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The Respiratory Protection Effectiveness Clinical Trial (ResPECT)

This study is ongoing, but not recruiting participants.
Sponsor:
Collaborators:
Centers for Disease Control and Prevention
Veterans Health Administration
University of Massachusetts, Amherst
Denver Health and Hospital Authority
Children's Hospital Colorado
VA Eastern Colorado Health Care System
VA New York Harbor Healthcare System
Michael Debakey Veterans Affairs Medical Center
Washington D.C. Veterans Affairs Medical Center
VA St. Louis Health Care System
Information provided by (Responsible Party):
Trish Perl, Johns Hopkins University
ClinicalTrials.gov Identifier:
NCT01249625
First received: November 29, 2010
Last updated: July 29, 2016
Last verified: July 2016
  Purpose
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.

Condition Intervention
Influenza
Respiratory Syncytial Viruses
Paramyxoviridae Infections
Coronavirus
Rhinovirus
Device: N95 Respirator
Device: Medical/surgical mask

Study Type: Interventional
Study Design: Allocation: Randomized
Endpoint Classification: Efficacy Study
Intervention Model: Parallel Assignment
Masking: Open Label
Primary Purpose: Prevention
Official Title: Incidence of Respiratory Illness in Outpatient Healthcare Workers Who Wear Respirators or Medical Masks While Caring for Patients

Resource links provided by NLM:


Further study details as provided by Johns Hopkins University:

Primary Outcome Measures:
  • Protective Effects of N95 Respirators vs Medical Masks [ Time Frame: 2010-2015 ] [ Designated as safety issue: No ]
    To determine and analyze the magnitude of the change, if any, in incidence of laboratory confirmed influenza in healthcare practitioners wearing N95 respirators (2009 CDC guidelines) compared to medical masks (2007 guidelines).

  • Incidence Determination of Influenza and Viral Respiratory Illness [ Time Frame: 2010-2015 ] [ Designated as safety issue: No ]
    To improve understanding about the burden of influenza and other viral respiratory illnesses among HCPs stationed in outpatient settings by evaluating the incidence of laboratory-confirmed influenza.


Secondary Outcome Measures:
  • Protective Effects [ Time Frame: 2010-2015 ] [ Designated as safety issue: No ]
    • To determine and analyze the magnitude of the change, if any, in incidence of acute respiratory illness, influenza-like illness, and lab-confirmed respiratory illness in HCPs wearing N95 respirators compared to medical masks.
    • To examine the relationship between incidence and possible risk factors, including compliance, attitudes and opinions of HCPs and workplace exposures.

  • Incidence determination [ Time Frame: 2010-2015 ] [ Designated as safety issue: No ]
    - To measure the incidence of acute respiratory illness, influenza-like illness, and lab-confirmed respiratory illness in selected outpatient settings.


Estimated Enrollment: 1600
Study Start Date: December 2010
Estimated Study Completion Date: December 2017
Estimated Primary Completion Date: May 2017 (Final data collection date for primary outcome measure)
Arms Assigned Interventions
Active Comparator: N95 Respirator
The investigators are comparing the 3M 1860 N95 respirator against the Precept 15320 medical mask.
Device: N95 Respirator
Participants in this arm will be asked to wear an N95 respirator for the extent of the 12 week study period.
Other Names:
  • 3M Corporation 1860, 1860S, and 1870 models
  • Kimberly Clark Technol Fluidshield PFR95-170, PFR95-174
Active Comparator: Medical/surgical mask
The investigators are comparing the Precept 15320 medical/surgical mask against the 3M 1860 N95 respirator.
Device: Medical/surgical mask
Participants in this arm will be asked to wear a medical/surgical mask for the extent of the 12 week study period.
Other Names:
  • Precept 15320
  • Kimberly Clark Technol Fluidshield 47107

  Hide Detailed Description

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) - surgical masks or N95 respirators - as a key component of personal protective equipment (PPE) when faced with seasonal influenza or other respiratory illness. However, their relative protective effect is unknown, especially in the outpatient setting. 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 about PPE use: How do respirators (N95s) protect HCWs in the outpatient setting against influenza, influenza-like illness (ILI), acute respiratory illness (ARI) and other respiratory illnesses, as compared to surgical masks? This study will have the following outcomes:

  • An analysis to determine the most effective facial protective equipment to use to prevent disease transmission in the outpatient setting during a seasonal influenza outbreak, epidemic or pandemic event.
  • An analysis of the incidence of organism-specific rates of respiratory viral infections in the outpatient setting during influenza season.
  • An assessment of the incidence rate of organism-specific respiratory viral infections in the outpatient setting.

