Impact Of Public Health Interventions Research Articles

COVID-19 epidemic prediction and the impact of public health interventions: A review of COVID-19 epidemic models.

The coronavirus disease outbreak of 2019 (COVID-19) has been spreading rapidly to all corners of the word, in a very complex manner. A key research focus is in predicting the development trend of COVID-19 scientifically through mathematical modelling. We conducted a systematic review of epidemic prediction models of COVID-19 and the public health intervention strategies by searching the Web of Science database. 55 studies of the COVID-19 epidemic model were reviewed systematically. It was found that the COVID-19 epidemic models were different in the model type, acquisition method, hypothesis and distribution of key input parameters. Most studies used the gamma distribution to describe the key time period of COVID-19 infection, and some studies used the lognormal distribution, the Erlang distribution, and the Weibull distribution. The setting ranges of the incubation period, serial interval, infectious period and generation time were 4.9–7 days, 4.41–8.4 days, 2.3–10 days and 4.4–7.5 days, respectively, and more than half of the incubation periods were set to 5.1 or 5.2 days. Most models assumed that the latent period was consistent with the incubation period. Some models assumed that asymptomatic infections were infectious or pre-symptomatic transmission was possible, which overestimated the value of R0. For the prediction differences under different public health strategies, the most significant effect was in travel restrictions. There were different studies on the impact of contact tracking and social isolation, but it was considered that improving the quarantine rate and reporting rate, and the use of protective face mask were essential for epidemic prevention and control. The input epidemiological parameters of the prediction models had significant differences in the prediction of the severity of the epidemic spread. Therefore, prevention and control institutions should be cautious when formulating public health strategies by based on the prediction results of mathematical models.

The Impact of Public Health Interventions on Delaying and Mitigating against Replacement by SARS-CoV-2 Variants of Concern

Background. Public Health interventions have succeeded marginally to flatten the COVID-19 epidemic curve of the most recent wave in many regions. Several highly transmissible COVID-19 Variants of Concern (VOCs) have emerged internationally, with variable virulence and as-yet unclear antigenicity. Growing public fatigue highlights the importance of accurately quantifying the speed at which VOCs can replace the resident strain, to inform precise decision-making in terms of both timing and scale of interventions.Methods. Using a simple Susceptible-Infected-Removed (SIR) model, we derive a formula calculating the duration for a VOC to increase its frequency from one level to another. We evaluate the impact of interventions on VOC-related variables.Findings. After introduction of a VOC with a higher transmissibility than the resident strain, its prevalence increases exponentially, leading to eventual replacement by the VOC. Even if the outbreak caused by the resident strain has previously been brought under control, the epidemic is eventually going to increase exponentially, possibly after a transient declining phase. The initial VOC frequency, epidemic mean generation time, and reproduction number of the resident strain all contribute to determining the speed of VOC replacement and the resulting epidemic growth.Interpretation. Following the introduction of a VOC, the risk of a sudden surge of the total cases is hidden behind a transient decline. Maintaining or enhancing Public Health interventions are vital to slowing down the VOC replacement, curbing the epidemic growth, and increasing VOC doubling time.

COVID-19: The Impact of Public Health Interventions on the Outbreak: Public Health Perspective and Future Trends

In March 2019, before the first confirmed cases in Turkey, a scientific advisory board was created to develop guidelines for disease control and preventive health care. Public health interventions and treatment protocols were implemented by the health authority and scientific advisory board with daily analyses obtained from the databases. Soon after, the pandemic management process intensified with the closure of schools and partial curfews. Despite the decline in the number of cases and deaths, the number of cases plateaued and did not decrease further with the effect of the normalization period. With the end of the summer, the increase in spending time indoors, and the gradual start of face-to-face education in schools, the number of new cases has significantly increased. Therefore, the strict implementation of public health interventions has been established. In this period, the health literacy level of the population and the perception of risk also affected their compliance with health protocols. Moreover, vaccine studies and the application of practices for COVID-19 were deemed essential to effectively contain the virus. In the coming months, it is necessary to implement vaccination programs along with good planning and communication strategy. It is important to have a multi-sectoral approach, considering the possible security, health, economic, social, psychological, political, and social effects of this epidemic and other future outbreaks. Furthermore, it is necessary to comply with individual measures and restrictions for the benefit of society.

