Physical symptoms, psychological outcomes, quality of life, COVID-19 related attitudes, and behaviors were measured. The differences in the outcomes between participants who had COVID-19 social contacts and those who had not were analyzed. The survey included 1,447 non-infected eligible participants. Among those, 173 (12.0%) reported at least one confirmed/suspected case in their social contacts. In the multiple regression adjusted for demographic data, the presence of confirmed/suspected infection cases in one’s social contacts was significantly associated with poorer physical and mental outcomes, lower health-related quality of life, and different COVID-19 related attitudes and behaviors (p<0.05). In conclusion, people who had social contacts with COVID-19 patients were at risk of adverse health outcomes. Future studies are needed to understand the long-term impacts. Similarly, strategies to improve health outcomes for these people are needed.The novel coronavirus (COVID-19) infection has spread worldwide since late 2019. People who have social contacts with COVID-19 patients might be at higher risk of physical or mental health problems. This study investigated whether people who had social contacts with COVID-19 patients would have poorer physical or mental outcomes, and different attitudes and behaviors. Chinese adults were recruited to fill in an online survey using snowball sampling during 21 The novel coronavirus COVID-19) infection has spread worldwide since first detected in Wuhan, China in December 2019 9 infecti. TherefoPrevious studies had shown that healthcare workers and people with pre-existing chronic diseases were at higher risk of health problems when exposed to COVID-19 –9. We hyThis study aimed to fill the gap and determine whether people who had social contacts with COVID-19 patients had poorer physical and mental health and lower quality of life. We hypothesized that people who had social contacts with COVID-19 patients are more prone to develop psychological and physical symptoms. This knowledge will help determine the allocation of scarce medical resources to these high-risk individuals. Essential factors to adapt to the COVID-19 epidemic, such as lifestyles , social support, and self-efficacy, were also measured . This stThis study was approved by the Survey and Behavioural Research Ethics Committee of the Chinese University of Hong Kong. The study was also registered in the World Health Organization recognized registry (ChiCTR2000030223).www.wjx.cn)) and posters to their friends and family members. Participants voluntarily filled in the survey after reading the informed consent shown at the beginning of the survey.Participants were recruited to fill in the online survey between February 21–26, 2020. Participants were eligible if they were Chinese and aged over 18 years. Participants were excluded if they were infected by COVID-19. Potential participants were recruited using snowball sampling via WeChat, one of the most popular mobile applications (Apps) for communications in China. The participants were encouraged to pass on the survey link . Minor amendments were made according to the comments received. The questionnaire can be found under, Social contacts with suspected or confirmed COVID-19 patients  was self-reported. Other demographic data, including age, sex, marital status, work, educational level, self-reported household income level according to the local economy, and residential location, were also collected.Depressive, anxiety and post-traumatic stress disorder (PTSD) symptoms were assessed using the validated Chinese versions of the two-item Patient Health Questionnaire (PHQ-2), the two-item Generalized Anxiety Disorder Questionnaire (GAD-2), and two of the items from the Clinician-Administered PTSD Scale (recurrent dreams and avoidance about the COVID-19 epidemic), respectively –18; ScorPhysical health was assessed by the number of COVID-19 related symptoms  in the previous four weeks. Furthermore, the participants rated their overall health ranging from 1 (poor) to 5 (excellent). Similarly, life satisfaction was rated on a 7-point Likert scale ranging from 1 (very dissatisfied) to 7 (very satisfied) in response to the question “Are you satisfied with your life?”. Health-related quality of life (HRQOL) was measured by the validated Chinese version of the European quality of life 5-dimension (EQ5D-5L) Questionnaire, which also included a visual analog scale (EQ-VAS). Higher scores signified higher HRQOL .As participants who had social contacts with COVID-19 patients may have different perceptions of the COVID-19 epidemic and different daily lives, the questionnaire also asked about (i) their perceptions such as their perceived risk of being infected and perceived time needed to control the epidemic; (ii) impact on their daily lives such as expenditure for personal infection prevention  and health-seeking behavior ; and (iii) their loneliness level, which was measured by the three-item Chinese validated UCLA Loneliness Scale (UCLA-3) (score range 3–9); higher scores represent higher levels of loneliness .Covariates to health outcomes such as hours of moderate/vigorous exercise, hours of sedentary time, diet, screen time, social support, and self-efficacy were also asked. Their diet was measured by the number of days of having different kinds of food in the past week . Self-efQuality control was conducted by reliability and logic check as well as a minimal responding time. To check for reliability of answers, the survey had set two repeated questions at the beginning and the end of the survey in a different order. Participants were expected to provide the same or similar answers (score difference no more than one point). Only those surveys that passed the logic checks  and at least ≥250 seconds was spent to finish the entire survey, were considered valid.χ2) and two-sample t-test were used to compare the differences between those participants who had social contacts with COVID-19 patients and those who had not for categorical data and continuous data, respectively. Statistically significant outcomes, as defined by p<0.05, were included in the linear regression model or ordinal logistic regression model. The models were adjusted for age, gender, marital status, education, income, and employment. The strength of associations between COVID-19 patients' presence in one’s social circle and variables of interest were estimated by the adjusted odds ratio (OR) or adjusted coefficient together with their corresponding 95% CIs. As the program did not allow any empty answers, there was no missing data. Sample size calculation was based on the method that at least 10 participants to be included for one independent variable with interests in regression models. All data were analyzed using Stata version 13.1 .The baseline characteristics were presented using frequency (and percentage) together with their 95% confidence intervals (CIs) and mean (and standard deviation (SD) for categorical and continuous variables, respectively. Chi-square test , married (59.5%), employed (69.5%), and obtained a bachelor’s degree or above (72.5%). However, only 110 participants (7.6%) lived in Hubei province. Among the eligible participants, 12% had social contacts with COVID-19 patients  .In the univariate analysis, participants who had social contacts with COVID-19 patients were more likely to have more depressive symptoms, anxiety symptoms, suicidal ideation/thoughts, PTSD symptoms, somatic symptoms, poorer meaning in life, loneliness, lower HRQOL, more COVID-19 related symptoms, and lower life satisfaction . After cp = 0.009), higher perceived risk of being infected , and longer perceived time for successful epidemic control . Also, they had different health behaviors. For example, they paid more to prevent infection  and more likely to have visits to a doctor in the past four weeks . Finally, they reported lower perceived self-efficacy  , and going out distance) between the participants with or without COVID-19 patients in their social circle .The current study conducted in mainland China suggests that Chinese people who had social contacts with COVID-19 patients are at an increased risk of having adverse physical and mental outcomes. Furthermore, people who had social contacts with COVID-19 patients had more pessimistic perceptions , reduced self-efficacy, and had increased health utilization . These results highlighted the importance of allocating health resources to this high-risk group.Despite the lack of similar studies , 14, ourThe stress or “fight-or-flight” response theory may explain the results , 29. FacThe current study was one of the first to comprehensively assess the pandemic's adverse effects on people who had social contacts with COVID-19 patients. The use of validated questionnaires will also allow for easy comparison with other studies. Quality control was utilized to exclude unreliable answers and responses. This study will contribute to understanding the impacts of COVID-19 in the general population and has identified a high-risk group for targeted screening and interventions.Yet, some limitations of this study should be discussed. First, the current study used snowball sampling strategies and had recruited participants whose demographics might not represent the general population . However, the strict social distancing policy during COVID-19 had made other sampling methods infeasible. Second, the cross-sectional nature of the current study may limit the conclusion about causal effects in some scenarios. For example, although it is most likely that having social contacts with COVID-19 patients led to elevated anxiety levels, it is also possible that anxious people defined social contact  more loosely and subjectively felt a close proximity to the disease or COVID-19 patients. Third, the current cross-sectional design prevented the conclusion about the stability of these physical/mental symptoms and their long-term impacts.As the participants who had social contacts with COVID-19 patients had poorer physical and mental health outcomes, proactive strategies to screen and treat diseases should target this high-risk population. During the COVID-19 outbreaks, the delivery of healthcare services was hindered. New delivery methods are being developed, e.g., online interventions and telemedicine, although these are not targeted to people in social contacts with COVID-19 patients , 32. TheFuture studies are suggested to develop strategies to screen for/treat physical and mental illnesses in this high-risk group. Future studies of longer duration  are also suggested to detect the long-term effect on physical and psychological health. It is currently not clear whether these adverse effects will be resolved after the patients recovered or are cured.In summary, the current study found that people with confirmed or suspected COVID-19 patients in their social circle were at risk of having adverse health outcomes, including more physical, mental disease symptoms, and a lower HRQOL. Studies with longer duration are needed to understand the long-term impacts. Similarly, strategies to improve health outcomes for these people are needed.S1 TableP<0.05.Social support: When you need help, can you count on someone willing and able to meet your needs. Chi-square test (χ2) and two-sample t-test were used for categorical data and continuous data respectively. *(DOCX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.S2 File(SAV)Click here for additional data file.


