FluSAFE: Flu SMS Alerts to Freeze Exposure
Phase: NA
Conditions: Influenza, Acute Respiratory Infection, Influenza-like Illness
Sponsor: Columbia University

FluSAFE: Flu SMS Alerts 
to Freeze Exposure
ClinicalTrials.gov ID: NCT03274310
Document date: May 4, 2020

1 
We propose to conduct a randomized controlled trial assess whether a scalable, text message-based 
educational intervention triggered by text message-based surveillance for influenza-like illness (ILI) in 
households with one or more children can decrease laboratory-confirmed influenza transmission. 
Influenza infection results in an estimated 31 million outpatient visits, 55,000 to 974,200 hospitalizations 
(2014-15 estimate: 974,206), and 3000 to 49,000 deaths (2014-15 estimate: 49,006) annually.1-3
Membership in a household in which someone else has influenza is the major risk factor for contracting 
influenza.4-7  The household secondary attack rate (SAR) is as high as 19% based on laboratory-confirmed 
influenza and 30% based on symptoms.6-14  Non-pharmaceutical preventive measures including education have 
been found to decrease influenza transmission.15 For example, in one study during the 2009 influenza A H1N1 
pandemic, having had a household discussion about preventing transmission was significantly associated with 
decreased SAR.7 Most transmission occurs early in an illness,7,10,13 and it has been demonstrated that non- 
pharmaceutical interventions are effective only if started within 36 hours of index case symptom onset.16,17  Yet, 
most interventions are delayed because they are not initiated until care is sought.16,17 In one study, less than 
30% of households were identified within 36 hours when an index case was diagnosed at a medical visit.16 We 
reasoned that use of home surveillance with text-message symptom reporting to trigger an educational 
intervention has the potential to overcome delays in implementing preventative behaviors. 
In previous work, we demonstrated in one primarily Latino, urban sample, that text messaging by family 
members can be used to rapidly identify members with ILI/acute respiratory infections (ARI) early in an  
illness.18  This approach to early identification would enable implementation of an educational intervention 
targeted to the 36-hour timeframe. Providing education within a text message is a proven successful strategy   
to influence behavior.19-22  Using text message surveillance to trigger a novel educational intervention also  
allows inclusion of both medically and non-medically attended index cases, a key element in reducing influenza 
SAR since less than one-third of persons with laboratory-confirmed influenza seek medical care.18  Text 
messaging itself is scalable, low-cost, and can be used in low literacy populations.23 However, using text- 
message based surveillance to trigger a real-time text-message behavioral educational intervention to   
decrease household influenza transmission has not been assessed. In evaluating the impact of this 
intervention strategy, the primary outcome will be laboratory-confirmed influenza. The primary outcome will be 
determined using self-swabs among household members to detect SAR, including asymptomatic infection and 
medically- and non-medically attended illness. We have successfully piloted self-swab collection. 
Aim: To assess the impact of an educational intervention delivered by text messaging on transmission 
of influenza within households 
Primary Outcome Hypothesis: Educational text-messages will reduce laboratory-confirmed influenza 
household transmission from 12% to 5% 
Secondary Outcome Hypothesis: Educational text-messages will reduce ILI transmission from 23% to 11% 
Exploratory Aim: To compare the yield of text message ILI/ARI surveillance among subgroups in a 
diverse, community sample 
Hypothesis: Response rates to text messages will be similar among different sociodemographic groups. 
We will enroll and randomize 400 households (n=~1500 individuals) with ≥1 child recruited from four 
contiguous communities in New York City 1:1, stratified by community, to receive surveillance-only vs. 
surveillance plus text message educational intervention. We will have 88% power to detect a decrease in 
influenza SAR from 12% to 5%. These percentages are derived from the paper by Cowling et al., reporting a 
reduction from 12% to 5% between hand hygiene and usual care arms in SAR for influenza when a non-text 
message-based behavioral intervention was started ≤36 hours of index illness onset.16  For surveillance, 
households in both arms will receive text messages 3x/week during each influenza season (Nov-Mar) and 
report household members with ILI/ARI symptoms. For those in the educational intervention arm, when an 
ILI/ARI is reported, educational text messages will be sent targeted to decrease household transmission. 
Both arms will obtain one self-swab of the index case, and self-swabs of all other household members 
on days 3 and 5 from index onset.7,10,13,24 Swabs will be returned via postage-paid envelope and analyzed 
using an FDA-approved reverse-transcriptase multiplex PCR assay to identify influenza, including sub-types, 
as well as other viral respiratory pathogens, e.g., parainfluenza etc. We will also collect symptom reports from 
all household members. The primary outcome will be SAR of laboratory-confirmed influenza. Secondarily, we 
will assess SAR based on ILI/ARI symptoms, and of non-influenza viruses. Response rates will be compared 
between arms and by demographic factors (age, education, race/ethnicity). Trial results could provide support 
for text-based strategies to reduce influenza transmission. 
SPECIFIC AIMS 

2 
 
 
Impact of Influenza: Influenza infection results in an estimated 31 million outpatient visits, 55,000 to 974,200 
hospitalizations (2014-15 estimate: 974,206), and 3000 to 49,000 deaths (2014-15 estimate: 49,006) 
annually.1-3 The burden of influenza also includes high cost from direct medical expenses, days lost from work 
or school,25  and inappropriate use of antibiotics.26 Children and adolescents 6 months-18 years old are at 
increased risk of influenza morbidity and mortality, and influenza is one of the most common causes of 
hospitalization for the pediatric population.1  School-aged children and adolescents also serve as an important 
reservoir, transmitting influenza to household members.1,27 
Household Transmission of Influenza. Membership in a household in which someone else has influenza is 
the major risk factor for contracting influenza.4-7 The secondary attack rate of influenza in a household is as high 
as 19% based on laboratory-confirmed seasonal and pandemic influenza, and as high as 30% based on 
symptoms for ILI/ARI.6-14  Non-pharmaceutical preventive measures including education have been found to 
reduce household transmission.15  One study conducted during the 2009 H1N1 pandemic found that having had 
a household discussion about how to prevent transmission was the only household-level factor          
significantly associated with decreased secondary attack rates.7  However, since most transmission occurs in 
the first few days of illness,7,10,13 it is important that the intervention occur as soon as possible after onset of 
symptoms in the index case. Cowling et al. demonstrated an effect of non-pharmaceutical intervention (hand 
hygiene alone and hand hygiene plus face mask) only when initiated within 36 hours of symptom onset in the 
index case.16 Seuss et al. also found an impact of mask or mask plus hand hygiene only in households for 
which the intervention was also started within 36 hours.17 Interventions will be delayed when they depend on 
the index case seeking care.16,17 In Cowling’s study, less than 30% of households were identified within 36 
hours of onset based on identifying index cases at a medical visit. During the 2009 H1N1 influenza A  
pandemic, surveillance based on medical visits for ILI was not completely useful for detecting or monitoring the 
outbreak due in part to the lack of timeliness of visits.28 These failures to capture cases within 36 hours arise 
from two factors: time between onset of ILI and seeing a medical provider, and cases that are not medically 
attended at all. We reported that less than one-third of persons with laboratory-confirmed influenza in a 
community surveillance study sought medical care.18  Influenza cases have the potential to transmit infection to 
household members during the first 36 hours of symptoms; the earlier in this period and the higher the 
proportion with ILI who receive an educational intervention, the greater the potential to reduce transmission. 
Text Messaging and Community-based ILI/ARI Surveillance. To address the limitations of rapid 
identification of cases based on medical attendance, several studies have had people report via an automated 
telephone system,29  but this strategy depends on participants actively remembering to initiate the report. 
Others have used email, but use may differ by demographics and may not be feasible in diverse populations.30 
While others have used Internet surveillance,31 or other sources like Google Flu Trends, Twitter 32-34 or 
crowdsourcing,35  these data are only available in aggregate. Furthermore, they cannot be verified, be used to 
measure transmission, nor determine an infectious etiology since respiratory samples are not taken. In 
addition, internet-based communications may have HIPAA limitations.36 
Text messaging, however, could facilitate rapid surveillance and data collection from large numbers of 
participants from diverse populations by using a personalized, patient-centered approach. Text-messaging is 
not internet-based and does not have internet-related HIPAA limitations. Currently, 90% of adults in the U.S. 
own a cell phone, with high rates in both minority and non-minority populations (90% White, 90% Black, 92% 
Latino).23  Smart phone use, although increasing, is lower (64%).23  Text messaging can also accommodate 
different languages, and messages can be tailored to each participant by using response algorithms. Text- 
messaging can be used effectively in low-literacy populations.18-22  Another advantage of text messaging is its 
scalability. After the initial investment to develop the platform, the cost of each additional message is negligible, 
allowing large numbers of participants to be monitored simultaneously and serially over time. Text messaging 
can also be centralized and standardized across large geographic areas, allowing surveillance and 
assessment of distinct demographic subgroups or geographic areas while still maintaining the economy of 
centralized data collection, management, and analysis. Finally, it allows captured of both medically-attended 
and non-medically attended disease. 
As part of an ongoing community-based study funded by CDC, we have demonstrated the feasibility of 
using text messaging to conduct ILI/ARI surveillance in one primarily Latino, urban neighborhood community.18 
During the first three years of the study, 62,650 text messages were sent to households and responses were 
received for 48,308 (77.1%). The response rate did not decrease over time. The majority (88.9%) of responses 
B. RESEARCH STRATEGY 
SIGNIFICANCE 

