I, OVERVIEW OF THE STUDY

1.1 Background’

On August 10, 1990, Congress passed the Fire Safe Cigarette Act of 1990. The act authorized the
U.S. Consumer Product Safety Commission (CPSC) to conduct research and assess the feasibility of
developing a performance standard to reduce cigarette ignition propensity. As stated in the act, cigarette-
ignited fires caused 1,492 deaths in 1987, 3,809 serious injuries, and $395 million in property damage.

The act requires the CPSC to conduct several research projects, including a study of fires started by
cigarettes. A fire incident study was planned to identify the kinds of cigarettes, the characteristics of the
smokers, and the other factors involved in such fires. The data will help the CPSC determine the
relationship between various characteristics of cigarettes and smokers and the risk of fire.

In October 1987, CPSC staff, with the help of the International Association of Fire Chiefs (AFC),
conducted a pilot fire-incident study (Feasibility Study of Obtaining Field Data on Cigarette-Ignited Fires)
to determine the relative risk of different cigarette types. Nine fire departments in cities throughout the
United States participated. Data were collected over a period of two months and demonstrated the
feasibility of a full-scale effort. This pilot study did not include collection for a comparison or control
group.

The National Fire Protection Association (NFPA), under contract with the CPSC, has undertaken
the current fire-incident study. It has been collecting data on actual incidents of cigarette-related fires
at eight sites. The collection began in November 1991, and was completed in December 1992.
Personnel of participating fire departments were trained to collect the information in person at the scene
of a fire. The smoker or smokers thought to be responsible for igniting the fire were interviewed. If
a person was incapacitated because of injuries, a family member was interviewed as a proxy respondent.

The number of completed interviews for this part of the study was 564.

‘Excerpts taken from the OMB package submitted in April 1992.

A-1


Ill. DESIGN OF THE REINTERVIEW STUDY

The reinterview sample comprised 600 cases selected from households with three or fewer smokers.
First, 200 households were selected where the initial respondent was a non-smoker. Households were
selected with probability proportional to the number of smokers, and one smoker was randomly picked
for reinterview within each household. Thus, each smoker in the original sample of cases for which a
non-smoker proxy provided the data has an equal overall probability of selection for the reinterview
sample.

Next, a sample of 200 households was selected from the group where a smoker was the original
respondent. Selection was proportional to the total number of smokers minus one. During interviewing
a smoker was randomly selected who was not the original respondent.

Finally, a sample of 200 additional households was selected where the smoker was the initial
respondent for the household. For this sample, the initial respondent was reinterviewed.

This approach produced 294 completed reinterviews, with 97 that were originally nonsmoker proxy
interviews, 95 that were smoker proxy interviews, and 102 that were originally self reports.

The reinterview study was restricted to those households with three or fewer smokers in order to
reduce the difficulty of identifying the original respondent, since the names of individuals were not
collected as part of the original survey. This restricted set comprised 95 percent of the households in the
original study. Only one respondent was interviewed in any household at the reinterview. The person
to be interviewed was identified by the original reporting status and by demographic information such
as age, sex, and education. If there was any question as to whether the respondent was the person
originally inte:viewed, the case was replaced. Similarly, if a respondent refused, no attempt was made
to convert the refusal for the reinterview. Close to 100 interviews were completed in each of the three
respondent groups. Because of the decision-rules, twice as many cases were randomly assigned as were

ultimately thought to be needed.

B-13


Table 5
Continuous Characteristics in Categorical Form (Continued)

E. Column Length (mm) Non-fire Fire
Number _ Percent Number _— Percent
Under 60 353 219 & 12.1
60-69 938 58.2 362 64.2
Over 69 820 19.9 134 23.8
Total 1,611 100.0 564 100.0
F. Filter Length (mm) Non-fire Fire
Number Percent Number Percent
No filter 49 3.0 59 10.5
18-25 698 43.3 320 56.7
Over 25 864 53.6 185 32.8
Total 1,611 100.0 564 100.0
G. Total Length (mm) Non-fire Fire
Number _— Percent Number _— Percent
Under 80 148 9.2 6 10.6
80-84 869 53.9 356 63.1
85-99 383 23.8 7 17.2
Over 99 211 13.1 51 9.0
Total 1,611 100.0 564 100.0
HL Amount of Tobacco (mg) Non-fire Fire
Number __ Percent Number _— Percent
Under 600 2 1.4 7 12
600-699 390 24.2 67 119
700-799 75 48.1 322 57.1
800-899 383 23.8 129 22.9
900-999 nu 0.7 2 0.4
Over 999 2 18 37 66
Total 1,611 100.0 564 100.0
L Smoker Age (years) Non-fire Fire
Number _— Percent Number _— Percent
Under 35 736 45.7 222, 39.4
35-64 766 A475 269 47.7
Over 64 109 6.8 23 12.9
Total 1,611 100.0 564 100.0


Fire Safe Cigarette Act of 1990

Under the Cigarette Safety Act of 1984 (P.L. 98-567), the Technical Study Group on Cigarette and Little
Cigar Fire Safety (TSG) found that it is technically feasible and may be commercially feasible to develop
a cigarette that will have a significantly reduced propensity to ignite furniture and mattresses. Further-
more, they found that the overall impact of such a cigarette on other aspects of the United States
society and economy may be minimal.

Recognizing that cigarette-ignited fires continue to be the leading cause of fire deaths in the United
States, the Fire Safe Cigarette Act of 1990 (P.L. 101-352) was passed by the 101st Congress and signed
into law on August 10, 1990. The Act deemed it appropriate for the U.S. Consumer Product Safety

Commission to complete the research recommended by the TSG and provide, by August 10, 1993, an

assessment of the practicality of a cigarette fire safety performance standard.

Three particular tasks were assigned to the National Institute of Standards and Technology's Building
and Fire Research Laboratory:

+ develop a standard test method to determine cigarette ignition propensity,

* compile performance data for cigarettes using the standard test method, and

* conduct laboratory studies on and computer modeling of ignition physics to develop valid,
user-friendly predictive capability.

Three tasks were assigned to the Consumer Product Safety Commission:

+ design and implement a study to collect baseline and follow-up data about the characteristics of
cigarettes, products ignited, and smokers involved in fires,

+ develop information on societal costs of cigarette-ignited fires, and

* in consultation with the Secretary of Health and Human Services, develop information on changes
in the toxicity of smoke and resultant health effects from cigarette prototypes.

The Act also established a Technical Advisory Group to advise and work with the two agencies.
This report is one of six describing the research performed and the results obtained. Copies of

these reports may be obtained from the U.S. Consumer Product Safety Commission,
Washington, DC 20207.


TABLE 11.3

SMOKER BEHAVIOR AND CIGARETTE CHARACTERISTICS CONTROLLING
FOR SEX, RACE AND HOME OWNERSHIP

Non-Smoker 2
Self Smoker Proxy Proxy Total Sample x p=
Controlling for Sex
Smokes 20+ Per Day
Male 53.4 61.3 47.1 54.1 11.98 0.002
Female 46.7 45.3 41.7 45.5 1.44 0.488
Filtered ,
Male 92.8 95.4 91.6 93.2 3.67 0.159
Female 96.7 98.7 96.1 97.1 3.05 0.218
Length
Male
Regular/King 69.3 W17 83.6 73.6 21.7 0.000
Long 27.5 25.8 16.0 24.2
Extra Long 3.1 2.5 0.4 2.2
Female
Regular/King 53.3 55.5 66.1 56.1 9.84 0.043
Long 40.4 39.2 30.6 38.4
Extra Long 6.3 5.3 3.3 5.6

B-9



contains two or more listed households.) Telephone numbers generated by the procedure are checked
against a list of working 100-banks. Those in nonworking banks are not included in the sample. This
procedure improves survey efficiency by reducing the number of calls made to nonworking telephone

numbers.

3.5 Stratification and Allocation of the Sample

Stratifying telephone exchanges by income is not straightforward, because income estimates are
available for geographically defined areas-counties, zip codes, and census tracts-that do not correspond
to the areas covered by telephone exchanges. Further, the stratification cannot be consistent across sites
because telephone companies use different criteria for assigning telephone exchanges. We defined 17
sampling strata for the survey. Within each service area, the sample was stratified by median household
income, estimated for each telephone exchange. The initial plan was to define two strata per site, with
the low-income stratum defined as those telephone exchanges with median incomes below the 20th
percentile of median household income for all exchanges in the site. At some of the sites we found that
the exchanges defined as low-income by this criterion contained substantially less than 20% of a site’s
total adult population. Using strata which contain too few households would not be effective in achieving
our goal of increasing the representation of low-income households, so it was decided to increase the cut-
off point to make the low income strata larger. Conversely, raising the cut-off too high would "dilute"
the low income stratum, again making it more difficult to increase the representation of low income
households. Thus, a maximum median income of $21,000 was set as a cut-off for inclusion in the low
income stratum, The revised cut-off points are shown in Table III.1. The Dallas service area was
divided into three income strata because after dividing into two strata, the distribution of median
household income for the exchanges in the low income stratum suggested that this stratum contained two

distinct subgroups, a very low income subgroup, and a low income subgroup.


TABLE IV.2

FINAL STATUS OF HOUSEHOLDS ORIGINALLY SAMPLED--TOTALS

Total Households
Number Percent _Callls _
Eligible
Complete 1,503 12.9 2.9
Complete--no address 29 0.2 5.8
Final refusal 65 0.6 6.1
Subtotal 1,597 13.7 3.1
Ineligible Residence
No smoker > 12 4,618 39.7 3.0
Subtotal 4,618 39.7 3.0
Eligibility Unknown.
Language barrier 24 0.2 3.8
Final refusal 385 3.3 3.4
Maximum dialings 567 4.9 20.0
Effort ended 91 0.8 12.3
Other 1 0.0 18.0
Subtotal 1,068 9.2 13.7
Nonresidence
Nonworking/new number 2,317 19.9 1.5
Not a residence 2,036 17.5 2.3
Other 3 0.0 2.7
Subtotal 4,356 37.4 1.9
Total 11,639 100.0 3.6

A-21



several categories that should be excluded from the analysis. These were (1)
households definitely outside the fire service area, (2) households where the tract
could not be determined or could only be imputed from zip codes, and (3)
households that had cigarette fires (9 smokers). The first three were all groups
that might not or did not fall in the boundaries of the fire service districts of
interest. After excluding these four categories, the smoker survey file contained
data from 1,130 households and 1,611 smokers.

Questionnaires Used and Resulting Data Elements

The questionnaire used to collect data from cigarette fires was designed in
such a way that the data could be coded directly from the forms without the need
for coding sheets, There were two versions of the questionnaire used in the course
of the data collection phase of the study.

The education question originally had three choices -- completed high
school, did not complete high school and undetermined. The choice added was
"some college or more." The income question originally had four choices -- under
$10,000/year, $10,001 to $20,000/year, over $20,000/year and undetermined. The
revised form expanded on the third option. The choices for over $20,000 per year
became $20,000 to $29,999/year, $30,000 to $39,999/year and $40,000 or more. The
smoker survey used even more categories for income and education and more
categories for race. In the end, the analysis needed all useable cases, so for
analysis purposes, these added options were folded down to the choices on the
original fire incident questionnaire.

The other continuous-variable smoker characteristic was the age of the
smoker, which was measured in years and used in its continuous form.

Both NFPA and Mathematica collected data ‘on census tract and used that
data to assign proxy income and education variables to each smoker. In each
case, the proxy variable was a characteristic of the tract (e.g., median household
income, percent of population below poverty line). This was done as a backup in
case the rate of non-reporting of income or education was so great that a sufficient,
sample size could only by achieved through the use of proxy variables.

Thanks to the excellent efforts of the eight participating fire departments,
however, this backup proved unnecessary. There were complete data for 439 fire
cases and 1,281 non-fire smoker cases. This was still within the target sample
size for both groups. In addition, early analyses showed, as expected, that the
proxy variables produced much weaker statistical models.

9


TABLE IV,2

NUMBER OF DAYS ELAPSING BETWEEN ORIGINAL INTERVIEW
AND REINTERVIEW BY TYPE

Mean Median

Reinterview Sample as Whole 66.7 80
Original Self-Reporter 68.0 82
Original Smoker Proxy 66.5 78
Original Nonsmoker Proxy 65.7 80
Cases Brand Code Matched 68.4 82
Cases Brand Code Mismatched 63.5 45

B-18


For other measures, the percentage of mismatches was highest for income (45.4 percent) and lowest
for age within 2 years (10.3 percent). For income and number of smokers in the household the highest
degree of mismatch was for non-smoker proxies.

The mismatch on income category for smoker proxies is substantially higher at 64.6 percent than
the 35.1 percent for the self-reports and 36.7 for nonsmoker proxies. This sizeable difference suggests
that the three groups may differ on other personal characteristics which may be associated with knowledge
of household income. For example, more self-reporters were women and survey experience indicates
that more women answer the telephone. If this is the case, perhaps more male head of households were
smoker proxies (although complete information is not available from the data set). Other studies indicate
that more adult females answer “don’t know" to household income questions and that when the answer
is given it is often different from that reported by the male adult "head" of household. However, this
is only one possible explanation for the high level of mismatch in the smoker proxy group for the income
question.

Although the degree of mismatch was quite high for the income question the overall reliability was
similar for the two versions of the income question (seen in Table IV.6) (43.1 percent for version 1
overall compared to 47.7 percent for version 2). Table IV.6 shows a larger discrepancy between the two
versions within each of the three respondent groups than overall, however, ranging from a 15 percentage
point difference when the original respondent was a smoker proxy, to a 9-11 percentage point difference
for the other groups. The differences within subgroups are not large enough to be statistically significant
due to small sample sizes.

Finally, the degree of mismatch on age was highest where the original respondent was a non-smoking
proxy, but the difference between this group and the self respondents in percent mismatched is not large

enough to be statistically significant at even the 10 percent level.

B-24


TABLE IIL.4

HOUSEHOLDS INSIDE/OUTSIDE FIRE SERVICE AREA (FSA)

Household in Household Not Tract Not
Stratum FSA Tract® in FSA’ Determined Total

Baltimore

Low-Income 28 0 1 29

Remainder 121 5 2 128
Cleveland

Low-Income 47 8 2 57

Remainder 62 19 3 84
Columbus

Low-Income 21 1 0 22

Remainder 115 24 6 145
Dallas

Very Low-Income 9 0 1) 9

Low-Income 20 0 0 20

Remainder 151 35 10 196
Denver

Low-Income 22 0 1 23

Remainder 61 18 5 84
Houston

Low-Income 58 1 0 59

Remainder 219 30 9 258
Philadelphia

Low-Income 72 1 4 71

Remainder 216 4 14 234
Portland

Low-Income 9 1 0 10

Remainder 90 3 4 97
Total 1,321 150 61 1,532

* The tract information was imputed on the basis of zip code for 83 of the 1,321 households in this
column.

> The tract information was imputed on the basis of zip code for 12 of the 150 households in this column.



Primary Concise Model of Cigarette Fire Risk

The principal or primary model for the analysis was then run. Called
Model 2, it is identical to Model 1, except that its cigarette characteristics were
limited to those found significant in Model 1, i.e., filter, filter length, pack, and
porosity. See Table 8, which also includes more of the relevant statistics for each
characteristic than were shown in Table 7. Note that Model 2 also includes
smoker characteristics not found significant, because the objective of the analysis
is to control for smoker characteristic effects.

Table 8 includes Beta parameter estimates, standard errors, Wald chi-
squares,!0 and p values for each variable. It also includes estimated adjusted
odds ratios and 95% confidence intervals around the odds ratios.

The odds ratio is exp (Beta) for a categorical variable like pack. For
example, the odds ratio for pack is exp (.6189) = 1.857. This means that after
adjusting for the effects of other cigarette characteristics, smoker characteristics,
and city in the model, the odds of a cigarette fire increase by 85.7% if the cigarette
was from a soft pack rather than a hard pack.

For a continuous variable like porosity, the odds ratio is exp (c beta) where c
is the unit of change.!! For example, the odds ratio for porosity is exp (.0127) =
1.013. This means that for an increase of 1 CORESTA unit of porosity, the odds of
a cigarette fire increase 1.3%. However, a unit of change will not have the same
meaning for all variables. A more meaningful unit of change for a continuous
variable like porosity might be the standard deviation, which is 13.8. If this is
used as the unit of change, then the odds ratio for porosity is 1.192. This means
that after adjusting for all the other variables in the model, the odds of having a
cigarette fire increase 19.2% for every increase of 18.8 CORESTA units (one
standard deviation) in porosity.

Two of the significant cigarette characteristics involve the filter - (presence
of) filter and filter length. Therefore, it is necessary to consider them together. In

10The Wald chi-square is calculated by dividing the beta estimate by the standard
error and squaring the result. Any p value equal to or less than .05 is considered
significant.

11David W. Hosmer and Stanley Lemeshow, Applied Logistic Regression, New
York, N.Y.: John Wiley and Sons, 1989.


Education * Percentage with high school education in tract
Education * Filtered cigarette

Education * Number of cigarettes (amount)

Education * Porosity

Education * Citrate

Density * Filtered cigarette
Density * Circumference
Density * Porosity

Density * Citrate

Porosity * Filtered cigarette
Porosity * Circumference
Porosity * Citrate

Citrate * Filtered cigarette
Citrate * Circumference

Age * Amount of tobacco

Race * Amount of tobacco
Gender * Amount of tobacco
Income * Amount of tobacco
Education * Amount of tobacco
Density * Amount of tobacco
Porosity * Amount of tobacco
Citrate * Amount of tobacco

A-47


Conclusions

1, After controlling for all smoker characteristics and city, logistic
regression modeling showed four cigarette characteristics to be significant - filter,
filter length, porosity, and pack type. This means there are already commercially
available cigarettes that exhibit reduced ignition propensity when one controls for
smoker characteristics.

2. Filter, filter length and porosity all affect air intake, which therefore
appears to be an important physical element in the combustion process associated
with risk.

3. Analysis limited to only filtered cigarettes showed the same
characteristics to be significant, plus tobacco column length.

4. Extension of the analysis to two-way interaction terms did not change
any of the conclusions on which cigarette characteristics are important but did
indicate that the role of pack type was different for men vs. women.


The brand list used for this survey was developed under the direction of the CPSC’s Division of
Epidemiology. The list is part of the CATI instrument. The three-digit brand ‘code includes the
manufacturer code as the first digit. Three hundred brands were included in the list and "other, specify"

answer choices were also utilized when an exact brand was not specified.

A6


were then combined in one model with all of the variables from Model 2 (see Table
10).

The strength of the two interaction terms involving pairs of smoker
characteristics are of limited interest, once it is clear that they do not eliminate
any cigarette characteristics from significance. Instead, the interaction term of
greatest interest is pack x gender. Note that with this term present, both of the
primary terms, pack and gender, cease to be significant.

Referring back to the general logistic regression model equations, it may be
seen that, in calculating an odds ratios for two characteristics, all other
characteristics are held constant. In the case of pack and gender, then, the odds
ratios can be calculated based on exp (.0558 pack - .8722 gender + .9931 pack x
gender), using the Beta parameters from Table 10. For females, this expression
goes from 1.00 to 1.06 when switching from a hard pack to a soft pack, indicating a
slight 6% increase in the odds of having a cigarette fire (which was found not to be
statistically significant). For males, however, this expression goes from 0.69 to
1.97, for an increase of 185% the odds of having a cigarette fire if the cigarette is
from a soft pack rather than a hard pack.

As noted earlier, pack type is an unusual variable. If the higher risk
associated with a soft pack is a reflection of the added vulnerability of cigarettes in
such packs to crushing, bending, or other impact-related modifications, then it
may be plausible that these effects would be greater for males, carrying packs in
their pockets, than for females, carrying packs in their purses. This remains
speculation, however.


© response rate for smoker households = .60

On the basis of these assumptions, we estimated that the number of phone numbers needed to
produce interviews with 1,500 smoker households would be 1,500/(.55*.575*.6) ~ 8000. The working
household telephone rate and response rate were conservative estimates based on experience with previous
telephone surveys. The smoking rate was provided by Abt.

The observed working rate for RDD telephone numbers and the achieved response rate were higher
than the rates used to determine the maximum number of sample telephone numbers needed. However,
the rate for incidence of smoking was less than half the rate estimated by Abt. We found that an average
of 3.9 households had to be screened in order to find one household in which anyone smoked.
Consequently, the study required more sample telephone numbers than was originally expected. In all,

11,639 sample telephone numbers were used.’

