In locations where few people have received Covid-19 vaccines, health systems remain vulnerable to spikes in SARS-CoV-2 infections. Triage tools, which could include biomarkers, to identify patients with moderate Covid-19 infection suitable for community-based management would be useful in the event of surges. In consultation with FIND (Geneva, Switzerland) we shortlisted seven biomarkers for evaluation, all measurable using point-of-care tests, and either currently available or in late-stage development.
METHODS
We prospectively recruited unvaccinated adults with laboratory-confirmed Covid-19 presenting to two hospitals in India with moderate symptoms, in order to develop and validate a clinical prediction model to rule-out progression to supplemental oxygen requirement. Moderate disease was defined as oxygen saturation (SpO2) ≥ 94% and respiratory rate < 30 breaths per minute (bpm), in the context of systemic symptoms (breathlessness or fever and chest pain, abdominal pain, diarrhoea, or severe myalgia). All patients had clinical observations and blood collected at presentation, and were followed up for 14 days for the primary outcome, defined as any of the following: SpO2 < 94%; respiratory rate > 30 bpm; SpO2/fraction of inspired oxygen (FiO2) < 400; or death. We specified a priori that each model would contain three easily ascertained clinical parameters (age, sex, and SpO2) and one of the seven biomarkers (C-reactive protein (CRP), D-dimer, interleukin-6 (IL-6), neutrophil-to-lymphocyte ratio (NLR), procalcitonin (PCT), soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), or soluble urokinase plasminogen activator receptor (suPAR)), to ensure the models would be implementable in high patient-throughput, low-resource settings. We evaluated the models’ discrimination, calibration, and clinical utility in a held-out external temporal validation cohort.
ETHICS
Ethical approval was given by the ethics committees of AIIMS and CMC, India, the Oxford Tropical Research Ethics Committee, UK; and by the MSF Ethics Review Board.
ClinicalTrials.gov number, NCT04441372.
RESULTS
426 participants were recruited, of which 89 (21.0%) met the primary outcome. 257 participants comprised the development, and 166 the validation, cohorts. The three models containing NLR, suPAR, or IL-6 demonstrated promising discrimination (c-statistics: 0.72 to 0.74) and calibration (calibration slopes: 1.01 to 1.05) in the held-out validation cohort. Furthermore, they provided greater utility than a model containing the clinical parameters alone (c-statistic = 0.66; calibration slope = 0.68). The inclusion of either NLR or suPAR improved predictive performance such that the ratio of correctly to incorrectly discharged patients increased from 10:1 to 23:1 or 25:1 respectively. Including IL-6 resulted in a similar proportion (~21%) of correctly discharged patients as the clinical model, but without missing any patients requiring supplemental oxygen.
CONCLUSION
We present three clinical prediction models that could help clinicians identify patients with moderate Covid-19 suitable for community-based management. These models are readily implementable and, if validated, could be of particular relevance for resource-limited settings.
CONFLICTS OF INTEREST
None declared.
Background
Every year, 60% of deaths from diarrhoeal disease occur in low and middle-income countries due to inadequate water, sanitation, and hygiene. In these countries, diarrhoeal diseases are the second leading cause of death in children under five, excluding neonatal deaths. The approximately 100,000 people residing in the Bentiu Internally Displaced Population (IDP) camp in South Sudan have previously experienced water, sanitation, and hygiene outbreaks, including an ongoing Hepatitis E outbreak in 2021. This study aimed to assess the gaps in Water, Sanitation, and Hygiene (WASH), prioritise areas for intervention, and advocate for the improvement of WASH services based on the findings.
Methods
A cross-sectional lot quality assurance sampling (LQAS) survey was conducted in ninety-five households to collect data on water, sanitation, and hygiene (WASH) coverage performance across five sectors. Nineteen households were allocated to each sector, referred to as supervision areas in LQAS surveys. Probability proportional to size sampling was used to determine the number of households to sample in each sector block selected using a geographic positioning system. One adult respondent, familiar with the household, was chosen to answer WASH-related questions, and one child under the age of five was selected through a lottery method to assess the prevalence of WASH-related disease morbidities in the previous two weeks. The data were collected using the KoBoCollect mobile application. Data analysis was conducted using R statistical software and a generic LQAS Excel analyser. Crude values, weighted averages, and 95% confidence intervals were calculated for each indicator. Target coverage benchmarks set by program managers and WASH guidelines were used to classify the performance of each indicator.
Results
The LQAS survey revealed that five out of 13 clean water supply indicators, eight out of 10 hygiene and sanitation indicators, and two out of four health indicators did not meet the target coverage. Regarding the clean water supply indicators, 68.9% (95% CI 60.8%-77.1%) of households reported having water available six days a week, while 37% (95% CI 27%-46%) had water containers in adequate condition. For the hygiene and sanitation indicators, 17.9% (95% CI 10.9%-24.8%) of households had handwashing points in their living area, 66.8% (95% CI 49%-84.6%) had their own jug for cleansing after defaecation, and 26.4% (95% CI 17.4%-35.3%) of households had one piece of soap. More than 40% of households wash dead bodies at funerals and wash their hands in a shared bowl. Households with sanitary facilities at an acceptable level were 22.8% (95% CI 15.6%-30.1%), while 13.2% (95% CI 6.6%-19.9%) of households had functioning handwashing points at the latrines. Over the previous two weeks, 57.9% (95% CI 49.6–69.7%) of households reported no diarrhoea, and 71.3% (95% CI 62.1%-80.6%) reported no eye infections among children under five.
Conclusion
The camp’s hygiene and sanitation situation necessitated immediate intervention to halt the hepatitis E outbreak and prevent further WASH-related outbreaks and health issues. The LQAS findings were employed to advocate for interventions addressing the WASH gaps, resulting in WASH and health actors stepping in.
In locations where few people have received COVID-19 vaccines, health systems remain vulnerable to surges in SARS-CoV-2 infections. Tools to identify patients suitable for community-based management are urgently needed.
METHODS
We prospectively recruited adults presenting to two hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 in order to develop and validate a clinical prediction model to rule-out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 bpm; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex and SpO2) and one of seven shortlisted biochemical biomarkers measurable using commercially-available rapid tests (CRP, D-dimer, IL-6, NLR, PCT, sTREM-1 or suPAR), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration and clinical utility of the models in a held-out temporal external validation cohort.
RESULTS
426 participants were recruited, of whom 89 (21.0%) met the primary outcome. 257 participants comprised the development cohort and 166 comprised the validation cohort. The three models containing NLR, suPAR or IL-6 demonstrated promising discrimination (c-statistics: 0.72 to 0.74) and calibration (calibration slopes: 1.01 to 1.05) in the validation cohort, and provided greater utility than a model containing the clinical parameters alone.
CONCLUSIONS
We present three clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources.