Impact of School Cycles and Environmental Forcing on the Timing of Pandemic Influenza Activity in Mexican States,May-December 2009

While a relationship between environmental forcing and influenza transmission has been established in inter-pandemic seasons, the drivers of pandemic influenza remain debated. In particular, school effects may predominate in pandemic seasons marked by an atypical concentration of cases among children. For the 2009 A/H1N1 pandemic, Mexico is a particularly interesting case study due to its broad geographic extent encompassing temperate and tropical regions, well-documented regional variation in the occurrence of pandemic outbreaks, and coincidence of several school breaks during the pandemic period. Here we fit a series of transmission models to daily laboratory-confirmed influenza data in 32 Mexican states using MCMC approaches, considering a meta-population framework or the absence of spatial coupling between states. We use these models to explore the effect of environmental, school–related and travel factors on the generation of spatially-heterogeneous pandemic waves. We find that the spatial structure of the pandemic is best understood by the interplay between regional differences in specific humidity (explaining the occurrence of pandemic activity towards the end of the school term in late May-June 2009 in more humid southeastern states), school vacations (preventing influenza transmission during July-August in all states), and regional differences in residual susceptibility (resulting in large outbreaks in early fall 2009 in central and northern Mexico that had yet to experience fully-developed outbreaks). Our results are in line with the concept that very high levels of specific humidity, as present during summer in southeastern Mexico, favor influenza transmission, and that school cycles are a strong determinant of pandemic wave timing.     

Characterizing the epidemiology of the 2009 influenza A/H1N1 pandemic in Mexico

Background

Mexico''s local and national authorities initiated an intense public health response during the early stages of the 2009 A/H1N1 pandemic. In this study we analyzed the epidemiological patterns of the pandemic during April–December 2009 in Mexico and evaluated the impact of nonmedical interventions, school cycles, and demographic factors on influenza transmission.

Methods and Findings

We used influenza surveillance data compiled by the Mexican Institute for Social Security, representing 40% of the population, to study patterns in influenza-like illness (ILIs) hospitalizations, deaths, and case-fatality rate by pandemic wave and geographical region. We also estimated the reproduction number (R) on the basis of the growth rate of daily cases, and used a transmission model to evaluate the effectiveness of mitigation strategies initiated during the spring pandemic wave. A total of 117,626 ILI cases were identified during April–December 2009, of which 30.6% were tested for influenza, and 23.3% were positive for the influenza A/H1N1 pandemic virus. A three-wave pandemic profile was identified, with an initial wave in April–May (Mexico City area), a second wave in June–July (southeastern states), and a geographically widespread third wave in August–December. The median age of laboratory confirmed ILI cases was ∼18 years overall and increased to ∼31 years during autumn (p<0.0001). The case-fatality ratio among ILI cases was 1.2% overall, and highest (5.5%) among people over 60 years. The regional R estimates were 1.8–2.1, 1.6–1.9, and 1.2–1.3 for the spring, summer, and fall waves, respectively. We estimate that the 18-day period of mandatory school closures and other social distancing measures implemented in the greater Mexico City area was associated with a 29%–37% reduction in influenza transmission in spring 2009. In addition, an increase in R was observed in late May and early June in the southeast states, after mandatory school suspension resumed and before summer vacation started. State-specific fall pandemic waves began 2–5 weeks after school reopened for the fall term, coinciding with an age shift in influenza cases.

Conclusions

We documented three spatially heterogeneous waves of the 2009 A/H1N1 pandemic virus in Mexico, which were characterized by a relatively young age distribution of cases. Our study highlights the importance of school cycles on the transmission dynamics of this pandemic influenza strain and suggests that school closure and other mitigation measures could be useful to mitigate future influenza pandemics. Please see later in the article for the Editors'' Summary     

The geographic synchrony of seasonal influenza: a waves across Canada and the United States

Background

As observed during the 2009 pandemic, a novel influenza virus can spread globally before the epidemic peaks locally. As consistencies in the relative timing and direction of spread could form the basis for an early alert system, the objectives of this study were to use the case-based reporting system for laboratory confirmed influenza from the Canadian FluWatch surveillance program to identify the geographic scale at which spatial synchrony exists and then to describe the geographic patterns of influenza A virus across Canada and in relationship to activity in the United States (US).

