Estimating life expectancy adjusted by self-rated health status in the United States: national health interview survey linked to the mortality

Prognostic awareness and willingness to explore prognosis in older adults with cancer

INTRODUCTION

Prognostic awareness varies widely among older adults with cancer. Accurate prognostic awareness helps to ensure delivery of care that is aligned with the patient's goals. Understanding factors associated with poor prognostic awareness in older adults with cancer may help identify which patients may need interventions to improve prognostic awareness. In this study, we assessed factors associated with poor prognostic awareness in older adults with cancer.

MATERIALS AND METHODS

We conducted a cross-sectional analysis of older patients with cancer referred to a geriatric oncology clinic at the University of Rochester. We provided paper questionnaires for patients to complete prior to their clinic assessment. Questionnaires asked patients to estimate their overall life expectancy and the life expectancy of a person of the same age with normal health. Prognostic awareness was considered poor if patients estimated living at least as long as a person of the same age with normal health. We assessed independent demographic and clinical variables (age, sex, race, income, religion, living situation, education, marital status, and cancer type and stage), aging-related factors (comorbidities, cognition, depression, social support, nutritional status, and physical function), and willingness to discuss prognosis. Factors significant at p ≤ 0.15 on bivariate analyses were included in the multivariable logistic regression model.

RESULTS

We included 257 patients; the mean age was 80 years (standard deviation [SD] 6.8, range 55-97), 37% were female, 71% were White, and 44% were married. Nearly two-thirds of patients (62%) had poor prognostic awareness: 7% estimated they would live longer than and 55% estimated they would live as long as a person of the same age with normal health. Half (49%) were willing to discuss prognosis, 29% were not, and 22% did not answer. On multivariable analysis, factors associated with poor prognostic awareness were older age [one-year increase; adjusted odds ratio (AOR) 1.07, 95% confidence interval (CI) 1.02-1.12], race other than White (AOR 2.35, 95% CI 1.09-5.06), unwillingness to discuss prognosis (AOR 3.33, 95% CI 1.54-7.18), and stage I-III cancer (vs. stage IV, AOR 3.83, 95% CI 1.8-8.17).

DISCUSSION

In a cohort of older patients with cancer, approximately two-thirds had poor prognostic awareness. Older age, race other than White, stage I-III cancer, and unwillingness to discuss prognosis were associated with higher odds of poor prognostic awareness. Interventions aiming to improve patients' prognostic awareness may need to gauge patients' willingness to discuss prognosis.

Healthy lifestyle, statin, and mortality in people with high CVD risk: A nationwide population-based cohort study

Objective

To examine the joint association of healthy lifestyles and statin use with all-cause and cardiovascular mortality in high-risk individuals, and evaluate the survival benefits by life expectancy.

Methods

During 2015-2021, participants aged 35-75 years were recruited by the China Health Evaluation And risk Reduction through nationwide Teamwork. Based on number of healthy lifestyles related to smoking, alcohol drinking, physical activity, and diet, we categorized them into: very healthy (3-4), healthy (2), and unhealthy (0-1). Statin use was determined by self-report taking statin in last two weeks.

Results

Among the 265,209 included participants at high risk, 6979 deaths were observed, including 3236 CVD deaths during a median 3.6 years of follow-up. Individuals taking statin and with a very healthy lifestyle had the lowest risk of all-cause (HR: 0.70; 95 %CI: 0.57-0.87) and cardiovascular mortality (0.56; 0.40-0.79), compared with statin non-users with an unhealthy lifestyle. High-risk participants taking statin and with a very healthy lifestyle had the highest years of life gained (5.90 years at 35-year-old [4.14-7.67; P < 0.001]) compared with statin non-users with an unhealthy lifestyle among high-risk people. And their life expectancy was comparable with those without high risk but with a very healthy lifestyle (4.49 vs. 4.68 years).

Conclusion

The combination of preventive medication and multiple healthy lifestyles was associated with lower risk of all-cause and cardiovascular mortality and largest survival benefits. Integrated strategy to improve long-term health for high-risk people was urgently needed.

An individually adjusted approach for communicating epidemiological results on health and lifestyle to patients

If scientific research on modifiable risk factors was more accessible to the general population there is a potential to prevent disease and promote health. Mobile applications can automatically combine individual characteristics and statistical models of health to present scientific information as individually tailored visuals, and thus there is untapped potential in incorporating scientific research into apps aimed at promoting healthier lifestyles. As a proof-of-concept, we develop a statistical model of the relationship between Self-rated-health (SRH) and lifestyle-related factors, and a simple app for conveying its effects through a visualisation that sets the individual as the frame of reference. Using data from the 6th (n = 12 981, 53.4% women and 46.6% men) and 7th (n = 21 083, 52.5% women and 47.5% men) iteration of the Tromsø population survey, we fitted a mixed effects linear regression model that models mean SRH as a function of self-reported intensity and frequency of physical activity (PA), BMI, mental health symptoms (HSCL-10), smoking, support from friends, and HbA1c ≥ 6.5%. We adjusted for socioeconomic and demographic factors and comorbidity. We designed a simple proof-of-concept app to register relevant user information, and use the SRH-model to translate the present status of the user into suggestions for lifestyle changes along with predicted health effects. SRH was strongly related to modifiable health factors. The strongest modifiable predictors of SRH were mental health symptoms and PA. The mean adjusted difference in SRH between those with 10-HSCL index = 1.85 (threshold for mental distress) and HSCL-10 = 1 was 0.59 (CI 0.61-0.57). Vigorous physical activity (exercising to exhaustion ≥ 4 days/week relative to sedentary) was associated with an increase on the SRH scale of 0.64 (CI 0.56-0.73). Physical activity intensity and frequency interacted positively, with large PA-volume (frequency ⨯ intensity) being particularly predictive of high SRH. Incorporating statistical models of health into lifestyle apps have great potential for effectively communicating complex health research to a general audience. Such an approach could improve lifestyle apps by helping to make the recommendations more scientifically rigorous and personalised, and offer a more comprehensive overview of lifestyle factors and their importance.

