Determinants of late detection and advanced-stage diagnosis of breast cancer in Nigeria
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ABSTRACT: Objective
To describe the risk factors for late detection and advanced-stage diagnosis among patients who detected their BC early. Method
Using secondary data, we analyzed the impact of socio-demographic factors, premorbid experience, BC knowledge, and health-seeking pattern on the risk of late detection and advanced-stage diagnosis after early BC detection. Test of statistical significance in SPSS and EasyR was set at 5% using Sign-test, chi-square tests (of independence and goodness of fit), odds ratio, or risk ratio as appropriate. Result
Most socio-demographic factors did not affect detection size or risk of disease progression in the 405 records analyzed. High BC knowledge, p-value = 0.001, and practicing breast self-examination (BSE) increased early detection, p-value = 0.04, with a higher probability (OR 1.6 (95% CI 1.1–2.5) of detecting <2cm lesions. Visiting alternative care (RR 1.5(95% CI 1.2–1.9), low BC knowledge (RR 1.3(95% CI 1.1–1.9), and registering concerns for hospital care increased the risk of advanced-stage diagnosis after early detection (64% (95% CI 55–72)). Adhering to the monthly BSE schedule reduced the risk of advanced-stage diagnosis by -25% (95% CI -49, -1.1) in the presence of socioeconomic barriers. Conclusion
Strategies to increase BC knowledge and BSE may help BC downstaging, especially among women with common barriers to early diagnosis.
SUBMITTER: Agodirin O
PROVIDER: S-EPMC8565753 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundDisrupted and delayed Medicaid coverage has been consistently associated with lower rates of cancer screening and early-stage cancer diagnosis compared with continuous coverage. However, the relationships between Medicaid coverage timing, breast cancer treatment delays, and survival are less clear.MethodsUsing the linked Missouri Cancer Registry-Medicaid claims data, we identified 4583 women diagnosed with breast cancer between 2007 and 2016. We used logistic regression to estimate odds ratios (ORs) of late-stage diagnosis and treatment delays for prediagnosis (>30 days, >90 days, and >1 year before diagnosis) vs peridiagnosis enrollment. Cox proportional hazards models were used to estimate the hazard ratio (HR) of breast cancer-specific mortality for pre- vs postdiagnosis enrollment.ResultsPatients enrolled in Medicaid more than 30 days before diagnosis were less likely to be diagnosed at a late stage compared with those enrolled in Medicaid peridiagnosis (OR = 0.69, 95% confidence interval [CI] = 0.60 to 0.79). This result persisted using enrollment 90-day (OR = 0.64, 95% CI = 0.56 to 0.74) and 1-year thresholds (OR = 0.55, 95% CI = 0.47 to 0.65). We did not observe a difference in the likelihood of treatment delays between the 2 groups. After adjustment for sociodemographic factors, there was no statistically significant difference in the risk of breast cancer mortality for patients enrolled more than 30 days prediagnosis relative to patients enrolled peridiagnosis (HR = 0.98, 95% CI = 0.83 to 1.14), but a lower risk was observed for patients enrolled prediagnosis when using 90 days (HR = 0.85, 95% CI = 0.72 to 0.999) or 1 year (HR = 0.79, 95% CI = 0.66 to 0.96) as the threshold.ConclusionsWomen with breast cancer who enroll in Medicaid earlier may benefit from earlier diagnoses, but only longer-term enrollment may have survival benefits.
