Evaluation of a geriatrics primary care model using prospective matching to guide enrollment

https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01360-4

Abstract

Background

Few definitive guidelines exist for rigorous large-scale prospective evaluation of nonrandomized programs and policies that require longitudinal primary data collection. In Veterans Affairs (VA) we identified a need to understand the impact of a geriatrics primary care model (referred to as GeriPACT); however, randomization of patients to GeriPACT vs. a traditional PACT was not feasible because GeriPACT has been rolled out nationally, and the decision to transition from PACT to GeriPACT is made jointly by a patient and provider. We describe our study design used to evaluate the comparative effectiveness of GeriPACT compared to a traditional primary care model (referred to as PACT) on patient experience and quality of care metrics.

Methods

We used prospective matching to guide enrollment of GeriPACT-PACT patient dyads across 57 VA Medical Centers. First, we identified matches based an array of administratively derived characteristics using a combination of coarsened exact and distance function matching on 11 identified key variables that may function as confounders. Once a GeriPACT patient was enrolled, matched PACT patients were then contacted for recruitment using pre-assigned priority categories based on the distance function; if eligible and consented, patients were enrolled and followed with telephone surveys for 18 months.

Results

We successfully enrolled 275 matched dyads in near real-time, with a median time of 7 days between enrolling a GeriPACT patient and a closely matched PACT patient. Standardized mean differences of < 0.2 among nearly all baseline variables indicates excellent baseline covariate balance. Exceptional balance on survey-collected baseline covariates not available at the time of matching suggests our procedure successfully controlled many known, but administratively unobserved, drivers of entrance to GeriPACT.

Conclusions

We present an important process to prospectively evaluate the effects of different treatments when randomization is infeasible and provide guidance to researchers who may be interested in implementing a similar approach. Rich matching variables from the pre-treatment period that reflect treatment assignment mechanisms create a high quality comparison group from which to recruit. This design harnesses the power of national administrative data coupled with collection of patient reported outcomes, enabling rigorous evaluation of non-randomized programs or policies.


https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01360-4


Sampling strategies to evaluate the prognostic value of a new biomarker on a time-to-event end-point

Abstract

Background

The availability of large epidemiological or clinical data storing biological samples allow to study the prognostic value of novel biomarkers, but efficient designs are needed to select a subsample on which to measure them, for parsimony and economical reasons. Two-phase stratified sampling is a flexible approach to perform such sub-sampling, but literature on stratification variables to be used in the sampling and power evaluation is lacking especially for survival data.

Methods

We compared the performance of different sampling designs to assess the prognostic value of a new biomarker on a time-to-event endpoint, applying a Cox model weighted by the inverse of the empirical inclusion probability.

Results

Our simulation results suggest that case-control stratified (or post stratified) by a surrogate variable of the marker can yield higher performances than simple random, probability proportional to size, and case-control sampling. In the presence of high censoring rate, results showed an advantage of nested case-control and counter-matching designs in term of design effect, although the use of a fixed ratio between cases and controls might be disadvantageous. On real data on childhood acute lymphoblastic leukemia, we found that optimal sampling using pilot data is greatly efficient.

Conclusions

Our study suggests that, in our sample, case-control stratified by surrogate and nested case-control yield estimates and power comparable to estimates obtained in the full cohort while strongly decreasing the number of patients required. We recommend to plan the sample size and using sampling designs for exploration of novel biomarker in clinical cohort data.


https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01283-0

Evaluating complex interventions in context: systematic, meta-narrative review of case study approaches

Abstract

Background

There is a growing need for methods that acknowledge and successfully capture the dynamic interaction between context and implementation of complex interventions. Case study research has the potential to provide such understanding, enabling in-depth investigation of the particularities of phenomena. However, there is limited guidance on how and when to best use different case study research approaches when evaluating complex interventions. This study aimed to review and synthesise the literature on case study research across relevant disciplines, and determine relevance to the study of contextual influences on complex interventions in health systems and public health research.

Methods

Systematic meta-narrative review of the literature comprising (i) a scoping review of seminal texts (n = 60) on case study methodology and on context, complexity and interventions, (ii) detailed review of empirical literature on case study, context and complex interventions (n = 71), and (iii) identifying and reviewing ‘hybrid papers’ (n = 8) focused on the merits and challenges of case study in the evaluation of complex interventions.