3.4 Random Digit Dialing Methodology

The random digit dialing (RDD) sample for this study was created using a single-stage systematic
selection procedure designed to produce a sample that has equal probability of selection within strata.”
The selection procedure is "list-assisted" in the sense that it identifies "working 100-banks" of telephone
numbers, from which the sample numbers are selected. (A 100-bank is a set of 100 telephone numbers

associated with the eight digits beginning a phone number. A 100-bank is identified as "working" if it

'The observed rates were:

© working household telephone number rate = .603
© smoking prevalence rate = 1/3.89 = .257
© response rate for smokers = .833

The sampling procedure was designed to ensure (within strata) an equal and known probability of
selection for all residential telephone numbers. However, there was no guarantee that each and every
telephone household had an equal probability of being selected, because roughly 7% of telephone
households were served by two or more telephone numbers. As a practical matter it is difficult to
measure the number of lines becasue respondents confuse the number of lines with the number of outlets.

A-9




TABLE IV.1

PERCENTAGE OF CASES WHERE REINTERVIEW RESPONSES DO NOT MATCH ORIGINAL RESPONSE,
BY TYPE OF RESPONDENT TO ORIGINAL INTERVIEW

Percentage (%) Mismatch and Number of Cases
Original Respondent
1 2 3
Variable Self ‘Smoker Proxy Nonsmoker Proxy Total Sample ¢ Df p= Significant Contrasts*
1. # Smokers in HH iver 28.4 144 17.9 11.07 2 0.004 1-2, 2-3
Sample Size 99 95 97 291
2. Brand Code 25.5 34.4 35.7 32.0 3.04 2 0.218 None
Sample Size 98 93 90 | 281
3. Length of Cigarette Ta 19.6 23.3 16.4 9.94 2 0.007 1-2, 1-3
(Regular/Long/Extra Long
Sample Size 98 92 90 280
| 4, Fihered or Not 0.0 0.0 0.0 0.0 NA NA | NA i None
i
Sample Size 98 95 90 283
5. Pack Type (soft or hard) 14 10.0 us 128 9.22 2 0.010 | 1-3, 23
Sample Size 95 90 88 273
T
6. Mentholated or Not 41 9.5 6.1 68 2.22 2 | 0330 None
Sample Size 98 95 87 280
7. Amount Smoked Per Day 13.1 22.6 25.3 20.0 4.76 2 i 0.092 13
(Whether more than a pack) |
Sample Size 99 93 83 215 |
'
8. Annual HH Income 35.1 64.4 36.7 | 45.4 6.33 2 | 0.042 1-2, 2.3
Gn $10,000 iniervals) | i
Sample Size i 7 B 68 218 i
9. Age Within 2 Years 82 18 15.2 10.3 93.68 2 0.159 NA
Sample Size | 98 93 92 283

Includes only cases where a valid response (other than don’t know) was provided on both surveys.

‘Comparisons where the between group difference is significant at the 5 percent level. Contrast 1-2 is self va. smoker proxy, 2-3 is smoker proxy vs. non-smoker proxy, 1-3 is self va. non-smoker proxy.

B-16


Table A-3

Logistic Regression
Comparison of Results
Based on Two Sample Designs
Random Sample _— Cluster Sample
Standard Standard
Error Error

Intercept 4306 44
Filter 4856 50
Filter length 0164 02
Porosity 0046 01
Pack .1749 .18
Gender .1304 .13
Age 0045 00
Race 1413 15
Education 1458 15
Incomel 1627 17
Income2 -1599 17
cl .2246 24
2 2423 .26
3 .3802 38
4 3333 34
c5 .2321 23
c6 .2106 21
c7 -2535 .29

As noted in the text, the sample of smokers (non-fire) was based on a clustered
sample. Smokers were not picked independently, but rather as part of a larger
unit known as a cluster (in this case household). To determine the effect of
clustering, two logistic regression runs were made: (1) where the sample
selected was assumed to be chosen by a simple random sample and (2) where
the sample selected was based on a clustered sample design and household is
the cluster. The difference between the two sample design assumptions
should be reflected in the standard errors.

The results for the cluster sample design were based on SUDAAN, a software
statistical program developed by Research Triangle Institute, which performs
logistic regression on complex sample designs.


VI. ADDITION OF CENSUS AND OTHER DATA
6.1 Census Tracts

Census tracts were identified for all but 61 interviewed households. The file layout shows a "source
of census tract" code in column 149. There were three sources for the census tract.

If the randomly generated sample telephone number belonged to a listed telephone household, then
a census tract was added to the sample record on the basis of the address published with the listed
telephone number. These cases are identified by a 1 in column 149.

When the telephone number did not belong to a listed telephone household, the address supplied by
the respondent in the interview was used to identify a census tract. (The respondent-supplied address is
found in columns 68-148). This method used computer matching of the respondent address with a file
containing street names, and house number ranges from each ZIP code and census tract. Census tracts
identified through this method are denoted by a 2 in column 149."

In cases where the computer match failed, a census tract was imputed exclusively on the basis of the
ZIP code provided by the respondent. This imputation added the census tract associated with the ZIP

code’s center of population. A value of 3 in column 149 indicates that the tract was imputed.”

"In 95 cases, the household address could not be matched with a census tract. Failure to match may
have occurred for several reasons: The respondent may have misreported the street name or house
number (perhaps thinking that deliberate misreporting would ensure confidentiality), or this information
may have been misrecorded by the interviewer. Respondents who refused to give an address were asked
to provide an intersection near their house. Some of these
"intersections" were found to be nonexistent.

The census tracts imputed on the basis of ZIP code should not be relied on with the same confidence
as the census tracts obtained using the other methods. An urban ZIP code area can contain many census
tracts (we estimate an average of nine tracts per ZIP code), so values of 3 in column 149 should be
interpreted as a warning that the census tract is only an approximation. Values of 1 and 2 indicate that
the census tract can be relied on to be correct.

A-29



with different physical characteristics. Furthermore, risk-related cigarette
characteristics are often correlated with each other, risk-related smoker
characteristics are often correlated with each other, and it is likely that some risk-
related cigarette characteristics are correlated with risk-related smoker
characteristics through differences in the brand preferences of different smoker
groups.

A cigarette fire is the result of three elements, each of which is necessary
for fire to occur and each of which may be characterized by greater or lesser
degrees of susceptibility. First is the contact between heat source (cigarette) and
potential fuel source. Second is the susceptibility of the fuel source to ignition,
when so exposed. Third is the propensity of the cigarette to ignite fires when put
in this position.

Contact between cigarette and potential fuel source usually is a result of
carelessness, and people differ in the degree of their carelessness. Therefore,
smokers will have different probabilities of discarding cigarettes in place where
ignition is possible, and cigarettes may have different probabilities of being
discarded.

Potential fuel sources (e.g., upholstered furniture) may be more or less
susceptible to ignition by a given intensity and time of heat exposure, for example,
as a function of design or material composition. This translates into different
probabilities of ignition, given exposure, for different substitutable items (e.g., two
different couches), and this in turn translates into different probabilities of
ignition, given exposure for different smokers, based on their relative likelihood of
owning one or the other type of item.

Finally, cigarettes may differ in their propensities to ignite fires, given
exposure to a specified potential fuel source. The probability of ignition, given
exposure, may then be a function of cigarette characteristics that make it more or
less likely that the cigarette will burn with a more or less intense heat or for
longer or shorter periods of time after discard.

Smoker characteristics such as age, gender, poverty, and education are
used to analyze this complex interaction of three types of elements, It is not
asserted that poverty directly causes fires, but poverty may be associated, for
example, with a higher likelihood of excess use of alcohol, which could lead to a
higher degree of carelessness in handling cigarettes. Less education does not
directly cause fires but may be correlated with a reduced awareness of or


Volume 4. Cigarette Fire Incident Study

Table of Contents

Cigarette Fire Incident Study (Overall Analysis)
Executive Summary........ ccc ccc cence cc cccccccses
General Overview. .... 2... cece ccc c cece cece en eneee
Data Collection and Set-up for Analysis..........
Preliminary Analysis...... 2... cccewcccccccnccccece
General Logistic Regression Model.........cseeeee
Complete Model of Cigarette Fire Risk............
Concise Model of Fire Risk...........ccseeceeneee
Conclusions. ... 2... cece e ccc cce cnc cc cece ccccescees

BX 0) 01-11.

Cigarette Consumer Survey......... ccc cc cece cc cccccccs

Self-Proxy Comparisons for the Consumer Survey........

i
1
8
11
13
23
26
33
34

Bl


This resulted in an expanded sample of 506 cases for the fire group, and
1,540 for the non-fire group, and it reduced the percentage of missing cases to just
5.9%. Model 1 with all ten cigarette characteristics was run with this data set.
The same four cigarette characteristics again were found significant: filter, filter
length, pack, and porosity. It is also worth noting that the Beta coefficients for the
four cigarette characteristics changed very little from the original sample. (See
Table A-5.)

Table A-6 displays a set of ratios directly from the data, showing the
number of fires divided by the number of non-fire smokers, according to a three-
way cross-tabulation by filter length, column length, and income. (Filter length
was consistently one of the significant cigarette characteristics, column length
was significant when only filtered cigarettes were studied, and income was the
strongest smoker characteristic.) This display shows that even with the data
divided into this many categories, the relationships between fire risk and certain
characteristics are strong enough to show up in simple-to-read displays.
Although these tabulations do not control for the effects of other smoker and
cigarette characteristics, they agree with the logistic regression analysis that does
control for other smoker and cigarette characteristics.

Table A-7 shows a similar display for the pack type and gender variables.
As in the discussion of the interaction term on pack and gender, this simpler, less
controlled analysis shows a high odds ratio for soft vs. hard packs for males
smokers (2.53) and a smaller odds ratio for soft vs. hard packs for female smokers
(1.79). Again, no other smoker or cigarette characteristics are controlled for in
these calculations.

Other Patterns of Fire Cases

There were 23 civilian deaths and 57 civilian injuries in the 564 fire cases.
Two of the deaths occurred in one incident, and 20 of the civilian injuries occurred
in multiple-injury incidents - five incidents with two injuries, two incidents with
three injuries, and one incident with four injuries.

Tables A-8 and A-9 display the patterns by form and type of material first
ignited. Most fires began with the ignition of mattresses or bedding, upholstered
furniture, or trash.


were supplied by CACI Marketing Systems. It provided census statistics for each tract in each fire-
service area (see section IV.B). We used this list of tracts for the five areas where we had no existing
tract inclusion list.

On the basis of this comparison, we were able to create a flag record (FSA, for "fire-service area”).
The FSA flag has values of 1, 2, or 3, where 1 indicates the record pertains to a household in a tract that
is entirely or partly inside the fire-service area,* 2 indicates the household was in a tract outside the fire-
service area, and 3 indicates that we are unable to tell whether or not the household was inside the fire-
service area. Records are marked 3 if we have no tract for the household.

Table III.4 shows the results of an analysis of how often interviewed households were outside the
fire-service areas. As expected, more interviews with households outside the fire-service areas occurred
in cities for which we had overestimated the population. In such cases, our definition of the fire-service
area included telephone exchanges serving a significant number of households outside the area. Overall,
9.8% of the interviewed households were outside the associated fire-service area. Census tracts could
not be determined for an additional 4.0 percent of the households interviewed.

Two caveats to the data user are in order. First, the FSA flag indicates where the interviewed
household is in a tract that was completely or partially included in a fire-service area. This is not the
same as saying that the FSA flag identifies households in the fire-service area, because a household in
a tract that is only partly in the fire-service area may or may not be in the fire-service area. These
“border tracts" (shown in Table III.5) exist in Columbus, Cleveland and Houston. Second, the analysis
resulting in Table III.4 included households for which the tract information was imputed solely on the
basis of zip code (see Section VI.A). Data users wanting to analyze data only for households that are
known to be inside the fire-service areas should exclude all households in border tracts or with imputed

tracts.

3In all but three sites - Cleveland, Columbus and Houston - all tracts are entirely within the fire
service areas. In Cleveland, Columbus and Houston, some tracts are partly inside.

A-IS


where HHyoy is the number of households known not to have smokers. HHRATE is the same rate used
in the overall response rate, discussed earlier,

For the entire sample, the overall response rate was 87.17%. The response rate for smokers was
83.30%. Tables IV.3 and IV.4 summarize the response rates by site, by income strata, and for all sites
combined, respectively.

Neither the overall response rate nor the response rate for smokers varied greatly between fire
service areas. (The lowest rates were in Philadelphia.) The overall response rate ranged from 85.69%

to 89.05%, and the response rate for smokers ranged from 80.64% to 87.18%.

A-24


Cigarette Fire Incident study

4

PREPARED FOR THE U.S. Consumer § nie :

Propucr Sarety Commission

BY THE NATIONAL FIRE PROTECTION

oe ae

ASSOCIATION AND MaTHEmatica

Pouicy Researcn, Inc.

Aucust 1993





cigarette characteristics (porosity, density, etc.) are when analyzed for these cases by length. In the worst
cases, it affects fewer than 16.4 percent because some provided a UPC code which more accurately
matches the data in any case.

The differences between the original responses and reinterview responses appear to differ randomly
and are not systematically biased toward a particular response. This was assessed by crosstabulating the
original survey responses for reinterview sample members with the reinterview response. The marginal
distributions, presented in Table [V.4, are very similar for the two sources of data, and examination of
the off-diagonal elements of the crosstabulation shows that the mismatches are very evenly distributed
with the reinterview responses being equally likely to be shorter or longer than the original responses.
Furthermore, this pattern occurs for all three groups of original respondent types. Thus, while the
proportion of mismatches is higher for the both groups of proxy respondents, the overall distribution does
not appear to have been affected by the differences.

The other cigarette characteristic for which the proportion of mismatches was significantly greater
for proxies than for self-respondents was pack type (soft or hard). Again, the overall distribution is quite
similar for the original survey response and the reinterview with the smokers themselves (Table IV.5),
but the original respondents were slightly more likely to indicate soft pack than were the reinterview
respondents. Examination of the original survey-reinterview crosstabulation for each of the three
respondent groups separately shows that this pattern occurs for all three groups, including the group of
original self respondents. While the proportion of mismatches is clearly lower for the self-respondents
(7.4 percent) than for the two proxy groups (especially the non-smoker proxy group, at 21.6 percent),
the pattern of a higher reported use of hard packs in the original interview than in the reinterview exists
for all groups. Thus, the difference may be due more to the passage of time than an indication that non-

smoker proxies at the original interview gave frequent incorrect responses.

B-21




Table

IL1

11.2

IL3

Iv.1

IV.2

IV.3

IV.4

IV.5

1V.6

IV.7

IV.8

TABLES

Page
ORIGINAL SURVEY SMOKER BEHAVIOR AND CIGARETTE
CHARACTERISTICS .. 0.0... ec cee eee B46
SMOKER BEHAVIOR AND CIGARETTE CHARACTERISTICS
BY SEX, HISPANIC, RACE AND HOME OWNERSHIP ............ B-8
SMOKER BEHAVIOR AND CIGARETTE CHARACTERISTICS
CONTROLLING FOR SEX, RACE AND HOME OWNERSHIP ...... B-10
PERCENTAGE OF CASES WHERE REINTERVIEW RESPONSES
DO NOT MATCH ORIGINAL RESPONSE, BY TYPE
OF RESPONDENT TO ORIGINAL INTERVIEW ..............0- B-16
NUMBER OF DAYS ELAPSING BETWEEN ORIGINAL,
INTERVIEW AND REINTERVIEW BY TYPE ................. B-18
FREQUENCY OF BRAND CHANGE BY TYPE OF REPORT
AND BRAND CODE MATCH STATUS ............ 000000005 B-20
A COMPARISON OF THE DISTRIBUTION ON LENGTH OF
CIGARETTE FOR THE ORIGINAL AND REINTERVIEW
SAMPLE ..... 0.0... cee ccc eee teenies B-22
A COMPARISON OF THE DISTRIBUTIONS OF PACK TYPE
FOR THE ORIGINAL AND REINTERVIEW SAMPLE ............ B-23
NON-MATCH RESPONSES TO INCOME QUESTION BY
VERSION, OVERALL AND BY TYPE OF ORIGINAL
RESPONDENT .... 2... ccc cee ccc eee ene B-25
MISSING DATA BY TYPE OF ORIGINAL RESPONDENT ......... B-27

NON-MATCH RESPONSES TO INCOME QUESTION BY
VERSION, OVERALL AND BY TYPE OF ORIGINAL
RESPONDENT ..... 0... 2c cece ee eee B-29


Forward selection is a variable selection procedure by which variables are
inserted one at a time into the model until a satisfactory regression equation is
found. In stepwise regression, after each insertion of a variable, there is testing
performed to see if any previously inserted variable can now be discarded due to
relationships that exist among the variables currently in the model.

Results were similar, and the same four characteristics were found
significant: filter, filter length, pack, and porosity.

Also numerous different combinations of characteristics were run, and
results indicated that these four characteristics were the ones most often were
found to be significant.


(1) _ Ifthe survey UPC matched a CCD UPC and the survey key matched a CCD key then,
if the brand codes were the same, the UPC match was used. Otherwise the key match
was used.

(2) If the survey UPC matched a CCD UPC but there was not match on keys, the UPC
match was used.

(3) __ If there was no match on UPC codes but there was a match on keys, the key match was
used.

(4) If there was one missing element (other than brand code) in the survey key and there
was only one match with the CCD when this element was excluded, the corresponding
CCD key was used,

(5) If there were no missing elements in the survey key and that key matched only one CCD
key when only one element was excluded, the corresponding CCD key was used.

(6 ) Ifa match as in the previous two items resulted in more than one potential CCD key
match but the characteristics were identical across all the potential matches then one of
the potential matches was arbitrarily selected.

Table VI.1 depicts the status of the cases in the sample when matched to the manufacturer’s data.

A-31


TABLE IV.4

RESPONSE RATES BY INCOME STRATUM

RR nocers

RRovert
SRATE

HHRATE
Smoker Households

Low Income All Exchanges
Exchanges Other Exchanges Combined
84.62 83.01 83.30
87.88 87.02 87.17
31.19 24.61 25.69
46.41 64.09 60.32

306 1,226 1,532

A-26


to the probability of careless disposal of a cigarette, but these are examples of
characteristics that could not be reliably measured through field interviews.
Similarly, there may be unidentified cigarette characteristics that, if known,
would prove to be strongly linked to fire risk.

The possibility of important hidden smoker characteristics is a concern
only if these characteristics have a strong link to fire risk that is also very
different in form from the correlation to fire risk shown by the smoker
characteristics in the model. To take the example cited, drug and alcohol use are
known to be correlated with such characteristics as age, gender, and race - all
variables included in the model.5 The included smoker variables therefore may
gain some of their apparent statistical strength by acting as proxies for variables
like drug or alcohol use that are not explicitly included. This effect is not a
problem because the analysis is concerned only with the strength of cigarette
characteristics after controlling for smoker characteristics, not with the relative
importance of individual smoker characteristics.

However, it is also possible that the hidden smoker characteristics could
add to the apparent statistical strength of some of the cigarette characteristics.
This theoretical possibility could occur only if a cigarette characteristic were
strongly correlated with a hidden smoker characteristic and was not strongly
correlated with any of the included smoker characteristics. This possibility is
remote, and no patterns in the analysis or data from other sources have been put
forward to indicate this is the case.

A more likely possibility is that one or more hidden cigarette characteristics
could be adding to the apparent statistical strength of the included cigarette
characteristics. For example, the measured citrates are probably not the only
additives used. The possibility that hidden cigarette characteristics may be
significant bears on the interpretation of the results of the analysis. If the
analysis shows that cigarette characteristics are significant factors in fire risk
after controlling for smoker characteristics, then it follows that cigarette redesign
can lead to reduced fire risk, but the best approach to that redesign may not
involve exclusive, or even primary, concentration on the particular cigarette
characteristics found significant. Instead, these results may be more useful as

5See, for example, Table 198 and the general population tables in Statistical
Abstract of the United States: 1992, 112th edition, Washington: Bureau of the
Census, 1992.




