availability of sweets also including greater abandonment of specific foods and adoption of others.

A simulation study for geographic cluster detection analysis on population-based health survey data using spatial scan statistics

Background

In public health and epidemiology, spatial scan statistics can be used to identify spatial cluster patterns of health-related outcomes from population-based health survey data. Although it is appropriate to consider the complex sample design and sampling weight when analyzing complex sample survey data, the observed survey responses without these considerations are often used in many studies related to spatial cluster detection.

Methods

We conducted a simulation study to investigate which data type from complex survey data is more suitable for use by comparing the spatial cluster detection results of three approaches: (1) individual-level data, (2) weighted individual-level data, and (3) aggregated data.

Results

The results of the spatial cluster detection varied depending on the data type. To compare the performance of spatial cluster detection, sensitivity and positive predictive value (PPV) were evaluated over 100 iterations. The average sensitivity was high for all three approaches, but the average PPV was higher when using aggregated data than when using individual-level data with or without sampling weights.

Conclusions

Through the simulation study, we found that use of aggregate-level data is more appropriate than other types of data, when searching for spatial clusters using spatial scan statistics on population-based health survey data.

Identifying subgroups of age and cohort effects in obesity prevalence

The obesity epidemic represents an important public health issue in the United States. Studying obesity trends across age groups over time helps to identify crucial relationships between the disease and medical treatment allowing for the development of effective prevention policies. We aim to define subgroups of age and cohort effects in obesity prevalence over time by considering an optimization approach applied to the age-period-cohort (APC) model. We consider a heterogeneous regression problem where the regression coefficients are age dependent and belong to subgroups with unknown grouping information. Using the APC model, we apply the alternating direction method of multipliers (ADMM) algorithm to develop a two-step algorithm for (1) subgrouping of cohort effects based on similar characteristics and (2) subgrouping age effects over time. The proposed clustering approach is illustrated for the United States population, aged 18-79, during the period 1990-2017.

Diallyl trisulfide inhibits cell migration and invasion of human melanoma a375 cells via inhibiting integrin/facal adhesion kinase pathway

Melanoma is the leading cause of death from skin disease due to its propensity for metastasis. Studies have shown that integrin-mediated focal adhesion kinase (FAK) signal pathway is implicated in cell proliferation, survival and metastasis of tumor cells. Our previous results indicated that diallyl trisulfide (DATS) provided its antimelanoma activity via inducing cell cycle arrest and apoptosis. The aim of this study was to explore DATS mediated antimetastatic effect and the corresponding mechanism in human melanoma A375 cells. We found that DATS exhibited an inhibitory effect on the abilities of migration and invasion in A375 cells under noncytotoxic concentrations analyzed by wound healing assays and Matrigel invasion chamber system. DATS attenuated invasion of A375 cells with characteristic of decreased activities and protein expressions of matrix metalloproteinase-2 (MMP-2) and MMP-9. Moreover, DATS exerted an inhibitory effect on cell adhesion of A375 cells, which is in correlation with the change in integrin signaling pathway. Results of Western blotting showed that DATS decreased the levels of several integrin subunits, including α4, α5, αv, β1, β3 and β4. Subsequently, DATS induced a strong decrease in total FAK, phosphorylated FAK Tyr-397,-576, -577, and disorganized F-actin stress fibers, resulting in a nonmigratory phenotype. These results suggest that the antimetastatic potential of DATS for human melanoma cells might be due to the disruption of integrin/FAK signaling pathway.

Distribuição espacial da obesidade em área urbana no Brasil

Resumo A distribuição espacial de uma doença é importante para o diagnóstico e o conhecimento epidemiológico da situação e das tendências de saúde, permitindo uma melhor compreensão acerca dos fatores que determinam o estado de saúde das populações. O objetivo do estudo foi analisar a distribuição espacial da obesidade em adultos em Belo Horizonte, a partir da base de dados do Sistema de Vigilância de Fatores de Risco e de Proteção para Doenças Crônicas por Inquérito Telefônico de 2008 a 2010. A obesidade foi definida como índice de massa corporal 30 kg/m2. Foi desenvolvida uma base georreferenciada com os dados ambientais a partir do endereço e CEP do local. Empregou-se a estatística de varredura espacial. Uma análise comparativa das variáveis ambientais referentes aos conglomerados de maior e menor prevalência de obesidade foi realizada. Foi encontrado um conglomerado de indivíduos obesos na área central da cidade, sem significância estatística. Verificou-se, também, agrupamento significativo de indivíduos não obesos no leste da cidade. Esses resultados sugerem que as razões para a baixa prevalência de obesidade em áreas urbanas brasileiras podem estar relacionadas à melhor organização social e alta disponibilidade de comércios de alimentos e de locais para a prática de atividade física.

Spatial clustering of physical activity and obesity in relation to built environment factors among older women in three U.S. states

BACKGROUND

Identifying spatial clusters of chronic diseases has been conducted over the past several decades. More recently these approaches have been applied to physical activity and obesity. However, few studies have investigated built environment characteristics in relation to these spatial clusters. This study's aims were to detect spatial clusters of physical activity and obesity, examine whether the geographic distribution of covariates affects clusters, and compare built environment characteristics inside and outside clusters.

