AbstractIn Italy, approximately 400.000 new cases of malignant tumors are recorded every year. The average of annual deaths caused by tumors, according to the Italian Cancer Registers, is about 3.5 deaths and about 2.5 per 1,000 men and women respectively, for a total of about 3 deaths every 1,000 people. Long-term (at least a decade) and spatially detailed data (up to the municipality scale) are neither easily accessible nor fully available for public consultation by the citizens, scientists, research groups, and associations. Therefore, here we present a ten-year (2009–2018) database on cancer mortality rates (in the form of Standardized Mortality Ratios, SMR) for 23 cancer macro-types in Italy on municipal, provincial, and regional scales. We aim to make easily accessible a comprehensive, ready-to-use, and openly accessible source of data on the most updated status of cancer mortality in Italy for local and national stakeholders, researchers, and policymakers and to provide researchers with ready-to-use data to perform specific studies. Methods For a given locality, year, and cause of death, the SMR is the ratio between the observed number of deaths (Om) and the number of expected deaths (Em): SMR = Om/Em (1) where Om should be an available observational data and Em is estimated as the weighted sum of age-specific population size for the given locality (ni) per age-specific death rates of the reference population (MRi): Em = sum(MRi x ni) (2) MRi could be provided by a public health organization or be estimated as the ratio between the age-specific number of deaths of reference population (Mi) to the age-specific reference population size (Ni): MRi = Mi/Ni (3) Thus, the value of Em is weighted by the age distribution of deaths and population size. SMR assumes value 1 when the number of observed and expected deaths are equal. Following eqns. (1-3), the SMR was computed for single years of the period 2009-2018 and for single cause of death as defined by the International ICD-10 classification system by using the following data: age-specific number of deaths by cause of reference population (i.e., Mi) from the Italian National Institute of Statistics (ISTAT, (http://www.istat.it/en/, last access: 26/01/2022)); age-specific census data on reference population (i.e., Ni) from ISTAT; the observed number of deaths by cause (i.e., Om) from ISTAT; the age-specific census data on population (ni); the SMR was estimated at three different level of aggregation: municipal, provincial (equivalent to the European classification NUTS 3) and regional (i.e., NUTS2). The SMR was also computed for the broad category of malignant tumors (i.e. C00-C979, hereinafter cancer macro-type C), and for the broad category of malignant tumor plus non-malignant tumors (i.e. C00-C979 plus D0-D489, hereinafter cancer macro-type CD). Lower 90% and 95% confidence intervals of 10-year average values were computed according to the Byar method.

Number and rate of new cancer cases diagnosed annually from 1992 to the most recent diagnosis year available. Included are all invasive cancers and in situ bladder cancer with cases defined using the Surveillance, Epidemiology and End Results (SEER) Groups for Primary Site based on the World Health Organization International Classification of Diseases for Oncology, Third Edition (ICD-O-3). Random rounding of case counts to the nearest multiple of 5 is used to prevent inappropriate disclosure of health-related information.

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Video on Risk factors of Lung Cancer - ![https://youtu.be/0vVRp5eNDlA?feature=shared]

Dataset: 1. GENDER: Gender of the individual (M: Male, F: Female) 2. AGE: Age of the individual 3. SMOKING: Smoking status (2: Yes, 1: No) 4. YELLOW_FINGERS: Presence of yellow fingers (2: Yes, 1: No) 5. ANXIETY: Anxiety level (2: High, 1: Low) 6. PEER_PRESSURE: Peer pressure level (2: High, 1: Low) 7. CHRONIC DISEASE: Presence of chronic disease (2: Yes, 1: No) 8. FATIGUE: Fatigue level (2: High, 1: Low) 9. ALLERGY: Allergy status (2: Yes, 1: No) 10. WHEEZING: Wheezing condition (2: Yes, 1: No) 11. ALCOHOL CONSUMING: Alcohol consumption status (2: Yes, 1: No) 12. COUGHING: Presence of coughing (2: Yes, 1: No) 13. SHORTNESS OF BREATH: Shortness of breath condition (2: Yes, 1: No) 14. SWALLOWING DIFFICULTY: Difficulty in swallowing (2: Yes, 1: No) 15. CHEST PAIN: Presence of chest pain (2: Yes, 1: No) 16. LUNG_CANCER: Lung cancer diagnosis (2: Yes, 1: No)

• Data has 309 rows and 16 columns with floating variables, integer, object which ranges from 0 - 308

• Lung cancer is the uncontrollable growth of abnormal cells in one or both of the lungs. Cigarette smoking causes most lung cancers when smoke gets in the lungs. Lung cancer kills 1.8 million people each year, more than any other cancer. It has an 80-90% death rate, and is the leading cause of cancer death in men, and the second leading cause of cancer death in women.