Stroke causes death, disability and increases the use of healthcare resources worldwide. The outcome of intravenous thrombolysis and mechanical endovascular thrombectomy highly depends on the delay from symptom onset to initiation of definitive treatment. The purpose of this study was to compare the various patient transportation strategies to minimize pre-hospital delays.Emergency medical services (EMS) mission locations and ambulance response times in Finland with urgent stroke-suspected dispatch codes were collected from Emergency Response Centre (ERC) records between 1 January 2016 and 31 December 2016. Four transport scenarios were simulated for each mission, comparing ground and helicopter transportation to hospital with different treatment capabilities.In 2016, a total of 20,513 urgent stroke-suspected missions occurred in Finland. Of these, we were able to locate and calculate a route to scenario-based hospitals in 98.7%  of the missions.For ground transport, the estimated median pre-hospital time to a thrombolysis-capable and thrombectomy-capable hospital were 54.5 min , 31.7–111.4) and 94.4 min , respectively. Should patients be transported on the ground to thrombectomy-capable hospitals only, the pre-hospital time would increase in 11,003 (54.4%) of missions, most of which were in rural areas.With the fastest possible transportation method, the estimated mean transport time to a thrombectomy-capable hospital was 80.84 min . Helicopter transportation was the fastest method in 68.8%  of missions, and the time saved was greater than 30 min in 27.1% (5475) of missions. In rural areas, helicopter transportation was the fastest option in nearly all missions if dispatched simultaneously with ground ambulance.Helicopter transportation may significantly decrease pre-hospital delays for stroke patients, especially in rural areas, but the selection of an optimal transportation method or chain of methods should be determined case-by-case. Acute ischemic stroke is the second most common cause of death worldwide  and resuIntravenous thrombolysis with tissue plasminogen activator (tPA) within 4.5 h after symptom onset is standard care for acute ischemic stroke. Recently, several randomized control trials also demonstrated the efficacy and safety of intra-arterial mechanical treatment –7. In 20In most countries with organized acute healthcare services, equity in access to healthcare services is valued . SeveralHelicopter emergency medical services (HEMS) has been shown to effectively shorten pre-hospital times in certain patient groups , althougThis study aimed to 1) compare strategies for stroke patient transportation to determine an optimal strategy to minimize pre-hospital time and 2) report predicted interregional and urban-rural variations in delays in access to thrombolysis- or thrombectomy-capable hospitals. The study focuses on operational and logistics issues; thus, no economical evaluation is performed.The Finnish Emergency Response Centre (ERC) is a state agency that is responsible for handling calls made to the public emergency number, 112. The ERC handles requests for EMS, police, fire and rescue, and emergency social services. Currently, there are six national ERCs to where 112 calls are routed based on the callers’ geographic locations.In EMS-related calls, dispatchers use a computer-aided and criteria-based risk assessment system to classify missions to one of four urgency classes  and 83 symptom and findings-based or injury mechanism-based classes. Dispatchers do not attempt to identify the patient, nor do they have access to electronic patient records. Legally, the caller or victim is not a patient until he or she is reached by an EMS unit. Every call is registered in ERC records.n = 5) have thrombectomy capability. Currently, the suspected-stoke criteria-based risk assessment tool used in ERCs is not designed or able to identify potential thrombolysis or thrombectomy candidates, and therefore, missions are mostly over-triaged [EMS mission data between 1 January 2016 and 31 December 2016 with urgent suspected-stroke dispatch codes were collected from ERC records. In this study, we analysed only the mission and urgency class code, mission location, and response time of the first ambulance unit. The patients were not identified, nor were patients’ hospital or EMS records accessed. The incident location and ground ambulance response time data were combined with the estimated transportation time to determine the time delay from an emergency call to patient arrival to the nearest hospital with stroke thrombolysis or thrombectomy capabilities. In Finland, only university hospitals  and thrombolysis capability Direct ground ambulance transportation to the nearest hospital with thrombectomy capability Helicopter transportation to the nearest university hospital with simultaneous ground EMS dispatchHelicopter transportation to the nearest university hospital with HEMS dispatched by EMS after primary evaluationWe estimated the pre-hospital time for four scenarios for each mission:The catchment area for each hospital was determined by calculating a convex hull around a set of mission locations with the fastest road access to the same hospital. In this study, catchment areas did not align with administrative areas.The area type for a mission scene was determined for each mission using the existing area classification method. Missions were divided into four groups by their area type: core urban, other urban, dispersed settlement, and other rural .The open source GraphHopper  routing The results were reported as median times with a 95% confidence interval (CI). To support operative decision making with thrombectomy candidates, the effect of the estimated ground transportation time on the optimal transportation method was also reported.In 2016, ERCs dispatched 20,513 urgent stroke missions. We were able to locate and calculate the routes to the hospitals in 20,240 (98.7%) missions, which were included in the study.With ground transportation only, the estimated median pre-hospital time to thrombolysis-capable and thrombectomy-capable hospitals was 54.5 min  and 94.4 min , respectively.If the patients were transported only to thrombectomy-capable hospitals, the pre-hospital time would increase in 11,003 (54.4%) missions. The median increase was 66.8 min . In rural areas, the pre-hospital time would increase in roughly 70% of the missions  of the missions, with a median time benefit of 23.7 min , if HEMS was dispatched simultaneously with ground EMS , the estimated mean time to a thrombectomy-capable hospital was 80.84 min . HEMS transportation was the fastest method in 68.8% 13,921) of missions, and the time saved was greater than 30 min in 27.1% (5475) of missions  may offer faster diagnosis and treatment initiation for certain patient groups. However, the outcomes are dependent on out-of-hospital and in-hospital processes and transportation delays.The analysis suggests that the balance between optimal transportation methods is somewhat labile. The effect of relatively small changes in assumptions, driving times, or unit locations on mission onset may change the optimum transportation method and the optimum receiving hospital. The time to transport a patient from a hospital helipad to an emergency room (ER) may negate the time benefit from faster helicopter transportation. The options are often dichotomous and/or limiting and choices in one mission might alter the available choices in simultaneous missions or the next mission in the same area. Machine-learning systems might offer a reasonable method to estimate the outcomes of different options and support decision-making in real-time.To use methods presented in this study to support decision-making in everyday practice, both pre-hospital and in-hospital delays in every phase of the logistic chain should be studied with comprehensive and accurate data collection. An economic evaluation should be performed to further evaluate costs and benefits of different transport strategies. Due to constantly changing unit locations, weather or road conditions, pre-calculated response time maps, and plans may lead to the selection of non-optimal transportation methods. A real-time decision support system utilizing current unit location, weather, and traffic data would be beneficial for decision-making during individual missions.Helicopter transportation may significantly decrease stroke patients’ pre-hospital time, especially in rural areas, but the selection of an optimal transportation method or chain of methods should be determined case-by-case.Additional file 1. A thematic map showing the distribution of stroke-suspected missions in 2016 in Finland with the fastest estimated transportation method indicated.


Through a comprehensive review of more than 2400 published papers, Nc2Eye catalogs 7088 manually curated ncRNA-ED associations involving 4363 ncRNAs across eight species. We also provide detailed descriptions and annotation information for each ncRNA-disease association such as ncRNA categories, experimental methods, expression pattern and related clinical drugs. To further expand the pathogenic ncRNAs, we also collected more than 90 high-throughput EDs-related transcriptome datasets. Furthermore, a user-friendly interface was constructed for convenient and flexible data browsing, querying, and retrieving. We believe that Nc2Eye is a timely and valuable knowledgebase for significantly improving and useful for discovery of new diagnostic and therapeutic biomarkers.Eye diseases (EDs) represent a group of disorders affecting the visual system, most of which can lead to visual impairment and blindness. Accumulating evidence reveals that non-coding RNAs (ncRNAs) are closely associated with a wide variety of EDs. However, abundant associations between ncRNAs and EDs are scattered across the published literature, obstructing a global view of ncRNA-ED associations. A public resource of high-quality manually curated ncRNAomics knowledge associated with EDs remains unavailable. To address this gap, we thus developed Nc2Eye ( Eye diseases (EDs) represent a group of disorders affecting the visual system. It was estimated that 217 million people (3.1% of the world population) suffered from moderate to severe visual disturbance in the world in 2015, while 36 million were blind . There aPVT1 is up-regulated in RB tissues and silencing PVT1 can suppress the tumor growth , such as microRNA (miRNA), long non-codingRNA (lncRNA) and circlar RNA (circRNA), are a large category of functional RNA molecules involved in regulating many biological processes . Numerour growth . Overexpactivity . miR-27all lines  and Sun With the progression of disease-related ncRNA study, some disease-associated ncRNA resources were constructed, such as LncRNADisease , miR2DisTo fill the gap, we constructed Nc2Eye, a manually curated database, to provide experimentally validated ncRNA-ED associations. The current version of Nc2Eye contains 7088 associations between 104 EDs and 4363 ncRNAs in eight species, through a comprehensive review of more than 2400 published papers. To investigate the underlying molecular mechanism of EDs, Nc2Eye gathered more than 90 high-throughput transcriptome datasets in EDs, and normal controls. In addition, Nc2Eye provided a user-friendly interface for convenient and flexible data browsing, querying, retrieving and submitting. Nc2Eye is a timely and valuable resource to significantly improve our understanding of ncRNA dysfunction in EDs.To ensure the accuracy and reliability in the data collection process, we manually curated all Nc2Eye entries by the following steps. First, we searched the