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Smoke-free Ordinance
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This letter was accepted May 25, 2010.
doi:10.2105/AJPH.2010.202119
Contributors
E. J. Hahn originated the letter and took the lead on
writing. N. L. York added citations and edited the letter.
M. K. Rayens reviewed the design and analysis and
edited the letter.
Acknowledgments
The authors are supported by a research grant from the
National Heart, Lung, and Blood Institute (grant
R01HL086450).
Note. The content is solely the responsibility of the
authors and does not necessarily represent the official
views of the National Heart, Lung, and Blood Institute or
the National Institutes of Health.
References
1. Ferketich AK, Liber A, Pennell M, Nealy D,
Hammer J, Berman M. Clean indoor air ordinance
coverage in the Appalachian region of the United States.
Am J Public Health. 2010;100(7):1313–8.
2. Ahijevych K, Kuun P, Christman S, Wood T,
Browning K, Wewers ME. Beliefs about tobacco among
Appalachian current and former users. Appl Nurs Res.
2003;16(2):93–102.
3. Denham SA, Meyer MG, Toborg MA. Tobacco
cessation in adolescent females in Appalachian
communities. Fam Community Health. 2004;27(2):
170–181.
4. Shannon LM, Havens JR, Mateyoke-Scrivner A,
Walker R. Contextual differences in substance use for
rural Appalachian treatment-seeking women. Am J Drug
Alcohol Abuse. 2009;35(2):59–62.
5. York NL, Hahn EJ, Rayens MK, Talbert J. Local
elected officials’ views on smoke-free policy in Kentucky.
Kentucky J Communication. 2008;27(2):125–146.
6. Stillman F, Yang G, Figueiredo V, Hernandez-
Avila M, Samet J. Building capacity for tobacco
control research and policy. Tob Control. 2006;15:
i18–i23.
7. York NL, Hahn EJ, Rayens MK, Talbert J.
Community readiness for local smoke-free policy change.
Am J Health Promot. 2008;23:112–120.
8. Americans for Nonsmokers’ Rights Foundation.
Overview list–how many smokefree laws? 2010. Avail-
able at http://www.no-smoke.org/pdf/mediaordlist .
Accessed May 17, 2010.
9. O’Connor JC, MacNeil A, Chriqui JF, Tynan M,
Bates H, Eidson SK. Preemption of local smoke-free air
ordinances: the implications of judicial opinions for
meeting national health objectives. J Law Med Ethics.
2008;36(2):403–12, 214.
10. Nykiforuk C, Campbell S, Cameron R, Brown S,
Eyles J. Relationships between community characteris-
tics and municipal smoke-free bylaw status and strength.
Health Policy. 2007;80(2):358–368.
11. US Dept of Health and Human Services. The Health
Consequences of Involuntary Exposure to Tobacco Smoke:
A Report of the Surgeon General. Atlanta, GA: Dept of
Health and Human Services, Public Health Service,
Centers for Disease Control and Prevention, National
Center for Chronic Disease and Prevention and Pro-
motion, Office of Smoking and Health; 2006.
BERMAN ET AL. RESPOND
We certainly agree with Hahn et al. that the
ultimate goal is for residents of the Appalachian
region to be protected by comprehensive, anti-
preemptive statewide smoke-free laws, but we
evidently disagree about the best strategy to
reach that goal.1
Although we do not intend to downplay the
determined efforts and tremendous accom-
plishments of local advocates in parts of
Appalachia, our study found that years of local
advocacy for smoke-free laws have produced
only modest results. Moreover, the communities
that have successfully passed comprehensive
smoke-free laws have tended to be those with
relatively high socioeconomic profiles, likely
further exacerbating the health disparities that
already exist within the region.
We believe that all residents of the Appala-
chian region deserve protection from breathing
toxic environmental tobacco smoke, and
a community-by-community approach simply
will not move the process along quickly
enough. Hahn et al.’s own research has
found that there is strong public support for
smoke-free laws in the Appalachian region, and
the recent passage of a strong smoke-free law in
North Carolina—combined with encouraging
progress toward a smoke-free law in Virginia—
suggests that statewide success is possible even
in historically tobacco-growing regions.
We urge a strategy that will enlist the
support of local leaders as part of a larger effort
to pass and implement comprehensive state-
wide smoke-free laws. j
Micah Berman, JD
Amy K. Ferketich, PhD
Alex Liber, BS
Michael Pennell, PhD
Darren Nealy, JD
Jana Hammer, JD
About the Authors
Amy K. Ferketich, Alex Liber, and Michael Pennell are with
the Ohio State University, Columbus. Micah Berman is
with the New England School of Law, Boston, MA. Darren
Nealy and Jana Hammer are with the School of Law,
Capital University, Columbus.
