Non-contact infrared versus axillary and tympanic thermometers in children attending primary care: a mixed-methods study of accuracy and acceptability

Non-Contact Infrared Thermometers and Thermal Scanners for Human Body Temperature Monitoring: A Systematic Review

In recent years, non-contact infrared thermometers (NCITs) and infrared thermography (IRT) have gained prominence as convenient, non-invasive tools for human body temperature measurement. Despite their widespread adoption in a range of settings, there remain questions about their accuracy under varying conditions. This systematic review sought to critically evaluate the performance of NCITs and IRT in body temperature monitoring, synthesizing evidence from a total of 72 unique settings from 32 studies. The studies incorporated in our review ranged from climate-controlled room investigations to clinical applications. Our primary findings showed that NCITs and IRT can provide accurate and reliable body temperature measurements in specific settings and conditions. We revealed that while both NCITs and IRT displayed a consistent positive correlation with conventional, contact-based temperature measurement tools, NCITs demonstrated slightly superior accuracy over IRT. A total of 29 of 50 settings from NCIT studies and 4 of 22 settings from IRT studies achieved accuracy levels within a range of ±0.3 °C. Furthermore, we found that several factors influenced the performance of these devices. These included the measurement location, the type of sensor, the reference and tool, individual physiological attributes, and the surrounding environmental conditions. Our research underscores the critical need for further studies in this area to refine our understanding of these influential factors and to develop standardized guidelines for the use of NCITs and IRT.

Agreement of Temperatures Measured Using a Non-Contact Infrared Thermometer With a Rectal Digital Thermometer in Horses

Evaluating the body temperature of horses is an essential tool for monitoring horse health and biosecurity in groups of horses. Temperatures of horses and foals are determined most often using rectal thermometry. Rectal thermometry has limitations that include safety considerations for horses and humans. Thus, we investigated the agreement between a noncontact infrared thermometer (NCIT) and a rectal digital thermometer in 142 horses and 34 foals. For each horse and foal, measurements using the NCIT were collected from the forehead (n = 2) or neck (n = 1) and with a rectal digital thermometer (n = 1). Although the NCIT demonstrated good reliability (i.e. repeatability of measurements), a large negative bias (nearly 2°F (-16.7°C) in adult horses and >3°F (-16.1°C) in foals) was observed between readings from the NCIT and the rectal thermometer in healthy horses. Although horses with febrile illness were not included in the study, our results indicate that the large and inconsistent bias observed with the NCIT indicates that these devices will not be a suitable substitute for rectal thermometry for obtaining valid estimates of core body temperature in horses.

Influence of Hospital Environmental Variables on Thermometric Measurements and Level of Concordance: A Cross-Sectional Descriptive Study

During a pandemic, and given the need to quickly screen febrile and non-febrile humans, it is necessary to know the concordance between different thermometers (TMs) and understand how environmental factors influence the measurements made by these instruments.

OBJECTIVE

The objective of this study is to identify the potential influence of environmental factors on the measurements made by four different TMs and the concordance between these instruments in a hospital setting.

METHOD

The study employed a cross-sectional observational methodology. The participants were patients who had been hospitalised in the traumatology unit. The variables were body temperature, room temperature, room relative humidity, light, and noise. The instruments used were a Non Contract Infrared TM, Axillary Electronic TM, Gallium TM, and Tympanic TM. A lux meter, a sound level meter, and a thermohygrometer measured the ambient variables.

RESULTS

The study sample included 288 participants. Weak significant relationships were found between noise and body temperature measured with Tympanic Infrared TM, r = -0.146 (p < 0.01) and likewise between environmental temperature and this same TM, r = 0.133 (p < 0.05). The concordance between the measurements made by the four different TMs showed an Intraclass Correlation Coefficient (ICC) of 0.479.

CONCLUSIONS

The concordance between the four TMs was considered "fair".

Accuracy of parents’ subjective assessment of paediatric fever with thermometer measured fever in a primary care setting

Background

Fever is a common symptom of benign childhood illness but a high fever may be a sign of a serious infection. Temperature is often used by parents to check for illness in their children, and the presence of a high temperature can act as a prompt to consult a healthcare professional. It would be helpful for GPs to understand how well parental assessment of the presence of fever correlates with temperature measurement in the clinic in order to incorporate the history of the child’s fever into their clinical assessment.

Methods

Secondary analysis of a cross-sectional diagnostic method comparison study. Parents were asked whether they thought their child had fever before their temperature was measured by a researcher. Fever was defined as a temperature of 38 °C and higher using either an axillary or tympanic thermometer.