TABLE VIL.1

DISTRIBUTION OF HOUSEHOLDS IN SAMPLE AND POPULATION BY STRATUM

Stratum

Percent of Sample Households
in Stratum

Estimated Percent
of Population

Baltimore Low-Income
Baltimore Remainder
Cleveland Low-Income
Cleveland Remainder
Columbus Low-Income
Columbus Remainder
Dallas Very Low-Income
Dallas Low-Income
Dallas Remainder
Denver Low-Income
Denver Remainder
Houston Low-Income
Houston Remainder
Philadelphia Low-Income
Philadelphia Remainder
Portland Low-Income
Portland Remainder

Total

1.10
7.59
2.64
4.46
1.28
9.88
0.33
1.66
13,72
1.04
6.64
3.69
19.54
4.10
15.40
0.58
6.34
100.00

1.16
7.45
2.82
4.99
1.30
9.70
0.42
1.83
13.46
1.15
6.59
3.74
19.15
3.94
15.18
0.61
6.51
__100.00

Households in Stratum

A-38


TABLE 111.5

TRACTS PARTIALLY INSIDE FIRE SERVICE AREAS

Cleveland Columbus: Houston

1051 3.1 74.24 88.22 au 253 446.01 545.12
1052 12 49 88.25 212 254 448 545.32
1061 19 75.2 92.1 222.01 263 449.2 547.98
1231 25.1 75.31 93.61 222.02 264 450 548.98
1232 26 75.32 93.62 223.01 273 451.32 549
1244 32 75.33 93.71 224.01 322.01 452.01 550
1371 43 75.34 93.73 224.02 334 452.12 552
1413 44 75.4 93.74 224.03 336 452.22 555.01
1922 AS 75.5 93.81 226.01 341 529.01 555.12
St 76 93.86 226.02 354 530.01 556.01
62.2 TA 93.9 228.01 359.11 530.02 559.01
62.3 77.21 94.1 228.02 361 530.03 701.03
63.1 77.22 94.2 229 362 531.01 71.14
63.21 71.3 94.3 230.01 370.1 $31.02 701.15
63.3 714 94.5 230.03 370.2 531.03 701.24
63.4 WAL 94.9 230.04 371.02 532.01 701.25
63.53 78.12 95.2 232 37. 532.02 701.33
63.6 78.3 95.9 233 371.21 533.01 703.12
63.7 79.3 97.4 234 372 533.02 703.13
63.81 79.4 97.5 235 373.03 533.03 703.22
63.82 79.5 98 236 373.04 534.01 705
63.91 81.2 237 373.11 534.02 901.03
63.92 813 238 373.21 535.1 901.22
64.1 81.4 240.02 416.05 $35.2 902.02

67.1 81.6 241.01 417.02 536.02

68.21 82.1 241.02 433.3 537.22

69.31 82.4 242 434.02 538.11

69.41 82.91 243 436.13 538.12

69.44 83.11 244.01 436.23 539

69.45 83.12 244.22 437.11 $40.01

69.5 83.22 245.12 437.12 $40.01

69.9 83.3 245.22 437.22 $40.22

mA 83.4 247.2 437.32 541.2

T1A2 83.5 248 438.06 542.02

NAS 83.6 249.03 438.21 $42.11

12 82.7 249.22 438.31 542,97

nr 83.8 249.32 440.06 543

Bo 83.91 250 441.02 544

74.1 85 251 444.04 $45.01

A-I7


Table 8
Model 2 — Only Significant Cigarette

Characteristics from Model 1
Wald 95%
Standard Chi- Odds Confidence
Beta Error square p Ratio Interval

Intercept - 2.8921 4489 28.4 .000
Filter! 1.2204 4850 6.3 011 *
Filter length! - .0748 0163 20.8 .000 928 (.899, .958)
Porosity 0127 .0045 8.0 004 1.013 (1.004, 1.022)
Pack 6189 .1750 12.5 000 1.857 (1.817, 2.617)
Gender A241 13802 10.6 001 1.528 (1.184, 1.972)
Age 0052 0045 13 249 1.005 (.996,1.014)
Race - 2752 1411 3.8 052 159 (575, 1.001)
Education 4321 1455 8.8 003 1.540 = (1.158, 2.048)
Incomel 1.8034 1624 123.3 000 6.070 (4.415, 8.345)
Income2 1.1998 1596 56.5 000 3.319 (2.427, 4.539)
cl 0880 2245 0.2 691
c2 -.2499 2419 11 302
c3 - 1.8740 .3800 24.3 000
4 - 9326 3332 78 005
cS - 5816 2318 6.3 012
c6 -.1910 2102 0.8 364
c7 - 1167 2533 0.2 645

Model chi-square** = 362.7 with 17 degrees of freedom p= .000

Note that the logistic model was based on 439 cases from the fire group and 1,281
from the non-fire group that had complete data (no missing data).

1Filter is reflected in these two variables: filter (whether the cigarette is filtered,
and filter length (if the cigarette is filtered, how long is the filter), note in our
sample the minimum value for filter length is 18 when a filter is present.

The Wald chi-square is calculated by dividing the beta estimate by the standard
error and squaring the result. Any p value equal to or less than .05 is considered
significant. -

The odds ratio is exp (Beta) for a categorical variable like pack. For a continuous
variable like porosity the odds ratio is exp (c Beta) where c is the unit of change
and in the above table is assumed to be 1, though in the text other options are
discussed.

*See discussion in text. Odds ratio for filter is only meaningful in combination
with filter length.

**Model chi-square is the difference between -2 log likelihood for all the
parameters in the model and -2 log likelihood with only the intercept in the model.

x


Table A-6

Ratio of Fires to No Fires
According to Column Length, Filter Length, and Income
Income

Cigarette Under $10,000 - At Least
Characteristic $10,000 19,999 $20,000
Total 1.00 66 14

Filter length

0 mm 191 1.50 59

18 - 25 mm 1.18 65 .20

26 - 35 mm -70 38 06
Column length under 60 mm AB 53 08

Filter length

0 mm - - -

18 - 25 mm A5 1.00 22

26 - 35 mm 46 45 06
Column length 60-69 mm 1.10 57 15

Filter length

0 mm 2.00 3.50 40

18-25 mm 1.26 62 48

26 - 35 mm 52 -30 07
Column length over 69 mm 1.17 50 17

Filter length

0 mm 1.88 1.10 86

18 - 25 mm - - -

26 - 35 mm 1.02 39 14


Among the measures of smoking behavior and cigarette characteristics, differences were found in
the amount smoked, whether the smoker uses filtered cigarettes, and the length of the cigarette as noted
in Table 11.1). Smokers for whom a smoker proxy provided the data are more likely than self-reporters
to consume a pack or more a day, but those for whom a non-smoker proxy provided the information are
less likely to smoke this much. The explanation for this most likely relates to characteristics of those
falling into each group.

Regarding whether filter cigarettes are smoked, and the length of the cigarette, the pattern is more
expected. In both cases, the two types of proxy responses (smoker and non-smoker) differ from self-
reports in the same direction, with the difference being larger for non-smoker proxy responses. The
differences for length of cigarette are quite large (77.8 percent of non-smoker proxies reporting regular
or king, compared to 60.7 percent of self reports,) suggesting that this detail is too subtle for many non-
smokers to report on accurately. The differences for type of pack and whether the cigarette is menthol
were small and not statistically significant.

An analysis was then performed to examine whether differences in reports of smoking behavior and
cigarette characteristics could be explained by differences in the types of households or smokers that were
reported on.

The first step was to examine differences in smoking behavior and cigarette characteristics by sex
and race. The results are presented in Table I1.2. Substantial differences in length of cigarettes are found
by sex and whether Hispanic. Filtered cigarettes were reported differentially by sex, and to a smaller
extent, by race and homeowner status. The number of cigarettes reported smoked differed by sex of
smoker, race and whether Hispanic, with differences of 8 to 28 percentage points observed.

The next step was to see if the differences in reports by respondent type remained when personal
and household characteristics were controlled. Because race and whether Hispanic overlap, the two
categories were combined to include all Hispanics, and three groups of non-Hispanics: White, Black and

other. The results of the analysis are shown in Table II.3. When gender is controlled for, the



Contract No.: CPSC-C-92-1001, Task Orders 003 and 005
MPR Reference No.: 8071

CIGARETTE FIRE SAFETY SURVEY

FINAL REPORT
OMB #3041-0110
January, 1993 °
Authors:
Donna Eisenhower
Peter Forbes
John Hall
Submitted to: Submitted by:
U.S. Consumer Product Safety Commission Mathematica Policy Research, Inc.
5401 Westbard Avenue P.O. Box 2393
Bethesda, Maryland 20816 Princeton, N.J. 08543-2393
(609) 799-3535
Project Officer: Project Director:

William Zamula Donna Eisenhower


TABLE IV.7 (continued)

Self Smoker Proxy Non-Smoker Proxy ¢
(a = 102) (a = 95) (a- 98) Total p=
Mentholated
Data Missing From
Both 1.0 0.0 1.0 07
Reinterview 29 0.0 1.0 14
Original 0.0 0.0 9.2 3.1 22.97 0.001
Amount Smoked
Data Missing From
Both 0.0 0.0 1.0 03
Reinterview 29 0.0 2.0 1.7
Original 0.0 21 12.2 48 22.62 0.001
Income
Data Missing From
Both 108 14 6.1 8.1
Reinterview 78 1A 98 98
Original 98 8.4 10.2 8.1 6.00 0.424
Age of Smoker
Data Missing From
Both 0.0 0.0 0.0
Reinterview 29 0.0 1.0
Original 1.0 2A 5.1 6.66 0.155

NOTE: — The X statistic reported in the fifth column is for a test of whether the distributions of the responses for the three types of respondents differ by more than might be expected due to normal
sampling variability, if the three samples had each been drawn from the same population. The p value in the last column gives the probability of observing a dispersion as large as that
which is actualiy observed if the sampies had been drawn from the same population.

B-28


Table A-7

Ratio of Fires to No Fires
According to Pack and Gender
Gender
Pack Type Male __ ———Female__
Fire NoFire Ratio Fire NoFire Ratio
Hard 45 231 19 B23 191 18
Soft 302 615 A9 183 573 32


TABLE IV.1

DISTRIBUTION OF SMOKERS IN THE SAMPLE BY TYPE OF INTERVIEW

Cumulative Cumulative
Type Frequency Percent Frequency Percent
Self-reported 1,128 51.1 1,128 51.1
Smoker proxy 577 26.2 1,705 77.3
Nonsmoker proxy 501 22.7 2,206 ___100.0

A-20


6.2 Census Data

Three pieces of tract-level 1990 census data were added to each record in the final version of the
survey file. They were: (1) median household income for the tract; (2) the percentage of the population
aged 25 and above who had completed at least a high school diploma; and (3) percentage of persons in
the tract below the poverty level. These data were obtained for each tract in the eight cities. The quality

of the data was verified against paper copies of the same data. No errors or omissions were found.

6.3 Other Merged Data

The survey data for a given case in the sample was matched to the cigarette characteristic data
provided by cigarette manufacturers. The cigarette characteristic data included information on the
following: density, porosity, citrate, and circumference (from which the amount of tobacco could also
be calculated). The data from the two sources were matched on a seven-digit code called a "key code."

The seven digits of this code are as follows:

Ist digit Manufacturer

2nd and 3rd digits Brand code

4th digit Length of cigarette
5th digit Filter or not

6th digit Soft or hard pack
7th digit Mentholated or not

Of the 2,206 smokers in the original sample for the comparison group, 1,969 were matched to data
provided by cigarette manufacturers.
The following rules applied for matching and merging the two data sets (the cigarette characteristic

data provided by the manufacturers will be referred to as CCD):

A-30


Table A-1

Logistic Regression
Model 1 Without Columbus
Beta p
Intercept 10.4679 71
Filter 1.9849 .001
Filter length - 0899 .000
Porosity 0111 015
Pack 6039 001
Density - 0319 -193
Menthol - 0211 898
Amount of tobacco 0055 457
Circumference - .2999 564
Citrate -.1715 341
Column length - 0333 700
Gender 4034 .003
Age 0047 313
Race ~ 2592 .087
Education 4331 004
Incomel 1.8016 000
Income2 1.2308 000
cl .1419 541
c2 - .2001 415
c4 - 9154 .006
05 - 5683 015
c6 -.1251 .560
e7 - 0783 759

Model chi-square* = 323.7 with 22 degrees of freedom pp = .000

Note that the logistic model was based on 429 cases from the fire group and
1,142 from the non-fire group that had complete data (no missing data).

The p value is based on a Wald chi-square statistic for the Beta estimate. Any p
value equal to or less than .05 is considered significant.

*Model chi-square is the difference between -2 log likelihood for all the
parameters in the model and -2 log likelihood with only the intercept in the
model,


difference, one would expect more accurate and reliable data with the least amount of time elapsing
between interviews. Because there is in fact less reliability with the least amount of elapsed time one
might conclude that the amount of elapsed time between interviews does not explain the relatively high
level of mismatch on brand code for the sample as a whole.

As part of the reinterview, respondents were asked how frequently they changed the brand of
cigarette they smoked. Table IV.3 presents this data by type of original report and match or mismatch
on brand code. While the number of cases in the most frequent categories are smaller, there is a pattern
for the most frequent brand changers to have a greater percentage of mismatch than those who seldomly
or never change their brand. This is as expected--if a person frequently changed their brand they would
be less likely to recall what brand they were smoking two months or more before the interview. Also,
while respondents were asked to report the usual brand they smoked, some respondents said they had no
“usual” brand. In those cases, they were asked to report the brand they smoked most often and if that
was not possible the brand they smoked closest to the interview. Individuals who had no usual brand may
have reported accurately at the time of the interview but could not remember accurately at a later time.
(Recall that respondents were asked to think back and report as of the date of the original interview.)
Nonetheless, even among self respondents who say they never change brands, 20 percent gave a different
brand at reinterview than they did in the initial interview.

Differences in a proxy’s ability to report on the length of cigarette someone else smoked is somewhat
understandable. This question provided three answer choices requiring a finer distinction of (1) regular
or kings (2) long or deluxe and (3) extra long. While most of the others have two answers indicating the
presence or absence of a characteristic. This information may be to refined for some of those proxy
reporters reporting for others in a household. The degree to which this fact affects the use of the data

for the 16.4 percent having a mismatch depends on how different the

B-19


This empirical study evaluates the reliability and degree of missing information for self and proxy
reports of cigarette-related information. The study is based on comparisons of original responses given
by proxies for a smoker to subsequently obtained responses from the actual smoker. Original self-reports
are compared to self-reports in reinterview of the same person as a measure of reliability. This difference
in test-retest reliability can than be factored out of proxy-self report comparisons to draw some
conclusions about the validity of proxy responses. The real issue is whether self-reports provide any
higher quality information than proxy reports when problems of reliability that exist even for the self-
reported data are factored out.

The remainder of this report is organized as follows:

© preliminary comparisons of the self-reported and proxy-reported data from the original

survey

e the design of the reinterview study

© the report of the findings based on the reinterview data

© final conclusions


difference in number of ‘cigarettes smoked per day remains for male smokers but not for women.
Controlling for gender substantially reduces the difference in the proportion reported to be smoking filter
cigarettes; however, substantial differences in length of cigarette remain for both men and women.

When controlling for race, the difference in reports for amount smoked is greater than average
among Blacks, and Hispanics and lower among Whites. The pattern of the smoker proxies being more
likely than self or non-smoker proxies to report consumption of 20 or more cigarettes a day holds for all
the racial groups.

As in the case of controlling for sex, when race is controlled for, differences in reports of smoking
filtered cigarettes are greatly reduced. Differences in reports of cigarette length are reduced for Blacks;
for Hispanics, the overall pattern changes from non-smoker proxies being most likely to report regular
or king size length, to non-smoking proxies being most likely.

Controlling for home ownership reduces the differences on smoking filtered cigarettes, but has little
effect on the other two measures.

These comparisons (the usual type of assessment of the validity of proxy responses) suggest that
there are sizeable differences between the data reported for smokers who responded to the survey
themselves and the data reported for smokers by proxy respondents. Whether these differences are due
to reporting error by proxies or to differences between the individuals who responded themselves and
those whose information was supplied by a proxy cannot be ascertained from these comparisons.
However, differences between the two groups of smokers on basic demographic factors do not appear
to account for the differences in the responses. The next section presents a direct assessment of the

correspondence between proxy and self reports for the same individuals.

B-12


Among the measures of smoking behavior and cigarette characteristics, the most notable differences
across respondent groups are in the percent of mismatches on brand code, length of cigarette, pack type
and amount smoked. For each of these variables the difference between the group with the highest
mismatch and that with the lowest is 10 percentage points or more. However, only the differences for
length and pack type were statistically significant at the 5 percent level. While the observed mismatch
on these two variables was highest for the non-smoker proxies, the only large (significant at the 5 percent
level) difference between the two proxy groups was for pack type.

The results must be evaluated in light of the degree of mismatch between the self-reports at the
original and reinterview since that is as accurate as proxy responses can be expected to get. The degree
of mismatch for the individuals who originally supplied data on themselves (self-respondents) is
surprisingly high for some variables. For example, the self-mismatch for brand code is 25.5%, lower
than the degree of mismatch for the two proxy groups (34.4 and 35.1 percent, respectively) but higher
than what one might expect. Because brand code is perhaps the most essential cigarette characteristic
collected, two factors will be examined to explain the degree of mismatch, namely:

e the difference in elapsed time between the original interview and the reinterview for the

matches and mismatches

e the frequency of brand change cases as reported at the reinterview for matches and

mismatches
Table IV.2 presents the mean number of days which elapsed between the original interview and the
reinterview by type of case. The mean number of days which elapsed between the interview and the
reinterview for the sample as a whole was 66.7; 68 for the original self reporter; 66.5 for the smoker
proxy; and 65.7 for the nonsmoker proxy. The range and distributions for elapsed time were also similar
about the same. The nonsmoker proxy had more mismatches on the whole and slightly less time elapsed
between the original interview and reinterview. Similarly, those cases where the brand mismatched had

the least number of elapsed days (63.5) between interviews. While there is some

B-17



Cigarette Fire tncident Study

Beatrice Harwood
Terry L. Kissinger

U.S. Consumer Product Safety Commission

J. Karter, Jr., Alison L. Mi

Rita F. Fahy, John R. Hall, Jr.

Donna Elsenhower, Peter Forbe:

John Hall,

Mathematica Policy Research, Inc.

August 1993



U.S. Department of Commerce
National Institute of Standards and Technology
Gaithersburg, MD 20899

Official Business
Penalty for Private Use $300




that served a fire-service area. Before performing the tract/exchange analysis, we identified the census
tracts that comprise the city, because census tracts do not always match city boundaries. In Cleveland
the match was not problematic. However, in Houston and Columbus many tracts were both inside and
outside the city. We had to decide what minimum percentage of the tract had to be inside the city before
we would classify it as being inside the city. In the case of Houston, we decided to use tracts that were
25% or more inside the city. For Columbus, we used tracts that were at least 50% inside the city. The
tract inclusion criterion was stronger for Columbus than Houston because Columbus tracts matched city
boundaries poorly, and we wanted to reduce calls to households outside the city.

Creating a list of telephone numbers for households inside a specified urban area is inherently
difficult, because telephone exchanges do not perfectly overlap with city or census tract boundaries. The
best possible frame would be a list of telephone numbers for all telephone households in the service areas
and would exclude all others. Because that frame was not available, we had to decide which exchanges
to include to achieve a high degree of coverage of households inside a service area while minimizing
coverage of households outside it. In order to do that, some exchanges with listed numbers inside the
service area were excluded from the frame. But few of the numbers in the excluded exchanges were
within the service area, which was covered well by the remaining exchanges. In each service area, 95%

or more of the listed households were on the frame of included exchanges.

3.3 Sample Size
The sampling design called for collecting data for all smokers age 12 years and older in 1,500
households. The survey design allowed for proxy reports by any adult about all smokers in the
household. The assumptions Abt Associates used to determine the number of telephone numbers needed
to produce interviews with 1,500 "smoker households" were:
proportion of possible telephone numbers that were active, household numbers in sampled
exchanges =.55

© smoking prevalence rate = 1/1.74 = .575

A-38




For regression coefficients, we recommend a jackknife or balanced repeated replications (BRR)
approach. These are available for logistic regression in WESLOG or CPLEX. SUDAAN will compute
standard errors for logistic regression coefficients, but uses the Taylor series approximations. CPLEX
is available free of charge from the Bureau of the Census. SUDAAN is available from Research Triangle
Institute and WESLOG from Westat, Inc. MPR has SUDAAN and is obtaining CPLEX.

In a jackknife estimation of the variance of regression coefficients, the sample is divided into k
random groups, each of size m. In the present case we would divide the households, rather than the
smokers, into groups. One could use each household as a group or could form larger groups. The
jackknife estimate of standard errors requires k + 1 estimates of the coefficients, one with all cases in

the model, plus k estimates, each with one “group” omitted. Then:

B = the regression coefficient with all cases in the model

5, = the regression coefficient with the ath random subgroup omitted
: 1 §

B = os B
. Kani

The variance of Bz is then:

‘ 1 ae
v (By) Fa-D Der 6, - BY

One then may use WB,) directly or compute average design effects, where:

vB.)
Def, = ——
v(B)

A-43



Variable
Filter

Menthol

Pack

Gender

Race

Education

Incomel

Income2

City codes

Table6
Codes for the Categorical Cigare'

Characteristics and Smoker Gharattoristics Used

in the Logistic Regression Analysis

(1 = filter
0 = unfiltered)

(1 = menthol
0 = non-menthol)

(1 = soft
0 = hard)

(1 = male
0 = female)

(1 = nonwhite
0 = white)

(1 = not high school graduate
= high school graduate)

(1 = income less than $10,000
0 = income not less than $10,000)

(1 = income $10,000 - $19,999
0 =income not in the range of $10,000 -
$19,999)

(Note: When income1 and income?2 are both
0, the reference group income is $20,000
or more.)