Correspondence should be sent to Amy K. Ferketich,
Division of Epidemiology, The Ohio State University College
of Public Health, B-209 Starling-Loving Hall, 320 West
10th Avenue, Columbus, OH 43210 (e-mail: aferketich@
cph.osu.edu). Reprints can be ordered at http://www.ajph.org
by clicking the ‘‘Reprints/Eprints’’ link.
This letter was accepted June 7, 2010.
doi:10.2105/AJPH.2010.202333
Contributors
M. Berman drafted the letter. A. K. Ferketich worked
with M. Berman on the content. A. Liber, D. Nealy, M.
Pennell, and J. Hammer all read the letter, provided edits,
and agreed with the content.
Reference
1. Rayens MK, Hahn EJ, Langley RE, Zhang M.
Public support for smoke-free laws in rural communities.
Am J Prev Med. 2008;34(6):519–522.
LOCAL SMOKE-FREE ORDINANCES
ARE PASSING IN TOBACCO-GROWING
STATES
Ferketich et al. studied demographic factors
associated with the passage of clean indoor air
ordinances in Appalachian communities.1
Based on reported lack of progress at the local
level despite strong public support for clean
indoor air laws, they recommended that efforts
be focused on the state level instead. This
conclusion goes beyond the data that they
present and does not consider the power of
the tobacco industry in state-level politics.2–6
Additionally, recent experience in South Caro-
lina,7 a state Ferketich et al. included in their
research, shows that strong progress on local
clean indoor air ordinances is possible even in
an Appalachian, tobacco-growing state.
South Carolina’s weak state clean indoor air
ordinances passed in 1996 with an assumed
preemption clause pushed by cigarette manu-
facturer lobbyists and hospitality industry allies
that halted clean indoor ordinance progress for
a decade. However, between May 2006 and
January 2008, local advocates, supported by
national tobacco control technical assistance
and funding, challenged this presumed pre-
emption by passing clean indoor air ordinances
in 12 localities, two of which were sued under
claims that state preemption did not allow
local clean air ordinances. In March 2008,
the state Supreme Court ruled that local clean
indoor air ordinances were not preempted;
since then, advocates have passed 21 more
local clean indoor air ordinances. The passage
of these local clean indoor air ordinances as
of May 2010 has been recognized as the
highest number of strong local ordinances
passed in any US state for two years in a row.8
November 2010, Vol 100, No. 11 | American Journal of Public Health Letters | 2013
LETTERS
Cigarette manufacturing interests responded
with an effort to pass explicit state preemption.
During the 2007 and 2008 legislative session,
11 neutral to strong clean indoor air bills were
introduced; three were co-opted to include
weak clean indoor air provisions and preemp-
tive language as a result of tobacco manufac-
turer lobbying. Tobacco control advocates
stopped all weak bills with preemption and
convinced legislators to delay state laws, which
could become vehicles for preemption.7
The trajectory of South Carolina clean in-
door air ordinance progress provides a strong
counterpoint to Ferketich et al.’s conclusion
that there is a lack of motivation among
tobacco control advocates at the local level in
tobacco-growing states and that clean indoor
air ordinance efforts should focus at the state
level. Developing clean indoor air laws at the
state level without strong local support pro-
vides an opportunity for cigarette manufac-
turers to preempt more comprehensive local
activity, where tobacco manufacturers have
less sway.3,9 In contrast, developing the capac-
ity of local advocates can result in a strong
smoke-free movement through local smoke-
free ordinance adoption. j
Sarah Sullivan, BA
Stanton A. Glantz, PhD
About the Authors
The authors are with the Center for Tobacco Control
Research and Education, University of California, San
Francisco.
Correspondence should be sent to Stanton A. Glantz, PhD,
Professor of Medicine, Center for Tobacco Control Research
and Education, 530 Parnassus Suite 366, University of
California, San Francisco, CA 94143-1390 (e-mail:
glantz@medicine.ucsf.edu). Reprints can be ordered at
http://www.ajph.org by clicking the ‘‘Reprints/Eprints’’ link.
This letter was accepted June 23, 2010.
doi:10.2105/AJPH.2010.204156
Contributors
Both authors contributed to writing this letter.
Acknowledgments
This work was supported by the National Cancer Institute
(grant CA-61021).
References
1. Ferketich AK, Liber A, Pennell M, Nealy D,
Hammer J, Berman M. Clean indoor air ordinance
coverage in the Appalachian region of the United States.
Am J Pub Health. 2010;100:1313–1318.
2. Givel MS, Glantz SA. Tobacco lobby political
influence on US state legislatures in the 1990s. Tob
Control. 2001;10:124–134.
