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We discuss the use of quantitative data and methods to understand where and how COVID-19 spreads, to estimate and predict its impacts on population health and wellbeing, and to plan effective public health responses. Geographic approaches often involve developing multi-scalar and dynamic models that incorporate geographic processes and variability, harnessing big and real-time data on people’s mobilities and interactions, and paying attention to how gender, ethnicity, and other dimensions of people’s identities intersect with larger structures in impacting the uneven geographies of COVID-19 risk. Our chapter addresses each of these topics while highlighting the need for critical and place-based approaches that are sensitive to local and regional variability in COVID-19 processes and impacts.
Sara L. McLafferty; Aida Guhlincozzi; Fikriyah Winata. Counting COVID: Quantitative Geographical Approaches to COVID-19. Ecology of Tuberculosis in India 2021, 409 -416.
AMA StyleSara L. McLafferty, Aida Guhlincozzi, Fikriyah Winata. Counting COVID: Quantitative Geographical Approaches to COVID-19. Ecology of Tuberculosis in India. 2021; ():409-416.
Chicago/Turabian StyleSara L. McLafferty; Aida Guhlincozzi; Fikriyah Winata. 2021. "Counting COVID: Quantitative Geographical Approaches to COVID-19." Ecology of Tuberculosis in India , no. : 409-416.
Health researchers and policy-makers increasingly use volunteered geographic information (VGI) to analyze spatial variation in health and wellbeing and to develop interventions. As socially constructed data, health VGI reflect the people who perceive issues and choose to report them, and the digital systems that structure the reporting process. We propose a conceptual framework that describes the interlocking effects of socioeconomic, behavioral, geographic, and technological processes on VGI accuracy and credibility. GIS and statistical methods are used to analyze social and geographical biases in health-related VGI through a case study of bed bug complaint data from New York City's 311 system. Reports of bed bug infestation from 311 are mapped and modeled to uncover associations with socioeconomic and built environment characteristics. Factors associated with bed bug report credibility are examined by comparing characteristics of confirmed reports with those for reports in which inspectors found no evidence of infestation (negative reports). A multilevel model of credibility incorporating report-, building-, and tract-level variables reveals strong geographical and socioeconomic biases, with negative reports generated more frequently from high-value residential buildings located in high-income neighborhoods with predominately white, non-Hispanic populations. Using 311 data for all bed bug reports, rather than confirmed reports, obscures the burden of these pests in high poverty neighborhoods and diminishes socioeconomic disparities. Mistaken reporting also has economic costs, as each report triggers an inspection by city inspectors that entails time, monetary, and opportunity costs.
Sara McLafferty; Daniel Schneider; Kathryn Abelt. Placing volunteered geographic health information: Socio-spatial bias in 311 bed bug report data for New York City. Health & Place 2020, 62, 102282 .
AMA StyleSara McLafferty, Daniel Schneider, Kathryn Abelt. Placing volunteered geographic health information: Socio-spatial bias in 311 bed bug report data for New York City. Health & Place. 2020; 62 ():102282.
Chicago/Turabian StyleSara McLafferty; Daniel Schneider; Kathryn Abelt. 2020. "Placing volunteered geographic health information: Socio-spatial bias in 311 bed bug report data for New York City." Health & Place 62, no. : 102282.
Rural populations experience a myriad of cancer disparities ranging from lower screening rates to higher cancer mortality rates. These disparities are due in part to individual-level characteristics like age and insurance status, but the physical and social context of rural residence also plays a role. Our objective was two-fold: 1) to develop a multilevel conceptual framework describing how rural residence and relevant micro, macro, and supra-macro factors can be considered in evaluating disparities across the cancer control continuum and 2) to outline the unique considerations of multilevel statistical modeling in rural cancer research. We drew upon several formative frameworks that address the cancer control continuum, population-level disparities, access to health care services, and social inequities. Micro-level factors comprised individual-level characteristics that either predispose or enable individuals to utilize health care services or that may affect their cancer risk. Macro-level factors included social context (e.g. domains of social inequity) and physical context (e.g. access to care). Rural-urban status was considered a macro-level construct spanning both social and physical context, as “rural” is often characterized by sociodemographic characteristics and distance to health care services. Supra-macro-level factors included policies and systems (e.g. public health policies) that may affect cancer disparities. Our conceptual framework can guide researchers in conceptualizing multilevel statistical models to evaluate the independent contributions of rural-urban status on cancer while accounting for important micro, macro, and supra-macro factors. Statistically, potential collinearity of multilevel model predictive variables, model structure, and spatial dependence should also be considered.
