The Respiratory Protection Effectiveness Clinical Trial
Condition: Influenza; Respiratory Syncytial Viruses; Paramyxoviridae Infections; Coronavirus; Rhinovirus
MeSH terms: Paramyxoviridae Infections
Study type: Interventional

Summary: 
      Despite widespread use of respiratory protective equipment in the U.S. healthcare workplace,
      there is very little clinical evidence that respirators prevent healthcare personnel (HCP)
      from airborne infectious diseases. Scientific investigation of this issue has been quite
      complicated, primarily because the use of respirators has become "the standard of care" for
      protection against airborne diseases in some instances, even without sufficient evidence to
      support their use. The key question remains: How well do respirators prevent airborne
      infectious diseases? The answer to this important question has important medical, public
      health, political and economic implications.
    

Detailed description: 
      Prevention strategies are key in limiting the transmission of respiratory viruses such as
      influenza. Among non-pharmacologic interventions, there is intense interest in the use of
      facial protective equipment (FPE) - medical masks (MM) or N95 respirators (N95) - as a key
      component of personal protective equipment (PPE) when faced with respiratory illness
      including influenza. However, their relative protective effect is unknown, especially in the
      outpatient setting (OPD). To plan for future epidemics and best manage limited supplies of
      FPE, evidence is needed to guide planning activities and policy makers. This project aims to
      answer a key question: How do N95s protect healthcare workers (HCWs) in the OPD against
      influenza, influenza-like illness (ILI), acute respiratory illness (ARI) and other causes of
      respiratory illnesses including respiratory viral illness (RV), as compared to MMs? The study
      outcomes are to:

        -  determine the most effective FPE equipment to use to prevent disease transmission in the
           OPD during an influenza outbreak, epidemic or pandemic event.

        -  the incidence of organism-specific rates of ARI in the OPD during the RV season.

        -  the incidence rate of organism-specific respiratory viral infections in the OPD.

      Clinics (or their functional partitions) will be randomized into either the N95 or MM arm
      using a stratified randomization scheme to ensure comparability between the two arms. All
      participants, regardless of study arm, will be fit-tested for an N95. The study will require
      a 16-18 week period that will be initiated when viral surveillance data indicates that
      influenza season has begun. Participants will be recruited through informational meetings
      with clinic staff. Participants will have blood drawn before week 1 and after the end of the
      active portion of the study to assess seroconversion over the study period to capture the
      incidence of non-symptomatic influenza. All participants will fill out a pre-study survey and
      post on knowledge, attitudes and beliefs regarding influenza, influenza vaccinations, and
      appropriate PPE.

      During the first week, participants will fill out a form with basic demographic and workplace
      information. Participants will be asked to 1. wear participant's appropriate FPE when in
      close contact with patients with suspected or confirmed influenza or RV for the next 12-16
      weeks; 2. fill out a daily form assessing exposure to ILI and FPE use, and weekly forms
      assessing RV symptoms and medication use. The investigators will collect nasal and throat
      (pharyngeal) swabs twice during the study for all participants, and when participants report
      having an ILI or ARI. Study staff will make unannounced visits to the clinics to observe FPE
      and hand hygiene compliance rates.

      Participants will be compensated for participation (as detailed below). To be included the
      clinical site leadership has agreed to have one of more participants in the trial. To be
      included the subject(s): (1) meets the definition of "healthcare personnel" (2) is able to
      read and sign informed consent (3) agrees to all requirements of the protocol, including fit
      testing and diary keeping (4) is 18 years old or greater (6) passes fit testing for one of
      the study supplied N95 and agrees to use that model for the entire 16 week period of the
      study. Subjects are excluded if: (1) subject self-identified as having severe heart, lung,
      neurological or other systemic disease that one or more Investigator believes could preclude
      safe participation (2) are known to not tolerate wearing FPE for any period (3) facial hair,
      or other issue such as facial adornments, preclude respirator Occupational Safety and Health
      Administration (OSHA)-compliant fit testing or proper mask fit during the study period (4)
      subjects are advised by Occupational Health (or other qualified clinician) to not wear the
      same or similar respirator or medical mask models used in this study (5) in the opinion of
      the Investigator, may not be able to reasonably participate in the trial for any reason (6)
      Self-identified as in, or will be in the third trimester of pregnancy, during the study
      period.

      Participants will be compensated for participation. Due to space limitations details are not
      presented here but are available from the overall study PI's.

