ABSTRACT
BackgroundDue to preexisting conditions, older adults are at higher risk of COVID-19 related severe complications. Current evidence is limited on access to care for older adults during the COVID-19 pandemic. ObjectivesTo examine the extent of reduced access to care among older American adults during the COVID-19 pandemic, identify predictors and reasons of reduced access. Materials and methodsUsing publicly available data from the COVID-19 module (interim release) of the Health and Retirement Study, we undertook descriptive analyses of older adults stratified by sex, age group, race, education, marital status, employment, receipt of social security benefits, health insurance, number of limitations in activities of daily living and pre-existing conditions. Associations between reduced access to care and predictors were estimated using a multivariable logistic regression model. ResultsAbout 30% of respondents delayed or avoided care during the pandemic. Reduced access was more likely to be reported by respondents that were female, younger, educated, not receiving social security benefits, with limitations in daily activities and three preexisting conditions. In terms of the reasons, the majority of the respondents (45.9%) reported that their visit was either cancelled or rescheduled by the provider; 13.9% thought they could wait, 10.9% could not get an appointment, 9.1% found it unaffordable, and 7.4% were afraid to visit the provider. Respondents reported of reduced access to doctors visits, surgery, prescription filling, and dental care. ConclusionsWe suggest urgent attention on improving access to care for older adults during the pandemic. For nonemergency conditions and routine care that can be delivered virtually, telehealth services can be strengthened. Additionally, health messaging can reemphasize that neglecting medical care might lead to increased morbidity and mortality among older adults from preexisting illnesses.
ABSTRACT
BackgroundThe recent CoVID-19 pandemic has emerged as a threat to global health. Though current evidence on the epidemiology of the disease is emerging, very little is known about the predictors of recovery. ObjectivesTo describe the epidemiology of confirmed CoVID-19 patients in Republic of Korea and identify predictors of recovery. Materials and methodsUsing publicly available data for confirmed CoVID-19 cases from the Korea Centers for Disease Control and Prevention from January 20, 2020 to April 30, 2020, we undertook descriptive analyses of cases stratified by sex, age group, place of exposure, date of confirmation and province. Correlation was tested among all predictors (sex, age group, place of exposure and province) with the Pearsons correlation coefficient. Associations between recovery from CoVID-19 and predictors were estimated using a multivariable logistic regression model. ResultsMajority of the confirmed cases were females (56%), from 20-29 age group (24.3%), and primarily from three provinces -- Gyeongsangbuk-do (36.9%), Gyeonggi-do (20.5%) and Seoul (17.1%). Case fatality ratio was 2.1% and 41.6% cases recovered. Older patients, patients from provinces such as Daegu, Gyeonggi-do, Gyeongsangbuk-do, Jeju-do, Jeollabuk-do and Jeollanam-do, and those contracting the disease from healthcare settings had lower recovery. ConclusionsOur study adds to the very limited evidence base on potential predictors of survival among confirmed CoVID-19 cases. We call additional research to explore the predictors of recovery and support development of policies to protect the vulnerable patient groups.
ABSTRACT
BackgroundThe recent pandemic of CoVID-19 has emerged as a threat to global health security. There are a very few prognostic models on CoVID-19 using machine learning. ObjectivesTo predict mortality among confirmed CoVID-19 patients in South Korea using machine learning and deploy the best performing algorithm as an open-source online prediction tool for decision-making. Materials and methodsMortality for confirmed CoVID-19 patients (n=3,299) between January 20, 2020 and April 30, 2020 was predicted using five machine learning algorithms (logistic regression, support vector machine, K nearest neighbor, random forest and gradient boosting). Performance of the algorithms was compared, and the best performing algorithm was deployed as an online prediction tool. ResultsThe random forest algorithm was the best performer in terms of predictive ability (accuracy=0.981), discrimination (area under ROC curve=0.886), calibration (Matthews Correlation Coefficient=0.459; Brier Score=0.063) and. The best performer algorithm (random forest) was deployed as the online CoVID-19 Community Mortality Risk Prediction tool named CoCoMoRP (https://ashis-das.shinyapps.io/CoCoMoRP/). ConclusionsWe describe the development and deployment of an open-source machine learning tool to predict mortality risk among CoVID-19 confirmed patients using publicly available surveillance data. This tool can be utilized by potential stakeholders such as health providers and policy makers to triage patients at the community level in addition to other approaches.
ABSTRACT
OBJECTIVE@#To evaluate microscopy, OptiMAL(®) and multiplex PCR for the identification of Plasmodium falciparumm (P. falciparum) and Plasmodium vivax (P. vivax) from the field isolates of Bikaner, Rajasthan (Northwest India).@*METHODS@#In this study, a multiplex PCR (P. falciparum and P. vivax) was further developed with the incorporation of Plasmodium malariae (P. malariae) specific primer and also a positive control. The performance of microscopy, plasmodium lactate dehydrogenase (pLDH) based malaria rapid diagnostic test OptiMAL(®) and 18S rRNA gene based multiplex PCR for the diagnosis of P. falciparum and P. vivax was compared.@*RESULTS@#The three species multiplex PCR (P. falciparum, P. vivax and P. malariae) with an inbuilt positive control was developed and evaluated. In comparison with multiplex PCR, which showed the sensitivity and specificity of 99.36% (95%CI, 98.11%-100.00%) and 100.00% (95%CI, 100.00%-100.00%), the sensitivity and specificity of microscopy was 90.44% (95%CI, 88.84%-95.04%) and 99.22% (95%CI, 97.71%-100.00%), and OptiMAL(®) was 93.58% (95%CI, 89.75%-97.42%) and 97.69% (95%CI, 95.10%-100.00%). The efficiencies were 99.65%, 95.10% and 95.45% for multiplex PCR, microscopy and OptiMAL(®), respectively.@*CONCLUSIONS@#Our results raise concerns over the overall sensitivities of microscopy and OptiMAL(®), when compared to the multiplex PCR and thus stress the need for new molecular interventions in the accurate detection of the malarial parasites. This further highlights the fact that further developments are needed to improve the performance of rapid diagnostic tests at field level.
