# Are peak **expiratory flow**  **rates of asthmatics impaired APHA**  **2004 Annual**  **Meeting Session**  **#31 18.0**  **Paper #83634**  

# rates of asthmatics impaired **by levels of SO**  2 

# in the EPA **'safe' zone?** 

*****The project**  **was reviewed**  **and approved by the**  **University of Medicine and**  **Dentistry of New Jersey’**  **s Institutional**  **Review Board**  **for human subject research at**  **Robert Wo**  **od Johnson**  **Medical School**  **and New Jersey**  **Medical School**  **.**

# Authors Stanley H. We iss, MD 1,2 

## Azadeh **Ta sslimi, BA**  **1** 

## - **Pr esenter**  **(tassliaz@umdnj.edu)** Amy Davidow, PhD 1 

### Kathy Belby

, RN 1 

### Shahnaz Alimokhtari, MS 3 

### Clif ford P. W eisel, PhD 2,3 **(1) Dept.**  **of Pr**  **eventive Medicine and Community**  **Health, UMDNJ** 

**– New Jersey Medical**  **School, 30 Bergen St, Suite ADMC 1614,**  **Newark, NJ 07107, 973**  **- 972**  **- 4623,**  **(2) NJ School of Public Health,**  **(3) Envir** **onmental and Occupational**  **Health Sciences Institute,**  **UMDNJ**

**– EOHSI/R** 

**WJMS & NJ**  **School of Public Health, 170**  **Fr elinghuysen**  **Rd, EOHSI 3 -**  **314, Piscataway**  **, NJ**  **08855.**

# Purpose

**Determine if changes in peak flow expiratory flow rate (PEFR)** 

**among asthmatic school children**  **were related to air pollution from**  **sulfur dioxide (SO 2**  

## ).

# SETTING 3 Rural communities in Warren County, NJ (Northwestern NJ) 1. Belvidere 2. White Township 3. Harmony Township

# WA**RREN COUNTY**  **, NJ**

# School **Surveys**

**Fall 2002** A survey of asthma symptoms was conducted of all students with the cooperation of the public schools in the community. In conjunction with review with the school nurses and related f/u, these data were used to determine the actual pediatric and adolescent asthma rates by age, gender and other parameters. **Hierarchical estimates**  **of prevalence for**  **the 1641 students (K -**  **12) were**  : **“Physician -**  **diagnosed, Current Asthma”**  **11% “Undiagnosed**  **Current Respiratory Symptoms,**  **that can be consistent**  **with asthma”** 11%
- 18% **“Physician -**  **diagnosed Asthma,**  **no current symptoms”**  7%
- 11% **“No Evidence of Asthma”**  57%
- 71%

# PROSPECTIVE STUDY **FEBRUAR**

**Y**

**– JUNE 2003**  ELIGIBILITY

for ENROLLMENT

: All ‘ **Physician -**  **Diagnosed’ A**  **sthmatic** students enrolled in the public school systems of Belvidere, White To wnship and Harmony To wnships and in grades 5 through 12, who had asthma symptoms during the prior year or had prescription medicine for asthma

# WEB **- BASED**  **QUESTIONNAIRE** GOAL: Capture All Relevant Information & Minimize # Of Questions Asked In Order To Maximize Participation & Compliance Rate **COMPONENTS (SYMPT OMS**  **AND POTENTIAL MODIFIERS):**  1. General Health, 2. Prevalence & Severity Symptoms Related to Asthma & Resp Problems, 3. Measurement of Peak Expiratory Flow Rate 4. Medication Use, 5. Encounters w/ Potential Triggers of Asthma (i.e. smoke, animal fur, etc) **DESIGNED WITH PULL**  **- DOWN**  **MENUS SO THA**  **T IT** **COULD BE**  **COMPLETED ON A**  **DAIL Y**  **BASIS AND T** 