(cl, c2, c3, c4, c5, c6, c7 are the codes
representing dummy variables used for the
cities

cl = 1if city is Baltimore

c2 = 1if city is Cleveland

3 = 1if city is Columbus

c4 = 1if city is Denver

c5 = 1if city is Houston

c6 = 1if city is Philadelphia

c7 = 1if city is Portland

(Note: When cl to c7 are all 0, the reference
city is Dallas.)

Abbreviation
Filter

Menthol

Pack

Gender

Race

Educ

Incl

Inc2

Same as
variable used


Table3

City, by Group
Non-fire Fire

City Number Percent Number Percent
Baltimore 203 126 110 19.5
Cleveland 139 8.6 3 13.8
Columbus 17 11.0 17 3.0
Dallas 204 126 % 13.3
Denver 9 6.2 26 46
Houston 296 18.4 6 12.1
Philadelphia 359 22.3 133 23.6
Portland 14 83 YG 10.1

1,611 100.0 564 100.0

Chi-square = 68.6 df=7 p=.000

Chi-square is a test statistic of the hypothesis that both the non-fire and fire
groups have the same distribution with respect to the characteristic being
analyzed. df refers to degrees of freedom. p is the probability that the chi-square
statistic would be as large as the value shown if there were in fact no difference
between the two distributions.

Non-fire refers to people who smoke and did not have a fire. Fire refers to people
who smoke and did have a fire.


Contract No.: CPSC-C-92-1001, Task Order 005
MPR Reference No.: 8071

SELF-PROXY COMPARISONS FOR THE
CIGARETTE FIRE SAFETY SURVEY
FINAL REPORT

OMB #3041-0110

February, 1993
Authors:
Donna Eisenhower
John Hall
Randy Brown
Submitted to: Submitted by:
U.S. Consumer Product Safety Commission Mathematica Policy Research, Inc.
5401 Westbard Avenue P.O. Box 2393
Bethesda, Maryland 20816 Princeton, N.J. 08543-2393
(609) 799-3535
Project Officer: Project Director:

William Zamula Donna Eisenhower


Menthol and citrate had been found to have marginal significance in the earlier
studies and were not found significant here. Density and circumference, which
were found to be significant in laboratory studies of experimental cigarettes, were
not found significant in this analysis. As noted earlier, density and
circumference had s0 little variation in commercially available cigarettes that
their potential impact on fire risk was not really measured by this study.

Note that filter, filter length, and porosity all bear on the air intake in some
fashion. This suggests a common physical process of combustion bearing on
ignition and risk. However, air intake is a complex phenomenon and occurs in a
different form in a discarded cigarette than in a cigarette being smoked.

Filtered Cigarettes Only

With the apparent importance of filter, it was considered useful to see what
model predicted cigarette fire risk best among filtered cigarettes.

An initial model was run and included nine cigarette characteristics (all
except total cigarette length and filter), the smoker characteristics, and city. Four
cigarette characteristics were found significant: filter length, pack, porosity, and
column length. A model with just those four cigarette characteristics, the
smoker characteristics, and city was run and is displayed in Table 9. The new
variable added compared to the earlier model is tobacco column length. The odds
ratio for column length is 1.034. This means that after adjusting for the effects of
the other variables in the model, for each increase of 1 unit (mm) in column
length, the odds of a cigarette fire increase by 3.4%.

Interaction Terms

The next model considered included interaction terms. Numerous two-way
interactions among the four cigarette characteristics and the smoker
characteristics terms in Model 2 were examined. All of the interaction terms
were evaluated with all of the variables from Model 2 already in the model.!2
Three interaction terms were found significant: pack x gender (p < .005),
education x income (p < .005), and race x income (p < .05). These interactions

12Significance was evaluated by comparing model chi-square for Model 2 (main
effects) to Model 2 plus the interaction of interest.

2B


II. PRELIMINARY COMPARISONS OF SELF

AND PROXY-REPORTED DATA FROM THE ORIGINAL SURVEY

In the original survey, one respondent in each household answered questions not only about
household level data, but about the personal characteristics and smoking behavior of all smokers
identified, plus the characteristics of cigarettes smoked by all smokers. Although the respondents were
self-selected (interviews were conducted with any adult member of the household 18 years of age or older
who either answered the telephone or was the first eligible adult to come to the telephone), it is
instructive to see if there are differences in responses by respondent characteristics. For individual level

data, respondents are characterized as:

© self-reporters (smokers reporting their own data)
© smoker-proxies (data provided by smokers about other smokers in the household)

© non-smoker proxies (non-smokers providing data on smokers)

This section addresses the differences in the distributions of cigarette-related information as reported by
self-reporters, smoker proxies, and nonsmoker proxies in the original survey.

The analysis beginning with Table II.1 consists of a tabular presentation of distributions for various
variables. Chi-square (X’) statistics are used to indicate the strength of any differences seen between
groups. While the report refers to levels of statistical significance, the size of the percentage difference
must be carefully evaluated. Finally, even if the means or distributions are the same for self and proxy-
reported data, the proxies may still be reporting differently for individual cases than the smokers
themselves would have (with errors balancing out). The reinterview data provide more control for these

factors; the results are presented in Section IV.

B-5


Data on non-response are shown in Tables IV.7 and IV.8. The figures in these tables show the
percentage of cases where responses were missing from:

© both the initial interview and the reinterview

e the reinterview only

© the original interview only

Data missing from both interviews indicate no change in the quality of data. If the original
Tespondent was a proxy, data missing from the reinterview indicates that the proxy provided more
information than the self-reporter at reinterview, while data missing from the original interview "only"
indicate that the proxy provided less information. The amount of data that is missing is another indication
of the relative quality of data provided by the three groups of original respondents.

The comparisons also indicate that non-smoker proxies were less likely than the other groups to
provide data that the smoker would have provided as a self-reporter. Noteworthy differences are seen
for several smoking measures: whether filtered or mentholated cigarettes are smoked, and amount
smoked. Smaller differences are seen for length of cigarette and pack type. For other measures, the
most noticeable result is the trivial difference on income. The difference on age of smoker is also small.

Table IV.8 presents a comparison of missing data by version of the income question. No differences
are seen overall, and among the subgroups defined by original respondent, differences are seen only for

non-smoking proxies, but these differences lead to no conclusions about whether one version is superior.

B-26




The proxy information provides a means of obtaining data on more smokers in the fire service area.
An important issue is whether the proxy report is as accurate and reliable as the data that would have
been obtained from the actual smoker. Self-reported data are usually assumed to be more accurate and
reliable. However, the survey literature suggests that the distribution of responses from proxies often
differs from that of self-respondents without allowing us to conclude which is better. This is because
there is rarely an external means available or used to validate the self and proxy reports, or the study
design is limited in some other manner.

Moore (1988) after completing a review of the literature on self-proxy reporting spanning three
decades concludes that this "research has not produced conclusive evidence of consistent response bias
or response error variance differences due to the self/proxy status." He attributes this finding to the
methodological shortcomings of much of this literature but cautions that “lack of convincing evidence of
quality differences is not synonymous with convincing evidence of no quality differences." The literature
is further complicated by findings such as these reported by Mathiowetz and Groves (1985) in reviewing
the health survey literature, they found that “although early studies indicate less agreement between the
interview report and medical record data for proxy reports than for self reports, more recent studies
indicate no difference in response error by type of respondent, or suggest that in some cases proxy reports
may be more accurate."

Whether the self report is of higher quality than a proxy report will depend upon the individual, their
circumstances in relation to the subject matter, and the subject matter itself. Proxy reporting for the
mentally impaired or for children has been preferred to no data at all. Proxy reporting in cases where
a self-report may be subject to a high level of social desirability or sensitivity might be preferred.
However, the best report is one that can be recalled and reported most accurately. The acceptability of

who will report must be evaluated in light of this criterion.

B-2




Therefore

Prob (cigarette fire) = exp (Bo + Bi Xi + B2 Xo +... + Bp Xp)
Prob (no cigarette fire)

The odds ratio can be used to determine the change in odds as a function of
a change of so many units in a variable. For example, if column length is
decreased by 10 millimeters, how much do the odds change of having a cigarette
fire, after controlling for the effects of other cigarette and smoker characteristics?


greatly reduces interviewer and processing errors. Interviewer errors on this study should also be small
as a result of the extensive training conducted. Frequency distributions of the data do not indicate any

unexpected or unusual values.

7.2 Need for Weighting and Imputation

Sample weights are used when the sample is distributed differently, on important characteristics, than
the study population. The differences in distribution may result from the study design (e.g.,
oversampling) or from differential response rates or frame coverage. Imputation refers to a set of
procedures for adding values for items missing from cases that are otherwise complete.

Weights. The choices of whether to use sample weights, and if so, how to construct the weights,
depends on the use of the data. In the present survey, the data may be used separately to make
descriptive statements about smokers in the fire service areas. Their primary use will be, combined with
data from the household fire survey, to analyze the effects of smoker and cigarette characteristics on the
likelihood of a household fire occurring.

With regard to making descriptive statements, the sample was designed to provide estimates that are
self-weighting with respect to all smokers in the areas included in the sample frame. Sample strata were
defined by fire service area and median household income. By targeting the sample so that completed
interviews with eligible households are distributed across strata in approximately the same proportion as
are estimates of all households, the need to use weights for descriptive analysis should be eliminated.
This approach to design has provided the advantage of explicitly controlling for the distribution of
households across low-income and other areas. If we were to compute sample weights, we would
calculate the stratum-specific probabilities of selection and response rates, and weight by the inverse of
these. We would then check the weighted sample distribution against our best estimate of the population

distribution and adjust to that distribution.* However, since the sample distribution already fits that of

“We would prefer to use external estimates of the distribution of smokers, but since we do not have
such estimates, the distribution of households allows us to estimate the distribution of smokers from

A-36


consistent practice of the rules of fire-safe behavior.

The principal hypothesis to be addressed in this study, then, is as follows:
Are there cigarette characteristics that are statistically related to higher risk in
cigarette-initiated fires, after adjusting for the effects of smoker characteristics?

If the answer is yes, then that would support the view that fire risk can be
reduced through changes to cigarette characteristics. If the answer is no,
however, then that could mean that cigarette modifications would not be an
effective way to reduce fire risk, that risk only appears to be related to some
cigarette characteristics because of their correlation with smoker characteristics.
At the least, it would suggest that fire risk is not sensitive to changes in cigarette
characteristics within the ranges of characteristics found in current commercial
cigarettes.

If fire risk is related to cigarette characteristics and smoker
characteristics, the relative strength of each relationship may not be the only, or
even the primary, consideration in choosing which characteristics to emphasize
in a program to reduce cigarette fire risk. Also important are the degree of
sensitivity of fire risk to the characteristic and, as important, the feasibility and
cost of making changes in the characteristic. If age and gender turn out to be
strongly related to fire risk, for example, this has no direct practical value,
because age and gender cannot be altered. It may have indirect value if one can
develop an awareness program to change some other risk-related characteristic
(e.g., carelessness) and tailor it especially to high-risk age or gender groups.

More subtly, if poverty or education proved to be highly correlated with fire
risk, they can be changed, but only slowly and at very great cost. By comparison,
changes to the physical characteristics of cigarettes can be made more quickly
and less expensively. All this needs to be considered in interpreting the
implications of this analysis.

To help answer these questions, this report will examine the effects of
numerous cigarette and smoker characteristics all at the same time in order to
adjust for associations and differences in the distributions among these
characteristics. These results will indicate what characteristics are significant
after adjusting for the effects of the other characteristics.


IV. DATA COLLECTION METHODOLOGY FOR THE COMPARISON GROUP

4.1 Description and Schedule

The data collection for the comparison group for this study was conducted from Mathematica’s
Telephone Center in Princeton, New Jersey, using computer-assisted telephone interviewing (CATI). The
interviewer training took place on August 1, 1992. During September 1992, a decision was made to
increase the sample size from 1,500 smokers to 1,500 households. The final interviews were completed
by October 31, 1992.

An adult member of the household who was 18 years of age or older was interviewed about the
characteristics of all smokers 12 years of age and older in the household. One minute per call was spent
attempting to establish any contact, whether answered or not, with a sampled household. After an eligible
member of a household with at least one smoker was contacted, four minutes were spent on average to
complete an interview.

Table IV.1 depicts the distribution of smokers in the sample. Self-reporters are respondents who
smoke and provided information for themselves. A smoker proxy is a smoker who provided information
for another smoker in the household. A nonsmoker proxy is a nonsmoker who provided information for
a smoker in the household. As the table indicates, 51.1 percent were self-reporters, 26.2 percent were
smoker proxies, and 22.7 percent were nonsmoker proxies.

Table IV.2 represents the final "closeout" status of all households originally included in the sample.
Households that were outside the fire service areas or that provided incomplete or incorrect information
that made matching the survey data to the manufacturer’s data on cigarette characteristics impossible were
deleted or flagged. As Table IV.2 shows, an average of 3.9 households had to be screened to reach a
household with at least one smoker. In other words, a little over one quarter of the sample of households
had a smoker. The next section provides details on response rates. Appendix C provides details on the

final status of cases sampled for each of the eight sites.

A-19



TABLE IV.3

RESPONSE RATES BY SITE
Baltimore Cleveland Columbus Dallas Denver Houston Philadelphia Portland
RRenokers 82.95 85.77 87.18 84.33 83.54 82.86 80.64 81.89
RRyverat 87.43 89.05 88.78 87.84 87.19 86.76 85.68 85.94
SRATE 30.74 33.18 24.42 23.67 23.37 22.92 27.21 25.86
HHRATE 65.29 55.77 67.79 54.42 51.37 55.49 74.59 60.82
Smoker 157 141 167 225 107 317 311 107

Households

A-25


And finally, special thanks to Beatrice Harwood and Dr. Terry Kissinger of
CPSC. Their contributions to our work and our analysis went far beyond the
requirements of project officers.

iii


I. BACKGROUND FOR THE STUDY

On August 10, 1990, Congress passed The Fire Safe Cigarette Act of 1990. The act authorized the
U.S. Consumer Product Safety Commission (CPSC) to conduct research and assess the feasibility of
developing a performance standard to reduce cigarette ignition propensity. Data have now been collected
by two organizations which will help the CPSC determine the relationship between various characteristics
of cigarettes and smokers and the risk of fire.

The National Fire Protection Association (NFPA), under contract with the Consumer Product Safety
Commission, has undertaken a fire-incident study, based on data they have collected on cigarette-related
fires at eight sites, The data collection began in November 1991 and was completed in December 1992.
Personnel of participating fire departments were trained to collect the information in person at the scene
of a fire. Mathematica Policy Research, Inc., under subcontract with Market Facts, Inc., has completed
the collection of data for a comparison group, to be used in determining the effect of characteristics of
smokers and cigarettes on the probabilities of household fires.

Mathematica was also responsible for the design, collection, and analysis of this methodological
study to evaluate the data quality of self and proxy reports used in the original Comparison Survey. This
study was done by comparing results between the original respondent whether a self-report, smoker proxy
report, or non smoker proxy report to actual self-reports at a reinterview. The methodological study was
conducted in response to concerns expressed by the Technical Advisory Group created by the Fire Safe
Cigarette Act of 1990.

Data for the comparison group was collected for all smokers in a household. The information was
reported by one household member 18 years of age or older. For the total of 2,206 smokers, 51.1%
were self-reports, 26.2% were smoker proxy reports and 22.7% were nonsmoker-proxy reports. Self-
reports for all smokers in a household or selecting one smoker per household in the same numbers was

not feasible in the survey. This methodological study assesses the quality of the proxy-reported data.

B-1


¢,

RR. = ‘SCREEN

owndl “ HH + (Nyy, * HHRATE)

with HHRATE is defined as:

HH
HH + Ny

HHRATE =
where HH is the number of households identified and Nx, is the number of nonresidential numbers
identified.

The smoker response rate is the number of completed interviews with smoking households (Cs)
divided by the estimated number of smoking households (HHs). The denominator of the smoker response
rate is the sum of: (1) the number of households known to have smokers (HHgx); plus (2) a survey-based

estimate (HHg,) of the number of other households containing smokers. Thus:

C, ¢,

‘s Ss

wor HT” HA, + HA,

The estimate HHs, has two components: (1) a portion of the telephone numbers known to be
households, but where smoking status was unknown (HHgp,;, and (2) a portion of the numbers called

without determining whether or not they were household numbers (Nyxpx). Thus:

HH = (HHg, * SRATE) + (Nyx * HHRATE * SRATE)

For SRATE, we used the prevalence of smokers among households, as estimated from the survey:

Hy
HH, + HH,

‘NON

SRATE =

A-23


Chapter

at

il

CONTENTS

Page
BACKGROUND FOR THE STUDY ......... 20... 00. ce cee e cence B-1
PRELIMINARY COMPARISONS OF SELF AND PROXY-REPORTED
DATA FROM THE ORIGINAL SURVEY .............0 00 ee euee B-5
DESIGN OF THE REINTERVIEW STUDY ..............0-000 00s B-13
FINDINGS BASED ON THE REINTERVIEW DATA ..............05 B-15
CONCLUSIONS ....... 0.0... B-31


these conditions do not invalidate the comparisons, they should be considered as possible sources of
“noise” in conducting the analyses. First, although both samples were drawn from the same fire service
areas the actual coverage is somewhat different. The present survey collected data only from smokers
in telephone households, while the study of households with fires collected data from smokers thought
to cause a fire and is without regard to presence of a telephone. Second, as is usually the case, there may
be method effects. The present survey was conducted by telephone on all smokers in a household, with
one respondent reporting for all smokers in the household therefore, proxy data is collected. The
household fire study collected data only for the smoker in a household who was suspected of causing a
fire, used in-person interviewing, and allowed for proxy responses only in cases when the desired
respondent was unavailable due to injury. However, the most recent literature indicates little difference
in data quality between telephone and inperson methodologies.”

Despite these potential concerns, there is little reason to expect substantial biases from the use of
different data sources. The vast majority (over 90 percent) of households in these areas have telephones.
The degree of geographic undercoverage was small, and the identification of the census tracts for over
96 percent of the sample insures that few persons outside the service areas will be included in the final
data set. Given these considerations, it is our opinion that the resulting bias will be small, but we cannot
directly measure the extent of the bias from the survey data.

Sampling Error. The sample for this survey is not a simple random sample, and therefore proper

analysis of the data requires that the effects of departures from simple random sampling (called design

2De Leeuw, Edith D., and Johannes van der Zouwen. "Data Quality in Telephone and Face to Face
Surveys: A Comparative Meta-Analysis." In Telephone Survey Methodology, edited by Paul P.
Biemer, Robert M. Groves, Lars E. Lyberg, James T. Massey, William L. Nicholls II, and Joseph
Wakesberg. New York: John Wiley & Sons, Inc., 1988, pp. 283-299.

A-34


Mathematica Policy Research, Inc., under a subcontract with Market Facts, Inc., was selected to
collect data for a comparison group. The potential respondent universe for the comparison survey
consisted of cigarette smokers with telephones in the same eight sites across the U.S. The eight sites
represented fire department service areas from which the fire-incident cases were sampled. The service
areas were typically entire city areas, although some service areas also included census tracts outside the
city. The boundaries of the survey areas are discussed in greater detail in section 6.1.

The size of the target population in each site was estimated using national smoking rates by age

group:

Estimated Total Number

Site of Cigarette Smokers
Denver, CO 110,200
Baltimore, MD 173,800
Cleveland, OH 119,200
Columbus, OH 151,300
Portland, OR 104,000
Philadelphia, PA 375,600
Dallas, TX 241,800
Houston, TX 388,900
TOTAL 1,664,800

The eight sites contain a total of 2,762,000 households.

The comparison group was interviewed by a random digit dialing survey conducted by a computer-
assisted telephone interview (CATI). The survey was conducted with 1,532 households that had at least
one smoker. One adult over 18 years of age in each household was interviewed about each smoker in
the household. This method produced completed information on 2,206 smokers. Response rates for the
comparisqn survey were 87% overall and 83% for households with smokers. A household where an
adult reported that no one in the household smoked was considered a completed interview in the overall

response rate. Response rates are discussed in greater detail in section 4.2.

