Family member and service provider experiences and perspectives of a digital surveillance and service navigation approach in multicultural context: a qualitative study in identifying the barriers and enablers to Watch Me Grow-Electronic (WMG-E) program with a culturally diverse community.

BACKGROUND: Children and families from priority populations experienced significant psychosocial and mental health issues to the COVID-19 pandemic. Yet they also faced significant barriers to service access, particularly families from culturally and linguistically diverse (CALD) backgrounds. With most child and family health nurse clinics ceasing in-person consultations due to the pandemic, many children missed out on health and developmental checks. The aim of this study was to investigate the perspectives and experiences of family members and service providers from an urban, CALD community regarding the implementation of a digital, developmental surveillance, Watch Me Grow-Electronic (WMG-E) program. METHODS: Semi-structured interviews were conducted with 17 family members, service navigators, and service providers in a multicultural community in South Western Sydney, Australia. This qualitative study is an implementation evaluation which formed as part of a larger, two-site, randomised controlled trial of the WMG-E program. A reflexive thematic analysis approach, using inductive coding, was adopted to analyse the data. RESULTS: Participants highlighted the comprehensive and personalised support offered by existing child and family health services. The WMG-E was deemed beneficial because the weblink was easy and quick to use and it enabled access to a service navigator who support family access to relevant services. However, the WMG-E was problematic because of technology or language barriers, and it did not facilitate immediate clinician involvement when families completed the weblink. CONCLUSIONS: Families and service providers in this qualitative study found that using WMG-E empowered parents and caregivers to access developmental screening and learn more about their child's development and engage with relevant services. This beds down a new and innovative solution to the current service delivery gap and create mechanisms that can engage families currently not accessing services, and increases knowledge around navigating the health and social care services. Notwithstanding the issues that were raised by families and service providers, which include accessibility challenges for CALD communities, absence of clinical oversight during screening, and narrow scope of engagement with available services being offered, it is worth noting that improvements regarding these implementation factors must be considered and addressed in order to have longevity and sustainability of the program. TRIAL REGISTRATION: The study is part of a large randomised controlled trial (Protocol No. 1.0, Version 3.1) was registered with ANZCTR (registration number: ACTRN12621000766819) on July 21st, 2021 and reporting of the trial results will be according to recommendations in the CONSORT Statement.

Approaches for Evaluating Visit-to-Visit Blood Pressure Variability as a Cardiovascular Disease Risk Factor: A Scoping Review.

Visit-to-visit blood pressure variability (BPV) is associated with cardiovascular disease (CVD) and its mortality, independent of mean blood pressure (BP). However, in real world clinical practice this phenomenon is under-appreciated by clinicians. Serial BPV measured at clinical visits are frequently considered random fluctuations. This scoping review aims to review methodologies for estimating BPV, including metrics, frequency of BP measurements, BPV observation and follow-up durations. The review also compares studies that used electronic health record (EHR) data and those that used non-EHR data to assess BPV. We found little or no consensus on metrics used for BPV estimation in either study using EHR or non-EHR data. The non-EHR studies followed a stricter protocol for BP measurement than the EHR-based studies. Both groups of studies used comparable methodologies to estimate BPV.

Strategies to Address Statin Medication Intolerance Among Patients at Risk of Cardiovascular Disease Identified Through Electronic Health Records: A Literature Review and Pooled Analysis.

