Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on cancer incidence, mortality, and survival. Incidence data, available through 2014, were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data, available through 2015, were collected by the National Center for Health Statistics. In 2018, 1,735,350 new cancer cases and 609,640 cancer deaths are projected to occur in the United States. Over the past decade of data, the cancer incidence rate (2005-2014) was stable in women and declined by approximately 2% annually in men, while the cancer death rate (2006-2015) declined by about 1.5% annually in both men and women. The combined cancer death rate dropped continuously from 1991 to 2015 by a total of 26%, translating to approximately 2,378,600 fewer cancer deaths than would have been expected if death rates had remained at their peak. Of the 10 leading causes of death, only cancer declined from 2014 to 2015. In 2015, the cancer death rate was 14% higher in non-Hispanic blacks (NHBs) than non-Hispanic whites (NHWs) overall (death rate ratio [DRR], 1.14; 95% confidence interval [95% CI], 1.13-1.15), but the racial disparity was much larger for individuals aged <65 years (DRR, 1.31; 95% CI, 1.29-1.32) compared with those aged ≥65 years (DRR, 1.07; 95% CI, 1.06-1.09) and varied substantially by state. For example, the cancer death rate was lower in NHBs than NHWs in Massachusetts for all ages and in New York for individuals aged ≥65 years, whereas for those aged <65 years, it was 3 times higher in NHBs in the District of Columbia (DRR, 2.89; 95% CI, 2.16-3.91) and about 50% higher in Wisconsin (DRR, 1.78; 95% CI, 1.56-2.02), Kansas (DRR, 1.51; 95% CI, 1.25-1.81), Louisiana (DRR, 1.49; 95% CI, 1.38-1.60), Illinois (DRR, 1.48; 95% CI, 1.39-1.57), and California (DRR, 1.45; 95% CI, 1.38-1.54). Larger racial inequalities in young and middle-aged adults probably partly reflect less access to high-quality health care. CA Cancer J Clin 2018;68:7-30. © 2018 American Cancer Society.

https://acsjournals.onlinelibrary.wiley.com/doi/epdf/10.3322/caac.21442

Cancer statistics, 2018

The number of cancer survivors continues to increase because of both advances in early detection and treatment and the aging and growth of the population. For the public health community to better serve these survivors, the American Cancer Society and the National Cancer Institute collaborate to estimate the number of current and future cancer survivors using data from the Surveillance, Epidemiology, and End Results cancer registries. In addition, current treatment patterns for the most prevalent cancer types are presented based on information in the National Cancer Data Base and treatment-related side effects are briefly described. More than 15.5 million Americans with a history of cancer were alive on January 1, 2016, and this number is projected to reach more than 20 million by January 1, 2026. The 3 most prevalent cancers are prostate (3,306,760), colon and rectum (724,690), and melanoma (614,460) among males and breast (3,560,570), uterine corpus (757,190), and colon and rectum (727,350) among females. More than one-half (56%) of survivors were diagnosed within the past 10 years, and almost one-half (47%) are aged 70 years or older. People with a history of cancer have unique medical and psychosocial needs that require proactive assessment and management by primary care providers. Although there are a growing number of tools that can assist patients, caregivers, and clinicians in navigating the various phases of cancer survivorship, further evidence-based resources are needed to optimize care. CA Cancer J Clin 2016;66:271-289. © 2016 American Cancer Society.

https://onlinelibrary.wiley.com/doi/pdf/10.3322/caac.21349

Cancer treatment and survivorship statistics, 2016.

Secret History: Return of the Black Death Channel 4, 7-8pm In 1348 the Black Death swept through London, killing people within days of the appearance of their first symptoms. Exactly how many died, and why, has long been a mystery. This Secret History documentary follows experts as they pick through the evidence and reveal why the plague killed on such a scale. And they ask, what might be coming next?

Medscape

Background
Decision aids are intended to help people participate in decisions that involve weighing the benefits and harms of treatment options often with scientific uncertainty.

Objectives
To assess the effects of decision aids for people facing treatment or screening decisions.

Search methods
For this update, we searched from 2009 to June 2012 in MEDLINE; CENTRAL; EMBASE; PsycINFO; and grey literature. Cumulatively, we have searched each database since its start date including CINAHL (to September 2008).

Selection criteria
We included published randomized controlled trials of decision aids, which are interventions designed to support patients' decision making by making explicit the decision, providing information about treatment or screening options and their associated outcomes, compared to usual care and/or alternative interventions. We excluded studies of participants making hypothetical decisions.

Data collection and analysis
Two review authors independently screened citations for inclusion, extracted data, and assessed risk of bias. The primary outcomes, based on the International Patient Decision Aid Standards (IPDAS), were:

A) 'choice made' attributes;

B) 'decision-making process' attributes.