The Impact of Public Health Interventions on The First and Second Waves of COVID-19 in Trinidad And Tobago – A Simple Epidemic Model

Introduction The COVID-19 pandemic has resulted in more than 35 million confirmed cases worldwide. Currently, there is no specific treatment for the disease or available vaccine to reduce the spread of COVID-19. As such, countries rely on a range of public health interventions to assist in halting the spread of transmission. Caribbean countries have also adopted many public health interventions. In this paper, we use mathematical modelling to demonstrate the impact of public health interventions on the progression of COVID-19 in order to provide timely decision support. Methods A cohort Markov model, based on the concept of the SEIR model, was built to reflect the characteristics of the COVID-19 virus. Five possible public health interventions in the first wave and a projection of current second wave were simulated using the constructed model. Results The model results indicate that the strictest combined interventions of complete border closure and lockdown were the most effective with the number of deaths less than ten in the first wave. For the current second wave, it will take around 30 days for the pandemic to pass its peak after implementing the wearing of face masks policy. Conclusions This paper shows the impact of common public health interventions on the COVID-19 pandemic, using Trinidad and Tobago as an example. Such impacts may be useful in reducing delays in decision-making and improving compliance by populations. However, given the limitations associated with mathematical models, decision-making should be guided by economic assessments, infectious disease and public health expertise.

Decreased influenza activity during the COVID-19 pandemic-United States, Australia, Chile, and South Africa, 2020.

Transplant recipients are among the groups for whom the updated recommendations for 2020–2021 influenza vaccination should generally be considered essential, notably in the face of the COVID‐19 pandemic.

Impact of Public Health Interventions to Curb SARS-CoV-2 Spread by an Evidence-Educated Delphi-Panel and Tailored SEIR Model

Background: With the absence of immunization, public health interventions are the basis for curbing the spread of the SARS-CoV-2 virus. Evidence of impact of non-pharmaceutical interventions (NPI) on SARS-CoV-2 spread in Germany is scarce. The objectives of this study were to use a Delphi-panel based assessment of the effectiveness of different COVID-19 specific prevention measures in order to retrospectively approximate and to prospectively predict the SARS-CoV-2 pandemic progression via a SEIR model (SEIR: Susceptible - Exposed - Infectious - Removed). The SEIR model will be made available with a modifiable user interface.Methods: We applied an evidence-educated Delphi-panel approach to elicit the impact of NPIs being discussed in Germany on the SARS-CoV-2 transmission rate R0. Initial estimates were discussed and agreed upon in two Delphi stages resulting in a final set of average efficacy and compliance estimates for each NPI. Effectiveness was defined as the product of efficacy and compliance. A discrete, deterministic SEIR model with time step of 1 day, a latency period of 1.·8 days, duration of infectiousness of 5 days, and a share of the total population of 15% assumed to be protected by immunity was developed in order to estimate the impact of selected NPI measures on pandemic course. The model was populated with the Delphi-panel results and varied in sensitivity analyses.Results: Efficacy and compliance estimates were obtained as follows (ranked by effectiveness): test and isolate 49% (efficacy)/78% (compliance), keeping distance 42%/74%, personal protection masks (cloth masks or other face masks) 33%/79%, ban of large public events 26%/96%, contact reduction 33%/59%, closure of non-essential stores 10%/97%, closure of schools 10%/100%, working from home 12%/66%, closure of restaurants 8%/96%, and improved hand hygiene 7%/54%. Applying these NPI effectiveness estimates to the SEIR model resulted in a valid replication of reported occurrence of the German SARS-CoV-2 pandemic.Conclusion: Employing an evidence-educated Delphi-panel approach for generating NPI effectiveness estimates is feasible and could help to generate model simulations with close replication of reported infected cases in Germany. Future curbing scenarios require a combination of NPIs. A Delphi-panel based NPI assessment and modelling might support public health policy decision making by informing sequence and number of needed public health measures.Funding Statement: None.Declaration of Interests: None declared.Ethics Approval Statement: Not applicable.