      During the study period, study staff will be making unannounced visits to the clinics to
      measure hand hygiene and FPE compliance. The information collected will not be shared with
      the supervisors or administration of the clinics.

      Respiratory Protection Effectiveness Clinical Trial (ResPECT) Analysis Plan

        1. Analysis timeline and procedures

           A pre-specified analysis plan for the primary manuscript of ResPECT was initially
           approved by all study principal and study-site lead investigators on April 2016 and
           updated in May 2017 to reflect the addition of the Laboratory Detected Respiratory
           Infection (LDRI) outcome to the analysis. At the time of this revision (a) all data
           collection is complete, (b) all laboratory specimen samples have been tested for the
           primary and secondary outcomes, and (c) the database housing all of the ResPECT data
           contained no information about which clinics were assigned to which arm of the study.
           Once the analysis plan is updated and changes submitted to the site IRB's, the data
           coordinating center will release labels that identify separate arms of the study to the
           ResPECT statisticians who will use those codes to implement the analysis as described in
           this document.

        2. General outline of analysis framework

      The ResPECT study was a cluster-randomized trial that used constrained randomization (i.e.
      matching) to ensure balance across arms. The analysis described in this document is an
      unmatched analysis, i.e. the analysis does not explicitly account for the matching. This has
      been described as an appropriate approach to analyzing data arising from a matched design.

      The final analysis of ResPECT outcome data will consist of intention-to-treat (ITT) and
      per-protocol analyses (PP) for each of the five study outcomes defined below. For each
      analysis, the investigators will fit and report results from both adjusted and unadjusted
      models. Unadjusted models will be analyzed at the cluster-level, and will only include a main
      effect estimate for the mask and the cluster-level random effects to account for repeated
      measures of related clusters across multiple seasons. Adjusted models will be analyzed at the
      individual-level and will include individual-level covariates and random effects to account
      for repeated measures of the same individual across seasons.

      2.a Intention-to-treat analysis The ITT analysis will include all of the ResPECT participants
      who were randomized- i.e., those assigned a mask based on participant's clinic affiliation.
      Participant's data will be included according to participant's treatment assignment,
      regardless of participant's adherence to protocol, subsequent withdrawal, failure to provide
      requested data/samples, or loss to follow-up. This analysis is intended to capture a more
      realistic outcome of intervention by acknowledging that noncompliance and protocol deviations
      are an unavoidable part of clinical practice.

      In this study, any person who was eligible according to the baseline survey will be included
      in the ITT analysis. The outcomes for many participants will be missing, particularly those
      who withdrew during the course of the study. This missingness could conceivably be (a)
      related to outcome/illness status if individuals were more likely to quit the study because
      the individuals became sick, or (b) related to the assigned intervention if those assigned
      one mask over another were more likely to withdraw from study participation. The
      investigators will assess possible relationships between self-reported reasons for withdrawal
      and measured variables. Approaches for imputing missing data are addressed below.

      2.b Per-protocol analysis Any participant who completed at least eight weeks of study
      participation will be included in the per protocol analysis. This strategy will include some
      participants who only had one blood draw or who are missing reliable serological data due to
      timing of or lack of information on vaccination (see Participant flow for ResPECT study
      analysis approaches showing ITT and per protocol cohorts and Decision Algorithm for
      serological influenza outcome adjudication below). These inclusion/exclusion criteria were
      decided on by the study PIs.

      The reasons for missing participant blood samples include loss to follow-up with or without
      formal withdrawal/deactivation, sample loss due to handling/labeling error, or insufficient
      sample volume. Since the serologic definition of influenza seroconversion is a 4-fold
      increase in titer, unpaired serology cannot be assigned an influenza seroconversion status
      and must be imputed. Missing serologic data will not exclude the patient from the polymerase
      chain reaction (PCR)-laboratory assessment. Hence, if an individual is missing a second blood
      draw but had lab-confirmed influenza by PCR, then this individual will be considered to have
      had a lab-confirmed influenza outcome. This may create non-random missingness, but it was
      decided by PIs that since this would not impact many study participants the risk of bias to
      the overall study was very low.

      2.c Handling of missing data via imputation methods There will be substantial missing data in
      the outcome (lab-confirmed influenza) and other covariates. The missing data will be imputed
      using standard multiple imputation techniques, creating imputed datasets with no missing
      values for each analysis. Each of these datasets will be analyzed using the regression models
      described below. The results from all of the analyses will be pooled using standard multiple
      imputation techniques for combining estimates across imputed datasets.

