Exploring County-Level Correlations in Cancer Rates and Trends

A Multivariate Ordinary Least Squares Regression Model

By Noah Rippner [source]

About this dataset

This dataset offers a unique opportunity to examine the pattern and trends of county-level cancer rates in the United States at the individual county level. Using data from cancer.gov and the US Census American Community Survey, this dataset allows us to gain insight into how age-adjusted death rate, average deaths per year, and recent trends vary between counties – along with other key metrics like average annual counts, met objectives of 45.5?, recent trends (2) in death rates, etc., captured within our deep multi-dimensional dataset. We are able to build linear regression models based on our data to determine correlations between variables that can help us better understand cancers prevalence levels across different counties over time - making it easier to target health initiatives and resources accurately when necessary or desired

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How to use the dataset

This kaggle dataset provides county-level datasets from the US Census American Community Survey and cancer.gov for exploring correlations between county-level cancer rates, trends, and mortality statistics. This dataset contains records from all U.S counties concerning the age-adjusted death rate, average deaths per year, recent trend (2) in death rates, average annual count of cases detected within 5 years, and whether or not an objective of 45.5 (1) was met in the county associated with each row in the table.

To use this dataset to its fullest potential you need to understand how to perform simple descriptive analytics which includes calculating summary statistics such as mean, median or other numerical values; summarizing categorical variables using frequency tables; creating data visualizations such as charts and histograms; applying linear regression or other machine learning techniques such as support vector machines (SVMs), random forests or neural networks etc.; differentiating between supervised vs unsupervised learning techniques etc.; reviewing diagnostics tests to evaluate your models; interpreting your findings; hypothesizing possible reasons and patterns discovered during exploration made through data visualizations ; Communicating and conveying results found via effective presentation slides/documents etc.. Having this understanding will enable you apply different methods of analysis on this data set accurately ad effectively.

Once these concepts are understood you are ready start exploring this data set by first importing it into your visualization software either tableau public/ desktop version/Qlikview / SAS Analytical suite/Python notebooks for building predictive models by loading specified packages based on usage like Scikit Learn if Python is used among others depending on what tool is used . Secondly a brief description of the entire table's column structure has been provided above . Statistical operations can be carried out with simple queries after proper knowledge of basic SQL commands is attained just like queries using sub sets can also be performed with good command over selecting columns while specifying conditions applicable along with sorting operations being done based on specific attributes as required leading up towards writing python codes needed when parsing specific portion of data desired grouping / aggregating different categories before performing any kind of predictions / models can also activated create post joining few tables possible , when ever necessary once again varying across tools being used Thereby diving deep into analyzing available features determined randomly thus creating correlation matrices figures showing distribution relationships using correlation & covariance matrixes , thus making evaluations deducing informative facts since revealing trends identified through corresponding scatter plots from a given metric gathered from appropriate fields!

Research Ideas

• Building a predictive cancer incidence model based on county-level demographic data to identify high-risk areas and target public health interventions.
• Analyzing correlations between age-adjusted death rate, average annual count, and recent trends in order to develop more effective policy initiatives for cancer prevention and healthcare access.
• Utilizing the dataset to construct a machine learning algorithm that can predict county-level mortality rates based on socio-economic factors such as poverty levels and educational attainment rates

Acknowledgements

If you use this dataset i...

Cancer Mortality & Incidence Rates: (Country LVL)

Investigating Cancer Trends over time

By Data Exercises [source]

About this dataset

This dataset is a comprehensive collection of data from county-level cancer mortality and incidence rates in the United States between 2000-2014. This data provides an unprecedented level of detail into cancer cases, deaths, and trends at a local level. The included columns include County, FIPS, age-adjusted death rate, average death rate per year, recent trend (2) in death rates, recent 5-year trend (2) in death rates and average annual count for each county. This dataset can be used to provide deep insight into the patterns and effects of cancer on communities as well as help inform policy decisions related to mitigating risk factors or increasing preventive measures such as screenings. With this comprehensive set of records from across the United States over 15 years, you will be able to make informed decisions regarding individual patient care or policy development within your own community!