Statins are a group of medications that lower lipid and are used for primary and secondary prevention of cardiovascular diseases (CVD). Patients can either be partially (15%) or completely (5%) intolerant to statins. Symptoms of statin intolerance can include muscle aches (myalgia), weakness, cramps, myopathy, diabetes mellitus, and elevated creatine kinase levels. Decreasing statin intolerance also improves statin adherence, which in turn results in lower number of CVD events among patients. Studies on statin intolerance is often embedded within studies of statin adherence. However, relevant data can be obtained from digital health systems. This preliminary literature review looks at studies from the past 10 years to identify and determine the effectiveness of strategies to address statin intolerance. The NLA definition for statin intolerance was used in this review. The initial search results on EMBASE, PubMed, SCOPUS, and CINAHL showed 91 articles and applying the inclusion and exclusion criteria, four articles were used in this review and pooled analysis. The study patients were identified through electronic health records. The pooled analysis was done using the Metafor package in R, applying a random-effect model to estimate pooled effect size. The findings suggest that using fixed dose combination therapy and switching from a lipophilic statin to a hydrophilic statin, while correcting metabolic abnormalities, or initiating evolocumab alongside statin can address statin intolerance. The overall relative risk (RR) was 0.40 (95% CI, 0.09 to 1.70) with I2 90%, and the overall odds ratio (OR) was 0.11 (95% CI, 0.01 to 1.59) with I2 94%, suggesting that the interventions work well in addressing statin intolerance. Since statin intolerance is has a vast range of effects, further research works may be done on exploring the possibility of using digital health systems to identify and provide targeted interventions to patients.

Association Between Visit to Visit Blood Pressure Variability and Cardiovascular Outcome: A Meta-Analysis.

Visit-to-visit (VVV) blood pressure variability (BPV) is associated with cardiovascular disease. However, in practice, BPV at sequential clinic visits is often regarded as mere random fluctuations and frequently under-appreciated by the clinicians. Therefore, this meta-analysis aims to compare the effect size of VVV BPV on cardiovascular outcome, by comparing studies that have used the electronic health record (EHR) and non-EHR data. The pooled hazard ratio for VVV BPV is comparable between studies using EHR and non-EHR data. Studies using EHR reported a pooled hazard ratio (HR) for VVV systolic BPV of 1.22 (95% CI: 1.07-1.38), while non-EHR studies had a HR of 1.16 (95% CI: 1.10-1.22). The pooled HR for VVV diastolic BPV in EHR studies was 1.09 (95% CI: 0.86-1.39), whereas non-EHR studies showed a HR of 1.10 (95% CI: 1.04-1.17). EHR data is a reliable source for assessing BPV, which in turn can predict the CVD outcomes.

What role can 'health decision-makers' play in supporting knowledge translation of health and medical research?

CEmbedding research users into the research process can better support its translation into health systems and services. Still, the role of health decision-makers (HDMs) as research partners is poorly understood. HDMs, such as policymakers, administrators, directors or other managers, understand the broader contexts of a health service and have a mandate to facilitate change where appropriate, so they could play an important partnership role in research activities.

Correction: Mobile App Intervention of a Randomized Controlled Trial for Patients With Obesity and Those Who Are Overweight in General Practice: User Engagement Analysis Quantitative Study.

[This corrects the article DOI: 10.2196/45942.].

Seamless EMR data access: Integrated governance, digital health and the OMOP-CDM.

Objectives In this overview, we describe theObservational Medical Outcomes Partnership Common Data Model (OMOP-CDM), the established governance processes employed in EMR data repositories, and demonstrate how OMOP transformed data provides a lever for more efficient and secure access to electronic medical record (EMR) data by health service providers and researchers.Methods Through pseudonymisation and common data quality assessments, the OMOP-CDM provides a robust framework for converting complex EMR data into a standardised format. This allows for the creation of shared end-to-end analysis packages without the need for direct data exchange, thereby enhancing data security and privacy. By securely sharing de-identified and aggregated data and conducting analyses across multiple OMOP-converted databases, patient-level data is securely firewalled within its respective local site.Results By simplifying data management processes and governance, and through the promotion of interoperability, the OMOP-CDM supports a wide range of clinical, epidemiological, and translational research projects, as well as health service operational reporting.Discussion Adoption of the OMOP-CDM internationally and locally enables conversion of vast amounts of complex, and heterogeneous EMR data into a standardised structured data model, simplifies governance processes, and facilitates rapid repeatable cross-institution analysis through shared end-to-end analysis packages, without the sharing of data.Conclusion The adoption of the OMOP-CDM has the potential to transform health data analytics by providing a common platform for analysing EMR data across diverse healthcare settings.

Mobile App Intervention of a Randomized Controlled Trial for Patients With Obesity and Those Who Are Overweight in General Practice: User Engagement Analysis Quantitative Study.

