Long-term Mammography Screening Trends and Predictors of Return to Screening after the COVID-19 Pandemic: Results from a Statewide Registry.

Purpose To evaluate long-term trends in mammography screening rates and identify sociodemographic and breast cancer risk characteristics associated with return to screening after the COVID-19 pandemic. Materials and Methods In this retrospective study, statewide screening mammography data of 222 384 female individuals aged 40 years or older (mean age, 58.8 years ± 11.7 [SD]) from the Vermont Breast Cancer Surveillance System were evaluated to generate descriptive statistics and Joinpoint models to characterize screening patterns during 2000-2022. Log-binomial regression models estimated associations of sociodemographic and risk characteristics with post-COVID-19 pandemic return to screening. Results The proportion of female individuals in Vermont aged 50-74 years with a screening mammogram obtained in the previous 2 years declined from a prepandemic level of 61.3% (95% CI: 61.1%, 61.6%) in 2019 to 56.0% (95% CI: 55.7%, 56.3%) in 2021 before rebounding to 60.7% (95% CI: 60.4%, 61.0%) in 2022. Screening adherence in 2022 remained substantially lower than that observed during the 2007-2010 apex of screening adherence (66.1%-67.0%). Joinpoint models estimated an annual percent change of -1.1% (95% CI: -1.5%, -0.8%) during 2010-2022. Among the cohort of 95 644 individuals screened during January 2018-March 2020, the probability of returning to screening during 2020-2022 varied by age (eg, risk ratio [RR] = 0.94 [95% CI: 0.93, 0.95] for age 40-44 vs age 60-64 years), race and ethnicity (RR = 0.84 [95% CI: 0.78, 0.90] for Black vs White individuals), education (RR = 0.84 [95% CI: 0.81, 0.86] for less than high school degree vs college degree), and by 5-year breast cancer risk (RR = 1.06 [95% CI: 1.04, 1.08] for very high vs average risk). Conclusion Despite a rebound to near prepandemic levels, Vermont mammography screening rates have steadily declined since 2010, with certain sociodemographic groups less likely to return to screening after the pandemic. Keywords: Mammography, Breast, Health Policy and Practice, Neoplasms-Primary, Epidemiology, Screening Supplemental material is available for this article. © RSNA, 2024.

Clinical factors associated with patterns of endocrine therapy adherence in premenopausal breast cancer patients.

INTRODUCTION: Patients with hormone receptor positive breast cancer are recommended at least five years of adjuvant endocrine therapy, but adherence to this treatment is often suboptimal. We investigated longitudinal trends in adjuvant endocrine therapy (AET) adherence among premenopausal breast cancer patients and identified clinical characteristics, including baseline comorbidities and non-cancer chronic medication use, associated with AET adherence. METHODS: We included stage I-III premenopausal breast cancer patients diagnosed during 2002-2011 and registered in the Danish Breast Cancer Group clinical database who initiated AET. We used group-based trajectory modeling to describe AET adherence patterns. We also linked patients to Danish population-based registries and fit multinomial logistic models to compute odds ratios (ORs) and 95% confidence intervals (95% CIs) associating clinical characteristics with AET adherence patterns. RESULTS: We identified three adherence patterns among 4,353 women-high adherers (57%), slow decliners (36%), and rapid decliners (6.9%). Women with stage I disease (vs. stage II; OR: 1.9, 95% CI 1.5, 2.5), without chemotherapy (vs. chemotherapy; OR: 4.3, 95% CI 3.0, 6.1), with prevalent comorbid disease (Charlson Comorbidity Index score ≥ 1 vs. 0; OR: 1.6, 95% CI 1.1, 2.3), and with a history of chronic non-cancer medication use (vs. none; OR: 1.3, 95% CI 1.0, 1.8) were more likely to be rapid decliners compared with high adherers. CONCLUSIONS: Women with stage I cancer, no chemotherapy, higher comorbidity burden, and history of chronic non-cancer medication use were less likely to adhere to AET. Taking steps to promote adherence in these groups of women may reduce their risk of recurrence.

