Letter to the Editor

Response to �Does filter media type really affect BRS?�

Dear Editor,This letter is in response to a letter to you from

Jeffrey Siegel and Jim Rosenthal (Does filter mediatype really affect BRS? Indoor Air 2009; 19: 346–347).Dr. Siegel and Mr. Rosenthal raise an importantquestion regarding the conclusions to our analysis ofthe Building Assessment Survey and Evaluation(BASE) Study data in Buchanan et al. 2008 (IndoorAir 18, 2: 144–155); is the composition of material usedin filter media causally related with increased preva-lence of building related symptoms (BRS) in the BASEStudy data? In this response we attempt address thepoint regarding the effects of filter efficiency raised inthe letter.

Background

We initially hypothesized that filter efficiency is asso-ciated with occupant symptoms. Our early analyses didnot support this hypothesis, however, a secondaryhypothesis that filter media type is associated withoccupant symptoms did reveal the results shared inBuchanan et al. In that paper we showed in broadunadjusted models that statistically significantincreased risks of a range of BRS (lower respiratory,cough, upper respiratory, dry eyes, fatigue and diffi-culty concentrating, and headache) existed in a set of91 BASE study buildings that used polyester orsynthetic (PS) fiber filter media relative to buildingsthat used exclusively other filter materials (fiberglass[FG], cotton, cellulose, and material blends). We thenshowed that similar, stronger and statistically signifi-cant, associations existed in models with more refineddefinitions of filter type and with adjustment forconfounding. In brief, we analyzed a subset of 34buildings from the BASE Study dataset to determinethe separate and joint associations of filter medium (PSor FG) and outdoor ozone concentration (above/belowthe median, 67.6 lg/m3) with BRS. Using logisticregression models and general estimating equations(GEE), we estimated odds ratios (ORs) and 95%confidence intervals for the association of filtermedium, ozone, and filter medium • ozone with BRS.The interesting finding was that the joint exposure toboth PS + high ozone,1 relative to FG + low ozone,

had significant associations with lower and upperrespiratory, cough, eye, fatigue, and headache BRS(ORs ranged from 2.26 to 5.90).

Additional methods

As discussed in the paper, adjustments for confoundingin the refined models included a range of filter-relatedvariables collected via inspection in the BASE studyincluding filter condition, filter fit, filter shape, and adichotomous variable for filter efficiency based on thefilter minimum efficiency reporting value (MERV). It isthe MERV rating scheme in the analyses that havebeen questioned in the letter we are responding to. Weregret not providing greater detail in the paper on howthe MERV values in our analyses were acquired, as thisinformation may have avoided unnecessary specula-tion. The letter is correct in its observation that �…ASHRAE Standard 52.2 was not published until 1999and the data in the BASE study was collected between1994 and 1998.� However, the speculation that �… sometype of conversion from average dust spot efficienciesto MERVs had to have been done to make the data fitthis variable� was not entirely correct. The fact is thatthe BASE Study protocol included visual assessment offilter parameters including collection of filter manufac-turer and model data. In 2002 when we started thisexercise, we were able to conduct an extensive review ofpre and post 1999 product literature and make contactwith many very helpful filter manufacturers. In manycases the filters identified in the BASE Study buildingswere still on the market after 1999.In the analysis for the paper we developed a

procedure to provide the best estimates of MERVequivalency to the filters in the database. The proce-dure used the following logic. Many filter productswere not removed from the market because of theadoption of the MERV rating. In these cases we wereable to acquire the manufacturers� published MERVrating for the particular filters of interest. Those filtersfor which no listed MERV value was available hadestimated values assigned based on best engineeringjudgment (conducted by a team of ventilation engi-neers) using published efficiency data by particle size,as well as ASHRAE efficiency rating of the filter,published arrestance, and initial or average dust spotefficiency, and information on the material and shapeof the filter.

1We define �high ozone� in this letter as in Buchanan et al. 2008, above the

BASE Study median, 67.6 lg/m3.