BACKGROUND: The Health eLiteracy for Prevention in General Practice trial is a primary health care-based behavior change intervention for weight loss in Australians who are overweight and those with obesity from lower socioeconomic areas. Individuals from these areas are known to have low levels of health literacy and are particularly at risk for chronic conditions, including diabetes and cardiovascular disease. The intervention comprised health check visits with a practice nurse, a purpose-built patient-facing mobile app (mysnapp), and a referral to telephone coaching. OBJECTIVE: This study aimed to assess mysnapp app use, its user profiles, the duration and frequency of use within the Health eLiteracy for Prevention in General Practice trial, its association with other intervention components, and its association with study outcomes (health literacy and diet) to determine whether they have significantly improved at 6 months. METHODS: In 2018, a total of 22 general practices from 2 Australian states were recruited and randomized by cluster to the intervention or usual care. Patients who met the main eligibility criteria (ie, BMI>28 in the previous 12 months and aged 40-74 years) were identified through the clinical software. The practice staff then provided the patients with details about this study. The intervention consisted of a health check with a practice nurse and a lifestyle app, a telephone coaching program, or both depending on the participants' choice. Data were collected directly through the app and combined with data from the 6-week health check with the practice nurses, the telephone coaching, and the participants' questionnaires at baseline and 6-month follow-up. The analyses comprised descriptive and inferential statistics. RESULTS: Of the 120 participants who received the intervention, 62 (52%) chose to use the app. The app and nonapp user groups did not differ significantly in demographics or prior recent hospital admissions. The median time between first and last app use was 52 (IQR 4-95) days, with a median of 5 (IQR 2-10) active days. App users were significantly more likely to attend the 6-week health check (2-sided Fisher exact test; P<.001) and participate in the telephone coaching (2-sided Fisher exact test; P=.007) than nonapp users. There was no association between app use and study outcomes shown to have significantly improved (health literacy and diet) at 6 months. CONCLUSIONS: Recruitment and engagement were difficult for this study in disadvantaged populations with low health literacy. However, app users were more likely to attend the 6-week health check and participate in telephone coaching, suggesting that participants who opted for several intervention components felt more committed to this study. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12617001508369; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=373505. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2018-023239.

Strategies to Improve Statin Medication Adherence Among Patients at Risk of Cardiovascular Disease Identified Through Electronic Health Records: A Literature Review.

Statin is a group of lipid/cholesterol-lowering medications that is commonly used for primary and secondary prevention of cardiovascular diseases (CVD). In Australia, this is the first line of pharmacological therapy for CVD risk management. High-risk patients who do not adhere to lipid-modifying medicines have an increased risk of CVD mortality, hospitalization, and revascularization. However, studies show that 67% of patients are non-adherent to statins. As such, improving statin adherence through various strategies is very important. This literature review delves into the studies from the past 10 years to identify the various strategies used and their effectiveness to improve statin adherence. The initial search results on PubMed showed 157 articles and based on the inclusion and exclusion criteria, 7 articles were finally used for this review. The patients in the studies were identified through electronic health records. The findings suggest that education, counselling and motivation through face-to-face interaction, phone calls or text messages, reminder messages and frequent follow-up visits are good strategies to improve statin adherence. Alongside these, simplifying regimens, switching combinations of medicines, or using alternate dosing have also been shown to improve statin adherence. In summary, counselling and face-to-face interaction are effective methods for improving statin adherence. The use of electronic health record (EHR) systems combined with targeted interventions delivered to patients identified to be non-adherent to statin may further improve statin adherence.

Visit-to-Visit Blood Pressure Variability in Cardiovascular Disease.

Visit-to-visit blood pressure variability (BPV) is associated with cardiovascular disease (CVD), independently of mean blood pressure (BP). However, in real world clinical practice, this phenomenon is frequently considered as random fluctuation. This review aimed to investigate the differences among studies investigating visit-to-visit BPV and CVD using electronic health record (EHR) and clinical trial data. Our review suggests that BP values in clinical trial data are derived using a stricter protocol compared to EHR data. Furthermore, there was no consensus on metrics used in estimation of BPV.