Secondary outcomes were behavioral, health, and health-system effects. We pooled results using mean differences (MD) and relative risks (RR), applying a random-effects model.

Main results
This update includes 33 new studies for a total of 115 studies involving 34,444 participants. For risk of bias, selective outcome reporting and blinding of participants and personnel were mostly rated as unclear due to inadequate reporting. Based on 7 items, 8 of 115 studies had high risk of bias for 1 or 2 items each.

Of 115 included studies, 88 (76.5%) used at least one of the IPDAS effectiveness criteria: A) 'choice made' attributes criteria: knowledge scores (76 studies); accurate risk perceptions (25 studies); and informed value-based choice (20 studies); and B) 'decision-making process' attributes criteria: feeling informed (34 studies) and feeling clear about values (29 studies).

A) Criteria involving 'choice made' attributes:

Compared to usual care, decision aids increased knowledge (MD 13.34 out of 100; 95% confidence interval (CI) 11.17 to 15.51; n = 42). When more detailed decision aids were compared to simple decision aids, the relative improvement in knowledge was significant (MD 5.52 out of 100; 95% CI 3.90 to 7.15; n = 19). Exposure to a decision aid with expressed probabilities resulted in a higher proportion of people with accurate risk perceptions (RR 1.82; 95% CI 1.52 to 2.16; n = 19). Exposure to a decision aid with explicit values clarification resulted in a higher proportion of patients choosing an option congruent with their values (RR 1.51; 95% CI 1.17 to 1.96; n = 13).

B) Criteria involving 'decision-making process' attributes:

Decision aids compared to usual care interventions resulted in:

a) lower decisional conflict related to feeling uninformed (MD -7.26 of 100; 95% CI -9.73 to -4.78; n = 22) and feeling unclear about personal values (MD -6.09; 95% CI -8.50 to -3.67; n = 18);

b) reduced proportions of people who were passive in decision making (RR 0.66; 95% CI 0.53 to 0.81; n = 14); and

c) reduced proportions of people who remained undecided post-intervention (RR 0.59; 95% CI 0.47 to 0.72; n = 18).

Decision aids appeared to have a positive effect on patient-practitioner communication in all nine studies that measured this outcome. For satisfaction with the decision (n = 20), decision-making process (n = 17), and/or preparation for decision making (n = 3), those exposed to a decision aid were either more satisfied, or there was no difference between the decision aid versus comparison interventions. No studies evaluated decision-making process attributes for helping patients to recognize that a decision needs to be made, or understanding that values affect the choice.

C) Secondary outcomes

Exposure to decision aids compared to usual care reduced the number of people of choosing major elective invasive surgery in favour of more conservative options (RR 0.79; 95% CI 0.68 to 0.93; n = 15). Exposure to decision aids compared to usual care reduced the number of people choosing to have prostate-specific antigen screening (RR 0.87; 95% CI 0.77 to 0.98; n = 9). When detailed compared to simple decision aids were used, fewer people chose menopausal hormone therapy (RR 0.73; 95% CI 0.55 to 0.98; n = 3). For other decisions, the effect on choices was variable.

The effect of decision aids on length of consultation varied from 8 minutes shorter to 23 minutes longer (median 2.55 minutes longer) with 2 studies indicating statistically-significantly longer, 1 study shorter, and 6 studies reporting no difference in consultation length. Groups of patients receiving decision aids do not appear to differ from comparison groups in terms of anxiety (n = 30), general health outcomes (n = 11), and condition-specific health outcomes (n = 11). The effects of decision aids on other outcomes (adherence to the decision, costs/resource use) were inconclusive.

Authors' conclusions
There is high-quality evidence that decision aids compared to usual care improve people's knowledge regarding options, and reduce their decisional conflict related to feeling uninformed and unclear about their personal values. There is moderate-quality evidence that decision aids compared to usual care stimulate people to take a more active role in decision making, and improve accurate risk perceptions when probabilities are included in decision aids, compared to not being included. There is low-quality evidence that decision aids improve congruence between the chosen option and the patient's values.

New for this updated review is further evidence indicating more informed, values-based choices, and improved patient-practitioner communication. There is a variable effect of decision aids on length of consultation. Consistent with findings from the previous review, decision aids have a variable effect on choices. They reduce the number of people choosing discretionary surgery and have no apparent adverse effects on health outcomes or satisfaction. The effects on adherence with the chosen option, cost-effectiveness, use with lower literacy populations, and level of detail needed in decision aids need further evaluation. Little is known about the degree of detail that decision aids need in order to have a positive effect on attributes of the choice made, or the decision-making process.