3. Traynor MP, Begay ME, Glantz SA. New tobacco
industry strategy to prevent local tobacco control. JAMA.
1993;270(4):479–486.
4. Siegel M, Carol J, Jordan J, et al. Preemption in
tobacco control: review of an emerging public health
problem. JAMA. 1997;278(10):858–863.
5. Office on Smoking and Health, National Center
for Chronic Disease Prevention and Health Promotion,
Centers for Disease Control and Prevention. Preemptive
State Tobacco-Control Laws – United States, 1982-1998.
MMWR Morb and Mortal Wkly Rep. 1999;47(51):
1112–1114.
6. Ibrahim JK, Glantz SA. The Rise and Fall of
Tobacco Control Media Campaigns, 1967-2006. Am J
Public Health. 2007;97:1383–1396.
7. Sullivan SE, Barnes RL, Glantz SA. Shifting
attitudes towards tobacco control in tobacco country:
Tobacco industry political influence and tobacco policy
making in South Carolina. San Francisco, CA: Center for
Tobacco Control Research and Education, University of
California, San Francisco; 2009. Available at: http://
escholarship.org/uc/item/278790h5. Accessed July 29,
2010.
8. South Carolina Tobacco Collaborative. S.C. recog-
nized for 2nd year of smoke free leadership. Available at:
http://www.sctobacco.org/news.aspx?article=1046.
Published June 6, 2010. Accessed July 29, 2010.
9. Samuels B, Glantz SA. The politics of local tobacco
control. JAMA. 1991;266(15):2110–2117.
BERMAN AND FERKETICH RESPOND
Contrary to Sullivan and Glantz’s assertion, our
article never stated that ‘‘there is a lack of
motivation among tobacco control advocates at
the local level in tobacco-growing states,’’ nor
did we intend any such implication. We have
deep admiration and profound respect for the
efforts of local tobacco control advocates,
particularly those working in the challenging
political environment of Appalachia.
Recognizing the limitations of a community-
by-community approach identified in our
research, we urged local tobacco control ad-
vocates to prioritize the adoption of compre-
hensive statewide smoke-free laws while not
abandoning efforts to promote local smoke-
free ordinances. Sullivan and Glantz fear that
tobacco manufacturers may seek to weaken
proposed statewide laws and insert preemp-
tive provisions that would limit local authority.
We have no doubt that tobacco companies
will attempt such tactics. But, as the experience
in South Carolina shows, well-organized to-
bacco control advocates are fully capable of
defeating the tobacco industry at the state
level.
Around the country, tobacco control advo-
cates have been far more successful in pursuing
the adoption of anti-preemptive state laws than
the tobacco industry has been in seeking
preemption. In just the last five years, explicit
anti-preemptive language clarifying that local
smoke-free ordinances can be broader than
state law has been enacted (either as part of
a smoke-free law or separately) in Arizona,
Arkansas, Colorado, Georgia, Hawaii, Idaho,
Illinois, Louisiana, Maryland, Minnesota,
Nevada, New Jersey, New Mexico, North
Dakota, and Ohio.1
Preemptive statewide laws are an appropri-
ate area of concern, but as tobacco control
advocates around the country have demon-
strated, the best defense is a good offense. j
Micah Berman, JD
Amy K. Ferketich, PhD
About the Authors
Micah Berman is with New England Law, Boston, MA. Amy
K. Ferketich is with the Ohio State University, Columbus.
Correspondence should be sent to Amy K. Ferketich,
Division of Epidemiology, The Ohio State University College
of Public Health, B-209 Starling-Loving Hall, 320 West 10th
Avenue, Columbus, OH 43210 (e-mail: aferketich@cph.
osu.edu). Reprints can be ordered at http://www.ajph.org by
clicking the ‘‘Reprints/Eprints’’ link.
This letter was accepted June 30, 2010.
doi:10.2105/AJPH.2010.204628
Contributors
M. Berman drafted the letter. A. K. Ferketich worked
with M. Berman on the content.
Reference
1. Americans for Nonsmokers’ Rights. History of Pre-
emption of Smokefree Air by State. April 1, 2010.
Available at: http://www.protectlocalcontrol.org/docs/
HistoryofPreemption . Accessed July 29, 2010.
RELATIVE MEASURES ALONE TELL
ONLY PART OF THE STORY
In their article on HIV/AIDS mortality, Rubin
et al. approvingly cite Braveman’s definition of
a health inequality as ‘‘a difference in which
disadvantaged social groups . . . systematically
experience worse health or greater health risks
than more advantaged social groups.’’1(p1053)
Later in her work, Braveman discusses two
common effect measures used when comparing
two groups, the rate ratio and the rate difference,
and observes that ‘‘both absolute and relative
2014 | Letters American Journal of Public Health | November 2010, Vol 100, No. 11
LETTERS
Copyright of American Journal of Public Health is the property of American Public Health Association and its
content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s
express written permission. However, users may print, download, or email articles for individual use.