Results

We identified four broad (and to some extent overlapping) research traditions, all using case study in a slightly different way and with different goals: 1) developing and testing complex interventions in healthcare; 2) analysing change in organisations; 3) undertaking realist evaluations; 4) studying complex change naturalistically. Each tradition conceptualised context differently—respectively as the backdrop to, or factors impacting on, the intervention; sets of interacting conditions and relationships; circumstances triggering intervention mechanisms; and socially structured practices. Overall, these traditions drew on a small number of case study methodologists and disciplines. Few studies problematised the nature and boundaries of ‘the case’ and ‘context’ or considered the implications of such conceptualisations for methods and knowledge production.

Conclusions

Case study research on complex interventions in healthcare draws on a number of different research traditions, each with different epistemological and methodological preferences. The approach used and consequences for knowledge produced often remains implicit. This has implications for how researchers, practitioners and decision makers understand, implement and evaluate complex interventions in different settings. Deeper engagement with case study research as a methodology is strongly recommended.


https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01418-3

Pre-statistical harmonization of behavrioal instruments across eight surveys and trials

Abstract

Background

Data harmonization is a powerful method to equilibrate items in measures that evaluate the same underlying construct. There are multiple measures to evaluate dementia related behavioral symptoms. Pre-statistical harmonization of behavioral instruments in dementia research is the first step to develop a statistical crosswalk between measures. Studies that conduct pre-statistical harmonization of behavioral instruments rarely document their methods in a structured, reproducible manner. This is a crucial step which entails careful review, documentation and scrutiny of source data to ensure sufficient comparability between items prior to data pooling. Here, we document the pre-statistical harmonization of items measuring behavioral and psychological symptoms among people with dementia. We provide a box of recommended procedure for future studies.

Methods

We identified behavioral instruments that are used in clinical practice, a national survey, and randomized trials of dementia care interventions. We rigorously reviewed question content and scoring procedures to establish sufficient comparability across items as well as item quality prior to data pooling. Additionally, we standardized coding to Stata-readable format, which allowed us to automate approaches to identify potential cross-study differences in items and low-quality items. To ensure reasonable model fit for statistical co-calibration, we estimated two-parameter logistic Item Response Theory models within each of the eight studies.

Results

We identified 59 items from 11 behavioral instruments across the eight datasets. We found considerable cross-study heterogeneity in administration and coding procedures for items that measure the same attribute. Discrepancies existed in terms of directionality and quantification of behavioral symptoms for even seemingly comparable items. We resolved item response heterogeneity, missingness and skewness, conditional dependency prior to estimation of item response theory models for statistical co-calibration. We used several rigorous data transformation procedures to address these issues, including re-coding and truncation.

Conclusions

This study highlights the importance of each aspect involved in the pre-statistical harmonization process of behavioral instruments. We provide guidelines and recommendations for how future research may detect and account for similar issues in pooling behavioral and related instruments.

https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01431-6

Mediation analysis methods used in observational research: a scoping review and recommendations

Abstract

Background

Mediation analysis methodology underwent many advancements throughout the years, with the most recent and important advancement being the development of causal mediation analysis based on the counterfactual framework. However, a previous review showed that for experimental studies the uptake of causal mediation analysis remains low. The aim of this paper is to review the methodological characteristics of mediation analyses performed in observational epidemiologic studies published between 2015 and 2019 and to provide recommendations for the application of mediation analysis in future studies.

Methods

We searched the MEDLINE and EMBASE databases for observational epidemiologic studies published between 2015 and 2019 in which mediation analysis was applied as one of the primary analysis methods. Information was extracted on the characteristics of the mediation model and the applied mediation analysis method.

Results

We included 174 studies, most of which applied traditional mediation analysis methods (n = 123, 70.7%). Causal mediation analysis was not often used to analyze more complicated mediation models, such as multiple mediator models. Most studies adjusted their analyses for measured confounders, but did not perform sensitivity analyses for unmeasured confounders and did not assess the presence of an exposure-mediator interaction.