• The global cancer burden is estimated to have risen to 18.1 million new cases and 9.6 million deaths in 2018. One in 5 men and one in 6 women worldwide develop cancer during their lifetime, and one in 8 men and one in 11 women die from the disease. Worldwide, the total number of people who are alive within 5 years of a cancer diagnosis, called the 5-year prevalence, is estimated to be 43.8 million.

Breast cancer is the most frequently diagnosed cancer and the most frequent cause for cancer-related deaths in women worldwide. Globally, breast cancer accounted for 2.08 million out of 18.08 million new cancer cases (incidence rate of 11.6%) and 626,679 out of 9.55 million cancer-related deaths (6.6% of all cancer-related deaths) in 2018. 1,2 In India, breast cancer has surpassed cancers of the cervix and the oral cavity to be the most common cancer and the leading cause of cancer deaths. In 2018, 159,500 new cases of breast cancer were diagnosed, representing 27.7% of all new cancers among Indian women and 11.1% of all cancer deaths.

In india breast cancer cases reporting and diagnotics have increased 10 times in past 3 years . All thanks to the various cancer awareness initiatives by both private and govt. organisations.

This dataset, released February 2021, contains the statistics of premature mortality by various causes for people below 75 years, over the years 2014 to 2018. Causes for death include cancer (colorectal, lung, breast), diabetes, circulatory system diseases (ischaemic heart disease, cerebrovascular disease), respiratory system diseases (chronic obstructive pulmonary disease), and external causes (road traffic injuries, suicide and self-inflicted injuries) The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Data compiled by PHIDU from deaths data based on the 2014 to 2018 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries of Births, Deaths and Marriages and the National Coronial Information System. The population is the ABS Estimated Resident Population (ERP) for Australia, 30 June 2014 to 30 June 2018. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

Lung Cancer Prediction

Air Pollution, Alcohol, Smoking & Risk of Lung Cancer

About this dataset

This dataset contains information on patients with lung cancer, including their age, gender, air pollution exposure, alcohol use, dust allergy, occupational hazards, genetic risk, chronic lung disease, balanced diet, obesity, smoking, passive smoker, chest pain, coughing of blood, fatigue, weight loss ,shortness of breath ,wheezing ,swallowing difficulty ,clubbing of finger nails and snoring

How to use the dataset

Lung cancer is the leading cause of cancer death worldwide, accounting for 1.59 million deaths in 2018. The majority of lung cancer cases are attributed to smoking, but exposure to air pollution is also a risk factor. A new study has found that air pollution may be linked to an increased risk of lung cancer, even in nonsmokers.

The study, which was published in the journal Nature Medicine, looked at data from over 462,000 people in China who were followed for an average of six years. The participants were divided into two groups: those who lived in areas with high levels of air pollution and those who lived in areas with low levels of air pollution.

The researchers found that the people in the high-pollution group were more likely to develop lung cancer than those in the low-pollution group. They also found that the risk was higher in nonsmokers than smokers, and that the risk increased with age.

While this study does not prove that air pollution causes lung cancer, it does suggest that there may be a link between the two. More research is needed to confirm these findings and to determine what effect different types and levels of air pollution may have on lung cancer risk

Research Ideas

• predicting the likelihood of a patient developing lung cancer
• identifying risk factors for lung cancer
• determining the most effective treatment for a patient with lung cancer

Acknowledgements

License

See the dataset description for more information.

