Although use of colonoscopy has increased substantially among elderly Medicare beneficiaries, no one has described colonoscopy-related adverse events in a representative sample of Medicare patients.
...To determine risk for adverse events after outpatient colonoscopy in elderly patients.
Population-based, matched cohort study.
Surveillance, Epidemiology, and End Results cancer registry areas.
Random 5% sample of Medicare beneficiaries, age 66 to 95 years, who underwent outpatient colonoscopy between 1 July 2001 and 31 October 2005 (n = 53 220), matched with beneficiaries who did not have colonoscopy.
Medicare claims were used to measure the rate of serious gastrointestinal events (bleeding and perforation), other gastrointestinal events, and cardiovascular events resulting in a hospitalization or emergency department visit within 30 days after colonoscopy compared with matched beneficiaries who did not have colonoscopy. Logistic regression was used to estimate adjusted predictive risks for adverse events and to assess whether these events varied by age, comorbid conditions, or type of colonoscopy.
Persons undergoing colonoscopy had a higher risk for adverse gastrointestinal events than their matched group. Rates of adverse events after colonoscopy increased with age. Patients having polypectomy had higher risk for all adverse events compared with their matched group and with the screening and diagnostic colonoscopy groups. Comorbid conditions increased the risk for adverse events. Patients with a history of stroke, chronic obstructive pulmonary disease, atrial fibrillation, or congestive heart failure had significantly higher risk for serious gastrointestinal events.
The analysis relied on the diagnosis and procedure codes recorded on the Medicare claims.
Risks for adverse events after outpatient colonoscopy among elderly Medicare beneficiaries were low; however, they increased with age with specific comorbid conditions and depending on whether polypectomy was done. These data may inform decisions on whether to perform colonoscopy in persons of advanced age or those with comorbid conditions.
The ERSPC (European Randomized Study of Screening for Prostate Cancer) found that screening reduced prostate cancer mortality, but the PLCO (Prostate, Lung, Colorectal, and Ovarian Cancer Screening ...Trial) found no reduction.
To evaluate whether effects of screening on prostate cancer mortality relative to no screening differed between the ERSPC and PLCO.
Cox regression of prostate cancer death in each trial group, adjusted for age and trial. Extended analyses accounted for increased incidence due to screening and diagnostic work-up in each group via mean lead times (MLTs), which were estimated empirically and using analytic or microsimulation models.
Randomized controlled trials in Europe and the United States.
Men aged 55 to 69 (ERSPC) or 55 to 74 (PLCO) years at randomization.
Prostate cancer screening.
Prostate cancer incidence and survival from randomization; prostate cancer incidence in the United States before screening began.
Estimated MLTs were similar in the ERSPC and PLCO intervention groups but were longer in the PLCO control group than the ERSPC control group. Extended analyses found no evidence that effects of screening differed between trials (P = 0.37 to 0.47 range across MLT estimation approaches) but strong evidence that benefit increased with MLT (P = 0.0027 to 0.0032). Screening was estimated to confer a 7% to 9% reduction in the risk for prostate cancer death per year of MLT. This translated into estimates of 25% to 31% and 27% to 32% lower risk for prostate cancer death with screening as performed in the ERSPC and PLCO intervention groups, respectively, compared with no screening.
The MLT is a simple metric of screening and diagnostic work-up.
After differences in implementation and settings are accounted for, the ERSPC and PLCO provide compatible evidence that screening reduces prostate cancer mortality.
National Cancer Institute.
We investigated differences in net cancer survival (survival observed if the only possible cause of death was the cancer under study) estimated using new approaches for relative survival (RS) and ...cause-specific survival (CSS).
We used SEER data for patients diagnosed in 2000 to 2013, followed-up through December 31, 2014. For RS, we used new life tables accounting for geography and socio-economic status. For CSS, we used the SEER cause of death algorithm for attributing cancer-specific death. Estimates were compared by site, age, stage, race, and time since diagnosis.
Differences between 5-year RS and CSS were generally small. RS was always higher in screen-detectable cancers, for example, female breast (89.2% vs. 87.8%) and prostate (98.5% vs. 93.7%) cancers; differences increased with age or time since diagnosis. CSS was usually higher in the remaining cancer sites, particularly those related to specific risk factors, for example, cervix (70.9% vs. 68.3%) and liver (20.7% vs. 17.1%) cancers. For most cancer sites, the gap between estimates was smaller with more advanced stage.
RS is the preferred approach to report cancer survival from registry data because cause of death may be inaccurate, particularly for older patients and long-term survivors as comorbidities increase challenges in determining cause of death. However, CSS proved to be more reliable in patients diagnosed with localized disease or cancers related to specific risk factors as general population life tables may not capture other causes of mortality.