      2. d Process for determining participant membership in ITT and per protocol cohorts
      Participants signed informed consent. Those who failed to meet inclusion criteria or did not
      complete screening were excluded. Those who met the inclusion criteria were randomly assigned
      to a mask group and formulate the ITT cohort. The 'per protocol' cohort will not include
      those who withdrew before participating (i.e., those who do not fill out any daily or weekly
      surveys), or discontinued the intervention (withdraw with less than 8 weeks of
      participation). The 'per protocol' cohort will include those who completed at least 8 weeks
      of study. The investigators define, for each participant, the amount of time that
      participants participated as the difference between the clinic activation date and latest of
      either the automatically-generated time-stamp of the last completed daily or weekly survey or
      the collection date of the last swab, with a maximum of 12 weeks. Those who participated for
      at least 8 weeks (56 days) according to this calculation will be included in the 'per
      protocol' cohort. For analyses using person time, the investigators will use the latest of
      the following; the last survey completed date or collection date from a swab collection.

      Decision Algorithm for serological influenza outcome adjudication:

      This decision algorithm documents the process for which ResPECT participants will be
      determined to have had laboratory-confirmed influenza based on serological testing only. The
      possible outcomes are: laboratory confirmed influenza confirmed by serology (LCI-S) and no
      laboratory confirmed influenza event confirmed by serology (no LCI-S). In some cases,
      outcomes (either LCI-S or no LCI-S) will be imputed. The algorithm to classify and/or impute
      these outcomes is as follows:

      Step 1: Determine Study Completion Determine if participants have completed the study (and
      thus in the 'per protocol' cohort) or if participants have not and thus are in the ITT cohort

      Step 2: Determine serological influenza outcome for those in the 'per protocol' cohort 2a.
      For those individuals in the 'per protocol' cohort who have two serological samples,
      collected at the beginning and end of the season according to protocol, and who experience a
      four-fold rise in influenza hemagglutination inhibition antibody (HAI) titer to exactly 0
      strains, classify the serological influenza outcome as no LCI-S.

      2b. For those individuals in the 'per protocol' cohort who have two serological samples,
      collected at the beginning and end of the season according to protocol, and who experience a
      four-fold rise in influenza HAI antibody titer to one or more strains, classify the
      serological influenza outcome as LCI-S.

      2c. For those individuals in the 'per protocol' cohort who do not have two serological
      samples, collected at the beginning and end of the season according to protocol or who are
      missing vaccination info or were vaccinated during the study, impute the serological
      influenza outcome as LCI-S. Missing LCI-S status will be imputed using standard multiple
      imputation techniques, creating multiple imputed datasets with no missing values for each
      analysis.

      Step 3: Impute the LCI-S outcome for the ITT cohort Some members of the ITT cohort did not
      complete all weeks of the study and may be missing a serological outcome for the same reasons
      mentioned above. For these individuals, the serological influenza outcome must be imputed.
      Missing LCI-S status will be imputed using standard multiple imputation techniques, creating
      multiple imputed datasets with no missing values for each analysis.

      2.d Model and variable selection

      This data is from a cluster-randomized clinical trial. The investigators anticipate that the
      constrained randomization will ensure balance across important covariates. The clinics were
      pair-matched by the following characteristics:

      Study site Clinic size Clinic type (ED/Urgent care, Primary Care, Outpatient, Enhanced)
      Enhanced PPE (whether HCWs wore enhanced PPE during patient procedures, e.g. in dental and
      dialysis clinics) Patient population (Pediatric, Adult, or mixed)

      Because these variables were matched on, the investigators will not adjust for any of them in
      the multivariable regression models. However, cluster-level random intercepts as well as
      additional participant-level covariates will be added to the model to adjust for possible
      residual confounding that is not controlled for by the cluster-randomized design. These
      covariates will be individual-level variables including:

      Age, Gender, Race (White, Black or African American, Asian, Native Hawaiian or Pacific
      Islander, American Indian or Alaskan Native) and ethnicity (Hispanic or Latino) Number of
      household members under 5 (this has been noted as a strong risk factor for influenza),
      Categorical occupation risk level (low, medium, or high), Binary season-specific flu
      vaccination status (was or was not vaccinated), Proportion of daily surveys where an
      individual reported exposure to someone with respiratory symptoms, and Individual-level
      (self-reported) measures of mask and hand hygiene compliance.