Conclusions

To ensure a causal interpretation of the effect estimates in the mediation model, we recommend that researchers use causal mediation analysis and assess the plausibility of the causal assumptions. The uptake of causal mediation analysis can be enhanced through tutorial papers that demonstrate the application of causal mediation analysis, and through the development of software packages that facilitate the causal mediation analysis of relatively complicated mediation models.


https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01426-3

Major adverse cardiovascular event definitions used in observational analysis of administrative databases: a systematic review

Abstract

Background

Major adverse cardiovascular events (MACE) are increasingly used as composite outcomes in randomized controlled trials (RCTs) and observational studies. However, it is unclear how observational studies most commonly define MACE in the literature when using administrative data.

Methods

We identified peer-reviewed articles published in MEDLINE and EMBASE between January 1, 2010 to October 9, 2020. Studies utilizing administrative data to assess the MACE composite outcome using International Classification of Diseases 9th or 10th Revision diagnosis codes were included. Reviews, abstracts, and studies not providing outcome code definitions were excluded. Data extracted included data source, timeframe, MACE components, code definitions, code positions, and outcome validation.

Results

A total of 920 articles were screened, 412 were retained for full-text review, and 58 were included. Only 8.6% (n = 5/58) matched the traditional three-point MACE RCT definition of acute myocardial infarction (AMI), stroke, or cardiovascular death. None matched four-point (+unstable angina) or five-point MACE (+unstable angina and heart failure). The most common MACE components were: AMI and stroke, 15.5% (n = 9/58); AMI, stroke, and all-cause death, 13.8% (n = 8/58); and AMI, stroke and cardiovascular death 8.6% (n = 5/58). Further, 67% (n = 39/58) did not validate outcomes or cite validation studies. Additionally, 70.7% (n = 41/58) did not report code positions of endpoints, 20.7% (n = 12/58) used the primary position, and 8.6% (n = 5/58) used any position.

Conclusions

Components of MACE endpoints and diagnostic codes used varied widely across observational studies. Variability in the MACE definitions used and information reported across observational studies prohibit the comparison, replication, and aggregation of findings. Studies should transparently report the administrative codes used and code positions, as well as utilize validated outcome definitions when possible.

https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01440-5


Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis

Abstract

Background

Novartis and the University of Oxford’s Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression.

Method

The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2200 centres worldwide. For the “IL-17” project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA)).

Results

A fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project.

Conclusions

An informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools.


https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01409-4

Impact of the COVID-19 pandemic on publication dynamics and non-COVID-19 research production

Abstract

Background

The COVID-19 pandemic has severely affected health systems and medical research worldwide but its impact on the global publication dynamics and non-COVID-19 research has not been measured. We hypothesized that the COVID-19 pandemic may have impacted the scientific production of non-COVID-19 research.

Methods

We conducted a comprehensive meta-research on studies (original articles, research letters and case reports) published between 01/01/2019 and 01/01/2021 in 10 high-impact medical and infectious disease journals (New England Journal of Medicine, Lancet, Journal of the American Medical Association, Nature Medicine, British Medical Journal, Annals of Internal Medicine, Lancet Global Health, Lancet Public Health, Lancet Infectious Disease and Clinical Infectious Disease). For each publication, we recorded publication date, publication type, number of authors, whether the publication was related to COVID-19, whether the publication was based on a case series, and the number of patients included in the study if the publication was based on a case report or a case series. We estimated the publication dynamics with a locally estimated scatterplot smoothing method. A Natural Language Processing algorithm was designed to calculate the number of authors for each publication. We simulated the number of non-COVID-19 studies that could have been published during the pandemic by extrapolating the publication dynamics of 2019 to 2020, and comparing the expected number to the observed number of studies.