Spatial Patterns of an SIS Epidemic Model with Diffusion

We consider an SIS spatial epidemic model with nonlinear incidence rate and diffusion, that consists of susceptible S and infective I individual populations which interact randomly in their physical environment. We use pattern formation to explain the spread and control of the epidemic over a course of time. Turing instability conditions are established and analysed for the model to exhibit spatial patterns. We find the exact Turing space in the parameter regime for these conditions to hold. An implicit pseudo-spectral method is used to numerically approximate the system and the patterns form reveal that the susceptible and infected populations behave of the same way. In some examples these populations are in isolation from each other. This happens because the susceptible individuals diffuse or move away from the infected individuals to avoid contact and the possibility of getting infected with the disease. Rigorous numerical experimentation reveals that the model has rich dynamics. We find that whenever the transmission rate β is less than the treatment r, there is no outbreak but for β ≥ r there is a possibility of having an outbreak. The results obtained extend well the findings of pattern formation in epidemic models and may have direct implications for the study of disease spread and control and perhaps the mechanistic impact of public health interventions on epidemics.

The impact of public health interventions on critical illness in the pediatric emergency department during the SARS-CoV-2 pandemic.

Study objectiveThe impact of public health interventions during the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) pandemic on critical illness in children has not been studied. We seek to determine the impact of SARS‐CoV‐2 related public health interventions on emergency healthcare utilization and frequency of critical illness in children.MethodsThis was an interrupted time series analysis conducted at a single tertiary pediatric emergency department (PED). All patients evaluated by a provider from December 31 through May 14 of 6 consecutive years (2015‐2020) were included. Total patient visits (ED and urgent care), shock trauma suite (STS) volume, and measures of critical illness were compared between the SARS‐CoV‐2 period (December 31, 2019 to May 14, 2020) and the same period for the previous 5 years combined. A segmented regression model was used to explore differences in the 3 outcomes between the study and control period.ResultsTotal visits, STS volume, and volume of critical illness were all significantly lower during the SARS‐CoV‐2 period. During the height of public health interventions, per day there were 151 fewer total visits and 7 fewer patients evaluated in the STS. The odds of having a 24‐hour period without a single critical patient were >5 times higher. Trends appeared to start before the statewide shelter‐in‐place order and lasted for at least 8 weeks.ConclusionsIn a metropolitan area without significant SARS‐CoV‐2 seeding, the pandemic was associated with a marked reduction in PED visits for critical pediatric illness.

Impact of bystander-focused public health interventions on cardiopulmonary resuscitation and survival: a cohort study

Bystander cardiopulmonary resuscitation (CPR) increases an individual's chance of survival from out-of-hospital cardiac arrest (OHCA), but the frequency of bystander CPR is low in many communities. We aimed to assess the cumulative effect of CPR-targeted public health interventions in Singapore, which were incrementally introduced between 2012 and 2016. We did a secondary analysis of a prospective cohort study of adult, non-traumatic OHCAs, through the Singapore registry. National interventions introduced during this time included emergency services interventions, as well as dispatch-assisted CPR (introduced on July 1, 2012), a training programme for CPR and automated external defibrillators (April 1, 2014), and a first responder mobile application (myResponder; April 17, 2015). Using multilevel mixed-effects logistic regression, we modelled the likelihood of receiving bystander CPR with the increasing number of interventions, accounting for year as a random effect. The Singapore registry contained 11 465 OHCA events between Jan 1, 2011, and Dec 31, 2016. Paediatric arrests, arrests witnessed by emergency medical services, and healthcare-facility arrests were excluded, and 6788 events were analysed. Bystander CPR was administered in 3248 (48%) of 6788 events. Compared with no intervention, likelihood of bystander CPR was not significantly altered by the addition of emergency medical services interventions (odds ratio [OR] 1·33 [95% CI 0·98-1·79]; p=0·065), but increased with implementation of dispatch-assisted CPR (3·72 [2·84-4·88]; p<0·0001), with addition of the CPR and automated external defibrillator training programme (6·16 [4·66-8·14]; p<0·0001), and with addition of the myResponder application (7·66 [5·85-10·03]; p<0·0001). Survival to hospital discharge increased after the addition of all interventions, compared with no intervention (OR 3·10 [95% CI 1·53-6·26]; p<0·0001). National bystander-focused public health interventions were associated with an increased likelihood of bystander CPR, and an increased survival to hospital discharge. Understanding the combined impact of public health interventions might improve strategies to increase the likelihood of bystander CPR, and inform targeted initiatives to improve survival from OHCA. National Medical Research Council, Clinician Scientist Award, Singapore and Ministry of Health, Health Services Research Grant, Singapore.