Revisiting Transfer Learning Method for Tuberculosis Diagnosis.

Transfer learning (TL) has been proven to be a good strategy for solving domain-specific problems in many deep learning (DL) applications. Typically, in TL, a pre-trained DL model is used as a feature extractor and the extracted features are then fed to a newly trained classifier as the model head. In this study, we propose a new ensemble approach of transfer learning that uses multiple neural network classifiers at once in the model head. We compared the classification results of the proposed ensemble approach with the direct approach of several popular models, namely VGG-16, ResNet-50, and MobileNet, on two publicly available tuberculosis datasets, i.e., Montgomery County (MC) and Shenzhen (SZ) datasets. Moreover, we also compared the results when a fully pre-trained DL model was used for feature extraction versus the cases in which the features were obtained from a middle layer of the pre-trained DL model. Several metrics derived from confusion matrix results were used, namely the accuracy (ACC), sensitivity (SNS), specificity (SPC), precision (PRC), and F1-score. We concluded that the proposed ensemble approach outperformed the direct approach. Best result was achieved by ResNet-50 when the features were extracted from a middle layer with an accuracy of 91.2698% on MC dataset.Clinical Relevance- The proposed ensemble approach could increase the detection accuracy of 7-8% for Montgomery County dataset and 4-5% for Shenzhen dataset.

General practitioners' perspectives regarding early developmental surveillance for autism within the australian primary healthcare setting: a qualitative study.

BACKGROUND: Significant challenges remain in the early identification of child developmental disabilities in the community. Implementing supports and services early in the life course has been shown to promote positive developmental outcomes for children at high likelihood of developmental disabilities, including autism. As part of a cluster randomised controlled trial, this study seeks to examine and compare the perspectives and experiences of Australian general practitioners (GPs) in relation to a digital developmental surveillance program for autism and usual care pathway, in general practice clinics. METHODS: A qualitative research methodology with semi-structured interviews and thematic inductive analysis underpinned by grounded theory was utilised. All GPs from South Western Sydney (NSW) and Melbourne (Victoria) who participated in the main program ("GP Surveillance for Autism") were invited to the interview. GPs who provided consent were interviewed either over online or in-person meeting. Interviews were audio-recorded, transcribed, and coded using NVivo12 software. Inductive interpretive approach was adopted and data were analysed thematically. RESULTS: Twenty-three GPs across the two sites (NSW: n = 11; Victoria: n = 12) agreed to be interviewed; data saturation had reached following this number of participants. Inductive thematic coding and analysis yielded eight major themes and highlighted common enablers such as the role of GPs in early identification and subsequent supports, enhanced communication between clinicians/professionals, relationship-building with patients, and having standardised screening tools. Specific facilitators to the feasibility and acceptability of a digital screening program for the early identification of developmental disabilities, including the early signs of autism, and encouraging research and education for GPs. However, several practical and socioeconomic barriers were identified, in addition to limited knowledge and uptake of child developmental screening tools as well as COVID-19 lockdown impacts. Common and specific recommendations involve supporting GPs in developmental/paediatrics training, streamlined screening process, and funding and resources in the primary healthcare services. CONCLUSIONS: The study highlighted the need for practice and policy changes, including further training of GPs alongside sufficient time to complete developmental checks and appropriate financial remuneration through a Medicare billing item. Further research is needed on implementation and scale up of a national surveillance program for early identification of developmental disabilities, including autism.

Understanding the convergence of social enterprise, digital health, and citizen engagement for co-producing integrated Person-Centred health services: A critical review and theoretical framework.

