The burden, admission, and outcome of COVID-19 in Africa: protocol for a systematic review and meta-analysis

Incidence and characterisation of post-COVID-19 symptoms in hospitalized COVID-19 survivors: A single centre, prospective observational cohort study to recognize the syndemic connotations in India

BACKGROUND

Long COVID or post-COVID-19 syndrome is the persistence of signs and symptoms that develop during or after COVID-19 infection for more than 12 weeks and are not explained by an alternative diagnosis. In spite of health care recouping to pre pandemic states, post-COVID-19 state tends to be less recognised from low middle income country settings and holistic therapeutic protocols do not exist. Owing to the syndemic nature of the COVID 19, it is important to characterise post-COVID-19 syndrome.

OBJECTIVE

We aimed to determine the incidence of post-COVID-19 symptoms in a cohort of inpatients who recovered from COVID-19 from February 2021 to July 2021 from a tertiary care centre in South India. In addition, we aimed at comparing the prevalence of post-COVID-19 manifestations in non-ICU and ICU patients, assessing the persistence, severity and characteristics of post-COVID-19 manifestations and elucidating the risk factors associated with the presence of post-COVID-19 manifestations.

METHODS

120 adult patients admitted with COVID-19 in the specified time frame were recruited into the study after informed written consent. The cohort included 50 patients requiring Intensive care unit and 70 patients with non-intensive care. The follow-up was conducted on the second and sixth week after discharge with a structured questionnaire. The questionnaire was filled by the patient/family member of the patient during their visit to the hospital for follow-up at 2 weeks and through telephone follow up at 6 weeks.

RESULTS

: Mean age of the cohort was 55 years and 55% were males. Only (5%) of the cohort had taken the first dose of COVID-19 vaccination.58.3% had mild COVID-19 and 41.7% had moderate to severe COVID-19 infection. 60.8% (n=73) of patients had at least one persistent symptom at sixth week of discharge. 50 (41.7%) patients required intensive care during their inpatient stay. Presence of persistent symptoms at 6 weeks was not associated with severity of illness, age or requirement for intensive care. Fatigue was the most common reported persistent symptom with a prevalence of 55.8% followed by dyspnoea (20%) and weight loss (16.7%). Female sex (OR 2.4, 95% CI: 1.03-5.58, P=.04) and steroid administration during hospital stay (OR: 4.43; 95% CI: 1.9-10.28, P=.001), were found to be significant risk factors for the presence of post-COVID-19 symptoms at 6 weeks as revealed by logistic regression analysis.

CONCLUSIONS

60.8% of inpatients treated for COVID-19 had post-COVID-19 symptoms at 6 week's post- discharge from hospital. Incidence of post-COVID-19 syndrome in the cohort did not significantly differ across the mild, moderate and severe COVID-19 severity categories. Female sex and steroid administration during hospital stay were identified as predictors of persistence of post-COVID-19 symptoms at 6 weeks.

CLINICALTRIAL

Acute and chronic exposure to air pollution in relation with incidence, prevalence, severity and mortality of COVID-19: a rapid systematic review

BACKGROUND

Air pollution is one of the world's leading mortality risk factors contributing to seven million deaths annually. COVID-19 pandemic has claimed about one million deaths in less than a year. However, it is unclear whether exposure to acute and chronic air pollution influences the COVID-19 epidemiologic curve.

METHODS

We searched for relevant studies listed in six electronic databases between December 2019 and September 2020. We applied no language or publication status limits. Studies presented as original articles, studies that assessed risk, incidence, prevalence, or lethality of COVID-19 in relation with exposure to either short-term or long-term exposure to ambient air pollution were included. All patients regardless of age, sex and location diagnosed as having COVID-19 of any severity were taken into consideration. We synthesised results using harvest plots based on effect direction.

RESULTS

Included studies were cross-sectional (n = 10), retrospective cohorts (n = 9), ecological (n = 6 of which two were time-series) and hypothesis (n = 1). Of these studies, 52 and 48% assessed the effect of short-term and long-term pollutant exposure, respectively and one evaluated both. Pollutants mostly studied were PM2.5 (64%), NO2 (50%), PM10 (43%) and O3 (29%) for acute effects and PM2.5 (85%), NO2 (39%) and O3 (23%) then PM10 (15%) for chronic effects. Most assessed COVID-19 outcomes were incidence and mortality rate. Acutely, pollutants independently associated with COVID-19 incidence and mortality were first PM2.5 then PM10, NO2 and O3 (only for incident cases). Chronically, similar relationships were found for PM2.5 and NO2. High overall risk of bias judgments (86 and 39% in short-term and long-term exposure studies, respectively) was predominantly due to a failure to adjust aggregated data for important confounders, and to a lesser extent because of a lack of comparative analysis.

CONCLUSION

The body of evidence indicates that both acute and chronic exposure to air pollution can affect COVID-19 epidemiology. The evidence is unclear for acute exposure due to a higher level of bias in existing studies as compared to moderate evidence with chronic exposure. Public health interventions that help minimize anthropogenic pollutant source and socio-economic injustice/disparities may reduce the planetary threat posed by both COVID-19 and air pollution pandemics.

Prevalence and Associated Factors for Depressive Symptomatology in Chinese Adults During COVID-19 Epidemic

Background: The coronavirus disease 2019 (COVID-19) has been rapidly transmitted worldwide, which contributed to various psychological problems (such as fear, depression, and anxiety) among the general population in China. The purpose of this study is to investigate the prevalence and associated factors of depressive symptoms among Chinese adults.

Methods: A cross-sectional study of Chinese adults was conducted during 17–29 February 2020. Symptoms of depression were assessed using the Center for Epidemiologic Studies Depression scale (CES-D).