Results

Among the 22,525 studies assessed, 6319 met the inclusion criteria, of which 1022 (16.2%) were related to COVID-19 research. A dramatic increase in the number of publications in general journals was observed from February to April 2020 from a weekly median number of publications of 4.0 (IQR: 2.8–5.5) to 19.5 (IQR: 15.8–24.8) (p < 0.001), followed afterwards by a pattern of stability with a weekly median number of publications of 10.0 (IQR: 6.0–14.0) until December 2020 (p = 0.045 in comparison with April). Two prototypical editorial strategies were found: 1) journals that maintained the volume of non-COVID-19 publications while integrating COVID-19 research and thus increased their overall scientific production, and 2) journals that decreased the volume of non-COVID-19 publications while integrating COVID-19 publications. We estimated using simulation models that the COVID pandemic was associated with a 18% decrease in the production of non-COVID-19 research. We also found a significant change of the publication type in COVID-19 research as compared with non-COVID-19 research illustrated by a decrease in the number of original articles, (47.9% in COVID-19 publications vs 71.3% in non-COVID-19 publications, p < 0.001). Last, COVID-19 publications showed a higher number of authors, especially for case reports with a median of 9.0 authors (IQR: 6.0–13.0) in COVID-19 publications, compared to a median of 4.0 authors (IQR: 3.0–6.0) in non-COVID-19 publications (p < 0.001).

Conclusion

In this meta-research gathering publications from high-impact medical journals, we have shown that the dramatic rise in COVID-19 publications was accompanied by a substantial decrease of non-COVID-19 research.


https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01404-9

Modular literature review: a novel systematic search and review method to support priority setting in health policy and practice

Abstract

Background

There is an unmet need for review methods to support priority-setting, policy-making and strategic planning when a wide variety of interventions from differing disciplines may have the potential to impact a health outcome of interest. This article describes a Modular Literature Review, a novel systematic search and review method that employs systematic search strategies together with a hierarchy-based appraisal and synthesis of the resulting evidence.

Methods

We designed the Modular Review to examine the effects of 43 interventions on a health problem of global significance. Using the PICOS (Population, Intervention, Comparison, Outcome, Study design) framework, we developed a single four-module search template in which population, comparison and outcome modules were the same for each search and the intervention module was different for each of the 43 interventions. A series of literature searches were performed in five databases, followed by screening, extraction and analysis of data. “ES documents”, source documents for effect size (ES) estimates, were systematically identified based on a hierarchy of evidence. The evidence was categorised according to the likely effect on the outcome and presented in a standardised format with quantitative effect estimates, meta-analyses and narrative reporting. We compared the Modular Review to other review methods in health research for its strengths and limitations.

Results

The Modular Review method was used to review the impact of 46 antenatal interventions on four specified birth outcomes within 12 months. A total of 61,279 records were found; 35,244 were screened by title-abstract. Six thousand two hundred seventy-two full articles were reviewed against the inclusion criteria resulting in 365 eligible articles.

Conclusions

The Modular Review preserves principles that have traditionally been important to systematic reviews but can address multiple research questions simultaneously. The result is an accessible, reliable answer to the question of “what works?”. Thus, it is a well-suited literature review method to support prioritisation, decisions and planning to implement an agenda for health improvement.


https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01463-y

Feasibility of a hybrid clinical trial for respiratory virus detection in toddlers during the influenza season

Abstract

Background

Traditional clinical trials are conducted at investigator sites. Participants must visit healthcare facilities several times for the trial procedures. Decentralized clinical trials offer an interesting alternative. They use telemedicine and other technological solutions (apps, monitoring devices or web platforms) to decrease the number of visits to study sites, minimise the impact on daily routine, and decrease geographical barriers for participants. Not much information is available on the use of decentralization in randomized clinical trials with vaccines.

Methods

A hybrid clinical trial may be assisted by parental recording of symptoms using electronic log diaries in combination with home collected nasal swabs. During two influenza seasons, children aged 12 to 35 months with a history of recurrent acute respiratory infections were recruited in 12 primary health centers of the Valencia Region in Spain. Parents completed a symptom diary through an ad hoc mobile app that subsequently assessed whether it was an acute respiratory infection and requested collection of a nasal swab. Feasibility was measured using the percentage of returned electronic diaries and the validity of nasal swabs collected during the influenza season. Respiratory viruses were detected by real-time PCR.

Results

Ninety-nine toddlers were enrolled. Parents completed 10,476 electronic diaries out of the 10,804 requested (97%). The mobile app detected 188 potential acute respiratory infections (ARIs) and requested a nasal swab. In 173 (92%) ARI episodes a swab was taken. 165 (95.4%) of these swabs were collected at home and 144 (87.3%) of them were considered valid for laboratory testing. Overall, 152 (81%) of the ARIs detected in the study had its corresponding valid sample collected.