PubMed database  with theAfter comprehensively reviewing more than 2400 published papers, a total of 7088 associations between 104 EDs and 4363 ncRNAs in eight species were manually collected. There were 132 of these ncRNAs were reported to be related with drugs in 14 EDs. Distribution of ncRNA-associated entries in each species and ncRNA category is listed in A great number of studies based on high-throughput experiments have emerged in recent years . Mining http://nc2eye.bio-data.cn/.Finally, all data in Nc2Eye were organized using MySQL (version 5.7.26). The web interface was built in PHP and the web services were deployed using Apache (version 2.4.6). The Nc2Eye database is freely available at Nc2Eye constructed a user-friendly web interface for convenient and flexible data browsing, querying, retrieving and submitting . In the Eye diseases are a common group of disorders in the visual system, causing visual impairment and blindness. Emerging evidence demonstrated that the dysregulation of ncRNAs played critical roles in various EDs. We developed Nc2Eye, a curated knowledgebase of experimentally validated ncRNAs associated with EDs, which provides a global landscape of ncRNAs in EDs.hsa-miR-155 has the most connection with diseases and it is associated with 16 diseases, such as DR and RB, indicating the importance of this miRNA in ophthalmopathy. In addition, we tracked the number of ncRNA-ED publications each year (We could find some important messages behind the large and complex data resources by analyzing the data from Nc2Eye. Top10 diseases with the most ncRNA associations were listed . Diabetiach year  and founIn conclusion, Nc2Eye, an eye disorders-specialized database, provides global insights into ncRNA functions in various EDs. We believe that Nc2Eye will be beneficial to researchers to dissect the underlying mechanism of ophthalmopathy. In the future, a confidence score system will be developed to estimate the reliability of a specific ncRNA-ED association according to experimental evidence. In addition, we plan to update the database every 3 months to extend newly ED-related relationships and make it more powerful.http://nc2eye.bio-data.cn/download.html.Publicly available datasets were analyzed in this study. This data can be found here: HC and LX conceived and designed the experiments. YZ, ZX, and FY collected and analyzed the data. FG designed the website. YZ and LX wrote and revised the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This cross-sectional study compares use of low-value care among individuals enrolled in traditional Medicare and those enrolled in Medicare Advantage and examines trends in use from 2006 to 2015. Were there differences in use of low-value care between individuals enrolled in traditional fee-for-service Medicare (TM) and those enrolled in Medicare Advantage (MA) from 2006 to 2015?In this cross-sectional study of 11 677 individuals enrolled in TM and 5164 individuals enrolled in MA, there were no significant differences between individuals enrolled in TM and those enrolled in MA in most measures of use of low-value care. There was no significant decrease in most measures of use of low-value care among individuals enrolled in TM or MA over time.Use of low-value care is prevalent in TM and MA, suggesting that managed care enrollment is not associated with decreased use of low-value care. Decreasing use of low-value care is a major goal for Medicare given the potential to decrease costs and harms. Compared with traditional fee-for-service Medicare (TM), Medicare Advantage (MA) is more strongly financially incentivized to decrease use of low-value care.To compare use of low-value care among individuals enrolled in TM and those enrolled in MA overall and to examine trends in use of low-value care in both programs from 2006 to 2015.This cross-sectional study analyzed individuals enrolled in TM and MA using data from the 2006 to 2015 Medical Expenditure Panel Survey. To account for differences in characteristics between individuals enrolled in TM and those enrolled in MA, a propensity score–based approach was used. Data were analyzed from August 2020 through January 2021.Being enrolled in MA or TM.Binary measures of use were collected for 13 low-value services in 4 categories  and 4 low-value composites corresponding to the categories .P = .02). For the NSAID use for hypertension, heart failure, or kidney disease metric, 807 of 7832 individuals enrolled in TM  received the care, and 447 of 3566 individuals enrolled in MA  received the care, for a rate of use that was significantly higher among individuals enrolled in MA, by 2.9 percentage points . Overall, there were no decreases in use of low-value care in TM or MA over time.Among 11 677 individuals enrolled in TM and 5164 individuals enrolled in MA, 9429 (56.0%) were women and the mean (SD) age was 74.5 (6.3) years. Of 13 low-value services and 4 low-value composites, statistically significant differences were found in 2 measures. For the low-value medication composite, 2054 of 11 636 eligible individuals enrolled in TM  received the care, and 981 of 5141 eligible individuals enrolled in MA  received the care, for a rate of use that was significantly higher among individuals enrolled in MA, by 2.2 percentage points (95% CI, 0.5-3.8 percentage points; This cross-sectional study found that use of low-value care was similarly prevalent in MA and TM, suggesting that MA enrollment was not associated with decreased provision of low-value care compared with TM. Thus, decreasing use of low-value care has potential for cost savings and harm reduction in the US health care system.Low-value care is defined as a service that provides little to no clinical benefit but incurs health care costs.10 A 2014 study2 found that among 26 low-value services in 6 categories , 24% to 41% of individuals enrolled in traditional fee-for-service Medicare (TM) received at least 1 low-value service in 2009. The rate of use of low-value care varied across services, ranging from 0.1% for electroencephalography for headache to 12.4% for imaging for low-risk low back pain. Among the 6 categories of low-value care, the most prevalent were imaging, which accounted for 43% of low-value care, and cancer screening, which accounted for 31% of low-value care.Use of low-value care is especially prevalent among older adults in the US.11 Understanding whether individuals enrolled in TM and those enrolled in MA had different rates of use of low-value care may be of high policy relevance because MA plans are financially incentivized to use lower-cost care through capitated payments. These plans can also implement network and prior authorization restrictions to control health care use. Furthermore, MA plans can offer supplemental benefits to address the needs of individuals with chronic illness.13 Indeed, MA tends to deliver care more efficiently than TM by increasing use of primary care visits15 and decreasing use of unnecessary care or intensity of care.20 However, to our knowledge, evidence on use of low-value care in MA is limited. A 2017 study5 examined use of low-value care in MA but was limited to using data from a single insurer. Evidence suggests that low-value services among individuals enrolled in MA varied widely in frequency and cost, with spending on low-value care in 2014 totaling $100.8 million for individuals enrolled in MA.5Although most individuals enrolled in Medicare receive care through TM, 36% of individuals enrolled in Medicare were enrolled in Medicare Advantage (MA), known as Medicare managed care, in 2020.1 have been launched to decrease use of low-value care. Evidence suggests that Choosing Wisely was associated with modest decreases in use of low-value care in TM and MA.22 Concurrently, new delivery models have been implemented to decrease use of unnecessary care. Evidence suggests that Medicare accountable care organization (ACO) programs were associated with decreases in use of low-value care, but the magnitude of the decreases was small.22 However, it remains unknown whether this trend occurred in both TM and MA.Another important policy question is how use of low-value care in TM and MA has changed over time. Since 2012, several initiatives, including Choosing Wisely,We addressed these knowledge gaps regarding use of low-value care in TM and MA. First, we compared use of low-value care between individuals enrolled in TM and those enrolled in MA. Second, we examined trends in use of low-value care in the programs from 2006 to 2015.STROBE) reporting guideline.This cross-sectional study was exempted from review and informed consent by the institutional review board of Drexel University because the data were deidentified and publicly available. Reporting followed the Strengthening the Reporting of Observational Studies in Epidemiology . The MEPS is a nationally representative survey of the US noninstitutionalized civilian population. This survey annually collects information on respondents’ demographic and socioeconomic characteristics, health status, and health care use. The data are collected in person via 3 rounds per year.23 and 202024 suggest differences in coding between MA and TM. However, the MEPS offers self-reported health status, which allowed us to control for a measure of health that is comparable across TM and MA populations.Specifically, we used the following 6 data sets from the MEPS: the full-year consolidated data files, longitudinal files, medical condition files, outpatient visit files, office-based medical clinic visit files, and prescribed medicine files. Compared with claims data, the MEPS has several strengths for comparing use of low-value care between individuals enrolled in TM and MA. First, the MEPS offers service-specific data for those enrolled in MA, which allowed us to identify these individuals’ use of low-value care. By comparison, claims data for this population is limited. Second, the MEPS provides comprehensive information on socioeconomic and health status, which are associated with rates of health services use but are imperfectly captured in claims data. Third, clinician coding patterns may be associated with differences in health risks as constructed from claims data; studies from 2014We included individuals enrolled in Medicare for the entire year sampled and excluded those who were younger than 65 years, were dual eligible for Medicare and Medicaid, and switched between TM and MA within the year. The MEPS asked respondents whether they enrolled in MA or TM in each round of data collection (conducted 3 times every year). We defined individuals as enrolled in TM if they were continuously enrolled in the program throughout the 3 rounds in the year, and we identified individuals as enrolled in MA if they were continuously enrolled in the program throughout the 3 rounds.25 and 201926 that identified low-value care using the MEPS, we identified 13 low-value services in 4 categories. Use of low-value care was measured as a binary outcome. For each measure,36 we identified individuals who were eligible for the measure  and then determined whether those individuals received the particular low-value service  (36Following studies from 2016merator) . We exclThe cancer composite included 3 low-value cancer screenings: cervical, colorectal, and prostate cancer screening in older adults. We used self-reports to identify use of cancer screening in a given year. Using longitudinal files, we excluded individuals diagnosed with each type of cancer in the prior year. The treatment composite had 2 low-value antibiotic use measures: antibiotic for acute upper respiratory infection and antibiotic for influenza. The medication