Self-rated physical health predicts mortality in aging persons beyond objective health risks

Previous studies on self-rated health and mortality have usually not differentiated between physical and mental health, respectively have not considered physical diseases. This study aims to determine self-rated physical and mental health from middle to old age, examine associations with mortality adjusted for objective risk factors and assess effect modification by gender. In a large population-based sample (N = 14,993 at baseline), self-rated physical and mental health were rated separately by a single-item. Associations to mortality were modelled by Cox regressions, adjusting for potential confounding variables. Most participants rated their physical (79.4%), resp. mental health (82.3%) as good. Poor self-rated physical health was lowest in the youngest group (19.6%, age 35-44), and highest in midlife (29.1%, age 55-64). Poor self-rated mental health was lowest among the oldest (18.5%), and highest from 45 to 54 years (29.3%). Poor self-rated physical, but not mental health was predictive of mortality when adjusting for objective risk factors. Male gender and poor self-rated physical health interacted (RERI 0.43 95%-CI 0.02-0.85). Self-rated physical health was best in the youngest and worst in the midlife group, this pattern was reversed regarding self-rated mental health. Poor self-rated physical, but not mental health was predictive of mortality, adjusting for objective risk factors. It was more strongly predictive of mortality in men than in women. Poor subjective physical health ratings, should be taken seriously as an unfavorable prognostic sign, particularly in men.

Divergences and gaps in life expectancy and health-adjusted life expectancy in Mexico: Contribution analysis of the Global Burden of Disease Study 2019

INTRODUCTION

Life expectancy (LE) and Health-adjusted life expectancy (HALE) are summary indicators that reflect a population's general life conditions and measure inequities in health outcomes. The objective of this study was to identify the differences in LE and HALE by sex, age group, and state in Mexico from 1990 to 2019. Also, to evaluate whether the changes in HALE are related to sociodemographic indicators and indicators of access to and quality of health services.

METHODS

A secondary analysis was performed based on the Global Burden of Disease, Injuries, and Risk Factors Study (GBD). Data were obtained for LE (by sex and state) and HALE (by sex, age group, and state) for the years 1990, 2010, and 2019. The correlations between HALE with the Socio-Demographic Index (SDI) and with the Healthcare Access and Quality (HAQ) Index were estimated for 1990 and 2019 (by total population and sex).

RESULTS

LE and HALE had an absolute increase of 6.7% and 6.4% from 1990 to 2019, mainly among women, although they spent more years in poor health (11.8 years) than men. The patterns of LE and HALE were heterogeneous and divergent by state. In 2019, the difference in HALE (for both sex) between the states with the highest (Hidalgo) and the lowest (Chiapas) value was 4.6 years.

CONCLUSIONS

Progress in LE and HALE has slowed in recent years; HALE has even had setbacks in some states. Gaps between men and women, as well as between states, are persistent. Public and population policymaking should seek to lengthen LE and focus on ensuring that such years are spent in good health and with good quality of life.

A New Personalized Oral Cancer Survival Calculator to Estimate Risk of Death From Both Oral Cancer and Other Causes

Importance

Standard cancer prognosis models typically do not include much specificity in characterizing competing illnesses or general health status when providing prognosis estimates, limiting their utility for individuals, who must consider their cancer in the context of their overall health. This is especially true for patients with oral cancer, who frequently have competing illnesses.

Objective

To describe a statistical framework and accompanying new publicly available calculator that provides personalized estimates of the probability of a patient surviving or dying from cancer or other causes, using oral cancer as the first data set.

Design, Setting, and Participants

The models used data from the Surveillance, Epidemiology, and End Results (SEER) 18 registry (2000 to 2011), SEER-Medicare linked files, and the National Health Interview Survey (NHIS) (1986 to 2009). Statistical methods developed to calculate natural life expectancy in the absence of the cancer, cancer-specific survival, and other-cause survival were applied to oral cancer data and internally validated with 10-fold cross-validation. Eligible participants were aged between 20 and 94 years with oral squamous cell carcinoma.

Exposures

Histologically confirmed oral cancer, general health status, smoking, and selected serious comorbid conditions.

Main Outcomes and Measures

Probabilities of surviving or dying from the cancer or from other causes, and life expectancy in the absence of the cancer.

Results

A total of 22 392 patients with oral squamous cell carcinoma (13 544 male [60.5%]; 1476 Asian and Pacific Islander [6.7%]; 1792 Black [8.0%], 1589 Hispanic [7.2%], 17 300 White [78.1%]) and 402 626 NHIS interviewees were included in this calculator designed for public use for patients ages 20 to 86 years with newly diagnosed oral cancer to obtain estimates of health status-adjusted age, life expectancy in the absence of the cancer, and the probability of surviving, dying from the cancer, or dying from other causes within 1 to 10 years after diagnosis. The models in the calculator estimated that patients with oral cancer have a higher risk of death from other causes than their matched US population, and that this risk increases by stage.

Conclusions and relevance

The models developed for the calculator demonstrate that survival estimates that exclude the effects of coexisting conditions can lead to underestimates or overestimates of survival. This new calculator approach will be broadly applicable for developing future prognostic models of cancer and noncancer aspects of a person's health in other cancers; as registries develop more linkages, available covariates will become broader, strengthening future tools.