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Attention to survivors of adult cancers formally began more than 30 years ago with the founding of the National Coalition for Cancer Survivorship by representatives from 20 organisations who envisioned an organisation that would address survivorship issues and include friends, family, and caregivers. Since then, progress has been made in cancer care delivery, which has created challenges for and barriers to provision of optimal follow-up care to patients and survivors living with cancer as a chronic illness. Focus on post-treatment cancer care, including monitoring for long-term and late effects, and concerns regarding the effect of a cancer diagnosis and treatment on quality of life have gained momentum in the past 10 years. This impetus is largely a result of the 2005 Institute of Medicine Report From Cancer Patient to Cancer Survivor: Lost in Transition. Although the issues raised in the report were hardly novel, they gave a new and powerful voice to the cancer survivorship movement that demanded a call to action. In this Series paper, we provide an overview of the issues surrounding provision of cancer survivorship and follow-up care in the USA and discuss potential solutions to these challenges.

The diagnosis and treatment of patients with cancer requires access to imaging to ensure accurate management decisions and optimal outcomes. Our global assessment of imaging and nuclear medicine resources identified substantial shortages in equipment and workforce, particularly in low-income and middle-income countries (LMICs). A microsimulation model of 11 cancers showed that the scale-up of imaging would avert 3·2% (2·46 million) of all 76·0 million deaths caused by the modelled cancers worldwide between 2020 and 2030, saving 54·92 million life-years. A comprehensive scale-up of imaging, treatment, and care quality would avert 9·55 million (12·5%) of all cancer deaths caused by the modelled cancers worldwide, saving 232·30 million life-years. Scale-up of imaging would cost US$6·84 billion in 2020–30 but yield lifetime productivity gains of $1·23 trillion worldwide, a net return of $179·19 per $1 invested. Combining the scale-up of imaging, treatment, and quality of care would provide a net benefit of $2·66 trillion and a net return of $12·43 per $1 invested. With the use of a conservative approach regarding human capital, the scale-up of imaging alone would provide a net benefit of $209·46 billion and net return of $31·61 per $1 invested. With comprehensive scale-up, the worldwide net benefit using the human capital approach is $340·42 billion and the return per dollar invested is $2·46. These improved health and economic outcomes hold true across all geographical regions. We propose actions and investments that would enhance access to imaging equipment, workforce capacity, digital technology, radiopharmaceuticals, and research and training programmes in LMICs, to produce massive health and economic benefits and reduce the burden of cancer globally.

The COVID-19 pandemic presents clinicians a unique set of challenges in managing breast cancer (BC) patients. As hospital resources and staff become more limited during the COVID-19 pandemic, it becomes critically important to define which BC patients require more urgent care and which patients can wait for treatment until the pandemic is over. In this Special Communication, we use expert opinion of representatives from multiple cancer care organizations to categorize BC patients into priority levels (A, B, C) for urgency of care across all specialties. Additionally, we provide treatment recommendations for each of these patient scenarios. Priority A patients have conditions that are immediately life threatening or symptomatic requiring urgent treatment. Priority B patients have conditions that do not require immediate treatment but should start treatment before the pandemic is over. Priority C patients have conditions that can be safely deferred until the pandemic is over. The implementation of these recommendations for patient triage, which are based on the highest level available evidence, must be adapted to current availability of hospital resources and severity of the COVID-19 pandemic in each region of the country. Additionally, the risk of disease progression and worse outcomes for patients need to be weighed against the risk of patient and staff exposure to SARS CoV-2 (virus associated with the COVID-19 pandemic). Physicians should use these recommendations to prioritize care for their BC patients and adapt treatment recommendations to the local context at their hospital.

Despite the significant advances made in our understanding of cancer and how to treat it over the last hundred years, there are wide global disparities in access to cancer care and in who gets to benefit from cutting-edge cancer research.

Abstract Background Cancer mortality is high in sub-Saharan Africa (SSA), partly due to inadequate treatment access. We explored access to and affordability of cancer treatment regimens for the top 10 cancers utilizing examples from Kenya, Uganda, and Rwanda. Materials and Methods Population, healthcare financing, minimum wage, and cancer incidence and mortality data were obtained from the WHO, World Bank, public sources, and GLOBOCAN. National Essential Medicines List (NEML) alignment with 2019 WHO EML was assessed as a proportion. Cancer regimen pricing was calculated using public and proprietary sources and methods from prior studies. Affordability through universal healthcare coverage (UHC) was assessed as 1-year cost <3× gross national income per capita; and to patients out-of-pocket (OOP), as 30-day treatment course cost <1 day of minimum wage work. Results A total of 93.4% of the WHO EML cancer medicines were listed on the 2019 Kenya NEML, and 70.5% and 41.1% on Uganda (2016) and Rwanda (2015) NEMLs, respectively. Generic chemotherapies were available and affordable to governments through UHC to treat non-Hodgkin’s lymphoma, cervical, breast, prostate, colorectal, ovarian cancers, and select leukemias. Newer targeted agents were not affordable through government UHC purchasing, while some capecitabine-based regimens were not affordable in Uganda and Rwanda. All therapies were not affordable OOP. Conclusion All cancer treatment regimens were not affordable OOP and some were not covered by governments. Newer targeted drugs were not affordable to all 3 governments. UHC of cancer drugs and improving targeted therapy affordability to LMIC governments in SSA are key to improving treatment access and health outcomes. This study evaluated the access to, and affordability of treatment regimens based on 2019 WHO Essential Medicines List (EML) indications for the 10 most common cancers in Kenya, Uganda, and Rwanda. The findings demonstrate that all cancer treatment regimens were unaffordable to patients paying out-of-pocket and that novel essential targeted agents were not always available on country EMLs and were unaffordable to governments in all 3 countries through universal healthcare coverage purchasing.