3 
were received within 4 hours of messages being sent and 96.2% responses were received within 12 hours 
(section- preliminary studies). Based on our community study, the CDC has now included the use of text 
message surveillance as part of their pandemic influenza research plans.37
Text Message Reporting and Real-time Behavioral Intervention to Reduce Transmission. While text 
messaging surveillance can rapidly assess if someone in a household is ill, one potential benefit of this type of 
surveillance that has not yet been studied is the ability to trigger a real-time educational intervention to 
decrease transmission by exploiting this rapid identification in order to decrease transmission from both 
medically-attended and non-attended index cases. Providing education within a text message is a proven 
successful strategy to influence behavior. We have demonstrated the impact of text message educational 
reminders to improve influenza vaccination as well as other vaccinations.19-22  Text messaging has also been 
used for other behavioral interventions such as smoking cessation.38
The Health Belief Model provides a conceptual basis for the intervention we are proposing to test.39,40
According to this model, four main factors affect health-related behaviors: perceived susceptibility of 
Figure 1: Health Belief Model and Behavioral Intervention to Decrease 
Respiratory Virus Transmission (modified from Rosenstock et al) 
contracting the disease; perceived severity of the 
disease; perceived barriers to adoption of a 
behavior to avoid the disease; and the perceived 
effectiveness of the behavior. While the model is 
usually focused on an individual’s behavior to 
protect him/herself from developing a disease, for 
infection transmission, the model can also include 
behaviors to protect oneself and other household 
members. Lastly, a cue to action must be present to 
trigger the behavior. 
This conceptual framework will be used to 
design educational information to be sent within text 
messages (cue to action) (Figure 1). First, 
messages will include information explaining that an 
ill individual can transmit the viral respiratory illness 
to their household members, and that transmission could be harmful to their household members. Next, 
messages will include easy to implement behavioral suggestions, and finally will include information regarding 
the effectiveness of these behaviors. We will also include an interactive component, thereby allowing users to 
gain further information and enhance engagement.20,41  Consistent with health communication theory, these 
messages will come from a credible source (e.g. the family’s health care provider), be in easily understood 
language, and will recommend behaviors that can be immediately and easily implemented.42,43
Disadvantaged Populations and Households with Children are at Higher Risk for Influenza. The study 
population will include disadvantaged and other high-risk populations. Understanding the impact of an 
intervention to decrease influenza household transmission in a low-income, urban population may be 
particularly important as they may be at higher risk for infection and transmission due to overcrowding.44,45 In 
addition, they are at greater risk of having non-medically attended disease as being low-income, Latino, 
publicly insured, foreign born and having a lower education level are factors associated with lower likelihood of 
having a primary health care provider and seeking care for illness.18,46 Also, each household will have at least 
one child which potentially can lead to higher infection risk.27 Thus, the risk and impact may be greater, and 
strategies that work in such populations – such as text messaging – need to be tested. 
Thus, the significance of our proposed trial arises from the high secondary attack rate of influenza in 
households with children, the high morbidity and cost of influenza, and the use of new HIPAA-compliant 
communications technology, specifically text messaging, that is low cost, scalable, and usable in high-risk 
populations, to implement home surveillance-linked text-message based educational and behavioral 
intervention. No trials of this approach have been reported or are being conducted to our knowledge. 
This study is innovative in four ways. (1) It will assess the effectiveness of delivering a real-time text- 
message-based behavioral intervention to decrease influenza transmission within households. (2) It will also 
determine if the intervention decreases transmission of other respiratory viruses. (3) It will use text messaging 
for large-scale ILI/ARI surveillance among varied sociodemographic groups and assess group-specific 
response rates. (4) It will obtain self-swabs from participants with ILI/ARI to detect respiratory pathogens. 
INNOVATION 

4 
First, while we have previously demonstrated the effective use of text messaging surveillance for ILI, 
we have not assessed coupling an educational intervention targeted at decreasing influenza transmission to 
this approach to surveillance. We are not aware of any trials of this strategy for reducing influenza SAR. 
Second, while the primary target for most surveillance efforts is influenza, because of the affordability 
and availability of commercially available multiplex PCR kits, we will be able to assess the impact of the 
intervention on other non-influenza respiratory pathogens, which are more common than influenza.18  Thus, the 
use of text messaging to provide a real-time health behavioral intervention during respiratory illness could 
provide new insights into prevention strategies for non-influenza viral pathogens. This is also novel. 
Third, while we are successfully using text messaging for ILI/ARI surveillance in a predominately Latino 
community, it is not known if it will be as effective in other populations included in this study-- African- 
Americans and White, non-Latinos of varying sociodemographic status. In addition, in our ongoing MoSAIC 
study, families are very closely followed, which may decrease the generalizability of their text response rates.  
In this proposed study, we will also assess the feasibility of using text messaging in a more diverse and 
geographically widespread population with less frequent contact with research staff to demonstrate its 
feasibility, effectiveness, and potential scalability. 
Finally, the feasibility of collecting respiratory tract specimens is limited by the time and effort required 
for an ill individual to come to a central site to have a sample taken, as done in some studies,10 or the time and 
expense required for staff to go to an ill person’s home to obtain a sample as done in our ongoing  study. We 
and others have shown that self-swabs can be used to identify respiratory pathogens during acute illness, 
(section preliminary studies).47-50  However, a study coupling text message ILI/ARI surveillance with self-swabs 
as the mode of data collection has not, to our knowledge, been conducted among participants from varied 
socio-demographic  populations. 
PROJECT TEAM: This interdisciplinary research team has expertise and experience in all key study aspects 
including community-based sample recruitment and retention, community ILI/ARI surveillance, text message 
surveillance, using multiplex reverse-transcriptase polymerase chain reaction (RT-PCR), and text messaging- 
based behavioral interventions. The investigators also have a long history of collaborative work. 
Dr. Melissa Stockwell, the contact PI, has expertise in monitoring ILI/ARI in a community household 
sample. She is PI of a CDC funded grant, the MoSAIC study (U01P000618), which includes community-based 
surveillance for ARI/ILI using text messages.18,47,51  Additionally, she has provided expertise to the CDC on 
pandemic influenza planning including community surveillance and conducted two studies of the 2009  
influenza A H1N1 pandemic.52,53 She was PI on an NIH funded grant, the ACURI study (RC1 MD004109), an 
educational intervention to improve care for ARI,54 and is experienced with the use of text messages for 
vaccine adverse event surveillance, and for other behavioral interventions including promoting vaccination,18-22
including serving as PI on four grants (R01HS022677, R40MC17169, U01IP000313, U01IP000618). 
Dr. Stockwell has worked closely over the last six years with Dr. Elaine Larson, a PI on the proposed 
study, who is a Co-I on the MoSAIC project18,47,51 and was Co-PI on the ACURI study.54   Dr. Stockwell is a 
member of the Steering Committee of the Columbia Center for Interdisciplinary Research to Reduce Infections 
(CIRI) directed by Dr. Larson. Dr. Larson is an expert in antimicrobial resistance, infection prevention, 
epidemiology, and clinical and nursing research. She was a chair of the CDC’s Healthcare Infection Control 
Practices Advisory Committee. Dr. Larson also has expertise in monitoring households for ILI as PI of CDC 
and NIH- funded trials related to household transmission of ARI/ILI.29,55(R01 NR05251; U01CI000442). 
Dr. Stockwell also works closely with Dr. Lisa Saiman, a PI on this proposed study and a Co-I on 
MoSAIC.18,47,51   They have also collaborated on follow-up of exposed children after measles cases at Columbia 
University Medical Center (CUMC), and an assessment of measles and influenza vaccination status in children 
admitted to the pediatric ED.56  Drs. Larson and Saiman have a long-standing collaborative relationship over 13 
years, including being co-authors on 35 publications, as well as serving as Co-PI’s and Co-I’s on related grants 
(R01 NR010821)(R01HS021470) and on an NINR-funded T32 “Interdisciplinary Research to Prevent 
Infections”. Dr. Saiman is also on the Steering Committee of CIRI, is the hospital epidemiologist at Morgan 
Stanley Children’s Hospital and has expertise in healthcare-associated infections, and outbreak investigations 
of bacterial and viral pathogens. Dr. Saiman has had a long-standing interest in infections of the respiratory 
tract and has performed a household study of nasal carriage of methicillin-resistant Staphylococcus aureus in 
which self-swabs were successfully obtained in adults and children and mailed to her laboratory.57  She has 
APPROACH 