      The investigators will attempt to include all of the above-listed variables in the analysis.
      No variable selection will be performed to optimize the goodness of fit of the model. No Type
      I error rate adjustments will be made. Variables will be left out only if the variable
      contribute to instability in model estimation: e.g. collinearity (identified by variance
      inflation factors) or insufficient data to impute covariate status. In the model design
      stage, the investigators identified a full set of covariates that would satisfy the sample
      size recommendation that the investigators have no more than m/15 parameters in the model,
      where m = min(n1, n2) and n1 and n2 are the numbers in each of the response variable
      categories. Based on preliminary estimates of the total number of influenza outcomes
      expected, the investigators aimed to keep the number of estimated parameters below 25.

      The following variables were considered but not included in the analysis for the final model.
      Justification is provided.

      Follow-up variables such as contact with household members with flu: noisy, lacking flu
      confirmation, and too reliant on self-reporting biases.

      Cumulative study-based vaccination status (i.e. ever vaccinated, never vaccinated): would be
      collinear with seasonal vaccine status.

      Absence from work: not directly related to outcome, chose to include average number of hours
      worked instead.

      Dummy variables of clinic types: while these encode important questions, they aren't the main
      purpose of the central study, and were characteristics that were matched on.

      Size of household: for parsimony, the investigators will include number of household members
      under 5 instead.

      Clinic size: was used in matching for randomization. Comorbid conditions: hard to justify
      including some and not others, of secondary relevance to the main outcome.

      Average number of hours worked per week defined each season for each individual: there was a
      minimum number of hours worked defined in inclusion criteria, so this range will not be
      substantial.

      Smoking status: secondary relevance to main outcome.

      2.e Pre-specified exploratory analyses In addition to the pre-specified analyses of primary
      and secondary outcomes, the investigators will run several pre-specified exploratory analyses
      to assess the impact of vaccine coverage and protocol compliance with the study outcomes.

      Using the models described in Sections 3 and 4 below, the investigators will consider adding
      additional covariates to the models from the primary and secondary analyses. Specifically,
      the investigators will examine the impact of covariates specific to a particular
      cluster-season including:

      Vaccine coverage among participants in the cluster Hand-hygiene compliance rate Measure of
      how often any HCW in the clinic wore any mask, MM or N95 Proportion of clinic HCW enrolled in
      study and size of clinic

      Additionally, the investigators will assess interaction terms considering the following
      variables:

      Interaction of cluster-level mask compliance with mask group Interaction of individual-level
      vaccination status with mask group

      Finally, the investigators will investigate combinations of cluster-level, seasonal,
      individual-level and cluster-seasonal random effects to capture different possible
      correlation structures of the data. The magnitude of each variance component will dictate
      whether the variance components are included in the final model.

      3. Analysis plan for primary outcome: laboratory-confirmed influenza

      3.a Outcome definitions A dichotomous variable will indicate whether or not a participant had
      an episode of laboratory-confirmed influenza during a single influenza season. As specified
      in the protocol, individuals who have a PCR-confirmed influenza infection collected within
      seven days of symptom onset or who have a 4-fold rise in antibody titer will be considered as
      a positive case, As described above, the investigators will implement a per-protocol analysis
      and an ITT analysis.

      3.b Planned descriptive analysis The descriptive analysis will focus on aggregated
      participant numbers across the groups specified in "respect outcome tables.xlsx". The tables
      are as follows: 1) demographics, comprised of a breakout across treatment arms of
      characteristics including age, race, gender, occupation, clinic characteristics, vaccination
      status, and comorbid conditions, 2) Adjudication, where tallies of ResPECT participants are
      broken down into categories depending on participant's eligibility for the ITT and PP
      analyses and influenza adjudication outcome by year, 3) Nasopharyngeal swab lab results,
      where participants are broken out by year and mask type across the possible influenza and
      non-influenza viruses tested during the study, and 4) Summary results of lab-confirmed
      influenza, lab-confirmed non-influenza, ARI, LCRI, LDRI and ILI across intervention arms
      only.