This is an approximately 16-18 week study. The investigators will initiate the study when viral surveillance data indicates that influenza season has begun. Recruitment will be accomplished 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; this will also allow us to capture the incidence of non-symptomatic influenza. All participants will fill out a pre-study survey on knowledge, attitudes and beliefs regarding influenza, influenza vaccinations, and appropriate PPE. All participants, regardless of study arm, will be fit-tested for an N95 respirator.

During the first week, participants will fill out a form with basic demographic and workplace information. Clinics (or their functional partitions) will be placed into either the N95 respirator or surgical/medical mask arm using a stratified randomization scheme to ensure comparability between the two arms. Participants will be asked to wear their appropriate FPE when in close contact with patients with suspected or confirmed influenza or respiratory disease for the next 12-16 weeks. Participants will be asked to fill out a daily form assessing exposure to influenza-like-illness and FPE use, and weekly forms assessing influenza-like symptoms and medication use. The investigators will collect nasal and throat swabs twice during the study for all participants, and also when participants report that they have an influenza-like illness. Study staff will make unannounced visits to the clinics to observe FPE and hand hygiene compliance rates. At the end of the study, participants will be asked to fill out a post-study survey on knowledge, attitudes, and opinions about FPE.

Participants will be compensated for their participation (up to $599 total, as detailed below). Inclusion criteria are: (1) Clinical site leadership has agreed to have one or more staff participate in the trial (2) Subject meets the definition of "healthcare personnel" (3) Subject able to read and sign informed consent (4) Subject agrees to all requirements of the protocol, including fit testing and diary keeping (5) Subject's age is 18 or greater (6) Subject passes fit testing for one of the study supplied respirator models and agrees to use that model for the entire 16 week period of the study. Exclusion criteria are: (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) Known to not tolerate wearing respiratory protective equipment for any period (3) Facial hair, or other issue such as facial adornments, precluding respirator OSHA-compliant fit testing or proper mask fit during the study period (4) 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 their participation. Each participant who completes the requirements of the study will receive a total of $ 500 (12 weeks) or $599 (16 weeks). They will receive $55 for completing the pre-study package (pre-study survey, baseline survey, and initial blood draw). For each week the participant meets the study requirements, he will be paid $10.00 for completing the weekly & daily surveys. Each randomized swab set (nasal + throat) is worth $60. There is also a post-study package (post-study survey and final blood draw) worth $70. Following the study, a bonus of up to $135 will be given to participants who complete the entire study, pro-rated on number of completed weekly and work-shift exposure surveys.

During the study period, study staff will be making unannounced visits to the clinics to see if participants are washing their hands and wearing their FPE as discussed during the educational session. These observations are for study use only. The information collected will not be shared with the supervisors or administration of the clinics.

ResPECT Study Analysis Plan Approved by all Principal Investigators, July 2016

  1. Analysis timeline and procedures

    This document presents a pre-specified analysis plan for the primary manuscript of the ResPECT Study. This document was finalized in July 2016 and approved by all ResPECT Study PIs. At the time of writing, final laboratory specimen samples were being tested, and the database housing all of the ResPECT data contained no information about which clinics were assigned to which arm of the study.

    Once all laboratory sample results are obtained and this analysis plan has been uploaded and registered to clinicaltrials.gov, the data coordinating center will release labels that identify separate arms of the study to the ResPECT statisticians. The statisticians 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. [1]

The final analysis of ResPECT Study outcome data will consist of intention-to-treat and per-protocol analyses for each of the four study outcomes defined below. For each analysis, we 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 the same cluster 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 intention-to-treat (ITT) analysis will include all of the ResPECT participants who were randomized— i.e., those assigned a mask based on their clinic affiliation. Their data will be included according to their treatment assignment, regardless of their 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. For additional information on ITT in randomized controlled trials, see [2].

