Questions & Hypotheses

Q: How do cancer rates vary across different racial groups for specific cancer types (lung, colorectal, breast)?

H: We hypothesize that certain racial groups, particularly African Americans, have higher incidence rates for specific cancers compared to non-Hispanic whites. These disparities may be influenced by a combination of genetic, environmental, socio-economic, and healthcare access factors. Historical marginalization and current socio-economic challenges disproportionately affect African Americans, potentially leading to higher exposure to risk factors such as smoking, unhealthy diets, and inadequate healthcare.

Correlation Matrix The heatmap identifies strong positive correlations between total cancer rates and cancer rates in older age groups, and significant correlations between lung cancer rates for different racial groups. The correlation matrix helps to identify the strength and direction of relationships between different variables. It is particularly useful for understanding which factors are strongly associated with cancer rates. Strong positive correlations between total cancer rates and cancer rates in older age groups suggest that age is a significant factor in cancer incidence. Significant correlations between lung cancer rates for different racial groups highlight the importance of considering race in cancer research. This supports the hypothesis that race influences cancer incidence for specific cancer types.

Q: How can a person’s background impact various aspects of life and ultimately lead to differing outcomes in health?

H: Background can potentially affect health in so many different ways. For one, access to healthcare/insurance or how a person is treated by healthcare professionals can be impacted by their race, gender, geographic location, and socioeconomic status (not to mention, a lot of these influence each other). Not having access to healthy food is another factor that can be detrimental to someone’s health. To look more into these hypotheses, my group can look into disparities in health insurance, differential treatment by healthcare professionals due to race or gender, and research where food deserts are.

Mean Colorectal Cancer Rates by Race This bar chart shows noticeable disparities in colorectal cancer rates among racial groups, with Black populations having higher mean colorectal cancer rates compared to others. This supports the hypothesis that a person's background, including race, impacts health outcomes.

Q: Do race and gender have an effect on a person’s chance of being diagnosed with cancer?

H: We predict that race and gender do have an effect on a person’s chance of being diagnosed with cancer.

Mean Lung Cancer Rates by Race This bar chart shows significant differences in lung cancer rates among racial groups, with White and Black populations having higher mean lung cancer rates compared to Asian and Hispanic populations. This visualization supports the hypothesis that race influences cancer incidence.

Mean Lung Cancer Rates by Sex This bar chart shows differences in lung cancer rates between males and females. Males tend to have higher lung cancer rates compared to females, supporting the hypothesis that gender influences cancer incidence.

Q: What are the geographic patterns in cancer incidence and how do they correlate with socioeconomic factors?

H: We predict that areas with lower socioeconomic status will exhibit higher cancer incidence rates due to limited access to healthcare and higher exposure to risk factors. Lower socioeconomic status often correlates with higher cancer rates, likely due to factors such as limited healthcare access, lifestyle behaviors, environmental exposures, and occupational hazards.

Mean Lung Cancer Rates by Age Group This bar chart shows an increasing trend in lung cancer rates with age, suggesting that older populations are more susceptible to lung cancer. This supports the hypothesis that age, along with socio-economic factors, influences cancer incidence.

Q: How do age and sex interact with race to influence cancer diagnosis rates?

H: We hypothesize that the intersection of age, sex, and race will reveal compounded disparities, particularly affecting minority women and older adults. This hypothesis posits that older age increases cancer risk due to cumulative exposure to risk factors and genetic mutations, while minority status may exacerbate this risk due to disparities in healthcare access and socio-economic conditions. Moreover, minority women might experience higher diagnosis rates of certain cancers (e.g., breast and cervical cancer) compared to non-Hispanic white women, due to combined effects of gender-specific health issues, socio-cultural factors, and potential biases in healthcare delivery.

Mean Breast Cancer Rates by Sex and Age Group This bar chart shows higher prevalence of breast cancer in older age groups, underscoring the importance of early detection and screening in younger age groups. This supports the hypothesis that age and sex interact with race to influence cancer diagnosis rates.