5 
also performed epidemiologic studies of the impact of the 2009 influenza A H1N1 pandemic at CUMC.58-60 She 
oversees the laboratory component of the MoSAIC study. 
Drs. Stockwell, Saiman and Larson have also worked closely with Dr. Haomiao Jia, the statistician for 
this study and for the MoSAIC project. Additionally, Dr. Jia collaborated with Dr. Larson on a project assessing 
predictors of influenza.61  His work with Dr. Saiman has included analysis of knowledge, attitude and practice 
surveys, epidemiology studies, and interventional studies. Dr. Stockwell also has a history of collaboration with 
Dr. Philip LaRussa, a co-investigator, as they co-direct a CDC-funded research program to assess adverse 
events post-vaccination using text messaging surveillance.62-64  Dr. Saiman works with Dr. LaRussa who helps 
oversee the laboratory component of the MoSAIC study. Drs. Stockwell and Larson collaborated with Dr. Dodi 
Meyer on the NIH-funded ACURI study.54  Dr. Meyer is the Director of Columbia University’s Community 
Pediatrics program and an expert in cultural competency and health literacy. Dr. Stockwell has worked 
previously with Dr. Elizabeth Cohn, an advisor; they both serve on the Steering Committee of Columbia 
University’s Patient-Centered Outcomes Research Initiative. Dr. Larson has a long-standing relationship with 
Dr. Cohn, including 8 publications. Dr. Cohn is a founding member of the Communities of Harlem Health 
Revival, a coalition of 72 community and faith-based organizations that seeks to improve the health of urban 
residents in upper Manhattan, and will aid recruitment. 
OVERVIEW: We propose to conduct a randomized controlled trial of real-time text message-based educational 
intervention to reduce transmission of laboratory-confirmed influenza in 400 households sampled from four 
diverse, contiguous urban communities in New York City. The primary outcome is household transmission of 
laboratory-confirmed influenza. The secondary outcome is household transmission of symptomatic ILI/ARI and 
of non-influenza respiratory viruses among household contacts of index cases. 
PRELIMINARY  STUDIES 
1. Recruitment of Participants to Clinical Studies Including Trials in Our Setting.
1.1 MoSAIC: MObile Surveillance for ARI/ILI in the Community (U01 IP000618):18  Drs. Stockwell, Saiman
and Larson recruited 158 households (84.5% of 187 households approached) in year 1 and 175 additional
households in subsequent years 2-4 from one of the communities proposed in this study. The current
surveillance population is over 1200 people. Many households recruited in year 1 are still in the study in year 4.
Near all (99%) of the study population are Latino, and 72% are Spanish-speaking.
1.2 Appropriate Care of URIs in Children of Latino Immigrants: The ACURI Project (RC1MD004109):54
Drs. Stockwell and Larson recruited 355 households, including 1460 individuals, from one of the communities 
in this proposed study. The mean number of people per household was 4 (range, 2 to 8 members) and nearly 
all (94%) households were retained through an 8-month study period. Near all (92%) of the study population 
are Latino, and 87% were Spanish-speaking. 
1.3 Impact of Non-Pharmaceutical Interventions on URIs and Influenza in Crowded, Urban Households 
(U01 CI000442):29  Dr. Larson recruited 509 households, including 2788 individuals. The mean number of 
household members was 4.5, and mean duration of participation was 55.5 weeks with an 87% retention rate. 
Most (96%) individuals were Latino. 
1.4 Effect of Antibacterial Home Cleaning and Handwashing Products on Infectious Disease Symptoms 
(R01NR05251):55  Dr. Larson recruited 238 households, including 1178 persons. The mean number of 
household members was 5 (range, 3 to 13 members).  Retention rates through the 48-week study were 94.1%. 
Most (98%) individuals were Latino. 
2. ARI/ILI Surveillance in a Community-Based Sample and Text Messaging Surveillance
2.1 MoSAIC: MObile Surveillance for ARI/ILI in the Community:18  Drs. Stockwell, Saiman, and Larson have
used text messaging to conduct surveillance for ARI/ILI. During the first three years of the study, 62,650 text 
messages were sent to households; responses were received for 48,308 (77.1%). The response rate has not 
decreased over time. The majority (88.9%) of households who responded did so within 4 hours of messages 
being sent; 96.2% responded within 12 hours. The median time from symptom onset to research-assistant 
obtained nasal swab is 2 days; 75-80% of specimens were collected within the optimal 48-72 hour window.18
2.2 Appropriate Care of URI in Children of Latino Immigrants: The ACURI Project:54  Drs. Stockwell and 
Larson conducted illness surveillance in household using weekly telephone reporting. There were 1,829 
illnesses reported. Research staff conducted home visits on a random sample of participants with telephone 
reports of medication use and the telephone reports were 93.7% accurate (95% CI 86.9%-97.4%). 

6 
2.3 Effect of Antibacterial Home Cleaning and Hand Washing Products on Infectious Disease 
Symptoms:55  Dr. Larson conducted ARI/ILI surveillance in households using weekly telephone reporting. 
Research staff determined the sensitivity (93%) and specificity (97%) of the first 100 self-reports of symptoms 
through an in-person assessment conducted during a home visit. During 26.2%, 23.3% and 10.2% of 
household-months at least one household member had a runny nose, cough, and/or sore throat. 
2.4 Impact of Non-Pharmaceutical Interventions on URIs and Influenza in Households:29  Dr. Larson and 
colleagues used twice weekly structured, automated telephone messages using a toll-free number to detect 
symptoms of ARI/ILI in household members. There were 5,034 ARIs reported, of which 669 cases were 
consistent with ILI and 78 (11%) were laboratory-confirmed cases of influenza. 
2.5 Text Messaging and Vaccine Safety Surveillance:62-64  Drs. Stockwell and LaRussa have also 
successfully demonstrated text message vaccine adverse event surveillance in pediatric and obstetric patients. 
3. Identifying Etiology of ARI/ILI 
3.1 MoSAIC: MObile Surveillance for ARI/ILI in the Community:18 In this study, 59.5% of 1677 samples 
were positive for a respiratory pathogen; 21.4% of positive samples collected November-March were positive 
for influenza. In addition, 14 other respiratory pathogens were detected including subspecies of coronavirus and 
parainfluenza, respiratory syncytial virus (RSV), adenovirus, human metapneumovirus (hMPV), rhinovirus/ 
enterovirus (RV/EV), Chlamydophila pneumoniae and Mycoplasma pneumoniae. 
3.2 Impact of Non-Pharmaceutical Interventions on URIs and Influenza in Crowded, Urban  
Households:29  33.3% of deep nasal swabs obtained were positive for influenza of which 43.6% were influenza 
A and 56.4% were influenza B. Among the 66.7% of swabs that tested negative for influenza, 30.8% were 
positive for other viruses including RSV, parainfluenza, RV/EV, adenovirus, and hMPV. 
3.3 Viral surveillance: As a member of the NewYork-Presbyterian Hospital Department of Infection Prevention 
and Control and the hospital epidemiologist at Morgan Stanley Children’s Hospital, Dr. Saiman has helped to 
craft interventional strategies to prevent transmission of respiratory pathogens in healthcare settings which 
include tracking and trending community-onset and healthcare-associated respiratory viral pathogens. 
4. Conduct of Randomized Clinical Trials of ARI/ILI- and Vaccination-Related Behavioral Interventions 
that were Effective 
4.1 Appropriate Care of URIs in Children of Latino Immigrants: The ACURI Project:54 NCT01916031 Drs. 
Stockwell and Larson evaluated the impact of an educational intervention to increase health literacy and 
decrease pediatric emergency department (PED) visits for ARIs among predominantly Latino Early Head Start 
families. Intervention families with a young child with an ARI, were less likely to use an inappropriate over-the- 
counter medication, provide incorrect dosing, and/or bring their child to the pediatric emergency department. 
4.2 Impact of Non-pharmaceutical Interventions on URIs, Influenza in Crowded, Urban Households:29 
NCT00448981 Dr. Larson found mask wearing reduced secondary transmission of influenza. 
4.3 Text-messaging to Promote Health Literacy and Enhance Vaccine Uptake and Completion: Dr. 
Stockwell has conducted a number of randomized controlled trails demonstrating the effects of text message 
vaccination reminders that include educational information to increase administration of influenza and other 
vaccines.19-22,41,65  NCT01146912, NCT01662583, NCT01199666 
5. Collection of Self-Swab Samples: We have piloted the use of self-swabs in preparation for this study.  For 
30 individuals (15 adults and 15 children) meeting ARI/ILI criteria in our MoSAIC study, the ill adult was asked 
to obtain a self-swab, or if a child was ill, their parent obtained the swab. The self-swabs were obtained after 
the staff obtained a swab and left the home. The participants mailed self-swabs to our lab via a pre-stamped 
mailer. Nearly all (97%) households obtained the self-swab and mailed it back. Overall sensitivity for the self- 
swab to capture any respiratory pathogen or influenza was 83.3% and 87.5%, respectively. The kappa statistic 
between research and self-swab was 0.84. Specificity was 100%, and negative and positive predictive values 
were 78.6% and 100% respectively. Sensitivity for self swabs to detect respiratory pathogens for participants 
obtaining their own self-swab was 71.4% and for a parent obtaining a swab from their child was 90.9%; 
sensitivity for influenza was 100% for participants obtaining their own self-swab and 75.0% for a parent 
obtaining their child’s swab. There were no differences in demographic variables, including education level, or 
days between research staff obtaining the swab and receipt of self-swabs, among participants whose self- 
swab results correlated with the research swab results versus participants whose self-swabs results did not 
correlate with research swab results.47 

7 
We also conducted another pilot self-swab study among the household contacts of symptomatic index 
cases to assess the proportion of household members with asymptomatic infection with viral respiratory 
pathogens. We provided 181 households with swabs and instructions for obtaining self-swabs for all individuals 
(symptomatic and asymptomatic) in the household after a symptomatic family member (index case) was 
identified for up to seven days. We received 88% of daily swabs from index cases and 81% from non-index 
household members, which included both related and unrelated household members. 
STUDY DESIGN 
This is a randomized two arm clinical trial to be conducted over four calendar years with blinded 
ascertainment of the primary endpoint: PCR assay-based household transmission of influenza infection. 
STUDY SETTING AND POPULATION: 
This study will be conducted in twelve contiguous, but diverse Neighborhood 
Tabulation Areas (NTAs) in New York City (Figure 2).66 Washington Heights/Inwood 
includes Marble Hill-Inwood (MN 1), Washington Heights North (MN35), 
Washington Heights South (MN36), extending from West 155th Street to the 
northern tip of the island. Most (71%) residents are Latino, half are foreign born, and 
half are English-proficient. Central Harlem consists of Central Harlem North-Polo 
Grounds (MN03) and Central Harlem South (MN11), which extends from Central 
Park North to West 155th Street, and east-west from approximately St Nicholas 
Avenue to 5th Avenue. Most residents (63%) are Black. East Harlem consists of East 
Harlem North (MN34) and East Harlem South (MN33), which extends from 
approximately E 96th Street to West 145th Street and east-west from 5th Avenue to 
Harlem River. Most residents are either Hispanic (49%) or Black (31%). The area 
consisting of West Harlem, Hamilton Heights and Morningside Heights includes 
Hamilton Heights (MN04), Manhattanville (MN06), and Morninside Heights 
(MN09), extending from West 106th Street to West 155th Street and east-west from 
Hudson River to Central Park West/Bradhurst Avenue. The racial/ethnic compositions 
of this community are more variable. The last area consists of the Upper West Side 
(MN12) and Lincoln Square (MN14), extending from West 58th Street to West 106th 
Street and is bounded on the west by the Hudson River and on the east by Central 
Park/Central Park West. Most residents (67%) are White. The research team has 
experience recruiting participants from these communities.20,21,29,54,55,67
SAMPLING FRAME, ELIGIBILITY, EXCLUSIONS, SAMPLING, RECRUITMENT, AND RANDOMIZATION 
Sampling frame. In order for the study sample to be population-based, we have defined the sampling frame 
geographically, as just described. 
Eligibility and Exclusions. Eligibility and exclusion criteria for households are based on previous 
studies.18,29,55 Eligibility will require having at least 3 people per household with one or more <18 years of age, 
in order to ensure that enough people in the household are at risk (Table 2). Household will be defined as 
individuals who live in the dwelling including nuclear family members, relatives, friends and boarders. 
Sampling. As described below, we will use a roster-based approach and snowball sampling from cooperating 
organizations in the sampling frame, with random selection from these rosters.  
Recruitment. We will recruit potential households from a variety of sources, which we have used previously to 
recruit study participants, including community social service programs, Head Start programs, community 
health clinics, and private medical offices. We will recruit with the help of the following organizations: 
-
Union Settlement Association
-
Abyssinian Baptist Church
-
Communities of Harlem Health Revival (CHHR)
-
Children’s Aid Society
-
Upper West Side practices affiliated with Columbia University

8 
Table 2: Eligibility and Exclusion Criteria 
  Recruitment (continued).  
We will also encourage households to notify 
others in their social networks of the study, thus 
recruiting via snowball sampling. At each site, 
we will obtain a roster of patients, enrolled 
families, or clients (as applicable). From 
these rosters, the study statistician will 
identify a random sample to contact. Each 
family will be contacted in order to assess 
eligibility and interest in the study. We have 
had high enrollment rates (>80% of those 
approached to participate) in our previous studies.18,29,54 The project coordinator will assess the first eligibility 
criteria (number of household members, child <18 years-old in household and Spanish/English-speaking. If 
the household is eligible, the coordinator will ask who is the primary caregiver of the children in the 
household and then ask if  that person has a cellphone with text messaging and if they are willing to be the 
household reporter, as well as confirm that they and the household are not planning on moving in the next 
year. If these criteria are met, a home visit for enrollment will be scheduled. The household reporter will 
provide written consent, which will include being the household reporter and sending and receiving text 
messages. Other household members will be provided with an information sheet in English or Spanish 
explaining the study and the request for self-swabs from both ill participants and asymptomatic participants. 
In our text message surveillance study, these procedures were approved by the Columbia University Medical 
Center Institutional Review Board. Each study participant will be given a unique study ID coded to their 
household. Multiple other sources of contact information, if available, will be collected for the household 
reporter including landline and email address. 
Randomization Procedures. On study enrollment, we will randomize 400 households (100 from each 
community) 1:1 to surveillance + education vs. surveillance-only arm, stratified by community and sampling 
site. Randomization will be conducted by the programmer under the supervision of the study statistician using 
permuted blocks ensuring balance between the arms. The programmer will enter the study arm the household 
is randomized to into the text messaging platform database, but arm assignments will not be visible to other 
research staff. Thus, the statistician and research staff will be blinded to arm. 
Annual home visits will be made to all enrolled households wanting to continue the next respiratory 
season to facilitate retention and participation, answer questions, ensure contact information remains correct, 
update household composition changes, and provide swab kits. Households that continue to meet eligibility 
criteria (Table 2) may continue into the next respiratory season. Households will receive $50 on enrollment and 
$25 at this re-enrollment visit; this will aid with enrollment and retention. Participants will also receive 
compensation for reporting and returning self-swabs as outlined below. If a household no longer meets 
eligibility criteria during a season or if are lost to follow-up, we will replace households in the same season if 
there are ≥2 months left in the influenza season or the next year if there are <2months left. Households that 
leave the study will be replaced using the same procedures as for the baseline sample using 1:1 
randomization. Based on previous studies, we expect 10-20% of households to be replaced yearly.18,29,67 
TEXT MESSAGE SURVEILLANCE FOR ILI/ARI 
Both arms (surveillance + education vs. surveillance-only) will receive text messages to perform surveillance of 
household members for ILI/ARI. The surveillance period will run November through March each year. We have 
chosen that period since for the past 9 years (with the exception of the 2009 influenza A H1N1 pandemic) the 
earliest onset of influenza in these communities has been in November and we have seen the vast majority of 
influenza cases by March.18,68 If there are unusual influenza patterns, we will be able to start sample collection 
earlier or end it later. During the surveillance period, each household reporter in all households will  
automatically receive text messages three-times a week from the text messaging system (a.k.a. research staff- 
generated messages) asking if any household member has symptoms included in the study case definitions for 
ILI/ARI and asked to respond whether or not anyone is ill (Figure 3). Households will be texted on a Monday, 
Wednesday and Friday schedule to optimize days between reporting. We have chosen this schedule so that 
intervention can start within 36 hours of onset. Messages will be sent in English or Spanish based on the 
 
 
 
 
 
 
 
 
 

9 
household reporter preference; Dr. Stockwell has extensive experience creating 160-character limited text 
messages that are linguistically and culturally 
equivalent in English and Spanish (including 
messages for ILI/ARI surveillance). In addition to 
research staff-generated messages, the household 
reporter will also be able to text the key word,  
“sick” at any time during the surveillance periods to 
alert staff that someone in the household is ill 
(a.k.a. household (HH)-generated messages), 
which will lead to  a text message cascade (Figure 
3). Each household will also receive a refrigerator 
magnet with study contact information, and the 
number to text the keyword “sick”. They will also 
receive a thermometer and self-swab kits as below. 
Reporters will use their own cell phone and   
receive compensation as described below. This 
strategy for texting and reporting illness has been 
used successfully in the MoSAIC study. 
If the household reporter misses a week of 
reporting, they will be contacted by the research 
team using their contact information, and if needed 
via a home visit, to confirm their cell number, to 
identify reasons for not reporting, and to update contact information including any new number to be used. 
Based on our previous work, we expect to contact less than 20 (5%) households monthly. Similar to our 
previous studies, a payment of $15/month will be provided to household reporters who respond to ≥75% of the 
text messages during the previous month (i.e., at least 9 text messages/month) to defray the costs of the 
required text messages The payment will be made remotely via a refillable cash card. 
Confirmation of ARI/ILI Symptoms via Phone Calls. Any research staff or HH -generated message  
indicating illness in a household member will trigger an automatic email notifying research staff. Staff will 
immediately contact (within 2-4 hours) the household reporter to confirm ARI/ILI symptoms. The use of text 
messaging to perform the first level of surveillance allows staff to focus on households with ill participants,  
which we expect to be about 5-15 households at any given reporting day. In the MoSAIC study, we  
successfully contacted all families within 12 hours of reporting via text message, 7 days a week. Symptoms will 
be confirmed over the phone using the Symptoms Reporting Form used in previous studies18,29,54,69 
(Appendix).18,29 Anyone meeting case definition for ARI or ILI will be swabbed. The CDC-provided case 
definition for ARI is presence of at least two of the following: (1) rhinorrhea/nasal congestion; (2) sore throat; 
(3) cough; (4) fever/feverishness; (5) myalgia. The ILI definition is at least two of the following signs and 
symptoms: fever, cough, headache, sore throat, or myalgia.16 In addition, for those <1 years-old, a swab will be 
collected even if rhinorrhea is the only symptom; young children have viral illnesses that manifest differently 
than in adults.70 We are using ARI and ILI criteria to collect swabs, since a poor correlation between CDC ILI 
definitions and influenza has been noted by ourselves and others.71 
Obtaining of Self-Swab Nasal Samples. At enrollment, we will provide households with our pilot tested self- 
swab kits. The research staff will demonstrate to the household reporter how to obtain the swab, including an 
observation of the reporter taking a self-swab. Ill participants can self-swab or the household reporter can 
obtain the swab or a parent can obtain their child’s swab. Households will also be given a simple written 
instruction sheet (Appendix) and a link to a brief YouTube video that we will create to demonstrate how to take 
the swab; both will be in English and Spanish. We will offer support for the self-swabbing, if needed, in a step- 
wise fashion.49 We will first provide further instruction and support over the telephone, then, if needed, the 
research staff will visit the home to supervise collection, and if that fails, the staff will collect the specimen. In 
our pilot studies of self-swabbing of ill participants and of asymptomatic household members, all households 
reported that the process for taking the swabs went well without any issues. 
Samples will be collected from the anterior nares using flocked swabs,72 which have a sensitivity of 91- 
98.5% and specificity of 100% compared with nasopharyngeal aspirate.73,74  Each household will be provided 
with a kit that includes multiple nasal swabs, labeled containers with universal transport media, plastic  
 

10 
specimen bags, and pre-addressed/pre-stamped waterproof mailers with absorbent paper inside in case of 
spill. Households will be instructed on the importance of making sure the container is closed, how to put the 
container in the specimen bag, and how to place the bag in the mailer and seal the mailer. We will ask that the 
households keep their unused kits in their refrigerator so they will be highly likely to recall the kits’ location. 
Each household’s kit will contain a list of stickers with unique ID numbers for each household member matched 
to the person’s name. Families will be asked to put the relevant sticker and date of collection on the specimen. 
For the index case, we will ask that samples be obtained the same day as the report of illness to 
facilitate the yield of respiratory pathogens. We will then ask that everyone else in the household is swabbed 3 
and 5 days after the onset of symptoms in the index case. We have chosen this timing, to maximize yield for 
influenza, since it has been estimated that the time between influenza infection in an index case and infection 
of secondary household contacts is 3.4 days [3.1, 3.7].7,10,13,24 It was also found that a single sample on day 4 
or day 5 since the symptoms onset of the index case would have the most “detectable” infections circulating in 
households.24 Households will be texted reminders on day 3 and 5 requesting that these samples be taken, 
and will be asked to text back confirmation. Households that do not text back confirmation will be called. We 
will ask that swabs be kept in their refrigerator, until mailing, as in our pilot study. 
Once all samples are collected they will be placed in a pre-addressed, pre-stamped mailer and dropped 
into the regular mail, as in our pilot. They will be texted a final time on day 7 asking if swabs were sent back, 
and will be called if do not respond. An episode of ILI/ARI within a household will be considered completed 
when the last person within a household is symptom-free for 5 consecutive days; after that, new symptoms 
within a household would trigger consideration of a new ILI/ARI episode. As additional compensation for their 
time, households will receive $5.00 when the swabs are received. We will track kits that are used, and a new kit 
will be mailed overnight when a household as used up all kits. 
Laboratory Confirmation of Respiratory Pathogens. When a mailed swab sample is received, it will be 
logged into a database and placed in a designated refrigerator (2-8oC) prior to processing. Samples that   
cannot be fully processed within 48 hours will be aliquoted frozen at minus 800C. Samples will be analyzed 
using the FilmArray instrument Respiratory Panel developed by BioFire Diagnostics,75-77  which has FDA 510(k) 
clearance as an in vitro diagnostic device for nasal swab specimens, and is the testing system we use in our 
current MoSAIC study. The Respiratory Panel applies multiplex reverse-transcriptase polymerase chain 
reaction (RT-PCR) to identify 20 respiratory pathogens using a variety of gene targets for specific 
pathogens75(Appendix). Detected pathogens include Influenza A, Influenza A subtype H1, Influenza A subtype 
H3, Influenza A subtype H1 2009, Influenza B, adenovirus, 4 subtypes of coronavirus and 4 subtypes of 
parainfluenza, human metapneumovirus, respiratory syncytial virus, rhinovirus/enterovirus, Mycoplasma 
pneumoniae, Chlamydophilia pneumoniae and Bortadella pertussis. The sensitivity of FilmArray Respiratory 
Panel ranges from 87-100% and specificity from 89-100%. Previous studies have shown that the rates of 
detection of respiratory viruses by RT-PCR can be up to 2-fold higher than by conventional viral culture.78 
Testing will be performed within a research laboratory at Columbia University Medical Center  and the 
technologist, Ms. Wang, is the chief technologist on our MoSAIC study which also uses the FilmArray system. 
She will be supervised by Dr. Saiman. Dr. LaRussa will provide expertise in viral diagnostic testing. Quality 
control will be performed for all new lots and shipments using validation panels as per the manufacturer’s 
recommendations for research studies. The technical failure rate has been estimated to be 2-4%;76,77 but in our 
current study, the failure rate has been only 1.3%. In the event of technical failure, there is adequate residual 
viral transport media volume to perform a second test. For households with an influenza positive index case, if 
both d3 and d5 samples in a household member are negative, the d5 sample will be re-tested. The residual 
viral culture media containing the sample and swab will be stored in a minus 800C freezer. Results will not be 
shared with participants and Ms. Wang and project coordinator will be blinded to study arm. However, if a 
household’s index case has a negative result two episodes in a row, the study team may further assess the episodes 
and review with the household how to take the swab as well as the importance that symptoms be met. 
Blinding. Participants cannot be blinded as to which group they are randomized. Laboratory technicians 
ascertaining the primary endpoint will be blinded as to arm assignment, and the research staff will also be 
blinded as above. The data analysis will be blinded as to group assignment by using a dummy variable for 
intervention vs. comparison until the analyses are completed. 
TEXT MESSAGE-BASED  INTERVENTION 
While all households will receive the surveillance text messages, we will randomize households within each 
surveillance community to additionally receive educational text message about ways to decrease household 

11 
transmission (surveillance + education arm) vs. standard care (surveillance-only arm). We will then assess the 
impact of the educational text messages primarily on secondary attack rates of influenza within households. 
The comparison arm will not receive any educational text messages, only the surveillance ones as above. 
Message Generation. When an ILI/ARI is reported, research staff will log it into the text message application. 
If the household is in the surveillance + education arm, the system will automatically send one educational 
message to the household reporter a day for three days; if it is surveillance-only the system will not send a 
message. This allows research staff to remain blinded. Messages will be based on whether it is an adult or 
child who is the index case (Appendix). We have chosen three messages since after that most within- 
household transmission will have occurred. The household reporter will be instructed on enrollment that they 
are to read the text message to the household members. We have designed 4 sets of message to be used in 
order per household i.e. each household will start with set one (see Appendix for adult and child-specific sets 
of messages). The message will be targeted to be from the recruitment site (e.g. doctor’s office, Head Start). 
We will track reports of undelivered messages; these are visible in the text message application. 
 
Intervention Message Design Including Cultural Sensitivity. We have designed sample sets of messages 
(see Appendix) based on the Health Belief Model that includes the CDC recommended information to   
decrease influenza and other respiratory pathogen transmission, the relevant literature, and the team’s 
expertise in text messaging, influenza, disease transmission, health literacy and the community. As mentioned 
above, our team includes Dr. Dodi Meyer, an expert in cultural competency, who runs a health literacy program 
that has been federally funded, Health Education and Adult Literacy. Messages will include information that an 
Figure 4: Examples of Potential Set Text Messages for a Sick Adult 
ill individual can pass the respiratory illness to 
their household members, that transmission 
can harmful to household members, 
suggestions for behavior change, and the 
effectiveness of behavior change (Figure 4). 
Some of the messages will be interactive 
since in our previous vaccination-related 
intervention, we found that including one 
interactive message increased the 
intervention effect.20,41  While keeping within 
the 160-character count, we designed 
messages to be no more than a 5th grade 
reading level according to the Flesch–Kincaid 
readability test,79 and in accordance with national guidelines.80   The messages will have the same content for 
English and Spanish-speaking families, ensuring proper linguistic and cultural equivalency. In our previous text 
messaging studies, we used equivalent English and Spanish content and found no interaction between 
intervention effects and language of message.19  Using the same content in English and Spanish allows 
comparisons across the study population. Our team has expertise in completing such translations in 11 text 
messaging studies.18-22,41,62-65,81 We will follow the best practices for translation82 which include preparing a 
preliminary version in Spanish, back-translation by a second person unfamiliar with the original English 
messages, and examination of the original English, preliminary Spanish and back-translated English versions. 
We then will show the final draft versions to five other bilingual staff members in our Division of Child and 
Adolescent Health who represent different Spanish-speaking countries to ensure the messages are 
linguistically and culturally congruent. The next step will include pretesting messages, as delineated below. 
We will then adjust messages and repeat the above steps accordingly. Team members will meet a last time to 
assess content validity and equivalency of final messages.  
Pretesting. We will pre-test the educational messages in person with households in all 4 communities  
following our previous field-testing protocol. For the Washington Heights community, we will sample families 
from the MoSAIC study. For the West Harlem, Hamilton Heights and Morningside Heights area, we will recruit 
households from two of the health centers associated with NewYork-Presbyterian Hospital serving those 
neighborhoods, and for the Upper Westside area we will recruit from two general pediatric practices affiliated 
with CUMC. For the Harlem area, we will recruit through the Communities of Harlem Health Revival (letter of 
support). Households with whom we pretest messages must meet the same eligibility criteria as those for the 
study and will not be eligible to be randomized. After obtaining informed consent, the project coordinator will 
text the proposed messages and ask the respondent to feedback in their own words what the message meant 
Text Message Examples 
Health Belief Model Factor 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

12 
to them. The coordinator will also ask about any problems or suggested changes. This process will be iterative 
until no new message changes are made. The translation process will repeated with the final messages. We 
anticipate needing 40 households, 10 from each community, to pretest the messages. Households will receive 
a round-trip New York City subway Metrocard worth $5.00 for participating in the pre-test. 
 