Biomarkers predictive of a response to extended endocrine therapy in breast cancer: a systematic review and meta-analysis.

PURPOSE: Extension of adjuvant endocrine therapy beyond five years confers only modest survival benefit in breast cancer patients and carries risk of toxicities. This systematic review investigates the role of biomarker tests in predicting the clinical response to an extension of endocrine therapy. METHODS: We searched Ovid MEDLINE, Ovid Embase, Global Index Medicus, and the Cochrane Central Register of Controlled Trials using an iterative approach to identify full-text articles related to breast cancer, endocrine therapy, and biomarkers. RESULTS: Of the 1,217 unique reports identified, five studies were deemed eligible. Four investigated the Breast Cancer Index (BCI) assay in three distinct study populations. These studies consistently showed that BCI score was predictive of response to extended endocrine therapy among 1,946 combined patients, who were predominately non-Hispanic white and postmenopausal. CONCLUSIONS: Evidence in the setting of predictive tests for extended endocrine therapy is sparse. Most relevant studies investigated the use of BCI, but these study populations were largely restricted to a single age, race, and ethnicity group. Future studies should evaluate a variety of biomarkers in diverse populations. Without sufficient evidence, physicians and patients face a difficult decision in balancing the benefits and risks of endocrine therapy extension.

Obesity and Risk of Recurrence in Patients With Breast Cancer Treated With Aromatase Inhibitors.

Importance: Clinical studies confirm that obesity is a risk factor for recurrence in postmenopausal women with hormone receptor-positive (HR+) breast cancer. Evidence suggests that women with obesity do not obtain similar protection from aromatase inhibitors as women with healthy weight. Objective: To examine the associations of body mass index (BMI) with recurrence. Design, Setting, and Participants: The cohort study was conducted using data from the Danish Breast Cancer Group and enrolled postmenopausal women diagnosed with stage I to III HR+ breast cancer from 1998 through 2016. Data analysis was conducted from November 2022 to April 2023. Exposures: BMI was classified as (1) healthy weight (18.5-24.9), (2) overweight (25.0-29.9), (3) obesity (30.0-34.9), and (4) severe obesity (≥35.0) using the World Health Organization guidelines. Healthy weight was considered the reference group in statistical analyses. Main Outcomes and Measures: Follow-up began 6 months after breast cancer surgery and continued until the first event of recurrence, contralateral breast cancer, new primary malignant neoplasm, death, emigration, end of clinical follow-up at 10 years, or September 25, 2018. Cox regression was used to estimate crude and adjusted hazard ratios with 95% CIs, adjusting for patient, tumor, and treatment characteristics. Results: A total of 13 230 patients (median [IQR] age at diagnosis, 64.4 [58.6-70.2] years) with information on BMI were enrolled. There were 1587 recurrences with a median (IQR) potential estimated follow-up of 6.2 (3.6-8.5) years. Multivariable analyses revealed increased recurrence hazards associated with obesity (adjusted hazard ratio, 1.18 [95% CI, 1.01-1.37]) and severe obesity (adjusted hazard ratio, 1.32 [95% CI, 1.08-1.62]) vs patients with healthy weight. Patients with overweight had a greater risk, but the results were not statistically significant (adjusted hazard ratio, 1.10 [95% CI, 0.97-1.24]). Conclusions and Relevance: In this study, obesity was associated with an increased risk of breast cancer recurrence among postmenopausal patients with HR+ early-stage breast cancer treated with aromatase inhibitors. Physicians should be aware of the significance of obesity on breast cancer outcomes to secure optimal treatment benefit in all patients.

Exploring the presence of bovine leukemia virus among breast cancer tumors in a rural state.