A-2




non-fire group. In particular, Columbus accounted for 11.0% of the non-fire
group, but only 3.0% of the fire group.

City is neither a smoker nor a cigarette characteristic, but it may be
correlated with any or all of those characteristics. It is not necessary for the fire
and non-fire shares to match for each city, because the smokers in one city may,
for a variety of reasons, be more likely to have fires than the smokers in another
city. NFPA used reports to NFIRS and other information to estimate the
completeness of reporting, which ranged from 80% or higher (Baltimore,
Cleveland) to around 50% (Denver, Houston, Philadelphia, Portland) to around
25% (Columbus), with one undetermined (Dallas).

An appropriate means of addressing this is to insert dummy variables for
the cities, and this was done, (A dummy variable is a variable whose only values
are 1 and 0 and which can be used to indicate the presence or absence of a
condition. In this context, there were dummy variables for all but one of the
cities. For example, when the Baltimore variable was equal to 1, the fire or
smoker was from Baltimore; otherwise, the fire or smoker was not from
Baltimore. When all seven city variables were equal to 0, that indicated the city
was Dallas.)

Table 4 compares the non-fire and fire groups with respect to the eight
continuous-variable cigarette characteristics and the one continuous-variable
smoker characteristic. The difference in means, relative to the standard errors,
indicates the significance of the differences between fire and non-fire groups,
through the Student's t statistic.

For filtered cigarettes only, filter length had a mean of 25.14 mm for the fire
group and 23.52 mm for the non-fire group (t = 7.69, p< .001). Porosity had a
mean of 32.44 for the fire group and 30,96 for the non-fire group (t = 1.84, p< .1).
Amount of tobacco had a mean of 772.72 for the fire group and 748.50 for the non-
fire group (t = 5.87, p< .001). Tobacco column length had a mean of 65.95 for the
fire group and 64.98 for the non-fire group (t = 2.91, p< .005).

The last simple analyses prepared show the continuous cigarette
characteristics in categorical form (see Table 5). These tables illustrate a general
point cited earlier. Some characteristics that were significant risk factors may
not be statistically significant in the field study because the range of values among
commercial cigarettes is narrow, compared with the range examined in
experimental cigarettes. Tobacco density and cigarette circumference are apt


Logistic Regression Results
Model 1 (Expanded Sample Size)
Beta p
Intercept 15.5787 -370
Filter 1.8884 .001
Filter length - 0946 000
Porosity 0107 007
Pack 5705 001
Density - 0388 -100
Menthol 0472 -750
Amount of tobacco 0075 -290
Circumference - 4423 373
Citrate - 0793 621
Column length - 0548 505
Gender 3134 009
Age .0073 079
Race - 2159 117
Education 4454 001
Education (Missing) 7787 .008
Incomel1 1.7225 -000
Income2 1.1430 000
Income (Missing) - 0969 661
cl 0722 -740
c2 - 0204 -930
c8 - 14995 000
c4 - 6756 .028
c5 - 4144 061
cé6 0402 843
c7 0032 998
Model chi-square* = 427.8 with 25 degrees of freedom p = 0.000

All of our logistic model runs had included only those cases from the fire and
non-fire group where there was complete data (no missing data). In this
model, we also included cases where there was missing information only for
either education or income. Thus, this logistic regression model was based on
506 cases from the fire group and 1,540 from the non-fire group.

The p value is based on a Wald chi-square statistic for the beta estimate. Any p
value equal to or less than .05 is considered significant.

*Model chi-square is the difference between -2 log likelihood for all the

parameters in the model and -2 log likelihood with only the intercept in the
model.

41


Table A-9

Fire Cases, by Type of Material First Ignited

Type of Material First Ignited
Cotton, rayon, or cotton fabric or furnished goods
Man-made fabric, or finished goods
Untreated, uncoated paper
Unknown-type fabric, textile, or fur
Sawn wood

Polyurethane

Tobacco

Multiple items

Polyvinyl

Unknown-type plastic

Other known type of material*
Unknown type of material

Total cases

*Each specific type accounted for 4 or fewer cases

45

Number of Cases
306 (54.3%)
(13.7%)
(9.6%)
(3.7%)
(3.2%)
(1.6%)
(1.4%)
(1.4%)
(1.2%)
(0.9%)
(6.7%)
(2.3%)

564 (100.0%)


4.2 Response Rates Defined

In this section we consider two important measures of survey quality for this study: the overall
response rate (RR,,.ra), and the response rate for smokers (RR, mores). A response rate is the ratio of the
number of completed interviews with reporting units to the number of eligible reporting units in the
sample. (For computing RR,,.,n an eligible reporting unit is a telephone household. For computing
RRymoren and eligible reporting unit is a telephone household with one or more smokers.) A response
rate is simple to compute when the eligibility status of every reporting unit in the sample is known. When
eligibility is not known, assumptions need to be made about how many of the "unknown" units are
eligible.

In this study, the eligibility status of every unit is not known. For instance, some households may
have refused to be interviewed before we could establish whether the households contained a smoker.
Some telephone numbers were retired after a maximum number of dialings, without ever making a
contact that would allow us to determine whether the number served a household. In these cases, we used
the results of the survey to estimate two rates: a rate of smoking in known households, and a rate of
household numbers among the sample telephone numbers. We used these survey-based rates to estimate
the total number of households and the total number of households with one or more smokers.

The overall response rate is the number of households that completed the screening part of the
interview (C,.:n) divided by the estimated number of households in the sample. Both smoking and
nonsmoking households completed the screening part of the interview. The denominator for the overall
response rate is the number of known households (HH), plus an estimate of the number of households
called without successfully determining whether or not the number belonged to a household (Nyupx)-
This estimate uses a so-called “household rate" (HHRATE), which is an estimate of the proportion of all
telephone numbers in the sample that are household telephone numbers. Thus, the overall response rate

is:

A-22


given at that time. If a smoker changed his type of cigarette or cigarette package frequently, he or she
would clearly be better able than a proxy to remember the type of cigarette smoked or package purchased
two or three months earlier. The proxy’s response about smoking behavior at the time of interview may
well have been nearly as accurate as the smoker’s own response.

Mismatches between interviews for the household variables (income and number of smokers) were
also higher when the original interview was with a smoker proxy, which is likely to be due simply to the
consistency of the respondent rather than to systematic differences in the reliability of the response. Two
different smokers in a household may well respond differently if asked about household income at any
point in time. If one of these individuals were reasked about household income a few months later, the
likelihood that the respondent will give an answer consistent with their own earlier response is greater
than the likelihood that the respondent will give a response similar to the original response of the other
smoker. However, there is no reason to believe a priori that the original respondent provided a more
accurate estimate of household income than other smokers in the household would have given. The lower
incomes typically reported at the reinterview that at the original interview with a smoker proxy suggests
that these types of individuals may differ on a number of characteristics related to their knowledge of
household incomes (e.g., original respondents may be more or less likely to be the head of household
than those for whom a proxy provided the data initially). The difference between the original and
reinterview responses for smoker proxy cases is due entizely to reinterview respondents indicating that
there was only one smoker in the household. (By definition, smoker proxy cases were reported to have

two or more smokers in the household on the original interview.)

In the eventual analysis of the effects of smoker and cigarette characteristics on the likelihood of a
smoking related fire, more credible results will be obtained if the proxy responses were included than if
they were excluded. While excluding proxy cases would eliminate any potential biases due to

misreporting by proxies, these biases are likely to be relatively minor compared to the biases that would

B-32


TABLE IV.6

NON-MATCH RESPONSES TO INCOME QUESTION BY VERSION, OVERALL
AND BY TYPE OF ORIGINAL RESPONDENT

Version 1 Version 2 Total v p=
Percent Mismatch
Overall 43.1 47.7 45.4 0.46 0.496
n= 109 109 218
By Original Respondent
Self Report 30.8 39.5 35.1 0.64 0.424
n= 39 38 77
Smoker Proxy 56.7 72.2 64.4 1.90 0.168
n= 37 36 73
Non-Smoker Proxy 42.2 31.4 36.7 0.88 0.347
n= 33 35 68

NOTE: The X? statistic reported in the fifth column is for a test of whether the distributions of the responses for the three
types of respondents differ by more than might be expected due to normal sampling variability, if the three
samples had each been drawn from the same population. The p value in the last column gives the probability
of observing a dispersion as large as that which is actually observed if the samples had been drawn from the same
population.

B-25


part of a calibration for a standardized test method, which could then assess the
impact of all types of changes in cigarette design.


TABLE IV.7

MISSING DATA BY TYPE OF ORIGINAL RESPONDENT

Self Smoker Proxy Non-Smoker Proxy ¢
(a = 102) (a = 95) (a - 98) Total p=
Number of Smokers in Household
Data Missing From
Both 0.0 0.0 0.0 0.0
Reinterview 29 0.0 2.0 7
Original 0.0 0.0 0.0 0.0 2.6 0.265
Brand Code
Data Missing From
Both 0.0 0.0 0.0 0.0
Reinterview 2.9 0.0 1.0 1.4
0.0 0.0 0.0° 0.0 3.31 0.192
1.0 0.0 0.0 0.3
29 0.0 2.0 17
0.0 3.2 6.1 3.1 10.80 0.095
0.0 0.0 0.0 0.0
29 0.0 1.0 14
1.0 0.0 7d 2.7 14.39 0.006
Data Missing From
Both 2.9 0.0 1.0 1.4
Reinterview. 29 3.2 2.0 27
Original 1.0 21 7A 3.4 9.93 0.128

B-27



Chapter

18

mm.

IV.

VI.

VIL.

VII.

APPENDIX

CONTENTS
Page

OVERVIEW OF THE STUDY A-l
1.1 Background ............. A-l

1.2 Role of the Contractors A3
INSTRUMENT DESIGN AND BRAND LIST FOR
COMPARISON GROUP ........ 02... cece cece eee A-5S

SAMPLING DESIGN AND PROCEDURES
3.1 Target Population ...............
3.2 The Sample Frame and Its Coverage . . .
3.3 Sample Size... 2... ee eee
3.4 Random Digit Dialing Methodology .. .
3.5 Stratification and Allocation of the Sample

DATA COLLECTION METHODOLOGY FOR THE COMPARISON

GROUP 2... cece eee tenet ene nee
4.1 Description and Schedule
4.2 Response Rates Defined

DATA PROCESSING AND ENTRY .... 2.0... eee ee eee A-27
ADDITION OF CENSUS AND OTHER DATA A-29
6.1 Census Tracts 2.6... eee eee eee +. A-29
6.2 Census Data...... .. A30
6.3 Other Merged Data A-30
USING THE DATA .. 1... cece tenes A-33
7.1 Limitations in Using the Data ........ .. A33
7.2 Need for Weighting and Imputation .. . .. A36
7.3 Computing Sampling Errors . 2... 0... ee ee A-39
FREQUENCY AND CROSS-TABULATIONS OF THE SURVEY DATA ... A-45

SIMPLIFIED VERSION OF THE QUESTIONNAIRE




TABLE II.3

POPULATION, INTERVIEWS AND SCREENING BY STRATUM

Estimated

Smoker Households

Total Screen
Stratum Population Interviewed Households
Baltimore Low-Income. 93,722 29 wit)
Baltimore Remainder 611,873 128 470
Cleveland Low-Income 214,919 57 164
Cleveland Remainder 381,509 84 282
Columbus Low-Income 100,675 22 80
Columbus Remainder 767,221 145 612
Dallas Very Low-Income 39,957 9 21
Dallas Low-Income 175,629 20 104
Dallas Remainder 1,039,363 196 855
Denver Low-Income 72,218 23 67
Denver Remainder 459,841 84 408
Houston Low-Income 357,574 59 229
Houston Remainder 1,521,160 258 1,211
Philadelphia Low-Income 345,996 77 256
Philadelphia Remainder 1,206,954 234 953
Portland Low-Income 33,954 10 35
Portland Remainder 506,592 97 398
Total 7,929,157 1,532 6,215



General Logistic Regression Model

The logistic regression model has been well-established for years,
particularly for dose-response studies. Our situation is analogous to a dose-
response problem. As a hypothetical example, if the "dose" of, say, citrate is zero,
then the risk of a cigarette fire will be a function of the other variables. If the
"dose" of citrate is then increased (and if the amount of citrate were positively
correlated with risk), then the risk would rise, typically following an S-curve, with
the effect on risk low at low doses, while at high doses, the risk of fire could
approach certainty.

This technique requires a dichotomous outcome variable. In our situation,
the possible outcomes are that the smoker had a cigarette fire or did not. Smoker
and cigarette characteristics are the independent variables whose effects are to be
examined. The model takes this form®:

©
Prob (cigarette fire) = 14+ exp (Z)

where exp (Z) is the base of the natural logarithm raised to the power of Z,
Z = Bo + Bi Xi + Bo Xo+... + Bp Xp,

Bo is the intercept parameter,
Xi are independent variables,

fi are regression parameters.

It should be noted that because sampling was disproportionate in the two
groups (i.e., the fire group is overrepresented), the logistic equation cannot be
used in this report to predict the probability of having a fire.

An important feature of the logistic regression method is that it provides a
particularly simple form for calculating odds ratios. The odds is the quotient of
the probability of fire to the probability of no fire. The odds ratio is the ratio of the
odds corresponding to two different values of the independent variable. (It is a
format for expressing probabilities most familiar in sports betting contexts, e.g.,
10-to-1 odds that Team A will beat Team B.)

Prob (no cigarette fire) = 1 - Prob (cigarette fire)

6Marija J. Norusis/SPSS Inc., SPSS Advanced Statistics User's Guide, Chicago,
IL: SPSS Inc., 1990.


Within each stratum, allocation of the sample was proportional to the estimated number of (total)
households. In doing so, low income telephone households are oversampled, thus compensating for lower
telephone coverage rate for low income households. Table IH.2 summarizes the distribution of
households, persons, screened households, and smoker households interviewed in each of the eight fire-
service areas. Table III.3 provides additional information about the 17 site/income strata.

The Census/Estimated Population column of Table III.2 shows that the methods used to define the
fire-service areas resulted in an overestimation of the populations in most cases. This overestimation
resulted from a lack of fit between the fire-service areas and the areas serviced by the telephone
exchanges included. The overestimation was an unavoidable consequence of including enough telephone
exchanges to provide nearly complete coverage of the households inside the fire-service areas, because
a sizeable number of households outside the fire-service areas were consequently included in the sampling
frame.

In Philadelphia and Baltimore, the census population/estimated population ratio is very close to one.
The closeness of fit between the fire-service area and the areas serviced by the included exchanges is due
to the fact that Philadelphia County and Baltimore City County boundaries match the boundaries of the
included telephone exchanges closely. The populations of the other fire-service areas were overestimated
by anywhere from approximately 14% (Denver) to 37% (Columbus).

The implication of this lack of fit is that a number of interviews in some sites (specifically, those
other than Philadelphia and Baltimore) were conducted with households outside the fire-service areas.
To isolate the interviewed households known to be in the fire-service areas, we compared each
household’s census tract with lists of census tracts associated with each of the fire-service areas.

The lists for Denver, Portland, and Dallas were supplied with the initial sample documentation,

written by Abt. The lists of tracts inside Baltimore, Houston, Philadelphia, Cleveland, and Columbus


Il. INSTRUMENT DESIGN AND BRAND LIST FOR COMPARISON GROUP

A copy of a simplified version of the questionnaire can be found in Appendix A. The questions
appear in English and in Spanish. The draft instrument was pretested with nine respondents by Abt
Associates before Office of Management and Budget (OMB) approval. Mathematica further pretested
the CATI version of the instrument after receiving OMB approval. The latter pretest was used to develop
the interviewer training materials for the study. The question-by-question specifications and other
selected training materials are included as Appendix B of this report.

The questionnaire determined if the respondent was a household member over 18 years of age. Then
the number of household members 12 years of age or older who smoke at least one cigarette a day was
determined, Cigarette-related information was then reported by the respondent for every smoker in the
household. This information included the following:

UPC code if available from package
brand of cigarette

length

filtered or not
soft or hard pack
mentholated or not

The following information was collected for each smoker:

amount smoked
gender

age

race

education

Finally, the following information was collected for the household as a whole:

number of people residing in the household
household income

home ownership

incidence of fire

address

A-S


Table 1

Categorical Smoker Characteristics, by Group

A. Gender

Male
Female

Chi-square = 13.6

B. Race
White
Nonwhite

Chi-square = 18.9

C. Education
High school graduate

Not high school
graduate

Chi-square = 81.6
D. Income
Under $10,000

$10,000-19,999
$20,000 and up

Chi-square = 243.0

Non-fire Fire
Number Percent Number Percent
846 52.6 347 61.5
164 414 212 38.5
1,610 100.0 564 100.0
df=1 p-=.000 1 missing non-fire case
O missing fire cases
Non-fire Fire
Number Percent Number Percent
987 58.5 270 48.0
G4 415 ps8 52.0
1,601 100.0 563 100.0
df=1 p=.000 10 missing non-fire cases
1 missing fire case
Non-fire Fire
Number Percent Number Percent
1,260 81.6 300 61.7
285 18.4 186 38.3
1,545 100.0 486 100.0
df=1 p=.000 66 missing non-fire cases
78 missing fire cases
Non-fire Fire
Number Percent Number Percent
223, 16.6 223 45.6
270 20.1 148 30.3
847 63.2 palsy 241
1,340 100.0 489 100.0
df=2 p=.000 271 missing non-fire cases

75 missing fire cases

Chi-square is a test statistic of the hypothesis that both the non-fire and fire
groups have a similar distribution with respect to the characteristic being
analyzed. df refers to degrees of freedom. p is the probability that the chi-square
statistic would be as large as or larger than the value shown if there were in fact
no difference between the two distributions.

Non-fire refers to people who smoke and did not have a fire. Fire refers to people
who smoke and did have a fire.

“4


Table 9

Logistic Regression Results
Model for Filtered Cigarettes Only
Wald 95%
Standard —chi- Odds Confidence
Beta Error square p Ratio Interval
Intercept - 2.8989 8437 118 000
Filter length - 0957 0184 27.1 000 908 (.877, .942)
Porosity 0145 0046 9.9 002 1.015 (1.005, 1.024)
Pack 5779 1767 10.7 001 1.783 (1.261, 2.520)
Column length 0339 0140 59 016 1.034 (1.006, 1.063)
Gender 4317 -1845 10.3 001 1.540 (1.183, 2.004)
Age 0055 .0047 14 237 1.006 (.996, 1.015)
Race -.3117 1473 4.5 034 -732 (549, .977)
Education 3641 1517 5.8 016 1439 (1.069, 1.938)
Incomel 1.8569 1676 122.8 000 6404 (4.611, 8.894)
Income2 1.2239 -1657 54.5 -000 3.400 (2.457, 4.705)
cl 1732 2299 0.6 451
c2 + .2298 2476 0.9 353
3 - 19719 4134 22.8 000
4 - 9263 3433 7.3 .007
5 - BT47 .2388 5.8 016
c6 - 1337 2179 0.4 539
c7 - 0664 .2619 0.1 -800

Model chi-square* = 335.3 with 17 degrees of freedom p = 0.000

Note that the model is based on 397 cases for the fire group and 1,241 for the non-
fire group that had complete data on filtered cigarettes (no missing data).

The Wald chi-square is calculated by dividing the beta estimate by the standard
error and squaring the result. Any p value equal to or less than .05 is considered
significant.

The odds ratio is exp (Beta) for a categorical variable like pack. For a continuous
variable like porosity the odds ratio is exp (c Beta), where c is the unit of change
and in the above table is assumed to be 1, though in the text other options are
discussed.

*Model chi-square is the difference between -2 log likelihood for all the parameters
in the model and -2 log likelihood with only the intercept in the model.

31


Table

IL.1

HL2

M3

TL.4

TLS

IV.1

IV.2

Iv.3

IV.4

Vi

VILI

TABLES

HIGHEST MEDIAN INCOME EXCHANGE FOR LOW INCOME
STRATUM ... 2... cece eee nee

POPULATION, INTERVIEWS AND SCREENING BY SITE ........
POPULATION, INTERVIEWS AND SCREENING BY STRATUM ....
HOUSEHOLDS INSIDE/OUTSIDE FIRE SERVICE AREA (FSA) .....
TRACTS PARTIALLY INSIDE FIRE SERVICE AREAS ...........

DISTRIBUTION OF SMOKERS IN THE SAMPLE BY
TYPE OF INTERVIEW .. 1... 2. ee ec eee eee

FINAL STATUS OF HOUSEHOLDS ORIGINALLY
SAMPLED-TOTALS ....... 0.000. c ccc eens

NUMBERS AND PERCENTAGE OF SURVEY CASES
MATCHING WITH MANUFACTURER’S DATA ...............