INTRODUCTION: To achieve Universal Health Coverage and the United Nations' (UN) Sustainable Development Goals (SDGs) agenda for 2030, the World Health Organisation (WHO) recommended the use of social enterprise, digital technology, and citizen engagement in the delivery of Integrated People-Centred Health Services (IPCHS) as part of its strategic vision for 21st century primary care. METHODS: We conducted a hermeneutic review of frameworks, models and theories on social enterprise, digital health, citizen engagement and IPCHS. This involved multiple iterative cycles of (i) searching and acquisition, followed by (ii) critical analysis and interpretation of literature to assemble arguments and evidence for conceptual relationships until information saturation was reached. This process identified a set of constructs which we synthesised into a testable framework. RESULTS: Several interdisciplinary frameworks, models and theories explain how social enterprises could use digital technology, and citizen engagement to enable the technical and social integration required to facilitate people-centred primary care. Innovative approaches can be used to maintain financial sustainability while delivering IPCHS at lower cost to vulnerable and marginalised populations in both developed and developing countries. CONCLUSION: This framework provides a theoretical grounding to guide empirical inquiry into how social enterprises use digital technology to engage citizens in co-producing IPCHS.

A nationwide study of COVID-19 impact on mental health-related presentations among children and adolescents to primary care practices in Australia.

This study explored the impacts of COVID-19 on the mental health (MH)-related visits to general practices (GPs) among children and young people (CYP) up to 18 years of age in Australia. This study analysed national-level data captured by the NPS MedicineWise program on monthly CYP MH-related visits per 10,000 visits to GPs from January 2014 to September 2021. We considered the pre-COVID-19 period (January 2014-February 2020) and the COVID-19 period (March 2020-September 2021). We used a Bayesian structural time series (BSTS) model to estimate the impact of COVID-19 on MH-related GP visits per 10,000 visits. A total of 103,813 out of 7,690,874 visits to GP (i.e., about 135 per 10,000 visits) were related to MH during study period. The BSTS model showed a significant increase in the overall MH-related visits during COVID-19 period (33%, 95% Credible Interval (Crl) 8.5%-56%), particularly, visits related to depressive disorders (61%, 95% Crl 29%-91%). The greatest increase was observed among females (39%, 95% Crl 12%-64%) and those living in socioeconomically least disadvantaged areas (36%, 95% Crl 1.2-71%). Our findings highlight the need for resources to be directed towards at-risk CYP to improve MH outcomes and reduce health system burden.

Integrated Management Systems (IMS) to Support and Sustain Quality One Health Services: International Lessons from the COVID-19 Pandemic by the IMIA Primary Care Working Group.

OBJECTIVES: One Health considers human, animal and environment health as a continuum. The COVID-19 pandemic started with the leap of a virus from animals to humans. Integrated management systems (IMS) should provide a coherent management framework, to meet reporting requirements and support care delivery. We report IMS deployment during, and retention post the COVID-19 pandemic, and exemplar One Health use cases. METHODS: Six volunteer members of the International Medical Association's (IMIA) Primary Care Working Group provided data about any IMS and One Health use to support the COVID-19 pandemic initiatives. We explored how IMS were: (1) Integrated with organisational strategy; (2) Utilised standardised processes, and (3) Met reporting requirements, including public health. Selected contributors provided Unified Modelling Language (UML) use case diagram for a One Health exemplar. RESULTS: There was weak evidence of synergy between IMS and health system strategy to the COVID-19 pandemic. However, there were rapid pragmatic responses to COVID-19, not citing IMS. All health systems implemented IMS to link COVID test results, vaccine uptake and outcomes, particularly mortality and to provide patients access to test results and vaccination certification. Neither proportion of gross domestic product alone, nor vaccine uptake determined outcome. One Health exemplars demonstrated that animal, human and environmental specialists could collaborate. CONCLUSIONS: IMS use improved the pandemic response. However, IMS use was pragmatic rather than utilising an international standard, with some of their benefits lost post-pandemic. Health systems should incorporate IMS that enables One Health approaches as part of their post COVID-19 pandemic preparedness.

Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.