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 they became sick, or (b) related to the assigned intervention if those assigned one mask over another were more likely to withdraw from study participation. We 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 (see conference call meeting notes from March 21, 2016).

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 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 flu) as well as 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.[3]

Process for determining participant membership in ITT and Per Protocol cohorts Participants were consented. 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 (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. We define, for each participant, the amount of time that they 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, we 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 they 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 HI 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 HI 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. We 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, we 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) [4] Number of household members under 5 (this has been noted as a strong risk factor for influenza [5]), 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.

We 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 [6]. No Type I error rate adjustments will be made. Variables will be left out only if they 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, we identified a full set of covariates that would satisfy the sample size recommendation [7] that we have no more than m/15 parameters in our 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, we 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, we 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, we 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, we will consider adding additional covariates to the models from the primary and secondary analyses. Specifically, we will examine the impact of covariates specific to a particular cluster-season including:

Vaccine coverage among participants in the cluster Hand-hygiene compliance rate (based on observational data collected by RAs) Measure of how often any HCW in the clinic wore any mask, MM or N95 (based on observational data collected by RAs) Proportion of clinic HCW enrolled in study and size of clinic

Additionally, we 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, we 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 they 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 or who have a 4-fold rise in antibody titer will be considered as a positive case. As described above, we 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 Dr. Radonovich's "respect outcome tables.xlsx" (received on 01-11-2016, revised 04-12-2016). 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 their eligibility for the ITT and Per-Protocol 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, and ILI across intervention arms only.

3.c Planned Primary Analysis We 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 we 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% CI.

Our ITT and per-protocol 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 our primary outcome, we will conduct a sensitivity analysis that randomly assigns binary outcomes to participants who did not complete the study. Specifically, we will create a two-dimensional grid on which we vary the influenza attack rates in participants who dropped out of the study for both the medical mask (MM) and N95 arm, separately. We will fix the MM dropout attack rate between half and twice the observed MM attack rate, based on complete data. We 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), we will observe the sensitivity of our results to values of the missing data.

Additionally, we will compare rates reporting of symptomatic events in the two study arms. If we detect a statistically significant difference in symptomatic reporting between arms, we 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 influenza infection, as defined by Table 3 in the 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 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 4 days of the original symptomatic report (per protocol definition of LCRI and confirmed by PI decision on April 24, 2016). Events with multiple viruses detected will count as a single event of LCRI (confirmed by PI decision on 4/24/2016). If a swab tested positive but was not associated with a symptomatic event (i.e. was not collected between symptom onset and four 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 we assign them a single LCRI event (PI decision on 5/2/2016).

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 id, participants must report being symptom-free for at least seven days prior to the beginning of the second event (per PI decision May 2, 2016). 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. We 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 they participated in the study. We 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 they 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 our secondary outcomes will encounter instances of missing data, either in NP swab results 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 NP samples), we 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, we 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.

References Cluster Randomised Trials Hayes RJ, Moulton LH (2009) ISBN: 978-1-58488-816-1; 315 pages; Chapman and Hall/CRC.

Fisher LD, Dixon DO, Herson J, Frankowski RK, Hearron MS, Peace KE (1990). Intention to treat in clinical trials. In: Peace KE, editor. Statistical issues in drug research and development. New York: Marcel Dekker. pp. 331-50.

Schafer, Joseph L. "Multiple imputation: a primer." Statistical methods in medical research 8.1 (1999): pp. 3-15.

Collection of Race and Ethnicity Data in Clinical Trials. http://www.fda.gov/RegulatoryInformation/Guidances/ucm126340.htm#iii Williams CJ, Schweiger B, Diner G, Gerlach F, Haaman F, Krause G, Nienhaus A, Buchholz U. Seasonal influenza risk in hospital healthcare workers is more strongly associated with household than occupational exposures: results from a prospective cohort study in Berlin, Germany, 2006/07. BMC Infect Dis. 2010 Jan 12;10:8.

A. Gelman and E. Loken, unpublished. The garden of forking paths: Why multiple comparisons can be a problem, even when there is no "fishing expidition" or "p-hacking" and the research hypothesis was posited ahead of time. http://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf J Whitehead, Sample size calculations for ordered categorical data. Stat Med, 12:2257-2271, 1993.

