Purpose To summarise what is currently known about the psychosocial morbidity, experiences, and needs of people with cancer and their informal caregivers, who live in rural or regional areas of developed countries. Methods Eligible studies dating from August 2010 until May 2021 were identified through several online databases, including MEDLINE, EMBASE, PsychINFO, and RURAL (Rural and Remote Health Database). Results were reported according to the PRISMA guidelines and the protocol was registered on PROSPERO (CRD42020171764). Results Sixty-five studies were included in this review, including 20 qualitative studies, 41 quantitative studies, and 4 mixed methods studies. Qualitative research demonstrated that many unique psychosocial needs of rural people remain unmet, particularly relating to finances, travel, and accessing care. However, most (9/19) quantitative studies that compared rural and urban groups reported no significant differences in psychosocial needs, morbidity, or quality of life (QOL). Five quantitative studies reported poorer psychosocial outcomes (social and emotional functioning) in urban cancer survivors, while three highlighted poorer outcomes (physical functioning, role functioning, and self-reported mental health outcomes) in the rural group. Conclusion Recent research shows that rural people affected by cancer have unique unmet psychosocial needs relating to rurality. However, there was little evidence that rural cancer survivors report greater unmet needs than their urban counterparts. This contrasts to the findings from a 2011 systematic review that found rural survivors consistently reported worse psychosocial outcomes. More population-based research is needed to establish whether uniquely rural unmet needs are due to general or cancer-specific factors.

It appears safe to administer chemotherapy in rural towns under the supervision of medical oncologists from larger centres via teleoncology, provided that rural health care resources and governance arrangements are adequate.

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As COVID-19 vaccinations became available and were proven effective in preventing serious infection, uptake amongst individuals varied, including in medically vulnerable populations. This cross-sectional multi-site study examined vaccine uptake, hesitancy, and explanatory factors amongst people with serious and/or chronic health conditions, including the impact of underlying disease on attitudes to vaccination. A 42-item survey was distributed to people with cancer, diabetes, or multiple sclerosis across ten Australian health services from 30 June to 5 October 2021. The survey evaluated sociodemographic and disease-related characteristics and incorporated three validated scales measuring vaccine hesitancy and vaccine-related beliefs generally and specific to their disease: the Oxford COVID-19 Vaccine Hesitancy Scale, the Oxford COVID-19 Vaccine Confidence and Complacency Scale and the Disease Influenced Vaccine Acceptance Scale-Six. Among 4683 participants (2548 [54.4%] female, 2108 [45.0%] male, 27 [0.6%] other; mean [SD] age, 60.6 [13.3] years; 3560 [76.0%] cancer, 842 [18.0%] diabetes, and 281 [6.0%] multiple sclerosis), 3813 (81.5%) self-reported having at least one COVID-19 vaccine. Unvaccinated status was associated with younger age, female sex, lower education and income, English as a second language, and residence in regional areas. Unvaccinated participants were more likely to report greater vaccine hesitancy and more negative perceptions toward vaccines. Disease-related vaccine concerns were associated with unvaccinated status and hesitancy, including greater complacency about COVID-19 infection, and concerns relating to vaccine efficacy and impact on their disease and/or treatment. This highlights the need to develop targeted strategies and education about COVID-19 vaccination to support medically vulnerable populations and health professionals.

Introduction: A discussion of 'waves' of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only tenuous. Methods: We present an algorithm which processes a general time series to identify substantial, significant and sustained periods of increase in the value of the time series, which could reasonably be described as 'observed waves'. This provides an objective means of describing observed waves in time series. Results: The output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of non-pharmaceutical interventions correlates with a reduced number of observed waves and reduced mortality burden in those waves. Conclusion: It is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic.