Cost-effectiveness of public health strategies for COVID-19 epidemic control in South Africa: a microsimulation modelling study

Cost effectiveness and decision analysis for national airport screening options to reduce risk of COVID-19 introduction in Uganda, 2020

INTRODUCTION

Early during the COVID-19 outbreak, various approaches were utilized to prevent COVID-19 introductions from incoming airport travellers. However, the costs and effectiveness of airport-specific interventions have not been evaluated.

METHODS

We evaluated policy options for COVID-19-specific interventions at Entebbe International Airport for costs and impact on COVID-19 case counts, we took the government payer perspective. Policy options included; (1)no screening, testing, or mandatory quarantine for any incoming traveller; (2)mandatory symptom screening for all incoming travellers with RT-PCR testing only for the symptomatic and isolation of positives; and (3)mandatory 14-day quarantine and one-time testing for all, with 10-day isolation of persons testing positive. We calculated incremental cost-effectiveness ratios (ICERs) in US$ per additional case averted.

RESULTS

Expected costs per incoming traveller were $0 (Option 1), $19 (Option 2), and $766 (Option 3). ICERs per case averted were $257 for Option 2 (which averted 4,948 cases), and $10,139 for Option 3 (which averted 5,097 cases) compared with Option I. Two-week costs were $0 for Option 1, $1,271,431 Option 2, and $51,684,999 Option 3. The per-case ICER decreased with increase in prevalence. The cost-effectiveness of our interventions was modestly sensitive to the prevalence of COVID-19, diagnostic test sensitivity, and testing costs.

CONCLUSION

Screening all incoming travellers, testing symptomatic persons, and isolating positives (Option 2) was the most cost-effective option. A higher COVID-19 prevalence among incoming travellers increased cost-effectiveness of airport-specific interventions. This model could be used to evaluate prevention options at the airport for COVID-19 and other infectious diseases with similar requirements for control.

Analysis and prediction of COVID-19 epidemic in South Africa

The coronavirus disease-2019 (COVID-19) has been spreading rapidly in South Africa (SA) since its first case on 5 March 2020. In total, 674,339 confirmed cases and 16,734 mortality cases were reported by 30 September 2020, and this pandemic has made severe impacts on economy and life. In this paper, analysis and long-term prediction of the epidemic dynamics of SA are made, which could assist the government and public in assessing the past Infection Prevention and Control Measures and designing the future ones to contain the epidemic more effectively. A Susceptible-Infectious-Recovered model is adopted to analyse epidemic dynamics. The model parameters are estimated over different phases with the SA data. They indicate variations in the transmissibility of COVID-19 under different phases and thus reveal weakness of the past Infection Prevention and Control Measures in SA. The model also shows that transient behaviours of the daily growth rate and the cumulative removal rate exhibit periodic oscillations. Such dynamics indicates that the underlying signals are not stationary and conventional linear and nonlinear models would fail for long-term prediction. Therefore, a large class of mappings with rich functions and operations is chosen as the model class and the evolutionary algorithm is utilized to obtain the optimal model for long term prediction. The resulting models on the daily growth rate, the cumulative removal rate and the cumulative mortality rate predict that the peak and inflection point will occur on November 4, 2020 and October 15, 2020, respectively; the virus shall cease spreading on April 28, 2021; and the ultimate numbers of the COVID-19 cases and mortality cases will be 785,529 and 17,072, respectively. The approach is also benchmarked against other methods and shows better accuracy of long-term prediction.

The impact and applications of social media platforms for public health responses before and during COVID-19

Background

 Social media platforms have numerous potential benefits and drawbacks on public health, which have been described in the literature. The COVID-19 pandemic has exposed our limited knowledge regarding the potential health impact of these platforms, which have been detrimental to public health responses in many regions.

Objective

This review aims to highlight a brief history of social media in health care and report its potential negative and positive public health impacts, which have been characterized in the literature.

Methods

 We searched electronic bibliographic databases including PubMed, including Medline and Institute of Electrical and Electronics Engineers Xplore, from December 10, 2015, to December 10, 2020. We screened the title and abstracts and selected relevant reports for review of full text and reference lists. These were analyzed thematically and consolidated into applications of social media platforms for public health.

Results

 The positive and negative impact of social media platforms on public health are catalogued on the basis of recent research in this report. These findings are discussed in the context of improving future public health responses and incorporating other emerging digital technology domains such as artificial intelligence. However, there is a need for more research with pragmatic methodology that evaluates the impact of specific digital interventions to inform future health policy.

Conclusions

 Recent research has highlighted the potential negative impact of social media platforms on population health, as well as potentially useful applications for public health communication, monitoring, and predictions. More research is needed to objectively investigate measures to mitigate against its negative impact while harnessing effective applications for the benefit of public health.