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How to use the dataset

This dataset provides comprehensive US county-level cancer mortality and incidence rates from 2000 to 2014. It includes the mortality and incidence rate for each county, as well as whether the county met the objective of 45.5 deaths per 100,000 people. It also provides information on recent trends in death rates and average annual counts of cases over the five year period studied.

This dataset can be extremely useful to researchers looking to study trends in cancer death rates across counties. By using this data, researchers will be able to gain valuable insight into how different counties are performing in terms of providing treatment and prevention services for cancer patients and whether preventative measures and healthcare access are having an effect on reducing cancer mortality rates over time. This data can also be used to inform policy makers about counties needing more target prevention efforts or additional resources for providing better healthcare access within at risk communities.

When using this dataset, it is important to pay close attention to any qualitative columns such as “Recent Trend” or “Recent 5-Year Trend (2)” that may provide insights into long term changes that may not be readily apparent when using quantitative variables such as age-adjusted death rate or average deaths per year over shorter periods of time like one year or five years respectively. Additionally, when studying differences between different counties it is important to take note of any standard FIPS code differences that may indicate that data was collected by a different source with a difference methodology than what was used in other areas studied

Research Ideas

• Using this dataset, we can identify patterns in cancer mortality and incidence rates that are statistically significant to create treatment regimens or preventive measures specifically targeting those areas.
• This data can be useful for policymakers to target areas with elevated cancer mortality and incidence rates so they can allocate financial resources to these areas more efficiently.
• This dataset can be used to investigate which factors (such as pollution levels, access to medical care, genetic make up) may have an influence on the cancer mortality and incidence rates in different US counties

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: death .csv | Column name | Description | |:-------------------------------------------|:-------------------------------------------------------------------...

Table S1: Raw data extracted from GLOBOCAN 2018 dataset for all cancers and each cancer, for all age groups and age-group intervals, for females and males, and for mortality and incidence for Arab countries, the world, USA and Europe. This data was used to generate the pivot table in Table S2 which can be queried. All data in the manuscript was based on this data file.

This portion of the GapMinder data includes one year of numerous country-level indicators of health, wealth and development for 213 countries.

GapMinder collects data from a handful of sources, including the Institute for Health
Metrics and Evaluation, US Census Bureau’s International Database, United Nations Statistics Division, and the World Bank. Source: https://www.gapminder.org/

Variable Name , Description of Indicator & Sources Unique Identifier: Country

1. incomeperperson : 2010 Gross Domestic Product per capita in constant 2000 US$.The inflation but not the differences in the cost of living between countries has been taken into account. [Main Source : World Bank Work Development Indicators]

2. alcconsumption: 2008 alcohol consumption per adult (age 15+), litres Recorded and estimated average alcohol consumption, adult (15+) percapita consumption in liters pure alcohol [Main Source : WHO]

3. armedforcesrate: Armed forces personnel (% of total labor force) [Main Source : Work Development Indicators]

4. breastcancerper100TH : 2002 breast cancer new cases per 100,000 female Number of new cases of breast cancer in 100,000 female residents during the certain year. [Main Source : ARC (International Agency for Research on Cancer)]

5. co2emissions : 2006 cumulative CO2 emission (metric tons), Total amount of CO2 emission in metric tons since 1751. [*Main Source : CDIAC (Carbon Dioxide Information Analysis Center)] *

6. femaleemployrate : 2007 female employees age 15+ (% of population) Percentage of female population, age above 15, that has been employed during the given year. [ Main Source : International Labour Organization]

7. employrate : 2007 total employees age 15+ (% of population) Percentage of total population, age above 15, that has been employed during the given year. [Main Source : International Labour Organization]

8. HIVrate : 2009 estimated HIV Prevalence % - (Ages 15-49) Estimated number of people living with HIV per 100 population of age group 15-49. [Main Source : UNAIDS online database]

9. Internetuserate: 2010 Internet users (per 100 people) Internet users are people with access to the worldwide network. [Main Source : World Bank]

10. lifeexpectancy : 2011 life expectancy at birth (years) The average number of years a newborn child would live if current mortality patterns were to stay the same. [Main Source : 1) Human Mortality Database, 2) World Population Prospects: , 3) Publications and files by history prof. James C Riley , 4) Human Lifetable Database ]