PURPOSE: The bovine leukemia virus (BLV) is a deltaretrovirus that causes malignant lymphoma and lymphosarcomas in cattle globally and has high prevalence among large scale U.S. dairy herds. Associations between presence of BLV DNA in human mammary tissue and human breast cancer incidence have been reported. We sought to estimate the prevalence of BLV DNA in breast cancer tissue samples in a rural state with an active dairy industry. METHODS: We purified genomic DNA from 56 fresh-frozen breast cancer tissue samples (51 tumor samples, 5 samples representing adjacent normal breast tissue) banked between 2016 and 2019. Using nested PCR assays, multiple BLV tax sequence primers and primers for the long terminal repeat (LTR) were used to detect BLV DNA in tissue samples and known positive control samples, including the permanently infected fetal lamb kidney cell line (FLK-BLV) and blood from BLV positive cattle. RESULTS: The median age of patients from which samples were obtained at the time of treatment was 60 (40-93) and all were female. Ninety percent of patients had invasive ductal carcinoma. The majority were poorly differentiated (60%). On PCR assay, none of the tumor samples tested positive for BLV DNA, despite having consistent signals in positive controls. CONCLUSION: We did not find BLV DNA in fresh-frozen breast cancer tumors from patients presenting to a hospital in Vermont. Our findings suggest a low prevalence of BLV in our patient population and a need to reevaluate the association between BLV and human breast cancer.

Single-nucleotide polymorphisms and the effectiveness of taxane-based chemotherapy in premenopausal breast cancer: a population-based cohort study in Denmark.

PURPOSE: Taxane-based chemotherapy is the primary treatment for premenopausal breast cancer. Although being inconsistent, research suggests that variant alleles alter pharmacokinetics through reduced function of OATP transporters (limiting hepatic uptake), CYP-450 enzymes (hampering drug metabolism), and ABC transporters (decreasing clearance). Reduced function of DNA repair enzymes may hamper effectiveness through dose-limiting toxicities. We investigated whether single-nucleotide polymorphisms (SNPs) were associated with breast cancer recurrence or mortality in premenopausal women diagnosed with breast cancer. METHODS: We conducted a population-based cohort study of premenopausal women diagnosed with non-distant metastatic breast cancer in Denmark during 2007‒2011, when guidelines recommended adjuvant combination chemotherapy (taxanes, anthracyclines, and cyclophosphamide). Using archived formalin-fixed paraffin-embedded primary tumor tissue, we genotyped 26 SNPs using TaqMan assays. Danish health registries provided data on breast cancer recurrence (through September 25, 2017) and death (through December 31, 2019). We fit Cox regression models to calculate crude hazard ratios (HRs) and 95% confidence intervals (CIs) for recurrence and mortality across genotypes. RESULTS: Among 2,262 women, 249 experienced recurrence (cumulative incidence: 13%) and 259 died (cumulative incidence: 16%) during follow-up (median 7.0 and 10.1 years, respectively). Mortality was increased in variant carriers of GSTP1 rs1138272 (HR: 1.30, 95% CI 0.95-1.78) and CYP3A rs10273424 (HR: 1.33, 95% CI 0.98-1.81). SLCO1B1 rs2306283 (encoding OATP1B1) variant carriers had decreased recurrence (HR: 0.82, 95% CI 0.64-1.07) and mortality (HR: 0.77, 95% CI 0.60-0.98). CONCLUSION: Docetaxel effectiveness was influenced by SNPs in GSTP1, CYP3A, and SLCO1B1 in premenopausal women with non-distant metastatic breast cancer, likely related to altered docetaxel pharmacokinetics. These SNPs may help determine individual benefit from taxane-based chemotherapy.

Sampling Validation Data to Achieve a Planned Precision of the Bias-Adjusted Estimate of Effect.

Data collected from a validation substudy permit calculation of a bias-adjusted estimate of effect that is expected to equal the estimate that would have been observed had the gold standard measurement been available for the entire study population. In this paper, we develop and apply a framework for adaptive validation to determine when sufficient validation data have been collected to yield a bias-adjusted effect estimate with a prespecified level of precision. Prespecified levels of precision are decided a priori by the investigator, based on the precision of the conventional estimate and allowing for wider confidence intervals that would still be substantively meaningful. We further present an applied example of the use of this method to address exposure misclassification in a study of transmasculine/transfeminine youth and self-harm. Our method provides a novel approach to effective and efficient estimation of classification parameters as validation data accrue, with emphasis on the precision of the bias-adjusted estimate. This method can be applied within the context of any parent epidemiologic study design in which validation data will be collected and modified to meet alternative criteria given specific study or validation study objectives.