Cost-Effectiveness of COVID-19 Policy Measures: A Systematic Review

OBJECTIVES

The COVID-19 pandemic has had a major impact on our society, with drastic policy restrictions being implemented to contain the spread of the severe acute respiratory syndrome coronavirus 2. This study aimed to provide an overview of the available evidence on the cost-effectiveness of various coronavirus disease 2019 policy measures.

METHODS

A systematic literature search was conducted in PubMed, Embase, and Web of Science. Health economic evaluations considering both costs and outcomes were included. Their quality was comprehensively assessed using the Consensus Health Economic Criteria checklist. Next, the quality of the epidemiological models was evaluated.

RESULTS

A total of 3688 articles were identified (March 2021), of which 23 were included. The studies were heterogeneous with regard to methodological quality, contextual factors, strategies' content, adopted perspective, applied models, and outcomes used. Overall, testing/screening, social distancing, personal protective equipment, quarantine/isolation, and hygienic measures were found to be cost-effective. Furthermore, the most optimal choice and combination of strategies depended on the reproduction number and context. With a rising reproduction number, extending the testing strategy and early implementation of combined multiple restriction measures are most efficient.

CONCLUSIONS

The quality assessment highlighted numerous flaws and limitations in the study approaches; hence, their results should be interpreted with caution because the specific context (country, target group, etc) is a key driver for cost-effectiveness. Finally, including a societal perspective in future evaluations is key because this pandemic has an indirect impact on the onset and treatment of other conditions and on our global economy.

Demographics, comorbidities, and outcomes among young and middle-aged COVID-19 patients in Saudi Arabia

The impact of different sociodemographic and clinical characteristics on the COVID-19-related morbidity and mortality rates have been studied extensively around the world; however, there is a dearth of data on the impact of different clinical and sociodemographic variables on the COVID-19-related outcomes in Saudi Arabia. This study aimed to identify those at high risk of worse clinical outcomes, such as hospitalization and longer length of stay (LOS) among young and middle-aged adults (18 to 55 years). In this questionnaire-based cross-sectional study, 706 patients with real-time polymerase chain reaction (RT-PCR) confirmed COVID-19 infection were interviewed. Patients' demographic characteristics, dietary habits, medical history, and lifestyle choices were collected through phone interviews. Patients with chronic health conditions, such as diabetes and hypertension, reported a higher rate of hospitalization, ICU admission, oxygen-support needs, and a longer period of recovery and LOS. Multiple logistic regression showed that diabetes, hypertension, and pulmonary disease (e.g., asthma and chronic obstructive pulmonary disease (COPD)) were associated with a higher risk of hospitalization and longer LOS. Multiple logistic regression showed that symptoms of breathlessness, loss of smell and/or taste, diarrhea, and cough were associated with a longer recovery period. Similarly, breathlessness, vomiting, and diarrhea were associated with higher rates of hospitalization. The findings of this study confirm the similarity of the factors associated with worse clinical outcomes across the world. Future studies should use more robust designs to investigate the impact of different therapies on the COVID-19-related morbidity and mortality in Saudi Arabia.

College campuses and COVID-19 mitigation: clinical and economic value

BACKGROUND

Decisions around US college and university operations will affect millions of students and faculty amidst the COVID-19 pandemic. We examined the clinical and economic value of different COVID-19 mitigation strategies on college campuses.

METHODS

We used the Clinical and Economic Analysis of COVID-19 interventions (CEACOV) model, a dynamic microsimulation that tracks infections accrued by students and faculty, accounting for community transmissions. Outcomes include infections, $/infection-prevented, and $/quality-adjusted-life-year ($/QALY). Strategies included extensive social distancing (ESD), masks, and routine laboratory tests (RLT). We report results per 5,000 students (1,000 faculty) over one semester (105 days).

RESULTS

Mitigation strategies reduced COVID-19 cases among students (faculty) from 3,746 (164) with no mitigation to 493 (28) with ESD and masks, and further to 151 (25) adding RLTq3 among asymptomatic students and faculty. ESD with masks cost $168/infection-prevented ($49,200/QALY) compared to masks alone. Adding RLTq3 ($10/test) cost $8,300/infection-prevented ($2,804,600/QALY). If tests cost $1, RLTq3 led to a favorable cost of $275/infection-prevented ($52,200/QALY). No strategies without masks were cost-effective.

CONCLUSION

Extensive social distancing with mandatory mask-wearing could prevent 87% of COVID-19 cases on college campuses and be very cost-effective. Routine laboratory testing would prevent 96% of infections and require low cost tests to be economically attractive.