/pdf/decision-aids-for-people-facing-health-treatment-or-1j1706q17l.pdf

Decision aids for people facing health treatment or screening decisions

CA : A Cancer Journal for Clinicians

Rebuttal by Dr Hoffman

Post-acute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (PASC) is a complex condition with multisystem involvement. We assessed patients’ experience with a PASC clinic established at University of Iowa in June 2020. A survey was electronically mailed in June 2021 asking about (1) symptoms and their impact on functional domains using the Patient-Reported Outcomes Measurement Information System (PROMIS) measures (Global Health and Cognitive Function Abilities) (2) satisfaction with clinic services, referrals, barriers to care, and recommended support resources. Survey completion rate was 35% (97/277). Majority were women (67%), Caucasian (93%), and were not hospitalized (76%) during acute COVID-19. As many as 50% reported wait time between 1 and 3 months, 40% traveled >1 h for an appointment and referred to various subspecialities. Participants reported high symptom burden-fatigue (77%), “brain fog” (73%), exercise intolerance (73%), anxiety (63%), sleep difficulties (56%) and depression (44%). On PROMIS measures, some patients scored significantly low (≥1.5 SD below mean) in physical (22.7%), mental (15.9%), and cognitive (17.6%) domains. Approximately 61% to 93% of participants were satisfied with clinical services. Qualitative analysis added insight to their experience with healthcare. Participants suggested potential strategies for optimizing recovery, including continuity of care, a co-located multispecialty clinic, and receiving timely information from emerging research. Participants appreciated that physicians validated their symptoms and provided continuity of care and access to specialists.

https://doi.org/10.1177/23743735231151539

Patient Experiences with a Tertiary Care Post-COVID-19 Clinic

Introduction Chest imaging often incidentally finds indeterminate nodules that need to be monitored to ensure early detection of lung cancers. Health care systems need effective approaches for identifying these lung nodules. We compared the diagnostic performance of 2 approaches for identifying patients with lung nodules on imaging studies (chest/abdomen): (1) relying on radiologists to code imaging studies with lung nodules; and (2) applying a text search algorithm to identify references to lung nodules in radiology reports. Methods We assessed all radiology studies performed between January 1, 2016 and November 30, 2016 in a single Veterans Health Administration hospital. We first identified imaging reports with a diagnostic code for a pulmonary nodule. We then applied a text search algorithm to identify imaging reports with key words associated with lung nodules. We reviewed medical records for all patients with a suspicious radiology report based on either search strategy to confirm the presence of a lung nodule. We calculated the yield and the positive predictive value (PPV) of each search strategy for finding pulmonary nodules. Results We identified 12,983 imaging studies with a potential lung nodule. Chart review confirmed 8,516 imaging studies with lung nodules, representing 2,912 unique patients. The text search algorithm identified all the patients with lung nodules identified by the radiology coding (n = 1,251) as well as an additional 1,661 patients. The PPV of the text search was 72% (2,912/4,071) and the PPV of the radiology code was 92% (1,251/1,363). Among the patients with nodules missed by radiology coding but identified by the text search algorithm, 130 had lung nodules > 8 mm in diameter. Conclusions The text search algorithm can identify additional patients with lung nodules compared to the radiology coding; however, this strategy requires substantial clinical review time to confirm nodules. Health care systems adopting nodule-tracking approaches should recognize that relying only on radiology coding might miss clinically important nodules.

Applying a Text-Search Algorithm to Radiology Reports Can Find More Patients With Pulmonary Nodules Than Radiology Coding Alone.

31 Background: Androgen deprivation therapy (ADT) has been associated with an increased risk of developing diabetes (DM) and cardiovascular disease (CVD), though this is controversial, particularly for CVD. We prospectively assessed the relationship between ADT and incident DM and CVD in the Prostate Cancer Outcomes Study (PCOS), a population-based cohort of prostate cancer survivors followed longitudinally for 15 years from diagnosis. Methods: We identified men in the PCOS with non-metastatic prostate cancer diagnosed from 1994 to 1995 and followed through 2009 to 2010. We used multivariable logistic regression models to compare groups receiving short-term ADT (less than 2 years), prolonged ADT (2 years or more) and no ADT to assess the relationship between ADT exposure and subsequent diagnoses of DM and CVD (determined by patient report and cause of death data). We evaluated the effects of age at diagnosis, race, stage, and comorbidity on the development of DM and CVD. Results: Among 3,526 men with como...

Influence of age on incident diabetes (DM) and cardiovascular disease (CVD) among prostate cancer survivors receiving androgen deprivation therapy (ADT).