Results

Of 399 children recruited, 119 (29.8%) were believed by their parents to be febrile at the time of questioning and 23 (6.3%) had a fever as measured by a researcher in the clinic. 23.5% of children with a parental assessment of fever were found to have a fever in the clinic. Less than 1% of children whose parents thought they did not have a fever were found to be febrile in the clinic. Having more than one child did not improve accuracy of parents assessing fever in their child.

Conclusions

In the GP surgery setting, a child identified as afebrile by their parent is highly likely to be measured as such in the clinic. A child identified as febrile by their parent is less likely to be measured as febrile.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12875-022-01638-6.

Clinical Accuracy of Non-Contact Forehead Infrared Thermometer Measurement in Children: An Observational Study

We evaluated the clinical reliability and utility of temperature measurements using no-contact forehead infrared thermometers (NCFITs) by comparing their temperature measurements with those obtained using infrared tympanic thermometers (IRTTs) in children. In this observational, prospective, and cross-sectional study, we enrolled 255 children (aged 1 month to 18 years) from the pediatric surgery ward at a tertiary medical center in Korea. The mean age of the children was 9.05 ± 5.39 years, and 54.9% were boys. The incidence rate of fever, defined as an IRTT reading of ≥38.0 °C, was 15.7%. The ICC coefficient for the assessment of agreement between temperatures recorded by the NCFIT and IRTT was 0.87, and the κ-coefficient was 0.83. The bias and 95% limits of agreement were 0.15 °C (−0.43 to 0.73). For an accurate diagnosis of fever (≥38 °C), the false-negative rate was much lower, but the false-positive rate was higher, especially in 6-year-old children. Therefore, NCFITs can be used to screen children for fever. However, a secondary check is required using another thermometer when the child’s temperature is >38 °C. NCFITs are proposed for screening but not for measuring the temperature. For the latter, an accurate and reliable thermometer shall be used.

Clinical evaluation of non-contact infrared thermometers

Non-contact infrared thermometers (NCITs) are being widely used during the COVID-19 pandemic as a temperature-measurement tool for screening and isolating patients in healthcare settings, travelers at ports of entry, and the general public. To understand the accuracy of NCITs, a clinical study was conducted with 1113 adult subjects using six different commercially available NCIT models. A total of 60 NCITs were tested with 10 units for each model. The NCIT-measured temperature was compared with the oral temperature obtained using a reference oral thermometer. The mean difference between the reference thermometer and NCIT measurement (clinical bias) was different for each NCIT model. The clinical bias ranged from just under - 0.9 °C (under-reporting) to just over 0.2 °C (over-reporting). The individual differences ranged from - 3 to + 2 °C in extreme cases, with the majority of the differences between - 2 and + 1 °C. Depending upon the NCIT model, 48% to 88% of the individual temperature measurements were outside the labeled accuracy stated by the manufacturers. The sensitivity of the NCIT models for detecting subject's temperature above 38 °C ranged from 0 to 0.69. Overall, our results indicate that some NCIT devices may not be consistently accurate enough to determine if subject's temperature exceeds a specific threshold of 38 °C. Model-to-model variability and individual model accuracy in the displayed temperature were found to be outside of acceptable limits. Accuracy and credibility of the NCITs should be thoroughly evaluated before using them as an effective screening tool.

Non-contact infrared thermometers compared with current approaches in primary care for children aged 5 years and under: a method comparison study

BACKGROUND

Current options for temperature measurement in children presenting to primary care include either electronic axillary or infrared tympanic thermometers. Non-contact infrared thermometers could reduce both the distress of the child and the risk of cross-infection.

OBJECTIVES

The objective of this study was to compare the use of non-contact thermometers with the use of electronic axillary and infrared tympanic thermometers in children presenting to primary care.

DESIGN

Method comparison study with a nested qualitative study.

SETTING

Primary care in Oxfordshire.

PARTICIPANTS

Children aged ≤ 5 years attending with an acute illness.

INTERVENTIONS

Two types of non-contact infrared thermometers [i.e. Thermofocus (Tecnimed, Varese, Italy) and Firhealth (Firhealth, Shenzhen, China)] were compared with an electronic axillary thermometer and an infrared tympanic thermometer.

MAIN OUTCOME MEASURES

The primary outcome was agreement between the Thermofocus non-contact infrared thermometer and the axillary thermometer. Secondary outcomes included agreement between all other sets of thermometers, diagnostic accuracy for detecting fever, parental and child ratings of acceptability and discomfort, and themes arising from our qualitative interviews with parents.