11. oilperperson : 2010 oil Consumption per capita (tonnes per year and person) [Main Source : BP]

12. polityscore : 2009 Democracy score (Polity) Overall polity score from the Polity IV dataset, calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 the highest. [Main Source : Polity IV Project]

13. relectricperperson : 2008 residential electricity consumption, per person (kWh) . The amount of residential electricity consumption per person during the given year, counted in kilowatt-hours (kWh). [Main Source : International Energy Agency]

14. suicideper100TH : 2005 Suicide, age adjusted, per 100 000 Mortality due to self-inflicted injury, per 100 000 standard population, age adjusted . [Main Source : Combination of time series from WHO Violence and Injury Prevention (VIP) and data from WHO Global Burden of Disease 2002 and 2004.]

15. urbanrate : 2008 urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects) [Main Source : World Bank]

IntroductionComparable indicators on complete cancer prevalence are increasingly needed in Europe to support survivorship care planning. Direct measures can be biased by limited registration time and estimates are needed to recover long term survivors. The completeness index method, based on incidence and survival modelling, is the standard most validated approach.MethodsWithin this framework, we consider two alternative approaches that do not require any direct modelling activity: i) empirical indices derived from long established European registries; ii) pre-calculated indices derived from US-SEER cancer registries. Relying on the EUROCARE-6 study dataset we compare standard vs alternative complete prevalence estimates using data from 62 registries in 27 countries by sex, cancer type and registration time.ResultsFor tumours mostly diagnosed in the elderly the empirical estimates differ little from standard estimates (on average less than 5% after 10-15 years of registration), especially for low prognosis cancers. For early-onset cancers (bone, brain, cervix uteri, testis, Hodgkin disease, soft tissues) the empirical method may produce substantial underestimations of complete prevalence (up to 20%) even when based on 35-year observations. SEER estimates are comparable to the standard ones for most cancers, including many early-onset tumours, even when derived from short time series (10-15 years). Longer observations are however needed when cancer-specific incidence and prognosis differ remarkably between US and European populations (endometrium, thyroid or stomach).DiscussionThese results may facilitate the dissemination of complete prevalence estimates across Europe and help bridge the current information gaps.

Global Suicide, Mental Health, Substance Use Disorders Trends

Analyzing the Impact Across Countries

By [source]

About this dataset

This dataset contains comprehensive data on global suicide, mental health, substance use disorders, and economic trends from 1990 to 2017. Using this data, researchers can delve deep into the effects of these trends across countries and ultimately uncover important insights about the state of global health. The dataset contains information about suicide rates (per 100,000 people), mental disorder prevalence (as a percentage of population size in 2017), population share with substance use disorders (as a percentage from 1990-2016), GDP per capita by purchasing power parity (in terms of current US$ for 1990-2017) and net national income per capita adjusted for inflation effects(in current US$, as in 2016). Additionally it tracks unemployment rate among populations over time(populaton%, 1991-2017). All this will help us to better understand how issues such as suicide, mental health and substance use disorders are affecting the lives of people globally

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This dataset offers insights into how mental health, substance use disorders, and economic status can impact global suicide trends. To get the most out of this data set, it is important to note the various columns available and their purpose as outlined above.

To analyze global suicide rates, look at the column “Probability (%) of dying between age 30 and exact age 70 from any of cardiovascular disease, cancer, diabetes or chronic respiratory disease” for a summary of estimated suicide rates for different countries over time. Additionally the columns “Entity” and “Code” provide useful information on which country is being discussed in each row.

The column “Prevalence- Alcohol and Substance Use Disorders” provides an overview of substance use disorders across different countries while the year column indicates when these trends are taking place.

For economic indicators related to mental health there is data available on national income per capita (current US$, 2016) as well as unemployment rate (population % 1991-2017). Together these metrics give a detailed picture into how economics can be interlinked with mental health and potentially suicide rates.