Columns

File: cancer patient data sets.csv | Column name | Description | |:-----------------------------|:--------------------------------------------------------------------| | Age | The age of the patient. (Numeric) | | Gender | The gender of the patient. (Categorical) | | Air Pollution | The level of air pollution exposure of the patient. (Categorical) | | Alcohol use | The level of alcohol use of the patient. (Categorical) | | Dust Allergy | The level of dust allergy of the patient. (Categorical) | | OccuPational Hazards | The level of occupational hazards of the patient. (Categorical) | | Genetic Risk | The level of genetic risk of the patient. (Categorical) | | chronic Lung Disease | The level of chronic lung disease of the patient. (Categorical) | | Balanced Diet | The level of balanced diet of the patient. (Categorical) | | Obesity | The level of obesity of the patient. (Categorical) | | Smoking | The level of smoking of the patient. (Categorical) | | Passive Smoker | The level of passive smoker of the patient. (Categorical) | | Chest Pain | The level of chest pain of the patient. (Categorical) | | Coughing of Blood | The level of coughing of blood of the patient. (Categorical) | | Fatigue | The level of fatigue of the patient. (Categorical) | | Weight Loss | The level of weight loss of the patient. (Categorical) | | Shortness of Breath | The level of shortness of breath of the patient. (Categorical) | | Wheezing | The level of wheezing of the patient. (Categorical) | | Swallowing Difficulty | The level of swallowing difficulty of the patient. (Categorical) | | Clubbing of Finger Nails | The level of clubbing of finger nails of the patient. (Categorical) |

Cancer is a large group of diseases that can start in almost any organ or tissue of the body when abnormal cells grow uncontrollably, go beyond their usual boundaries to invade adjoining parts of the body and/or spread to other organs.

Cancer is the second leading cause of death globally, accounting for an estimated 9.6 million deaths, or one in six deaths, in 2018. Lung, prostate, colorectal, stomach and liver cancer are the most common types of cancer in men, while breast, colorectal, lung, cervical and thyroid cancer are the most common among women. (Source: WHO)

Dataset source : https://gco.iarc.fr/today/data/factsheets/populations/356-india-fact-sheets.pdf WHO

BackgroundCervical cancer incidence and mortality rates in the United States have substantially declined over recent decades, primarily driven by reductions in squamous cell carcinoma cases. However, the trend in recent years remains unclear. This study aimed to explore the trends in cervical cancer incidence and mortality, stratified by demographic and tumor characteristics from 1975 to 2018.MethodsThe age-adjusted incidence, incidence-based mortality, and relative survival of cervical cancer were calculated using the Surveillance, Epidemiology, and End Results (SEER)-9 database. Trend analyses with annual percent change (APC) and average annual percent change (AAPC) calculations were performed using Joinpoint Regression Software (Version 4.9.1.0, National Cancer Institute).ResultsDuring 1975–2018, 49,658 cervical cancer cases were diagnosed, with 17,099 recorded deaths occurring between 1995 and 2018. Squamous cell carcinoma was the most common histological type, with 34,169 cases and 11,859 deaths. Over the study period, the cervical cancer incidence rate decreased by an average of 1.9% (95% CI: −2.3% to −1.6%) per year, with the APCs decreased in recent years (−0.5% [95% CI: −1.1 to 0.1%] in 2006–2018). Squamous cell carcinoma incidence trends closely paralleled overall cervical cancer patterns, but the incidence of squamous cell carcinoma in the distant stage increased significantly (1.1% [95% CI: 0.4 to 1.8%] in 1990–2018). From 1995 to 2018, the overall cervical cancer mortality rate decreased by 1.0% (95% CI: −1.2% to −0.8%) per year. But for distant-stage squamous cell carcinoma, the mortality rate increased by 1.2% (95% CI: 0.3 to 2.1%) per year.ConclusionFor cervical cancer cases diagnosed in the United States from 1975 to 2018, the overall incidence and mortality rates decreased significantly. However, there was an increase in the incidence and mortality of advanced-stage squamous cell carcinoma. These epidemiological patterns offer critical insights for refining cervical cancer screening protocols and developing targeted interventions for advanced-stage cases.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

This table contains 4032 series, with data for years 1994/1998 - 2009/2013 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (6 items: Canada; Inuit Nunangat; Inuvialuit Region; Nunavut; ...) Sex (3 items: Both sexes; Males; Females) Indicators (2 items: Mortality; Potential years of life lost) Selected causes of death (16 items: Total, all causes of death; All malignant neoplasms (cancers); Colorectal cancer; Lung cancer; ...) Characteristics (7 items: Number; Rate; Low 95% confidence interval, rate; High 95% confidence interval, rate; ...).