RESULTS

A total of 401 children (203 boys) were recruited, with a median age of 1.6 years (interquartile range 0.79-3.38 years). The readings of the Thermofocus non-contact infrared thermometer differed from those of the axillary thermometer by -0.14 °C (95% confidence interval -0.21 to -0.06 °C) on average with the lower limit of agreement being -1.57 °C (95% confidence interval -1.69 to -1.44 °C) and the upper limit being 1.29 °C (95% confidence interval 1.16 to 1.42 °C). The readings of the Firhealth non-contact infrared thermometer differed from those of the axillary thermometer by -0.16 °C (95% confidence interval -0.23 to -0.09 °C) on average, with the lower limit of agreement being -1.54 °C (95% confidence interval -1.66 to -1.41 °C) and the upper limit being 1.22 °C (95% confidence interval 1.10 to 1.34 °C). The difference between the first and second readings of the Thermofocus was -0.04 °C (95% confidence interval -0.07 to -0.01 °C); the lower limit was -0.56 °C (95% confidence interval -0.60 to -0.51 °C) and the upper limit was 0.47 °C (95% confidence interval 0.43 to 0.52 °C). The difference between the first and second readings of the Firhealth thermometer was 0.01 °C (95% confidence interval -0.02 to 0.04 °C); the lower limit was -0.60 °C (95% confidence interval -0.65 to -0.54 °C) and the upper limit was 0.61 °C (95% confidence interval 0.56 to 0.67 °C). Sensitivity and specificity for the Thermofocus non-contact infrared thermometer were 66.7% (95% confidence interval 38.4% to 88.2%) and 98.0% (95% confidence interval 96.0% to 99.2%), respectively. For the Firhealth non-contact infrared thermometer, sensitivity was 12.5% (95% confidence interval 1.6% to 38.3%) and specificity was 99.4% (95% confidence interval 98.0% to 99.9%). The majority of parents found all methods to be acceptable, although discomfort ratings were highest for the axillary thermometer. The non-contact thermometers required fewer readings than the comparator thermometers.

LIMITATIONS

A method comparison study does not compare new methods against a reference standard, which in this case would be central thermometry requiring the placement of a central line, which is not feasible or acceptable in primary care. Electronic axillary and infrared tympanic thermometers have been found to have moderate agreement themselves with central temperature measurements.

CONCLUSIONS

The 95% limits of agreement are > 1 °C for both non-contact infrared thermometers compared with electronic axillary and infrared tympanic thermometers, which could affect clinical decision-making. Sensitivity for fever was low to moderate for both non-contact thermometers.

FUTURE WORK

Better methods for peripheral temperature measurement that agree well with central thermometry are needed.

TRIAL REGISTRATION

Current Controlled Trials ISRCTN15413321.

FUNDING

This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 24, No. 53. See the NIHR Journals Library website for further project information.

Proper use of noncontact infrared thermometry for temperature screening during COVID-19

Among the myriad of challenges healthcare institutions face in dealing with coronavirus disease 2019 (COVID–19), screening for the detection of febrile persons entering facilities remains problematic, particularly when paired with CDC and WHO spatial distancing guidance. Aggressive source control measures during the outbreak of COVID-19 has led to re-purposed use of noncontact infrared thermometry (NCIT) for temperature screening. This study was commissioned to establish the efficacy of this technology for temperature screening by healthcare facilities. We conducted a prospective, observational, single-center study in a level II trauma center at the onset of the COVID-19 outbreak to assess (i) method agreement between NCIT and temporal artery reference temperature, (ii) diagnostic accuracy of NCIT in detecting referent temperature \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 100.0\,^{\circ }{\mathrm{F}}$$\end{document}≥100.0∘F and ensuing test sensitivity and specificity and (iii) technical limitations of this technology. Of 51 healthy, non-febrile, healthcare workers surveyed, the mean temporal artery temperature was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$98.4\,^{\circ }{\mathrm{F}}$$\end{document}98.4∘F (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$95\%$$\end{document}95% confidence interval (CI) = \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[98.2,98.6]\,^{\circ }{\mathrm{F}}$$\end{document}[98.2,98.6]∘F). Mean NCIT temperatures measured from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${1}\,{\mathrm{ft}}$$\end{document}1ft, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${3}\,{\mathrm{ft}}$$\end{document}3ft, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${6}\,{\mathrm{ft}}$$\end{document}6ft distances were \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$92.2\,^{\circ }{\mathrm{F}}$$\end{document}92.2∘F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(95\%\ {\text {CI}}=[91.8\ 92.67]\,^{\circ }{\mathrm{F}})$$\end{document}(95%CI=[91.892.67]∘F), \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$91.3\,^{\circ }{\mathrm{F}}$$\end{document}91.3∘F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(95\%\ {\text {CI}}=[90.8\ 91.8]\,^{\circ }{\mathrm{F}})$$\end{document}(95%CI=[90.891.8]∘F), and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$89.6\,^{\circ }{\mathrm{F}}$$\end{document}89.6∘F\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(95\%\ {\text {CI}}=[89.2 \ 90.1]\,^{\circ }{\mathrm{F}})$$\end{document}(95%CI=[89.290.1]∘F), respectively. From statistical analysis, the only method in sufficient agreement with the reference standard was NCIT at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${1}\,{\mathrm{ft}}$$\end{document}1ft. This demonstrated that the device offset (mean temperature difference) between these methods was \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-6.15\,^{\circ }{\mathrm{F}}$$\end{document}-6.15∘F (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$95\%\ {\text {CI}}=[-6.56,-5.74]\,^{\circ }{\mathrm{F}}$$\end{document}95%CI=[-6.56,-5.74]∘F) with 95% of measurement differences within \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-8.99\,^{\circ }{\mathrm{F}}$$\end{document}-8.99∘F (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$95\%\ {\text {CI}}=[-9.69,-8.29]\,^{\circ }{\mathrm{F}}$$\end{document}95%CI=[-9.69,-8.29]∘F) and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-3.31\,^{\circ }{\mathrm{F}}$$\end{document}-3.31∘F (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$95\%\ {\text {CI}}= [-4.00,-2.61]\,^{\circ }{\mathrm{F}}$$\end{document}95%CI=[-4.00,-2.61]∘F). By setting the NCIT screening threshold to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$93.5\,^{\circ }{\mathrm{F}}$$\end{document}93.5∘F at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${1}\,{\mathrm{ft}}$$\end{document}1ft, we achieve diagnostic accuracy with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$70.9\%$$\end{document}70.9% test sensitivity and specificity for temperature detection \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\ge 100.0\,^{\circ }{\mathrm{F}}$$\end{document}≥100.0∘F by reference standard. In comparison, reducing this screening criterion to the lower limit of the device-specific offset, such as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$91.1\,^{\circ }{\mathrm{F}}$$\end{document}91.1∘F, produces a highly sensitive screening test at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$98.2\%$$\end{document}98.2%, which may be favorable in high-risk pandemic disease. For future consideration, an infrared device with a higher distance-to-spot size ratio approaching 50:1 would theoretically produce similar results at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${6}\,{\mathrm{ft}}$$\end{document}6ft, in accordance with CDC and WHO spatial distancing guidelines.

Management of acute fever in children: Consensus recommendations for community and primary healthcare providers in sub-Saharan Africa

Fever is one of the most common reasons for unwell children presenting to pharmacists and primary healthcare practitioners. Currently there are no guidelines for assessment and management of fever specifically for community and primary healthcare workers in the sub-Saharan Africa region. This multidisciplinary consensus guide was developed to assist pharmacists and primary healthcare workers in sub-Saharan Africa to risk stratify and manage children who present with fever, decide when to refer, and how to advise parents and caregivers. Fever is defined as body temperature ≥ 37.5 °C and is a normal physiological response to illness that facilitates and accelerates recovery. Although it is often associated with self-limiting illness, it causes significant concern to both parents and attending healthcare workers. Clinical signs may be used by pharmacy staff and primary healthcare workers to determine level of distress and to distinguish between a child with fever who is at high risk of serious illness and who requires specific treatment, hospitalisation or specialist care, and those at low risk who could be managed conservatively at home. In children with warning signs, serious causes of fever that may need to be excluded include infections (including malaria), non-infective inflammatory conditions and malignancy. Simple febrile convulsions are not in themselves harmful, and are not necessarily indicative of serious infection. In the absence of illness requiring specific treatment, relief from distress is the primary indication for prescribing pharmacotherapy, and antipyretics should not be administered with the sole intention of reducing body temperature. Care must be taken not to overdose medications and clear instructions should be given to parents/caregivers on managing the child at home and when to seek further medical care.

Parents' concerns and beliefs about temperature measurement in children: a qualitative study

Background

Nearly 40% of parents with children aged 6 to 17 months consult a healthcare professional when their child has a high temperature. Clinical guidelines recommend temperature measurement in these children, but little is known about parents’ experiences of and beliefs about temperature measurement. This study aimed to explore parents’ concerns and beliefs about temperature measurement in children.