BACKGROUND: Tuberculosis (TB) was the leading infectious cause of mortality globally prior to COVID-19 and chest radiography has an important role in the detection, and subsequent diagnosis, of patients with this disease. The conventional experts reading has substantial within- and between-observer variability, indicating poor reliability of human readers. Substantial efforts have been made in utilizing various artificial intelligence-based algorithms to address the limitations of human reading of chest radiographs for diagnosing TB. OBJECTIVE: This systematic literature review (SLR) aims to assess the performance of machine learning (ML) and deep learning (DL) in the detection of TB using chest radiography (chest x-ray [CXR]). METHODS: In conducting and reporting the SLR, we followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 309 records were identified from Scopus, PubMed, and IEEE (Institute of Electrical and Electronics Engineers) databases. We independently screened, reviewed, and assessed all available records and included 47 studies that met the inclusion criteria in this SLR. We also performed the risk of bias assessment using Quality Assessment of Diagnostic Accuracy Studies version 2 (QUADAS-2) and meta-analysis of 10 included studies that provided confusion matrix results. RESULTS: Various CXR data sets have been used in the included studies, with 2 of the most popular ones being Montgomery County (n=29) and Shenzhen (n=36) data sets. DL (n=34) was more commonly used than ML (n=7) in the included studies. Most studies used human radiologist's report as the reference standard. Support vector machine (n=5), k-nearest neighbors (n=3), and random forest (n=2) were the most popular ML approaches. Meanwhile, convolutional neural networks were the most commonly used DL techniques, with the 4 most popular applications being ResNet-50 (n=11), VGG-16 (n=8), VGG-19 (n=7), and AlexNet (n=6). Four performance metrics were popularly used, namely, accuracy (n=35), area under the curve (AUC; n=34), sensitivity (n=27), and specificity (n=23). In terms of the performance results, ML showed higher accuracy (mean ~93.71%) and sensitivity (mean ~92.55%), while on average DL models achieved better AUC (mean ~92.12%) and specificity (mean ~91.54%). Based on data from 10 studies that provided confusion matrix results, we estimated the pooled sensitivity and specificity of ML and DL methods to be 0.9857 (95% CI 0.9477-1.00) and 0.9805 (95% CI 0.9255-1.00), respectively. From the risk of bias assessment, 17 studies were regarded as having unclear risks for the reference standard aspect and 6 studies were regarded as having unclear risks for the flow and timing aspect. Only 2 included studies had built applications based on the proposed solutions. CONCLUSIONS: Findings from this SLR confirm the high potential of both ML and DL for TB detection using CXR. Future studies need to pay a close attention on 2 aspects of risk of bias, namely, the reference standard and the flow and timing aspects. TRIAL REGISTRATION: PROSPERO CRD42021277155; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=277155.

Using machine learning to predict cardiovascular risk using self-reported questionnaires: Findings from the 45 and Up Study.

BACKGROUND: Machine learning has been shown to outperform traditional statistical methods for risk prediction model development. We aimed to develop machine learning-based risk prediction models for cardiovascular mortality and hospitalisation for ischemic heart disease (IHD) using self-reported questionnaire data. METHODS: The 45 and Up Study was a retrospective population-based study in New South Wales, Australia (2005-2009). Self-reported healthcare survey data on 187,268 participants without a history of cardiovascular disease was linked to hospitalisation and mortality data. We compared different machine learning algorithms, including traditional classification methods (support vector machine (SVM), neural network, random forest and logistic regression) and survival methods (fast survival SVM, Cox regression and random survival forest). RESULTS: A total of 3687 participants experienced cardiovascular mortality and 12,841 participants had IHD-related hospitalisation over a median follow-up of 10.4 years and 11.6 years respectively. The best model for cardiovascular mortality was a Cox survival regression with L1 penalty at a re-sampled case/non-case ratio of 0.3 achieved by under-sampling of the non-cases. This model had the Uno's and Harrel's concordance indexes of 0.898 and 0.900 respectively. The best model for IHD hospitalisation was a Cox survival regression with L1 penalty at a re-sampled case/non-case ratio of 1.0 with Uno's and Harrel's concordance indexes of 0.711 and 0.718 respectively. CONCLUSION: Machine learning-based risk prediction models developed using self-reported questionnaire data had good prediction performance. These models may have the potential to be used in initial screening tests to identify high-risk individuals before undergoing costly investigation.

HIV treatment-as-prevention and its effect on incidence of HIV among cisgender gay, bisexual, and other men who have sex with men in Australia: a 10-year longitudinal cohort study.