Whitney E. Zahnd; Sara L. McLafferty; Jan M. Eberth. Multilevel analysis in rural cancer control: A conceptual framework and methodological implications. Preventive Medicine 2019, 129, 105835 -105835.
AMA StyleWhitney E. Zahnd, Sara L. McLafferty, Jan M. Eberth. Multilevel analysis in rural cancer control: A conceptual framework and methodological implications. Preventive Medicine. 2019; 129 ():105835-105835.
Chicago/Turabian StyleWhitney E. Zahnd; Sara L. McLafferty; Jan M. Eberth. 2019. "Multilevel analysis in rural cancer control: A conceptual framework and methodological implications." Preventive Medicine 129, no. : 105835-105835.
Disparities in breast cancer outcomes between rural and urban populations are of great interest as environmental, social, and technological processes unevenly affect rural and urban landscapes. This chapter examines research on rural-urban disparities in breast cancer. The first section briefly reviews recent literature on variations in breast cancer incidence, stage, survival, quality of life, treatment and costs in rural and urban areas. The next section critically evaluates this work by presenting six suppositions about rural and urban places and populations. Each supposition discusses a key issue concerning how rural and urban categories and disparities are defined, measured, and analyzed. I argue that rural and urban are complex, heterogeneous categories that are difficult to define and that change over time complicates our understanding of breast cancer disparities. The complexity, heterogeneity, and dynamism of urban and rural places and populations give rise to unequal and varying BC outcomes. In addition, people’s differing experiences of urban and rural places strongly influence important risks and exposures, while also affecting access to diagnosis and treatment facilities, leading to people- and place-based heterogeneity in breast cancer outcomes. Implications for future research on rural-urban disparities are discussed.
Sara McLafferty. Rural-Urban Disparities in Breast Cancer: Six Suppositions and Future Directions. Geospatial Approaches to Energy Balance and Breast Cancer 2019, 379 -398.
AMA StyleSara McLafferty. Rural-Urban Disparities in Breast Cancer: Six Suppositions and Future Directions. Geospatial Approaches to Energy Balance and Breast Cancer. 2019; ():379-398.
Chicago/Turabian StyleSara McLafferty. 2019. "Rural-Urban Disparities in Breast Cancer: Six Suppositions and Future Directions." Geospatial Approaches to Energy Balance and Breast Cancer , no. : 379-398.
Recent scholarship points to a protective association between green space and birth outcomes as well a positive relationship between blue space and wellbeing. We add to this body of literature by exploring the relationship between expectant mothers’ exposure to green and blue spaces and adverse birth outcomes in New York City. The Normalized Difference Vegetation Index (NDVI), the NYC Street Tree Census, and access to major green spaces served as measures of greenness, while proximity to waterfront areas represented access to blue space. Associations between these factors and adverse birth outcomes, including preterm birth, term birthweight, term low birthweight, and small for gestational age, were evaluated via mixed-effects linear and logistic regression models. The analyses were conducted separately for women living in deprived neighborhoods to test for differential effects on mothers in these areas. The results indicate that women in deprived neighborhoods suffer from higher rates adverse birth outcomes and lower levels of residential greenness. In adjusted models, a significant inverse association between nearby street trees and the odds of preterm birth was found for all women. However, we did not identify a consistent significant relationship between adverse birth outcomes and NDVI, access to major green spaces, or waterfront access when individual covariates were taken into account.
Kathryn Abelt; Sara McLafferty. Green Streets: Urban Green and Birth Outcomes. International Journal of Environmental Research and Public Health 2017, 14, 771 .
AMA StyleKathryn Abelt, Sara McLafferty. Green Streets: Urban Green and Birth Outcomes. International Journal of Environmental Research and Public Health. 2017; 14 (7):771.
Chicago/Turabian StyleKathryn Abelt; Sara McLafferty. 2017. "Green Streets: Urban Green and Birth Outcomes." International Journal of Environmental Research and Public Health 14, no. 7: 771.