METHODS

In 2004, Nurses' Health Study participants from California, Massachusetts, and Pennsylvania completed survey items on physical activity (N = 22,599) and weight-status (N = 19,448). The spatial scan statistic was utilized to detect spatial clustering of higher and lower likelihood of obesity and meeting physical activity recommendations via walking. Clustering analyses and tests that adjusted for socio-demographic and health-related variables were conducted. Neighborhood built environment characteristics for participants inside and outside spatial clusters were compared.

RESULTS

Seven clusters of physical activity were identified in California and Massachusetts. Two clusters of obesity were identified in Pennsylvania. Overall, adjusting for socio-demographic and health-related covariates had little effect on the size or location of clusters in the three states with a few exceptions. For instance, adjusting for husband's education fully accounted for physical activity clusters in California. In California and Massachusetts, population density, intersection density, and diversity and density of facilities in two higher physical activity clusters were significantly greater than in neighborhoods outside of clusters. In contrast, in two other higher physical activity clusters in California and Massachusetts, population density, diversity of facilities, and density of facilities were significantly lower than in areas outside of clusters. In Pennsylvania, population density, intersection density, diversity of facilities, and certain types of facility density inside obesity clusters were significantly lower compared to areas outside the clusters.

CONCLUSIONS

Spatial clustering techniques can identify high and low risk areas for physical activity and obesity. Although covariates significantly differed inside and outside the clusters, patterns of differences were mostly inconsistent. The findings from these spatial analyses could eventually facilitate the design and implementation of more resource-efficient, geographically targeted interventions for both physical activity and obesity.

The spatial distribution of overweight and obesity among a birth cohort of young adult Filipinos (Cebu Philippines, 2005): an application of the Kulldorff spatial scan statistic

OBJECTIVES

The objectives of the study were to test for spatial clustering of obesity in a cohort of young adults in the Philippines, to estimate the locations of any clusters, and to relate these to neighborhood-level urbanicity and individual-level socioeconomic status (SES).

SUBJECTS

Data are from a birth cohort of young adult (mean age 22 years) Filipino males (n=988) and females (n=820) enrolled in the Cebu Longitudinal Health and Nutrition Survey.

METHODS

We used the Kulldorff spatial scan statistic to detect clusters associated with unusually low or high prevalences of overweight or obesity (defined using body mass index, waist circumference and body fat percentage). Cluster locations were compared to neighborhood-level urbanicity, which was measured with a previously validated scale. Individual-level SES was adjusted for using a principal components analysis of household assets.

RESULTS

High-prevalence clusters were typically centered in urban areas, but often extended into peri-urban and even rural areas. There were also differences in clustering by both sex and the measure of obesity used. Evidence of clustering in males, but not females, was much weaker after adjustment for SES.

Racial differences in the built environment--body mass index relationship? A geospatial analysis of adolescents in urban neighborhoods

BACKGROUND

Built environment features of neighborhoods may be related to obesity among adolescents and potentially related to obesity-related health disparities. The purpose of this study was to investigate spatial relationships between various built environment features and body mass index (BMI) z-score among adolescents, and to investigate if race/ethnicity modifies these relationships. A secondary objective was to evaluate the sensitivity of findings to the spatial scale of analysis (i.e. 400- and 800-meter street network buffers).

METHODS

Data come from the 2008 Boston Youth Survey, a school-based sample of public high school students in Boston, MA. Analyses include data collected from students who had georeferenced residential information and complete and valid data to compute BMI z-score (n = 1,034). We built a spatial database using GIS with various features related to access to walking destinations and to community design. Spatial autocorrelation in key study variables was calculated with the Global Moran's I statistic. We fit conventional ordinary least squares (OLS) regression and spatial simultaneous autoregressive error models that control for the spatial autocorrelation in the data as appropriate. Models were conducted using the total sample of adolescents as well as including an interaction term for race/ethnicity, adjusting for several potential individual- and neighborhood-level confounders and clustering of students within schools.

RESULTS

We found significant positive spatial autocorrelation in the built environment features examined (Global Moran's I most ≥ 0.60; all p = 0.001) but not in BMI z-score (Global Moran's I = 0.07, p = 0.28). Because we found significant spatial autocorrelation in our OLS regression residuals, we fit spatial autoregressive models. Most built environment features were not associated with BMI z-score. Density of bus stops was associated with a higher BMI z-score among Whites (Coefficient: 0.029, p < 0.05). The interaction term for Asians in the association between retail destinations and BMI z-score was statistically significant and indicated an inverse association. Sidewalk completeness was significantly associated with a higher BMI z-score for the total sample (Coefficient: 0.010, p < 0.05). These significant associations were found for the 800-meter buffer.

CONCLUSION

Some relationships between the built environment and adolescent BMI z-score were in the unexpected direction. Our findings overall suggest that the built environment does not explain a large proportion of the variation in adolescent BMI z-score or racial disparities in adolescent obesity. However, there are some differences by race/ethnicity that require further research among adolescents.