Different approaches for net survival estimation should be considered depending on cancer under study.
Stage is the most important prognostic factor for understanding cancer survival trends. Summary stage (SS) classifies cancer based on the extent of spread: In situ, Localized, Regional, or Distant. ...Continual updating of staging systems poses challenges to stage comparisons over time. We use a consistent summary stage classification and present survival trends for 25 cancer sites using the joinpoint survival (JPSurv) model.
We developed a modified summary stage variable, Long-Term Site-Specific Summary Stage, based on as consistent a definition as possible and applied it to a maximum number of diagnosis years, 1975-2019. We estimated trends by stage by applying JPSurv to relative survival data for 25 cancer sites in SEER-8, 1975-2018, followed through December 31, 2019. To help interpret survival trends, we report incidence and mortality trends using the joinpoint model.
Five-year relative survival improved for nearly all sites and stages. Large improvements were observed for localized pancreatic cancer 4.25 percentage points annually, 2007-2012 (95% confidence interval, 3.40-5.10), distant skin melanoma 2.15 percentage points annually, 2008-2018 (1.73-2.57), and localized esophagus cancer 1.18 percentage points annually, 1975-2018 (1.11-1.26).
This is the first analysis of survival trends by summary stage for multiple cancer sites. The largest survival increases were seen for cancers with a traditionally poor prognosis and no organized screening, which likely reflects clinical management advances.
Our study will be particularly useful for understanding the population-level impact of new treatments and identifying emerging trends in health disparities research.
Life expectancy is increasingly incorporated in evidence-based screening and treatment guidelines to facilitate patient-centered clinical decision-making. However, life expectancy estimates from ...standard life tables do not account for health status, an important prognostic factor for premature death. This study aims to address this research gap and develop life tables incorporating the health status of adults in the United States.
Data from the National Health Interview Survey (1986-2004) linked to mortality follow-up through to 2006 (age ≥ 40, n = 729,531) were used to develop life tables. The impact of self-rated health (excellent, very good, good, fair, poor) on survival was quantified in 5-year age groups, incorporating complex survey design and weights. Life expectancies were estimated by extrapolating the modeled survival probabilities.
Life expectancies incorporating health status differed substantially from standard US life tables and by health status. Poor self-rated health more significantly affected the survival of younger compared to older individuals, resulting in substantial decreases in life expectancy. At age 40 years, hazards of dying for white men who reported poor vs. excellent health was 8.5 (95% CI: 7.0,10.3) times greater, resulting in a 23-year difference in life expectancy (poor vs. excellent: 22 vs. 45), while at age 80 years, the hazards ratio was 2.4 (95% CI: 2.1, 2.8) and life expectancy difference was 5 years (5 vs. 10). Relative to the US general population, life expectancies of adults (age < 65) with poor health were approximately 5-15 years shorter.
Considerable shortage in life expectancy due to poor self-rated health existed. The life table developed can be helpful by including a patient perspective on their health and be used in conjunction with other predictive models in clinical decision making, particularly for younger adults in poor health, for whom life tables including comorbid conditions are limited.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract
Background
Studies conducted in Swedish populations have shown that men with lowest prostate-specific antigen (PSA) levels at ages 44–50 years and 60 years have very low risk of future ...distant metastasis or death from prostate cancer. This study investigates benefits and harms of screening strategies stratified by PSA levels.
Methods
PSA levels and diagnosis patterns from two microsimulation models of prostate cancer progression, detection, and mortality were compared against results of the Malmö Preventive Project, which stored serum and tracked subsequent prostate cancer diagnoses for 25 years. The models predicted the harms (tests and overdiagnoses) and benefits (lives saved and life-years gained) of PSA-stratified screening strategies compared with biennial screening from age 45 years to age 69 years.
Results
Compared with biennial screening for ages 45–69 years, lengthening screening intervals for men with PSA less than 1.0 ng/mL at age 45 years led to 46.8–47.0% fewer tests (range between models), 0.9–2.1% fewer overdiagnoses, and 3.1–3.8% fewer lives saved. Stopping screening when PSA was less than 1.0 ng/mL at age 60 years and older led to 12.8–16.0% fewer tests, 5.0–24.0% fewer overdiagnoses, and 5.0–13.1% fewer lives saved. Differences in model results can be partially explained by differences in assumptions about the link between PSA growth and the risk of disease progression.
Conclusion
Relative to a biennial screening strategy, PSA-stratified screening strategies investigated in this study substantially reduced the testing burden and modestly reduced overdiagnosis while preserving most lives saved. Further research is needed to clarify the link between PSA growth and disease progression.