Indoor Air 2009; 19: 526–528www.blackwellpublishing.com/inaPrinted in Singapore. All rights reserved

� 2009 John Wiley & Sons A/S

INDOOR AIRdoi:10.1111/j.1600-0668.2009.00616.x

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Overall MERV ratings for the BASE buildings werethen built up from the individual filter type informa-tion based on proportional flow rates in each buildingper air handling unit and location of each AHU. Theanalysis combined MERV values for multiple filterswithin single air-handlers by applying sequential filterefficiencies to residual particle fractions. It combinedMERV values for multiple air handling units within atest space by flow-weighted average based upon theproportion of supply per unit and assumption oflinearity of MERV scale.To address the comments in the letter, we have gone

back to the BASE dataset to make detailed use of thefilter efficiency data discussed above. The distributionsof filter efficiencies for FG and PS filters werecompared in using Students t-test and were plottedfor visual comparison. We then created a four-levelcategorical variable MERVC: MERV £ 3 (coded �0�);3< MERV £ 7 (coded �1�); 7< MERV £ 11 (coded�2�); and 11< MERV £ 14 (coded �4�). Identicalmethods with logistic regression models with GeneralEstimating Equations, as presented in Buchanan et al.,were run with the exception that the binary MERVvariable was replaced with the four-level categoricalvariable MERVC. The analyses shown in Tables 5 and6 of Buchanan et al. were re-run with these changes.A second multivariate logistic regression analysis withGeneral Estimating Equations was conducted with thelarger sample size of the 91 building �any PS� vs. �no PS�dataset.

Results and discussion

The cumulative probability distribution of MERVvalues for both the FG and PS filters are shown inFigure 1a for the 91 buildings. These distributions arenot statistically different (pooled two-sided Studentst-test P = 0.61), and the �any PS� and �no PS� filterbuildings span a similar MERV range. Similarly,Figure 1b shows the media type distributions for the34 buildings. FG or PS MERV distributions were notstatistically different (P = 0.83). Note that in thisrestricted dataset the PS filters minimum is MERV 6.5while that of the FG filter is MERV 1.Drawing attention above to the similarity of the FG

and PS MERV distributions in the 91 buildings, and toTable 2 of Buchanan et al. 2008, the statisticallysignificant crude associations between filter type inthe �any PS vs. no PS� buildings and BRS, stronglyindicates that filter efficiency is not an importantdeterminant of symptoms in this data set.We applied the four-level filter MERVC variable

described above, having greater resolution than thebinary variable �MERV� used in Buchanan et al., to themultivariate logistic regression models as discussedabove. Table 1 shows the equivalent analysis to Table 5in Buchanan et al. using MERVC. Table 2 provides

the MERVC logistic regression model estimated ORsfor the relationship of ozone to BRS in PS buildingsand for the risk in high ozone areas to occupants in PSbuildings relative to occupants in FG buildings.Comparison of Tables 1 and 2 to Buchanan et al.

Tables 5 and 6 reveal general similarity. The PSBuilding risk relative to FG increased in the MERVCmodel in all cases except for headache, which droppedslightly, although its confidence interval tightenedconsiderably. An additional analysis was conductedwith the 91 building data to address the observedimbalance of minimum MERV values in the 34building dataset. We replicated the adjusted GEEregression models with the MERVC categories usingthe �any PS vs. no PS� data that has very similar MERVranges (Figure 1a). This analysis showed that the �anyPS� at high ozone conditions relative to �no PS� and lowozone had statistically significant ORs ranging from1.7 to 3.6 for individual and grouped lower and upperrespiratory BRS. Similarly, the risk from high ozonerelative to low ozone buildings in �any PS� buildings

(a)

(b)

Fig. 1 Cumulative probability distributions of building-levelfilter MERV estimates from the 91 building �any PS� vs. �no PS�dataset (a), and from the 34 buildings that had only FG or onlyPS filters (b)

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Letter to the Editor

had statistically significant ORs ranging from 1.4 to1.8; and the �any PS� building risk relative to �no PS� inhigh ozone locations had statistically significant ORsranging from 1.6 to 1.9 for these symptoms.The MERVC variable in both the 34 and 91 building

logistic regression models were found to be statisticallysignificant at the 95% confidence level for one analysis.