This paper examines the effect of spatial aggregation error on statistical estimates of the association between spatial access to health care and late-stage cancer. Monte Carlo simulation was used to disaggregate cancer cases for two Illinois counties from zip code to census block in proportion to the age-race composition of the block population. After the disaggregation, a hierarchical logistic model was estimated examining the relationship between late-stage breast cancer and risk factors including travel distance to mammography, at both the zip code and census block levels. Model coefficients were compared between the two levels to assess the impact of spatial aggregation error. We found that spatial aggregation error influences the coefficients of regression-type models at the zip code level, and this impact is highly dependent on the study area. In one study area (Kane County), block-level coefficients were very similar to those estimated on the basis of zip code data; whereas in the other study area (Peoria County), the two sets of coefficients differed substantially raising the possibility of drawing inaccurate inferences about the association between distance to mammography and late-stage cancer risk. Spatial aggregation error can significantly affect the coefficient values and inferences drawn from statistical models of the association between cancer outcomes and spatial and non-spatial variables. Relying on data at the zip code level may lead to inaccurate findings on health risk factors.
Lan Luo; Sara McLafferty; Fahui Wang. Analyzing spatial aggregation error in statistical models of late-stage cancer risk: a Monte Carlo simulation approach. International Journal of Health Geographics 2010, 9, 51 -51.
AMA StyleLan Luo, Sara McLafferty, Fahui Wang. Analyzing spatial aggregation error in statistical models of late-stage cancer risk: a Monte Carlo simulation approach. International Journal of Health Geographics. 2010; 9 (1):51-51.
Chicago/Turabian StyleLan Luo; Sara McLafferty; Fahui Wang. 2010. "Analyzing spatial aggregation error in statistical models of late-stage cancer risk: a Monte Carlo simulation approach." International Journal of Health Geographics 9, no. 1: 51-51.
BACKGROUND: Differences in late‐stage cancer risk between urban and rural residents are a key component of cancer disparities. Using data from the Illinois State Cancer Registry from 1998 through 2002, the authors investigated the rural‐urban gradient in late‐stage cancer risk for 4 major types of cancer: breast, colorectal, lung, and prostate. METHODS: Multilevel modeling was used to evaluate the role of population composition and area‐based contextual factors in accounting for rural‐urban variation. Instead of a simple binary rural‐urban classification, a finer grained classification was used that differentiated the densely populated City of Chicago from its suburbs and from smaller metropolitan areas, large towns, and rural settings. RESULTS: For all 4 cancers, the risk was highest in the most highly urbanized area and decreased as rurality increases, following a J‐shaped progression that included a small upturn in risk in the most isolated rural areas. For some cancers, these geographic disparities were associated with differences in population age and race; for others, the disparities remained after controlling for differences in population composition, zip code socioeconomic characteristics, and spatial access to healthcare. CONCLUSIONS: The observed pattern of urban disadvantage emphasized the need for more extensive urban‐based cancer screening and education programs. Cancer 2009. © 2009 American Cancer Society.
Sara McLafferty; Fahui Wang. Rural reversal? Cancer 2009, 115, 2755 -2764.
AMA StyleSara McLafferty, Fahui Wang. Rural reversal? Cancer. 2009; 115 (12):2755-2764.
Chicago/Turabian StyleSara McLafferty; Fahui Wang. 2009. "Rural reversal?" Cancer 115, no. 12: 2755-2764.
Nancy Krieger has been one of the leading voices in documenting how social ‘axes of difference’, including race, ethnicity and class make people vulnerable to poor health and limit their access to effective health care. We discuss the importance of ‘locating’ diversity in health inequalities research. This includes critically dissecting racial and ethnic axes into more nuanced social categories that incorporate differences based on immigration and other factors. It also involves considering how diverse population groups vary in their perception and use of space for health-related activities and exposures. Examples relating to immigrant populations’ health and access to health care are discussed.
Sara McLafferty; Ranjana Chakrabarti. Locating diversity: race, nativity and place in health disparities research. GeoJournal 2009, 74, 107 -113.
AMA StyleSara McLafferty, Ranjana Chakrabarti. Locating diversity: race, nativity and place in health disparities research. GeoJournal. 2009; 74 (2):107-113.
Chicago/Turabian StyleSara McLafferty; Ranjana Chakrabarti. 2009. "Locating diversity: race, nativity and place in health disparities research." GeoJournal 74, no. 2: 107-113.