DISTRIBUTION OF HOUSEHOLDS IN SAMPLE AND
POPULATION BY STRATUM ........ 0.20. e eee eee eens


Table 4
Continuous Cigarette and Smoker

Characteristics, by Group
Non-fire
Number
a

Cases Mean Doaston Minimum Maximum
Density (mg/cc) 1611 239.30 10.68 172
Porosity (CORESTA) 1,611 30,96 13.06 10 is
Circumference (mm) 1,611 24.61 95 17 27
Citrate (%) 1,611 78 44 ) 3.1
Column length (mm) 1,611 64.98 6.72 51 90
Filter length* (mm) 1,562 25.14 451 18 35
Total length (mm) 1611 89.36 9.42 @ 120
Amount of tobacco (mg) 1,611 748.50 82.40 429,44 1,060.70
Age of smoker (years) 1,577 89.11 14.48 12 8&8

Fire
Number
oa Standard

Cases Mean Deviation Minimum Maximum
Density (mg/cc) 564 240.02 10.79 172 266
Porosity (CORESTA) 564 32.44 17.52 “4 185
Circumference (mm) 564 24.76 44 21 27
Citrate (%) 564 17 35 0 3.1
Column length (mm) 564 65.95 6.76 56 &
Filter length* (mm) 505 23.52 3.98 18 35
Total length (mm) 564 87.00 8.32 (ss) 120
Amount of tobacco (mg) 564 712.72 95.09 479.84 1,060.70
Age of smoker (years) 533 42.62 16.33 2 &

*Filter length includes only cases where filtered cigarettes were involved.

Non-fire refers to people who smoke and did not have a fire. Fire refers to people
who smoke and did have a fire.

NOTE: Significance tests for the difference between two means are phrased in
terms of the probability (p) that a difference in means would be at least as large as
the measured difference if the true population means were equal. In this table,
porosity was significant at the 0.1 level; column length was significant at the 0.005
level; and filter length, circumference and amount of tobacco were each
significant at the 0.001 level. Density, citrate, and total length were not significant
at the 0.1 level.

7




TABLE VI.1

NUMBERS AND PERCENTAGE OF SURVEY CASES

MATCHING MANUFACTURER’S DATA

Numbers Percentage
Matched by UPC N17 32.5
Matched by key code 1,059 48.0
Inferred match 193 8.7
No match possible due to 200 o1
missing data
No match possible due to no 37 L7

available key code in
manufacturer’s data set

A-32


Table A-2

Logistic Regression
Model 1 Excluding Proxy Cases*
Beta p
Intercept 11.1965 545
Filter 2.6830 001
Filter length -.1147 .000
Porosity 0094 054
Pack 4313 037
Density - 0326 188
Menthol 0237 898
Amount of tobacco 0055 464
Circumference - 3032 559
Citrate - 0794 683
Column length - .0323 -713
Gender 8069 000
Age - 0002 959
Race - .2536 135
Education 6199 -000
Incomel 1.6401 000
Income2 1.1031 .000
el 3990 -140
c2 0158 900
3 - 1.7910 .000
4 - 9253 .010
05 - 5350 039
6 - 0960 691
c7 2026 491

Model chi-square! = 328.2 with 23 degrees of freedom p=0.000

*This model for the smoker survey (non-fire) includes only data from smoker
self-report cases. It excludes smoker proxy and nonsmoker proxy cases.

Note that the logistic model was based on 439 cases from the fire group and 709
from the non-fire group that had complete data (no missing data).

The p value is based on a Wald chi-square statistic for the Beta estimate. Any p
value equal to or less than .05 is considered significant.

1Model chi-square is the difference between -2 log likelihood for all the
parameters in the model and -2 log likelihood with only the intercept in the
model.


V. DATA PROCESSING AND ENTRY

Data collected by the CATI system require little editing or coding. The “other, specify" answers
for brand codes were printed out. Some answers were matched to existing codes. The remaining
cigarette brands given under “other, specify" are listed in Appendix D. Frequency distributions on the

"cleaned" survey data do not indicate any unexpected or unreasonable values.

A-27






TABLE 11.3 (continued)

Non-Smoker 2
Self Smoker Proxy Proxy Total Sample x p=
Other
Regular/King 48.8 63.6 68.8 55.9 5.17 0.273
Long 46.3 36.4 18.8 38.2
Extra Long 4.9 0.0 12.5 5.9
Controlling for Homeowner
Smokes 20+ Per Day
Owns 51.8 56.3 47.0 51.7 4.64 0.098
Other 48.2 52.2 41.1 48.0 5.08 0.079
Filtered
Owns 93.9 96.4 91.9 94.1 4.84 0.089
Other 95.9 97.0 95.5 96.2 0.87 0.648
Length
Owns
Regular/King 60.0 66.3 74.4 65.2 18.70 0.001
Long 34.2 29.7 24.0 30.5
Extra Long 5.8 4.0 1.6 4.3
Other
Regular/King 61.5 64.5 80.0 65.6 21.00 0.000
Long 34.6 32.1 18.3 31.0
Extra Long 3.9 3.4 1.7 3.4

B-ll



The questionnaire used for the main study was done using CATI while the questionnaire used for
the reinterview was done using hard copy. The questionnaire used for the reinterview contained all of
the questions pertaining to cigarette-related information and a few demographic questions. The questions
were worded exactly as they were worded in the main study. An introductory phrase was added to most
questions which said "as of the date of the previous interview" to place the respondent in the context of
the interview date.

Finally, respondents from each of the three groups were randomly assigned to one of two versions
of the questionnaire. The only difference between the two versions was the wording of the categories
for the income question. In version one, for example, a category reads "$10,000 - 19,999 a year." In
version two, the category reads "$10,000 up to $20,000." There was a special need in the study to test
the subtle difference in wording. Both versions of the questionnaire appear as an attachment to this

report.

B-14


Table7

Model 1— The Most Complete
Logistic Regression Model

Beta p
Intercept 11.3542 540
Filter! 1.8397 .003
Filter length! - 0868 .000
Porosity 0111 015
Pack 6081 001
Density - 0336 171
Menthol .0080 960
Amount of tobacco .0060 420
Circumference - 3189 540
Citrate - .1353 440
Column length - 0386 660
Gender 4348 001
Age 0044 332
Race - 2760 066
Education 4357 .003
Incomel 1.8089 .000
Income2 1.2039 000
el 1351 560
c2 - 2034 407
3, - 1.8605 000
c4 - 9063 007
5 - 5665 015
c6 - 1281 550
c7 - 0837 743

Model chi-square* 372.5 with 23 degrees of freedom

p=.000

Note that the model is based on 439 cases from the fire group and 1,281 from the
non-fire group that had complete data (no missing data).

The p value is based on a Wald chi-square statistic for the Beta estimate. Any p
value equal to or less than .05 is considered significant.

Filter is reflected in two variables: filter (whether the cigarette is filtered) and
filter length (if the cigarette is filtered, how long is the filter). Note in our sample
the minimum value for filter length is 18 when a filter is present; this
discontinuity explains why two model variables are needed to address filter.

*Model chi-square is the difference between -2 log likelihood for all the parameters
in the model and -2 log likelihood with only the intercept in the model.


General Overview

Introduction

The Fire Safe Cigarette Act of 1990, which led to the research reported here,
had as its goal the completion of research begun under the Cigarette Safety Act of
1984, which had demonstrated that "it is technically feasible ... to develop
cigarettes that will have a significantly reduced propensity to ignite upholstered
furniture or mattresses,"! The tasks identified included development and
validation of a standard test method, additional studies of the societal costs of
injuries from cigarette-initiated fires and of the effects on cigarette smoke toxicity
of modifications to current cigarette designs, and the study described here on the
characteristics of cigarettes and smokers involved in cigarette-initiated fires.

Purpose
There are several purposes served by and potential benefits from the

proposed study: (a) identification of specific cigarette characteristics correlated
with differences in the risk of fire, which could be used in the development of
secondary test methods, also called non-ignition tests; and (b) quantification of the
relative importance of various cigarette characteristics in fire risk.

To understand how these questions rose to prominence, it is important to
review what we already know. National fire incident data bases have shown
consistently that cigarettes are the leading heat source in fatal U.S. fires. Even
with recent declines, the 1,245 civilian fire deaths in 1990 that were estimated to
have begun with lighted tobacco products (nearly all of them cigarettes)
represented roughly one of every four civilian fire deaths in the U.S.2 The
frequency of fire death from cigarette fires, however, is known to vary among
different sub-groups of the population, such as age groups and gender groups. At
the same time, laboratory studies have shown that under standardized test
conditions, the propensity of cigarettes to ignite fires is different for cigarettes

1Technical Study Group of the Cigarette Safety Act of 1984, Toward a Less Fire-
Prone Cigarette, Final Report of the Technical Study Group on Cigarette and
Little Cigar Fire Safety, October 1987, p. 1.

2Alison L. Miller, The U.S. Smoking-Material Fire Problem Through 1990,
Quincy, MA: National Fire Protection Association, Fire Analysis and Research
Division, March 1993, p. 1.


TABLE IV.8

NON-MATCH RESPONSES TO INCOME QUESTION BY VERSION, OVERALL
AND BY TYPE OF ORIGINAL RESPONDENT

Version i Version 2 Total xv p=

Data Missing Overall

From Both 10.6 5.6 8.1

Reinterview 10.6 9.0 9.8

Original 6.6 9.7 8.1 3.48 0.323

n= 151 144 295
By Original Respondent Self

From Both 11.5 10.0 10.7

Reinterview 11.5 4.0 78

Original 19 10.0 5.9 4.73 0.192

n= 52 50 102
Smoker Proxy

From Both 8.1 6.5 74

Reinterview 6.1 8.7 74

Original 10.2 6.5 8.4

n= 49 46 95 0.705 0.872
Non-Smoker Proxy

From Both 12.0 0.0 6.1

Reinterview 14.0 14.6 14,3

Original 8.0 12.5 10.2 6.42 0.093

n= 50 48 98

NOTE: The X? statistic reported in the fifth column is for a test of whether the distributions of the responses for the three
types of respondents differ by more than might be expected due to normal sampling variability, if the three
samples had each been drawn from the same population. The p value in the last column gives the probability
of observing a dispersion as large as that which is actually observed if the samples had been drawn from the same
population.

B-29



Table A-4

Logistic Regression
Comparison of Results of Model
Including Interaction Terms
Based on Two Sample Designs
Random Sample Cluster Sample
Standard Standard
Error Exror
Intercept 4355 44
Filter 4906 50
Filter length 0166 02
Porosity 0045 .00
Pack 2466 25
Pack x Gender 3306 32
Gender -1457 14
Age 0046 .00
Race -2508 26
Education 3812 .38
Incomel 2333 25
Income2 .2173 23
Educ x Incl 4382 45
Educ x Inc2 4565 A6
Race x Incl 3329 36
Race x Inc2 3403 36
cl .2293 24
c2 2464 27
63 3818 38
04 +3354 35,
5 2349 23,
c6 .2130 21
c7 2542 .29

As noted in the text, the sample of smokers (non-fire) was based on a
clustered sample. Smokers were not picked independently, but rather as
part of a larger unit known as a cluster (in this case household). To
determine the effect of clustering, two logistic regression runs were
made: (1) where the sample selected was assumed to be chosen by a
simple random sample and (2) where the sample selected was based on a
clustered sample design and household is the cluster. The difference
between the two sample design assumptions should be reflected in the
standard errors.

The results for the cluster sample design were based on SUDAAN, a
software statistical program developed by Research Triangle Institute,
which performs logistic regression on complex sample designs.




Appendix
Sensitivity Analysis

In addition to the analyses presented to this point, there were some
methodological issues related to the project that needed to be addressed.

One concerned the apparent low response rate of Columbus with respect to
the number of cigarette fires reported. A run was made of Model 1 (i.e., with all
10 cigarette characteristics) without Columbus. The same four cigarette
characteristics found significant with all the cities were again significant: filter,
filter length, porosity, and pack. It is also worth noting that the Beta coefficients
for these four cigarette characteristics and the smoker characteristics earlier
found significant changed very little. (See Table A-1.)

Another data quality question arose concerning the reliability of self-
reported and other-reported data for the smoker survey, that is, self-reported
answers vs, answers by others in the household. Mathematica did a separate
report that addresses that topic and related issues. Mathematica concluded, “In
the eventual analysis of the effects of smoker and cigarette characteristics on the
likelihood of a smoking fire we believe that more credible results would be
obtained if the proxy cases [i.e., reports by others] are included than if they are
excluded. While excluding proxy cases would eliminate any potential biases due
to misreporting by proxies, we [Mathematica] believe that these biases are likely to
be relatively minor compared to the biases that would be created by deleting these
cases.”18

Mathematica also recommended that estimates be made with proxy cases
removed, as a sensitivity test. Model 1 was then run with data from the smoker
survey that included only self-report cases. It therefore excluded both smoker
proxy and non-smoker proxy cases. This reduced the sample size for the non-fire
smoker group from 1,281 to 709 cases. In examining the results from Model 1
with proxies excluded, three of the cigarette characteristics found significant
earlier were again significant, filter, filter length, and pack, while porosity just
missed (p = .054). The size of the Beta coefficient did change somewhat, but that is

13Donna Eisenhower, John Hall, and Randy Brown, Self-Proxy Comparisons for
the Cigarette Fire Safety Survey Final Report, submitted by Mathematica Policy
Research, Inc., Princeton, NJ to U.S. Consumer Product Safety Commission,
Bethesda, MD, February, 1993.


TABLE I1.3 (continued)

Non-Smoker 2
Self Smoker Proxy Proxy Total Sample x p=
Controlling for Race
Smokes 20+ Per Day
Hispanic 18.5 35.3 23.7 24.6 4.78 0.092
White/Non-Hispanic 62.1 59.8 53.0 59.7 6.17 0.046
Black/Non-Hispanic 31.9 49.7 37.4 37.6 13.61 0.001
Other 415 63.6 58.8 49.2 2.53 0.282
Filtered
Hispanic 96.3 98.1 94.4 96.2 0.97 0.617
White/Non-Hispanic 94.9 96.1 92.5 94.8 3.76 0.153
Black/Non-Hispanic 94.8 98.1 95.1 95.7 2.94 0.230
Other 97.6 91.7 82.3 92.9 4.22 0.121
Length
Hispanic
Regular/King 70.9 90.2 81.0 79.3 8.84 0.0689
Long 27.8 78 19.0 19.7
Extra Long 1.3 2.0 0.0 1
White/Non-Hispanic
| Regular/King 59.0 61.9 76.2 63.0 24.79 0.000
! Long 36.0 39.5 22.9 33.2
i Extra Long 5.0 3.5 09 3.9
Black/Non-Hispanic
Regular/King 62.7 63.1 73.8 65.4 6.01 0.199
Long 31.6 32.2 23.8 36.1
Extra Long 5.5 4.7 2.3 4.6

B-10



examples of such narrow ranges. The experimental cigarettes were made to
show extremes: for tobacco density, the low range was from 130 to 170 mg/ce, the
high range from 230 to 8320 mg/cc. By comparison, among the commercial
cigarettes identified in the field study, there were no cigarettes at the 130 to 170
low-density range, and very few with values under 200. More than 90 percent of
the cigarettes were in the high-density range as defined for the experimental
cigarettes.

Regarding circumference, the experimental cigarettes were either 21 mm
or 25 mm in circumference. As can be seen from Table 5, only three percent of the
non-fire cases and one percent of the fire cases involved cigarettes with a
circumference within the range of the low circumference experimental
cigarettes. About 90 percent were between 24.5 and 25.4 mm, the value for the
large-circumference cigarettes.

These results for simple frequency counts and means for one variable at a
time are one way to get an idea of how these cigarette and smoker characteristics
are related to the risk of a cigarette fire. However, a better and more rigorous way
is to examine a multivariate model that will adjust or control for the effects of
other smoker and cigarette characteristics.

Dummy city variables were introduced so as to cover all the cities. Note that
city was put in the model not to compare cities, but to control for possible
differences in completeness of reporting of cigarette fires and other possible
differences by city.

Table 6 shows how the categorical variables were coded.

One additional rule was followed in constructing the multivariate model.
The model fits linear relationships of the variables, but three of the cigarette
characteristics - total cigarette length, tobacco column length, and filter length -
are linearly related. The first is the sum of the other two. This argues against
using all three at one time. Of the three, the last two variables refer to more clear-
cut, homogeneous physical properties - the tobacco and the filter - so they were the
ones retained.


Overview of Data Collection and Set-Up for Analysis

This section describes the steps leading up to the analysis, emphasizing key
points needed to put the analysis that follows in proper context.

CPSC authorized two organizations to undertake data collection projects.
NFPA conducted a cigarette fire incident data collection effort with the
cooperation of eight fire departments. Mathematica Policy Research, Inc., under
subcontract with Market Facts, Inc., conducted a study to collect data on a
comparison group of smokers who did not have fires and lived in the same
communities.

Selecting the Cities

With the approval of CPSC, NFPA selected eight fire departments to
participate in the cigarette fire incident data collection effort. Among the criteria
used to select the eight cities were willingness to participate and reported number
of cigarette fires. (This meant the samples were not simple random samples of
all U.S. smokers and cigarette fires.) The target total number of cigarette fires to
be collected, based on sample size recommendations given by Dr. Chan Dayton of
the University of Maryland, was between 400 and 600 fires for the eight
communities. The eight cities selected to participate were Baltimore MD,
Cleveland OH, Columbus OH, Dallas TX, Denver CO, Houston, TX, Philadelphia
PA, and Portland, OR.

The data collection period was 13 months, from December of 1991 through
December 1992. In all 647 forms were collected during that time period from the
eight communities. In terms of useable forms for the study, defined as those with
adequate information for precise cigarette brand identification, there were 564
useable forms.

Mathematica conducted their survey for the same eight communities so
that there would be a comparison group of smokers who did not have fires.
Mathematica designed and implemented a self-weighted sample of 1,532
households, obtaining information from all smokers in a household. Because the
resulting sample was not a simple random sample - it included all smokers in a
household - the possible effect of clustering on the analysis is of concern and will
be examined later in the report.

The smoker survey of 1,532 households obtained information on 1,969
smokers, Discussions among NFPA, CPSC, and Mathematica analysts identified

8


Executive Summary

Data were collected from eight cities on a wide range of cigarette and
smoker characteristics for a sample of smokers. Of these, 564 smokers had had
fires and were identified through fire department response to those fires, while
the other 1,611 smokers had not had fires and were identified through a telephone
sample survey of the communities. The characteristics analyzed included those
that had shown evidence of a relationship to the risk of a cigarette-initiated fire,
either in laboratory studies or in previous statistical analyses of fire experience.

The smoker characteristics analyzed were (household) income, education,
age, gender, and race. The cigarette characteristics analyzed were filter, tobacco
column length, filter length, circumference, density, amount of tobacco, menthol,
citrate, porosity and pack type. In addition, a variable was used to control for the
smoker's city.

After controlling for all smoker characteristics and city, logistic regression
modeling showed four cigarette characteristics to be significant - filter, filter
length, porosity, and pack type. Filter, filter length and porosity all affect air
intake, which therefore appears to be an important physical element in the
combustion process associated with risk. Analysis limited to filtered cigarettes
only showed the same characteristics to be significant, plus tobacco column
length. Extension of the analysis to two-way interaction terms did not change any
of the conclusions on which cigarette characteristics are important but did
indicate that the role of pack type was different for men vs. women.

Sensitivity analyses, shown in the appendix, supported the main
conclusions, which were that (1) cigarette characteristics are significant after
controlling for smoker characteristics and (2) the four specific cigarette
characteristics — filter, filter length, porosity, and pack — are the ones that are
significant. These analyses checked the impact of cluster sampling, sensitivity to
missing data on smoker characteristics, and sensitivity to non-fire smoker cases
with responses by people other than the smokers themselves.

All this means there are already commercially available cigarettes that
exhibit reduced ignition propensity when one controls for smoker characteristics.


TABLE IV.4

A COMPARISON OF THE DISTRIBUTIONS ON LENGTH OF CIGARETTE
FOR THE ORIGINAL AND REINTERVIEW SAMPLE

Original Survey

Reinterview Regular/ Long/ Extra Don’t
long deluxe long know Total
Regular/long 53% 7% 0% 2% 61.2%
Long/deluxe 1% 25% 1% <1% 34.4%
Extra long 0% 1% 2% <1% 3.8%
Don’t know <1% 0 0 <1% 0.7%
Total 60.5% 33.0% 3.1% 3.4% 100.0%

B-22




TABLE I.1

HIGHEST MEDIAN INCOME EXCHANGE FOR LOW INCOME STRATUM

SITE .
Baltimore
Cleveland
Columbus 19,274
Dallas 16,973"
Denver 15,028
Houston 19,882
Philadelphia 20,776
Portland 19,905.