The overall ORs for filter efficiency ranged from 0.58(dry skin, the only statistically significant observation)to 0.97 (lower respiratory) in the 91 building models.The ORs ranged from 0.69 to 1.70 in the 34 buildingdataset. ORs less than unity imply a possibly protectiverelationship; higher filter MERV associated with lowersymptoms. This observation should not be given muchcredence as the associations were largely notsignificant.It appears that the classification of filter MERV

values in the model into four-levels has improved thespecificity of filter performance, decreasing overall biasin the analyses. Misclassification bias is known to shiftthe OR toward the null (Kleinbaum et al., 1982) – theincreasing OR supports the notion that the MERVvalues are better classified in the four-level models.Likewise, the effect of using the larger data set with theless defined filter material characteristics pushed theORs toward the null. However, in this case the largersample size increased the number of statisticallysignificant results.

Conclusions

We would like to thank Dr. Siegel and Mr. Rosenthalfor pointing out that we had poorly described the filterefficiency data used in our paper, and for inspiring thisextended analysis of the BASE filter data. Theirspeculation on filter efficiency effects do not hold upunder close scrutiny because filter efficiency wasadequately controlled for in the published analyses.Furthermore, by re-analyzing using better specificity inthe filter efficiency data we find that the observedeffects of the polyester/synthetic filter material may bestronger and more consistent that previously stated. Asdiscussed in Buchanan et al., the observations ofassociations in these analyses do not imply causalrelationships. Further research is underway in attemptsto identify physical mechanisms that could explain thestatistical relationships presented in this work.

M. G. ApteIndoor Environment Department, Lawrence Berkeley

National Laboratory, Berkeley, CA, USAE-mail: MGApte@lbl.gov

Table 2 Results from the four-level MERVC models from the 34-building subset, for therelationship of ozone to BRS in PS buildings and for the risk in high ozone areas (abovevs. below 67.6 lg/m3) to occupants in PS buildings relative to occupants in FG buildings

BRSOzone risk in PS buildingsORa (95% CI)

PS building risk (rel. to FG Building)in high ozone ORa (95% CI)

Lower respiratory 4.37 (1.48–12.9) 4.58 (2.16–9.70)Cough 1.01 (0.36–2.85) 2.25 (0.95–5.30)Upper respiratory 2.14 (1.19–3.87) 2.47 (1.59–3.84)Dry eyes 1.31 (0.62–2.76) 1.71 (1.17–2.51)Fatigue/Concent.b 1.27 (0.70–2.31) 1.76 (1.27–2.43)Dry skin 0.65 (0.24–1.71) 0.35 (0.17–0.72)Headache 2.17 (1.23–3.84) 2.04 (1.42–2.92)

aStatistically significant associations (P < 0.05) are highlighted in bold.bFatigue and/or difficulty concentrating.

Table 1 Adjusted interaction model estimates, from the 34-building subset, for theeffect of filter materials, ozone, and filter materials + ozone using the four-level MERVCvariable

Occupants in: BRSFG buildingsORa (95% CI)

PS buildingsORa (95% CI)

Low O3c Lower respiratory 1.00 1.41 (0.58–3.41)

Cough 1.00 2.27 (0.71–7.22)Upper respiratory 1.00 1.10 (0.51–2.39)Dry eyes 1.00 1.62 (0.65–4.01)Fatigue/Concent.b 1.00 2.03 (1.09–3.77)Dry skin 1.00 0.69 (0.21–2.34)Headache 1.00 1.19 (0.68–2.05)

High O3d Lower respiratory 1.34 (0.53–3.39) 6.15 (2.08–18.2)

Cough 1.01 (0.40–2.55) 2.28 (0.85–6.15)Upper respiratory 0.96 (0.60–1.54) 2.37 (1.20–4.65)Dry eyes 1.24 (0.72–2.11) 2.12 (1.12–4.01)Fatigue/Concent.b 1.47 (0.94–2.29) 2.58 (1.46–4.57)Dry skin 1.28 (0.56–2.93) 0.45 (0.17–1.18)Headache 1.27 (0.72–2.22) 2.58 (1.30–5.10)

aAll odds ratios are relative to fiberglass and low ozone. Statistically significant asso-ciations (P < 0.05) are highlighted in bold.bFatigue and/or difficulty concentrating.c<67.6 lg/m3.d‡67.6 lg/m3.

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Letter to the Editor