Methods

Semi-structured qualitative interviews were conducted from May 2017 to June 2018 with 21 parents of children aged 4 months to 5.5 years, who were purposively sampled from the METRIC study (a method comparison study comparing non-contact infrared thermometers to axillary and tympanic thermometers in acutely ill children). Data analysis followed a thematic approach.

Results

Parents described the importance of being able to detect fever, in particular high fevers, and how this then influenced their actions. The concept of “accuracy” was valued by parents but the aspects of performance which were felt to reflect accuracy varied. Parents used numerical values of temperature in four main ways: determining precision of the thermometer on repeat measures, detecting a “bad” fever, as an indication to administer antipyretics, or monitoring response to treatment. Family and social networks, the internet, and medical professionals and resources, were all key sources of advice for parents regarding fever, and guiding thermometer choice.

Conclusions

Temperature measurement in children has diagnostic value but can either empower, or cause anxiety and practical challenges for parents. This represents an opportunity for both improved communication between parents and healthcare professionals, and technological development, to support parents to manage febrile illness with greater confidence in the home.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12875-020-01355-y.

Diagnostic accuracy of non-contact infrared thermometers and thermal scanners: a systematic review and meta-analysis

Infrared thermal screening, via the use of handheld non-contact infrared thermometers (NCITs) and thermal scanners, has been widely implemented all over the world. We performed a systematic review and meta-analysis to investigate its diagnostic accuracy for the detection of fever. We searched PubMed, Embase, the Cochrane Library, medRxiv, bioRxiv, ClinicalTrials.gov, COVID-19 Open Research Dataset, COVID-19 research database, Epistemonikos, EPPI-Centre, World Health Organization International Clinical Trials Registry Platform, Scopus and Web of Science databases for studies where a non-contact infrared device was used to detect fever against a reference standard of conventional thermometers. Forest plots and Hierarchical Summary Receiver Operating Characteristics curves were used to describe the pooled summary estimates of sensitivity, specificity and diagnostic odds ratio. From a total of 1063 results, 30 studies were included in the qualitative synthesis, of which 19 were included in the meta-analysis. The pooled sensitivity and specificity were 0.808 (95%CI 0.656-0.903) and 0.920 (95%CI 0.769-0.975), respectively, for the NCITs (using forehead as the site of measurement), and 0.818 (95%CI 0.758-0.866) and 0.923 (95%CI 0.823-0.969), respectively, for thermal scanners. The sensitivity of NCITs increased on use of rectal temperature as the reference. The sensitivity of thermal scanners decreased in a disease outbreak/pandemic setting. Changes approaching statistical significance were also observed on the exclusion of neonates from the analysis. Thermal screening had a low positive predictive value, especially at the initial stage of an outbreak, whereas the negative predictive value (NPV) continued to be high even at later stages. Thermal screening has reasonable diagnostic accuracy in the detection of fever, although it may vary with changes in subject characteristics, setting, index test and the reference standard used. Thermal screening has a good NPV even during a pandemic. The policymakers must take into consideration the factors surrounding the screening strategy while forming ad-hoc guidelines.

A Patient Self-Checkup App for COVID-19: Development and Usage Pattern Analysis

BACKGROUND

Clear guidelines for a patient with suspected COVID-19 infection are unavailable. Many countries rely on assessments through a national hotline or telecommunications, but this only adds to the burden of an already overwhelmed health care system. In this study, we developed an algorithm and a web application to help patients get screened.

OBJECTIVE

This study aims to aid the general public by developing a web-based application that helps patients decide when to seek medical care during a novel disease outbreak.

METHODS

The algorithm was developed via consultations with 6 physicians who directly screened, diagnosed, and/or treated patients with COVID-19. The algorithm mainly focused on when to test a patient in order to allocate limited resources more efficiently. The application was designed to be mobile-friendly and deployed on the web. We collected the application usage pattern data from March 1 to March 27, 2020. We evaluated the association between the usage pattern and the numbers of COVID-19 confirmed, screened, and mortality cases by access location and digital literacy by age group.

RESULTS

The algorithm used epidemiological factors, presence of fever, and other symptoms. In total, 83,460 users accessed the application 105,508 times. Despite the lack of advertisement, almost half of the users accessed the application from outside of Korea. Even though the digital literacy of the 60+ years age group is half of that of individuals in their 50s, the number of users in both groups was similar for our application.

CONCLUSIONS

We developed an expert-opinion-based algorithm and web-based application for screening patients. This innovation can be helpful in circumstances where information on a novel disease is insufficient and may facilitate efficient medical resource allocation.