Methodology/Principal Findings

Weekly laboratory confirmations for influenza A were obtained from the Canadian FluWatch and the US FluView surveillance programs from 1997/98 to 2006/07. For the six seasons where at least 80% of the specimens were antigenically similar, we identified the epidemic midpoint of the local/regional/provincial epidemics and analyzed trends in the direction of spread. In three out of the six seasons, the epidemic appeared first in Canada. Regional epidemics were more closely synchronized across the US (3–5 weeks) compared to Canada (5–13 weeks), with a slight gradient in timing from the southwest regions in the US to northeast regions of Canada and the US. Cities, as well as rural areas within provinces, usually peaked within a couple of weeks of each other. The anticipated delay in peak activity between large cities and rural areas was not observed. In some mixed influenza A seasons, lack of synchronization sub-provincially was evident.

Conclusions/Significance

As mixing between regions appears to be too weak to force a consistency in the direction and timing of spread, local laboratory-based surveillance is needed to accurately assess the level of influenza activity in the community. In comparison, mixing between urban communities and adjacent rural areas, and between some communities, may be sufficient to force synchronization.     

Phylogeography of the spring and fall waves of the H1N1/09 pandemic influenza virus in the United States

Spatial variation in the epidemiological patterns of successive waves of pandemic influenza virus in humans has been documented throughout the 20th century but never understood at a molecular level. However, the unprecedented intensity of sampling and whole-genome sequencing of the H1N1/09 pandemic virus now makes such an approach possible. To determine whether the spring and fall waves of the H1N1/09 influenza pandemic were associated with different epidemiological patterns, we undertook a large-scale phylogeographic analysis of viruses sampled from three localities in the United States. Analysis of genomic and epidemiological data reveals distinct spatial heterogeneities associated with the first pandemic wave, March to July 2009, in Houston, TX, Milwaukee, WI, and New York State. In Houston, no specific H1N1/09 viral lineage dominated during the spring of 2009, a period when little epidemiological activity was observed in Texas. In contrast, major pandemic outbreaks occurred at this time in Milwaukee and New York State, each dominated by a different viral lineage and resulting from strong founder effects. During the second pandemic wave, beginning in August 2009, all three U.S. localities were dominated by a single viral lineage, that which had been dominant in New York during wave 1. Hence, during this second phase of the pandemic, extensive viral migration and mixing diffused the spatially defined population structure that had characterized wave 1, amplifying the one viral lineage that had dominated early on in one of the world's largest international travel centers.     

Patterns of spread of influenza A in Canada

Understanding spatial patterns of influenza transmission is important for designing control measures. We investigate spatial patterns of laboratory-confirmed influenza A across Canada from October 1999 to August 2012. A statistical analysis (generalized linear model) of the seasonal epidemics in this time period establishes a clear spatio-temporal pattern, with influenza emerging earlier in western provinces. Early emergence is also correlated with low temperature and low absolute humidity in the autumn. For the richer data from the 2009 pandemic, a mechanistic mathematical analysis, based on a transmission model, shows that both school terms and weather had important effects on pandemic influenza transmission.     

Qualitative analysis of the level of cross-protection between epidemic waves of the 1918-1919 influenza pandemic

The 1918-1919 influenza pandemic was composed of multiple waves within a period of nine months in several regions of the world. Increasing our understanding of the mechanisms responsible for this multi-wave profile has important public health implications. We model the transmission dynamics of two strains of influenza interacting via cross-immunity to simulate two temporal waves of influenza and explore the impact of the basic reproduction number, as a measure of transmissibility associated to each influenza strain, cross-immunity and the timing of the onset of the second influenza epidemic on the pandemic profile. We use time series of case notifications during the 1918 influenza pandemic in Geneva, Switzerland, for illustration. We calibrate our mathematical model to the initial wave of infection to estimate the basic reproduction number of the first wave and the corresponding timing of onset of the second influenza variant. We use this information to explore the impact of cross-immunity levels on the dynamics of the second wave of influenza. Our results for the 1918 pandemic in Geneva, Switzerland, indicate that a second wave can occur whenever R₀₁<1.5 or when cross-immunity levels are less than 0.58 for our estimated R₀₂ of 2.4. We also explore qualitatively profiles of two-wave pandemics and compare them with real temporal profiles of the 1918 influenza pandemic in other regions of the world including several Scandinavian cities, New York City, England and Wales, and Sydney, Australia. Pandemic profiles are classified into three broad categories namely “right-handed”, “left-handed”, and “M-shape”. Our results indicate that avoiding a second influenza epidemic is plausible given sufficient levels of cross-protection are attained via natural infection during an early (herald) wave of infection or vaccination campaigns prior to a second wave. Furthermore, interventions aimed at mitigating the first pandemic wave may be counterproductive by increasing the chances of a second wave of infection that could potentially be more virulent than the first.     