      3.c Planned Primary Analysis The investigators will use an individual-level logistic
      regression model to estimate the difference in influenza infection between the N95 and
      medical mask groups. Let Y_ijs be an indicator of whether subject i in cluster j developed
      laboratory-confirmed influenza in season s, and MASK_js is an indicator of which mask the
      clinic was assigned to in season s (0 if medical mask and 1 if N95). Then the investigators
      will fit a version of this model
      logit[Pr(Y_{ijs}=1|MASK_{js})]=Beta_{0}+Beta_{1}*MASK_{js}+SUM_{k}(Theta_{k}*X_{k,ijs}+alpha_
      {j} + alpha_{i} where the alpha_{j} are the cluster-level random intercepts, the alpha_{i}
      are the individual-level random intercepts (both assumed to be normally distributed), and the
      X_{k} refer to the individual-level covariates listed in Section 2.d. Unadjusted analyses
      will drop individual-level covariates and random intercepts, but will retain the
      cluster-level random effects.

      For each fitted model, the estimated odds ratio comparing the odds of infection for those
      HCPs wearing N95s compared to those HCPs wearing medical masks (i.e. exp(Beta_{1}) will be
      reported, with a 95% confidence interval (CI).

      The ITT and PP will use the same model equation (shown above) but will use different subsets
      of participants from the full cohort as described above.

      3.d Planned Sensitivity Analysis To account for the unavoidable additional uncertainty
      regarding the missing data from the primary outcome, the investigators will conduct a
      sensitivity analysis that randomly assigns binary outcomes to participants who did not
      complete the study. Specifically, the investigators will create a two-dimensional grid on
      which the investigators vary the influenza attack rates in participants who dropped out of
      the study for both the medical mask (MM) and N95 arm, separately. The investigators will fix
      the MM dropout attack rate between half and twice the observed MM attack rate, based on
      complete data. The investigators will fix the N95 dropout attack rate between half and twice
      the observed N95 attack rate, based on complete data. By varying these two parameters across
      the grid, and for each combination, calculating the adjusted odds ratio (averaged across n=50
      imputed datasets for each point on the grid), the investigators will observe the sensitivity
      of the results to values of the missing data.

      Additionally, the investigators will compare rates reporting of symptomatic events in the two
      study arms. If the investigators detect a statistically significant difference in symptomatic
      reporting between arms, the investigators will include a covariate adjustment of person time
      in each model to account for the amount of person time under observation.

      4. Analysis plan for secondary outcomes

      4.a Definitions of secondary outcomes: Acute Respiratory Illness (ARI): This outcome is the
      incidence of ARI as a clinical syndrome. ARI will be defined as the occurrence of signs or
      symptoms of respiratory infection, as defined by Table 2 in the published protocol with or
      without laboratory confirmation.

      Influenza-Like Illness (ILI): This outcome is the incidence of ILI as a clinical syndrome.
      ILI will be defined as temperature of 100°F [37.8°C] or greater plus cough and/or a sore
      throat, with or without laboratory confirmation.

      Laboratory Confirmed Respiratory Illness (LCRI): This outcome is defined as a laboratory
      confirmed respiratory illness from any of the pathogens listed in Table 4 in the protocol.
      Laboratory confirmed respiratory illness is ARI combined with laboratory confirmation by
      reverse transcription-polymerase chain reaction (RT-PCR) of infection with any of the
      pathogens listed in Table 4 in an upper respiratory specimen swab after symptoms were
      reported and within seven days of the original symptomatic report (PP definition of LCRI and
      confirmed April, 2016). Events with multiple viruses detected will count as a single event of
      LCRI. If a swab that tested positive but was not associated with a symptomatic event (i.e.
      was not collected between symptom onset and seven days after symptom onset) then the incident
      does not count as a LCRI event. If an individual seroconverts to influenza, had symptoms at
      some time during the study, and does not have a PCR-confirmed pathogen event already, then
      the investigators will assign them a single LCRI event.

      Laboratory-detected respiratory infection (LDRI): For a participant with or without symptoms,
      a laboratory-detected infection is defined as: 1) detection of a respiratory pathogen by PCR
      or other laboratory methods or 2) serological evidence of infection (e.g., seroconversion)
      with a respiratory pathogen during the study surveillance period(s). In a case where two or
      more pathogens are identified in the same specimen, each pathogen will be considered to
      represent a separate infection (e.g., 2 pathogens as 2 events, 3 pathogens as 3 events) for
      that study participant for that time-point. Sequential detection of the same pathogens by PCR
      or other laboratory method in swabs collected at least 21 days apart will be considered
      separate infections.