Medication-Associated Phthalate Exposure and Childhood Cancer Incidence.

BACKGROUND: Human phthalate exposure is widespread through contact with myriad consumer products. Exposure is particularly high through medications formulated with phthalates. Phthalates disrupt normal endocrine signaling and are associated with reproductive outcomes and incidence of some cancers. We measured associations between gestational and childhood medication-associated phthalate exposures and the incidence of childhood cancers. METHODS: We identified all live births in Denmark between 1997 and 2017, including both children and birth mothers. Using drug ingredient data merged with the Danish National Prescription Registry, we measured phthalate exposure through filled prescriptions for mothers during pregnancy (gestational exposure) and for children from birth until age 19 years (childhood exposure). Incident childhood cancers were ascertained from the Danish Cancer Registry, and associations were estimated with Cox regression models. RESULTS: Among 1 278 685 children, there were 2027 childhood cancer cases diagnosed over 13.1 million person-years of follow-up. Childhood phthalate exposure was strongly associated with incidence of osteosarcoma (hazard ratio [HR] = 2.78, 95% confidence interval [CI] = 1.63 to 4.75). We also observed a positive association with incidence of lymphoma (HR = 2.07, 95% CI = 1.36 to 3.14), driven by associations with Hodgkin and non-Hodgkin lymphoma but not Burkitt lymphoma. Associations were apparent only for exposure to low-molecular phthalates, which have purportedly greater biological activity. CONCLUSIONS: Childhood phthalate exposure was associated with incidence of osteosarcoma and lymphoma before age 19 years. Lingering questions include which specific phthalate(s) are responsible for these associations, by what mechanisms they occur, and to what extent childhood cancer cases could be avoided by reducing or eliminating the phthalate content of medications and other consumer products.

Ultrasound for the Objective Measurement of Breast Lymphedema.

OBJECTIVES: Breast lymphedema has supplanted upper extremity lymphedema as a common and debilitating sequela of breast cancer treatment, but has no objective measurement. We assessed the utility of ultrasound-measured difference in dermal thickness between affected and unaffected breasts as a measure of breast lymphedema. We associated this measure with patient characteristics, treatment parameters, and patient-reported impact on quality of life. METHODS: We enrolled 30 invasive breast carcinoma patients treated with breast-conserving surgery, sentinel lymph node biopsy, and radiotherapy, and 10 control patients evaluated for benign breast conditions without prior breast surgery or radiotherapy. Patient and treatment variables were ascertained from medical records and radiotherapy instruments. Impacts on quality of life were measured with a modified Disability of the Arm, Shoulder, and Hand questionnaire. We characterized breast lymphedema by calculating the difference in ultrasound-measured dermal thickness between affected and unaffected breasts. Associations with patient characteristics, treatment, and quality of life were quantified with log-binomial regression models. RESULTS: Breast lymphedema was defined as a dermal thickness difference of >0.3 mm. Nineteen patients in the invasive group (63%) had breast lymphedema by this definition. We observed positive associations between ultrasound-defined breast lymphedema and surgical factors (size of primary tumor, number of lymph nodes removed), radiotherapy factors (breast volume irradiated, receipt of radiation boost), and patient-reported outcomes (sleep quality and overall confidence). CONCLUSIONS: Difference in dermal thickness is an easy and inexpensive measurement for quantifying breast lymphedema, and correlates with treatment parameters and patient-reported impacts on quality of life.

Preexisting stress-related diagnoses and mortality: A Danish cancer cohort study.