The role of religion and politics in the responses to the coronavirus pandemic raises the question of their influence on the risk of other diseases. This study focuses on age-adjusted death rates of cancer, heart disease, and infant mortality per 1000 live births before the pandemic (2018-2019) and COVID-19 in 2020-2021. Eight hypothesized predictors of health effects were analyzed by examining their correlation to age-adjusted death rates among U.S. states, percentage who pray once or more daily, Republican influence on state health policies as indicated by the percentage vote for Trump in 2016, percent of household incomes below poverty, median family income divided by a cost-of-living index, the Gini income inequality index, urban concentration of the population, physicians per capita, and public health expenditures per capita. Since prayer for divine intervention is common to otherwise diverse religious beliefs and practices, the percentage of people claiming to pray daily in each state was used to indicate potential religious influence. All of the death rates were higher in states where more people claimed to pray daily, and where Trump received a larger percentage of the vote. Except for COVID-19, the death rates were consistently lower in states with higher public health expenditures per capita. Only COVID-19 was correlated to physicians per capita, lower where there were more physicians. Corrected statistically for the other factors, income per cost of living explains no variance. Heart disease and COVID-19 death rates were higher in areas with more income inequality. All of the disease rates were in correlation with more rural populations. Correlation of daily prayer with smoking cigarettes, and neglect of public health recommendations for fruit and vegetable consumption and COVID-19 vaccination suggests that prayer may be substituted for preventive practices.

This dataset, released December 2016, contains statistics for deaths of people aged 0-74 years during the years 2010-2014 based on the following causes: cancer, diabetes, circulatory system diseases, respiratory systems diseases and external causes. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical …Show full descriptionThis dataset, released December 2016, contains statistics for deaths of people aged 0-74 years during the years 2010-2014 based on the following causes: cancer, diabetes, circulatory system diseases, respiratory systems diseases and external causes. The data is by Primary Health Network (PHN) 2017 geographic boundaries based on the 2016 Australian Statistical Geography Standard (ASGS). There are 31 PHNs set up by the Australian Government. Each network is controlled by a board of medical professionals and advised by a clinical council and community advisory committee. The boundaries of the PHNs closely align with the Local Hospital Networks where possible. For more information please see the data source notes on the data. Source: Data compiled by PHIDU from deaths data based on the 2010 to 2014 Cause of Death Unit Record Files supplied by the Australian Coordinating Registry and the Victorian Department of Justice, on behalf of the Registries of Births, Deaths and Marriages and the National Coronial Information System. Please note: AURIN has spatially enabled the original data. "*" - Indicates statistically significant, at the 95% confidence level. "**" - Indicates statistically significant, at the 99% confidence level. "~" - Indicates modelled estimates have Relative Root Mean Square Errors (RRMSEs) from 0.25 to 0.50 and should be used with caution. "~~" - Indicates modelled estimates have RRMSEs greater than 0.50 but less than 1 and are considered too unreliable for general use. '?' - Indicates modelled estimates are considered too unreliable. Blank cell - Indicates data was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data). Abbreviation Information: "ASR per #" - Indirectly age-standardised rate per specified population. "SDR" - Indirectly age-standardised death ratio. "95% C.I" - upper and lower 95% confidence intervals. Copyright attribution: Torrens University Australia - Public Health Information Development Unit, (2018): ; accessed from AURIN on 12/3/2020. Licence type: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Australia (CC BY-NC-SA 3.0 AU)