Results: A total of 3,399 respondents were included in the analysis. It was observed that 14.2% (481/3,399) of the participants were screened positive for depressive symptoms. In a multivariate logistic regression analysis, older age (OR = 0.98; 95% CI, 0.97–0.99), smoking (OR = 1.57; 95% CI, 1.10–2.26), self-rated health (good: OR = 0.49; 95% CI, 0.37–0.66; fairly: OR = 0.60; 95% CI, 0.45–0.80), having greater support scores (OR = 0.95; 95% CI, 0.94–0.96), knowledge about the main symptom of COVID-19 (very clearly: OR = 0.58; 95% CI, 0.42–0.79; relatively clearly: OR = 0.59; 95% CI, 0.44–0.79), and staying in Wuhan within 3 months before the outbreak of epidemic (OR = 1.78; 95% CI, 1.34–2.38) were associated with depressive symptoms.

Conclusion: A considerable proportion of the general population in China had depressive symptoms during the COVID-19 epidemic. Routine screening and targeted interventions for depression are needed among high-risk depressed individuals during the COVID-19 epidemic.

The values and limitations of mathematical modelling to COVID-19 in the world: a follow up report

We previously described a mathematical model to simulate the course of the COVID-19 pandemic and try to predict how this outbreak might evolve in the following two months when the pandemic cases will drop significantly. Our original paper prepared in March 2020 analyzed the outbreaks of COVID-19 in the US and its selected states to identify the rise, peak, and decrease of cases within a given geographic population, as well as a rough calculation of accumulated total cases in this population from the beginning to the end of June 2020. The current report will describe how well the later actual trend from March to June fit our model and prediction. Similar analyses are also conducted to include countries other than the US. From such a wide global data analysis, our results demonstrated that different US states and countries showed dramatically different patterns of pandemic trend. The values and limitations of our modelling are discussed.

The epidemiological characteristics of COVID-19 in Libya during the ongoing-armed conflict

COVID-19 can have even more dire consequences in countries with ongoing armed conflict. Libya, the second largest African country, has been involved in a major conflict since 2011. This study analyzed the epidemiological situation of the COVID-19 pandemic in Libya, examined the impact of the armed conflict in Libya on the spread of the pandemic, and proposes strategies for dealing with the pandemic during this conflict. We collected the available information on all COVID-19 cases in the different regions of Libya, covering the period from March 25^th to May 25^th 2020. The cumulative number of cases and the daily new cases are presented in a way to illustrate the patterns and trends of COVID-19, and the effect of the ongoing armed conflict was assessed regionally. A total of 698 cases of COVID-19 were reported in Libya within a period of three months. The number of cases varied from one region to another and was affected by the fighting. The largest number of cases were reported in the southern part of the country, which has been severely affected by the conflict in comparison to the eastern and western parts of the country. This study describes the epidemiological pattern of COVID-19 in Libya and how it has been affected by the ongoing-armed conflict. This conflict seems to have hindered access to populations and there by masked he true dimensions of the pandemic. Hence, efforts should be combined to combat these consequences.

The outcome of COVID-19 among the geriatric age group in African countries: protocol for a systematic review and meta-analysis

BACKGROUND

According to the World Health Organization (WHO), the outbreak of coronavirus disease in 2019 (COVID-19) has been declared as a pandemic and public health emergency that infected more than 5 million people worldwide at the time of writing this protocol. Strong evidence for the outcome of COVID-19 among the geriatric age group has not been published in Africa. Therefore, this protocol will be served as a guideline to conduct a systematic review and meta-analysis of the outcome of COVID-19 among the geriatric age group in Africa.

METHODS

Published and unpublished studies on the outcome of COVID-19 among the geriatric age group in Africa and written in any language will be included. Databases (PubMed / MEDLINE, Google Scholar, Google, EMBASE, Web of Science, Microsoft Academic, WHO COVID-19 database, Cochran Library, Africa Wide Knowledge, and Africa Index Medicus) from March to August 2020 will be searched. Two independent reviewers will select, screen, extract data, and assess the risk of bias. The proportion will be measured using a random-effects model. Subgroup analysis will be conducted to manage heterogeneity. The presence of publication bias will be assessed using Egger's test and visual inspection of the funnel plots. This systematic and meta-analysis review protocol will be reported per the PRISMA-P guidelines.

CONCLUSION

This systematic review and meta-analysis protocol will be expected to quantify the outcome of COVID-19 among the geriatric age group in Africa.

SYSTEMATIC REVIEW REGISTRATION

This protocol was submitted for registration with the International Prospective Register of Systematic Reviews (PROSPERO) in April 2020 and accepted with the registration number: (https://www.crd.york.ac.uk/PROSPERO). CRD42020180600.

Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator

Forecasting the epidemics of the diseases is very valuable in planning and supplying resources effectively. This study aims to estimate the epidemiological trends of the coronavirus disease 2019 (COVID-19) prevalence and mortality using the advanced α-Sutte Indicator, and its prediction accuracy level was compared with the most frequently adopted autoregressive integrated moving average (ARIMA) method. Time-series analysis was performed based on the total confirmed cases and deaths of COVID-19 in the world, Brazil, Peru, Canada and Chile between 27 February 2020 and 30 June 2020. By comparing the prediction reliability indices, including the root mean square error, mean absolute error, mean error rate, mean absolute percentage error and root mean square percentage error, the α-Sutte Indicator was found to produce lower forecasting error rates than the ARIMA model in all data apart from the prevalence testing set globally. The α-Sutte Indicator can be recommended as a useful tool to nowcast and forecast the COVID-19 prevalence and mortality of these regions except for the prevalence around the globe in the near future, which will help policymakers to plan and prepare health resources effectively. Also, the findings of our study may have managerial implications for the outbreak in other countries.