BACKGROUND: This study evaluated the association between preexisting stress-related diagnoses and mortality in a Danish population-based cancer cohort. METHODS: This study included Danish patients with cancer diagnosed in 1995-2011 who had a stress-related diagnosis before their cancer diagnosis. Cancer patients without a prior stress-related diagnosis were matched 5:1 to the stress disorder cohort by cancer site, age group, calendar period, and sex. The 5-year cumulative incidence of cancer-specific and all-cause mortality was computed by stress-related diagnosis category. Hazard ratios and 95% confidence intervals (CIs) associating stress-related diagnoses with mortality were computed by follow-up time, stress-related diagnosis category, stage, comorbidity status, and cancer type. RESULTS: This study identified 4437 cancer patients with a preexisting stress-related diagnosis and 22,060 matched cancer cohort members. The 5-year cumulative risk of cancer-specific mortality was 33% (95% CI, 32%-35%) for those with a preexisting stress-related diagnosis and 29% (95% CI, 28%-29%) for those without a prior stress-related diagnosis. Cancer patients with a preexisting stress-related diagnosis had a 1.3 times higher cancer-specific mortality rate than the comparison cohort members (95% CI, 1.2-1.5). This increase persisted across categories of stress-related diagnosis. The association varied by stage and cancer type, with more pronounced associations found among those with a late stage at diagnosis and hematological malignancies. CONCLUSIONS: Cancer patients with preexisting stress-related diagnoses had increased rates of cancer-specific and all-cause mortality. The results suggest that psychiatric comorbidities may be an important consideration for cancer prognosis, and cancer treatment informed by a patient's history may improve outcomes.

Circulating lipids and breast cancer prognosis in the Malmö diet and cancer study.

PURPOSE: Examine the association between circulating lipids and breast cancer outcomes in patients enrolled in the Malmö Diet and Cancer Study (MDCS). PATIENTS AND METHODS: Circulating lipid levels were measured in blood sampled upon enrollment in the female MDCS cohort (N = 17,035). We identified all MDCS participants with incident invasive breast cancer diagnosed between 1991 and 2014. Follow-up time began at breast cancer diagnosis and continued until the first event of breast cancer recurrence, death, emigration, or 5 years of follow-up. We estimated the incidence rates of recurrence at 5 years and fit Cox regression models to compute crude and adjusted hazard ratios (HRs) with 95% confidence intervals (95% CI) of breast cancer recurrence as well as all-cause mortality according to cohort-specific tertiles of apolipoprotein A-1 (Apo A-1) and apolipoprotein B (Apo B). RESULTS: We enrolled 850 eligible patients. During the 5 years of follow-up, 90 invasive breast cancer recurrences were diagnosed over 3807 person-years. In multivariable analyses, high baseline levels of Apo B were associated with an increased rate of recurrence (tertile 3 vs. 1, HR = 2.30 [95% CI 1.13-4.68]). However, high baseline levels of Apo B were not associated with all-cause mortality (tertile 3 vs. 1, HR = 1.23 [95% CI 0.68-2.25]). We observed no associations between levels of Apo A-1 and recurrence (tertile 3 vs. 1, HR = 1.34 [95% CI 0.70-2.58]) or all-cause mortality (tertile 3 vs. 1, HR = 1.12 [95% CI 0.61-2.05]). CONCLUSION: High pre-diagnostic levels of Apo B were associated with an increased risk of recurrence among breast cancer patients. Circulating Apo A-1 was not associated with breast cancer outcomes.

Hypoxia-inducible factor-1α expression and breast cancer recurrence in a Danish population-based case control study.