Q: What role does access to healthcare play in the disparities observed in cancer rates among different racial groups?

H: We predict that limited access to healthcare among minority groups contributes significantly to the observed disparities in cancer rates. This limited access includes fewer healthcare facilities in minority communities, lower insurance coverage, and financial barriers that restrict timely cancer screenings, early diagnoses, and effective treatments. Additionally, systemic healthcare biases and cultural barriers further exacerbate these disparities, leading to delayed care and poorer outcomes.

Actual vs Predicted Total Cancer Rates This scatter plot compares actual total cancer rates with predicted total cancer rates based on our regression model. The strong linear relationship between the two sets of values indicates a well-fitting model, suggesting that our model accurately captures the factors influencing cancer rates. However, some deviations from the line of best fit indicate areas where the model could be improved, such as incorporating more nuanced measures of healthcare access and quality. This supports the hypothesis that access to healthcare is a significant factor in determining cancer rates and highlights the need for better healthcare access to reduce disparities. The predictive model's success or failure raises questions about the limitations of quantitative analysis and the need for qualitative insights to fully understand cancer trends.

Q: How do historical and socio-cultural contexts influence current disparities in cancer incidence?

H: We hypothesize that historical inequities and cultural factors continue to impact current health outcomes, exacerbating cancer disparities among racial minorities. Historically entrenched racism, including discriminatory policies like residential segregation and unequal access to education and employment, has led to lasting socio-economic disadvantages and health inequities for minorities. Cultural factors, such as differing health beliefs and practices and historical mistrust of the medical system due to past abuses, further impede healthcare access and utilization among racial minorities. These historical and cultural legacies perpetuate disparities in cancer incidence by limiting preventive care, contributing to higher exposure to environmental risks, and reducing early diagnosis and treatment opportunities for minority populations.

Mean Breast Cancer Rates by Race This bar chart shows disparities in breast cancer rates among racial groups, with Black and White populations exhibiting higher mean breast cancer rates compared to others. This supports the hypothesis that historical and socio-cultural contexts influence cancer incidence.

Q: How effective are current cancer treatments across different racial groups?

H: We predict that African Americans and other minorities receive lower-quality cancer treatments and have worse outcomes compared to non-Hispanic whites. This disparity is likely due to systemic issues such as limited access to advanced treatment options, biases in treatment recommendations, and underrepresentation in clinical trials. Additionally, socio-economic challenges such as financial barriers, lower health literacy, and less access to high-quality healthcare facilities contribute to less effective cancer care for minorities, resulting in higher rates of cancer recurrence and mortality. These factors collectively lead to differences in treatment quality and health outcomes, exacerbating the existing disparities in cancer survival and recovery.

Boxplot of Lung Cancer Rates by Race The boxplot shows significant differences in lung cancer rates among racial groups. White individuals generally receive better cancer treatments and have better access to healthcare and insurance, which can lead to lower cancer incidence and better outcomes. Conversely, minority groups, including Black and Indigenous populations, often face barriers to high-quality healthcare and insurance, contributing to higher cancer rates and worse outcomes. This supports the hypothesis that treatment effectiveness varies across racial groups due to differences in healthcare access and quality.

Q: What is the impact of socio-economic status on cancer incidence and survival rates within racial groups?

H: We predict that socio-economic status (SES) has a strong influence on both the incidence and survival rates of cancer, with lower socio-economic groups experiencing worse outcomes. This effect is particularly pronounced among racial minorities, who often face longer delays in receiving treatment due to systemic barriers such as limited access to healthcare facilities, financial constraints, and systemic biases. Mistrust and communication issues within the healthcare system also contribute to delayed care and poorer outcomes. Implementing targeted health equity initiatives is crucial to mitigate these disparities.

Correlation Matrix The heatmap shows strong correlations between socio-economic status indicators and cancer incidence rates, underscoring the impact of socio-economic factors on health outcomes. This supports the hypothesis that SES influences cancer incidence and survival rates.