DATA COLLECTION AND MANAGEMENT FOR BOTH ARMS 
Survey Tools. Household and individual data will be gathered from the household reporter using two methods: 
a Demographic Survey adapted from previous studies18,29  and a Household Intake Form (Appendix). Surveys 
will be verbally administered by research staff at the baseline (enrollment) home visit in the reporter’s preferred 
language, English or Spanish. We will obtain the following data at enrollment and update it annually: 
- Individual characteristics (age, sex, race/ ethnicity, place of birth, length of time in U.S., preferred language, 
education, insurance, employment status, school/daycare, hours outside the home, general health status, 
presence of chronic respiratory disease or other illness, smoking/exposure to second-hand smoke). 
- Household characteristics (composition, household density [ratio of people to bedrooms]). 
Symptom Reports. We will call on day 10 after symptom onset of index case to assess ILI/ARI symptoms in 
household members, care sought, if anti-viral medication taken, and current influenza vaccination status 
(including type of vaccine) using the Follow-up form (Appendix). 
Data Management. All data collection and storage will be HIPAA compliant as outlined in the Human Subjects 
Protection. Dr. Stockwell will supervise file cleaning and perform quality checks at least monthly. All study 
related documents and swab samples will contain a unique identifier for each participant. 
STATISTICAL PLAN: Data analysis will be performed using SAS (Cary, NC). 
AIM: To assess the impact of an educational intervention delivered by text messaging on transmission 
of influenza within households 
All analyses will be based on randomized arm assignment (intention-to-treat). An ill person will be considered  
an ILI/ARI index case for a household if, at the onset of illness, no one else in the household had been 
symptomatic within the previous 5 days.29  Non-index household members will be defined as secondary cases if 
they are confirmed by RT-PCR as influenza-positive on day 3 or day 5 swab (symptomatic or asymptomatic).16 
Five days was selected based on our previous studies and the influenza incubation period.29  The secondary 
attack rate will be defined as the number of secondary cases of laboratory-confirmed influenza (both 
symptomatic and asymptomatic) divided by the number of household members minus one (the index case). 
Secondary attack rates will be compared between the arms - surveillance plus education vs. surveillance-only - 
using chi square tests and a generalized linear mixed model (logistic regression for binary response, mixed 
model for clustering within a household). We will assess other co-variates in the models including for the index 
patient and household contacts: age, sex, vaccination status including vaccine type, antiviral use (treatment or 
prophylaxis), previous illness episodes reported for the household, influenza season and years in study. 
Secondarily, we will assess the intervention’s impact on additional outcomes using the same analyses 
as above: defining secondary cases based on (1) ILI-criteria (fever, cough, headache, sore throat, or myalgia) 
within 5 days of illness of index case; (2) ARI criteria (≥2 of a) rhinorrhea/nasal congestion; b) sore throat; c) 
cough; d) fever/feverishness; e) myalgia); (3) any laboratory-confirmed pathogen that is the same as the index 
case including non-influenza pathogens. An additional set of secondary outcome measures will be to assess  
the secondary attack rate at the household (cluster) level defined as the percentage of households with at least 
one secondary case as defined in the analyses above. 
We will minimize the amount of missing household-time due to attrition by replacing households who  
are lost to follow up that year if there are ≥2 months left in the influenza season or the next year if there are <2 
months left. We recognize that reasons for dropping out may be correlated with the outcome; this cannot be 
tested directly in the data. We will therefore add an indicator in the models for replacement households and   
test for association. Also, we will perform sensitivity analyses analyzing only the originally randomized study 
sample (exclude replacement households). Weak or no association for the indicator variable and consistency in 
the association between random arm assignment and outcome between the whole sample and the sample 
excluding replacement households will be interpreted as evidence supporting use of the entire sample. We will 
also conduct sensitivity analyses in which we assign the outcome of secondary transmission in the   
replacement sample to infection to make it a worse case scenario, with the caveat that this will only be possible 
if the dropout rate is low. If dropout rate is high, the range of possible estimation is too large. 

13 
Missing Data. We will document missing data and classify by variable and arm. We will then assess any 
relationships between missingness and covariates such as demographics or arm. Missing data due to attrition 
was discussed above. Missing data due to other reasons will also need to be taken into account. The main 
concern related to the primary outcome will be household contacts who do not obtain swabs. We will impute 
these data using multiple imputation following the methodology of Rubin.83  A more conservative approach 
would be to consider all household members missing swab data to be failures8 (to have been infected with 
influenza), but this is highly unlikely to be an accurate reflection of the true case, unduly weights drop-outs in 
the analysis, and is not the currently preferred approach to missing data.84,85 Covariate data may also be 
missing.  We will also impute these variables using multiple imputation following the methodology of Rubin.83
Exploratory Aim: To compare the yield of text message ILI/ARI surveillance among subgroups in a 
diverse, community sample 
We will use Pearson chi square to assess relationship between text message response rates and 
community, reporter’s race/ethnicity, insurance and education. This will be defined as number of responses 
received per message sent. If more than one variable is significantly related to reporting rates (p<0.1), we will 
develop multivariable logistic models to assess this relationship. We will also assess response rates by arm. 
SAMPLE SIZE AND POWER 
We have based our sample size on the primary outcome. With 200 households per arm and an average of 3.8 
persons/household, we will have 88% power to detect a decrease in secondary attack rates of laboratory- 
confirmed influenza from 12% (surveillance-only) to 5% (surveillance + education), based on type I error of 5% 
and an intracluster correlation coefficient of 0.29. The intracluster correlation, expected transmission rates for 
the surveillance-only arm, and effect size are derived from the paper by Cowling et al., reporting a reduction 
from 12% to 5% between the hand hygiene and usual care arms in SAR when a non-text message based 
behavioral intervention was started at ≤36 hours of illness onset in the index case using laboratory-confirmed 
influenza as an outcome.16  In the MoSAIC study, secondary attack rates for laboratory-confirmed influenza 
have been 16.6% therefore an expected rate of 12% in the surveillance-only arm is conservative. We will also 
be powered for our secondary outcome; we will have 91% power to detect a decrease in symptomatic ILI/ARI 
from 23% to 11%.16 If we lose 80 households (20%) per season, we will still have at least 80% power to detect 
the decreases in both the primary and secondary outcome as above, even without household replacement. 
POTENTIAL CHALLENGES AND SOLUTIONS Table 3 
Challenge 
Solution 
1. Recruitment and
retention
In our previous community studies retention was excellent. In our MoSAIC study, 75.9% 
of households recruited in year 1 were still in the study in year 3. 
2. Non-representative
sample
Since eligibility criteria include ≥3 members and ≥1 child, the sample will not be fully 
representative of the communities. However, the benefits of these inclusion criteria 
include higher likelihood of infection and transmission. 
3. Reporting bias
3x/a week texting will decrease recall bias. We have shown under- and over-reporting to 
be low and confirmed the validity of household symptom self-reports. 
4. Cell phone numbers
change/disconnected
We will have multiple ways to contact household reporters and will contact those who 
have not reported for one month. 
5. Effect on participants
seeking care
Staff will not provide medical advice, and will refer participants to their own health 
provider, as was the process in our previous studies, without event. 
6. Missed samples
We will offer support for the self-swabbing as needed in a step-wise fashion.49
7. Self swabs being
obtained days 3 and 5
We have chosen these days in order to maximize yield for influenza while keeping testing 
feasible. We may miss some non-influenza viruses with longer incubation periods 
8. Using U.S Postal Service
This allows best access for individuals who may not live near a FedEx or UPS drop box. 
9. Reporting November
through March
We have based surveillance on usual influenza patterns; if there are unusual influenza 
patterns, we will be able to start sample collection early or end it later. 

. 
PUBLIC HEALTH IMPLICATIONS AND DISSEMINATION 
We propose to assess the impact of a text message-based educational intervention on household transmission 
primarily of influenza and secondarily of ILI and other respiratory viruses. If successful, information gathered 
could provide new insights in decreasing influenza both during seasonal outbreaks and pandemics. It may   also, 
in a future pandemic, be useful to help identify those in need of antiviral treatment and prophylaxis. 
TIMELINE  Table 4 
 
Pre-start 
mths 1-6 
mths 7-12 
mths 13-41 
mths 14-48 
IRB submission 
X 
 
Hire/train staff, Pretest messages, Set up intervention 
 
X 
 
Household recruitment, Randomization 
 
X 
 
Surveillance;  Intervention 
 
X 
Lab and Data Analysis, Manuscript Preparation 
 
X 
X 
 
 
 
 
1. 
Prevention and control of seasonal influenza with vaccines. Recommendations of the Advisory 
Committee on Immunization Practices--United States, 2013-2014. MMWR Recomm Rep. Sep 20 
2013;62(RR-07):1-43. 
2. 
Reed C. Challenges to estimating influenza disease burden from populaion-based survelliance data. 
Incidence, Severity, and Impact Conference 2016 Paris, France. 
3. 
Centers for Disease Control and Prevention. Estimated Influenza Illnesses and Hospitalizations Averted 
by Vaccination — United States, 2014–15 Influenza Season. Available at  
http://www.cdc.gov/flu/about/disease/2014-15.htm. Accessed on January 22, 2016. 
4. 
Longini IM, Jr., Koopman JS, Monto AS, Fox JP. Estimating household and community transmission 
parameters for influenza. Am J Epidemiol. May 1982;115(5):736-751. 
5. 
Cauchemez S, Carrat F, Viboud C, Valleron AJ, Boelle PY. A Bayesian MCMC approach to study 
transmission of influenza: application to household longitudinal data. Stat Med. Nov 30 
2004;23(22):3469-3487. 
6. 
Ferguson NM, Cummings DA, Fraser C, Cajka JC, Cooley PC, Burke DS. Strategies for mitigating an 
influenza pandemic. Nature. Jul 27 2006;442(7101):448-452. 
7. 
France AM, Jackson M, Schrag S, et al. Household transmission of 2009 influenza A (H1N1) virus after 
a school-based outbreak in New York City, April-May 2009. J Infect Dis. Apr 1 2010;201(7):984-992. 
8. 
Iyengar P, von Mollendorf C, Tempia S, et al. Case-ascertained study of household transmission of 
seasonal influenza - South Africa, 2013. J Infect. Nov 2015;71(5):578-586. 
9. 
Cowling BJ, Chan KH, Fang VJ, et al. Comparative epidemiology of pandemic and seasonal influenza 
A in households. N Engl J Med. Jun 10 2010;362(23):2175-2184. 
10. 
Petrie JG, Ohmit SE, Cowling BJ, et al. Influenza transmission in a cohort of households with children: 
2010-2011. PLoS One. 2013;8(9):e75339. 
11. 
Looker C, Carville K, Grant K, Kelly H. Influenza A (H1N1) in Victoria, Australia: a community case 
series and analysis of household transmission. PLoS One. 2010;5(10):e13702. 
12. 
Morgan OW, Parks S, Shim T, et al. Household transmission of pandemic (H1N1) 2009, San Antonio, 
Texas, USA, April-May 2009. Emerg Infect Dis. Apr 2010;16(4):631-637. 
13. 
Cauchemez S, Donnelly CA, Reed C, et al. Household transmission of 2009 pandemic influenza A 
(H1N1) virus in the United States. N Engl J Med. Dec 31 2009;361(27):2619-2627. 
14. 
Carrat F, Sahler C, Rogez S, et al. Influenza burden of illness: estimates from a national prospective 
survey of household contacts in France. Arch Intern Med. Sep 9 2002;162(16):1842-1848. 
15. 
Crabtree A, Henry B. Non-Pharmaceutical Measures to Prevent Influenza Transmission: The Evidence 
for Individual Protective Measures.   Available at   http://nccid.ca/publications/non-pharmaceutical- 
measures-to-prevent-influenza-transmission/ Accessed on January 22, 2016. 
16. 
Cowling BJ, Chan KH, Fang VJ, et al. Facemasks and hand hygiene to prevent influenza transmission 
in households: a cluster randomized trial. Ann Intern Med. Oct 6 2009;151(7):437-446. 
17. 
Suess T, Remschmidt C, Schink SB, et al. The role of facemasks and hand hygiene in the prevention of 
influenza transmission in households: results from a cluster randomised trial; Berlin, Germany, 2009- 
2011. BMC Infect Dis. 2012;12:26. 
REFERENCES 

19.
Stockwell MS, Kharbanda EO, Martinez RA, Vargas CY, Vawdrey DK, Camargo S. Effect of a text
messaging intervention on influenza vaccination in an urban, low-income pediatric and adolescent
population: a randomized controlled trial. JAMA. Apr 25 2012;307(16):1702-1708.
20.
Stockwell MS, Hofstetter AM, DuRivage N, et al. Text message reminders for second dose of influenza
vaccine: a randomized controlled trial. Pediatrics. Jan 2015;135(1):e83-91. PMCID: PMC4279072.
21.
Stockwell MS, Westhoff C, Kharbanda EO, et al. Influenza vaccine text message reminders for urban,
low-income pregnant women: a randomized controlled trial. Am J Public Health. Feb 2014;104 Suppl
1:e7-e12.
22.
Stockwell MS, Kharbanda EO, Martinez RA, et al. Text4Health: impact of text message reminder- 
recalls for pediatric and adolescent immunizations. Am J Public Health. Feb 2012;102(2):e15-21.
PMCID:  PMC3483980
23.
Brenner J. Pew Internet: Mobile , Pew Internet and American Life Project, Pew Research Center,
Available  at  http://www.pewinternet.org/fact-sheets/mobile-technology-fact-sheet/  Accessed  on  January 2,
2016.
24.
Lau MS, Cowling BJ, Cook AR, Riley S. Inferring influenza dynamics and control in households. Proc
Natl Acad Sci U S A. Jul 21 2015;112(29):9094-9099.
25.
Molinari NA, Ortega-Sanchez IR, Messonnier ML, et al. The annual impact of seasonal influenza in the
US: measuring disease burden and costs. Vaccine. Jun 28 2007;25(27):5086-5096.
26.
Lee GM, Friedman JF, Ross-Degnan D, Hibberd PL, Goldmann DA. Misconceptions about colds and
predictors of health service utilization. Pediatrics. Feb 2003;111(2):231-236.
27.
Fiore AE, Epperson S, Perrotta D, Bernstein H, Neuzil K. Expanding the recommendations for annual
influenza vaccination to school-age children in the United States. Pediatrics. Mar 2012;129 Suppl 2:S54-
62.
28.
Assessment of ESSENCE performance for influenza-like illness surveillance after an influenza
outbreak--U.S. Air Force Academy, Colorado, 2009. MMWR Morb Mortal Wkly Rep. Apr 8
2011;60(13):406-409.
29.
Larson EL, Ferng YH, Wong-McLoughlin J, Wang S, Haber M, Morse SS. Impact of non- pharmaceutical
interventions on URIs and influenza in crowded, urban households. Public Health Rep. Mar-Apr
2010;125(2):178-191. PMCID: PMC2821845.
30.
Zichuhr K., S. S. Digital differences. Pew Internet & American Life Project. Available at
http://www.pewinternet.org/~/media//Files/Reports/2012/PIP_Digital_differences_041312.pdf.
Accessed on January 26, 2016.
31.
Marquet RL, Bartelds AI, van Noort SP, et al. Internet-based monitoring of influenza-like illness (ILI) in the
general population of the Netherlands during the 2003-2004 influenza season. BMC Public Health.
2006;6:242.
32.
Bernardo TM, Rajic A, Young I, Robiadek K, Pham MT, Funk JA. Scoping review on search queries
and social media for disease surveillance: a chronology of innovation. J Med Internet Res.
2013;15(7):e147.
33.
Chew C, Eysenbach G. Pandemics in the age of Twitter: content analysis of Tweets during the 2009
H1N1 outbreak. PLoS One. 2010;5(11):e14118.
34.
Dugas AF, Jalalpour M, Gel Y, et al. Influenza forecasting with Google Flu Trends. PLoS One.
2013;8(2):e56176.
35.
Dalton C, Durrheim D, Fejsa J, et al. Flutracking: a weekly Australian community online survey of
influenza-like illness in 2006, 2007 and 2008. Commun Dis Intell Q Rep. Sep 2009;33(3):316-322.
36.
Department of Health and Human Services. Guide to Privacy and Security of Electronic Health
Information.  Available  at    https://http://www.healthit.gov/providers-professionals/guide-privacy-and- 
security-electronic-health-information/. Accessed on January 25, 2016.
37.
The Epidemiology Of Novel Influenza Virus Infection And Evaluation Of Antiviral And Vaccine
Effectiveness.  Pandemic influenza Studies Planning Workshops. May and June 2015.
38.
Free C, Phillips G, Watson L, et al. The effectiveness of mobile-health technologies to improve health
care service delivery processes: a systematic review and meta-analysis. PLoS Med.
2013;10(1):e1001363.
39.
Rosenstock IM. Why people use health services. Milbank Mem Fund Q. Jul 1966;44(3):Suppl:94-127.
40.
Durham DP, Casman EA. Incorporating individual health-protective decisions into disease transmission
models: a mathematical framework. J R Soc Interface. Mar 7 2012;9(68):562-570.
41.
Hofstetter AM, Vargas CY, Camargo S, et al. Impacting delayed pediatric influenza vaccination: a
randomized controlled trial of text message reminders Am J Prev Med Apr 2015;48(4):392-401