PurposeBone is one of the most common sites for the spread of malignant tumors. Patients with bone metastases whose prognosis was shorter than 3 months (early death) were considered as surgical contraindications. However, the information currently available in the literature limits our capacity to assess the risk likelihood of 3 month mortality. As a result, the study's objective is to create an accurate prediction model utilizing machine-learning techniques to predict 3 month mortality specifically among lung cancer patients with bone metastases according to easily available clinical data.MethodsThis study enrolled 19,887 lung cancer patients with bone metastases between 2010 and 2018 from a large oncologic database in the United States. According to a ratio of 8:2, the entire patient cohort was randomly assigned to a training (n = 15881, 80%) and validation (n = 4,006, 20%) group. In the training group, prediction models were trained and optimized using six approaches, including logistic regression, XGBoosting machine, random forest, neural network, gradient boosting machine, and decision tree. There were 13 metrics, including the Brier score, calibration slope, intercept-in-large, area under the curve (AUC), and sensitivity, used to assess the model's prediction performance in the validation group. In each metric, the best prediction effectiveness was assigned six points, while the worst was given one point. The model with the highest sum score of the 13 measures was optimal. The model's explainability was performed using the local interpretable model-agnostic explanation (LIME) according to the optimal model. Predictor importance was assessed using H2O automatic machine learning. Risk stratification was also evaluated based on the optimal threshold.ResultsAmong all recruited patients, the 3 month mortality was 48.5%. Twelve variables, including age, primary site, histology, race, sex, tumor (T) stage, node (N) stage, brain metastasis, liver metastasis, cancer-directed surgery, radiation, and chemotherapy, were significantly associated with 3 month mortality based on multivariate analysis, and these variables were included for developing prediction models. With the highest sum score of all the measurements, the gradient boosting machine approach outperformed all the other models (62 points), followed by the XGBooting machine approach (59 points) and logistic regression (53). The area under the curve (AUC) was 0.820 (95% confident interval [CI]: 0.807–0.833), 0.820 (95% CI: 0.807–0.833), and 0.815 (95% CI: 0.801–0.828), respectively, calibration slope was 0.97, 0.95, and 0.96, respectively, and accuracy was all 0.772. Explainability of models was conducted to rank the predictors and visualize their contributions to an individual's mortality outcome. The top four important predictors in the population according to H2O automatic machine learning were chemotherapy, followed by liver metastasis, radiation, and brain metastasis. Compared to patients in the low-risk group, patients in the high-risk group were more than three times the odds of dying within 3 months (P < 0.001).ConclusionsUsing machine learning techniques, this study offers a number of models, and the optimal model is found after thoroughly assessing and contrasting the prediction performance of each model. The optimal model can be a pragmatic risk prediction tool and is capable of identifying lung cancer patients with bone metastases who are at high risk for 3 month mortality, informing risk counseling, and aiding clinical treatment decision-making. It is better advised for patients in the high-risk group to have radiotherapy alone, the best supportive care, or minimally invasive procedures like cementoplasty.

ObjectiveProstate cancer (PC) is the second leading cause of cancer death in men in the United States after lung cancer in global incidence. Elderly male patients over 65 years old account for more than 60% of PC patients, and the impact of surgical treatment on the prognosis of PC patients is controversial. Moreover, there are currently no predictive models that can predict the prognosis of elderly PC patients undergoing surgical treatment. Therefore, we aimed to construct a new nomogram to predict cancer-specific survival (CSS) in elderly PC patients undergoing surgical treatment.MethodsData for surgically treated PC patients aged 65 years and older were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression models were used to identify independent risk factors for elderly PC patients undergoing surgical treatment. A nomogram of elderly PC patients undergoing surgical treatment was developed based on the multivariate Cox regression model. The consistency index (C-index), the area under the subject operating characteristic curve (AUC), and the calibration curve were used to test the accuracy and discrimination of the predictive model. Decision curve analysis (DCA) was used to examine the potential clinical value of this model.ResultsA total of 44,975 elderly PC patients undergoing surgery in 2010–2018 were randomly assigned to the training set (N = 31705) and validation set (N = 13270). the training set was used for nomogram development and the validation set was used for internal validation. Univariate and multivariate Cox regression model analysis showed that age, marriage, TNM stage, surgical style, chemotherapy, radiotherapy, Gleason score(GS), and prostate-specific antigen(PSA) were independent risk factors for CSS in elderly PC patients undergoing surgical treatment. The C index of the training set and validation indices are 0.911(95%CI: 0.899–0.923) and 0.913(95%CI: 0.893–0.933), respectively, indicating that the nomogram has a good discrimination ability. The AUC and the calibration curves also show good accuracy and discriminability.ConclusionsTo our knowledge, our nomogram is the first predictive model for elderly PC patients undergoing surgical treatment, filling the gap in current predictive models for this PC patient population. Our data comes from the SEER database, which is trustworthy and reliable. Moreover, our model has been internally validated in the validation set using the C-index,AUC and the and the calibration curve, showed that the model have good accuracy and reliability, which can help clinicians and patients make better clinical decision-making. Moreover, the DCA results show that our nomogram has a better potential clinical application value than the TNM staging system.