*Cut-off for middle income stratum was $22,751.

A-11


Table 5
Continuous Characteristics in Categorical Form

A. Density (mg/cc)

Under 170
170-199
200-229
230-239
240-259
260-282
Over 282

Total
B. Porosity (CORESTA)

Under 20
20-29

Under 17.0
17.0-19.9
20.0-22.4
22.5-24.4
24,5-25.4
25.5-27.4
Over 27.4

Total

D. Citrate (%)
Under 0.6

0.6

0.7-0.9

Over 0.9

Total

Non-fire
Number Percent

Fire
Number Percent
0 0.0
9 1.6
2% 46
357 63.3
158 28.0
4 2.5
0 0.0
564 100.0
Fire
Number __— Percent
81 14.4
2A4 43.3
133 23.6
9 17.5
7 1.2
564 100.0
Fire
Number _— Percent
0 0.0
0 0.0
4 0.7
31 5.5
525 93.1
4 0.7
0 0.0
564 100.0
Fire
Number _— Percent
117 20.7
195 34.6
181 32.1
71 12.6
564 100.0


be created by deleting these cases. If smokers who were the original respondents differ markedly from
other smokers in these households, as the comparisons suggest that they do, deleting these cases from the
analysis would yield a distorted sample of smokers and could lead to biased estimates of the relationship
between smoker characteristics and smoking-related fires. Furthermore, the loss of proxy cases (one-
fourth the sample if only nonsmoker proxy cases were deleted, one-half if both types of proxy cases were
dropped) would substantially increase the variance of the estimates.

Two other arguments can also be made in favor of retaining the proxy cases. First, as indicated
above, the differences between the original and reinterview responses exist only for some characteristics,
and even for these the differences are not necessarily indicative of "errors" made by proxies in reporting
for other smokers. Second, econometric studies suggest that the coefficients in linear regression models
are “attenuated” (biased toward zero) when estimated on data with random errors in measurement. To
the extent that the same effects occur in logit models, the bias in the estimates due to the measurement
error would be to understate the effects of cigarette characteristics on fires. Thus, results which show
a significant relationship would not be attributable to the measurement error and would be a conservative
estimate of the true effects.

It is also recommended, however, that estimates be obtained with proxy cases removed, as a
sensitivity test. If the cigarette characteristics continued to be significant predictors of the probability of
a fire even when only one observation per household is used, this will provide support for the findings
from the full model. Another sensitivity test that might be explored would be to select at random a single
smoker from household with multiple smokers, to avoid any effects of inherent differences between

original self-respondents and those for whom a proxy completed the interview.

B-33

‘Wy U.S. GOVERNMENT PRINTING OFFICE: 1993-341-832/ 83088


Smoker Characteristics Analyzed

Six smoker characteristics were proposed for analysis:
° Income (of the household)

° Education
° Age

° Gender

° Race

° Number of cigarettes smoked per day

The first five of these characteristics have been shown in numerous
statistical analyses to be highly correlated with either the risk of fire or the risk of
death in fire. It is also generally recognized that age, gender, and race are
significantly related, through brand choice, with many cigarette characteristics,
and it is likely that this relationship applies to the other smoker characteristics as
well.

Cigarette Characteristics Analyzed.

Eleven cigarette characteristics were proposed for analysis:
. Filter (present or not present)

. Tobacco column length

. Total cigarette length

° Filter length

° Circumference

Density (of tobacco)

° Amount of tobacco

° Menthol (present or not present)
° Citrate (in wrapping paper)

° Porosity (of wrapping paper)

. Pack type (soft or hard)

Filter length was introduced as a separate variable late in the analysis as a
response to a discussion of the implications of some of the preliminary analysis.

Amount of tobacco is a variable calculated from circumference, density and
column length. In earlier studies, it has also been called tobacco mass.


Vv. CONCLUSIONS

The rates of disagreement between the responses given by original proxy respondents and the
responses subsequently elicited from the smokers themselves in the reinterview survey are fairly high for
some of the questions; the overall range is from 0 to 64 percent. When compared to the percentage of
mismatches among smokers who were interviewed initially and then reinterviewed, significantly higher
tates of mismatch exist for five of the variables examined. One or both groups of proxy respondents had
significantly higher rates of mismatches than the self respondents for two of the five cigarette
characteristics, (length of cigarette, pack type), one of the two smoker characteristics (amount smoked),
and both of the household characteristics (income, number of smokers in household).

These significant differences, however, appear to be reflect more on the design of the reinterview
survey than on the quality of proxy responses at the original interview. That is, differences observed
between the data supplied at reinterview and these supplied originally differ in larger part because of the
passage of time (over two months on average) between the original survey and the reinterview, and to
the change in the variable over time that may make it difficult to recall the appropriate response for an
earlier point in time. Such problems of recall error are particularly likely for cigarette characteristics.
For example, one-fourth of smokers report a different brand at reinterview than they reported themselves
originally. Questions about cigarette characteristics may also have had ambiguous answers originally,
further increasing the difficulty of recall. For example, many smokers may alternate between different
lengths of cigarettes or pack type, depending upon what is readily available at the place of purchase. On
the other hand, for variables that are likely to be fairly stable, such as whether the smoker buys filtered
or unfiltered cigarettes, we observe no describable difference between original proxy and original self-
respondents in the percentage of mismatches between the two interviews.

The higher rate of mismatches between original and reinterview responses for the groups with proxy
respondents originally is therefore not surprising for the cigarette variables and does not necessarily mean

that proxies at the original interview gave responses different from what the actual smoker would have

B-31


Complete Model of Cigarette Fire Risk

The initial model included the following variables:

Cigarette characteristics — filter, filter length, porosity, pack,
density, menthol, amount of tobacco, circumference, citrate,
and tobacco column length.

Smoker characteristics — gender, age, race, education, and
income (two dummy variables).

In addition, city was also included as a variable, using seven
dummy variables.

The results of this logistic regression model can be seen as Model 1 in
Table 7.

Filter and filter length, porosity, and pack were the cigarette
characteristics found significant’ after controlling for the effects of other cigarette
characteristics, the smoker characteristics, and city. Density, menthol, amount
of tobacco, circumference, citrate, and tobacco column length were all cigarette
characteristics not found to be significant. Gender, education, and income were
smoker characteristics found significant. Age and race were not found
significant. The city variable was also found significant® and will be included in
all other models.

Many models were run omitting certain variables. Particular attention
was paid to models using different choices and combinations from the group of
cigarette characteristics that could have posed multicollinearity problems, i.e.,
amount of tobacco and the three variables that make it up - density, tobacco
column length, and circumference. In the end, no reason was found to do other
than proceed to the most obvious concise model based on Model 1, as shown in the
next section. As a further check, forward selection and stepwise regression
techniques were used with significance criteria levels specified at the .05 level.®

TSignificance is defined as p < .05.

8The city variable, as represented by the seven design variables, was found
significant at p < .005.

8SAS User’s Guide: Statistics, Cary, NC: SAS Institute, 1985.


The design effects of weighting results from the use of weights to compensate for differential
sampling rates and non-response. A weighted estimate (e.g., a mean) is not a simple statistic but a
complex one involving two variables--the variable of interest and the weighting variable. Thus, the
estimated variance of a weighted statistic must account for two sources of variation.”

The design effect of clustering Deff, reflects the fact that in a clustered sample, the units being
observed are not selected independently, but as part of larger units known as clusters.* The variance of
an estimate from a clustered sample has two components--between clusters and within clusters.° In the
present survey, the household is the cluster and individual smokers the unit of observation. Clustering,

like weighting can affect the sampling error of any statistic.

"The effect of weighting on sampling error can be estimated using the methods described below
for estimating clustering effects. A useful approximation for the design effect of weighting (Deff,) is
1 plus the relvariance (rv) of the weights:

Deff, = Var(Weighted) . 1+

rv
Var(SRS)
_ ast
rw,
where: n is the (unweighted) number of cases
n 2
1 n »» Ww,
s+ | ee LE"
n-1 it n

w, is the weight for the ith case.

8Clusters are called primary sampling units (PSUs) when there is more than one stage of sampling.
In the present survey, we sampled all eligible persons in a household, so using the term PSU to refer
to households could be confusing.

In estimating sampling error, the within cluster component of variance would be zero, since all
smokers in a household were sampled.

A-40




VII, USING THE DATA

The data will be used to make descriptive statements about smokers in the targeted fire service areas
and to compare smokers associated with household fires with smokers not associated with fires. Data
on smokers associated with fires were collected in a separate study.’ Logistic regression models will be
used to determine the effect of various characteristics of smokers and of cigarettes on the probability that
a household fire occurs.

In this section we suggest guidelines for using the data collected in the survey. Subsection 7.1
describes the limitations of the data. Subsections 7.2 and 7.3 deal with the specific issues of weighting,

imputation and computing sampling error.

7.1 Limitations in Using the Data
Data from this survey are subject to the usual limitations of survey data. The data are affected by
several sources of potential error:
¢ Sampling error because the data were collected from a sample of smokers, rather than the
entire population

e Error arising from non-response (both case and item level), and possible frame
undercoverage or overcoverage

¢ Response error due to questions being misinterpreted or information incorrectly recalled by
respondents

© Interviewer or processing error

In addition to the general issues of data limitations, there are conditions present in this survey that

affect the usefulness of the data for comparison with the data collected in households with fires. While

Tn the present survey nine smokers were identified whose households had experienced fires. We
suggest excluding these households from analyses that combine data from the two studies. The number
of fires observed in the present survey is very small and these smokers should have had a chance of
being included in the household fire study.

A-33


Citrate refers to chemicals - sodium or potassium citrates - that are added
to some cigarette paper to help the paper and the tobacco burn at the same rate.

Porosity is also a characteristic of the cigarette paper and measures the
rate of air flow, which may affect the burning properties of the cigarettes.
Porosity is measured in cubic centimeters of air per minute per square centimeter
of paper, at a specified ambient air pressure. The measurement is expressed in
“CORESTA" units, with higher values corresponding to higher air flow.

Pack type is a difficult characteristic to interpret since it is not inherently a
characteristic of the cigarette. Two hypotheses have been identified for the
influence of pack type on the risk of cigarette fire ignition: (a) It may be that
cigarettes in soft packs are more likely to be physically altered, due to impact on
the pack prior to being ignited, than are cigarettes in hard packs, which would be
expected to shield more effectively against such incidental impacts. The bending
or crushing or other modifications resulting from impact could affect ignition
propensity. (b) It may be that cigarettes are designed differently as a function of
pack type, either to cater to differences in customer preferences between choosers
of hard vs. soft packs or to help preserve cigarette freshness under two sets of
storage conditions.

Laboratory tests in the earlier study, under the Cigarette Safety Act of 1984,
indicated ignition propensity differences for density, porosity, and circumference,
in that order.’ Citrate showed only slight differences, except for certain classes of
cigarettes. Tobacco type (flue-cured vs. burley) showed negligible differences and
has not been analyzed in this report.

In a feasibility study of the type of analysis in this report , the U.S.
Consumer Product Safety Commission (CPSC) found potentially significant
differences in density, tobacco column length, circumference, amount of tobacco,
porosity, and filter.*

Note that both the cigarette characteristics and the smoker characteristics
are taken from a much larger universe of definable characteristics. Smoker
characteristics like frequency or degree of alcohol or drug use might be correlated

3Technical Study Group of the Cigarette Safety Act of 1984, Toward a Less Fire-
Prone Cigarette, October 1987, Table 3, p. 8.

4Beatrice Howard and Linda Fansler, Feasibility Study of Obtaining Field Data on
Cigarette-Ignited Fires, Technical Study Group of the Cigarette Safety Act of 1984,

pp. 5, 7.


be required, because households experiencing fires would be substantially overrepresented in the
combined data set.° In such a case, weights should be constructed to reflect the distribution of smoking
households by fire/non-fire status.

Imputation. Imputation is used to compensate for missing items within otherwise complete
interviews. We do not recommend replacing any missing items (especially keycode items) with imputed
values on a permanent basis. If values were imputed, the data set would have less precision than a data
set of the same size with no item non-response. Thus, standard deviations calculated on the imputed data
would be underestimated and other descriptive statistics may be distorted. Further, the use of artificial
values would make matching the data with the manufacturer’s data imprecise.

In conducting regression analysis, however, to avoid dropping a large number of cases, a procedure
may be employed that imputes values where cases are missing data for independent variables other than
those included in the "keycode." In this procedure, a constant value (usually zero or the sample mean)
is imputed for the missing variable(s). A binary variable is then created for each variable where values
are imputed. For each case, the binary indicator is set to 1 if the variable was originally missing and set
to zero otherwise. The binary indicator for a variable is then included in any regression equation that

contains that variable.

7.3, Computing Sampling Errors

The effects of departures from simple random sampling are usually grouped as the design effects of
clustering (Deff.) and weighting (Deff,). Although we recommend that sample weights not be used in
the analysis, we realize that some approaches to analyzing the data could call for weights. Thus, we will
briefly explain why weights affect sampling error, and how one would estimate Deff, should that be

required by some future part of the analysis.

‘In a regression model, not using weights would bias the intercept term but not the coefficients of
the independent variables.

A-39


Cross-tabulations are provided for the following, listed in order of appearance:

Age * Gender

Age * Race

Age * Education

Age * Percentage with high school education in tract
Age * Income

Age * Median income in tract

Age * Percentage below poverty in tract
Age * Filtered cigarette

Age * Number of cigarettes (amount)
Age * Density

Age * Amount of tobacco

Age * Porosity

Age * Citrate

Race * Gender

Race * Education

Race * Percentage with high school education in tract
Race * Income

Race * Median income in tract

Race * Percentage below poverty in tract
Race * Filtered cigarette

Race * Mentholated

Race * Number of cigarettes (amount)
Race * Density

Race * Porosity

Race * Citrate

Gender * Education

Gender * Percentage with high school education in tract
Gender * Income

Gender * Median income in tract

Gender * Percentage below poverty

Gender * Filtered cigarette

Gender * Number of cigarettes (amount)

Gender * Density

Gender * Porosity

Gender * Citrate

Income * Median income in tract
Income * Percentage below poverty
Income * Filtered cigarette

Income * Number of cigarettes (amount)
Income * Porosity

Income * Citrate

A-46


TABLE 11.2

SMOKER BEHAVIOR AND CIGARETTE CHARACTERISTICS
BY SEX, HISPANIC, RACE AND HOME OWNERSHIP

Sex Hispanic Race Owns Home

Male Female Yes No White Black Other Yes No
1. Smokes 20+ 54.1 45.5° 24.1 52.4° 57.4 38.0 35.1° 51.7 48.0
2. Filered Cigarette 93.2 97.1° 96.2 95.0 95.0 96.6 93.0° 94.1 96.2

3. Length
Regular or King 73.6 56.1 79.6 63.7 64.1 65.9 69.5 65.2 65.6
Long 24.2 38.4 19.4 32.2 32.2 29.7 27.7 30.5 31.0
Extra Long 2.2 5.6 1.0 4.1° 3.7 4.4 2.8 4.3 3.4
* ¥? statistic, p < 0.10

> y? statistic, p < 0.05

* x? statistic, p < 0.01

B-8


Ill. SAMPLING DESIGN AND PROCEDURES

In this section we describe the sample design, stratification, and selection procedures used in the
CPSC cigarette fire survey. After defining the target population, we present the sampling frame and its
coverage. Sample size calculations are then reviewed and the RDD methodology is briefly described.

Finally, the stratification and allocation of the sample are explained.

3.1 Target Population

The target population for this study was smokers age 12 and over in households with telephones in
the eight fire-service areas. The areas were defined in a variety of ways. Two (Baltimore and
Philadelphia) were easily defined, because the city, the fire-service area, and the county were
geographically identical. Three fire-service areas (Denver, Portland, and Dallas) were defined using a
list of tracts provided by CPSC. Three other fire-service areas were defined in terms of city limits
(Cleveland, Houston, and Columbus). Cleveland and Columbus each included three additional tracts

outside the city.

3.2 The Sample Frame and Its Coverage

The sample frame for this survey was the set of working telephone exchanges associated with each
of the eight service areas. Banks of 100 consecutive numbers with fewer than two listed household
numbers were eliminated. In order to limit the sampling frame to these eight fire-service areas, a census
tract/telephone exchange analysis was performed for each city. This analysis calculates the proportion
of listed telephone numbers in a telephone exchange that were inside a specified geographic region (in
this case, the fire-service area.) Using these reports, we were able to identify phone exchanges for
households in the service areas.

For Cleveland, Houston, and Columbus, where fire-services area were defined in terms of city

boundaries instead of tracts or counties, we used a two-step process to identify the telephone exchanges

A-7


TABLE 1.2

POPULATION, INTERVIEWS AND SCREENING BY SITE

Estimated Population Estimated Smoker Total

of Included Exchanges 1990 U.S. Census Population/ Households Screened
Site Population Census Interviewed Households
Baltimore 705,595 736,000 0.96 157 540
Cleveland 596,428 506,000 1,18 141 446
Columbus 867,896 633,000 1.37 167 692
Dallas 1,254,949 1,007,000 1.25 225 980
Denver 532,059 468,000 1.14 107 475
Houston 1,878,734 1,631,000 1.15 317 1,440
Philadelphia 1,552,950 1,586,000 0.98 311 1,209
Portland 540,546 437,000 1.24 107 433
Total 7,929,157 7,004,000 1.13 1,532 6,215

A-13


It should be pointed out that there was a problem on how the question on
number of cigarettes smoked was asked on the two surveys. The cigarette fire
incident survey used categories of up to a pack a day and more than a pack, while
the smoker survey used categories of less than a pack a day and a pack a day or
more. Analysis by Mathematica on part of their data confirmed that the response
of exactly a pack a day was so common that the two classification schemes could
not be treated as equivalent. Accordingly, it was necessary to omit this smoker
characteristic from the analysis.

The cigarette identifiers collected on the survey forms were length, filter,
pack type, and menthol, where length was a category variable of the type used in
labeling the cigarette for sale (e.g., King). Taken with brand identification, these
identifiers defined the unique packings, and three of them - filter, pack type, and
menthol - were directly useable as cigarette characteristics in the analysis. In
addition to these items, information on the physical characteristics of the
cigarette, supplied by the manufacturers to CPSC, was added to each record by
picking up the characteristics defined for the brand and type of cigarette. These
cigarette characteristics included density, porosity, circumference, citrate,
tobacco column length, and total cigarette length. Another characteristic -
amount of tobacco - was calculated based on the circumference, density, and
column length and added to the record. And a final characteristic - filter length -
was calculated during the analysis.

The increasingly popular generic cigarettes were captured through the use
of bar codes and label identification of company of manufacture. As noted earlier,
no case was used unless a positive brand identification was possible.


There are several methods for estimating the standard errors for statistics from a complex sample.
These methods we will discuss fall into two groups: Taylor series approximations and replication or
resampling methods.'° For the present survey, the most important statistics are regression coefficients,
for which replication methods (e.g., jackknife, balanced repeated replications) are usually preferred.

To estimate the variance of means or proportions for smokers, one could use commercially available
packages, such as WESVAR or SUDAAN, or one could use SAS or SPSS to estimate the components
of a variance estimate for a ratio mean based on the Taylor series. (MPR has written SAS routines to

perform these computations.) In the Taylor series approximation, we define:

ay = the number of households in stratum h
Xan = the number of smokers in the ath household in the hth stratum
Yon = the value of the variable y for the ath household in the hth stratum
H
x = the total number of cases (smokers) = L Ex,
hela
H
y = Z Ly, --(the sum of variable y across all households in all strata)
hela
Tr = y/x (the ratio mean)
H = the number of strata

‘Other groups of methods include generalized variance functions and random group methods. All
these methods are explained in Wolter (1985). Kalton (1983) gives easy to understand examples of
some of these methods.

"The formulae below were taken from Kalton, (1983), pp. 44-45. Equivalent formulae are found
in Kish (1965), p.192. The formula for V(r) is equivalent to that found in Wolter (1985), p.236.