Conclusions

Hybrid procedures used in this clinical trial with the influenza vaccine in toddlers were considered adequate, as we diagnosed most of the ARI cases on time, and had a valid swab in 81% of the cases. Hybrid clinical trials improve participant adherence to the study procedures and could improve recruitment and quality of life of the participants and the research team by decreasing the number of visits to the investigator site.

This report emphasises that the conduct of hybrid CTs is a valid alternative to traditional CTs with vaccines. This hybrid CT achieved high adherence of participant to the study procedures.

https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01474-9

Impact of vaccine prioritization strategies on mitigating COVID-19: an agent-based simulation study using an urban region in the United States

Abstract

Background

Approval of novel vaccines for COVID-19 had brought hope and expectations, but not without additional challenges. One central challenge was understanding how to appropriately prioritize the use of limited supply of vaccines. This study examined the efficacy of the various vaccine prioritization strategies using the vaccination campaign underway in the U.S.

Methods

The study developed a granular agent-based simulation model for mimicking community spread of COVID-19 under various social interventions including full and partial closures, isolation and quarantine, use of face mask and contact tracing, and vaccination. The model was populated with parameters of disease natural history, as well as demographic and societal data for an urban community in the U.S. with 2.8 million residents. The model tracks daily numbers of infected, hospitalized, and deaths for all census age-groups. The model was calibrated using parameters for viral transmission and level of community circulation of individuals. Published data from the Florida COVID-19 dashboard was used to validate the model. Vaccination strategies were compared using a hypothesis test for pairwise comparisons.

Results

Three prioritization strategies were examined: a minor variant of CDC’s recommendation, an age-stratified strategy, and a random strategy. The impact of vaccination was also contrasted with a no vaccination scenario. The study showed that the campaign against COVID-19 in the U.S. using vaccines developed by Pfizer/BioNTech and Moderna 1) reduced the cumulative number of infections by 10% and 2) helped the pandemic to subside below a small threshold of 100 daily new reported cases sooner by approximately a month when compared to no vaccination. A comparison of the prioritization strategies showed no significant difference in their impacts on pandemic mitigation.

Conclusions

The vaccines for COVID-19 were developed and approved much quicker than ever before. However, as per our model, the impact of vaccination on reducing cumulative infections was found to be limited (10%, as noted above). This limited impact is due to the explosive growth of infections that occurred prior to the start of vaccination, which significantly reduced the susceptible pool of the population for whom infection could be prevented. Hence, vaccination had a limited opportunity to reduce the cumulative number of infections. Another notable observation from our study is that instead of adhering strictly to a sequential prioritizing strategy, focus should perhaps be on distributing the vaccines among all eligible as quickly as possible, after providing for the most vulnerable. As much of the population worldwide is yet to be vaccinated, results from this study should aid public health decision makers in effectively allocating their limited vaccine supplies.


https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01458-9

Impact of the COVID-19 pandemic on publication dynamics and non-COVID-19 research production

Abstract

Background

The COVID-19 pandemic has severely affected health systems and medical research worldwide but its impact on the global publication dynamics and non-COVID-19 research has not been measured. We hypothesized that the COVID-19 pandemic may have impacted the scientific production of non-COVID-19 research.

Methods

We conducted a comprehensive meta-research on studies (original articles, research letters and case reports) published between 01/01/2019 and 01/01/2021 in 10 high-impact medical and infectious disease journals (New England Journal of Medicine, Lancet, Journal of the American Medical Association, Nature Medicine, British Medical Journal, Annals of Internal Medicine, Lancet Global Health, Lancet Public Health, Lancet Infectious Disease and Clinical Infectious Disease). For each publication, we recorded publication date, publication type, number of authors, whether the publication was related to COVID-19, whether the publication was based on a case series, and the number of patients included in the study if the publication was based on a case report or a case series. We estimated the publication dynamics with a locally estimated scatterplot smoothing method. A Natural Language Processing algorithm was designed to calculate the number of authors for each publication. We simulated the number of non-COVID-19 studies that could have been published during the pandemic by extrapolating the publication dynamics of 2019 to 2020, and comparing the expected number to the observed number of studies.