Walk for the Health of It Junior! – City of Fort Worth
Publie Health Department, Fort Worth – Karen Bell,
MPH, CHES
Walk for the Health of It Junior! was inspired by the adult ver-
sion Walk for the Health of It! to eombat the problem of obe-
sity and eardiovaseular disease by eneouraging physieal aetiv-
ity. frhe Junior! version targets 6th graders between the ages
of 11-12 years. The majority of the participants are Hispanie.
This program lasts 21 weeks with students completing walk-
ing logs of time walked during and outside of school. Parents
are asked to participate with the ehildren. A health educator
visits the sehool and teaehes the students about the importance
of exercise and the health benefits that result from regular
physieal activity. Anthropometrie measurements are taken for
all partieipating students before the program begins.
Walk Aeross Texas – Texas Cooperative Extension/TAMU
Sehqol of Rural Publie Health, College Station – Carol A.
Riee, PhD, RN
Walk Aeross Texas (WAT) is a fun, free fitness program aimed
at helping all people to establish the habit of physieal aetiv-
ity. Individuals participating in WAT can perform any aetivity
that Increases their heart rate. Using the Mileage Equivalents
list Ideated on the WAT website, individuals can convert time
spent on other activities, such as gardening, into miles walked
for the WAT eompetition. WAT participants log and track
their mileage in an on-line database.
African Ameriean Breastfeeding Promotion Campaign
– Texas Department of State Health Services WIC, Austin
– Tracy Erickson
This 9-month promotion campaign targeted African Ameri-
can men and women between the ages of 13-47. The goals
were increased awareness of the benefits of breastfeeding and
increased breastfeeding rates among African American WIC
participants. The brand and logo, “Breastmilk: 100% Natural
Ingredients”, was developed for the campaign and incorpo-
rated into all components. A brochure explaining the benefits
of breastfeeding to pregnant women was developed as well
as broehures for the woman’s partner and parents encourag-
ing them to support breastfeeding. The brochures were given
to al] African American WIC participants at initial pregnant
eertifications. Breastfeeding promotion bags were given to
healthcare providers to distribute. Posters were hung in WIC
clinifcs, hospitals, and healthcare provider offices. Exhibits,
flyeiis, and advertisements on TV, radio, billboards, and in the
newspaper spread the message in the African American com-
munity.
Eaeh recognized program was included in the 2006 Best Prac-
tices Listing and received statewide recognition at the Annual
Texas Public Health Association (TPHA) Conference and
ongoing promotion on www.EatSniartBeActiveTX.org and
www.dshs.state.tx.us/phn/phn.shtm. Additionally, representa-
tives of selected interventions had the opportunity to discuss
their intervention and lessons leamed at the TPHA Confer-
enee held in Piano, Texas.
The Economic Impact (or Lack
Thereof) of Smoke-Free
Ordinances
Philip Huang, MD, MPH
Secondhand smoke is reeognized as a significant health hazard
by all of the major health authorities. Fornier U.S. Surgeon
General Richard Carmona recently stated upon the release
of the latest Surgeon General’s report on seeondhand smoke
that “The scientific evidenee is now indisputable: secondhand
smoke is not a mere annoyance. It is a serious health hazard
that can lead to disease and premature death in children and
nonsmoking adults.”*” The report further noted that second-
hand smoke contains more than 50 cancer-causing chemicals,
and is itself a known human eareinogen. Nonsmokers who
are exposed to secondhand smoke inhale many of the same
toxins as smokers. Even brief exposure to secondhand smoke
has immediate adverse effeets on the cardiovascular system
and increases risk for heart disease and lung cancer, the report
says.’^’
In order to protect the public from the dangers of secondhand
smoke, many countries, states and local communities are
adopting comprehensive smoke-free ordinances. As of No-
vember 2006, 12 countries (Ireland, Italy, Seotland, England,
Norway, Sweden, New Zealand, Uganda, Malta, Uruguay,
Hong Kong, and Bhutan) and 16 states (California, Delaware,
New York, Connecticut, Maine, Massachusetts, Rhode Is-
land, Vermont, Washington, Hawaii, Ohio, Arizona, Montana,
New Jersey, Colorado and Utah) have passed comprehensive
smoke-free legislation that prohibits smoking in public places,
including all workplaces, restaurants and bars. In Texas, cur-
rently 9 cities (Austin, Beaumont, Benbrook, Copperas Cove,
El Paso, Houston, Laredo, Vernon and Victoria) have passed
ordinances that are considered 100% smoke-free and include
all workplaces, restaurants and bars.’^’ Houston just passed
their ordinance in October 2006, but it does not go fully into
effect until September 2007.