Impact of Public Health Interventions on Seasonal Influenza Activity During the COVID-19 Outbreak in Korea.

BackgroundCOVID-19 was introduced in Korea early and experienced a large outbreak in mid-February. We aimed to review the public health interventions used during the COVID-19 outbreak and describe the impact on seasonal influenza activity in Korea.MethodsNational response strategies and public health interventions, along with daily COVID-19 confirmed cases in Korea were reviewed during the pandemic. National influenza surveillance data were compared between seven sequential seasons. Characteristics of each season, including the rate of influenza-like illness (ILI), duration of epidemic, date of termination of epidemic, distribution of influenza virus strain and hospitalization were analyzed.ResultsAfter various public health interventions including enforced public education on hand hygiene, cough etiquette and staying at home with respiratory symptoms, universal mask use in public places, refrain from non-essential social activities and school closure, the duration of the influenza epidemic in 2019/2020 decreased by 6-12 weeks and the influenza activity peak rated 49.8 ILI/1,000 visits compared to 71.9-86.2 ILI/1,000 visits of previous seasons. During the period of enforced social distancing from week 9 to 17 of 2020, influenza hospitalization cases were 11.9-26.9-fold lower compared with previous seasons. During the 2019/2020 season, influenza B accounted for only 4%, in contrast with previous seasons in which influenza B accounted for 26.6% to 54.9% of all cases.ConclusionsEfforts to activate high level national response not only led to a decrease in COVID-19, but also substantial decrease in seasonal influenza activity. Interventions applied to control COVID-19 may serve as useful strategies for prevention and control of influenza in upcoming seasons.

Molecular surveillance over 14 years confirms reduction of Plasmodium vivax and falciparum transmission after implementation of Artemisinin-based combination therapy in Papua, Indonesia.

Genetic epidemiology can provide important insights into parasite transmission that can inform public health interventions. The current study compared long-term changes in the genetic diversity and structure of co-endemic Plasmodium falciparum and P. vivax populations. The study was conducted in Papua Indonesia, where high-grade chloroquine resistance in P. falciparum and P. vivax led to a universal policy of Artemisinin-based Combination Therapy (ACT) in 2006. Microsatellite typing and population genetic analyses were undertaken on available isolates collected between 2004 and 2017 from patients with uncomplicated malaria (n = 666 P. falciparum and n = 615 P. vivax). The proportion of polyclonal P. falciparum infections fell from 28% (38/135) before policy change (2004-2006) to 18% (22/125) at the end of the study (2015-2017); p<0.001. Over the same period, polyclonal P. vivax infections fell from 67% (80/119) to 35% (33/93); p<0.001. P. falciparum strains persisted for up to 9 years compared to 3 months for P. vivax, reflecting higher rates of outbreeding in the latter. Sub-structure was observed in the P. falciparum population, but not in P. vivax, confirming different patterns of outbreeding. The P. falciparum population exhibited 4 subpopulations that changed in frequency over time. Notably, a sharp rise was observed in the frequency of a minor subpopulation (K2) in the late post-ACT period, accounting for 100% of infections in late 2016-2017. The results confirm epidemiological evidence of reduced P. falciparum and P. vivax transmission over time. The smaller change in P. vivax population structure is consistent with greater outbreeding associated with relapsing infections and highlights the need for radical cure to reduce recurrent infections. The study emphasizes the challenge in disrupting P. vivax transmission and demonstrates the potential of molecular data to inform on the impact of public health interventions.

