Research Spotlights: October 2020

research spotlight

Achieving herd immunity to COVID-19 may not be a practical public health strategy

In response to COVID-19, affected countries have imposed a range of public health strategies to manage outbreaks. In a recent study funded by NIGMS, researchers use modeling techniques to assess the long-term potential of success using these approaches. Typically, these strategies have fallen into two categories—“suppression” and “mitigation.” Broadly speaking, the goal of the suppression strategy is to drastically reduce COVID-19 transmission rates and stop endogenous transmission in the target population, while the goal of the mitigation strategy is to achieve herd immunity by allowing the virus to spread through the population while mitigating disease burden as to not overwhelm the healthcare system. In practice, both strategies require the same types of control measures, social distancing and self-isolation, however the amount of time and intensity of the measures differ. To date, many countries with surges in COVID-19 outbreaks have employed a suppression strategy by mandating stringent social distancing measures which can have serious societal and economic repercussions. Several countries have started to consider or implement the mitigation strategy of establishing herd immunity to tackle the pandemic while lessening economic and societal impacts. In this study, the researchers sought to determine if and how countries could achieve herd immunity without overburdening the health care system, and to define the required control efforts needed.

The researchers used an age-stratified transmission model, with parameters to simulate COVID-19 viral transmission, including spread controlled by the self-isolation of symptomatic individuals and various levels of social distancing, in the United Kingdom (UK). The simulations indicated that without any control measures, the UK could have up to 410,000 deaths related to COVID-19, with 350,000 of those being from individuals over the age of 60. In contrast, if the suppression strategy was used, there were fewer deaths predicted with 62,000 in individuals over 60 years of age and 43,000 in those under 60. If self-isolation engagement is high (defined as at least 70 percent reduction in transmission), suppression can be achieved in two months regardless of social distancing measures, and potentially sooner if there are closings of school, work and social gathering places. When examining mitigation strategies that have the objective of building herd immunity, modeling results indicate that if social distancing is maintained at a fixed level, hospital capacity would have to significantly increase to prevent overwhelming the health care system. In order to achieve herd immunity in the UK with the current healthcare resources, it would be necessary to adjust levels of social distancing in real-time to ensure that the number of sick individuals is equal to but does not exceed hospital capacity. This delicate balance is to prevent the virus from spreading too quickly and overwhelming hospital resources, while not having the virus spreading too slowly, suppressing the epidemic.

In summary, consistent with previous observations and trends, these data confirmed that suppression of COVID-19 transmission is possible and feasible with prolonged (months) of social distancing. However, the modeling did not support achieving herd immunity as a practical endpoint since intervention levels would need to be carefully fine-tuned in an adaptive manner for an extended period of time. Such fine-tuning of social distancing renders this strategy impractical. Specifically, they found that for herd immunity to be achieved, social distancing must initially reduce the transmission rate to within a narrow range. Also, in order to compensate for susceptible population depletion, the extent of social distancing must be adaptive over time in a precise yet unfeasible way, and social distancing must be maintained for an extended period to ensure the healthcare system is not overwhelmed. It is important to note that there are still many unknowns about the nature, duration and effectiveness of COVID-19 immunity, and that the model used in this study assumes perfect long-lasting immunity. If COVID-19 immunity is not perfect, and there is a significant chance of reinfection, achieving herd immunity through widespread exposure is not likely. These modeling data, despite the unknowns, do provide information that can help to inform public health officials and other stakeholders with scenarios to help assess the consequences of alternative control strategies for controlling COVID-19 transmission, hospital burden, fatalities, and population-level immunity.

Citation: Brett TS, Rohani P. 2020. Transmission dynamics reveal the impracticality of COVID-19 herd immunity strategies. PNAS. DOI: 10.1073/pnas.2008087117

 

Smartphone applications based on ‘acceptance and commitment therapy’ are effective for smoking cessation

Digital interventions through smartphone applications are intended to improve treatment barriers for cessation of cigarette smoking, the leading cause of early death and disability worldwide. Despite the availability of nearly 500 English-language smartphone applications to date, only five randomized trials have tested the efficacy of some of these applications on abstinence rates. A study funded by NCI compared the efficacy of two smartphone applications—a more typical smoking cessation treatment model based on the United States Clinical Practice Guidelines (USCPG) versus an alternative treatment model based on acceptance and commitment therapy (ACT).

Researchers performed a blinded, parallel, two-group randomized clinical trial to compare these two types of therapies with participants that had a desire to quit smoking (n = 2,415, 70.4 percent women, 35.9 percent racial/ethnic minorities, mean age at enrollment, 38.2 years from all 50 U.S. states). Participants were either assigned the QuitGuide smartphone application (n = 1,201), based on USCPG guidelines, or the iCanQuit smartphone applications (n = 1,214), based on ACT. Follow-up visits were conducted at 3, 6, and 12 months after randomization.