BACKGROUND: Hypoxia-inducible factor-1α (HIF-1α) is a transcription factor that facilitates the adaptation of cancer cells to hypoxic conditions and may be prognostic of breast cancer recurrence. We evaluated the association of HIF-1α expression with breast cancer recurrence, and its association with timing of breast cancer recurrence. METHODS: In this population-based case-control study, we included women diagnosed with stage I-III breast cancer between 1985 and 2001, aged 35-69 years, registered in the Danish Breast Cancer Group. We identified 541 cases of breast cancer recurrence among women with estrogen receptor (ER)-positive disease who were treated with tamoxifen for at least 1 year (ER+ TAM+). We also enrolled 300 breast cancer recurrence cases among women with ER-negative disease, not treated with tamoxifen, who survived at least 1 year (ER-/TAM-). Controls were recurrence-free breast cancer patients at the time of case diagnosis, matched to recurrence cases on ER/TAM status, date of surgery, menopausal status, cancer stage, and county of residence. Expression of HIF-1α was measured by immunohistochemistry on tissue microarrays. We fitted logistic regression models to compute odds ratios (ORs) and 95% confidence intervals (CIs) associating HIF-1α expression with recurrence, and with timing of recurrence. RESULTS: HIF-1α expression was observed in 23% of cases and 20% of controls in the ER+/TAM+ stratum, and in 47% of cases and 48% of controls in the ER-/TAM- stratum. We observed a near-null association between HIF-1α expression in both ER/TAM groups (ER+/TAM+ OR = 1.21, 95%CI 0.88, 1.67 and ER-/TAM- OR = 0.97, 95%CI 0.68, 1.39). HIF-1α expression was not associated with time to recurrence among women in the ER+/TAM+ stratum, but was associated with early recurrence among women in the ER-/TAM- stratum. CONCLUSION: In this study, HIF-1α expression was not associated with breast cancer recurrence overall but may be associated with early recurrence among women diagnosed with ER- breast cancer.

State-level rurality and cigarette smoking-associated cancer incidence and mortality: Do individual-level trends translate to population-level outcomes?

County-level analyses demonstrate that overall cancer incidence is generally lower in rural areas, though incidence and mortality from tobacco-associated cancers are higher than in non-rural areas and have experienced slower declines over time. The goal of our study was to examine state-level rurality and smoking-related cancer outcomes. We used publicly-available national data to quantify rurality, cigarette smoking prevalence, and smoking-attributable cancer incidence and mortality at the state level and to estimate the population-attributable fraction of cancer deaths attributable to smoking for each state, overall and by gender, for 12 smoking-associated cancers. Accounting for a 15-year lag between smoking exposure and cancer diagnosis, the median proportion of smoking-attributable cancer deaths was 28.2% in Virginia (24.6% rural) and ranged from 19.9% in Utah (9.4% rural) to 35.1% in Kentucky (41.6% rural). By gender, the highest proportion of smoking-attributable cancer deaths for women (29.5%) was in a largely urban state (Nevada, 5.8% rural) and for men (38.0%) in a largely rural state (Kentucky). Regression analyses categorizing state-level rurality into low (0-13.9%), moderate (15.3-29.9%) and high (33.6-61.3%) levels showed that high rurality was associated with 5.8% higher cigarette smoking prevalence, higher age-adjusted smoking-associated cancer incidence (44.3 more cases per 100,000 population), higher smoking-associated cancer mortality (29.8 more deaths per 100,000 population), and 3.4% higher proportion of smoking-attributable cancer deaths compared with low rurality. Our findings highlight the magnitude of the relationship between state-level rurality and smoking-attributable cancer outcomes and the importance of tobacco control in reducing cancer disparities in rural populations.

Assessing Techniques for Quantifying the Impact of Bias Due to an Unmeasured Confounder: An Applied Example.