Female breast cancer is the most commonly diagnosed cancer in the world, with wide variations in reported survival by country. Women in low-income and middle-income countries (LMICs) in particular face several barriers to breast cancer services, including diagnostics and treatment. We aimed to estimate the potential impact of scaling up the availability of treatment and imaging modalities on breast cancer survival globally, together with improvements in quality of care. For this simulation-based analysis, we used a microsimulation model of global cancer survival, which accounts for the availability and stage-specific survival impact of specific treatment modalities (chemotherapy, radiotherapy, surgery, and targeted therapy), imaging modalities (ultrasound, x-ray, CT, MRI, PET, and single-photon emission computed tomography [SPECT]), and quality of cancer care, to simulate 5-year net survival for women with newly diagnosed breast cancer in 200 countries and territories in 2018. We calibrated the model to empirical data on 5-year net breast cancer survival in 2010–14 from CONCORD-3. We evaluated the potential impact of scaling up specific imaging and treatment modalities and quality of care to the mean level of high-income countries, individually and in combination. We ran 1000 simulations for each policy intervention and report the means and 95% uncertainty intervals (UIs) for all model outcomes. We estimate that global 5-year net survival for women diagnosed with breast cancer in 2018 was 67·9% (95% UI 62·9–73·4) overall, with an almost 25-times difference between low-income (3·5% [0·4–10·0]) and high-income (87·0% [85·6–88·4]) countries. Among individual treatment modalities, scaling up access to surgery alone was estimated to yield the largest survival gains globally (2·7% [95% UI 0·4–8·3]), and scaling up CT alone would have the largest global impact among imaging modalities (0·5% [0·0–2·0]). Scaling up a package of traditional modalities (surgery, chemotherapy, radiotherapy, ultrasound, and x-ray) could improve global 5-year net survival to 75·6% (95% UI 70·6–79·4), with survival in low-income countries improving from 3·5% (0·4–10·0) to 28·6% (4·9–60·1). Adding concurrent improvements in quality of care could further improve global 5-year net survival to 78·2% (95% UI 74·9–80·4), with a substantial impact in low-income countries, improving net survival to 55·3% (42·2–67·8). Comprehensive scale-up of access to all modalities and improvements in quality of care could improve global 5-year net survival to 82·3% (95% UI 79·3–85·0). Comprehensive scale-up of treatment and imaging modalities, and improvements in quality of care could improve global 5-year net breast cancer survival by nearly 15 percentage points. Scale-up of traditional modalities and quality-of-care improvements could achieve 70% of these total potential gains, with substantial impact in LMICs, providing a more feasible pathway to improving breast cancer survival in these settings even without the benefits of future investments in targeted therapy and advanced imaging. Harvard T H Chan School of Public Health, National Cancer Institute P30 Cancer Center Support Grant to Memorial Sloan Kettering Cancer Center, and Breast Cancer Research Foundation.