QuitGuide, based on USCPG, motivates users by using reason, logic, and factual information, and teaches users how to avoid smoking triggers. iCanQuit, based on ACT, differs from this approach in that it emphasizes values and teaches acceptance of smoking triggers and skills to overcome smoking urges. For example, QuitGuide will teach users that the best approach to prevent relapse is to avoid high-risk situations and learn how to distract oneself during an urge. Alternatively, ACT will teach users to openly track urges and practice perspective-taking or value-driven activities such as writing a smoke-free vision statement.

At the study end date, 87.2 percent of participants remained in the study. After 12 months of use with either smartphone application, users of iCanQuit (ACT method) had 1.49 times higher odds of quitting smoking (28.5 percent) compared with users of QuitGuide (USCPG method; 21.0 percent) and were more likely be abstinent from all tobacco products altogether, including e-cigarettes. The significant increase in 7- and 30-day abstinence for iCanQuit users was also found in earlier time points, three and six months into the study.

This study advances the evidence base for smartphone applications to aid smoking cessation with a much higher retention rate and longer follow-up period compared to prior smartphone application smoking cessation studies. The participants represented a broad demographic sample which increases the relevance of these findings to a diverse population. However, this study did not perform biochemical data collection to confirm abstinence in participants, but equal treatment between groups and the lack of face-to-face contact may minimize its impact on valid abstinence rates. In addition, this study may have been strengthened with the inclusion of a non-smartphone application treatment group in order to confirm the efficacy of this method to traditional, in-person methods. Overall, this study shows that smartphone applications can be effective in supporting smoking cessation and informs best practices for ongoing efforts to curb cigarette smoking.

Citation: Bricker JB, Watson NL, Mull KE, Sullivan BM, Heffner JL. 2020. Efficacy of Smartphone Applications for Smoking Cessation: A Randomized Clinical Trial. JAMA Intern Med. doi: 10.1001/jamainternmed.2020.4055

 

Insomnia Symptoms in the Transition from Childhood to Adolescence

Sleep has garnered attention over the years for its relationship with health outcomes. Research supported by the NHLBI, NIMH, and NCATS sought to study the natural history of insomnia symptoms in children that were followed into adolescence to establish population-based rates to better understand the course of insomnia, as well as risk factors. In youth, falling asleep and/or staying asleep are one of the most common parent-reported insomnia symptoms; with a prevalence in childhood and adolescence of 20–25 percent. Prior research has yielded varying estimates of persistence rates of insomnia symptoms in youth that range from zero percent over a 12-year period to 52 percent over a 4-year period. Due to this high variability in results, the researchers examined individual risk factors that contribute to the persistence and incidence of insomnia symptoms in youth.

In the present study the researchers followed a large, population-based, cohort sample of children into adolescence to evaluate the natural history of insomnia symptoms and a broad number of relevant clinically and objectively assessed risk factors. The sample was from the Penn State Child Cohort, which is a random, population-based sample of children (n = 700, 5–12 years at baseline), of whom 421 (53.9 percent male and 21.9 percent racial/ethnic minorities) were followed up as adolescents (12–23 years at follow-up). Subjects underwent polysomnography, clinical history, physical exam, and parent- and self-reported scales at baseline and follow-up. Insomnia symptoms were defined as a parent- or self-report of difficulty falling and/or staying asleep.

The results showed that the persistence of childhood insomnia symptoms was 56 percent (95 percent CI = 46.5–65.4), with only 30.3 percent (95 percent CI = 21.5–39.0) fully remitting with the incidence of insomnia symptoms being 31.1 percent (95 percent CI = 25.9–36.3). Several factors were associated with a higher persistence or incidence of insomnia symptoms including, female sex, racial/ethnic minority, and low socioeconomic status as well as psychiatric, behavioral or neurological disorders, obesity, smoking, and clear preference for activity in the evening.

In summary, childhood insomnia symptoms are highly persistent, with full remission occurring in only a third of children in the transition to adolescence. Disparities in youth with insomnia occur early in childhood and are associated with sex, racial/ethnic background, and socioeconomic status. However, other factors such as mental and/or physical health, lifestyle, and circadian risk factors play a key role in the incidence and persistence of chronic childhood insomnia symptoms into adolescence. This study provides findings that may allow for an informed risk-assessment approach to determine and monitor the likelihood for incidence and persistence of insomnia symptoms in certain youths, such as in girls, racial/ethnic minorities, children of low socioeconomic status, those with psychiatric/behavioral, neurological or metabolic disorders, and evening circadian preference. Additionally, these data indicate that childhood insomnia symptoms should not be expected to resolve or remit over the developmental period and that they should become the focus of integrated behavioral health strategies and/or interventions in this population.

Citation: Fernandez-Mendoza J, Bourchtein E, Calhoun S, Puzino K, Snyder CK, He F, Vgontzas AN, Liao D, Bixler E. 2020. Natural History of Insomnia Symptoms in the Transition from Childhood to Adolescence: Population Rates, Health Disparities and Risk Factors. Sleep. doi: 10.1093/sleep/zsaa187