The Transmission Dynamics of SARS-COV-2 in China: Modeling Study and the Impact of Public Health Interventions

Background: COVID-19 is breaking out and has become a global pandemic However, at present, the transmission dynamics remain unclear We aimed to establish a SE

Impact of population distribution shifts in sugar-sweetened beverage consumption on type II diabetes incidence in Ireland

PurposeWe simulate population shifts in the distribution of sugar-sweetened beverage (SSB) consumption and address previous methodological limitations to provide valid and reliable estimates of the potential impact of public health interventions on type II diabetes incidence in Ireland. MethodsA comparative risk assessment was conducted, using distribution shift calculations to estimate potential impact fractions (PIFs) for percentage reductions in SSB consumption. Data from the Survey of Lifestyle, Attitudes and Nutrition was analyzed. Individual risk of developing type II diabetes was estimated using a risk prediction algorithm. PIFs were calculated using risk estimates, changes in SSB consumption, and an appropriately specified relative risk. The impact of a 20% levy on SSBs was explored. Monte–Carlo simulation with 150,000 iterations estimated uncertainty intervals (UIs). PIFs were applied to 2016 census data, estimating the absolute incident cases that may potentially be avoided through reduced SSB consumption. ResultsOf the 7272 Survey of Lifestyle, Attitudes and Nutrition participants, 53.3% consumed SSBs. The 10-year rate of type II diabetes was estimated at 4.3% (95% confidence interval: 4.2%, 4.4%). Simulating a 100% reduction in SSB consumption, the population attributable fraction was 1.8% (95%UI: 0.1%, 3.3%). Population shifts in consumption after a 20% levy results in a PIF of 0.37% (95%UI: 0.02%, 0.7%). We estimate 135,850 incident type II diabetes cases over a 10-year period. Of these, 2446 (95% UI: 136, 4483) cases may be attributable to SSB consumption. ConclusionsOvercoming previous methodological limitations, unbiased estimates demonstrate that a population shift in SSB consumption can potentially play a role in the primary prevention of type II diabetes.

Abstract 314: The Impact of City-Level Public Health Interventions on Increased Bystander Cardiopulmonary Resuscitation

Introduction: Bystander cardiopulmonary resuscitation (B-CPR) may increase a victim’s chance of survival from sudden cardiac arrest (SCA), but B-CPR rates are low in many communities. Few studies have examined the association of city-wide public health interventions on B-CPR. Objectives: We sought to assess whether there is variation in B-CPR by intervention and location of arrest. We hypothesized that implementation of dispatch-assisted CPR (DA-CPR), CPR/AED training, and a first responder mobile application (myResponder) would increase B-CPR by two-fold. Methods: We conducted a retrospective study of adult, non-traumatic SCAs from the Singapore registry (4/2010-12/2016). Interventions included DA-CPR (7/2012 - present), CPR/AED training (04/2014 - present), and myResponder (4/2015 - present). Using logistic regression, we modeled the likelihood of receiving B-CPR by increased number of interventions over time. We examined these effects together, in the home, and public accounting for patient-level confounding. Results: From 2010-2016, the Singapore registry contained 12,546 SCA events. Excluding pediatric, EMS witnessed, and healthcare facility arrests, 7,476 were analyzed. Of these, mean age was 66±15 and 68% were male. B-CPR was administered in 45% of the events and varied by location (home: 43% v public: 52%). With implementation of DA-CPR, likelihood of B-CPR increased (OR: 3.5 (2.9-4.2) p&lt;0.01) compared to no intervention; with implementation of CPR/AED training, likelihood of B-CPR increased compared to no intervention (OR: 5.8 (4.8-7.0), p&lt;0.01). Lastly, implementation of myResponder resulted in a 7.09 increased likelihood of B-CPR compared to no intervention (OR: 7.1 (5.9-8.4), p&lt;0.01). Variation was seen when examining likelihood of B-CPR by all interventions compared to no intervention, in the home (OR: 8.7 (7.0-10.7)) and the public (OR: 4.0 (2.9-5.6)). Survival increased, corresponding to the increase in B-CPR. Conclusion: City-level public health interventions increased the likelihood of layperson B-CPR, while variation was seen in the home and public. Understanding the impact of public health interventions may shed light on strategies to increase B-CPR and inform targeted initiatives to improve survival from SCA.