Background
Second or later primary cancers account for approximately 20% of incident cases in the United States. Currently, cause‐specific survival (CSS) analyses exclude these cancers because the ...cause of death (COD) classification algorithm was available only for first cancers. The authors added rules for later cancers to the Surveillance, Epidemiology, and End Results cause‐specific death classification algorithm and evaluated CSS to include individuals with prior tumors.
Methods
The authors constructed 2 cohorts: 1) the first ever primary cohort, including patients whose first cancer was diagnosed during 2000 through 2016) and 2) the earliest matching primary cohort, including patients with any cancer who matched the selection criteria irrespective of whether it was the first or a later cancer diagnosed during 2000 through 2016. The cohorts' CSS estimates were compared using follow‐up through December 31, 2017. The new rules were used in the second cohort for patients whose first cancers during 2000 through 2016 were their second or later cancers.
Results
Overall, there were no statistically significant differences in CSS estimates between the 2 cohorts. Estimates were similar by age, stage, race, and time since diagnosis, except for patients with leukemia and those aged 65 to 74 years (3.4 percentage point absolute difference).
Conclusions
The absolute difference in CSS estimates for the first cancer ever cohort versus earliest of any cancers cohort in the study period was small for most cancer types. As the number of newly diagnosed patients with prior cancers increases, the algorithm will make CSS more inclusive and enable estimating survival for a group of patients with cancer for whom life tables are not available or life tables are available but do not capture other‐cause mortality appropriately.
An improved algorithm will allow researchers to estimate cause‐specific survival in patients with multiple cancers and will allow this framework to be more inclusive and more reflective of cancer survival for all recently diagnosed patients. The use of this cause‐specific survival framework may be particularly useful in studies of oncology outcomes, or when life tables do not adequately represent the patients' background mortality, or in special populations for which life tables are not available.
The utility of codes on Medicare Advantage (MA) data to capture cancer diagnoses and treatment for cancer patients is unknown.
This study compared cancer diagnoses and treatments on MA encounter data ...(MA data) with the Surveillance, Epidemiology, and End-Results (SEER) data.
Subjects were patients enrolled in either MA or Medicare fee-for-service (MFFS) when diagnosed with incident breast, colorectal, prostate, or lung cancer, 2015-2017, in a SEER cancer registry.
MA data, from 2 months before to 12 months following SEER diagnosis, were reviewed to identify cancer diagnoses, surgery, chemotherapy, and radiotherapy (RT). MA data were compared with SEER to determine their sensitivity to capture cancer diagnoses and sensitivity/specificity to identify surgeries. The agreement between SEER and Medicare data regarding receipt of chemotherapy and RT was measured by Kappa statistics. A similar comparison to SEER diagnoses/treatments was made using MFFS claims to provide context for the SEER-MA comparison.
The study included 186,449 patients, 38% in MA. MA data had 92%+ sensitivity to identify SEER cancer diagnosis and 90%+ sensitivity for cancer surgery. Specificity for surgery was >84%, except for breast cancer (52%). Kappa statistics for agreement between SEER and MA data regarding chemotherapy varied by cancer, 0.61-0.82, and for receipt of RT exceeded 0.75 for all cancers. Results observed for MFFS claims were similar to those in MA data.
For 4 common cancers, MA data included most cancer diagnoses and general types of cancer treatment reported in the SEER data. More research is needed to assess additional cancers and detailed treatments.
Abstract
Background
This study aims to quantify the extent and diversity of the cancer care workforce, beyond medical oncologists, to inform future demand because the number of cancer survivors is ...expected to grow in the United States.
Methods
Surveillance, Epidemiology, and End Results-Medicare data were used to evaluate health-care use of cancer survivors diagnosed between 2000 and 2014, enrolled in fee-for-service Medicare Parts A and B, and 65 years or older in 2008-2015. We calculated percentage of cancer survivors who saw each clinician specialty and their average annual number of visits in each phase of care. We projected the national number of individuals receiving care and number of annual visits by clinician specialty and phase of care through 2040.
Results
Cancer survivors had higher care use in the first year after diagnosis and last year of life phases. During the initial year after cancer diagnosis, most survivors were seen for cancer-related care by a medical oncologist (59.1%), primary care provider (55.9%), and/or other cancer-treating physicians (42.2%). The percentage of survivors with cancer-related visits to each specialty declined after the first year after diagnosis, plateauing after year 6-7. However, at 10 or more years after diagnosis, approximately 20% of cancer survivors had visits to medical oncologists and an average of 4 visits a year.
Conclusions
Cancer survivors had higher care use in the first year after diagnosis and last year of life. High levels of care use across specialties in all phases of care have important implications for models of survivorship care coordination and workforce planning.
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