information: findings from the health information national trends survey. J Health Commun. Oct-Nov 
2007;12(7):667-680. 
43.
Johnson JD, Meischke H. Cancer-related channel selection: an extension for a sample of women who
have had a mammogram. Women Health. 1993;20(2):31-44.
44.
Lofgren E, Fefferman NH, Naumov YN, Gorski J, Naumova EN. Influenza seasonality: underlying
causes and modeling theories. J Virol. Jun 2007;81(11):5429-5436.
45.
U.S. Department of Housing and Urban Development. Available at
http://www.huduser.org/portal//publications/pdf/Measuring_Overcrowding_in_Hsg.pdf.   Accessed   on
January 22, 2016. 
46.
CDC Monitoring the Nation's Health. Available at
http://www.cdc.gov/nchs/data/series/sr_10/sr10_260.pdf.    Accessed on January 22, 2016.
47.
Vargas CY, Wang L, Castellanos de Belliard Y, et al. Pilot study of participant-collected nasal swabs for
acute respiratory infections in a low-income, urban population. Clin Epidemiol. 2016;8:1-5. PMCID:
PMC4708198.
48.
Akmatov MK, Gatzemeier A, Schughart K, Pessler F. Equivalence of self- and staff-collected nasal
swabs for the detection of viral respiratory pathogens. PLoS One. 2012;7(11):e48508.
49.
Lambert SB, Allen KM, Nolan TM. Parent-collected respiratory specimens--a novel method for
respiratory virus and vaccine efficacy research. Vaccine. Mar 28 2008;26(15):1826-1831.
50.
Thompson MG, Ferber JR, Odouli R, et al. Results of a Pilot Study using Self-Collected Mid-Turbinate
Nasal Swabs for Detection of Influenza Virus Infection among Pregnant Women. Influenza Other Respir
Viruses. Feb 25 2015.
51.
Smithgall M, Vargas CY, Reed C, et al. Influenza Vaccine Effectiveness in a Low-Income, Urban
Community Cohort. Clin Infect Dis. Feb 1 2016;62(3):358-360. PMCID: PMC4706631.
52.
Stockwell MS, Rausch J, Sonnett M, Stanberry LR, Rosenthal SL. Parental reasons for utilization of an
urban pediatric emergency department during the 2009 H1N1 influenza epidemic. Pediatr Emerg Care.
Apr  2011;27(4):261-265.
53.
Stockwell MS, Martinez RA, Hofstetter A, Natarajan K, Vawdrey DK. Timeliness of 2009 H1N1 vaccine
coverage in a low-income pediatric and adolescent population. Vaccine. Apr 12 2013;31(16):2103- 2107.
54.
Stockwell MS, Catallozzi M, Larson E, et al. Effect of a URI-related educational intervention in early
head start on ED visits. Pediatrics. May 2014;133(5):e1233-1240. PMCID: PMC4006431.
55.
Larson EL, Lin SX, Gomez-Pichardo C, Della-Latta P. Effect of antibacterial home cleaning and
handwashing products on infectious disease symptoms: a randomized, double-blind trial. Ann Intern
Med. Mar 2 2004;140(5):321-329. PMCID: PMC2082058.
56.
Zachariah P, Posner A, Stockwell MS, et al. Vaccination Rates for Measles, Mumps, Rubella, and
Influenza Among Children Presenting to a Pediatric Emergency Department in New York City. J
Pediatric Infect Dis Soc. 2014;3(4):350-353.
57.
Stone A, Quittell L, Zhou J, et al. Staphylococcus aureus nasal colonization among pediatric cystic
fibrosis patients and their household contacts. Pediatr Infect Dis J. Oct 2009;28(10):895-899.
58.
Miroballi Y, Baird JS, Zackai S, et al. Novel influenza A(H1N1) in a pediatric health care facility in New
York City during the first wave of the 2009 pandemic. Arch Pediatr Adolesc Med. Jan 2010;164(1):24- 30.
59.
Baird JS, Buet A, Hymes SR, et al. Comparing the clinical severity of the first versus second wave of
2009 Influenza A (H1N1) in a New York City pediatric healthcare facility. Pediatr Crit Care Med. Jan 5
2012.
60.
Neu N, Plaskett T, Hutcheon G, Murray M, Southwick KL, Saiman L. Epidemiology of human
metapneumovirus in a pediatric long-term care facility. Infect Control Hosp Epidemiol. Jun
2012;33(6):545-550.
61.
Cohen B, Ferng YH, Wong-McLoughlin J, Jia H, Morse SS, Larson EL. Predictors of flu vaccination
among urban Hispanic children and adults. J Epidemiol Community Health. Mar 2012;66(3):204-209.
PMCID:  PMC3673314.
62.
Stockwell MS, Andres R, Fernandez N, Vargas C, Lara M, LaRussa P. Flunet: Real-time Influenza
Vaccine Adverse Event Surveillance. Platform presentation MHealth Summit  2011 Washington, DC.
2011.
63.
Stockwell MS, Broder K, Lewis P, et al. FeverFlu: Assessing Fever Rates in Children ages 24 to 59
months after LAIV or IIV During the 2013-14 Influenza Season. Presented to Advisory Committee on
Immunization Practices, June 2014.
64.
Stockwell MS, Broder K, LaRussa P, et al. Risk of fever after pediatric trivalent inactivated influenza
vaccine and 13-valent pneumococcal conjugate vaccine JAMA Pediatr Mar 2014;168(3):211-219

vaccination: A randomized controlled trial. Vaccine. Oct 26 2015;33(43):5741-5746. 
66.
New York City Department of City Planning. Neighborhood Tabulation Areas. Available at
https://data.cityofnewyork.us/City-Government/Neighborhood-Tabulation-Areas/cpf4-rkhq/data.
Accessed on July 6, 2017.
67.
Larson EL, Ferng YH, Wong-McLoughlin J, Wang S. Retention and protocol adherence of Hispanic
volunteers in a longitudinal trial. Am J Health Behav. Jul-Aug 2009;33(4):435-444.
68.
Hofstetter AM, Natarajan K, Rabinowitz D, et al. Timeliness of pediatric influenza vaccination compared
with seasonal influenza activity in an urban community, 2004-2008. Am J Public Health. Jul
2013;103(7):e50-58.
69.
Stockwell MS, Catallozzi M, Meyer D, Rodriguez C, Martinez E, Larson E. Improving care of upper
respiratory infections among Latino Early Head Start parents. J Immigr Minor Health. Dec
2010;12(6):925-931.
70.
Ohmit SE, Monto AS. Symptomatic predictors of influenza virus positivity in children during the
influenza season. Clin Infect Dis. Sep 1 2006;43(5):564-568.
71.
Conway NT, Wake ZV, Richmond PC, et al. Clinical Predictors of Influenza in Young Children: The
Limitations of “Influenza-Like Illness”. Journal of the Pediatric Infectious Diseases Society. September 3,
2012 2012.
72.
Daley P, Castriciano S, Chernesky M, Smieja M. Comparison of flocked and rayon swabs for collection of
respiratory epithelial cells from uninfected volunteers and symptomatic patients. J Clin Microbiol. Jun
2006;44(6):2265-2267.
73.
Abu-Diab A, Azzeh M, Ghneim R, et al. Comparison between pernasal flocked swabs and
nasopharyngeal aspirates for detection of common respiratory viruses in samples from children. J Clin
Microbiol. Jul 2008;46(7):2414-2417.
74.
Heikkinen T, Salmi AA, Ruuskanen O. Comparative study of nasopharyngeal aspirate and nasal swab
specimens for detection of influenza. BMJ. Jan 20 2001;322(7279):138.
75.
Poritz MA, Blaschke AJ, Byington CL, et al. FilmArray, an automated nested multiplex PCR system for
multi-pathogen detection: development and application to respiratory tract infection. PLoS One.
2011;6(10):e26047.
76.
Rand KH, Rampersaud H, Houck HJ. Comparison of two multiplex methods for detection of respiratory
viruses: FilmArray RP and xTAG RVP. J Clin Microbiol. Jul 2011;49(7):2449-2453.
77.
Pierce VM, Elkan M, Leet M, McGowan KL, Hodinka RL. Comparison of the Idaho Technology
FilmArray System to Real-Time PCR for Detection of Respiratory Pathogens in Children. J Clin
Microbiol. Feb 2011;50(2):364-371.
78.
Weinberg GA, Erdman DD, Edwards KM, et al. Superiority of reverse-transcription polymerase chain
reaction to conventional viral culture in the diagnosis of acute respiratory tract infections in children. J
Infect Dis. Feb 15 2004;189(4):706-710.
79.
Kincaid J, Fishburne R, Rogers R, Chissom B. Derivation of New Readability Formulas (Automated
Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy enlisted personnel.
Millington, TN: Naval Technical Training; 1975.
80.
Agency for Healthcare Research and Quality. Health Literacy Universal Precautions Toolkit, 2nd
Edition.  Available  at  http://www.ahrq.gov/professionals/quality-patient-safety/quality- 
resources/tools/literacy-toolkit/healthlittoolkit2.html. Accessed on January 19, 2016.
81.
Kharbanda EO, Stockwell MS, Fox HW, Andres R, Lara M, Rickert VI. Text message reminders to
promote human papillomavirus vaccination. Vaccine. Mar 21 2011;29(14):2537-2541.
82.
Guillemin F, Bombardier C, Beaton D. Cross-cultural adaptation of health-related quality of life
measures: literature review and proposed guidelines. J Clin Epidemiol. Dec 1993;46(12):1417-1432.
83.
Rubin DB. Multiple Imputation for Nonresponse in Surveys, New York: John Wiley & Sons, Inc. 1987.
84.
Little RJ, D'Agostino R, Cohen ML, et al. The prevention and treatment of missing data in clinical trials.
N Engl J Med. Oct 4 2012;367(14):1355-1360.
85.
Ware J, Harrington D, Hunter D, D'Agostino R. Missing Data. N Engl J Med. 2012;367:1353-135