While health systems have implemented multifaceted interventions to improve physician and patient communication in serious illnesses such as cancer, clinicians vary in their response to these initiatives. In this secondary analysis of a randomized trial, we identified phenotypes of oncology clinicians based on practice pattern and demographic data, then evaluated associations between such phenotypes and response to a machine learning (ML)-based intervention to prompt earlier advance care planning (ACP) for patients with cancer. Between June and November 2019, we conducted a pragmatic randomized controlled trial testing the impact of text message prompts to 78 oncology clinicians at 9 oncology practices to perform ACP conversations among patients with cancer at high risk of 180-day mortality, identified using a ML prognostic algorithm. All practices began in the pre-intervention group, which received weekly emails about ACP performance only; practices were sequentially randomized to receive the intervention at 4-week intervals in a stepped-wedge design. We used latent profile analysis (LPA) to identify oncologist phenotypes based on 11 baseline demographic and practice pattern variables identified using EHR and internal administrative sources. Difference-in-differences analyses assessed associations between oncologist phenotype and the outcome of change in ACP conversation rate, before and during the intervention period. Primary analyses were adjusted for patients' sex, age, race, insurance status, marital status, and Charlson comorbidity index. The sample consisted of 2695 patients with a mean age of 64.9 years, of whom 72% were White, 20% were Black, and 52% were male. 78 oncology clinicians (42 oncologists, 36 advanced practice providers) were included. Three oncologist phenotypes were identified: Class 1 (n = 9) composed primarily of high-volume generalist oncologists, Class 2 (n = 5) comprised primarily of low-volume specialist oncologists; and 3) Class 3 (n = 28), composed primarily of high-volume specialist oncologists. Compared with class 1 and class 3, class 2 had lower mean clinic days per week (1.6 vs 2.5 [class 3] vs 4.4 [class 1]) a higher percentage of new patients per week (35% vs 21% vs 18%), higher baseline ACP rates (3.9% vs 1.6% vs 0.8%), and lower baseline rates of chemotherapy within 14 days of death (1.4% vs 6.5% vs 7.1%). Overall, ACP rates were 3.6% in the pre-intervention wedges and 15.2% in intervention wedges (11.6 percentage-point difference). Compared to class 3, oncologists in class 1 (adjusted percentage-point difference-in-differences 3.6, 95% CI 1.0 to 6.1, p = 0.006) and class 2 (adjusted percentage-point difference-in-differences 12.3, 95% confidence interval [CI] 4.3 to 20.3, p = 0.003) had greater response to the intervention. Patient volume and time availability may be associated with oncologists' response to interventions to increase ACP. Future interventions to prompt ACP should prioritize making time available for such conversations between oncologists and their patients.

Substantial inequalities exist in cancer survival rates across countries. In addition to prevention of new cancers by reduction of risk factors, strategies are needed to close the gap between developed and developing countries in cancer survival and the effects of the disease on human suffering. We challenge the public health community's assumption that cancers will remain untreated in poor countries, and note the analogy to similarly unfounded arguments from more than a decade ago against provision of HIV treatment. In resource-constrained countries without specialised services, experience has shown that much can be done to prevent and treat cancer by deployment of primary and secondary caregivers, use of off-patent drugs, and application of regional and global mechanisms for financing and procurement. Furthermore, several middle-income countries have included cancer treatment in national health insurance coverage with a focus on people living in poverty. These strategies can reduce costs, increase access to health services, and strengthen health systems to meet the challenge of cancer and other diseases. In 2009, we formed the Global Task Force on Expanded Access to Cancer Care and Control in Developing Countries, which is composed of leaders from the global health and cancer care communities, and is dedicated to proposal, implementation, and evaluation of strategies to advance this agenda.

Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death. The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death. The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care. This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data. Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI -277 to -213; P<.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change -892), nausea score (-677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P=.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P<.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P=.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P=.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P=.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P=.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively. In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes. ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768. RR2-10.1136/bmjopen-2021-054675.

Background Butaro Cancer Center of Excellence (BCCOE) was founded to serve Rwanda’s rural low-income population, providing subsidized cancer diagnosis and treatment with transport stipends for the lowest-income patients. We examined whether travel distance to BCCOE was associated with advanced-stage diagnoses and treatment completion. Methods We conducted a retrospective cohort study using medical record data from BCCOE patients with pathologically-confirmed breast cancer from 2012–2016. Women with no prior surgery were included in the stage analysis; those with non-metastatic disease were included in the treatment analysis. We calculated travel distances using spatial analytic software and used multivariable logistic regression to examine the association of distance and other patient characteristics with late-stage diagnoses and treatment completion within one year of diagnosis. Results The analytic cohort for stage included 426 patients; 75.1% had late-stage (stage 3 or 4) disease. In univariable analyses, patients residing in BCCOE’s surrounding district had a lower proportion of late-stage diagnoses compared to those residing outside the district (57.9% v 76.8%, p  = 0.02). In adjusted analyses, odds of late-stage diagnosis were 2.46 (95% CI:1.21–5.12) times higher among those in distance quartile 4 (> 135.8 km) versus 1 (< 55.7 km); the effect of distance was less strong in sensitivity analyses excluding patients from BCCOE’s surrounding district. Patients from sectors with > 50% poverty had 2.33 times higher odds of late-stage diagnoses (95% CI:1.07–5.26) relative to those with poverty < 30%. In the treatment completion cohort ( n  = 348), 49.1% of patients completed surgery and chemotherapy within a year. In adjusted analyses, travel distance and poverty were not linearly associated with treatment completion. Conclusions At Rwanda’s first public cancer facility, sector-level poverty and longer travel distances were associated with late-stage breast cancer diagnoses, but less clearly associated with treatment completion, perhaps partly due to travel stipends provided to the lowest-income individuals undergoing treatment. Our findings support further investigation into wider use of travel stipends to facilitate early diagnosis and treatment completion.