TL;DR:

This article explores racial disparities in cancer outcomes in NC using the Mortality-Incidence Ratio (MIR) as a metric to quantify health equity. By comparing MIRs across racial groups with the white MIR and overall MIR as reference points, significant disparities were found, particularly among Black and Native American populations. The analysis underscores the importance of careful benchmark selection in health equity research and highlights the complex factors contributing to these disparities. Addressing these issues requires interdisciplinary research and targeted public health interventions to ensure equitable health outcomes for all

Key Findings:

– Black patients with melanoma had an MIR of 0.43, meaning 43% of those diagnosed in NC died, compared to just 7% of White patients.

– Native American populations faced extreme disparities in ovarian and esophageal cancers, with alarmingly high MIRs.

– Hispanic populations showed fewer disparities compared to the overall and white reference groups, but this finding may be misleading due to the tendency to view this diverse group as a monolith, which can obscure the unique disparities within subgroups.

Introduction

As I embark on my research career, my focus has increasingly centered on health equity—a concept that examines the fairness and justice of health outcomes across different populations. My PhD work aims to develop quantitative methods that better assess and address disparities in healthcare delivery and outcomes. This interest in health equity alongside recent experiences—ranging from my summer internship at the NIH focused on ovarian cancer to my current role in clinical data science within an Oncology clinical trial at Novant Health—have led me to this project.

The Mortality-Incidence Ratio (MIR) offers a powerful metric for this purpose, serving as an indicator of how lethal a disease is relative to its occurrence within a population. By examining the MIR across different racial groups, I aimed to quantify health equity within the realm of cancer care. This analysis compares MIRs using two reference points: the White MIR and the overall MIR, providing insights into how racial disparities manifest in cancer outcomes.

Background

Racial disparities in health outcomes are a critical and well-documented issue in public health, manifesting in various forms across different diseases. These disparities are often driven by a complex interplay of factors, including social determinants of health (such as education, income, and access to healthcare), genetic predispositions, and environmental exposures. The Mortality-Incidence Ratio (MIR) is particularly useful for examining these disparities because it quantifies the severity of a disease by comparing the mortality rate to the incidence rate within a population.

However, it is important to recognize that the MIR is just one piece of the puzzle. Health outcomes, especially in diseases as multifaceted as cancer, are influenced by numerous factors that extend beyond the scope of a single metric. These include healthcare access, the quality of care received, health education, and broader social and environmental determinants, such as healthy food insecurity, access to exercise, and pollution exposure. By understanding these interactions, we can better interpret the disparities revealed through MIR analysis and work toward more equitable health outcomes.

Methods

To investigate racial disparities in cancer outcomes, I conducted an analysis of Mortality-Incidence Ratios (MIRs) across various racial groups using data from the North Carolina Department of Health and Human Services (NC DHHS) for the years 2018-2022. The data was age-adjusted to the standard 2000 population to account for differences in age distribution across racial groups, ensuring that the comparisons were as accurate as possible.

MIRs were calculated by dividing the mortality rate by the incidence rate for each racial group. To provide a comprehensive view of disparities, I used two reference points for comparison: the MIR for the white population and the overall MIR, which represents the aggregated outcomes across all racial groups. This dual approach allowed me to assess how each racial group’s outcomes compared both to the population as a whole and to a specific racial group with historically better health outcomes.

Data

Key findings

The analysis revealed significant disparities in cancer outcomes across racial groups. For instance, from 2018 to 2022, the MIR for melanoma among Black patients in North Carolina was 0.43, indicating that 43% of Black individuals diagnosed with melanoma during this period died from the disease. In contrast, the MIR for melanoma among white patients was markedly lower, at 0.07 (7%).

This disparity likely reflects late-stage diagnosis among Black patients, which can result from several factors:

– Lack of Awareness: There may be a limited understanding among Black patients regarding their risk for melanoma, contributing to delayed diagnosis and treatment.

– Access to Specialized Care: Limited access to dermatological care or healthcare in general can exacerbate the severity of the disease by delaying diagnosis and treatment.

– Physician Training: Many physicians may not receive adequate training on the presentation of melanoma in Black patients, leading to missed or late diagnoses.

Native American populations faced particularly severe disparities in ovarian and esophageal cancers, with MIRs of 1 meaning that all patients diagnosed in 2018 – 2022 with these cancers died. These cancers are already aggressive, but the outcomes were disproportionately worse for Native American patients, likely due to:

– Healthcare Access: Persistent barriers to accessing quality healthcare.

– Intergenerational Trauma: Long-term, intergenerational oppression and its impacts on health.

Interestingly, the data showed fewer disparities for Hispanics when compared to the overall or white reference groups. However, this finding warrants caution. The Hispanic population is often treated as a monolithic group, despite being genetically and culturally diverse. This homogenization can obscure the nuances of disparities within this group. Additionally, underreporting due to immigration status may further distort the data. Notably, the American Cancer Society reports a lifetime cancer mortality risk of 1 in 5 for Hispanic men and 1 in 6 for Hispanic women—figures that are not fully reflected in the North Carolina data.

