Medicaid expansion in California and breast cancer incidence across neighborhoods with varying social vulnerabilities.

PURPOSE: To investigate changes in breast cancer incidence rates associated with Medicaid expansion in California. METHODS: We extracted yearly census tract-level population counts and cases of breast cancer diagnosed among women aged between 20 and 64 years in California during years 2010-2017. Census tracts were classified into low, medium and high groups according to their social vulnerability index (SVI). Using a difference-in-difference (DID) approach with Poisson regression models, we estimated the incidence rate, incidence rate ratio (IRR) during the pre- (2010-2013) and post-expansion periods (2014-2017), and the relative IRR (DID estimates) across three groups of neighborhoods. RESULTS: Prior to the Medicaid expansion, the overall incidence rate was 93.61, 122.03, and 151.12 cases per 100,000 persons among tracts with high, medium, and low-SVI, respectively; and was 96.49, 122.07, and 151.66 cases per 100,000 persons during the post-expansion period, respectively. The IRR between high and low vulnerability neighborhoods was 0.62 and 0.64 in the pre- and post-expansion period, respectively, and the relative IRR was 1.03 (95% CI 1.00 to 1.06, p = 0.026). In addition, significant DID estimate was only found for localized breast cancer (relative IRR = 1.05; 95% CI, 1.01 to 1.09, p = 0.049) between high and low-SVI neighborhoods, not for regional and distant cancer stage. CONCLUSIONS: The Medicaid expansion had differential impact on breast cancer incidence across neighborhoods in California, with the most pronounced increase found for localized cancer stage in high-SVI neighborhoods. Significant pre-post change was only found for localized breast cancer between high and low-SVI neighborhoods.

Multilevel mediation analysis on time-to-event outcomes: Exploring racial/ethnic disparities in breast cancer survival in California.

Background: Third-variable effect refers to the effect from a third-variable that explains an observed relationship between an exposure and an outcome. Depending on whether there is a causal relationship from the exposure to the third variable, the third-variable is called a mediator or a confounder. The multilevel mediation analysis is used to differentiate third-variable effects from data of hierarchical structures. Data Collection and Analysis: We developed a multilevel mediation analysis method to deal with time-to-event outcomes and implemented the method in the mlma R package. With the method, third-variable effects from different levels of data can be estimated. The method uses multilevel additive models that allow for transformations of variables to take into account potential nonlinear relationships among variables in the mediation analysis. We apply the proposed method to explore the racial/ethnic disparities in survival among patients diagnosed with breast cancer in California between 2006 and 2017, using both individual risk factors and census tract level environmental factors. The individual risk factors are collected by cancer registries and the census tract level factors are collected by the Public Health Alliance of Southern California in partnership with the Virginia Commonwealth University's Center on Society and Health. The National Cancer Institute work group linked variables at the census tract level with each patient and performed the analysis for this study. Results: We found that the racial disparity in survival were mostly explained at the census tract level and partially explained at the individual level. The associations among variables were depicted. Conclusion: The multilevel mediation analysis method can be used to differentiate mediation/confounding effects for factors originated from different levels. The method is implemented in the R package mlma.

Updated Methodology for Projecting U.S.- and State-Level Cancer Counts for the Current Calendar Year: Part I: Spatio-temporal Modeling for Cancer Incidence.

BACKGROUND: The American Cancer Society (ACS) and the NCI collaborate every 5-8 years to update the methods for estimating numbers of new cancer cases and deaths in the current year in the United States and in every state and the District of Columbia. In this article, we reevaluate the statistical method for estimating unavailable historical incident cases which are needed for projecting the current year counts. METHODS: We compared the current county-level model developed in 2012 (M0) with three new models, including a state-level mixed effect model (M1) and two state-level hierarchical Bayes models with varying random effects (M2 and M3). We used 1996-2014 incidence data for 16 sex-specific cancer sites to fit the models. An average absolute relative deviation (AARD) comparing the observed with the model-specific predicted counts was calculated for each site. Models were also cross-validated for six selected sex-specific cancer sites. RESULTS: For the cross-validation, the AARD ranged from 2.8% to 33.0% for M0, 3.3% to 31.1% for M1, 6.6% to 30.5% for M2, and 10.4% to 393.2% for M3. M1 encountered the least technical issues in terms of model convergence and running time. CONCLUSIONS: The state-level mixed effect model (M1) was overall superior in accuracy and computational efficiency and will be the new model for the ACS current year projection project. IMPACT: In addition to predicting the unavailable state-level historical incidence counts for cancer surveillance, the updated algorithms have broad applicability for disease mapping and other activities of public health planning, advocacy, and research.

Updated Methodology for Projecting U.S.- and State-Level Cancer Counts for the Current Calendar Year: Part II: Evaluation of Incidence and Mortality Projection Methods.

BACKGROUND: The American Cancer Society (ACS) and the NCI collaborate every 5 to 8 years to update the methods for estimating the numbers of new cancer cases and deaths in the current year for the U.S. and individual states. Herein, we compare our current projection methodology with the next generation of statistical models. METHODS: A validation study was conducted comparing current projection methods (vector autoregression for incidence; Joinpoint regression for mortality) with the Bayes state-space method and novel Joinpoint algorithms. Incidence data from 1996-2010 were projected to 2014 using two inputs: modeled data and observed data with modeled where observed were missing. For mortality, observed data from 1995 to 2009, 1996 to 2010, 1997 to 2011, and 1998 to 2012, each projected 3 years forward to 2012 to 2015. Projection methods were evaluated using the average absolute relative deviation (AARD) between observed counts (2014 for incidence, 2012-2015 for mortality) and estimates for 47 cancer sites nationally and 21 sites by state. RESULTS: A novel Joinpoint model provided a good fit for both incidence and mortality, particularly for the most common cancers in the U.S. Notably, the AARD for cancers with cases in 2014 exceeding 49,000 for this model was 3.4%, nearly half that of the current method (6.3%). CONCLUSIONS: A data-driven Joinpoint algorithm had versatile performance at the national and state levels and will replace the ACS's current methods. IMPACT: This methodology provides estimates of cancer data that are not available for the current year, thus continuing to fill an important gap for advocacy, research, and public health planning.