BackgroundHepatitis C virus (HCV) infection affects men and women differently, yet few studies have investigated sex differences in long-term mortality risk among the HCV-infected population. We conducted a population-based study to elucidate all-cause and cause-specific mortality among men and women with HCV infection.MethodsThe study population consisted of adult participants from the 1999–2018 National Health and Nutrition Examination Survey, including 945 HCV-infected and 44,637 non-HCV-infected individuals. HCV infection was defined as either HCV seropositivity or detectable HCV RNA. Participants were followed until the date of death or December 31, 2019, to determine survival status.ResultsThe HCV-infected population, both male and female, tended to be older, more likely to be Black, single, have lower income, lower BMI, higher prevalence of hypertension, and were more likely to be current smokers. During a median follow-up of 125.0 months, a total of 5,309 participants died, including 1,253 deaths from cardiovascular disease (CVD) and 1,319 deaths from cancer. The crude analysis showed that the risk of death from all causes and from cancer, but not from CVD, was higher in the HCV-infected population. After adjusting for potential confounders, we found that both HCV-infected men (HR 1.41, 95% CI 1.10–1.81) and women (HR 2.03, 95% CI 1.36–3.02) were equally at increased risk of all-cause mortality compared to their non-HCV infected counterparts (p for interaction > 0.05). The risk of cancer-related mortality was significantly increased in HCV-infected women (HR 2.14, 95% CI 1.01–4.53), but not in men, compared to non-HCV-infected counterparts. Among HCV-infected population, there was no difference in the risks of all-cause, CVD-related, or cancer-related death between men and women.ConclusionBoth men and women with HCV infection had an increased risk of death from all causes compared to their non-HCV infected counterparts, but we did not observe a significant sex difference.

Poorer survival in cancer patients with vs. without comorbidity has been reported for various cancer sites. For patients with colorectal cancer (CRC), limited data are available so far. Patients with CRC diagnosed between 2010 and 2018 were identified in a health claims database covering 20% of the German population. We assessed the prevalence of comorbidities at cancer diagnosis and categorized the patients into the groups: ‘none’, ‘somatic only’, ‘mental only’ or ‘both’ types of comorbidities. Hazard ratios (HR, with 95% confidence intervals) for five-year overall survival were estimated by Cox proportional hazard models, adjusted for age, sex and stage at diagnosis (advanced vs. non-advanced). We included 92,991 patients (females: 49.1%, median age: 72 years) with a median follow-up of 30 months. The proportions assigned to the groups ‘none’, ‘somatic only’, ‘mental only’ or ‘both’ were 24.7%, 65.5%, 1.4% and 8.4%. Overall, 32.8% of the patients died during follow-up. Compared to patients without comorbidities (‘none’), the adjusted HR regarding death from any cause was 1.11 (95% CI: 1.07–1.14) in the group ‘somatic only’, 1.74 (95% CI: 1.58–1.92) in the group ‘mental only’ and 1.92 (95% CI: 1.84–2.00) in the group ‘both’. For patients with ‘mental only’ comorbidities, the adjusted HR was higher in males than in females (HR = 2.19, 95% CI: 1.88–2.55 vs. HR = 1.55, 95% CI: 1.37–1.75). Our results suggest that patients with CRC and with mental comorbidities, particularly males, have a markedly lower overall survival compared to those without any or only somatic comorbidities.