Impact of Public Health Interventions on Drinking Water-Associated Outbreaks of Hepatitis A - United States, 1971-2017.

Hepatitis A virus (HAV) is an RNA virus primarily transmitted via the fecal-oral route and, in rare cases, causes liver failure and death in infected persons. Although drinking water-associated hepatitis A outbreaks in the United States are rarely reported (1), HAV was the most commonly reported etiology for outbreaks associated with untreated ground water during 1971-2008 (2), and HAV can remain infectious in water for months (3). This report analyzes drinking water-associated hepatitis A outbreaks reported to the Waterborne Disease and Outbreak Surveillance System (WBDOSS) during 1971-2017. During that period, 32 outbreaks resulting in 857 cases were reported, all before 2010. Untreated ground water was associated with 23 (72%) outbreaks, resulting in 585 (68.3%) reported cases. Reported outbreaks significantly decreased after introduction of Advisory Committee on Immunization Practices (ACIP) hepatitis A vaccination recommendations* and U.S. Environmental Protection Agency's (USEPA) public ground water system regulations.† Individual water systems, which are not required to meet national drinking water standards,§ were the only contaminated drinking water systems to cause the last four reported hepatitis A outbreaks during 1995-2009. No waterborne outbreaks were reported during 2009-2017. Water testing and treatment are important considerations to protect persons who use these unregulated systems from HAV infection.

Evaluation of Intervention Impact on Health Inequality for Resource Allocation.

We describe a simplified distributional cost-effectiveness analysis based on aggregate data to estimate the health inequality impact of public health interventions. We extracted data on costs, health outcomes expressed as quality-adjusted life years (QALYs), and target populations for interventions within National Institute for Health and Care Excellence (NICE) public health guidance published up to October 2016. Evidence on variation by age, gender, and index of multiple deprivation informed socioeconomic distributions of incremental QALYs, health opportunity costs, and the baseline distribution of health. Total population QALYs, summary measures of inequality, and a health equity impact plane show results by intervention and by guideline. A value for inequality aversion from a general population survey in England let us combine impacts on health inequality and total health into a single measure of intervention value. Our estimates suggest that of 134 interventions considered by NICE, 70 (52%) reduce inequality and increase health, 21 (16%) involve a tradeoff between improving health and improving health inequality, and 43 (32%) reduce health and increase health inequality. Fully implemented, the potential impact of all recommendations was 23,336,181 additional QALYs for the population of England and Wales and a reduction of the gap in quality-adjusted life expectancy between the healthiest and least healthy from 13.78 to 13.34 QALYs. The combined value of the additional health and reduction in inequality was 28,723,776 QALYs. Our analysis takes account of the fact that existing public health spending likely benefits the most disadvantaged. This simple method applied separately to economic evaluation produces evidence of intervention impacts on the distribution of health that is vital in determining value for money when health inequality reduction is a policy goal.

Tracing the Impact of Public Health Interventions on HIV-1 Transmission in Portugal Using Molecular Epidemiology.