  Eligibility

Ages Eligible for Study:   18 Years to 100 Years   (Adult, Senior)
Genders Eligible for Study:   Both
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria:

  • (1) Clinical site leadership has agreed to have one or more staff participate in the trial
  • (2) Subject meets the definition of "healthcare personnel"
  • (3) Subject able to read and sign informed consent
  • (4) Subject agrees to all requirements of the protocol, including fit testing and diary keeping
  • (5) Subject's age is 18 or greater
  • (6) Subject passes fit testing for one of the study supplied respirator models and agrees to use that model for the entire intervention period of the study (if in respirator arm).

Exclusion Criteria:

  • (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) Known to not tolerate wearing respiratory protective equipment for any period
  • (3) Facial hair, or other issue such as facial adornments, precluding respirator OSHA-compliant fit testing or proper mask fit during the study period
  • (4) 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.
  • (7) Subject rotating in 2 different ResPECT study clinic sites /clusters during the 12-week study period.
  • (8) Subject works less than 24 hours/week in the cluster/clinic in which they are recruited.
  • (9) Subject works less than 75% of the intervention period in that clinic.
  • (10) Subject is a previous participant of the ResPECT Study, but does not consent for data from previous flu season(s) to be linked.
  Contacts and Locations
Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the Contacts provided below. For general information, see Learn About Clinical Studies.

Please refer to this study by its ClinicalTrials.gov identifier: NCT01249625

Locations
United States, Colorado
Children's Hospital Colorado Infectious Disease
Aurora, Colorado, United States, 80045
Denver Health Medical Center
Denver, Colorado, United States, 80204
Denver Veteran's Administration Medical Center
Denver, Colorado, United States, 80220
United States, District of Columbia
Veterans Affairs Medical Center, Washington, DC
Washington, District of Columbia, United States, 20422
United States, Maryland
Johns Hopkins Health Sytstem
Baltimore, Maryland, United States, 21287
United States, New York
VA New York Harbor Healthcare System
New York City, New York, United States, 10010
United States, Texas
Houston VA Medical Center
Houston, Texas, United States, 77030
Sponsors and Collaborators
Johns Hopkins University
Centers for Disease Control and Prevention
Veterans Health Administration
University of Massachusetts, Amherst
Denver Health and Hospital Authority
Children's Hospital Colorado
VA Eastern Colorado Health Care System
VA New York Harbor Healthcare System
Michael Debakey Veterans Affairs Medical Center
Washington D.C. Veterans Affairs Medical Center
VA St. Louis Health Care System
Investigators
Principal Investigator: Trish M. Perl, MD Johns Hopkins University School of Medicine/Johns Hopkins Hospital
Principal Investigator: Lewis Radonovich, MD North Georgia/South Florida VA
Study Director: Derek Cummings, PhD Johns Hopkins University
Study Director: Michael Simberkoff, MD New York Harbor Healthcare System VA
Study Director: Connie S Price, MD University of Colorado (Denver Health)
Study Director: Charlotte Gaydos, PhD Johns Hopkins University
Study Director: Nicholas Reich, PhD University of Massachusetts, Amherst
  More Information

Additional Information:
Publications:

Publications automatically indexed to this study by ClinicalTrials.gov Identifier (NCT Number):
Responsible Party: Trish Perl, Trish M. Perl, MD, MSc, Johns Hopkins University
ClinicalTrials.gov Identifier: NCT01249625     History of Changes
Other Study ID Numbers: NA_00031266 
Study First Received: November 29, 2010
Last Updated: July 29, 2016
Health Authority: United States: Institutional Review Board

Keywords provided by Johns Hopkins University:
Influenza
H1N1
Respiratory protection
N95 respirator
Medical mask
Personal protective equipment
Facial protective equipment
Pandemic planning

Additional relevant MeSH terms:
Coronavirus Infections
Paramyxoviridae Infections
Coronaviridae Infections
Nidovirales Infections
RNA Virus Infections
Virus Diseases
Mononegavirales Infections

ClinicalTrials.gov processed this record on September 28, 2016