ObjectiveThe role of spousal support has been recognized to benefit patients with many chronic diseases and cancers. However, the impact of marital status on the survival of middle-aged and elderly patients with primary bone tumors remains elusive.Materials and methodsThe data of patients aged ≥ 45 years with primary bone tumors diagnosed between 2000 and 2018 were extracted from the Surveillance, Epidemiology, and End Results Database. Kaplan–Meier analysis was used to assess the overall survival and tumor-specific survival of patients. The Cox proportional hazards and Fine-and-Gray models were used to calculate the hazard ratios (HRs) and sub-distribution HRs (sHR) and the corresponding 95% confidence interval (CI) of all-cause mortality and tumor-specific mortality, respectively.ResultsA total of 5,640 primary bone tumors were included in the study. In 45–59 years cohort, married, unmarried, divorced and widowed accounted for 66.0, 21.0, 11.2, and 1.8%, respectively; while 64.3, 10.1, 8.8, and 16.8% in 60+ years cohort, respectively. The widowed patients had a lower proportion of early-stage tumors at diagnosis than that married, unmarried, and divorced patients (31.0% vs. 36% vs. 37.1% vs. 39.4%; P = 0.008), and had a higher proportion of patients who did not undergo surgery than that of married, unmarried, and divorced patients (38.6% vs. 21.3% vs. 24.6% vs. 24.4%; P < 0.001). The widowed population had an increased risk of all-cause mortality (HR, 1.68; 95% CI, 1.50–1.88; P < 0.001) and disease-related mortality (HR, 1.33; 95% CI, 1.09–1.61; P = 0.005) compared with the married population.ConclusionThe marital status of middle-aged and elderly people can affect the tumor stage at diagnosis, treatment, and survival prognosis of patients with primary bone cancer. Widowed patients are more inclined to choose non-surgical treatment and have the worst prognosis.

ObjectiveProstate cancer (PC) is the most common malignant tumour in men, and atherosclerotic cardiovascular disease (ASCVD) is the leading cause of non-cancer death in PC patients. The main purpose of this study was to investigate whether chemotherapy increases heart-specific mortality (HSM) in elderly patients with PC.MethodsPatient information was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2018. We included all elderly patients with PC. The multivariate logistic regression model was used to explore the influencing factors of patients receiving chemotherapy. Confounders were excluded using a 1:1 proportional propensity score match, and a competing risk model and cumulative incidence plot were used to analyze HSM and other cause mortality (OCM) in patients who received chemotherapy versus those who did not.ResultsA total of 135183 elderly prostate patients were enrolled in this study, of whom 1361 received chemotherapy. The multivariate logistic regression model showed that older patients were more likely to not receive chemotherapy, married patients were more likely to receive chemotherapy, and the higher the TNM stage and tumor histological grade, the more patients received chemotherapy. In the original cohort before unmatched, there was no significant difference in HSM between chemotherapy and non-chemotherapy patients (P = 0.27). After 1:1 matching, HSM was significantly higher in patients without chemotherapy than in patients with chemotherapy (HR 2.54; P =0.002).ConclusionsOur results indicate that HSM is significantly higher in patients without chemotherapy than in those with chemotherapy. Therefore, although chemotherapy can lead to cardiotoxicity in elderly patients with PC, chemotherapy does not increase the HSM of patients and will benefit patients in the long-term survival.

BackgroundSeveral studies have estimated daily intake of resistant starch (RS), but no studies have investigated the relationship of RS intake with mortality.ObjectiveWe aimed to examine associations between RS intake and all-cause and cause-specific mortality.MethodsData from US National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018 with 24-h dietary recall data was used in current study. The main exposure in this study was RS intake, and the main outcome was the mortality status of participants until December 31, 2019. The multivariable Cox proportional hazards regression models were developed to evaluate the hazard ratios (HRs) and 95% confidence interval (95% CI) of cardiovascular disease (CVD), cancer, and all-cause mortality associated with RS intake.ResultsA total of 42,586 US adults [mean (SD) age, 46.91 (16.88) years; 22,328 (52.43%) female] were included in the present analysis. During the 454,252 person-years of follow-up, 7,043 all-cause deaths occurred, including 1,809 deaths from CVD and 1,574 deaths from cancer. The multivariable-adjusted HRs for CVD, cancer, and all-cause mortality per quintile increase in RS intake were 1 (95%CI, 0.97–1.04), 0.96 (95%CI, 0.93–1), and 0.96 (95%CI, 0.95–0.98), respectively. The associations remained similar in the subgroup and sensitivity analyses.ConclusionHigher RS intake is significantly associated with lower cancer and all-cause mortality, but not significantly with CVD mortality. Future studies focusing on other populations with different food sources of RS and RS subtypes are needed to access the dose–response relationship and to improve global dietary recommendations.

Additional file 5: Table S2. Observed values of CMR, ASMRW, YYL and YYL rate in age groupds and the top four MNFGO cancer types in Shanghai PNA, 1995-2018.