Can Interactions between Timing of Vaccine-Altered Influenza Pandemic Waves and Seasonality in Influenza Complications Lead to More Severe Outcomes?

Vaccination can delay the peak of a pandemic influenza wave by reducing the number of individuals initially susceptible to influenza infection. Emerging evidence indicates that susceptibility to severe secondary bacterial infections following a primary influenza infection may vary seasonally, with peak susceptibility occurring in winter. Taken together, these two observations suggest that vaccinating to prevent a fall pandemic wave might delay it long enough to inadvertently increase influenza infections in winter, when primary influenza infection is more likely to cause severe outcomes. This could potentially cause a net increase in severe outcomes. Most pandemic models implicitly assume that the probability of severe outcomes does not vary seasonally and hence cannot capture this effect. Here we show that the probability of intensive care unit (ICU) admission per influenza infection in the 2009 H1N1 pandemic followed a seasonal pattern. We combine this with an influenza transmission model to investigate conditions under which a vaccination program could inadvertently shift influenza susceptibility to months where the risk of ICU admission due to influenza is higher. We find that vaccination in advance of a fall pandemic wave can actually increase the number of ICU admissions in situations where antigenic drift is sufficiently rapid or where importation of a cross-reactive strain is possible. Moreover, this effect is stronger for vaccination programs that prevent more primary influenza infections. Sensitivity analysis indicates several mechanisms that may cause this effect. We also find that the predicted number of ICU admissions changes dramatically depending on whether the probability of ICU admission varies seasonally, or whether it is held constant. These results suggest that pandemic planning should explore the potential interactions between seasonally varying susceptibility to severe influenza outcomes and the timing of vaccine-altered pandemic influenza waves.     

The global transmission and control of influenza

New strains of influenza spread around the globe via the movement of infected individuals. The global dynamics of influenza are complicated by different patterns of influenza seasonality in different regions of the world. We have released an open-source stochastic mathematical model of the spread of influenza across 321 major, strategically located cities of the world. Influenza is transmitted between cities via infected airline passengers. Seasonality is simulated by increasing the transmissibility in each city at the times of the year when influenza has been observed to be most prevalent. The spatiotemporal spread of pandemic influenza can be understood through clusters of global transmission and links between them, which we identify using the epidemic percolation network (EPN) of the model. We use the model to explain the observed global pattern of spread for pandemic influenza A(H1N1) 2009-2010 (pandemic H1N1 2009) and to examine possible global patterns of spread for future pandemics depending on the origin of pandemic spread, time of year of emergence, and basic reproductive number (). We also use the model to investigate the effectiveness of a plausible global distribution of vaccine for various pandemic scenarios. For pandemic H1N1 2009, we show that the biggest impact of vaccination was in the temperate northern hemisphere. For pandemics starting in the temperate northern hemisphere in May or April, vaccination would have little effect in the temperate southern hemisphere and a small effect in the tropics. With the increasing interconnectedness of the world's population, we must take a global view of infectious disease transmission. Our open-source, computationally simple model can help public health officials plan for the next pandemic as well as deal with interpandemic influenza.     

From pandemic to endemic: Spatial-temporal patterns of influenza-like illness incidence in a Swiss canton, 1918–1924

