A cross sectional survey of internet use among a highly socially disadvantaged population of tobacco smokers.

McCrabb S ，Twyman L ，Palazzi K ，Guillaumier A ，Paul C ，Bonevski B ... -  《-》

Tobacco Product Use and Associated Factors Among Middle and High School Students - National Youth Tobacco Survey, United States, 2021.

Gentzke AS ，Wang TW ，Cornelius M ，Park-Lee E ，Ren C ，Sawdey MD ，Cullen KA ，Loretan C ，Jamal A ，Homa DM ... -  《MMWR SURVEILLANCE SUMMARIES》

Healthcare workers' informal uses of mobile phones and other mobile devices to support their work: a qualitative evidence synthesis.

Healthcare workers sometimes develop their own informal solutions to deliver services. One such solution is to use their personal mobile phones or other mobile devices in ways that are unregulated by their workplace. This can help them carry out their work when their workplace lacks functional formal communication and information systems, but it can also lead to new challenges. To explore the views, experiences, and practices of healthcare workers, managers and other professionals working in healthcare services regarding their informal, innovative uses of mobile devices to support their work. We searched MEDLINE, Embase, CINAHL and Scopus on 11 August 2022 for studies published since 2008 in any language. We carried out citation searches and contacted study authors to clarify published information and seek unpublished data. We included qualitative studies and mixed-methods studies with a qualitative component. We included studies that explored healthcare workers' views, experiences, and practices regarding mobile phones and other mobile devices, and that included data about healthcare workers' informal use of these devices for work purposes. We extracted data using an extraction form designed for this synthesis, assessed methodological limitations using predefined criteria, and used a thematic synthesis approach to synthesise the data. We used the 'street-level bureaucrat' concept to apply a conceptual lens to our findings and prepare a line of argument that links these findings. We used the GRADE-CERQual approach to assess our confidence in the review findings and the line-of-argument statements. We collaborated with relevant stakeholders when defining the review scope, interpreting the findings, and developing implications for practice. We included 30 studies in the review, published between 2013 and 2022. The studies were from high-, middle- and low-income countries and covered a range of healthcare settings and healthcare worker cadres. Most described mobile phone use as opposed to other mobile devices, such as tablets. We have moderate to high confidence in the statements in the following line of argument. The healthcare workers in this review, like other 'street-level bureaucrats', face a gap between what is expected of them and the resources available to them. To plug this gap, healthcare workers develop their own strategies, including using their own mobile phones, data and airtime. They also use other personal resources, including their personal time when taking and making calls outside working hours, and their personal networks when contacting others for help and advice. In some settings, healthcare workers' personal phone use, although unregulated, has become a normal part of many work processes. Some healthcare workers therefore experience pressure or expectations from colleagues and managers to use their personal phones. Some also feel driven to use their phones at work and at home because of feelings of obligation towards their patients and colleagues. At best, healthcare workers' use of their personal phones, time and networks helps humanise healthcare. It allows healthcare workers to be more flexible, efficient and responsive to the needs of the patient. It can give patients access to individual healthcare workers rather than generic systems and can help patients keep their sensitive information out of the formal system. It also allows healthcare workers to communicate with each other in more personalised, socially appropriate ways than formal systems allow. All of this can strengthen healthcare workers' relationships with community members and colleagues. However, these informal approaches can also replicate existing social hierarchies and deepen existing inequities among healthcare workers. Personal phone use costs healthcare workers money. This is a particular problem for lower-level healthcare workers and healthcare workers in low-income settings as they are likely to be paid less and may have less access to work phones or compensation. Out-of-hours use may also be more of a burden for lower-level healthcare workers, as they may find it harder to ignore calls when they are at home. Healthcare workers with poor access to electricity and the internet are less able to use informal mobile phone solutions, while healthcare workers who lack skills and training in how to appraise unendorsed online information are likely to struggle to identify trustworthy information. Informal digital channels can help healthcare workers expand their networks. But healthcare workers who rely on personal networks to seek help and advice are at a disadvantage if these networks are weak. Healthcare workers' use of their personal resources can also lead to problems for patients and can benefit some patients more than others. For instance, when healthcare workers store and share patient information on their personal phones, the confidentiality of this information may be broken. In addition, healthcare workers may decide to use their personal resources on some types of patients, but not others. Healthcare workers sometimes describe using their personal phones and their personal time and networks to help patients and clients whom they assess as being particularly in need. These decisions are likely to reflect their own values and ideas, for instance about social equity and patient 'worthiness'. But these may not necessarily reflect the goals, ideals and regulations of the formal healthcare system. Finally, informal mobile phone use plugs gaps in the system but can also weaken the system. The storing and sharing of information on personal phones and through informal channels can represent a 'shadow IT' (information technology) system where information about patient flow, logistics, etc., is not recorded in the formal system. Healthcare workers may also be more distracted at work, for instance, by calls from colleagues and family members or by social media use. Such challenges may be particularly difficult for weak healthcare systems. By finding their own informal solutions to workplace challenges, healthcare workers can be more efficient and more responsive to the needs of patients, colleagues and themselves. But these solutions also have several drawbacks. Efforts to strengthen formal health systems should consider how to retain the benefits of informal solutions and reduce their negative effects.

Glenton C ，Paulsen E ，Agarwal S ，Gopinathan U ，Johansen M ，Kyaddondo D ，Munabi-Babigumira S ，Nabukenya J ，Nakityo I ，Namaganda R ，Namitala J ，Neumark T ，Nsangi A ，Pakenham-Walsh NM ，Rashidian A ，Royston G ，Sewankambo N ，Tamrat T ，Lewin S ... -  《Cochrane Database of Systematic Reviews》

Qualitative evidence synthesis informing our understanding of people's perceptions and experiences of targeted digital communication.