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Project description:ImportanceThere are stark disparities in cervical cancer burden in the United States, notably by race and ethnicity and geography. Late-stage diagnosis is an indicator of inadequate access to and utilization of screening.ObjectiveTo identify geospatial clusters of late-stage cervical cancer at time of diagnosis in Texas.Design, setting, and participantsThis population-based cross-sectional study used incident cervical cancer data from the Texas Cancer Registry from 2014 to 2018 of female patients aged 18 years or older. Late-stage cervical cancer cases were geocoded at the census tract level (n = 5265) using their residential coordinates (latitude and longitude) at the time of diagnosis. Statistical analysis was performed from April to September 2023.ExposuresCensus tract of residence at diagnosis.Main outcome and measuresLate-stage cervical cancer diagnosis (ie, cases classified by the National Cancer Institute Surveillance, Epidemiology and End Results summary stages 2 to 4 [regional spread] or 7 [distant metastasis]). A Poisson probability-based model of the SaTScan purely spatial scan statistics was applied at the census tract-level to identify geographic clusters of higher (hot spots) or lower (cold spots) proportions than expected of late-stage cervical cancer diagnosis and adjusted for age.ResultsAmong a total of 6484 female patients with incident cervical cancer cases (mean [SD] age, 48.7 [14.7] years), 2300 (35.5%) were Hispanic, 798 (12.3%) were non-Hispanic Black, 3090 (47.6%) were non-Hispanic White, and 296 (4.6%) were other race or ethnicity. Of the 6484 patients, 2892 with late-stage diagnosis (mean [SD] age, 51.8 [14.4] years were analyzed. Among patients with late-stage diagnosis, 1069 (37.0%) were Hispanic, 417 (14.4%) were non-Hispanic Black, 1307 (45.2%) were non-Hispanic White, and 99 (3.4%) were other race or ethnicity. SaTScan spatial analysis identified 7 statistically significant clusters of late-stage cervical cancer diagnosis in Texas, of which 4 were hot spots and 3 were cold spots. Hot spots included 1128 census tracts, predominantly in the South Texas Plains, Gulf Coast, and Prairies and Lakes (North Texas) regions. Of the 2892 patients with late-stage cervical cancer, 880 (30.4%) were observed within hot spots. Census tract-level comparison of characteristics of clusters suggested that hot spots differed significantly from cold spots and the rest of Texas by proportions of racial and ethnic groups, non-US born persons, and socioeconomic status.Conclusions and relevanceIn this cross-sectional study examining geospatial clusters of late-stage cervical cancer diagnosis, place-based disparities were found in late-stage cervical cancer diagnosis in Texas. These findings suggest that these communities may benefit from aggressive cervical cancer interventions.
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Project description:ObjectiveTo evaluate the effect of irregular screening behaviour on the risk of advanced stage breast cancer at diagnosis in Flanders.MethodsAll women aged 50-69 who were invited to the organized breast cancer screening and diagnosed with breast cancer before age 72 from 2001 to 2018 were included. All prevalent screen and interval cancers within 2 years of a prevalent screen were excluded. Screening behaviour was categorized based on the number of invitations and performed screenings. Four groups were defined: regular, irregular, only-once, and never attenders. Advanced stage cancer was defined as a stage III + breast cancer. The association between screening regularity and breast cancer stage at diagnosis was evaluated in multivariable logistic regression models, taking age of diagnosis and socio-economic status into account.ResultsIn total 13.5% of the 38,005 breast cancer cases were diagnosed at the advanced stage. Compared to the regular attenders, the risk of advanced stage breast cancer for the irregular attenders, women who participated only-once, and never attenders was significantly higher with ORadjusted:1.17 (95%CI:1.06-1.29) and ORadjusted:2.18 (95%CI:1.94-2.45), and ORadjusted:5.95 (95%CI:5.33-6.65), respectively.ConclusionsIn our study, never attenders were nearly six times more likely to be diagnosed with advanced stage breast cancer than regular attenders, which was much higher than the estimates published thus far. An explanation for this is that the ever screened women is a heterogeneous group regarding the participation profiles which also includes irregular and only-once attenders. The benefit of regular screening should be informed to all women invited for screening.