**AKE LESS THAN 10**  **MINUTES**

# AIR**- MONITORING**  **SITES AIR POLLUTION LEVELS**  **were collected continuously**  **at 3 MONITORING STATIONS**  **: SULFUR DIOXIDE (SO**  **2 )**  Emitted by Coal
- powered plants in the area Known respiratory irritant **PARTICULATE MATTER BELOW 2.5 microns (PM 2.5**  **)** Emitted by combustion processes & formed in atmosphere Suggested to be linked to cardiovascular & respiratory ailments **OTHER AIR POLLUTANTS (Volatile Organic Compounds**  **- VOCs)**  Data collected periodically on **70** VOCs, but limited number of measurements**STUDY**

**P AR**  **TICIP ANTS:**  51% (64/125) of asthmatic students (physician
- diagnosed), grades 5
- 12, were eligible and then gave informed consent to participate *Higher participation rate in the elementary and middle 

## PROSPECTIVE STUDY

school than in the high school. **RECRUITMENT I**  **NT O STUDY**  **O N ROLLING BASIS:** 
• Start
- January 30, 2003
• Last student accepted into the study
- May 6, 2003 All students were asked to continue to enter data until June 19, 2003. Thus, the potential **number of days of data entry varied**  **fr om**  **45 to 141 days**
.**TOTAL # OF STUDENT -**  **DAYS ON WHICH**  **SYMPTOMS, MEDICATIONS, AND PEFR**  **ENTERED =**  **4,777 TOTAL # OF STUDENT**  **- DAYS**  

# DA **TA C**  **OLLECTED**

**TOTAL # OF STUDENT -**  **DAYS ON WHICH**  **W/ SYMPTOMS & MEDICATIONS,**  **BUT NO PEFR ENTERED**  **= 824**  **TOTAL NUMBER OF**  **STUDENT -**  **DAYS ON WHICH SYMPTOMS &**  **MEDICATIONS WERE**  **ENTERED BY PARTICIPANTS**  **= 5,601** 

# Data **Exclusions**

• 3 subjects were dropped from analysis who had completed the web
- based survey on less than 14 days.

• Data Monitoring during the course of the study suggested that 1 subject had unreliable data; he was contacted during the course of the study and he could not resolve critical inconsistencies.
• Inconsistencies noted for other students were all satisfactorily resolved. This left **60 subjects for**

**analysis.**

# STATISTICAL **ANALYSIS** One monitoring site was at Belvidere HS. Initial statistical analysis was performed using Belvidere residents only, since SO2 was directly measured in their town. Examined PEFR

– Objective measure of respiratory function

– Continuous values, which offers analytic advantages Examined medication use

– ongoing/prophylactic/in response to problem Developed mixed model

# MIXED **MODEL**

• How much the change in peak flow rate is related to other changes (pollution, temperature, and other factors)

• Controls for each individual subject **This is an advantage because PEFR varies with a number of physiological variables, in particular, height, weight, age, & gender (1
- 6) **Outcome = Natural Logarithm (ln) of PEFR Method = Mixed Model w** **ith a Random**  **(Subject -**  **level) Effect**

# MIXED **MODEL Va**  **riables included in Stepwise**  **Assessments:**

• **Daily and daytime (6am**  **- 8pm**  **) -**  **maximum and mean: hourly & 5**  **- minute SO2** 
• **mean SO2 (linear**

**& non -**  **linear**

**terms)**
• **same day**

**, 1 -**  **and 2 -**  **day lagged SO2 same day**  **day lagged SO2**
• **peak and mean same**  **- day**  **, and mean lagged, PM2.5**
• **Above data ar e fr** **om each of the 3 monitoring stations** 
• **mean daytime** (7am - 6pm) **temperatur e**  • **age, gender**
• **same -**  **day medication use (contr oller)** 
• **pollen (tr ees, grass, w**  **eeds, mold)** 