BACKGROUND: Although HIV treatment-as-prevention reduces individual-level HIV transmission, population-level effects are unclear. We aimed to investigate whether treatment-as-prevention could achieve population-level reductions in HIV incidence among gay, bisexual, and other men who have sex with men (GBM) in Australia's most populous states, New South Wales and Victoria. METHODS: TAIPAN was a longitudinal cohort study using routine health record data extracted from 69 health services that provide HIV diagnosis and care to GBM in New South Wales and Victoria, Australia. Data from Jan 1, 2010, to Dec 31, 2019, were linked within and between services and over time. TAIPAN collected data from all cisgender GBM who attended participating services, resided in New South Wales or Victoria, and were 16 years or older. Two cohorts were established: one included HIV-positive patients, and the other included HIV-negative patients. Population prevalence of viral suppression (plasma HIV viral load <200 RNA copies per µL) was calculated by combining direct measures of viral load among the HIV-positive cohort with estimates for undiagnosed GBM. The primary outcome of HIV incidence was measured directly via repeat testing in the HIV-negative cohort. Poisson regression analyses with generalised estimating equations assessed temporal associations between population prevalence of viral suppression and HIV incidence among the subsample of HIV-negative GBM with multiple instances of HIV testing. FINDINGS: At baseline, the final sample (n=101 772) included 90 304 HIV-negative and 11 468 HIV-positive GBM. 59 234 patients in the HIV-negative cohort had two or more instances of HIV testing and were included in the primary analysis. Over the study period, population prevalence of viral suppression increased from 69·27% (95% CI 66·41-71·96) to 88·31% (86·37-90·35), while HIV incidence decreased from 0·64 per 100 person-years (95% CI 0·55-0·76) to 0·22 per 100 person-years (0·17-0·28). Adjusting for sociodemographic characteristics and HIV pre-exposure prophylaxis (PrEP) use, treatment-as-prevention achieved significant population-level reductions in HIV incidence among GBM: a 1% increase in population prevalence of viral suppression corresponded with a 6% decrease in HIV incidence (incidence rate ratio [IRR] 0·94, 95% CI 0·93-0·96; p<0·0001). PrEP was introduced in 2016 with 17·60% uptake among GBM that year, which increased to 36·38% in 2019. The relationship between population prevalence of viral suppression and HIV incidence was observed before the availability of PrEP (IRR 0·98, 95% CI 0·96-0·99; p<0·0001) and was even stronger after the introduction of PrEP (0·80, 0·70-0·93; p=0·0030). INTERPRETATION: Our results suggest that further investment in HIV treatment, especially alongside PrEP, can improve public health by reducing HIV incidence among GBM. FUNDING: National Health and Medical Research Council of Australia.

Digital health and capability maturity models-a critical thematic review and conceptual synthesis of the literature.

OBJECTIVE: A literature review of capability maturity models (MMs) to inform the conceptualization, development, implementation, evaluation, and mainstreaming of MMs in digital health (DH). METHODS: Electronic databases were searched using "digital health," "maturity models," and related terms based on the Digital Health Profile and Maturity Assessment Toolkit Maturity Model (DHPMAT-MM). Covidence was used to screen, identify, capture, and achieve consensus on data extracted by the authors. Descriptive statistics were generated. A thematic analysis and conceptual synthesis were conducted. FINDINGS: Diverse domain-specific MMs and model development, implementation, and evaluation methods were found. The spread and pattern of different MMs verified the essential DH foundations and five maturity stages of the DHPMAT-MM. An unanticipated finding was the existence of a new category of community-facing MMs. Common characteristics included:1. A dynamic lifecycle approach to digital capability maturity, which is:a. responsive to environmental changes and may improve or worsen over time;b. accumulative, incorporating the attributes of the preceding stage; andc. sequential, where no maturity stage must be skipped.2. Sociotechnical quality improvement of the DH ecosystem and MM, which includes:a. investing in the organization's human, hardware, and software resources andb. a need to engage and improve the DH competencies of citizens. CONCLUSIONS: The diversity in MMs and variability in methods and content can create cognitive dissonance. A metamodel like the DHPMAT-MM can logically unify the many domain-specific MMs and guide the overall implementation and evaluation of DH ecosystems and MMs over the maturity lifecycle.