Osler once said of medicine, “Life is short, the art is long.” Like clinical medicine, clinical teaching involves both science and art. The science of clinical teaching is growing at a steady, albeit frustratingly slow, pace. One important mission of JGIM is to increase the scientific understanding of clinical teaching by publishing high-quality research on clinical education. However, the art of clinical teaching is more subjective, involving the life stories, values, and interpersonal interactions of teachers, learners, and patients1. The art of medicine is a key source of inspiration for clinical teachers. When teachers get together over coffee (or something stronger), they are more likely to swap moving stories of powerful teaching encounters than the latest research findings.

As every clinical educator knows, bringing the best art and the best science into particular teaching encounters is challenging. Subtle nuances matter, such as the learning climate; the receptivity, interests, and needs of the learner, and the knowledge and interaction style of the teacher. As evidenced by discussions around the Innovations in Education series in JGIM and presentations at the national SGIM meeting, clinical educators are endlessly interested in trading “secrets” of what does and does not work in creating lively, creative teaching/learning encounters. In this spirit, JGIM introduces a new series of articles—Teaching Tips—whose overall goal is to provide practical, specific, “how to” suggestions for teaching from experienced educators that other clinical educators can try out and adapt to their own settings.

The idea for a Teaching Tips series was sparked by a proposal from the Evidence Based Medicine Teaching Scripts Working Group (herein termed the EBM Teaching Working Group), a group of EBM teachers from many institutions who have extensive experience in teaching EBM to clinical learners. About a year ago, the EBM Teaching Working Group, headed by Dr. Peter Wyer, approached JGIM with a proposal to write a series of articles with practical suggestions to clinical educators on how to teach specific topics in EBM. Previous papers in the series had appeared in the Canadian Medical Association Journal2 and had been positively received by readers of that journal.

The EBM Teaching Tips series makes its first appearance in this issue of JGIM in an article on how to teach about likelihood ratios as applied to diagnostic tests3. As a long-time teacher of likelihood ratios, with plenty of blank stares and stony silences among learners under his belt, one of us (BCW) now routinely incorporates the tips described in this article in his teaching. The EBM Teaching Tips are different from other types of JGIM articles in at least 2 respects. First, they are intended for immediate and practical application, and so adopt a script-like tone of great detail—providing specific examples, phrases, diagrams, and nonverbal behaviors to teach particular concepts. This tone will undoubtedly be new, and perhaps feel foreign, to some readers. Second, they are based on the field experiences of seasoned EBM teachers rather than quantitative research; they make no scientific claim to generalizability or outcomes. As a consequence of field testing, they have undergone explicit review and modification based on extensive use.

To foster the uptake and improvement of the EBM Teaching Tips, the Working Group has created a set of Web-based video demonstrations and discussion forums for the specific tips. These materials are available as extra supplementary material to the Teaching Tips articles on the JGIM web site. JGIM editors look forward to providing the forum for this experiment in communication among clinician educators and envision readers actively engaging in open and specific “shop talk” to refine and improve teaching around topics of common interest.

The future Editors for this series (Richard M. Hoffman, MD; Jeffrey Jackson, MD, MPH; and W. Scott Richardson, MD) envision Teaching Tips to include topics and authors other than Evidence-Based Medicine (traditionally defined) and the EBM Teaching Working Group. Encouraged by our review of the EBM Teaching Tips series, we invite clinician educators to submit articles for the Teaching Tips series. Like the EBM Teaching Tips, submitted articles will be subject to peer review and must meet specific content and formatting requirements that are described in the Instructions to Authors. In general, Teaching Tips articles will be expected to: (a) focus on topics directly related to clinical care by physicians of adult patients, (b) include a literature-based rationale for the importance of the topic, (c) be appropriate for medical students, residents, fellows, or faculty in clinical or small group teaching settings, (d) relate to a topic commonly taught by a large number of clinician educators, (e) include specific suggestions on teaching methods, and (f) describe the results of field testing and subsequent refinement of the teaching tips. Undoubtedly, our ideas and requirements for the series will change as we interact with authors of potential Teaching Tips.

Every clinician educator has had powerful experiences and strong opinions about the context and skills of clinical teaching—seeing the anxious look, the painful silence, or the dawning of insight and enthusiasm in our learners is simultaneously motivating and humbling. We look forward to creating an open and ongoing dialogue with, and among, our readers to describe and improve the art and science of clinical education.

/pdf/teaching-tips-a-new-series-in-jgim-47uln6ph81.pdf

Richard M. Hoffman

Papers

Rebuttal by Dr Hoffman

Patient Experiences with a Tertiary Care Post-COVID-19 Clinic

Applying a Text-Search Algorithm to Radiology Reports Can Find More Patients With Pulmonary Nodules Than Radiology Coding Alone.

Influence of age on incident diabetes (DM) and cardiovascular disease (CVD) among prostate cancer survivors receiving androgen deprivation therapy (ADT).

Teaching Tips—A new series in JGIM