ObjectivesThere are multiple instruments for measuring multimorbidity. The main objective of this systematic review was to provide a list of instruments that are suitable for use in studies aiming to measure the association of a specific outcome with different levels of multimorbidity as the main independent variable in community-dwelling individuals. The secondary objective was to provide details of the requirements, strengths and limitations of these instruments, and the chosen outcomes.MethodsWe conducted the review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO registration number: CRD42018105297). We searched MEDLINE, Embase and CINAHL electronic databases published in English and manually searched the Journal of Comorbidity between 1 January 2010 and 23 October 2020 inclusive. Studies also had to select adult patients from primary care or general population and had at least one specified outcome variable. Two authors screened the titles, abstracts and full texts independently. Disagreements were resolved with a third author. The modified Newcastle-Ottawa Scale was used for quality assessment.ResultsNinety-six studies were identified, with 69 of them rated to have a low risk of bias. In total, 33 unique instruments were described. Disease Count and weighted indices like Charlson Comorbidity Index were commonly used. Other approaches included pharmaceutical-based instruments. Disease Count was the common instrument used for measuring all three essential core outcomes of multimorbidity research: mortality, mental health and quality of life. There was a rise in the development of novel weighted indices by using prognostic models. The data obtained for measuring multimorbidity were from sources including medical records, patient self-reports and large administrative databases.ConclusionsWe listed the details of 33 instruments for measuring the level of multimorbidity as a resource for investigators interested in the measurement of multimorbidity for its association with or prediction of a specific outcome.

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Introduction Technology to enhance hypertension management is increasingly used in primary care; however, it has not been evaluated in an Asian primary care setting. We aimed to understand the clinical impact and cost-effectiveness of a technology-enabled home blood pressure monitor when deployed in primary care, and patients’ perspectives about the technology. Methods A quasi-experimental cohort study was conducted in a polyclinic in Singapore. In total, 120 patients with hypertension were assigned to the telemonitoring intervention group. Patients received a home blood pressure device connected to the clinical care team's dashboard through a mobile gateway. Tele-consultations and nurse-led tele-support were carried out using established clinical protocols. In total, 120 patients assigned to the control group continued to receive usual care in the polyclinic. Clinical outcomes, cost-effectiveness, and patient satisfaction were measured 6 months after recruitment. Results In total, 217 patients completed 6 months of follow-up. Telemonitoring intervention patients had significantly increased odds of having controlled blood pressure by a factor of 2.69 ( p = 0.01), with the greatest improvement in those whose blood pressure was uncontrolled at baseline ( p < 0.05). The incremental cost-effectiveness ratios for all patients was S$23,935.14/quality-adjusted life year (<1 gross domestic product per capita), which was very cost-effective based on World Health Organization cost-effectiveness thresholds. There was greater satisfaction in telemonitoring intervention group relating to the convenience of recording and sharing blood pressure measurements with the health care team, consultation advice received, understanding by the health care team of their condition, and were more motivated to monitor their blood pressure. Discussion Telemonitoring with tele-consultation improved blood pressure control and was more cost-effective than usual care. Patients receiving telemonitoring intervention were also more motivated and satisfied with their care.

BackgroundFree-text clinical records provide a source of information that complements traditional disease surveillance. To electronically harness these records, they need to be transformed into codified fields by natural language processing algorithms.ObjectiveThe aim of this study was to develop, train, and validate Clinical History Extractor for Syndromic Surveillance (CHESS), an natural language processing algorithm to extract clinical information from free-text primary care records.MethodsCHESS is a keyword-based natural language processing algorithm to extract 48 signs and symptoms suggesting respiratory infections, gastrointestinal infections, constitutional, as well as other signs and symptoms potentially associated with infectious diseases. The algorithm also captured the assertion status (affirmed, negated, or suspected) and symptom duration.Electronic medical records from the National Healthcare Group Polyclinics, a major public sector primary care provider in Singapore, were randomly extracted and manually reviewed by 2 human reviewers, with a third reviewer as the adjudicator. The algorithm was evaluated based on 1680 notes against the human-coded result as the reference standard, with half of the data used for training and the other half for validation.ResultsThe symptoms most commonly present within the 1680 clinical records at the episode level were those typically present in respiratory infections such as cough (744/7703, 9.66%), sore throat (591/7703, 7.67%), rhinorrhea (552/7703, 7.17%), and fever (928/7703, 12.04%). At the episode level, CHESS had an overall performance of 96.7% precision and 97.6% recall on the training dataset and 96.0% precision and 93.1% recall on the validation dataset. Symptoms suggesting respiratory and gastrointestinal infections were all detected with more than 90% precision and recall. CHESS correctly assigned the assertion status in 97.3%, 97.9%, and 89.8% of affirmed, negated, and suspected signs and symptoms, respectively (97.6% overall accuracy). Symptom episode duration was correctly identified in 81.2% of records with known duration status.ConclusionsWe have developed an natural language processing algorithm dubbed CHESS that achieves good performance in extracting signs and symptoms from primary care free-text clinical records. In addition to the presence of symptoms, our algorithm can also accurately distinguish affirmed, negated, and suspected assertion statuses and extract symptom durations.

An outbreak of typhoid caused by Salmonella typhi of the same Vi-phage type (D1) and of the same antibiogram was reported in a large psychiatric institution in Singapore. A total of 95 (4.8%) of the 1965 inmates were infected, 47 with symptoms and 48 asymptomatic. Transmission was through close person-to-person contact and not through contaminated food or water. The source of infection could not be established. The outbreak was brought under control by maintaining a high standard of environmental sanitation, active search for fever and diarrhoeal cases, identification of asymptomatic cases by rectal swabbing, and isolation of those found to be infected. Mass immunization with two doses of heat-phenol inactivated typhoid vaccine was also carried out concurrently. The vaccine was found to have an efficacy of 65.8% in preventing clinical illness.