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Project description:BackgroundMultiple studies have yielded important findings regarding the determinants of an advanced-stage diagnosis of breast cancer. We seek to advance this line of inquiry through a broadened conceptual framework and accompanying statistical modeling strategy that recognize the dual importance of access-to-care and biologic factors on stage.MethodsThe Centers for Disease Control and Prevention-sponsored Breast and Prostate Cancer Data Quality and Patterns of Care Study yielded a seven-state, cancer registry-derived population-based sample of 9,142 women diagnosed with a first primary in situ or invasive breast cancer in 2004. The likelihood of advanced-stage cancer (American Joint Committee on Cancer IIIB, IIIC, or IV) was investigated through multivariable regression modeling, with base-case analyses using the method of instrumental variables (IV) to detect and correct for possible selection bias. The robustness of base-case findings was examined through extensive sensitivity analyses.ResultsAdvanced-stage disease was negatively associated with detection by mammography (P < 0.001) and with age < 50 (P < 0.001), and positively related to black race (P = 0.07), not being privately insured [Medicaid (P = 0.01), Medicare (P = 0.04), uninsured (P = 0.07)], being single (P = 0.06), body mass index > 40 (P = 0.001), a HER2 type tumor (P < 0.001), and tumor grade not well differentiated (P < 0.001). This IV model detected and adjusted for significant selection effects associated with method of detection (P = 0.02). Sensitivity analyses generally supported these base-case results.ConclusionsThrough our comprehensive modeling strategy and sensitivity analyses, we provide new estimates of the magnitude and robustness of the determinants of advanced-stage breast cancer.ImpactStatistical approaches frequently used to address observational data biases in treatment-outcome studies can be applied similarly in analyses of the determinants of stage at diagnosis. Cancer Epidemiol Biomarkers Prev; 25(4); 613-23. ©2016 AACR.
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Project description:BackgroundDisparities in the stage at diagnosis for breast cancer have been independently associated with various contextual characteristics. Understanding which combinations of these characteristics indicate highest risk, and where they are located, is critical to targeting interventions and improving outcomes for patients with breast cancer.MethodsThe study included women diagnosed with invasive breast cancer between 2009 and 2018 from 680 U.S. counties participating in the Surveillance, Epidemiology, and End Results program. We used a machine learning approach called Classification and Regression Tree (CART) to identify county "phenotypes," combinations of characteristics that predict the percentage of patients with breast cancer presenting with late-stage disease. We then mapped the phenotypes and compared their geographic distributions. These findings were further validated using an alternate machine learning approach called random forest.ResultsWe discovered seven phenotypes of late-stage breast cancer. Common to most phenotypes associated with high risk of late-stage diagnosis were high uninsured rate, low mammography use, high area deprivation, rurality, and high poverty. Geographically, these phenotypes were most prevalent in southern and western states, while phenotypes associated with lower percentages of late-stage diagnosis were most prevalent in the northeastern states and select metropolitan areas.ConclusionsThe use of machine learning methods of CART and random forest together with geographic methods offers a promising avenue for future disparities research.ImpactLocal interventions to reduce late-stage breast cancer diagnosis, such as community education and outreach programs, can use machine learning and geographic modeling approaches to tailor strategies for early detection and resource allocation.
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Project description:The International Agency for Research on Cancer (IARC) has recently reported a 66% increase in the global number of cancer deaths since 1960. In the US alone, about one in eight women is expected to develop invasive breast cancer(s) (breast cancer) at some point in their lifetime. Traditionally, a BC diagnosis includes mammography, ultrasound, and some high-end molecular bioimaging. Unfortunately, these techniques detect BC at a later stage. So early and advanced molecular diagnostic tools are still in demand. In the past decade, various histological and immuno-molecular studies have demonstrated that BC is highly heterogeneous in nature. Its growth pattern, cytological features, and expression of key biomarkers in BC cells including hormonal receptor markers can be utilized to develop advanced diagnostic and therapeutic tools. A cancer cell's progression to malignancy exhibits various vital biomarkers, many of which are still underrepresented in BC diagnosis and treatment. Advances in genetics have also enabled the development of multigene assays to detect genetic heterogeneity in BC. However, thus far, the FDA has approved only four such biomarkers-cancer antigens (CA); CA 15-3, CA 27-29, Human epidermal growth factor receptor 2 (HER2), and circulating tumor cells (CTC) in assessing BC in body fluids. An adequately structured portable-biosensor with its non-invasive and inexpensive point-of-care analysis can quickly detect such biomarkers without significantly compromising its specificity and selectivity. Such advanced techniques are likely to discriminate between BC and a healthy patient by accurately measuring the cell shape, structure, depth, intracellular and extracellular environment, and lipid membrane compositions. Presently, BC treatments include surgery and systemic chemo- and targeted radiation therapy. A biopsied sample is then subjected to various multigene assays to predict the heterogeneity and recurrence score, thus guiding a specific treatment by providing complete information on the BC subtype involved. Thus far, we have seven prognostic multigene signature tests for BC providing a risk profile that can avoid unnecessary treatments in low-risk patients. Many comparative studies on multigene analysis projected the importance of integrating clinicopathological information with genomic-imprint analysis. Current cohort studies such as MINDACT, TAILORx, Trans-aTTOM, and many more, are likely to provide positive impact on long-term patient outcome. This review offers consolidated information on currently available BC diagnosis and treatment options. It further describes advanced biomarkers for the development of state-of-the-art early screening and diagnostic technologies.