# Best Fit for Model

• PEAK 5
- MINUTE DA YTIME (6AM
- 8PM) SO2 from Belvidere

• MEAN PRIOR DA

Y SO2 from Belvidere

• DA YTIME TEMPERA

TURE (7AM
- 6PM)**Same effect of SO 2**  **seen for White Tow nship students;** 

– **Consistent with the Belvidere monitoring**  **station being between a major source of SO2 & White Twp**  **No statistical association**  **for students in Harmony** 

## Model applied to White & Harmony **using Belvidere HS SO 2**  

## Data

**No statistical association**  **for students in Harmony Tow** **nship**

– **Harmony in a different**  **direction from the Belvidere**  **monitor**

– **other monitoring**  **site also not appropriate for exposure characterization**  **for Harmony residents Harmony Tw** **p residents w**  **ho attend Belvidere HS**  **were included in model** 

### FULL

**MODEL RESUL**  **TS DESCRIPTION**  **OF**

**STUDY P**  **AR TICIP**  **ANTS** HARMONY

TWP HS STUDENTS
- 4 WHITE TWP

STUDENTS
- 16 BEL VIDERE STUDENTS
- 27 **TO TA** **L IN FULL** 

**M ODEL**  **A NAL**  **YSIS**
- **47** FEMALES
- 28 (60%): MALES
- 19 (40%) Age Mean
- 13 yrs Range
- 9
- 19 yrs Median
- 13 yrs**SO 2**  **values in Belvidere: Ranged up to -**  **21** ppb for Daily **Mean** SO 2 On 16 ( **11** %) of 141 days, the daily mean SO2 was **> 10**  ppb **331** ppb for 5
- minute Daytime **Peak** SO 2 On 14 ( **10** %) of 141 days, the peak 5
- minute SO2 was > **100** ppb The effect on Peak flow on reaching these 2 levels of SO2 are displayed on the next slide...**Coefficient P**  **** **Va lue**  **% Reduction**  **in PEFR 5**  **- minute**  

## Results **of Final PROC Mixed**  **Model Wi** **th All 47 Subjects** 

**Daytime** (6am
- 8pm) **Maximum SO2** 
- 0.06932 0.0071 **- 0.69%**  **For a 100 ppb**  **incr ease**  **in SO2**
**Prior Day**  **Mean SO2**
- 1.1434 0.0004 **- 1.1%**  **For a 10 ppb**  **incr ease**  **in SO2**

### The total variability among the peak flow rates was not the same for all students; the study’s statistician examined the contribution of each of the 47 students to the model 

## Analyses **wer e conducted to determine**  **which subjects contributed particularly to the observed associations** 

to the model The statistical association between SO 2 

### levels and peak flow rates appeared to vary with individuals

– **2 students appear highly**  **sensitive to SO**  **2**



### – **33** students appear to be somewhat sensitive to SO 2



### – **12** students appear not to be sensitive to SO 2**Coefficient P**  **** **Va lue**  **% Reduction**  **in PEFR Ve**  **ry Sensitive**  **Subjects (n=2)**

**SO2 5 -**  **minute Daytime**  **Max**
- 0.3174 <0.0001 **- 3.1%**  **For a** **100 ppb incr**  **ease in SO2** 

**- 5.8%**  **For**

**a 10 ppb**  

## Post**- hoc sub**  **- gr** **oup Multivariate**  **analyses**

**SO2 Prior**

**Day Mean**
- 5.9878 <0.0001 **- 5.8%**  **For a** **10 ppb incr**  **ease in SO2**  **Somewhat Sensitive**  **Subjects (n=33)**

**SO2 5 -**  **minute Daytime**  **Max**
- 0.07286 0.0326 **- 0.73%**  **For a** **100 ppb incr**  **ease in SO2**  **SO2 Prior**