composite had 5 measures for use of low-value medication: use of an anxiolytic, a sedative, or a hypnotic in an adult older than 65 years; use of benzodiazepine for depression; use of opioid for headache; use of opioid for back pain; and use of nonsteroidal anti-inflammatory drug (NSAID) for hypertension, heart failure, or chronic kidney disease. For measures in the treatment and medication composites, we first used the medical condition files to identify individuals eligible for each measure and then used the prescribed medicine files to identify use of specific medications or services as an indication for each condition. Use of prescription drugs was identified via drug names or therapeutic drug classes. We identified therapeutic drug classes for each prescription using the National Drug Code directory, generic names, and the Multum Lexicon therapeutic classification database (Cerner Multum). The imaging composite included 3 measures capturing use of a low-value imaging test: magnetic resonance imaging (MRI) or computed tomography (CT) for back pain, radiograph for back pain, and MRI or CT for headache. Using medical condition files, we identified individuals eligible for each measure. Then, we used outpatient visit files and office-based clinic visit files to identify use of imaging during visit. For each measure in the treatment, medication, and imaging composites, we excluded individuals with competing diagnoses or with some conditions associated with clinical red flags .25 and 2019,26 we identified individuals enrolled in Medicare eligible for each measure using the Clinical Classification Software (CCS) for the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM).37 This software collapses diagnosis and procedure codes from the ICD-9-CM and then aggregates the codes into clinically meaningful categories that group similar conditions.Following studies from 2016We included individual-level demographic, socioeconomic, and health status variables. These were age, sex, race/ethnicity, marital status, family income, US census region of residence, perceived health status, perceived mental health status, presence of any limitation, presence of cognitive limitation, presence of functional limitation, presence of social limitation, and 13 chronic conditions.40 found that individuals enrolled in MA are healthier than those enrolled in TM, making a direct comparison between the 2 groups potentially biased. To address selective enrollment, we used a propensity score–based approach. Following a 2020 study,14 we computed the inverse probability of treatment weighting (IPTW) as a propensity for enrolling in MA based on the variables described. Enrollment in MA has grown rapidly over the past decade,11 suggesting potential differences in the composition of TM and MA populations over time. To account for changes in these compositions over time, we estimated IPTW separately for 5 periods . We then determined whether IPTW-weighted samples were balanced on individual-level demographic, socioeconomic, and health status variables between TM and MA populations overall as well as within each period. We also determined whether the propensity score had an adequate overlap between TM and MA populations in each period.We compared unadjusted and adjusted sample characteristics between individuals enrolled in TM and those enrolled in MA. Studies from 2012 to 2019To estimate differences in use of low-value care between individuals enrolled in TM and those enrolled in MA, we performed logit estimation after controlling for all covariates described previously, as well as MA enrollment, periods , interaction terms associated with MA enrollment and periods, interaction terms associated with census regions and periods, and share of individuals enrolled in Medicare enrolled in MA plans by year and census region. We applied IPTW while conducting the analysis. From the regression results, we calculated the mean adjusted values of the outcomes for individuals enrolled in TM and MA while holding constant all other variables except the variable of interest, allowing us to compare the outcome of interest between individuals enrolled in TM and those enrolled in MA. We then examined the difference in these adjusted mean outcomes among individuals enrolled in MA compared with those enrolled in TM.We also examined trends in use of low-value care among individuals enrolled in TM and MA using 4 low-value care composite measures. We conducted the same analysis as described previously and estimated the adjusted mean outcomes for individuals enrolled in TM and MA in each period. Because changes in data collection may be associated with changes in trends, we compared the numbers for individuals eligible for each measure across 5 periods. We also examined whether the sample size varied by year in association with sample fluctuations.We conducted several sensitivity analyses to determine the robustness of our findings. First, because MEPS has smaller sample sizes than claims data do, which could bias toward the null, we conducted bootstrapping with 1000 iterations to measure robust CIs. Second, we conducted a heterogeneity analysis to identify whether prevalence of low-value care varied by socioeconomic and health status. We performed the analysis stratified by race/ethnicity, family income, and perceived health status.P values were from 2-sided tests, and results were deemed significant at P < .05. Study data were analyzed using Stata statistical software version 16.1 (StataCorp) from August 2020 through January 2021.For all analyses, we clustered standard errors within individuals because some individuals were included in the data over the course of multiple years. We used survey weights to adjust sample characteristics to be representative of the Medicare population. All Among 11 677 individuals enrolled in TM and 5164 individuals enrolled in MA, mean [SD] age was 74.5 [6.3] years and 9429 (56.0%) were women . While tP = .02). For NSAID use for hypertension, heart failure, or kidney disease, 807 of 7832 eligible individuals enrolled in TM  received the care, and 447 of 3566 eligible individuals enrolled in MA  received the care, for a rate of use that was statistically significantly higher among individuals enrolled in MA, by 2.9 percentage points . For other outcomes, use of low-value service was higher among individuals enrolled in MA than those enrolled in TM except for colorectal cancer screening, but no difference was statistically significant.Our IPTW-adjusted analyses found no significant differences between individuals enrolled in TM and those enrolled in MA in use of most of the measures of low-value care . StatistOdds ratios (ORs) from logit estimation of use of low-value care between individuals enrolled in TM and those enrolled in MA are presented in eTable 3 in the P = .01) (P = .01), but the trend reversed, with statistically significantly higher rates among individuals enrolled in TM in 2012 to 2013  and 2014 to 2015 . The numbers of individuals eligible for each measure were consistent across the 5 periods  , but theThis cross-sectional study had 2 key findings on use of low-value care among individuals enrolled in TM and MA. First, few differences were found overall in use of low-value care between TM and MA populations. Statistically significant differences were found in the low-value medication composite and the NSAID use in hypertension, heart failure, or kidney disease measure. Second, there was little evidence for decreases in use of low-value care in TM and MA over time, with a decrease found in 1 measure.20 which found that MA provided lower rates of unnecessary care and intensive care than TM. There are several potential explanations for our findings. First, a 2017 study41 found that a large share of low-value care was low cost or very low cost; MA plans may not be motivated to reduce low-cost services given that the potential for cost saving from low-cost services may be modest. Second, MA plans may promote targeted investment in areas that are associated with quality score or payment levels, but low-value care metrics may not be targeted as part of these scores or payments. Third, there may still be misaligned incentives for clinicians in MA plans to decrease provision of low-value care. Some clinicians serving individuals enrolled in MA are still operating under volume-based incentives, which are associated with less leverage in decreasing low-value care. Use of low-value services in our study was higher among individuals enrolled in MA compared with those enrolled in TM in almost all services, although the differences were statistically insignificant for most services. These findings suggest the need to reconsider the design of financial incentives in the TM and MA programs.We found that use of low-value care in MA was as prevalent as in TM during the study period, suggesting that the structure of MA may not be associated with decreases in use of low-value care. These findings may appear to contradict results of studies from 2017 to 2020,25 This may suggest that recent efforts aimed at decreasing use of low-value care have a limited role and are associated with a limited outcome. However, 2 other findings in our study were notable. First, decreasing rates in the low-value cancer screening composite measure in both programs were of interest. This finding may suggest that it takes time to observe significant changes, owing to low participation rates in early years and time lags in changes in practice patterns. Second, individuals enrolled in MA had a higher likelihood of having a low-value imaging composite use compared with individuals enrolled in TM in 2006 to 2007, but low-value imaging composite rates remained lower among those enrolled in MA in later years. This may suggest that decreases in use of some low-value services are partly associated with MA’s more recent efforts to decrease low-value imaging services that are costly.2Our null findings for decreases in use of low-value care in TM or MA over time are consistent with the results of a 2016 study.ICD-9-CM diagnosis and procedure codes, and thus we could not precisely identify individuals with competing diagnoses or exclude all conditions associated with clinical red flags. Our identification of low-value service use might thus have included some inappropriate individuals and conditions. Second, there may be selection into MA. Although we accounted for differences in sample characteristics between individuals enrolled in TM and those enrolled in MA, unobserved differences in patient factors may have remained. Third, our measure of cancer screening was self-reported and thus may be subject to reporting bias. Fourth, the MEPS reports information on medical conditions, but there may be reporting bias because individuals were not necessarily required to self-report all information and the MEPS does not verify self-reported information. Fifth, we detected statistically insignificant differences in use of some low-value care between TM and MA populations, but this might be associated with the small sample size.This study has several limitations. First, we could not measure all potentially relevant exclusions when identifying the use of low-value care. The MEPS reports health conditions based on CCS or 3-digit This cross-sectional study found that there were no significant differences in most measures of use of low-value care between individuals enrolled in TM and those enrolled in MA from 2006 to 2015. These findings suggest that neither the current structure of the TM and MA programs nor recent efforts to decrease use of low-value care have been associated with significant outcomes.