Finally this dataset also allows you to investigate varying trends overtime between different countries by looking at any common metrics but only in one specific year using appropriate filters when exploring the data set in more detail

Research Ideas

• Analyzing the correlation between mental health and economic indicators.
• Identifying countries with the highest prevalence of substance use disorders and developing targeted interventions for those populations.
• Examining the impact of global suicide rates over time to increase awareness and reduce stigma surrounding mental health issues in different countries

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: share-with-alcohol-and-substance-use-disorders 1990-2016.csv | Column name | Description | |:-----------------------------------------------------|:-----------------------------------------------------------------------------------| | Entity | The name of the country. (String) | | Code | The ISO code of the country. (String) | | Year | The year of the data. (Integer) | | Prevalence - Alcohol and substance use disorders | The percentage of the population with alcohol and substance use disorders. (Float) | | **Prevalence ** | Both (age-standardized percent) (%) |

**File: crude suicide rate...

Cancer is one of the biggest health challenges worldwide. As of 2021, around 15% of all deaths were cancer deaths, making it one of the most common causes of death globally.

Cancers are a group of diseases in which abnormal cells multiply rapidly and can grow into tumors. They can develop in different parts of the body and, in some cases, spread to other organs through the blood and lymph systems.

As the global population grows larger and older, the number of cancer cases has also increased. However, the age-standardized death rate from cancer has declined over time in many countries — due to improvements in diagnosis, research, medical advances, and public health efforts, as well as reductions in risk factors such as smoking and some cancer-causing pathogens.

On this page, we explore global data and research on different types of cancer. This can help us better understand the risk factors for cancer, how cancer risks vary across the lifespan, how they differ worldwide, and how they have changed over time.

Table S2: Pivot table which can be queried for cancer incidence and mortality across different age groups in Arab countries and the data for the world, the USA and Europe. The table also includes different subregions in the Arab region. The table is based on the data extracted from the GLOBOCAN 2018 web database supplied in Table S1 in this study.

The Human Papillomavirus (HPV) test is a crucial technology for cervical cancer prevention because it enables programs to identify women with high-risk HPV infection who are at risk of developing cervical cancer. Current U.S. Preventive Services Task Force recommendations include cervical cancer screening every three years with cervical cytology alone or every five years with either high-risk HPV testing alone or high-risk HPV testing combined with cytology (co-testing). In Argentina, 7,548 new cervical cancer cases are diagnosed each year with 3,932 deaths attributed to this cause. Our study aims to show the clinical implementation of a cervical cancer screening program by concurrent HPV testing and cervical cytology (co-testing); and to evaluate the possible cervical cancer screening scenarios for Latin America, focusing on their performance and average cost. A cervical cancer screening five year program via co-testing algorithm (Hybrid-2-Capture/cytology) was performed on women aged 30-65 years old at a university hospital. Statistical analysis included a multinomial logistic regression, and two cancer screening classification alternatives were tested (cytology-reflex and HPV-reflex). A total of 2,273 women were included, 91.11% of the participants were double-negative, 2.55% double-positive, 5.90% positive-Hybrid-2-Capture-/negative-cytology, and 0.44% negative-Hybrid-2-Capture/positive-cytology. A thorough follow-up was performed in the positive-Hybrid-2-Capture group. Despite our efforts, 21 (10.93%) were lost, mainly because of changes on their health insurance coverage which excluded them from our screening algorithm. Of the 171 women with positive-Hybrid-2-Capture results and follow-up, 68 (39.77%) cleared the virus infection, 64 (37.43%) showed viral persistence, and 39 (22.81%) were adequately treated after detection via colposcopy/biopsy of histological HSIL (High-Grade Squamous Intraepithelial Lesion). The prevalence of high-risk HPV in this population was 192 women (8.45%), with HSIL histology detection rates of 17.32 per 1,000 screened women. A multinomial logistic regression analysis was performed over the women with positive-Hybrid-2-Capture considering the follow up (clearance, persistence and HSIL) as dependent variable, and the cytology test results (positive- or negative-cytology and Atypical Squamous Cells of Undetermined Significance, ASC-US) as independent variable. The model supported a direct association between cytology test results and follow up: negative-cytology/clearance, ASC-US/persistence, and positive-cytology/HSIL with the following probabilities of occurrence for these pairs 0.5, 0.647 and 0.647, respectively. Cytology could be considered a prognostic-factor in women with a positive-Hybrid-2-Capture. These findings suggest that the introduction of co-testing could diminish the burden of cervical cancer in low-and middle-income-countries, acting as a tool against inequity in healthcare.

