Analyzing spatial aggregation error in statistical models of late-stage cancer risk: a Monte Carlo simulation approach

<p>Abstract</p> <p>Purpose</p> <p>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.</p> <p>Methods</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Late-Stage Breast Cancer Diagnosis and Health Care Access in Illinois∗

The variations of breast cancer mortality rates from place to place reflect both underlying differences in breast cancer prevalence and differences in diagnosis and treatment that affect the risk of death. This article examines the role of access to health care in explaining the variation of late-stage diagnosis of breast cancer. We use cancer registry data for the state of Illinois by zip code to investigate spatial variation in late diagnosis. Geographic information systems and spatial analysis methods are used to create detailed measures of spatial access to health care such as convenience of visiting primary care physicians and travel time from the nearest mammography facility. The effects of spatial access, in combination with the influences of socioeconomic factors, on late-stage breast cancer diagnosis are assessed using statistical methods. The results suggest that for breast cancer, poor geographical access to primary health care significantly increases the risk of late diagnosis for persons living outside the city of Chicago. Disadvantaged population groups including those with low income and racial and ethnic minorities tend to experience high rates of late diagnosis. In Illinois, poor spatial access to primary health care is more strongly associated with late diagnosis than is spatial access to mammography. This suggests the importance of primary care physicians as gatekeepers in early breast cancer detection

Multilevel Analysis in Rural Cancer Control: A Conceptual Framework and Methodological Implications

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

Predicting the effect of hospital closure on hospital utilization patterns

Geographic patterns of hospital utilization were analyzed before and after the closure of Sydenham hospital in New York City. The purpose was to determine the accuracy with which hospital utilization patterns after closure could be predicted using standard spatial interaction modeling procedures. Gravity models were calibrated to represent travel to hospitals before and after closure for patients residing in Sydenham's primary service area. Using three variables, hospital size, distance and type, the models accurately described utilization patterns in each year. The distance parameter, however, changed substantially between the 2 years. In addition large errors were observed when the model calibrated before closure was used to predict utilization patterns afterward. The geographic distribution and likely causes of errors were analyzed, along with their implications for spatial modeling efforts.hospital closure gravity model New York City

The geographical restructuring of urban hospitals: Spatial dimensions of corporate strategy

In response to demographic, economic and regulatory change, hospitals in the United States are increasingly adopting the diverse corporate strategies of private firms. The ability of a hospital to adopt these strategies and survive depends greatly on the nature of the hospital institution and the economic potential of its geographical market area. A typology is developed relating institutional strategies such as merger, expansion, diversification and closure to the size of the hospital and the socio-economic status of the neighborhood in which it is located. Data describing the geographical distribution, since 1967, of hospital closures, mergers and facility expansions in New York City are used to examine institutional strategies in relation to the typology. Closure has been most common among small hospitals located in low SES neighborhoods, whereas facility expansion has been most prevalent among large hospitals located in high status neighborhoods. The result is an increased concentration of patient care in large tertiary hospitals. Mergers have provided a means of not only increasing institutional size and rationalizing services, but also of establishing footholds in 'profitable' market areas. The final section considers the role of the state in hospital restructuring.restructuring of hospitals corporate strategy mergers New York City

Neighborhood characteristics and hospital closures : A comparison of the public, private and voluntary hospital systems

This paper analyzes the neighborhood distribution of hospital closures in New York City between 1970 and 1981. Discriminant analysis procedures are used to compare the social, economic and health status characteristics of neighborhoods in which hospitals have closed with those of neighborhoods in which facilities have remained open. The results show that overall hospital closures have had a substantial distributional impact, with facilities in low-income, high infant mortality neighborhoods having the highest rates of failure. Closure of voluntary hospitals occured most frequently in disadvantaged neighborhoods; whereas municipal and proprietary hospital closures showed no differential neighborhood impact. Implications for the geographical accessibility to various groups to health care and for the efficiency and cost of hospital services are discussed.

Geographies of Frontline Workers: Gender, Race, and Commuting in New York City

The COVID-19 pandemic amplified social, economic, and environmental inequalities in American cities, including inequities in commuting and access to employment. Frontline workers—those who had to work on site during the pandemic—experienced these inequalities in every aspect of their daily lives. We examine the labor force characteristics and commuting of frontline workers in New York City with a focus on gender and race/ethnic disparities in wages and commuting modes and times. Using Census PUMS microdata for a sample of New York City residents in the 2015–2019 period, we identify frontline workers from detailed industry and occupation codes and compare characteristics of frontline workers with those of essential workers who could work remotely. The data highlight wide disparities between frontline and remote workers. Minority men and women are concentrated in the frontline workforce. The residential geographies of frontline and remote workers differ greatly, with the former concentrated in low- and moderate- income areas distant from work sites and with long commute times. Compared to men, women frontline workers rely heavily on public transit to commute and transit dependence is highest among Black and Latina women. Low-wage employment, long commute times, and transit dependence intersected to increase minority women’s economic and social vulnerability during the pandemic

Green Streets: Urban Green and Birth Outcomes

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

Immigration and Geographic Access to Prenatal Clinics in Brooklyn, NY: A Geographic Information Systems Analysis

We compared levels of geographic access to prenatal clinics in Brooklyn, NY, between immigrant and US-born mothers and among immigrant groups by country of birth. We used birth data to characterize the spatial distribution of mothers and kernel estimation to measure clinic density within a 2-mile radius of each mother. Results showed that geographic access to clinics differs substantially by country of birth. Certain groups (e.g., Pakistani, Bangladeshi) have relatively poor geographic access despite a high need for prenatal care