**Day Mean**
- 8.7320 0.0404 **- 0.87%**  **For a** **10 ppb incr**  **ease in SO2** **Student A** 

**– Bivariate Display of Data**  **Belvider e r**  **esident, grade 7, male**

**5-minute Daytime SO2 Max vs. ln of Peak Flow**

**Univariate Analysis**

**6.4 6.6**  **6.8 Prior Day SO2 Mean vs. ln of Peak Flow**  **Univariate Analysis**

**6.4 6.6**  **6.8**

**y = -0.1358x + 6.4426**  **R2 = 0.0067 at**  **SO2=0, ln(pf)=6.443 SO2=0.100ppm, ln(pf)=6.429**  **-1.4% in peak flow** 
**5.6 5.8**  **6 6.2**  **6.4**

**0 0.1**  **0.2 0.3**  **0.4**

**SO2 5-min day**

**time max (ppm) l** **n o** **f P** **F**

Subject A

Linear (Subject A) **y = -7.7717x + 6.4699**  **R2 = 0.165 at SO2=0, ln(pf)=6.470**  **at SO2=0.01ppm, ln(pf)=6.392 -7.5%** **in peak flow**
**5.6 5.8**  **6 6.2**  **6.4**

**0 0.005**  **0.01 0.015**  **0.02 0.025** 

**Prior Day**

**SO2 Mean (ppm) l** **n o** **f P** **F**

Subject A

Linear (Subject A)**Student B**

**– Bivariate Display of Data**  **White Tw** **p re**  **sident, grade 5, male**

**5-minute Daytime SO2 Max vs. ln of Peak Flow**

**Univariate Analysis**

**5.7 5.8**  **5.9 6**  **Prior Day SO2 Mean vs. ln of Peak Flow Univariate Analysis** 

**5.7 5.8**  **5.9 6** 

**y = -0.1866x + 5.8107**  **R2 = 0.0102 at SO2=0, ln(pf)=5.811**  **at SO2=0.100ppm, ln(pf)=5.792 -1.9%** **in peak flow**
**5 5.1**  **5.2 5.3**  **5.4 5.5**  **5.6 5.7** 

**0 0.1**  **0.2 0.3**  **0.4**

**SO2 5-Minute Day**

**time Max (ppm) l** **n o** **f P** **F**

Subject B

Linear (Subject B) **y = -10.379x + 5.8396**  **R2 = 0.2174 at SO2=0, ln(pf)=5.840**  **at SO2=0.01ppm, ln(pf)=5.736 -9.9%**  **in peak flow**
**5 5.1**  **5.2 5.3**  **5.4 5.5**  **5.6 5.7** 

**0 0.005**  **0.01 0.015**  **0.02 0.025** 

**Prior Day**

**SO2 Mean (ppm) l** **n o** **f P** **F**

Subject B

Linear (Subject B)

# Association between **Mean Prior -**  **Day SO**  **2** 

# and PEFR This model predicts overall for the 47 students, that for a 10 parts per billion (ppb) increase in prior
- day mean SO there is an increase in prior 2 

### there is an average decrement in PEFR of 1.1%. For the 2 most sensitive students, this effect was roughly 6 times greater (
- 5.8%), and this magnitude of effect could be of clinical significance.

## This model predicts overall for the 47 students, that for a 100 ppb increase in peak same
- day SO there is an average 

# Association **between Max**  **Same -**  **Day SO2 & PEFR** 
peak same
- day SO 2 

## there is an average decrement in PEFR of 0.69% For the 2 most Sensitive Students, this effect was nearly triple (
- 3.1%).

