Assessing the likelihood of contracting COVID-19 disease based on a predictive tree model: A retrospective cohort study

Transmission of Viruses from Restroom Use: A Quantitative Microbial Risk Assessment

Restroom use has been implicated in a number of viral outbreaks. In this study, we apply quantitative microbial risk assessment to quantify the risk of viral transmission by contaminated restroom fomites. We estimate risk from high-touch fomite surfaces (entrance/exit door, toilet seat) for three viruses of interest (SARS-CoV-2, adenovirus, norovirus) through eight exposure scenarios involving differing user behaviors, and the use of hand sanitizer following each scenario. We assessed the impacts of several sequences of fomite contacts in the restroom, reflecting the variability of human behavior, on infection risks for these viruses. Touching of the toilet seat was assumed to model adjustment of the seat (open vs. closed), a common touch point in single-user restrooms (home, small business, hospital). A Monte Carlo simulation was conducted for each exposure scenario (10,000 simulations each). Norovirus resulted in the highest probability of infection for all exposure scenarios with fomite surfaces. Post-restroom automatic-dispensing hand sanitizer use reduced the probability of infection for each virus by up to 99.75%. Handwashing within the restroom, an important risk-reduction intervention, was not found to be as effective as use of a non-touch hand sanitizer dispenser for reducing risk to near or below 1/1,000,000, a commonly used risk threshold for comparison.

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.

Epidemiological Predictors of Positive SARS-CoV-2 Polymerase Chain Reaction Test in Three Cohorts: Hospitalized Patients, Healthcare Workers, and Military Population, Serbia, 2020

(1) Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its resulting coronavirus disease 2019 (COVID-19) has caused a fast-moving pandemic. Diagnostic testing, aimed to identify patients infected with SARS-CoV-2, plays a key role in controlling the COVID-19 pandemic in different populations. (2) Methods: This retrospective cohort study aimed to investigate predictors associated with positive polymerase chain reaction (PCR) SARS-CoV-2 test results in hospitalized patients, healthcare workers (HCWs), and military personnel (MP) during 2020, before the widespread availability of COVID-19 vaccines. Persons with a positive test result were compared with persons with a negative test result in three cohorts during the study period. (3) Results: A total of 6912 respondents were tested, and 1334 (19.3%) of them had positive PCR SARS-CoV-2 test results. Contact with a known COVID-19 case within 14 days (p < 0.001; OR: 1.48; 95% CI: 1.25-1.76), fever (p < 0.001; OR: 3.66; 95% CI: 3.04-4.41), cough (p < 0.001; OR: 1.91; 95% CI: 1.59-2.30), headache (p = 0.028; OR: 1.24; 95% CI: 1.02-1.50), and myalgia/arthralgia (p < 0.001; OR: 1.99; 95% CI: 1.65-2.42) were independently associated with positive PCR SARS-CoV-2 test results in the cohort of MP. Furthermore, fever (p < 0.001; OR: 2.75; 95% CI: 1.83-4.13), cough (p < 0.001; OR: 2.04; 95% CI: 1.32-3.13), headache (p = 0.008; OR: 1.76; 95% CI: 1.15-2.68), and myalgia/arthralgia (p = 0.039; OR: 1.58; 95% CI: 1.02-2.45) were independently associated with positive PCR SARS-CoV-2 test results in the cohort of HCWs. Moreover, independent predictors of positive PCR SARS-CoV-2 test results in hospitalized patients were contact with a known COVID-19 case within 14 days (p < 0.001; OR: 2.56; 95% CI: 1.71-3.83), fever (p < 0.001; OR: 1.89; 95% CI: 1.38-2.59), pneumonia (p = 0.041; OR: 1.45; 95% CI: 1.01-2.09), and neurological diseases (p = 0.009; OR: 0.375; 95% CI: 0.18-0.78). (4) Conclusions: According to data gathered from cohorts of hospitalized patients, HCWs, and MP, before the widespread availability of COVID-19 vaccines in Serbia, we can conclude that predictors of positive PCR SARS-CoV-2 test results in MP and HCWs were similar. Accurate estimates of COVID-19 in different population groups are important for health authorities.

A literature review on the analysis of symptom-based clinical pathways: Time for a different approach?

Breathlessness is a common clinical presentation, accounting for a quarter of all emergency hospital attendances. As a complex undifferentiated symptom, it may be caused by dysfunction in multiple body systems. Electronic health records are rich with activity data to inform clinical pathways from undifferentiated breathlessness to specific disease diagnoses. These data may be amenable to process mining, a computational technique that uses event logs to identify common patterns of activity. We reviewed use of process mining and related techniques to understand clinical pathways for patients with breathlessness. We searched the literature from two perspectives: studies of clinical pathways for breathlessness as a symptom, and those focussed on pathways for respiratory and cardiovascular diseases that are commonly associated with breathlessness. The primary search included PubMed, IEEE Xplore and ACM Digital Library. We included studies if breathlessness or a relevant disease was present in combination with a process mining concept. We excluded non-English publications, and those focussed on biomarkers, investigations, prognosis, or disease progression rather than symptoms. Eligible articles were screened before full-text review. Of 1,400 identified studies, 1,332 studies were excluded through screening and removal of duplicates. Following full-text review of 68 studies, 13 were included in qualitative synthesis, of which two (15%) were symptom and 11 (85%) disease focused. While studies reported highly varied methodologies, only one included true process mining, using multiple techniques to explore Emergency Department clinical pathways. Most included studies trained and internally validated within single-centre datasets, limiting evidence for wider generalisability. Our review has highlighted a lack of clinical pathway analyses for breathlessness as a symptom, compared to disease-focussed approaches. Process mining has potential application in this area, but has been under-utilised in part due to data interoperability challenges. There is an unmet research need for larger, prospective multicentre studies of patient pathways following presentation with undifferentiated breathlessness.

Association between the reduction of face-to-face appointments and the control of patients with type 2 diabetes mellitus during the Covid-19 pandemic in Catalonia

AIM

To analyse the relation between face-to-face appointments and management of patients with type 2 diabetes mellitus (T2DM) visited in primary care practices (PCP).

METHODS

Retrospective study in 287 primary care practices (PCPs) attending>300,000 patients with T2DM. We analysed the results of 9 diabetes-related indicators of the Healthcare quality standard, comprising foot and retinopathy screening, blood pressure (BP) and glycemic control; and the incidence of T2DM. We calculated each indicator's percentage of change in 2020 with respect to the results of 2019.

RESULTS

Indicators' results were reduced in 2020 compared to 2019, highlighting the indicators of foot and retinopathy screening (-51.6% and -25.7%, respectively); the glycemic control indicator (-21.2%); the BP control indicator (-33.7%) and the incidence of T2DM (-25.6%). Conversely, the percentage of type 2 diabetes patients with HbA1c > 10% increased by 34%. PCPs with<11 weekly face-to-face appointments offered per professional had greater reductions than those PCPs with more than 40. For instance, a reduction of -60.7% vs -38.2% (p-value < 0.001) in the foot screening's indicator; -27.5% vs -12.5% (p-value < 0.001) in glycemic control and -40.2 vs -24.3% (p-value < 0.001) in BP control.

CONCLUSIONS

Reducing face-to-face visits offered may impact T2DM patients' follow-up and thus worsen their control.