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Disparities in Female Breast Cancer Stage at Diagnosis in New Jersey: A Spatial-Temporal Analysis.

ABSTRACT: Despite improvements in early detection of breast cancer, disparities persist in stage at diagnosis, which is an important prognostic factor.We used the space-time scan statistic in SaTScan to identify geographic areas and time periods with significantly elevated proportions of female breast cancer diagnosed at the in situ or distant stage in New Jersey. The analyses were conducted with census tracts as the geographic unit of analysis, elliptical spatial windows, 3-year temporal windows, and Poisson models. Statistical significance was determined by 999 Monte Carlo simulations (P < .05); significant clusters were mapped in ArcMap. Breast cancer cases within the clusters were compared with breast cancer cases outside the clusters on demographic, socioeconomic, and clinical factors using the Pearson chi-square test (P < .05). In addition, populations within the clusters were compared with the population outside the clusters on demographic and socioeconomic factors.After exclusions, 126 756 cases of primary female breast cancer diagnosed in 1997 to 2011 from the New Jersey State Cancer Registry were included in the analysis. One distant stage breast cancer cluster was identified in northeastern New Jersey from 1997 through 2011 (n = 26 244, relative risk [RR] = 1.42, P < .001). Two in situ breast cancer clusters were found in northeastern New Jersey from 2004 through 2011 (n = 12 496, RR = 1.35, P < .001) and in central New Jersey from 2006 through 2011 (n = 29 319, RR = 1.24, P < .001). The distant stage cluster contained relatively high percentages of minority and lower socioeconomic status (SES) breast cancer cases and populations, whereas the in situ clusters had relatively low percentages of minority and lower SES breast cancer cases and populations.Although there have been improvements since an earlier study of distant stage breast cancer diagnosed in 1995 to 1997, disparities in stage at diagnosis continue. These findings can be used by our local cancer control partners to target specific populations for interventions such as breast cancer education and mammography screening, as well as by state legislative and public health authorities for resource allocation.

SUBMITTER: Roche LM

PROVIDER: S-EPMC5548504 | biostudies-other | 2017 Sep/Oct

REPOSITORIES: biostudies-other

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Project description:Background: This study sought to determine if increased access to health insurance following the Affordable Care Act (ACA) resulted in an increased proportion of early-stage breast cancer diagnosis among women in Pennsylvania, particularly minorities, rural residents, and those of lower socioeconomic status. Materials and Methods: Data on 35,735 breast cancer cases among women 50-64 and 68-74 years of age in Pennsylvania between 2010 and 2016 were extracted from the Pennsylvania Cancer Registry and analyzed in 2019. Women 50-64 years of age were subdivided by race/ethnicity, area of residence, and socioeconomic status as measured by area deprivation index (ADI). We compared the proportions of early-stage breast cancer diagnosis pre-ACA (2010-2013) and post-ACA (2014-2016) for all women 50-64 years of age to all women 68-74 years of age. This comparison was also made between paired sociodemographic subgroups for women 50-64 years of age. Multivariable logistic regression models were constructed to assess how race, area of residence, ADI, and primary care physician (PCP) density interacted to impact breast cancer diagnosis post-ACA. Results: The proportion of early-stage breast cancer diagnosis increased by 1.71% post-ACA among women 50-64 years of age (p < 0.01), whereas women 68-74 years of age saw no change. Multivariable logistic regression analysis demonstrated that minority women had lower odds of early-stage breast cancer diagnosis pre-ACA, but not post-ACA, when controlling for ADI. Meanwhile, increased area-level socioeconomic advantage was associated with higher odds of being diagnosed with early-stage breast cancer pre- and post-ACA irrespective of controlling for race, area of residence, or PCP density. Conclusions: Enhanced access to health insurance under the ACA was associated with an increased proportion of early-stage breast cancer diagnosis in Pennsylvanian women 50-64 years of age and may have reduced racial, but not socioeconomic, disparities in breast cancer diagnosis.

Project description:The reasons for elevated breast cancer rates in the upper Cape Cod area of Massachusetts remain unknown despite several epidemiological studies that investigated possible environmental risk factors. Data from two of these population-based case-control studies provide geocoded residential histories and information on confounders, creating an invaluable dataset for spatial-temporal analysis of participants' residency over five decades.The combination of statistical modeling and mapping is a powerful tool for visualizing disease risk in a spatial-temporal analysis. Advances in geographic information systems (GIS) enable spatial analytic techniques in public health studies previously not feasible. Generalized additive models (GAMs) are an effective approach for modeling spatial and temporal distributions of data, combining a number of desirable features including smoothing of geographical location, residency duration, or calendar years; the ability to estimate odds ratios (ORs) while adjusting for confounders; selection of optimum degree of smoothing (span size); hypothesis testing; and use of standard software. We conducted a spatial-temporal analysis of breast cancer case-control data using GAMs and GIS to determine the association between participants' residential history during 1947-1993 and the risk of breast cancer diagnosis during 1983-1993. We considered geographic location alone in a two-dimensional space-only analysis. Calendar year, represented by the earliest year a participant lived in the study area, and residency duration in the study area were modeled individually in one-dimensional time-only analyses, and together in a two-dimensional time-only analysis. We also analyzed space and time together by applying a two-dimensional GAM for location to datasets of overlapping calendar years. The resulting series of maps created a movie which allowed us to visualize changes in magnitude, geographic size, and location of elevated breast cancer risk for the 40 years of residential history that was smoothed over space and time.The space-only analysis showed statistically significant increased areas of breast cancer risk in the northern part of upper Cape Cod and decreased areas of breast cancer risk in the southern part (p-value = 0.04; ORs: 0.90-1.40). There was also a significant association between breast cancer risk and calendar year (p-value = 0.05; ORs: 0.53-1.38), with earlier calendar years resulting in higher risk. The results of the one-dimensional analysis of residency duration and the two-dimensional analysis of calendar year and duration showed that the risk of breast cancer increased with increasing residency duration, but results were not statistically significant. When we considered space and time together, the maps showed a large area of statistically significant elevated risk for breast cancer near the Massachusetts Military Reservation (p-value range:0.02-0.05; ORs range: 0.25-2.5). This increased risk began with residences in the late 1940s and remained consistent in size and location through the late 1950s.Spatial-temporal analysis of the breast cancer data may help identify new exposure hypotheses that warrant future epidemiologic investigations with detailed exposure models. Our methods allow us to visualize breast cancer risk, adjust for known confounders including age at diagnosis or index year, family history of breast cancer, parity and age at first live- or stillbirth, and test for the statistical significance of location and time. Despite the advantages of GAMs, analyses are for exploratory purposes and there are still methodological issues that warrant further research. This paper illustrates that GAM methods are a suitable alternative to widely-used cluster detection methods and may be preferable when residential histories from existing epidemiological studies are available.