As those of us in the public health community are aware, ex-
posure to secondhand smoke is first and foremost a publie
health issue, and efforts to protect the public from involun-
tary exposure to secondhand smoke are similar to other public
health regulations to protect the public from a known public
health hazard. Nevertheless, whenever these issues are dis-
cussed, the potential adverse economic impact on private es-
tablishments (especially restaurants, bars and clubs) is raised
as a possible reason not to pass these ordinances.
Numerous studies nationwide have examined the economic
effeets of smoke-free ordinances. A study that appeared in
the journal Tobacco Control in 2003 reviewed 97 studies that
made statements about economic impact of smoke-free ordi-
Continued on page 32
TPHA Joumal Volume 58, Issue 3 31
nances. The authors concluded that “all of the best designed
studies report no impact or a positive impact of smoke-free
restaurant and bar laws on sales or employment.”
In Texas, we have published reports and produeed white papers
examining the economic impact of smoke-free ordinances on
restaurant sales in West Lake Hills, Arlington, Austin, Piano,
Wichita Falls and El Paso.”*”^ All of these reports examined
sales tax data obtained from the Texas Comptroller of Public
Accounts and none of the reports showed any adverse eco-
nomic impact resulting from the smoke-free ordinances. The
February 27, 2004 Morbidity and Mortality Weekly Report
(MMWR) article was our first to also look at impact on bar
and mixed beverage sales in El Paso (the first eommunity in
Texas to pass a smoke-free ordinance that included all bars).
Again, no adverse economic impact was noted on restaurant,
bar or mixed beverage sales as a result of their 100% smoke-
free ordinanee.
With the remainder of this artiele I will provide an update on
some of the latest data that we have from Austin, as it is one
of the most recent large Texas cities to implement a compre-
hensive 100% smoke-free that includes bars and elubs. Since
bars and clubs are the “final frontier” with regard to smoke-
free ordinances in Texas, there has been partieular interest in
any preliminary data on Austin bar sales, so I will foeus spe-
cifically on the bar and mixed beverage sales data.
On September 1, 2005, Austin’s 100% smoke-free ordinance
went into effect as a result of a voter-approved referendum.
Austin had previously had an ordinance that required smoke-
free restaurants and the new ordinance extended the protee-
tion to include all bars and clubs. What follows are data from
sales tax revenues reported to the Texas Comptroller of Public
Accounts for mixed beverage sales. Because the ordinanee
has only been in effect for a little over a year, and because
of delays in reporting, we cannot yet perform the statistical
analyses that we typieally use, however, some trends ean al-
ready be seen.
Figure 1 shows monthly mixed beverage sales from January
2000 through July 2006. Mixed beverage sales are broken
down by sales in bars, restaurants and total sales. As seen
in the figure, total mixed beverage sales have eontinued the
increasing trend that has been present since 2003/2004. The
breakdown between mixed beverage sales in restaurants ver-
sus bars shows that mixed beverage sales in bars have essen-
tially remained the same since implementation of the smoke-
free ordinance while mixed beverage sales in restaurants has
continued the inereasing trend.
Based on this preliminary data, it appears that sinee imple-
mentation of the September 2005 smoke-free ordinanee in
Austin, total mixed beverage sales eontinued an increasing
trend that was seen before the ordinanee, primarily driven by
increases in mixed beverage sales in restaurants, while mixed
beverage sales in bars remained about the same.
Finally, I want to eomment on the plausibility of all of the
studies that show no adverse economic impact resulting from
smoke-free ordinanees. In Texas, our Behavioral Risk Fae-
tor Surveillance System (BRFSS) data show that as of 2005,
eurrent adult cigarette use is now down to just under 20% – so
there are four times the number of adult non-smokers as smok-
ers. The 2004 BRFSS also included a question about how a
smoke-free ordinanee would affect people’s deeisions to go
out to restaurants, bars, bingo halls and bowling alleys. Forty
four pereent of the Texas adult respondents said they would
go out more often, 39% said it would make no differenee, and
only 13% said they would go out less often. These survey
results provide even more support for why it makes perfect
sense that economic cost studies do not show decreased sales
resulting from smoke-free ordinances.
In conclusion, as public health officials, the science is clear
that we have a responsibility to proteet the publie from the
health effects of exposure to secondhand smoke. Arguments
that sueh actions will have adverse economic impact on com-
munities are not supported by the data.