| S-EPMC7952319 | biostudies-literature
Project description:IntroductionHealth insurance expansions may increase early detection of cancer and reduce late-stage cancer incidence. The study assesses the effects of the Affordable Care Act Medicaid expansions on rates of early- and late-stage cancer diagnosis up to 3 years after implementation.MethodsPopulation-based quasi-experimental analysis of nonelderly adults was conducted in 732 counties from the 2010-2016 Surveillance, Epidemiology, and End Results Program cancer registry data. Multivariate event study regressions were estimated to compare annual changes in county-level rates of cancer diagnoses in states that expanded Medicaid with those that did not. Data analysis was performed from May to October 2019.ResultsMedicaid expansion was associated with an increase in early-stage cancer diagnoses of 21.3 per 100,000 population (95% CI=2.9, 35.2) or 9.14% of population in its first year; estimates for Years 2 and 3 were also positive but smaller and not statistically significant. There was a marginally significant reduction in late-stage diagnoses of 8.7 per 100,000 population (95% CI= -25.0, 3.4) or 5.7% of population relative to baseline, 3 years after Medicaid expansion. There was no detectable effect of expansion on total diagnoses.ConclusionsMedicaid expansions increased early-stage cancer diagnosis in the first year of expansion, but effects dissipated in subsequent years, suggesting a response to pent-up patient demand for screening and diagnostic services immediately after expansion. There was also suggestive evidence of reductions in late-stage diagnosis in the third year of Medicaid expansion, highlighting the potential role of public health insurance in improving cancer outcomes among nonelderly adults.
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Project description:Breast cancer presents high incidence and mortality rates, being considered an important public health issue. Analyze the spatial distribution pattern of late stage diagnosis and mortality for breast cancer and its correlation with socioeconomic and health service offer-related population indicators. Ecological study, developed with 161 Intermediate Region of Urban Articulation (IRUA). Mortality data were collected from the Mortality Information System (MIS). Tumor staging data were extracted from the Hospital Cancer Registry (HCR). Socioeconomic variables were obtained from the Atlas of Human Development in Brazil; data on medical density and health services were collected from the National Registry of Health Institutions (NRHI) and Supplementary National Health Agency. Global Moran's Index and Local Indicator of Spatial Association (LISA) were utilized to verify the existence of territorial clusters. Multivariate analysis used models with global spatial effects. The proportion of late stage diagnosis of breast cancer was 39.7% (IC 39.4-40.0). The mean mortality rate for breast cancer, adjusted by the standard world population was 10.65 per 100,000 women (± 3.12). The proportion of late stage diagnosis presented positive spatial correlation with Gini's Index (p = 0.001) and negative with the density of gynecologist doctors (p = 0.009). The adjusted mortality rates presented a positive spatial correlation with the Human Development Index (p<0.001) and density of gynecologist doctors (p<0.001). Socioeconomic and health service offer-related inequalities of the Brazilian territory are determinants of the spatial pattern of breast cancer morbimortality in Brazil.
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