Sirs:An 84-year-old male patient with known ischemic cardiomyopathy was admitted to the emergency department of Leipzig University Hospital with typical signs and symptoms of an acute coronary syndrome in the midst of the SARS-Cov2 pandemic on April 22, 2020. His angina rapidly resolved after angioplasty of a sub-total proximal occlusion of his right coronary artery. However, 12 h later his 81-year-old wife was brought to a secondary care hospital by the emergency doctor because of acute and severe shortness of breath and typical angina. Elevated Troponin, ECG- and echocardiographic abnormalities led to her urgent transfer to our hospital with the suspected diagnosis of acute myocardial infarction .Her medical history included chronic renal insufficiency and hypertension. Otherwise, the patient was previously well, mobile and in very good general condition. Clinical examination showed an obese woman . The laboratory findings are presented in Table The 12-lead-ECG at admission displayed a sinus rhythm with a previously known right bundle branch block and new negative T-waves in II, III, aVF and V3-6 , as shown in Fig. Cardiac MR Fig.  showed wIn summary, the findings fulfill the criteria of a Takotsubo (stress) cardiomyopathy .Focused evaluation of potential emotional or physical triggers revealed that the patient suffered from psychologic stress during the last few weeks. Due to media reports, she was highly afraid of SARS-CoV2 infections for her husband and herself, because age and hypertension were communicated as high risk for a severe course of COVID-19. Furthermore, she felt severely affected by the lockdown measures. Her condition acutely and severely exacerbated when her husband was admitted to the hospital, which she felt would place him at a high risk of death, and because she was not allowed to visit him due to the SARS-CoV2-specific regulations.The patient was provided with psychological support and the SARS-CoV2-negative couple was allowed to meet. Heart failure therapy including an angiotensin receptor blocker, beta blocker, mineraloreceptor antagonist and a loop diuretic was initiated and the symptoms of the patient improved. She was discharged home in stabilized condition.Two months later, the patient presented to the outpatient clinic. She was asymptomatic and the negative T-waves in II, III and aVF had disappeared Fig. . EchocarTakotsubo (stress) cardiomyopathy (TTS) predominantly affects women. Approximately one-third of the cases are caused by emotional triggers and one-third by physical stress. Typical symptoms are angina pectoris or dyspnea; therefore, myocardial infarction is the most relevant differential diagnosis. Hypo- or akinesis of the apical and mid left-ventricular segments is characteristic. Additional common findings include transient ST-elevations or negative T-waves in ECG and increased Troponin. Increased catecholamine concentrations likely contribute to the pathophysiology that is still incompletely understood , 2. PatiThis case is informative with regard to patient’s perceptions and an example for a collateral medical damage during the SARS-CoV2-pandemic. Collateral damages in this context range from patients avoiding contact to medical professionals despite symptoms, delayed or false diagnoses (because of changes in previously standardized processes) to indirect effects causing psychological and physical diseases –6. The f


Social contact, including remote contact , could help reduce social inequalities in depressive symptoms and loneliness among older adults.Data were from the 8th wave of the English Longitudinal Study of Aging (2016/17), stratified by age . Inverse probability weighting was used to estimate average effects of weekly in-person and remote social contact on depressive symptoms  and two measures of loneliness . We also estimated controlled direct effects of education, partner status, and wealth on loneliness and depressive symptoms under two scenarios: 1) universal infrequent (<weekly) in-person social contact; and 2) universal weekly remote social contact.Weekly in-person social contact was associated on average with reduced odds of loneliness, but associations with remote social contact were weak. Lower education raised odds of depressive symptoms and loneliness, but differences were attenuated with infrequent in-person contact. Respondents living alone experienced more depressive symptoms and loneliness than those living with a partner, and less wealth was associated with more depressive symptoms. With universal infrequent in-person contact, these differences narrowed among those aged under 65 but widened among those aged 65+. Universal weekly remote contact had relatively little impact on inequalities.Reduced in-person social contact may increase depressive symptoms and loneliness among older adults, especially for those aged 65+ who live alone. Reliance on remote social contact seems unlikely to compensate for social inequalities. •In-person but not remote contact associated with less depression/loneliness.•In-person contact < weekly reduces educational inequalities in depression/loneliness.•In those aged 65+ partner/wealth inequalities widen with <weekly in-person contact.•Weekly remote contact has little impact on inequalities in depression/loneliness. Social contact may be in-person or remote (i.e. via phone/internet etc.), but is distinct from loneliness, which is a perceived feeling of social isolation . One canThe nature of social contact continues to change in response to technological developments in digital communications but impacts of such advancements will not necessarily be felt equally. Remote contact has become especially salient during the COVID-19 pandemic. Many countries have enacted ‘lockdown’ social mitigation measures, where reduced physical proximity to others is intended to slow the infection transmission rate, but such measures are likely to have psychological impacts . Identif1.21.What are the effects of in-person and remote social contact on loneliness and depressive symptoms?2.To what extent are inequalities in loneliness and depressive symptoms affected by making in-person social contact infrequent for everyone?3.To what extent are inequalities in loneliness and depressive symptoms affected by making remote social contact frequent for everyone?We use data from the English Longitudinal Study of Aging, to estimate answers to the following questions:22.1The English Longitudinal Study of Ageing (ELSA) is a large-scale, representative, longitudinal panel study of people aged 50 and over living in private households in England . The cor2.22.2.1Depressive symptoms were identified with the 8-item version of the Centre for Epidemiologic Studies Depression Scale CES-D; , with sc2.2.2We considered three potential sources of inequality in loneliness and depressive symptoms: 1) education, compared respondents with A level equivalent or higher qualifications (i.e. beyond completion of ordinary schooling) versus those with lesser or no qualifications; 2) partner status, comparing those living with a partner  versus those not living with a partner; and 3) wealth, measured using quintiles of net total non-pension household wealth  . For wea2.2.3Respondents were asked how often, on average, they met up with their children, other family members or friends (separate questions for each), and we compared those meeting up with at least one of their children, other family members or friends at least weekly versus those meeting up less frequently or reporting no children, family or friends. Weekly remote contact was coded similarly using questions on frequency of contact by telephone, letter, email or text with children, other family or friends (again separate questions were asked for each).2.2.4Confounding variables included: sex, five-year age group, ethnicity (white versus ethnic minority), government office region, any children in household (yes/no), any children outside household (yes/no), number of people in household other than respondent and partner, whether respondent felt close to their children, other family or friends, housing tenure (owner-occupant/rented), economic activity (in paid employment/not), social class  of last known occupation coded according to the British Registrar General's scheme which indicates occupational differences in status and economic resources , number 2.3All models used weights to adjust for sampling and drop-out . In prelFor research question 1 we separately estimated the effects of in-person and remote social contact on loneliness and depressive symptoms, using inverse probability weighting (IPW) . This isFor research questions 2 and 3, we assume social contact may mediate effects of either education, partner status or wealth on loneliness and depressive symptoms outcomes, as shown in Total exposure effects were estimated using IPW see  to estim33.13.23.33.4Living alone was associated with more depressive symptoms and loneliness than living with a partner. CDE estimates for universal infrequent in-person social contact indicated that differences in loneliness would widen among those aged over 65 but narrow among those aged under 65, while inequalities in depressive symptoms were attenuated in both age groups. CDE estimates for ensuring weekly remote contact for everyone indicated relatively little impact on these inequalities in those aged 65+ and there was some indication of inequalities widening among those aged under 65. Findings for partner status were similar with socioeconomic confounders re-positioned as pre-exposure confounders .Being in the least wealthy compared to the wealthiest quintile was not clearly associated with loneliness except for the UCLA loneliness measure among the 65+ age group but was associated with greater odds of depressive symptoms in both age groups. CDE estimates for universal infrequent in-person social contact indicated attenuation of this inequality in depressive symptoms among those aged under 65 and the inequality in loneliness among those aged 65+, but increased inequalities in depressive symptoms among those aged 65+. CDE estimates of ensuring weekly remote contact for everyone indicated some attenuation of the inequalities in loneliness and depressive symptoms among the 65+ age group, but little impact on the inequality in depressive symptoms among those aged under 65. Results for other wealth quintiles  were sim44.1On average, in-person social contact appeared associated with some reduced risk of loneliness in older adults in England, while estimated effects of remote social contact were relatively weak. Nevertheless, estimating effects of restricting everyone to infrequent (<weekly) in-person social contact suggested narrower inequalities in depressive symptoms and loneliness by education. Inequalities in depressive symptoms and loneliness by partner status and in depressive symptoms by wealth were impacted differently depending on age, with restricted in-person contact narrowing these inequalities in those aged <65 but widening them among adults aged 65+. Estimates for ensuring everyone received weekly remote contact also indicated some attenuation of inequalities but impacts were relatively weak compared to restricted in-person contact.4.2Our findings are consistent with others showing associations between social contact and depressive symptoms and loneliness , with inOur findings suggest that reductions in in-person social contact, e.g. under social mitigation measures related to COVID-19, are likely to lead on average to increases in loneliness among older adults. However, impacts may be experienced unequally and we estimated that those aged 65+ who live alone were particularly at risk for depressive symptoms and loneliness under restrictions to in-person contact. This has been anticipated , with re4.3Depressive symptoms and loneliness were modelled here as separate outcomes. The two are known to be related  and the While adjustment was made for earlier measures of social contact depression and loneliness, this was as post-exposure confounders, i.e. assuming that education, partner status and wealth were causally precedent), and we assumed a causal direction from social contact to depressive symptoms and loneliness where these were measured concurrently. Our results could also be accounted for by depressive symptoms or loneliness affecting concurrent measures of social contact, or by residual confounding.Our estimates based on pre-COVID-19 data may not necessarily generalise. For example, infrequent social contact under COVID-19 mitigation measures, may have different effects to infrequent social contact experienced under other circumstances . Moreove4.4Reductions in in-person social contact could on average result in increased loneliness among older adults. Adults aged 65+ who lived alone appeared especially vulnerable to reductions in social contact, with greater estimated increases in risk for depressive symptoms and loneliness than similarly aged adults living with a partner. Inequalities in depressive symptoms and loneliness by education could narrow as in-person contact is reduced, as those with more education seemed to derive more benefit from contact. Remote social contact seemed insufficient to mitigate adverse impacts or inequalities, and more attention is needed into how remote contact can become more efficacious, or to other strategies for addressing inequalities in depressive symptoms and loneliness among older adults.55.1Average Treatment Effects (ATEs) were estimated by regressing outcomes on each social contact variable, with each respondent assigned a weight equal to P(M)/P. M is their observed level of social contact. E represents the main exposure variables , which are confounders for the effect of social contact. X and C represent sets of pre and post-exposure confounders as listed in 5.2Outcomes were regressed on each exposure  with each respondent assigned a weight as P(E)/P(E|X), where E represents respondents’ observed values for the exposure in question, and X represents pre-exposure confounders as listed in 5.3are due to the exposure are not adjusted out, and the indirect effect of the exposure on the outcome via C in Outcomes were regressed on the exposure , mediator , and an interaction term for the two factors. These marginal structural models were weighted using a combination of the weight for the total effect of the exposure /P(E|X)) and a modified version of the social contact weight, this time calculated as P(M|E)/P. As the numerator of this weight is conditional on the exposure in question (E), it serves to balance intermediate confounders (C), but only within levels of the exposure, so differences in C that are due to the exposure are retained. The probabilities required to calculate these weights were estimated via logistic regression models of E and M . This means estimates are adjusted for differences in pre-exposure confounders, and for differences in post-exposure confounders that are not due to the exposure. In contrast with traditional regression analyses, differences in post-exposure confounders that The paper is based on secondary analysis of data that was subject to ethical review at the point of collection.Michael J. Green: Conceptualization, Methodology, Visualisation, Writing - original draft, Writing - review & editing. Elise Whitley: Conceptualization, Methodology, Data curation, Investigation, Formal analysis, Writing - original draft, Writing - review & editing. Claire L. Niedzwiedz: Methodology, Writing - review & editing. Richard J. Shaw: Methodology, Writing - review & editing. S. Vittal Katikireddi: Conceptualization, Methodology, Writing - review & editing.None.