## CURRENT FEDERAL GUIDELINES

**SO 2**  

## Air Quality Standards A guideline has been proposed by the US Environmental Protection Agency (EPA), based on the conclusion that in some localized situations, **5 -**  **minute** SO2 concentrations above 1600 μg/m3 might pose a health threat to sensitive individuals. **[ Draft**  **Guideline Document for**  **Ambient Monitoring of 5** **- Minute**  **SO2 Concentrations**  **, EPA, Office**  **of Air Quality**  **Planning and Standards,**  **Research Triangle Park, NC, July 20, 2000;**  **see also http://airqap.com/monitoring/Benchmarks%**  **20for% 20SO2%**  **20&% 20**  **PM25.htm ].**  This corresponds to an average over the five minutes of 

### 615 ppb.

## CURRENT FEDERAL GUIDELINES

**SO 2**  

## Air Quality Standards **Most air pollution monitors measure**  **the amount of**  **pollution in the air on almost**  **a continuous basis. Current**  **guidelines assume that higher levels of pollution**  **can be tolerated without effect for short periods of time.**  **Thus, the National Ambient**  **Air Quality Standards have**  **an averaging time as well as a concentration**  **associated with them. Typically, the longer the**  **averaging time is, the lower is the level of the**  **standard. The averaging times**  **utilized in development**  **of standards represent simplifications**  **of a m ore**  **com plicated**  **statistic. Refer to 40 CFR Part 50 (in the Code of Federal Regulations) for**  **guidance on how the standard are required to be computed**  **for regulatory purposes.**

# SUMMARY Weobserved significant ef fects on peak flow rate at levels well below the federal 24 hour standard (mean daily value) for SO 2 of 140ppb, which had been set to deal with short term ef

fects in the overall population. Some asthmatics appear to be especially sensitive to SO 2 . Relatively low levels of SO 2 had a magnitude of ef fect on respiratory function that raise clinical concern. **Our data suggest that even mor**  **e transient exposur**  **e, to levels far**

**below t**  **his guideline, may pose a health thr**  **eat to sensitive individuals.** Further analyses to examine asthma symptoms are in progress.

# LIMITATIONS The grouping of which students were most sensitive to SO2 was determined through data analysis, not a priori. Thus, further follow
- up may be useful to prospectively assess whether the same students remain sensitive. However, if medication regimens change, this might affect sensitivity. Modeling to incorporate medication changes may be useful, but will be complicated.

# LIMITATIONS

• The statistical association does not rule out that the ef fect is due to an unmeasured factor that correlates with both SO 2 

### and peak flow
.

• The study was conducted in a single region, and thus cannot be assumed to necessarily generalize to other communities.

# RECOMMENDATIONS Given that some persons may be especially sensitive to SO 2 

## , the definition of what constitutes a “safe level” may need to be revised. Some asthmatics may need to be especially cognizant of SO 2 

## pollution in their region and may need to attempt to modify their exposure when levels are high.

# Acknowledgements **Supported in part by grants fr** **om Roche Vi** **tamin (DSM)**  **Inc thr ough the SEP**  **Mechanism of NJ Department**  **of Envir** **onmental Pr** **otection -**  **see also**  **http://dsmcap.com/ Students**  **& their**

**families who took time to make the entries**  **Special thanks ar**  **e due to Beecher**  **Zollinger**

**, MD**  **and the school nurses Jill Br oscius, RN,**  **Ly nn Serniuk, RN, Lois Hampshir**  **e, RN, and**  **Donna Golda,**  **RN.**

# Supplemental Slides

# USE OF WEB **- BASED**  **QUESTIONNAIRE**

# POTENTIAL ADVANTAGES **PAPER VERSION Administration**  **of daily**  **WEB BASED Web based system**  **accessed by daily**  **questionnaires can**  **be time and labor intensive,**  **paper needs periodic**  **retrieval accessed by**  **participate at his/her**  **convenience, entry**  **often faster, contact**  **with staff minimized** 

### PAPER VERSION **Compliance, particularly** **for children/adolescents,**  

### WEB BASED **Children/adolescents are on computers**  **daily so smaller**  

# POTENTIAL ADVANTAGES

**children/adolescents, is**  **often low for daily**  **questionnaires Compliance**  **rate unknown**  **until attempting to retrieve**  **data daily** **so smaller intrusion in their**  **lives Compliance**  **checked in real time** 