Project description:ObjectiveIn low- and middle-income countries (LMICs), advanced-stage diagnosis of breast cancer (BC) is common, and this contributes to poor survival. Understanding the determinants of the stage at diagnosis will aid in designing interventions to downstage disease and improve survival from BC in LMICs.MethodsWithin the South African Breast Cancers and HIV Outcomes (SABCHO) cohort, we examined factors affecting the stage at diagnosis of histologically confirmed invasive breast cancer at five tertiary hospitals in South Africa (SA). The stage was assessed clinically. To examine the associations of the modifiable health system, socio-economic/household and non-modifiable individual factors, hierarchical multivariable logistic regression with odds of late-stage at diagnosis (stage III-IV), was used.ResultsThe majority (59%) of the included 3497 women were diagnosed with late-stage BC disease. The effect of health system-level factors on late-stage BC diagnosis was consistent and significant even when adjusted for both socio-economic- and individual-level factors. Women diagnosed in a tertiary hospital that predominantly serves a rural population were 3 times (OR = 2.89 (95% CI: 1.40-5.97) as likely to be associated with late-stage BC diagnosis when compared to those diagnosed at a hospital that predominantly serves an urban population. Taking more than 3 months from identifying the BC problem to the first health system entry (OR = 1.66 (95% CI: 1.38-2.00)), and having luminal B (OR = 1.49 (95% CI: 1.19-1.87)) or HER2-enriched (OR = 1.64 (95% CI: 1.16-2.32)) molecular subtype as compared to luminal A, were associated with a late-stage diagnosis. Whilst having a higher socio-economic level (a wealth index of 5) reduced the probability of late-stage BC at diagnosis, (OR = 0.64 (95% CI: 0.47-0.85)).ConclusionAdvanced-stage diagnosis of BC among women in SA who access health services through the public health system was associated with both modifiable health system-level factors and non-modifiable individual-level factors. These may be considered as elements in interventions to reduce the time to diagnosis of breast cancer in women.

Project description:BackgroundA sizeable fraction of tuberculosis (TB) cases go undiagnosed. By analysing data from enhanced demographic, microbiological and geospatial surveillance of TB registrations, we aimed to identify modifiable predictors of inequitable access to diagnosis and care.MethodsGovernmental community health workers (CHW) enumerated all households in 315 catchment areas during October-December 2015. From January 2015, government TB Officers routinely implemented enhanced TB surveillance at all public and private TB treatment registration centres within Blantyre (18 clinics in total). This included collection from registering TB patients of demographic and clinical characteristics, a single sputum sample for TB microscopy and culture, and geolocation of place of residence using an electronic satellite map application. We estimated catchment area annual TB case notification rates (CNRs), stratified by microbiological status. To identify population and area-level factors predictive of CHW catchment area TB case notification rates, we constructed Bayesian spatially autocorrelated regression models with Poisson response distributions. Worldpop data were used to estimate poverty.ResultsIn total, the 315 CHW catchment areas comprised 753,489 people (range 162 to 13,066 people/catchment area). Between 2015 and 2017, 6077?TB cases (61% male; 99% HIV tested; 67% HIV positive; 55% culture confirmed) were geolocated, with 3723 (61%) resident within a CHW catchment area. In adjusted models, greater distance to the nearest TB registration clinic was negatively correlated with TB CNRs, which halved for every 3.2-fold (95% CI 2.24-5.21) increase in distance. Poverty, which increased with distance from clinics, was negatively correlated with TB CNRs; a 23% increase (95% CI 17-34%) in the mean percentage of the population living on less than US$2 per day corresponded to a halving of the TB case notification rates.ConclusionsUsing enhanced surveillance of TB cases in Blantyre, we show an ecological relationship consistent with an 'inverse care law' whereby poorer neighbourhoods and those furthest from TB clinics have lower relative CNRs. If confirmed as low case detection, then pro-poor strategies to facilitate equitable access to TB diagnosis and treatment are required.

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Disparities in Female Breast Cancer Stage at Diagnosis in New Jersey: A Spatial-Temporal Analysis.

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