In pandemics, past and present, there is no textbook definition of when a pandemic is over, and how and when exactly a respiratory virus transitions from pandemic to endemic spread. In this paper we have compared the 1918/19 influenza pandemic and the subsequent spread of seasonal flu until 1924. We analysed 14,125 reports of newly stated 32,198 influenza-like illnesses from the Swiss canton of Bern. We analysed the temporal and spatial spread at the level of 497 municipalities, 9 regions, and the entire canton. We calculated incidence rates per 1000 inhabitants of newly registered cases per calendar week. Further, we illustrated the incidences of each municipality for each wave (first wave in summer 1918, second wave in fall/winter 1918/19, the strong later wave in early 1920, as well as the two seasonal waves in 1922 and 1924) on a choropleth map. We performed a spatial hotspot analysis to identify spatial clusters in each wave, using the Gi* statistic. Furthermore, we applied a robust negative binomial regression to estimate the association between selected explanatory variables and incidence on the ecological level. We show that the pandemic transitioned to endemic spread in several waves (including another strong wave in February 1920) with lower incidence and rather local spread until 1924 at least. At the municipality and regional levels, there were different patterns of spread both between pandemic and seasonal waves. In the first pandemic wave in summer 1918 the probability of higher incidence was increased in municipalities with a higher proportion of factories (OR 2.60, 95%CI 1.42–4.96), as well as in municipalities that had access to a railway station (OR 1.50, 95%CI 1.16–1.96). In contrast, the strong fall/winter wave 1918 was very widespread throughout the canton. In general, municipalities at higher altitude showed lower incidence. Our study adds to the sparse literature on incidence in the 1918/19 pandemic and subsequent years. Before Covid-19, the last pandemic that occurred in several waves and then became endemic was the 1918–19 pandemic. Such scenarios from the past can inform pandemic planning and preparedness in future outbreaks.     

Lessons learned from the 1918–1919 influenza pandemic

The 1918 influenza pandemic was one of the most virulent strains of influenza in history. Phylogenic evidence of the novel H1N1 strain of influenza discovered in Mexico last spring (2009) links it to the 1918 influenza strain. With information gained from analyzing viral genetics, public health records and advances in medical science we can confront the 2009 H1N1 influenza on a global scale. The paper analyses the causes and characteristics of a pandemic, and major issues in controlling the spread of the disease. Wide public vaccination and open communication between government and health sciences professionals will be an essential and vital component in managing the 2009 H1N1 pandemic and any future pandemics.     

Measured dynamic social contact patterns explain the spread of H1N1v influenza

Patterns of social mixing are key determinants of epidemic spread. Here we present the results of an internet-based social contact survey completed by a cohort of participants over 9,000 times between July 2009 and March 2010, during the 2009 H1N1v influenza epidemic. We quantify the changes in social contact patterns over time, finding that school children make 40% fewer contacts during holiday periods than during term time. We use these dynamically varying contact patterns to parameterise an age-structured model of influenza spread, capturing well the observed patterns of incidence; the changing contact patterns resulted in a fall of approximately 35% in the reproduction number of influenza during the holidays. This work illustrates the importance of including changing mixing patterns in epidemic models. We conclude that changes in contact patterns explain changes in disease incidence, and that the timing of school terms drove the 2009 H1N1v epidemic in the UK. Changes in social mixing patterns can be usefully measured through simple internet-based surveys.     

Transmission of a 2009 pandemic influenza virus shows a sensitivity to temperature and humidity similar to that of an H3N2 seasonal strain

In temperate regions of the world, influenza epidemics follow a highly regular seasonal pattern, in which activity peaks in midwinter. Consistently with this epidemiology, we have shown previously that the aerosol transmission of a seasonal H3N2 influenza virus is most efficient under cold, dry conditions. With the 2009 H1N1 pandemic, an exception to the standard seasonality of influenza developed: during 2009 in the Northern Hemisphere, an unusually high level of influenza virus activity over the spring and summer months was followed by a widespread epidemic which peaked in late October, approximately 2.5 months earlier than usual. Herein we show that aerosol transmission of a 2009 pandemic strain shows a dependence on relative humidity and temperature very similar to that of a seasonal H3N2 influenza virus. Our data indicate that the observed differences in the timings of outbreaks with regard to the seasons are most likely not due to intrinsic differences in transmission between the pandemic H1N1 and seasonal H3N2 influenza viruses.     