### PAPER VERSION **Paper questionnaires require subsequent**  **entry w**  **ith associated** 

### WEB BASED **Data is directly a**  **dded to data base, minimal**  **errors** 

# POTENTIAL ADVANTAGES

**entry w**  **ith associated transcription errors**  **errors Checks can be made to**  **restrict to valid entries**  **Data can be reviewed quickly** **s o participants**  **can be contacted for**  **questions**

# SURVEY **INSTRUMENT Section**  **1 (28 questions) Details on symptoms, triggers, location and peak**  **flow r** **ate Each includes**  **time of day & duration Section**  **2 Details on medication**  **taken Amount**  **and time of day Picklist and check box entries**  **Entry for current and previous tw**  **o day** **s permitted**  **Stop & restart after each major section possible** **The home page contains a login, r elevant links, important** 

**announcements, and information. These ar**  **e changed periodically .** 

# SYMPT **OM SECTION Tr** **acking of individual health and asthma**  **related sy mptoms**  **along with when problems**  **occurred Exposure to asthma triggers (smoke,**  **allergens, animal, etc) What microenvironments**  **visited Peak flow rates (up to 4 values)**  **Summary v**  **erification provided before**  **saving Internal checks**  **for general and specific sy**  **mptoms**

# MEDICAL **USE SECTION Comprehensive**  **medication tracking includes**  **time of intake and dosage A user’s medications are chosen from a**  **medication database**  **(contains over 200 medications, both**  **prescription and over the counter,**  **characterize by type).**  **Checks for medicine combinations and**  **dosage that are not medically advisable** 

# CONCLUSIONS
 

### Web Based Questionnaires **are feasible for daily entry from**  **adolescent and children (grades 4**  **– 12)**  **– data from 64 students**  **over 2 –**  **4 data from 64 students**  **months obtained**   

### Real time monitoring can help **improve compliance and quality**  **When combined with telephone/**  **e -**  **mail reminders and incentives**

## CONCLUSIONS

**(continued)**  

### Data entry errors are greatly reduced  

### Entry **can be more rapid than paper –**  **(only 5**  **– 10 minutes per day required)** 

**10 minutes per day required)**  

### Has potential for use in large **epidemiological studies for population**  **who typically access computer** 

# PILOT STUDY 10 students, grades 5
- 12, with physician
- diagnosed asthma, were enrolled in a Pilot Study to: 1) Ve rify that web site functioned correctly 2) Ve rify accuracy of Data Collection 3) Elicit feedback from students to improve Site for ease of entry 4) Field
- tested Baseline Questionnaire that ascertained the general health of the subjects and their medication; medication data were used to individualize the web questionnaire for each subject.

# Confirmation **of Validity of**

**Survey Responses**  All data were first reviewed to verify that no inconsistencies existed in the input that might have resulted from either inadvertent or intentional errors. Each subject’ s responses were plotted chronologically to examine trends in the data and for individual data points that dif fered from the previous and next days data.

# MIXED **MODEL Accounts for** 

**differ ent number** 

**of r epeat measur**  **ements made on each individual**  **Implements random effects in a statistical model**  **framework & permits modeling the covariance structur**  **e of the data** 

• It can compute ef

ficient estimates of fixed ef fects and valid errors estimates within the Generalized Linear Modeling (GLM) procedure.
• It is important to model the covariance structure for repeated measures data because measurements taken close in time are often highly correlated.
• A log transformation of the peak flow rate was done so that the dependent variable of the GLM was close to normally distributed.**Least Sensitive Subjects (n=12)**  **Coefficient P**  **- value**  **SO2 5 -**  **minute Daytime**  **Maximum 0.006307**  **NS** 

## Post**- hoc sub**  **- gr** **oup Multivariate**  **analyses**

**Maximum 0.006307**  **NS SO2 Prior** 

**Day Mean -**  **0.00048 NS** 

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