      For all of these endpoints, an individual may experience any or all of the outcomes more than
      once during the course of the 12-week study. Within the same study identification (ID),
      participants must report being symptom-free for at least seven days prior to the beginning of
      the second event, except for LDRI which has the longer 21-day window separating events. As in
      the primary endpoint section, the secondary outcomes analysis will also include a
      per-protocol and an ITT analysis. A general description of these approaches is provided
      above, with specific modifications discussed below.

      4.b Planned secondary outcome ITT analysis As in the primary outcome ITT, this analysis will
      include all of the randomized ResPECT participants regardless of withdrawal status,
      participation, or protocol adherence. Secondary outcomes will be characterized using a
      per-week rate of infection so that all participants may be included. The investigators will
      use a covariate-adjusted individual-level log-linear Poisson regression analysis with person
      time as an offset term as well as cluster-level and individual-level random intercepts. For
      the ITT analysis, the amount of person time will be fixed at 12 weeks for each participant,
      regardless of how much time participant participated in the study. The investigators will
      include the same covariates as described in the primary outcome analysis section above in the
      Poisson regression model for the ITT and per-protocol analyses. Unadjusted models will
      include only the cluster-level random intercepts.

      For each fitted model, the estimated incidence rate ratio between the N95 and medical mask
      arm will be estimated and reported, with a 95% CI.

      4.c Secondary outcome per-protocol analysis Per-protocol analyses will use the same Poisson
      regression methods described for the secondary outcome ITT analyses. Additionally, the
      per-protocol analyses will include ResPECT study participants who completed at least 8 weeks
      (starting at the time of site activation) of the 12-week trial. All randomized participants
      will be included unless participant withdrew, were administratively withdrawn, or deactivated
      before participating for at least 8 weeks.

      Calculation of person-weeks for each participant will proceed as follows: for individuals who
      withdrew, completion date will be determined by the earliest withdrawal or deactivation date;
      in the event that these dates conflict, the earlier date will be used. For all other
      participants, active participation time will be calculated as the time between clinic
      activation and the latest of either the automatically-generated timestamp of the last
      completed daily or weekly survey or the collection date of the last swab, up to 12 weeks.

      4.d Missing covariate data for secondary outcomes The analysis approaches for the secondary
      outcomes will encounter instances of missing data, either in or failure to report relevant
      information on self-reported forms. Areas in which these issues may require special handling
      are 1) missing swab collection dates, 2) missing swab results, and 3) incomplete symptomatic
      event reporting.

      Missing swab collection dates are relevant for matching swab results to symptomatic event
      reports. Where this data is missing (often in the case of swabs collected using take-home
      kits, where participants self-collected the nasopharyngeal (NP) samples), the investigators
      will attempt to match swab results to symptomatic reporting events using the swab number or
      process of elimination (ie, only one event was reported and only 1 symptomatic swab was
      provided).

      Missing swab results may occur due to practical considerations (running out of PCR plates),
      participant noncompliance, or handling errors. These results are truly missing, cannot be
      recovered, and therefore must be discarded. There are also a few instances (<30 out of
      >11,000, or <0.27%) in which results cannot be reliably matched to the correct individual due
      to barcode transcription errors. These will be discarded if there is any doubt about the
      correct assignment barcode. Since these errors did not arise in a systematic way and comprise
      a very small portion of the overall available and reliable swab samples, this decision should
      not affect the analysis outcome.

      A few instances also exist in which participants provided a symptomatic swab but failed to
      complete a symptomatic event form. Since the participant provided no details to accompany the
      biological specimen, the investigators will not include these data in the analysis of ILI
      events (which require specific symptom reports). However, positive symptomatic swab data
      lacking specific symptom data will be included in the ARI and LCRI.
    

Intervention name(s): N95 Respirator; Medical/surgical mask
Intervention type(s): Device; Device
Intervention description(s): Participants in this arm will be asked to wear an N95 respirator for the extent of the 12 week study period.; Participants in this arm will be asked to wear a medical/surgical mask for the extent of the 12 week study period.
Other intervention name(s): 3M Corporation 1860, 1860S, and 1870 models; Kimberly Clark Technol Fluidshield Particulate Filter Respirator (PFR) 95-170; PFR 95-174; Precept 15320; Kimberly Clark Technol Fluidshield 47107
Allocation: Randomized
Intervention model: Parallel Assignment
Masking: Double (Investigator, Outcomes Assessor)
Primary purpose: Prevention
Keywords: Influenza; H1N1; Respiratory protection; N95 respirator; Medical mask; Personal protective equipment; Facial protective equipment; Pandemic planning