A-41


Table 2
Categorical Cigarette Characteristics, by Group

A. Filter Non-fire Fire
Number Percent Number Percent
Filtered 1,562 97.0 505 89.5
Unfiltered 49 3.0 59 10.5
1,611 100.0 564 100.0
Chi-square = 48.7 df=1 p=.000 0 missing cases
B. Pack Non-fire Fire
Number Percent Number Percent
Soft 1,189 73.8 485 86.0
Hard 422 26.2 ye) 14.0
1,611 100.0 564 100.0
Chi-square = 35.0 df=1 p=.000 0 missing cases
C. Menthol Non-fire Fire
Number Percent Number Percent
Nonmenthol 978 60.7 331 58.6
Menthol 633 39.3 233 414
1,611 100.0 564 100.0
Chi-square = 0.7 df=1 p=.399 0 missing cases

Chi-square is a test statistic of the hypothesis that both the non-fire and fire
groups have a similar distribution with respect to the characteristic being
analyzed. df refers to degrees of freedom. p is the probability that the chi-square
statistic would be as large as or larger than the value shown if there were in fact
no difference between the two distributions.

Non-fire refers to people who smoke and did not have a fire. Fire refers to people
who smoke and did have a fire.


Table A-8
Fire Cases, by Form of Material First Ignited

Form of Material First Ignited Number of Cases
Mattress or bedding 256 (45.4%)
Upholstered furniture 143 (25.4%)
Trash 47 (8.3%)
Clothing 2 (3.5%)
Floor covering 4 (2.5%)
Papers 8 (1.4%)
Curtain or drapery 7 (1.2%)
Multiple items 7 (1.2%)
Structural element or framing 6 (1.1%)
Box or bag 6 (1.1%)
Other known form of material* 44 (7.8%)
Unknown form of material 6 (1.1%)
Total cases 564 (1.1%)

*Each specific form accounted for 4 or fewer cases.


the estimated population, we have accomplished by sample control, what would have been done by
weighting. Table VII.1 indicates by stratum the estimated distribution of households the population, and
cooperative® households identified in the sample. Based on the distribution obtained, it is not necessary
to use sample weights in conducting the data analysis.

While we conclude that sample weights are not needed, there are other approaches to descriptive
analysis that would lead to different decisions about weighting. For instance, to make estimates about
smokers in telephone households one would weight to the estimated distribution of all telephone
households in the frame or in the service areas.

For regression analysis using only the present survey, weights should not be required, even if one
took a different view toward weighting for descriptive analysis. Any household weights would be
constant within strata. In a multiple regression model, variables can be added to control for the stratum-
specific effects that would be addressed by the sample weights that would be computed.

For analyses combining data from the present survey with that from the household fire survey,
weights should not be needed if the objective is to estimate the coefficients of the independent variables
in a logistic regression model. However, to estimate the likelihood of a household fire, whether

unconditionally, or conditioned on certain values of the dependent variables, weights would

the sample. The prevalence of smokers in cooperative households provides our best estimate of the
prevalence of smokers in the population.

SBy cooperative households we mean those that provided information on the number of smokers
in the household.

A-37


going from an unfiltered cigarette to a filtered cigarette, one incurs the
categorical-variable factor for filter presence (1.2204) and the factor for the length
of the filter (-.0743 times filter length). Now the minimum value for filter length is
18(mm). So for a filtered cigarette with filter length of 18 the odds ratio is

exp (1.2204 - .0743 x 18) = exp (-.1170) = .890. This means that after adjusting for all
of the other variables in the model, a filtered cigarette with filter length 18 mm
has an 11.0% (1.00-0.89) lower odds of being involved in a cigarette fire than an
unfiltered one. For every additional unit of change (millimeter) in filter length,
the odds ratio is exp (- .0748) = .928. This means for every increase of filter length
of 1 unit, the odds of a cigarette fire decrease another 7.2% (1.000 - 0.928).

Now consider the odds ratios for the smoker characteristics.

The odds of a cigarette fire are increased by 507% for people with household
incomes $10,000 or less compared to people with household incomes of $20,000 or
more, The odds of having a cigarette fire are increased by 232% for people with
incomes of $10,000 - $19,999 compared to people with household incomes of $20,000
or more. Income is clearly the strongest variable in the model.

The odds ratio for education is 1.540. This means that after adjusting for all
of the cigarette characteristics and other smoker characteristics in the model, the
odds of having a cigarette fire increase by 54.0% if a person didn’t graduate from
high school as compared to a person who did graduate. The odds ratio for gender
is 1.528. This means that the odds of a cigarette fire for a male was 52.8% higher
than for a female, after adjusting for all of the other smoker characteristics and
cigarette characteristics.

In terms of the principal purposes of this project, Model 2 answers the
question as to whether cigarette characteristics are significant factors in cigarette
fire risk after controlling for smoker characteristics. They clearly are.

Of the four cigarette characteristics found to be significant in Model 1 and
so used in Model 2 - filter, filter length, porosity, and pack - porosity had been
identified as significant in earlier laboratory work and in the pilot study for this
project. Filter had also been identified in the pilot study and had been only
partially addressed in the earlier laboratory work. Pack type and filter length had
not been considered in the earlier studies as characteristics. Pack type is an
unusual variable, as noted earlier, and a possible explanation for its strength will
emerge in the analyses on interactions among variables.

Tobacco column length had been a strong variable in the pilot study (and
proved to be significant in the model of filtered cigarettes only, shown later).

27


TABLE IV.5

COMPARISON OF ORIGINAL AND REINTERVIEW RESPONSES ON
CIGARETTE PACKAGING FOR REINTERVIEW SAMPLE CASES

Original Survey Response
Reinterview Soft Hard Don’t
Pack Pack know Total
Soft Pack 63% 4% 2% 68.7%
Hard Pack 8% 19% 1% 28.5%
Don’t know 1% 1% 1% 2.8%
Total 72.2% 23.7% 4.1% 100.0%

NOTE: Data are for 291 individuals.

B-23



Prelimi Analysi

Earlier sections indicated how results of simple comparisons of fire and
non-fire data in past analyses have shaped the choices of cigarette and smoker
characteristics to be analyzed in this project. The current effort provided a unique
opportunity for more preliminary analysis of this type.

Tables 1-2 present simple comparisons, one variable at a time, of the fire
and non-fire groups with respect to four smoker characteristics and three
cigarette characteristics, respectively. The characteristics are the ones that were
defined as categorical (as opposed to continuous) variables in the data collection.
Statistical measures of the degree of difference between fire and non-fire groups
on each of these characteristics are also presented. Note that these analyses are
able to use more of the collected data, because only cases missing data on the one
characteristic analyzed need be excluded.

Keep in mind that these are basic one variable tabulations and do not
control for other smoker or cigarette characteristics.

Males accounted for a higher percentage of smokers who had cigarette fires
(61.5%) than of smokers who did not have fires (52.6%). Whites accounted for a
smaller percentage of smokers who had cigarette fires (48.0%) than of smokers
who did not have fires (58.5%), while smokers who didn’t complete high school
comprised a much higher percentage of smokers who had cigarette fires (38.3%)
than of smokers who did not have fires (18.4%).

Smokers with the lowest household incomes (under $10,000) accounted for a
considerably higher share of smokers who had cigarette fires (45.6%) than of
smokers who did not have fires (16.6%). Also, smokers with household incomes
in the range of $10,000-19,999 comprised a larger percentage of smokers who had
fires (30.3%) than of smokers who did not have fires (20.1%).

Unfiltered cigarettes were the choice of more smokers who had cigarette
fires (10.5%) than of smokers who did not have fires (3.0%). Cigarettes from a soft
pack were used by 73.8% of the smokers who did not have fires, but by 86.0% of the
smokers who had fires. Menthol cigarettes were used about equally between the
two groups. (This is the only one of the seven comparisons without a statistically
significant difference.)

Table 3 displays a breakdown by city for the non-fire and fire groups. Some
cities accounted for a considerably smaller percentage of the fire group than of the

1


The objective of the project is to plan and carry out the initial phases of a cigarette fire-exposure
study that, when combined with a cigarette fire-incident study in the same areas, will enable the CPSC
to assess the relative risk of a cigarette fire in relation to cigarette and smoker characteristics.

Analysis comparing the results of the two surveys will employ a logistic regression model or other
statistical tests of differences, and the comparison survey data will allow valid use of logistic models.

This analysis will be completed sometime in April 1993.

1.2 Role of the Contractors

Abt Associates, Inc., designed the study, including the sample and instrument, for the comparison
group. NFPA was responsible for collecting the fire-incidence data and analyzing the entire data set for
the study as a whole. Mathematica Policy Research, Inc., under a subcontract with Market Facts, Inc.,
was responsible for developing a CATI version of the questionnaire and collecting and processing the data
for the comparison group. Mathematica with CPSC approval made some minor changes to the
questionnaire and some changes to the sample design. Mathematica will be available as a statistical
consultant to NFPA for the analysis.

The remainder of the report discusses the results of the survey conducted on the comparison group

by Mathematica.


TABLE IV.3

FREQUENCY OF BRAND CHANGE BY TYPE OF REPORT AND BRAND CODE MATCH STATUS

SELF SMOKER PROXY NONSMOKER PROXY
Match Mismatch Match Mismatch Match Mismatch

Frequently

Number 1 4 2 7 1 2

Percent 20 80 22 B 33 67
Once in a While

Number 12 7 7 8 5 rt

Percent 63 37 47 53 31 69
Seldomly

Number 19 5 20 8 17 14

Percent 19 a n 29 55 45
Never

Number al 10 32 i 34 13

Percent 80 20 18 25 nR 28





TABLE IL.1

ORIGINAL SURVEY
SMOKER BEHAVIOR AND CIGARETTE CHARACTERISTICS
(Percent Distribution by Type of Respondent)

Smoker Self Non-Smoker
Report J Smoker Proxy Proxy Total Sample
1. Smokes 20 or More 49.8 54.6 44.8 49.9
Cigarettes a Day
Sample Size 1,117 535 462 2,114
V =9.490 DF=2 p = 0.00%
2. _ Soft Pack Cigarettes 1S 74.7 07 | ng
Sample Size 1,092 529 446 2,067
 =3.125 DF=2 p = 0.210
3. Smokes Menthol 39.3 40.8 38.0 39.42
Sample Size 1,112 552 449 2,093
 =0.787 DF=2 p = 0.675
4. Smokes Filtered 99.9 96.8 93.2 _ 95.0
Sample Size 1,118 mi 557 456 2,131
vv =683 DF=2 p = 0.033
5. Length
Regular/King 60.7 65.0 77.8 65.3
Long 35.5 31.4 21.7 30.9
Extra Long 4.9 3.7 15 3.8
Sample Size 1,114 545 j 456 2,115
? =39.22 DF=4 p = 0.000

Table includes only cases where a valid response (other than don’t know) was provided

*p" is the probability that the x2 _ statistic would be this large if there were no differences between the

groups of respondents. Values of p less thay 0.05 indicate statistically significant differences at the
percent level.

B46


Acknowledgements

This study would not have been possible without tremendous effort and
cooperation from a great many people.

First and foremost, we wish to thank the fire departments in our eight
cities. In particular, we wish to thank the following chiefs for agreeing to
participate in this study and the project coordinators for all their hard work and
determination: Fire Chiefs Peter O'Connor and Herman Williams and project
coordinators Lieutenant Earl DeVincentz and Captain Kenneth Morris of
Baltimore, Maryland; Fire Chief Walter Zimmerer and project coordinators
Lieutenant Richard Klein and Captain Ray Masarik of Cleveland, Ohio; Fire
Chief Harmon Dutko and project coordinators Captain Tom Huston and Captain
Tim Carty of Columbus, Ohio; Fire Chief Dodd Miller and project coordinator
Deputy Chief T.W. Oney of Dallas, Texas; Fire Chief Richard Gonzales and
project coordinators Lieutenant Joe Sarconi and Captain Gregory Taft of Denver,
Colorado; Fire Chiefs Robert Clayton and E.A. Corral and project coordinators
Captain Michael Shrum, Assistant Chief Carl Hooker and Assistant Chief
Dennis Holder of Houston, Texas; Fire Commissioners Roger Ulshafer and
Harold Hairston and project coordinators Fire Marshal John Skarbek, Deputy
Chief Robert Wauhop and Battalion Chief Michael Mulderrig of Philadelphia,
Pennsylvania; and Fire Chief George Monogue, Fire Marshal Lynn Davis, project
coordinator Assistant Fire Marshal Jim Crawford, and Captain Paul Barrett of
Portland, Oregon.

Our thanks and appreciation extend to every member of the eight
participating fire departments for gathering the information so vital to this
project.

Thanks, too, to our colleagues at Mathematica - Donna Eisenhower, Randy
Brown, and John Hall - who helped not only with the coordination of two complex
sub-tasks but also with good ideas for every stage of the project.

We also wish to thank the members of the Technical Advisory Group,
whose helpful comments on our research plans and draft reports - and extensive
cooperation on data sources - varied from very useful to absolutely essential.

Thanks to our secretarial team of Helen Columbo, Nancy Schwartz and
Norma Candeloro, whose tireless support throughout the project was a major
factor in our success.

ii


effects)® be assessed and taken into account in conducting analyses, and interpreting and presenting
wsults. Section 7.3 below suggests methods of computing sampling error.

Potential Bias Due to Non-Response and Coverage Problems. Error from non-response and from
undercoverage arise because potential respondents could not be interviewed or were excluded from the
sample frame, respectively and the omitted individuals may differ substantially from those that were
interviewed. Overcoverage means that some persons living outside the service areas may have been
interviewed. Overcoverage can cause bias if those who are erroneously included differ from the study
population.

The major sources of undercoverage (discussed in more detail in Section 3.2) relates to selecting
telephone exchanges for the RDD sample. For reasons of efficiency, we excluded exchanges in which
only a small proportion of the exchanges’ listed telephone numbers were in the service area. However,
the excluded exchanges never contained more than 5 percent of the total telephone households in the
service area.

The high response rate achieved (83% for smokers, see Section 4.2) alleviates much of the concern
regarding non-response. Steps taken to identify interviewed households living outside the service area
reduce the impact of overcoverage. The strategy of defining strata by household income and allocating
the sample proportional to all households should offset the problems related to undercoverage.

Response, Interviewer and Processing Efforts. Response error, and interviewer and processing
errors, occur when the respondent (intentionally or unintentionally) gives incorrect reports, or when the

responses are incorrectly coded or changed in processing. The use of computer-assisted interviewing

3A design effect (Deff) is the ratio for the sample variance given the actual sample design to the
variance that would be obtained with a (hypothetical) simple random sample (SRS) of the same size.
Thus:

Var(DESIGN)

Var(SRS)
SRS estimates of standard errors are multiplied by the square root of the design effect (deft) to obtain
more accurate estimates of standard errors for constructing confidence intervals or performing
significance tests.

Deff =

A-35


Table 10

Logistic Regression Results
Model Including Interaction Terms
Wald
Standard Chi-

Beta Error square p
Intercept - 1.8058 4647 15.1 000
Filter! 1.4473 4900 8.7 003
Filter length! - 0819 0165 24.6 -000
Porosity 0122 0044 U7 005
Pack 0559 .2465 0.1 821
Pack x Gender 9931 3303 9.0 003
Gender -.8722 2987 16 .213
Age .0060 0046 17 192,
Race - 7365 2506 8.6 003
Educ - 2476 -3808 0.4 516
Incl 1.2695 .2329 29.7 .000
Inc2 9396 .2168 18.8 .000
Educ x Incl 1.1868 4378 TA 007
Educ x Inc2 3347 4558 0.5 463
Race x Incl 6227 3327 3.5 061
Race x Inc2 -7920 3399 5.4 020
cl .1228 .2292 0.3 592
c2 - 1.8923 .2460 0.6 441
3 - 1.8627 3816 23.8 .000
c4 - 8879 3352 10 008
5 - 5667 2345 5.8 016
6 - 2116 .2126 1.0 320
c7 - 1104 .2539 0.2 664

Model chi-square* = 389.1 with 22 df p =.000

Note that the logistic model was based on 439 cases for the fire group and 1,281
for the nonfire group that had complete data (no missing data).

1Filter is a combination of these two variables: filter (whether the cigarette is
filtered) and filter length (if the cigarette is filtered, how long is the filter); note
that in our sample the minimum value for filter length is 18 when a filter is
present.

The Wald chi-square is calculated by dividing the beta estimate by the standard
error and squaring the result. Any p value equal to or less than .05 is
considered significant.

*Model chi-square is the difference between -2 log likelihood for all the
parameters in the model and -2 log likelihood with only the intercept in the
model.


IV. FINDINGS BASED ON THE REINTERVIEW DATA

This section examines the data from the random sample selected for reinterview, assessing the
reliability of proxy responses provided in the main interview by comparing the responses on a follow-up
survey of randomly selected smokers with the proxy responses obtained on the initial interview. In
addition, because the sample includes reinterviews with some individuals who were interviewed
themselves in the initial sample, the (test-retest) reliability of data is measured, and the reliability of
responses by the type of the initial respondent can be compared. The degree to which individual data
items are missing for the original survey and the reinterview survey is also examined. For the income
question, the reinterview also tested two versions of the question that used slightly different wording.

Analysis of reinterview data included the variables measuring smoker behavior and cigarette
characteristics, two household characteristics--number of smokers in household and household income--
and smoker’s age. The income variable was included because of interest in testing two versions of
question wording.

The analysis examines first the degree to which reinterview responses match those of the initial
survey and how this differs by type of initial respondent. The degree to which the reinterview respondent
(always a self report) was able to provide data not reported by proxy respondents is then examined.

The percentage of mismatches varies across variables and across original respondent groups. Table
IV.1 presents the percentage of responses that do not match, given that data was provided on both the
original survey and the reinterview. Overall, the percentage of mismatches ranges from zero for whether
the cigarette was filtered to 45 percent for income category. The percentages of mismatches for cigarette
information ranges from zero for filtered to 32 percent for brand code. Several differences between
groups are also seen. Except for household characteristics, the degree of mismatch is highest for cases

where the original respondent was a non-smoker proxy.

B-15


vy) = Ea, 570),

v(x) = Xa, 5°(X),
cy) = Xa, SOY),
where:
sy), = z [yn - 2¥u/4,)P/(@, - 1) --(the sum of the within stratum variances of y)
°@), = z [x,, ~ (2x,/a,)P/(@, - 1) --(the sum of the within stratum variances of x)
sy), = E ia - (x,/4a,)] Da, - CyWa)l@, - 1) (the sum of the within stratum

covariances of x and y)

The variance v(r) of the ratio mean r is then approximately:

vr) + [¥y) + 7? VQ) - 2r (c @y))x?

The standard error of the ratio mean would be the square root of v(r). Standard errors can be
estimated for all variables of interest, or estimates of an average design effect can be calculated. Simple
random sample estimates of standard errors are then multiplied by the square root of the average design
effects.

A less precise, but useful approximation, since no subsampling was done within households, would

be to compute standard errors as if the household were the unit of observation. Thus for a statistic y:

yy) = E wg SOs
hel a,

where:

w, is the proportion of the population in stratum H.

A-42


to be expected with the change in sample size. (See Table A-2.)

Another issue is the sample design of the smoker survey. As noted earlier,
the sample was not a simple random sample. Smokers were picked not
independently but rather as part of a larger unit known as a cluster (in this case,
household), If a household was selected, all smokers in the household were
included in the sample. To determine the effect of clustering, two logistic
regression runs were compared: (1) one where the sample selected was assumed
to be chosen by a simple random sample, and (2) one where the sample selected
was based on a clustered sample design, with household as the cluster. All of the
logistic regression runs that have been made thus far were made under the
simple random sample assumption.

A software statistical program, SUDAAN, developed by Research Triangle
Institute, was used to perform logistic regression on a clustered sample design.
The difference between the two sample design assumptions should be reflected in
the standard errors of the Beta coefficients. The models examined were Model 2,
with the four cigarette characteristics, both with and without the three interaction
terms. Results for most of the variables seem to indicate little difference in the
standard errors for the two sample designs (See Tables A-3 and A-4.) In
particular, none of the cigarette or smoker characteristics that had been
significant ceased to be significant.

Another issue is the missing data question. As was noted earlier, before
the initial logistic regression run, the data base contained 564 cases for the fire
group and 1,611 cases for the non-fire group. The data base of cases with complete
data (no missing data) has 439 cases for the fire group and 1,281 for the non-fire
group. This means that 79.1% of the original 2,175 cases were used in most of the
logistic regression runs. This is more than adequate to do the analysis, but a
question still arises about the 20.9% cases where there were one or more missing
data items.

To help reduce that number and see whether results are affected, the
following sensitivity analysis was done. The two data items where there was the
largest number of missing-data cases were education and income. In this
sensitivity analysis model, we decided to include cases where there was missing
information only for either education or income (i.e., no missing data for any of
the other variables). In the latter cases, two additional dummy variables were
used to identify missing data for education and income, respectively.