Health communication is an area where changing technologies, particularly digital technologies, have a growing role to play in delivering and exchanging health information between individuals, communities, health systems, and governments.[1] Such innovation has the potential to strengthen health systems and services, with substantial investments in digital health already taking place, particularly in low‐ and middle‐income countries. Communication using mobile phones is an important way of contacting individual people and the public more generally to deliver and exchange health information. Such technologies are used increasingly in this capacity, but poor planning and short‐term projects may be limiting their potential for health improvement. The assumption that mobile devices will solve problems that other forms of communication have not is also prevalent. In this context, understanding people's views and experiences may lead to firmer knowledge on which to build better programs. A qualitative evidence synthesis by Heather Ames and colleagues on clients' perceptions and experiences of targeted digital communication focuses on a particular type of messaging – targeted messages from health services delivered to particular group(s) via mobile devices, in this case looking at communicating with pregnant women and parents of young children, and with adults and teenagers about sexual health and family planning.[2] These areas of reproductive, maternal, newborn, child, and adolescent health (RMNCAH) are where important gains have been made worldwide, but there remains room for improvement. Ames and colleagues sought to examine and understand people's perceptions and experiences of using digital targeted client communication. This might include communication in different formats and with a range of purposes related to RMNCAH – for example, receiving text message reminders to take medicines (e.g. HIV medicines) or go to appointments (such as childhood vaccination appointments), or phone calls offering information or education (such as about breastfeeding or childhood illnesses), support (e.g. providing encouragement to change behaviours) or advice (such as advising about local healthcare services). These communication strategies have the potential to improve health outcomes by communicating with people or by supporting behaviour change. However, changing people's health behaviours to a significant and meaningful degree is notoriously challenging and seldom very effective across the board. There are a multitude of systematic reviews of interventions aiming to change behaviours of both patients and providers, with the overall objective of improving health outcomes – many of which show little or no average effects across groups of people.[3] This evidence synthesis is therefore important as it may help to understand why communicating with people around their health might (or might not) change behaviours and improve consequent health outcomes. By examining the experiences and perspectives of those receiving the interventions, this qualitative evidence synthesis allows us to better understand the interventions' acceptability and usefulness, barriers to their uptake, and factors to be considered when planning implementation. The synthesis looked at 35 studies from countries around the world, focussing on communication related to RMNCAH. Of the 35 studies, 16 were from high‐income countries, mainly the United States, and 19 were from low‐ or middle‐income countries, mainly African countries. Many of the studies presented hypothetical scenarios. The findings from the synthesis are mixed and give us a more nuanced picture of the role of targeted digital communication. People receiving targeted digital communications from health services often liked and valued these contacts, feeling supported and connected by them. However, some also reported problems with the use of these technologies, which may represent barriers to their use. These included practical or technical barriers like poor network or Internet access, as well as cost, language, technical literacy, reading or issues around confidentiality, especially where personal health conditions were involved. Access to mobile phones may also be a barrier, particularly for women and adolescents who may have to share or borrow a phone or who have access controlled by others. In such situations it may be difficult to receive communications or to maintain privacy of content. The synthesis also shows that people's experiences of these interventions are influenced by factors such as the timing of messages, their frequency and content, and their trust in the sender. Identifying key features of such communications by the people who use them might therefore help to inform future choices about how and when such messaging is used. The authors used their knowledge from 25 separate findings to list ten implications for practice. This section of the review is hugely valuable, making a practical contribution to assist governments and public health agencies wishing to develop or improve their delivery of digital health. The implications serve as a list of points to consider, including issues of access (seven different aspects are considered), privacy and confidentiality, reliability, credibility and trust, and responsiveness to the needs and preferences of users. In this way, qualitative evidence is building a picture of how to better communicate with people about health. For example, an earlier 2017 Cochrane qualitative evidence synthesis by Ames, Glenton and Lewin on parents' and informal caregivers' views and experiences of communication about routine childhood vaccination provides ample evidence that may help program managers to deliver or plan communication interventions in ways that are responsive to and acceptable to parents.[4] The qualitative synthesis method, therefore, puts a spotlight on how people's experiences of health and health care in the context of their lives may lead to the design of better interventions, as well as to experimental studies which take more account of the diversity that exists in people's attitudes and decision‐making experiences.[5] In the case of this qualitative evidence synthesis by Ames and colleagues, the method pulled together a substantial body of research (35 data‐rich studies were sampled from 48 studies identified, with the high‐to‐moderate confidence in the evidence for 13 of the synthesized findings). The evidence from this review can inform the development of interventions, and the design of trials and their implementation. While waiting for such new trials or trial evidence on effects to emerge, decision‐makers can build their programs on the highly informative base developed by this review. This qualitative evidence synthesis, alongside other reviews, has informed development by the World Health Organization of its first guideline for using digital technologies for health systems strengthening,[1, 6] part of a comprehensive program of work to better understand and support implementation of such new technologies.

Ryan R ，Hill S 《Cochrane Database of Systematic Reviews》

Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses. Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS. Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments. Level III, diagnostic study.

Lee CC ，Chen CW ，Yen HK ，Lin YP ，Lai CY ，Wang JL ，Groot OQ ，Janssen SJ ，Schwab JH ，Hsu FM ，Lin WH ... -  《-》