BackgroundEstimation of temporal changes in human immunodeficiency virus (HIV) transmission patterns can help to elucidate the impact of preventive strategies and public health policies.MethodsPortuguese HIV-1 subtype B and G pol genetic sequences were appended to global reference data sets to identify country-specific transmission clades. Bayesian birth-death models were used to estimate subtype-specific effective reproductive numbers (Re). Discrete trait analysis (DTA) was used to quantify mixing among transmission groups.ResultsWe identified 5 subtype B Portuguese clades (26–79 sequences) and a large monophyletic subtype G Portuguese clade (236 sequences). We estimated that major shifts in HIV-1 transmission occurred around 1999 (95% Bayesian credible interval [BCI], 1998–2000) and 2000 (95% BCI, 1998–2001) for subtypes B and G, respectively. For subtype B, Re dropped from 1.91 (95% BCI, 1.73–2.09) to 0.62 (95% BCI,.52–.72). For subtype G, Re decreased from 1.49 (95% BCI, 1.39–1.59) to 0.72 (95% BCI, .63–.8). The DTA suggests that people who inject drugs (PWID) and heterosexuals were the source of most (>80%) virus lineage transitions for subtypes G and B, respectively.ConclusionsThe estimated declines in Re coincide with the introduction of highly active antiretroviral therapy and the scale-up of harm reduction for PWID. Inferred transmission events across transmission groups emphasize the importance of prevention efforts for bridging populations.

Scaling up prevention and treatment towards the elimination of hepatitis C: a global mathematical model

SummaryBackgroundThe revolution in hepatitis C virus (HCV) treatment through the development of direct-acting antivirals (DAAs) has generated international interest in the global elimination of the disease as a public health threat. In 2017, this led WHO to establish elimination targets for 2030. We evaluated the impact of public health interventions on the global HCV epidemic and investigated whether WHO's elimination targets could be met.MethodsWe developed a dynamic transmission model of the global HCV epidemic, calibrated to 190 countries, which incorporates data on demography, people who inject drugs (PWID), current coverage of treatment and prevention programmes, natural history of the disease, HCV prevalence, and HCV-attributable mortality. We estimated the worldwide impact of scaling up interventions that reduce risk of transmission, improve access to treatment, and increase screening for HCV infection by considering six scenarios: no change made to existing levels of diagnosis or treatment; sequentially adding the following interventions: blood safety and infection control, PWID harm reduction, offering of DAAs at diagnosis, and outreach screening to increase the number diagnosed; and a scenario in which DAAs are not introduced (ie, treatment is only with pegylated interferon and oral ribavirin) to investigate the effect of DAA use. We explored the effect of varying the coverage or impact of these interventions in sensitivity analyses and also assessed the impact on the global epidemic of removing certain key countries from the package of interventions.FindingsBy 2030, interventions that reduce risk of transmission in the non-PWID population by 80% and increase coverage of harm reduction services to 40% of PWID could avert 14·1 million (95% credible interval 13·0–15·2) new infections. Offering DAAs at time of diagnosis in all countries could prevent 640 000 deaths (620 000–670 000) from cirrhosis and liver cancer. A comprehensive package of prevention, screening, and treatment interventions could avert 15·1 million (13·8–16·1) new infections and 1·5 million (1·4–1·6) cirrhosis and liver cancer deaths, corresponding to an 81% (78–82) reduction in incidence and a 61% (60–62) reduction in mortality compared with 2015 baseline. This reaches the WHO HCV incidence reduction target of 80% but is just short of the mortality reduction target of 65%, which could be reached by 2032. Reducing global burden depends upon success of prevention interventions, implemention of outreach screening, and progress made in key high-burden countries including China, India, and Pakistan.InterpretationFurther improvements in blood safety and infection control, expansion or creation of PWID harm reduction services, and extensive screening for HCV with concomitant treatment for all are necessary to reduce the burden of HCV. These findings should inform the ongoing global action to eliminate the HCV epidemic.FundingWellcome Trust.