Results

Among the 22,525 studies assessed, 6319 met the inclusion criteria, of which 1022 (16.2%) were related to COVID-19 research. A dramatic increase in the number of publications in general journals was observed from February to April 2020 from a weekly median number of publications of 4.0 (IQR: 2.8–5.5) to 19.5 (IQR: 15.8–24.8) (p < 0.001), followed afterwards by a pattern of stability with a weekly median number of publications of 10.0 (IQR: 6.0–14.0) until December 2020 (p = 0.045 in comparison with April). Two prototypical editorial strategies were found: 1) journals that maintained the volume of non-COVID-19 publications while integrating COVID-19 research and thus increased their overall scientific production, and 2) journals that decreased the volume of non-COVID-19 publications while integrating COVID-19 publications. We estimated using simulation models that the COVID pandemic was associated with a 18% decrease in the production of non-COVID-19 research. We also found a significant change of the publication type in COVID-19 research as compared with non-COVID-19 research illustrated by a decrease in the number of original articles, (47.9% in COVID-19 publications vs 71.3% in non-COVID-19 publications, p < 0.001). Last, COVID-19 publications showed a higher number of authors, especially for case reports with a median of 9.0 authors (IQR: 6.0–13.0) in COVID-19 publications, compared to a median of 4.0 authors (IQR: 3.0–6.0) in non-COVID-19 publications (p < 0.001).

Conclusion

In this meta-research gathering publications from high-impact medical journals, we have shown that the dramatic rise in COVID-19 publications was accompanied by a substantial decrease of non-COVID-19 research.

https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01404-9

Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses

Abstract

Background

Systematic reviews are the cornerstone of evidence-based medicine. However, systematic reviews are time consuming and there is growing demand to produce evidence more quickly, while maintaining robust methods. In recent years, artificial intelligence and active-machine learning (AML) have been implemented into several SR software applications. As some of the barriers to adoption of new technologies are the challenges in set-up and how best to use these technologies, we have provided different situations and considerations for knowledge synthesis teams to consider when using artificial intelligence and AML for title and abstract screening.

Methods

We retrospectively evaluated the implementation and performance of AML across a set of ten historically completed systematic reviews. Based upon the findings from this work and in consideration of the barriers we have encountered and navigated during the past 24 months in using these tools prospectively in our research, we discussed and developed a series of practical recommendations for research teams to consider in seeking to implement AML tools for citation screening into their workflow.

Results

We developed a seven-step framework and provide guidance for when and how to integrate artificial intelligence and AML into the title and abstract screening process. Steps include: (1) Consulting with Knowledge user/Expert Panel; (2) Developing the search strategy; (3) Preparing your review team; (4) Preparing your database; (5) Building the initial training set; (6) Ongoing screening; and (7) Truncating screening. During Step 6 and/or 7, you may also choose to optimize your team, by shifting some members to other review stages (e.g., full-text screening, data extraction).

Conclusion

Artificial intelligence and, more specifically, AML are well-developed tools for title and abstract screening and can be integrated into the screening process in several ways. Regardless of the method chosen, transparent reporting of these methods is critical for future studies evaluating artificial intelligence and AML.


https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-021-01451-2

Effectiveness of exercise interventions on mental health and health-related quality of life in women with polycystic ovary syndrome: a systematic review

Abstract

Background

Polycystic ovary syndrome (PCOS) is a complex condition, impacting cardio-metabolic and reproductive health, mental health and health-related quality of life. The physical health benefits of exercise for women with PCOS are well-established and exercise is increasingly being recognised as efficacious for improving psychological wellbeing. The aim of this review was to summarise the evidence regarding the effectiveness of exercise interventions on mental health outcomes in women with PCOS.

Methods

A systematic search of electronic databases was conducted in March of 2020. Trials that evaluated the effect of an exercise intervention on mental health or health-related quality of life outcomes in reproductive aged women with diagnosed PCOS were included. Methodological quality was assessed using the modified Downs and Black checklist. Primary outcomes included symptoms of depression and anxiety, and health-related quality of life.

Results

Fifteen articles from 11 trials were identified and deemed eligible for inclusion. Exercise demonstrated positive improvements in health-related quality of life in all of the included studies. Half of included studies also reported significant improvements in depression and anxiety symptoms. There was large variation in methodological quality of included studies and in the interventions utilised.