Hepatitis D virus (HDV) infection is a major global health issue around the world. There are approximately 15–20 million individuals infected with HDV worldwide. HDV infection usually causes increased mortality compared with infection with hepatitis B virus (HBV) alone. However, testing for the detection of HDV is not widely available in Taiwan. Therefore, the General Biologicals Corporation (GB) HDV Ab kit was developed for detecting anti-HDV antibodies.A total of 913 serum and 462 EDTA-treated plasma samples were obtained from HBsAg-positive individuals in three hospitals in Taiwan from June 2014 to November 2017. We used three commercially available ELISA kits, DiaPro HDV Ab, DiaSorin ETI-AB-DELTAK-2 and GB HDV Ab, which were utilized strictly according to the instructions of the manufacturers.A comparative study of the results from the GB HDV Ab kit and the other commercial ELISA kits (DiaPro and DiaSorin) was performed to determine their efficacy for anti-HDV detection. The results indicated that the sensitivity of the GB HDV Ab kit for serum and EDTA samples was 100% compared to that of the DiaPro and DiaSorin kits, whereas the specificity for serum and EDTA samples was 99.3 and 98.1%, respectively. In addition, the overall agreement of the results of the GB HDV Ab kit for the serum and EDTA samples was 99.3 and 98.3%, respectively. It is worth noting that the performance of the GB HDV Ab kit was not affected by interference from triglyceride, bilirubin, hemoglobin, or human anti-mouse antibody. The limit of detection of the GB HDV Ab kit is approximately 100-fold lower than that of the other two commercial kits.The GB HDV Ab kit, which presented equivalent sensitivity and specificity compared to both certified anti-HDV kits, would be a suitable kit for HDV diagnosis in Taiwan. Hepatitis D virus (HDV) is a small single-stranded circular RNA with a negative polarity of approximately 1700 bases that produces hepatitis D antigen (HDAg) . Its repPrevious epidemiological studies estimated that approximately 15–20 million people are infected with HDV worldwide . HoweverHowever, the exact number of HDV infections is underestimated due to several factors. The first factor is the lack of knowledge of the regional HDV prevalence in low-income countries. Another is the rare systematic detection of anti-HDV antibodies in HBsAg-positive individuals, and the last is technical issues with enzyme immunoassays (EIAs), such as laboratory developed test (LDT) with lower sensitivity and specificity. These factors might lead to misdiagnosis and/or underestimation of HDV infection. Therefore, it is urgently necessary to establish sensitive and specific tools for HDV detection.According to its genetic diversity, HDV has been divided into eight major genotypes (HDV-1 to HDV-8) , 20. AmoIn this study, 1375 HBsAg-positive specimens were examined to assess the performance of the GB HDV Ab kit and to compare the data from this kit with those obtained by using the two most commonly available ELISA kits from DiaPro and DiaSorin.A total of 913 serum and 462 EDTA-treated plasma samples were obtained from HBsAg-positive individuals in three hospitals in Taiwan from June 2014 to November 2017. The three hospitals were Kaohsiung Chang Gung Memorial Hospital (KCGMH), Linkou Chang Gung Memorial Hospital (LCGMH) and National Taiwan University Hospital (NTUH).Ethical approval was obtained from the Institutional Review Board (IRB) of KCGMH (106-3378C), LCGMH (201700109B0) and NTUH (201612075DSB). Informed consent was sought and documented for all participants in this study.Three commercially available ELISA kits, DiaPro HDV Ab CE 0318), DiaSorin ETI-AB-DELTAK-2 (CE 0459) and GB HDV Ab (TFDA No. 005807), were utilized strictly according to the instructions of the manufacturers, as detailed in Table 8, DiaSorTreponema pallidum, and HBsAg, was spiked into the serum and EDTA-plasma from HDV-positive patients.Interference testing was performed according to the CLSI EP07-A2 guidelines . BrieflyThe detection limits of the three commercially available ELISA kits were determined by using decreasing concentrations of anti-HDV in normal human plasma (NHP) or guinea pig sera. Anti-HDV was obtained from two standard sensitivity panels, including ACCURUNⓇ 127 Anti-Hepatitis Delta Positive Control (CE-IVD) from SeraCare Life Sciences and polyclonal anti-HDV from guinea pig.P values < 0.05) was assessed by the two-tailed Student’s t-test. The sensitivity, specificity, and overall agreement with the 95% CI were estimated for each kit.All calculations were performed using Microsoft Excel 2016. Statistical significance  and DiaSorin (29-fold) kits; for polyclonal anti-HDV antibodies from guinea pig, the detection limit of the GB HDV Ab kit was 29-fold, which was better than that of the DiaPro (27-fold) and comparable to that of the DiaSorin (29-fold) kits . The results showed that the GB kit had superior analytical sensitivity compared to the DiaPro and DiaSorin kits. The detection limit of the GB HDV Ab kit for ACCURUN 127 was 2In the current study, a total of 913 serum specimens and 462 EDTA-treated plasma samples from HBV-infected individuals from three hospitals in Taiwan obtained from June 2014 to November 2017 were tested with commercially available HDV detection ELISA kits from GB, DiaPro and DiaSorin, and the results are summarized in Table n = 7), 65.8% for the HDV-2 genotype (n = 25) and 15.8% for the HDV-4 genotype (n = 6) was observed. All the samples were positive for HDV antibodies, as detected by the GB HDV Ab kit . However, HB and MAPC in EDTA-treated plasma might affect the values of the COI determined by the GB HDV Ab kit.To determine which analytes might interfere with the performance of the GB HDV Ab kit, interference assays were performed. Interference test samples were generated by spiking potential interfering materials into serum and EDTA-treated plasma containing anti-HDV plasma (ACCURUN 127) Table . The resIn addition, the detection ranges for the COI and OD values of the positive samples with serum and EDTA-treated plasma obtained with the GB HDV Ab kit were wider than those of the DiaPro kit Fig. . These rIn this study, the performance of three commercial HDV ELISA kits  was investigated for serum and EDTA-treated plasma. An important issue that was encountered was the discordance between the results of the GB and DiaPro kits. Therefore, another available kit, DiaSorin, was used to resolve this issue. Among 913 serum samples, 86 HDV-positive samples and 827 HDV-negative samples were determined by the DiaPro kit. On the other hand, 108 HDV-positive samples and 805 HDV-negative samples were determined by the GB HDV Ab kit. Therefore, the 22 inconsistent samples were reconfirmed with the DiaSorin kit, resulting in 15 HDV-positive samples, 6 HDV-negative samples, and one equivocal result. Then, we assumed that the real status of the 912 serum samples reflected 101 HDV-positive samples and 811 HDV-negative samples. By doing so, the overall sensitivity and specificity of the GB HDV Ab kit were shown to be 100 and 99.3%, whereas those of the DiaPro kit were shown to be 85.1 and 100%, respectively. The results for the serum samples indicated that the performance of the GB HDV Ab kit was better than that of the DiaPro kit. Among the 462 EDTA samples, 26 HDV-positive samples and 436 HDV-negative samples were determined by the DiaPro kit. However, 38 HDV-positive samples and 424 HDV-negative samples were determined by the GB HDV Ab kit. Therefore, the 12 inconsistent samples were also reconfirmed by using the DiaSorin kit, which resulted in 4 HDV-positive samples and 8 HDV-negative samples. Then, we assumed that the 462 EDTA samples included 30 HDV-positive samples and 432 HDV-negative samples. Therefore, the overall sensitivity and specificity of the GB HDV Ab kit were 100 and 98.1%, respectively, whereas those of the DiaPro were 86.7 and 100%, respectively. Therefore, the present study revealed the better performance of the GB HDV Ab kit in terms of specificity and sensitivity compared to that of the DiaPro kit. In addition, the detection limit of the GB HDV Ab kit was better than that of the DiaPro and DiaSorin . Although not yet understood, the slight background observed in EDTA-plasma with hemoglobin (HB) or MAPC might be due to unspecific binding of the antibodies. The results indicated that the GB HDV Ab kit is suitable for detecting anti-HDV antibodies in serum samples.In addition, the ELISA assay is usually affected by binding to natural unknown proteins, such as human anti-animal antibodies . TherefoThe GB HDV Ab kit can successfully detect anti-HDV antibodies from patients infected with HDV genotypes 1, 2 and 4, which are commonly found in Taiwan. The GB kit has higher sensitivity and specificity than commercial ELISA kits. In addition, the GB HDV Ab kit showed improved performance during interference testing of serum samples, a wider detection range and a lower detection limit than commercial kits. The results indicated that the GB HDV Ab kit might be suitable for screening patients infected with HDV.