Figure
30000000 •
25000000 •
20000000 –
15000000 –
10000000 –
5000000 –
n –< 20 00 :
1 -Austin iViixed Beverage Saie
(in Doiiars) ordin
A A— . AA . ., A A y
20
01
i
20
02
i
20
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—
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ance
20
04
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20
05
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20
06
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Restaurants
1. Carmona, Richard. “News Release: New Surgeon General’s
report Foeuses on the Effeets of Seeondhand Smoke.” June
27, 2006. U.S. Department of Health and Human Services.
Accessed Deeember 1, 2006 http://www.hhs.gov/news/press/
2006pres/20060627.html.
2. U.S. Department of Health and Human Serviees. The
Health Consequences of Involuntary Exposure to Tobaceo
Smoke: A Report of the Surgeon General. U.S. Department
of Health and Human Services, Centers for Disease Control
and Prevention, Coordinating Center for Health Promotion,
National Center for Chronic Disease Prevention and Health
Promotion, Office on Smoking and Health, 2006.
3. Gingiss, P, Roberts-Gray, C, Boerm, M, Greer, K, Sline,
R. “Texas Smoke-Free Ordinanee Database.” Houston Health
Network for Evaluation and Training Systems. Accessed De-
eember 1, 2006 http://txshsord.eoe.uh.edu/default.aspx.
4. Huang P, Tobias S, Kohout S, et al. “Assessment of the
impaet of a 100% Smoke-Free Ordinance on Restaurant Sales
-West Lake Hills, Texas, 1992-1994.” MMWR, 1995;44:370-
372.
5. Hayslett J, Huang P. “Impact of Clean Indoor Air Ordi-
Continued on page 33
32 Volume 58, Issue 3 TPHA Joumal
nances on Restaurant Revenues in Four Texas Cities: Ar-
lington, Austin, Piano and Wichita Falls, 1987-1999.” Texas
Department of State Health Services, March 2000, Accessed
December 1, 2006 http://www,dshs.state.tx.us/tobacco/pdf/
cleanairord
6. Huang P, McCusker M. “Impact of a Smoking Ban on Res-
taurant and Bar Revenues – El Paso, Texas 2002.” MMWR,
2004;53:150-152.
From the
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Chronic Disease Grand Rounds (Podcast/video/handouts)
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Videos: Learn about misunderstandings surrounding chronic
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To acceso re)>ort go to: httpi/Mww.who Jnt/chptehronio.disease.report/en/indexhtml
TPHA Joumal Volume 58, Issue 3
Visit the TPHTC website www.txphtrainingcenter.org to see
about available and upcoming trainings and news. For ques-
tions or suggestions, you may contact Liz Trevino, TPHTC
Coordinator at 817.735.0311 or etrevino@hsc.unt.edu
On the Hunt for a Reliable
Definition of Chronic Disease
and Other Health Information
Carolyn Medina, M.A., MLIS Librarian
Texas Department of State Health Services
Austin, Texas
Where do people go to get more infonnation about a chronic
health issue? According to the Pew Intemet & American Life
Project October 2006 report,*” 80% of Intemet users go on-
line looking for health information but 75% do not consis-
tently check the source and date of the infomiation they find
online. Where could these people go and know they are get-
ting current expert information? 1 decided to go on a hunt and
see what I could find on the Intemet.
First of all, I wanted to know what exactly is a chronic dis-
ease? A definition was not found in a medical encyclopedia,
nor in a medical dictionary. Going to Google,* ‘̂ typing in
Continued on page 34
33
Walk for the Health of It Junior! – City of Fort Worth
Publie Health Department, Fort Worth – Karen Bell,
MPH, CHES
Walk for the Health of It Junior! was inspired by the adult ver-
sion Walk for the Health of It! to eombat the problem of obe-
sity and eardiovaseular disease by eneouraging physieal aetiv-
ity. frhe Junior! version targets 6th graders between the ages
of 11-12 years. The majority of the participants are Hispanie.
This program lasts 21 weeks with students completing walk-
ing logs of time walked during and outside of school. Parents
are asked to participate with the ehildren. A health educator
visits the sehool and teaehes the students about the importance
of exercise and the health benefits that result from regular
physieal activity. Anthropometrie measurements are taken for
all partieipating students before the program begins.
Walk Aeross Texas – Texas Cooperative Extension/TAMU
Sehqol of Rural Publie Health, College Station – Carol A.
Riee, PhD, RN
Walk Aeross Texas (WAT) is a fun, free fitness program aimed
at helping all people to establish the habit of physieal aetiv-
ity. Individuals participating in WAT can perform any aetivity
that Increases their heart rate. Using the Mileage Equivalents
list Ideated on the WAT website, individuals can convert time
spent on other activities, such as gardening, into miles walked
for the WAT eompetition. WAT participants log and track
their mileage in an on-line database.