2478. Impact of 20 Years of Varicella Vaccination on the Epidemiology of Herpes Zoster in the United States: An Interrupted Time Series Analysis

BackgroundThe mechanism for reactivation of varicella zoster virus as herpes zoster (HZ) is not well understood. One hypothesis postulates that re-exposure to circulating wild-type varicella can boost individual immunity and prevent reactivation (“Exogenous Boosting,” EB). The validity of this hypothesis has been debated and the evidence to evaluate it is limited. Dynamic transmission model outcomes of impact and cost-effectiveness of universal varicella vaccination (UVV) are sensitive to EB characterization and assumptions, occasionally leading to conclusions that UVV programs may not be cost-effective and could lead to temporary increases in HZ incidence. The goal of this study was to use data from 20 years of UVV in the United States from 1996 to 2016 to evaluate whether the hypothesized increases in HZ incidence have been realized.MethodsThis is a retrospective study of de-identified administrative claims data from the US MarketScan® databases between 1991 and 2016. The incidence of HZ was analyzed by calendar year and age category using interrupted time series (ITS) analysis implemented through a negative binomial generalized linear regression model over three time periods: pre-UVV (1991–1995); 1 dose UVV (1996–2006); and 2 dose UVV (2007–2016). The ITS approach (Bernal et al., IJE, 2017) is an effective way to evaluate the impact of public health interventions implemented at specific time points.ResultsHZ incidence in the pre-UVV period increased at annual rates between 3.67% and 12.38%, with the highest increases in the 0–17 and 65+ age groups. The rate of HZ increase was lower in the 1 dose UVV period compared with the pre-UVV period for all age groups except for minor increases in the 18–35 (0.52%) and 55–65 (0.14%) groups. During the 2 dose UVV period, the rate of increase in HZ was lower in all groups than in the pre-UVV period, with the largest reductions in the 0–17 (−22.58%), 65+(−10.68%), and 18–<35 (−3.57%) age groups.ConclusionThis evaluation of the impact of UVV on rates of change in HZ does not support the hypothesis of an increase in HZ incidence due to UVV. While overall HZ incidence rates have been increasing year on year, the rate of that increase has been declining in both UVV periods. Our findings have implications on the assumptions used in economic evaluations of UVV programs. Disclosures L. Wolfson, Merck & Co., Inc.: Employee and Shareholder, Salary. V. Daniels, Merck & Co., Inc.: Employee and Shareholder, Salary. Y. T. Chen, Merck & Co., Inc.: Employee and Shareholder, Salary. W. Ou, Merck & Co., Inc.: Employee and Shareholder, Salary.

Impact of Public Health Interventions on Obesity and Type 2 Diabetes Prevention: A Simulation Study

Little is known about what interventions worked or did not work in slowing the obesity epidemic. The long-term comparative effectiveness of environmental and behavioral public health interventions for obesity and type 2 diabetes prevention over an individual's life course is relatively unexplored. The potential impact and long-term collective effectiveness of environmental and behavioral interventions on obesity and type 2 diabetes throughout the life course was evaluated. The Virtual Los Angeles Obesity Model developed in 2016 was used to estimate the incidence and prevalence of obesity and type 2 diabetes under current and hypothetical interventions among 98,000 individuals born in 2009 and followed from birth to age 65 years. Analyses were performed in 2016 and completed in 2018. The 48-year risk of type 2 diabetes was 0.533 (95% CI=0.446, 0.629) under the natural course, 0.451 (95% CI=0.334, 0.570) under the physical activity intervention, and 0.443 (95% CI=0.389, 0.495) under the fast-food intervention. The 64-year risk of obesity was 0.892 (95% CI=0.879, 0.903) under the natural course, 0.876 (95% CI=0.850, 0.899) under the physical activity intervention, and 0.864 (95% CI=0.856, 0.873) under the fast-food intervention. The other interventions had little or no long-term effects. When all the interventions were applied, the population risk ratios were 0.942 (95% CI=0.914, 0.967) and 0.634 (95% CI=0.484, 0.845) for obesity and type 2 diabetes, respectively. Implementing health interventions continuously throughout the life span and in combination with other interventions could substantially halt the obesity and the type 2 diabetes epidemics.