Alcohol use and related injuries are a leading risk factor for deaths and disabilities in Australia, particularly for Aboriginal and Torres Strait Islander people. An improved understanding of individual and geographical community characteristics that are significantly associated with higher rates of alcohol-related injuries for specific populations can contribute to more effective efforts aimed at reducing alcohol-related injuries. For Aboriginal and non-Aboriginal Australians in New South Wales, this study used emergency department (ED) data to investigate rates of alcohol-related injuries, whether differences in rates vary between communities, and individual and community characteristics significantly associated with alcohol-related injuries. Differences in rates of alcohol-related injuries between Aboriginal and non-Aboriginal people varied significantly between communities. Being younger than 38 years old was significantly associated with increased risk of alcohol-related injuries, independent of Aboriginal status and gender. Increased disadvantage of the geographical community inhabited was associated with increased alcohol-related injuries for males. For Aboriginal males, living in a regional community was significantly associated with increased alcohol-related injuries, compared to living in major cities. Conversely, for non-Aboriginal people, living in regional communities was significantly associated with fewer alcohol-related injuries. It is therefore likely that an explanation for between-community differences can be found in regional communities. Alcohol use is a leading risk factor for deaths and disability for those younger than 50 years of age , particuIndividual characteristics that are significantly associated with increased alcohol-related injuries include being younger, male, unmarried and unemployed ,11,12,13This study had three aims. First, to estimate and specify rates of alcohol-related injuries by individual and community characteristics in New South Wales (NSW) Australia, separately for Aboriginal and non-Aboriginal Australians. Second, to investigate whether differences in rates of alcohol-related injuries between Aboriginal and non-Aboriginal Australians vary across geographical communities. Third, to identify individual and community characteristics that are significantly associated with alcohol-related injuries separately for Aboriginal and non-Aboriginal Australians.The research was approved by the NSW Aboriginal Health and Medical Research Council Ethics Committee (no. 987/13) and the NSW Population and Health Services Research Ethics Committee (no. 2014/02/516).An epidemiological study using routinely collected ED presentation data in NSW between 1 January 2012 and 31 December 2014. NSW is the most populous state of Australia (an estimated population of approximately 7.5 million), of whom 2.9% identify as Aboriginal and/or Torres Strait Islander. The majority (77%) of the NSW population resides in the major cities  .n = 811,776).The Emergency Department Data Collection (EDDC), held by the NSW Ministry of Health, is a routinely collected dataset containing presentation data from public hospital EDs in NSW. For this study, the NSW Ministry of Health provided de-identified unit-level data for ED presentations involving persons aged 13 years and older for the period from l January 2012 to 31 December 2014 [This study used a proxy measure to identify alcohol-related injuries. This proxy measure was developed and validated by Young et al.  internatIndividual characteristics were Aboriginal status, age and the gender of patient as identified in the ED data. Aboriginal status was recoded as Aboriginal  or non-Aboriginal. Gender was coded as male or female. Age was grouped into 5-year age categories, starting with 13 years old as the youngest age available in the data and allowing to split between those who are legally able to purchase and drink alcohol from those who are not . The resulting age categories are: 13–17, 18–22, 23–27, 28–32, 33–37, 38–42, 43–47, 48–52, 53–57, 58–62, 63–67 and 68+. Splitting up the age groups into 5-year categories provides the most detailed insight into which groups are most at risk of alcohol-related injuries.n = 86 (0.03%) alcohol-related injuries) were excluded because no data on community characteristics were available for those POAs.Community characteristics were obtained by postcode area (POA), based on the postcode of residence of the patient. Though a variety of geographical units can be applied to identify a ‘community’ in Australia, POA was preferred because postcodes were included in the ED dataset and community characteristics of interest were more readily available by POA, compared to other geographical units. Four postcodes (Socioeconomic status (SES)—categorical. The SES of the geographical community was determined using the Index for Relative Disadvantage, which includes variables reflecting the relative disadvantage in an area compared to the rest of the state [he state . Each POhe state . Quintilhe state .Remoteness—categorical. Remoteness of the POAs was identified using the Australian Standard Geographical Classification remoteness structure, which defines remoteness based on road distance to service centres. POAs were grouped into three categories: major cities, regional and remote [d remote .Liquor licences—continuous. Information on liquor licensing in the communities was obtained from the NSW Office of Liquor, Gambling and Racing [d Racing . Types oPopulation characteristics—continuous. For each POA, data on the proportion of young males (20–29 years old) and the proportion of Aboriginal Australians were obtained from the ABS using 2011 POA Census data, the most recent information at the time of analysis .All analyses were conducted using STATA 14  ; confidep < 0.01); therefore, all models in this study were stratified by gender. Prior to assessing community variation in the difference in rate ratios between Aboriginal and non-Aboriginal patients, the statistically significant differences in the rate ratios of alcohol-related injuries between Aboriginal and non-Aboriginal patients needed to be established. These were investigated using single-level Poisson models with alcohol-related injuries as the outcome variable and individual characteristics (age categories) and categorical community characteristics (SES and remoteness) as independent variables. To investigate the community variation in the rate ratios between Aboriginal and non-Aboriginal patients, a random intercept for POA was added to these single level Poisson models.Preliminary single-level Poisson models with alcohol-related injuries as the outcome variable identified that the effect of Aboriginal status on alcohol-related injuries was modified by gender  categorical community characteristics (SES and remoteness), continuous community characteristics  and a random intercept for POA.A total of 125,834 alcohol-related injuries  were identified in NSW between l January 2012 and 31 December 2014 for patients aged 13 years and older (6611 (4.32%) Aboriginal patients and 119,223 (95.67%) non-Aboriginal patients). In terms of remoteness, age-standardised rates of alcohol-related injuries increased as remoteness increased for Aboriginal males and non-Aboriginal patients, but not for Aboriginal females where age-standardised rates were comparable in major city and remote areas. For Aboriginal patients, no clear pattern seemed to be observable in age-standardised rates of alcohol-related injuries by SES. For non-Aboriginal patients, age-standardised rates increased with increasing disadvantage.p < 0.01) and females (p < 0.01).The unadjusted rates of alcohol-related injuries for Aboriginal males were estimated to be 3.96 (CI 3.80–4.13) times higher compared to non-Aboriginal males and 4.23 (CI 4.02–4.45) times higher for Aboriginal females compared to non-Aboriginal females. When the random intercept for POA was added, thus controlling for geographical community variation in the rate ratios of alcohol-related injuries, these differences dropped to 3.22 (CI 3.08–3.37) for males and 3.59 (CI 3.40–3.79) for females. This provided evidence that rate ratios of Aboriginal compared to non-Aboriginal alcohol-related injuries vary significantly between geographic communities for both males  were most likely to experience alcohol-related injuries. For non-Aboriginal Australians, regional communities were associated with lower rates of alcohol-related injuries compared to major cities, but rates of alcohol-related injuries increased for Aboriginal males in regional communities. Increased disadvantage was associated with increased alcohol-related injuries for Aboriginal and non-Aboriginal males. Communities with a higher percentage of young males had higher rates of alcohol-related injuries for Aboriginal and non-Aboriginal people.The extent to which rates of alcohol-related injuries differed between Aboriginal and non-Aboriginal people varied between communities. This was true for males and females, all age groups and all communities, regardless of remoteness and SES. The analyses presented in It is likely that other factors not measured in this study contribute to higher rates of alcohol-related injuries experienced by Aboriginal people. Previous studies into risk factors for non-alcohol-specific injuries have identified a range of risk factors that are unique to Indigenous Peoples both in Australia and internationally. These risk factors include historical factors , disproportionately high experience of social stressors at a younger age   and commThe observation that alcohol-related injuries were significantly lower in regional areas for non-Aboriginal Australians is surprising considering increased remoteness has been associated with increased high risk drinking and other alcohol-related harms, such as assaults ,17,18,33In line with previous research, this study clearly highlights the need for alcohol-related injury prevention efforts to continue to target young people ,11,33,35While the use of administrative data to monitor and assess alcohol-related outcomes is beneficial ,42, therFirstly, the EDDC data do not include all EDs in NSW. Between 2011–2012 and 2014–2015, the EDDC covered between 97% and 99% of metropolitan EDs and 81–94% of rural and regional EDs. It does not include private hospitals, and it excludes some smaller and remote EDs ,43,44. MSecond, the use of a proxy measure might not be the ideal way of identifying alcohol-related injuries in EDs, however it is currently identified the most reliable method . While tFurther contributing to issues with the proxy measure is the less than optimal reliability of the primary diagnosis used to identify injury-related presentations. The accuracy of the primary diagnosis is at least 85% for ED patients subsequently admitted to the hospital  . The relThe proxy measure used in this study has not been explicitly validated for Aboriginal Australians. Proxy measures have been widely used in previous research into community-level alcohol-related ED presentations for the general population in Australia, which includes Aboriginal Australians ,48,49, bFinally, this paper likely does not paint a complete picture of alcohol-related injuries due to its use of only one dataset in one Australian state. Alcohol-related injuries are also reported to general practice, ambulance services and other primary health care settings. Triangulating data from these data sources can further improve our understanding as to what contributes to increased alcohol-related injuries. Further similar studies in other parts of Australia might provide a completer picture of what contributes to alcohol-related injuries amongst Aboriginal Australians.This study used administrative data to assess alcohol-related injury presentations to EDs for Aboriginal and non-Aboriginal people in NSW. This study showed that rates of alcohol-related injuries are higher among Aboriginal Australians than non-Aboriginal Australians, but that these differences vary between community. A potential explanation for this can be found in regional communities, where rates of alcohol-related injuries are increased for Aboriginal males but decreased for non-Aboriginal people. Therefore, initiatives are required to target reductions in alcohol misuse and injuries for non-Aboriginal and Aboriginal males. The target areas for these should be driven by data; given findings that there is community variation, this study indicates a need in both urban and regional settings. Prevention efforts targeting young people are required.