Conclusions

The available evidence indicates that exercise is effective for improving health-related quality of life and PCOS symptom distress. Exercise also shows some efficacy for improving symptoms and/or prevalence of depression and anxiety in women with PCOS. However, due to large heterogeneity of included studies, conclusions could not be made regarding the impact of exercise intervention characteristics. High-quality trials with well reported exercise intervention characteristics and outcomes are required in order to determine effective exercise protocols for women with PCOS and facilitate translation into practice.


https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-12280-9

Economic burden of varicella in Europe in the absence of universal varicella vaccination

Abstract

Background

Though the disease burden of varicella in Europe has been reported previously, the economic burden is still unknown. This study estimated the economic burden of varicella in Europe in the absence of Universal Varicella Vaccination (UVV) in 2018 Euros from both payer (direct costs) and societal (direct and indirect costs) perspectives.

Methods

We estimated the country specific and overall annual costs of varicella in absence of UVV in 31 European countries (27 EU countries, plus Iceland, Norway, Switzerland and the United Kingdom). To obtain country specific unit costs and associated healthcare utilization, we conducted a systematic literature review, searching in PubMed, EMBASE, NEED, DARE, REPEC, Open Grey, and public heath websites (1/1/1999–10/15/2019). The number of annual varicella cases, deaths, outpatient visits and hospitalizations were calculated (without UVV) based on age-specific incidence rates (Riera-Montes et al. 2017) and 2018 population data by country. Unit cost per varicella case and disease burden data were combined using stochastic modeling to estimate 2018 costs stratified by country, age and healthcare resource.

Results

Overall annual total costs associated with varicella were estimated to be €662,592,061 (Range: €309,552,363 to €1,015,631,760) in Europe in absence of UVV. Direct and indirect costs were estimated at €229,076,206 (Range €144,809,557 to €313,342,856) and €433,515,855 (Range €164,742,806 to €702,288,904), respectively. Total cost per case was €121.45 (direct: €41.99; indirect: €79.46). Almost half of the costs were attributed to cases in children under 5 years, owing mainly to caregiver work loss. The distribution of costs by healthcare resource was similar across countries. France and Germany accounted for 49.28% of total annual costs, most likely due to a combination of high numbers of cases and unit costs in these countries.

Conclusions

The economic burden of varicella across Europe in the absence of UVV is substantial (over 600 M€), primarily driven by caregiver burden including work productivity losses.


https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-12343-x

History of drinking problems diminishes the protective effects of within-guideline drinking on 18-year risk of dementia and CIND

Abstract

Objective

To examine the moderating effect of older adults’ history of drinking problems on the relationship between their baseline alcohol consumption and risk of dementia and cognitive impairment, no dementia (CIND) 18 years later.

Method

A longitudinal Health and Retirement Study cohort (n = 4421) was analyzed to demonstrate how older adults’ baseline membership in one of six drinking categories (non-drinker, within-guideline drinker, and outside-guideline drinker groups, divided to reflect absence or presence of a history of drinking problems) predicts dementia and CIND 18 years later.

Results

Among participants with no history of drinking problems, 13% of non-drinkers, 5% of within-guideline drinkers, and 9% of outside-guideline drinkers were classified as having dementia 18-years later. Among those with a history of drinking problems, 14% of non-drinkers, 9% of within-guideline drinkers, and 7% of outside-guideline drinkers were classified with dementia. With Non-Drinker, No HDP as reference category, being a baseline within-guideline drinker with no history of drinking problems reduced the likelihood of dementia 18 years later by 45%, independent of baseline demographic and health characteristics; being a baseline within-guideline drinker with a history of drinking problems reduced the likelihood by only 13% (n.s.). Similar patterns obtained for the prediction of CIND.

Conclusions

For older adults, consuming alcohol at levels within validated guidelines for low-risk drinking may offer moderate long-term protection from dementia and CIND, but this effect is diminished by having a history of drinking problems. Efforts to predict and prevent dementia and CIND should focus on older adults’ history of drinking problems in addition to how much alcohol they consume.


https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-021-12358-4

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