Discussion

The disparities observed using different reference points underscore the importance of selecting appropriate benchmarks in health equity research. The more pronounced disparities identified using the white MIR suggest that this group benefits from factors—such as better access to care—that improve outcomes, making it a stringent reference point. In contrast, the overall MIR, which averages outcomes across all racial groups, may obscure significant disparities that are critical to understanding and addressing health equity.

Quantifying health equity through metrics like the MIR is essential for identifying where interventions are most needed. However, achieving health equity requires more than just identifying disparities—it demands concerted efforts to address the underlying causes, which are often deeply rooted in social, economic, and political contexts. While health equity may not be a focal point in current political discussions, it is a critical area that must be continuously upheld and prioritized.

Interdisciplinary research is key to advancing our understanding of health disparities. By integrating insights from epidemiology, sociology, economics, and other fields, we can work toward a future where the MIR differential approaches zero across all racial groups. As we look ahead, it will be important to monitor how policy changes, such as the recent expansion of Medicare, impact these disparities—potentially revealing whether the core issue lies in access to care or other systemic factors.

Sources:

Click to access 2022-MortalityRatesFinal-RaceEthnicity.pdf

Click to access 2022-IncidenceRatesFinal-5YearRaceEthnicity.pdf

Why are so many Black patients dying of skin cancer? | AAMC

Melanoma Among Non-Hispanic Black Americans.

Racial differences in time to treatment for melanoma – PMC

The ongoing racial disparities in melanoma: An analysis of the Surveillance, Epidemiology, and End Results database (1975–2016)).

Cancer statistics for American Indian and Alaska Native individuals, 2022: Including increasing disparities in early onset colorectal cancer

How recognizing diversity among Hispanics could improve health outcomes | AAMC

Cancer Facts & Figures for Hispanics & Latinos 2018-2020

In my recent project, I explored the intersection of data visualization and sentiment analysis by creating dynamic word clouds. This project allowed me to harness new techniques in Python and develop a fresh approach to visualizing text data. Here’s an overview of the project and the skills I gained.

Project Overview

The goal of this project was to enhance data visualization capabilities and apply sentiment analysis to textual data. By creating word clouds, I aimed to visually represent the frequency and significance of words, making it easier to identify key themes and emotions within the text.

Skills and Techniques

1. Sentiment Analysis in Python
   • I used Python libraries such as NLTK (Natural Language Toolkit) and TextBlob for sentiment analysis. These tools enabled me to analyze the sentiment of text data, categorizing it into positive, negative, or neutral sentiments. This analysis was crucial for understanding the emotional tone of the content and provided valuable insights into how different words and phrases contribute to the overall sentiment.
2. Creating Word Clouds
   • I utilized the WordCloud library in Python to generate visually appealing word clouds. This involved preprocessing text data to remove common stopwords and punctuation, ensuring that the word clouds accurately reflected the most relevant terms. I experimented with various shapes, colors, and fonts to enhance the visual impact and align with the project’s objectives.
3. New Styles of Data Visualization
   • The project pushed the boundaries of traditional data visualization by incorporating creative design elements into the word clouds. I explored different styles and formats to represent text data in a way that was both informative and engaging. This approach allowed me to present data in a more visually dynamic manner, making it easier to convey complex information at a glance.
4. Code and Implementation
   • Here is a brief overview of the code used in this project:

# Start with loading all necessary libraries
import numpy as np
import pandas as pd
from os import path
from PIL import Image
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator

import matplotlib.pyplot as plt
%matplotlib inline

import warnings
warnings.filterwarnings("ignore")

from nltk.tokenize import word_tokenize
# Creating the diary input function to create a diary dictionary
def collect_entries():
    entries = []
    while True:
        date = input("Enter the date (YYYY-MM-DD) or type 'done' to finish: ")
        if date.lower() == 'done':
            break
        entry = input("Enter the diary entry: ")
        entries.append({"date": date, "entry": entry})
    return entries

diary_data = collect_entries()


import nltk
from nltk.tokenize import word_tokenize
nltk.download('punkt')
#create empty string 
text = ''
#concatenating entries to empty string for wordcloud
for entry in diary_data:
    text += entry["entry"] + " "
wordcloud = WordCloud(background_color="white").generate(text)

plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.show()

Wordcloud workbook

Outcomes and Reflections

This project demonstrated the power of combining sentiment analysis with creative data visualization techniques. By generating word clouds and analyzing sentiment, I was able to provide a comprehensive view of the textual data. The visual representations not only highlighted key themes but also offered insights into the emotional tone of the content.

The skills gained from this project include advanced text processing, sentiment analysis, and innovative data visualization techniques. These skills are essential for effectively communicating insights and enhancing data-driven decision-making.

Looking Ahead

I’m excited to continue exploring new ways to visualize data and analyze text. This project has opened up possibilities for applying these techniques to various contexts, from business analytics to academic research. If you have any questions or would like to discuss this project further, please feel free to reach out.