African Ameriean Breastfeeding Promotion Campaign
– Texas Department of State Health Services WIC, Austin
– Tracy Erickson
This 9-month promotion campaign targeted African Ameri-
can men and women between the ages of 13-47. The goals
were increased awareness of the benefits of breastfeeding and
increased breastfeeding rates among African American WIC
participants. The brand and logo, “Breastmilk: 100% Natural
Ingredients”, was developed for the campaign and incorpo-
rated into all components. A brochure explaining the benefits
of breastfeeding to pregnant women was developed as well
as broehures for the woman’s partner and parents encourag-
ing them to support breastfeeding. The brochures were given
to al] African American WIC participants at initial pregnant
eertifications. Breastfeeding promotion bags were given to
healthcare providers to distribute. Posters were hung in WIC
clinifcs, hospitals, and healthcare provider offices. Exhibits,
flyeiis, and advertisements on TV, radio, billboards, and in the
newspaper spread the message in the African American com-
munity.
Eaeh recognized program was included in the 2006 Best Prac-
tices Listing and received statewide recognition at the Annual
Texas Public Health Association (TPHA) Conference and
ongoing promotion on www.EatSniartBeActiveTX.org and
www.dshs.state.tx.us/phn/phn.shtm. Additionally, representa-
tives of selected interventions had the opportunity to discuss
their intervention and lessons leamed at the TPHA Confer-
enee held in Piano, Texas.
The Economic Impact (or Lack
Thereof) of Smoke-Free
Ordinances
Philip Huang, MD, MPH
Secondhand smoke is reeognized as a significant health hazard
by all of the major health authorities. Fornier U.S. Surgeon
General Richard Carmona recently stated upon the release
of the latest Surgeon General’s report on seeondhand smoke
that “The scientific evidenee is now indisputable: secondhand
smoke is not a mere annoyance. It is a serious health hazard
that can lead to disease and premature death in children and
nonsmoking adults.”*” The report further noted that second-
hand smoke contains more than 50 cancer-causing chemicals,
and is itself a known human eareinogen. Nonsmokers who
are exposed to secondhand smoke inhale many of the same
toxins as smokers. Even brief exposure to secondhand smoke
has immediate adverse effeets on the cardiovascular system
and increases risk for heart disease and lung cancer, the report
says.’^’
In order to protect the public from the dangers of secondhand
smoke, many countries, states and local communities are
adopting comprehensive smoke-free ordinances. As of No-
vember 2006, 12 countries (Ireland, Italy, Seotland, England,
Norway, Sweden, New Zealand, Uganda, Malta, Uruguay,
Hong Kong, and Bhutan) and 16 states (California, Delaware,
New York, Connecticut, Maine, Massachusetts, Rhode Is-
land, Vermont, Washington, Hawaii, Ohio, Arizona, Montana,
New Jersey, Colorado and Utah) have passed comprehensive
smoke-free legislation that prohibits smoking in public places,
including all workplaces, restaurants and bars. In Texas, cur-
rently 9 cities (Austin, Beaumont, Benbrook, Copperas Cove,
El Paso, Houston, Laredo, Vernon and Victoria) have passed
ordinances that are considered 100% smoke-free and include
all workplaces, restaurants and bars.’^’ Houston just passed
their ordinance in October 2006, but it does not go fully into
effect until September 2007.
As those of us in the public health community are aware, ex-
posure to secondhand smoke is first and foremost a publie
health issue, and efforts to protect the public from involun-
tary exposure to secondhand smoke are similar to other public
health regulations to protect the public from a known public
health hazard. Nevertheless, whenever these issues are dis-
cussed, the potential adverse economic impact on private es-
tablishments (especially restaurants, bars and clubs) is raised
as a possible reason not to pass these ordinances.
Numerous studies nationwide have examined the economic
effeets of smoke-free ordinances. A study that appeared in
the journal Tobacco Control in 2003 reviewed 97 studies that
made statements about economic impact of smoke-free ordi-
Continued on page 32
TPHA Joumal Volume 58, Issue 3 31
nances. The authors concluded that “all of the best designed
studies report no impact or a positive impact of smoke-free
restaurant and bar laws on sales or employment.”
In Texas, we have published reports and produeed white papers
examining the economic impact of smoke-free ordinances on
restaurant sales in West Lake Hills, Arlington, Austin, Piano,
Wichita Falls and El Paso.”*”^ All of these reports examined
sales tax data obtained from the Texas Comptroller of Public
Accounts and none of the reports showed any adverse eco-
nomic impact resulting from the smoke-free ordinances. The
February 27, 2004 Morbidity and Mortality Weekly Report
(MMWR) article was our first to also look at impact on bar
and mixed beverage sales in El Paso (the first eommunity in
Texas to pass a smoke-free ordinance that included all bars).