We aimed to identify, synthesise and evaluate randomised control trial evidence on the effects of healthcare professional training on the delivery quality of health behaviour change interventions and, subsequently, on patient health behaviours.Systematic review with narrative synthesis of effects on delivery quality and meta-analysis of health behaviour outcomes. We searched: Medline, EMBASE, PsychInfo, AMED, CINAHL Plus and the Cochrane Central Register of Control Trials up to March 2019. Studies were included if they were in English and included intervention delivery quality as an outcome. The systematic review was registered on PROSPERO (registration: CRD42019124502).P < 0.0001, I2 = 0%). No significant difference was found between short (≤6-months) and long-term (> 6-months) outcomes .Twelve-studies were identified as suitable for inclusion. All studies were judged as being high risk of bias with respect to training quality outcomes. However with respect to behavioural outcomes, only two of the six studies included in the meta-analysis had a high risk and four had some concerns. Educational elements (e.g. presentations) were used in all studies and nine included additional practical learning tasks. In eight studies reporting delivery quality, 54% of healthcare professional communication outcomes and 55% of content delivery outcomes improved in the intervention arm compared to controls. Training that included both educational and practical elements tended to be more effective. Meta-analysis of patient health behavioural outcomes in six-studies found significant improvements (Standardised mean difference (SMD): 0.20, 95% confidence interval: 0.11 to 0.28, Delivery quality of health behaviour change interventions appears to improve following training and consequently to improve health behaviours. Future studies should develop more concise /integrated measures of delivery quality and develop optimal methods of training delivery. Health impairing behaviours, such as smoking, over-eating and prolonged sedentary time are the underlying cause of many diseases (90% of cancers  and 30% 2 = 93.3%) [The effectiveness of interventions to change health impairing behaviours has been shown to vary . For exa= 93.3%) . Some in= 93.3%) .P < 0.001) [A fundamental issue for effectiveness in behavioural interventions is quality of delivery , 9. Deli< 0.001) . With re< 0.001) .Despite knowledge of the importance of delivery quality, systematic reviews have highlighted the need to improve intervention fidelity . A recenTo improve fidelity and consistency (of communication and content delivery) when delivering interventions, and ultimately their effectiveness, many studies and current NICE guidelines advise high quality healthcare professional training , 14–16. This systematic review and meta-analysis followed the methods outlined by the Cochrane handbook for systematic reviews  and was An electronic search was performed up until 19th March 2019 using; Medline, EMBASE, PsychInfo, AMED, CINAHL Plus and the Cochrane Central Register of Control Trials, for studies published in English. The search terms used, were informed by past reviews , 16 and The training intervention must be delivered to any healthcare professional. For this systematic review a healthcare professional is a role that involves contact with patients and delivery of care and or treatment, which includes but not limited to: physicians, surgeons, nurses, physiotherapists and healthcare assistants. Included studies must assess quality of intervention delivery and be a randomised control trial.Studies that only train participants who have contact with patients but do not deliver any care or treatment were excluded from this study, for example; hospital porter or ward clerk. Along with studies: (1) not assessing quality of intervention delivery; (2) not including a minimal or no-intervention control group; (3) not undertaking an RCT; and (4) not published in English.Following duplicate removal, titles and abstracts were independently screened by two reviewers  (50% each), reviewers subsequently checked each other’s included studies and 20% of the excluded studies for agreement. The full texts of all potentially eligible studies were then screened independently by two reviewers , with disagreements resolved via discussion. Inter-rater reliability was calculated using the AC1 statistic . ReferenTwo reviewers  independently extracted data from half of the included studies, with the other reviewer checking data extraction. The following characteristics were extracted: (1) methodology ; (2) participants ; (3) training interventions ; and (4) outcomes , patient health behaviour change, follow-up times).Study quality was independently assessed by two reviewers  using the Cochrane risk-of-bias tool , with diA narrative synthesis of influences of training on delivery quality was performed. As part of this, for each study we calculated the percentage of delivery quality outcomes that significantly improved compared to control. We defined short-, medium- and long-term timeframes as <six-months, ≥six to < 12-months and ≥ 12-months respectively.To analyse health behaviour outcomes, we performed a random effects meta-analysis in Cochrane Review Manager version 5.3, using standardised mean differences (SMD) to pool effect sizes. We included data in the meta-analysis that related to the primary health behaviour targeted by the intervention. In studies where health behaviours were not reported, but other outcomes were reported that would change following the targeted behaviour (e.g. blood pressure), these outcomes were used. Behaviour change outcomes were collated at two follow up times (short-term: six-months or less and long-term: over six-months). Publication bias was not assessed due to the limited number of included studies.The search results and screening process is shown in Fig. The characteristics and outcomes of included studies are detailed in supplementary Table Regarding health behaviour outcomes, eight-studies provided patient level data, with only six studies providing data suitable for meta-analysis. The six-studies assessed a range of health behaviours, two studies examined diabetes self-care, the remaining studies examined four different health behaviours; regularity of breast feeding, smoking cessation, asthma self-care and hypertension management. Three of these studies , 25, 26 Risk of bias assessement results are highlighted in Tables In summary, the high overall risk of bias was heavily influenced by the high risk of reporting bias found in all 12 studies whereby multiple outcome measures were used to assess delivery quality (a mean 11 outcome measures were used per study), with little consideration for statistical correction . As wellWithin the 12 included studies a total of 132-outcomes were identified. Sixty-eight outcomes from eight-studies related to communication skills, 64-outcomes from seven-studies related to delivery of intended intervention content. Communication skills and delivery of intervention content improved significantly for 54% (37 of 68) and 55% (35 of 64) of outcomes respectively.Two main types of training intervention were observed. These were communication training  and content-related training (training in the delivery of specific intervention content), produced similar results, significantly improving 50 and 49% of outcomes, respectively. Training using both educational and practical elements seemed to be more effective, significantly improving 52% of outcomes, whereas education only training significantly improved only 23% of outcomes. Effectiveness was also highest in the medium-term (between six to 12-months) with significant improvements in 81% of communication and 66% of content-delivery outcomes), compared with the long-term (over 12-months), where significant improvements were found in 50% of communication outcomes and 42% of content-delivery outcomes.Across the three-studies , 20, 26 Across the three studies , 25, 29 Three studies , 23, 28 Three-studies , 21, 27,Across the three-studies , 25, 29,Across the two-studies , 30 thatA total of six studies, with 2802 participants, were included in the meta-analysis. The results are shown in Fig. P < 0.01, I2 = 0%). Sub-group analysis highlighted a greater effect in the short-term (median six-months)  compared to the long-term (median 12 months) , however this difference was not significant (P = 0.31).The analysis indicated that healthcare professional training resulted in a small significant improvement in patient health behaviour compared to control , although this seems to decrease at 12-months. The latter finding should be interpreted with caution due to only three studies reporting outcomes at 12 months. Healthcare professional training also appears to have a significant effect on health behaviour change for up to 12 months.P < 0.05) in compliance with desired practice compared to the control group. This review, like ours, concluded that the inclusion of both practical and educational elements within healthcare professional training is associated with improvement in health behaviour change at the patient level.These results support current NICE guidelines that state high-quality healthcare professional training is important to improve patient outcomes in health behaviour change interventions . In addiThis review is the first to evaluate the effectiveness of training interventions for health behaviour change in healthcare professionals. We used rigorous systematic reviewing methods , includiThis review supports the current NICE recommendations on the importance of healthcare professional training to enhance the effectiveness of health behaviour change interventions . PolicymThere is a need for further research in this area to increase the number of high-quality trials of effectiveness of additional training to improve the quality of delivery of health behaviour change interventions. This should include research on the long-term effectiveness of healthcare professional training on both delivery quality and (objectively measured) health behaviour outcomes.In addition to the above and to help overcome the current lack of clarity of healthcare professional delivery quality outcomes , it is sThis systematic review suggests that healthcare professional training is an effective tool for improving the quality of delivery of health behaviour change interventions and that providing such training improves patient health behaviours. The data also suggest that the use of both educational and practical elements seem to be key for effective healthcare professional training. The effect on delivery quality seems to last for up to 12 months but the longer-term effects are uncertain. The findings of this systematic review with respect to delivery quality should be interpreted with caution due to the high risk of bias identified in the studies reviewed. With respect to effects on behaviour change, the findings are more robust, as the studies in meta-analysis had a lower risk of bias.Additional file 1: Table S1. Example of the search strategy for Medline (Ovid).Additional file 2: Table S2. Study characteristics.