Source data files for the paper of the above title

Background: Esophageal adenocarcinoma (EAC) remains a leading cause of cancer-related deaths worldwide and demonstrates a predominant rising incidence in Western countries. Recently, immunotherapy has dramatically changed the landscape of treatment for many advanced cancers, with the benefit in EAC thus far been limited to a small fraction of patients.Methods: Using somatic mutation data of The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium, we delineated the somatic mutation landscape of EAC patients from US and England. Based on the expression data of TCGA cohort, multiple bioinformatics algorithms were utilized to perform function annotation, immune cell infiltration analysis, and immunotherapy response assessment.Results: We found that RYR2 was a common frequently mutated gene in both cohorts, and patients with RYR2 mutation suggested higher tumor mutation burden (TMB), better prognosis, and superior expression of immune checkpoints. Moreover, RYR2 mutation upregulated the signaling pathways implicated in immune response and enhanced antitumor immunity in EAC. Multiple bioinformatics algorithms for assessing immunotherapy response demonstrated that patients with RYR2 mutation might benefit more from immunotherapy. In order to provide additional reference for antitumor therapy of different RYR2 status, we identified nine latent antitumor drugs associated with RYR2 status in EAC.Conclusion: This study reveals a novel gene whose mutation could be served as a potential biomarker for prognosis, TMB, and immunotherapy of EAC patients.

Number of facilities (outlets sampled) and geographical location of participating cities [33].

Trends in mortality caused by colorectal cancer in Latin American countries, sub-regions and SDI countries clusters, 1990–2019.

IntroductionIn Ghana, prices for cancer medicines are characterized by high retail markups, forex fluctuations and high variation in prices of medicines. Most patients cannot afford the cancer medicines. There is a problem of unaffordability and limited availability of essential cancer medicines which suggests potential inequity in patient access to cancer medicines. The study objective was to assess the prices, availability, and affordability of cancer medicines in Ghana. Prices of cancer medicines are a major contributor to the cost of treatment for cancer patients and the comparison of these cost was assessed to determine the affordability.MethodThe methods developed and standardized by the World Health Organization (WHO) in collaboration with the Health Action International (HAI), was adapted and used to measure prices, availability, and affordability of cancer medicines in Ghana. The availability of cancer medicines was assessed as percentage of health facilities stocked with listed medicines. The price of cancer medicines (of different brands as well as the same medicine manufactured by different pharmaceutical industries) available in the public hospitals, private hospitals, and private pharmacies was assessed, and the percentage variation in prices was calculated. Medicine prices were compared with the Management Sciences Health’s International Reference Prices to obtain a Median Price Ratio (MPR). The affordability of cancer medicines was determined using the treatment cost of a course of therapy for cancer conditions in comparison with the daily wage of the unskilled Lowest-Paid Government Worker.ResultsOverall availability of cancer medicines was very low. The availability of Lowest Priced Generic (LPG) in public hospitals, private hospitals, and private pharmacies was 46%, 22%, and 74% respectively. The availability of Originator Brand (OB) in public hospitals, private hospitals, and private pharmacies was 14%, 11%, and 23% respectively. The lowest median price [United States Dollars (USD)] for the LPG was 0.25, and the highest median price was 227.98. For the OB, the lowest median price was 0.41 and the highest median price was 1321.60. The lowest and highest adjusted MPRs of OBs and LPGs was 0.01 and 10.15 respectively. Some prices were 20.60 times more expensive. Affordability calculations showed that patients with colorectal and multiple myeloma cancer would need 2554 days wages (5286.40 USD) and 1642 days wages (3399.82 USD) respectively to afford treatment.ConclusionThe availability of cancer medicines was very low, and less than the WHO target of 80%. There were considerable variations in the prices of different brands of cancer medicines, and affordability remains suboptimal, as most patients cannot afford the cancer medicines. Comprehensive policies, regulations and multifaceted interventions that provides tax incentives, health insurance, and use of generics to improve cancer medicines availability, prices, and affordability, for the masses should be developed and implemented in Ghana.

Expected sample sizes by country and income level.