Optimizing tactics for use of the U.S. antiviral strategic national stockpile for pandemic influenza

In 2009, public health agencies across the globe worked to mitigate the impact of the swine-origin influenza A (pH1N1) virus. These efforts included intensified surveillance, social distancing, hygiene measures, and the targeted use of antiviral medications to prevent infection (prophylaxis). In addition, aggressive antiviral treatment was recommended for certain patient subgroups to reduce the severity and duration of symptoms. To assist States and other localities meet these needs, the U.S. Government distributed a quarter of the antiviral medications in the Strategic National Stockpile within weeks of the pandemic's start. However, there are no quantitative models guiding the geo-temporal distribution of the remainder of the Stockpile in relation to pandemic spread or severity. We present a tactical optimization model for distributing this stockpile for treatment of infected cases during the early stages of a pandemic like 2009 pH1N1, prior to the wide availability of a strain-specific vaccine. Our optimization method efficiently searches large sets of intervention strategies applied to a stochastic network model of pandemic influenza transmission within and among U.S. cities. The resulting optimized strategies depend on the transmissability of the virus and postulated rates of antiviral uptake and wastage (through misallocation or loss). Our results suggest that an aggressive community-based antiviral treatment strategy involving early, widespread, pro-rata distribution of antivirals to States can contribute to slowing the transmission of mildly transmissible strains, like pH1N1. For more highly transmissible strains, outcomes of antiviral use are more heavily impacted by choice of distribution intervals, quantities per shipment, and timing of shipments in relation to pandemic spread. This study supports previous modeling results suggesting that appropriate antiviral treatment may be an effective mitigation strategy during the early stages of future influenza pandemics, increasing the need for systematic efforts to optimize distribution strategies and provide tactical guidance for public health policy-makers.     

Seroprevalence Following the Second Wave of Pandemic 2009 H1N1 Influenza in Pittsburgh,PA, USA

Background

In April 2009, a new pandemic strain of influenza infected thousands of persons in Mexico and the United States and spread rapidly worldwide. During the ensuing summer months, cases ebbed in the Northern Hemisphere while the Southern Hemisphere experienced a typical influenza season dominated by the novel strain. In the fall, a second wave of pandemic H1N1 swept through the United States, peaking in most parts of the country by mid October and returning to baseline levels by early December. The objective was to determine the seroprevalence of antibodies against the pandemic 2009 H1N1 influenza strain by decade of birth among Pittsburgh-area residents.

Methods and Findings

Anonymous blood samples were obtained from clinical laboratories and categorized by decade of birth from 1920–2009. Using hemagglutination-inhibition assays, approximately 100 samples per decade (n = 846) were tested from blood samples drawn on hospital and clinic patients in mid-November and early December 2009. Age specific seroprevalences against pandemic H1N1 (A/California/7/2009) were measured and compared to seroprevalences against H1N1 strains that had previously circulated in the population in 2007, 1957, and 1918. (A/Brisbane/59/2007, A/Denver/1/1957, and A/South Carolina/1/1918). Stored serum samples from healthy, young adults from 2008 were used as a control group (n = 100). Seroprevalences against pandemic 2009 H1N1 influenza varied by age group, with children age 10–19 years having the highest seroprevalence (45%), and persons age 70–79 years having the lowest (5%). The baseline seroprevalence among control samples from 18–24 year-olds was 6%. Overall seroprevalence against pandemic H1N1 across all age groups was approximately 21%.

Conclusions

After the peak of the second wave of 2009 H1N1, HAI seroprevalence results suggest that 21% of persons in the Pittsburgh area had become infected and developed immunity. Extrapolating to the entire US population, we estimate that at least 63 million persons became infected in 2009. As was observed among clinical cases, this sero-epidemiological study revealed highest infection rates among school-age children.     

Substantial Impact of School Closure on the Transmission Dynamics during the Pandemic Flu H1N1-2009 in Oita,Japan

Background

School closure is considered as an effective measure to prevent pandemic influenza. Although Japan has implemented many class, grade, and whole school closures during the early stage of the pandemic 2009, the effectiveness of such a school closure has not been analysed appropriately. In addition, analysis based on evidence or data from a large population has yet to be performed. We evaluated the preventive effect of school closure against the pandemic (H1N1) 2009 and examined efficient strategies of reactive school closure.

Materials and Methods

Data included daily reports of reactive school closures and the number of infected students in the pandemic in Oita City, Japan. We used a regression model that incorporated a time delay to analyse the daily data of school closure based on a time continuous susceptible-exposed-infected-removed model of infectious disease spread. The delay was due to the time-lag from transmission to case reporting. We simulated the number of students infected daily with and without school closure and evaluated the effectiveness.