PURPOSE: To compare the magnitude of bias due to unmeasured confounding estimated from various techniques in an applied example. PATIENTS AND METHODS: We examined the association between dibutyl phthalate (DBP) and incident estrogen receptor (ER)-positive breast cancer in a Danish nationwide cohort (N=1,122,042). Cox regression analyses were adjusted for age and active drug compounds contributing to DBP exposure. We estimated the hazard ratios (HRs) that would have been observed had one of the DBP sources been unmeasured and calculated the strength of confounding by comparing to the fully adjusted HR. We performed a quantitative bias analysis (QBA) of the "unmeasured" confounder, using external information to specify the bias parameters. Upper bounds on the bias were estimated and E-values were calculated. RESULTS: The adjusted HR for incident ER-positive breast cancer among women with high-level (≥10,000 cumulative milligrams) versus no DBP exposure was 2.12 (95% confidence interval 1.12 to 4.05). Removing each DBP source in isolation resulted in negligible change in the HR. The bias estimates from the QBA ranged from 1.00 to 1.01. The estimated maximum impact of unmeasured confounding ranged from 1.01 to 1.51. E-values ranged from 3.46 to 3.68. CONCLUSION: The impact of bias due to simulated unmeasured confounding was negligible, in part, because the unmeasured variable was not independent of controlled variables. When a suspected confounder cannot be measured in the study data, our exercise suggests that QBA is the most informative method for assessing the impact. E-values may best be reserved for situations where uncontrolled confounding emanates from an unknown confounder.

The Importance of Making Assumptions in Bias Analysis.

Quantitative bias analyses allow researchers to adjust for uncontrolled confounding, given specification of certain bias parameters. When researchers are concerned about unknown confounders, plausible values for these bias parameters will be difficult to specify. Ding and VanderWeele developed bounding factor and E-value approaches that require the user to specify only some of the bias parameters. We describe the mathematical meaning of bounding factors and E-values and the plausibility of these methods in an applied context. We encourage researchers to pay particular attention to the assumption made, when using E-values, that the prevalence of the uncontrolled confounder among the exposed is 100% (or, equivalently, the prevalence of the exposure among those without the confounder is 0%). We contrast methods that attempt to bound biases or effects and alternative approaches such as quantitative bias analysis. We provide an example where failure to make this distinction led to erroneous statements. If the primary concern in an analysis is with known but unmeasured potential confounders, then E-values are not needed and may be misleading. In cases where the concern is with unknown confounders, the E-value assumption of an extreme possible prevalence of the confounder limits its practical utility.

Identifying key barriers to effective breast cancer control in rural settings.

Breast cancer is the most common cancer and the second most common cause of cancer mortality among women in the United States. Efforts to promote breast cancer control in rural settings face specific challenges. Access to breast cancer screening, diagnosis, and treatment services is impaired by shortages of primary care and specialist providers, and geographic distance from medical facilities. Women in rural areas have comparable breast cancer mortality rates compared to women in urban settings, but this is due in large part to lower incidence rates and masks a substantial rural/urban disparity in breast cancer survival among women diagnosed with breast cancer. Mammography screening utilization rates are slightly lower among rural women than their urban counterparts, with a corresponding increase in late stage breast cancer. Differences in breast cancer survival persist after controlling for stage at diagnosis, largely due to disparities in access to treatment. Travel distance to treatment centers is the most substantial barrier to improved breast cancer outcomes in rural areas. While numerous interventions have been demonstrated in controlled studies to be effective in promoting treatment access and adherence, widespread dissemination in public health and clinical practice remains lacking. Efforts to improve breast cancer control in rural areas should focus on implementation strategies for improving access to breast cancer treatments.

Bias Analysis Gone Bad.

Quantitative bias analysis comprises the tools used to estimate the direction, magnitude, and uncertainty from systematic errors affecting epidemiologic research. Despite the availability of methods and tools, and guidance for good practices, few reports of epidemiologic research incorporate quantitative estimates of bias impacts. The lack of familiarity with bias analysis allows for the possibility of misuse, which is likely most often unintentional but could occasionally include intentional efforts to mislead. We identified 3 examples of suboptimal bias analysis, one for each common bias. For each, we describe the original research and its bias analysis, compare the bias analysis with good practices, and describe how the bias analysis and research findings might have been improved. We assert no motive to the suboptimal bias analysis by the original authors. Common shortcomings in the examples were lack of a clear bias model, computed example, and computing code; poor selection of the values assigned to the bias model's parameters; and little effort to understand the range of uncertainty associated with the bias. Until bias analysis becomes more common, community expectations for the presentation, explanation, and interpretation of bias analyses will remain unstable. Attention to good practices should improve quality, avoid errors, and discourage manipulation.