Results

The model with a 3-day delay from transmission to reporting yielded the best fit using R ² (the coefficient of determination). This result suggests that the recommended period of school closure is more than 4 days. Moreover, the effect of school closure in the simulation of school closure showed the following: the number of infected students decreased by about 24% at its peak, and the number of cumulative infected students decreased by about 8.0%.

Conclusions

School closure was an effective intervention for mitigating the spread of influenza and should be implemented for more than 4 days. School closure has a remarkable impact on decreasing the number of infected students at the peak, but it does not substantially decrease the total number of infected students.     

Epidemiologic and economic effectiveness of school closure during influenza epidemics and pandemics

Epidemiologic and economic effectiveness of school closure during influenza epidemics and pandemics is discussed. Optimal effect of school closure is observed when this measure is taken at the start of the epidemic or pandemic and for a sufficiently long time. School closure during high morbidity among schoolchildren, in the middle (at the peak) and by the end of epidemic or pandemic does not influence significantly the spread of influenza or morbidity. Significant economic losses and other negative consequences of school closure are noted. School closure may be the most appropriate during the emergence of influenza pandemic when the pandemic vaccine is not yet available, however timely mass immunization of schoolchildren against influenza may be a more appropriate measure than school closure for the reduction of influenza morbidity and spread during seasonal influenza epidemics.     

The influenza pandemic of 2009: lessons and implications

Influenza is a moving target, which evolves in unexpected directions and is recurrent annually. The 2009 influenza A/H1N1 pandemic virus was unlike the 2009 seasonal virus strains and originated in pigs prior to infecting humans. Three strains of viruses gave rise to the pandemic virus by antigenic shift, reassortment, and recombination, which occurred in pigs as 'mixing vessels'. The three strains of viruses had originally been derived from birds, pigs, and humans. The influenza hemagglutinin (HA) and neuraminidase (NA) external proteins are used to categorize and group influenza viruses. The internal proteins (PB1, PB1-F2, PB2, PA, NP, M, and NS) are involved in the pathogenesis of influenza infection. A major difference between the 1918 and 2009 pandemic viruses is the lack of the pathogenic protein PB1-F2 in the 2009 pandemic strains, which was present in the more virulent 1918 pandemic strains. We provide an overview of influenza infection since 1847 and the advent of influenza vaccination since 1944. Vaccines and chemotherapy help reduce the spread of influenza, reduce morbidity and mortality, and are utilized by the global rapid-response organizations associated with the WHO. Immediate identification of impending epidemic and pandemic strains, as well as sustained vigilance and collaboration, demonstrate continued success in combating influenza.     

Effectiveness of non-adjuvanted pandemic influenza A vaccines for preventing pandemic influenza acute respiratory illness visits in 4 U.S. communities

We estimated the effectiveness of four monovalent pandemic influenza A (H1N1) vaccines (three unadjuvanted inactivated, one live attenuated) available in the U.S. during the pandemic. Patients with acute respiratory illness presenting to inpatient and outpatient facilities affiliated with four collaborating institutions were prospectively recruited, consented, and tested for influenza. Analyses were restricted to October 2009 through April 2010, when pandemic vaccine was available. Patients testing positive for pandemic influenza by real-time RT-PCR were cases; those testing negative were controls. Vaccine effectiveness was estimated in logistic regression models adjusted for study community, patient age, timing of illness, insurance status, enrollment site, and presence of high-risk medical conditions. Pandemic virus was detected in 1,011 (15%) of 6,757 enrolled patients. Fifteen (1%) of 1,011 influenza positive cases and 1,042 (18%) of 5,746 test-negative controls had record-verified pandemic vaccination >14 days prior to illness onset. Adjusted effectiveness (95% confidence interval) for pandemic vaccines combined was 56% (23%, 75%). Adjusted effectiveness for inactivated vaccines alone (79% of total) was 62% (25%, 81%) overall and 32% (-92%, 76%), 89% (15%, 99%), and -6% (-231%, 66%) in those aged 0.5 to 9, 10 to 49, and 50+ years, respectively. Effectiveness for the live attenuated vaccine in those aged 2 to 49 years was only demonstrated if vaccination >7 rather than >14 days prior to illness onset was considered (61%∶ 12%, 82%). Inactivated non-adjuvanted pandemic vaccines offered significant protection against confirmed pandemic influenza-associated medical care visits in young adults.