Again, no adverse economic impact was noted on restaurant,
bar or mixed beverage sales as a result of their 100% smoke-
free ordinanee.
With the remainder of this artiele I will provide an update on
some of the latest data that we have from Austin, as it is one
of the most recent large Texas cities to implement a compre-
hensive 100% smoke-free that includes bars and elubs. Since
bars and clubs are the “final frontier” with regard to smoke-
free ordinances in Texas, there has been partieular interest in
any preliminary data on Austin bar sales, so I will foeus spe-
cifically on the bar and mixed beverage sales data.
On September 1, 2005, Austin’s 100% smoke-free ordinance
went into effect as a result of a voter-approved referendum.
Austin had previously had an ordinance that required smoke-
free restaurants and the new ordinance extended the protee-
tion to include all bars and clubs. What follows are data from
sales tax revenues reported to the Texas Comptroller of Public
Accounts for mixed beverage sales. Because the ordinanee
has only been in effect for a little over a year, and because
of delays in reporting, we cannot yet perform the statistical
analyses that we typieally use, however, some trends ean al-
ready be seen.
Figure 1 shows monthly mixed beverage sales from January
2000 through July 2006. Mixed beverage sales are broken
down by sales in bars, restaurants and total sales. As seen
in the figure, total mixed beverage sales have eontinued the
increasing trend that has been present since 2003/2004. The
breakdown between mixed beverage sales in restaurants ver-
sus bars shows that mixed beverage sales in bars have essen-
tially remained the same since implementation of the smoke-
free ordinance while mixed beverage sales in restaurants has
continued the inereasing trend.
Based on this preliminary data, it appears that sinee imple-
mentation of the September 2005 smoke-free ordinanee in
Austin, total mixed beverage sales eontinued an increasing
trend that was seen before the ordinanee, primarily driven by
increases in mixed beverage sales in restaurants, while mixed
beverage sales in bars remained about the same.
Finally, I want to eomment on the plausibility of all of the
studies that show no adverse economic impact resulting from
smoke-free ordinanees. In Texas, our Behavioral Risk Fae-
tor Surveillance System (BRFSS) data show that as of 2005,
eurrent adult cigarette use is now down to just under 20% – so
there are four times the number of adult non-smokers as smok-
ers. The 2004 BRFSS also included a question about how a
smoke-free ordinanee would affect people’s deeisions to go
out to restaurants, bars, bingo halls and bowling alleys. Forty
four pereent of the Texas adult respondents said they would
go out more often, 39% said it would make no differenee, and
only 13% said they would go out less often. These survey
results provide even more support for why it makes perfect
sense that economic cost studies do not show decreased sales
resulting from smoke-free ordinances.
In conclusion, as public health officials, the science is clear
that we have a responsibility to proteet the publie from the
health effects of exposure to secondhand smoke. Arguments
that sueh actions will have adverse economic impact on com-
munities are not supported by the data.
Figure
30000000 •
25000000 •
20000000 –
15000000 –
10000000 –
5000000 –
n –< 20 00 :
1 -Austin iViixed Beverage Saie
(in Doiiars) ordin
A A— . AA . ., A A y
20
01
i
20
02
i
20
03
1
— Total — Bars
—
s
ance
20
04
\
20
05
j
20
06
j
Restaurants
1. Carmona, Richard. “News Release: New Surgeon General’s
report Foeuses on the Effeets of Seeondhand Smoke.” June
27, 2006. U.S. Department of Health and Human Services.
Accessed Deeember 1, 2006 http://www.hhs.gov/news/press/
2006pres/20060627.html.
2. U.S. Department of Health and Human Serviees. The
Health Consequences of Involuntary Exposure to Tobaceo
Smoke: A Report of the Surgeon General. U.S. Department
of Health and Human Services, Centers for Disease Control
and Prevention, Coordinating Center for Health Promotion,
National Center for Chronic Disease Prevention and Health
Promotion, Office on Smoking and Health, 2006.
3. Gingiss, P, Roberts-Gray, C, Boerm, M, Greer, K, Sline,
R. “Texas Smoke-Free Ordinanee Database.” Houston Health
Network for Evaluation and Training Systems. Accessed De-
eember 1, 2006 http://txshsord.eoe.uh.edu/default.aspx.
4. Huang P, Tobias S, Kohout S, et al. “Assessment of the
impaet of a 100% Smoke-Free Ordinance on Restaurant Sales
-West Lake Hills, Texas, 1992-1994.” MMWR, 1995;44:370-
372.
5. Hayslett J, Huang P. “Impact of Clean Indoor Air Ordi-
Continued on page 33
32 Volume 58, Issue 3 TPHA Joumal
nances on Restaurant Revenues in Four Texas Cities: Ar-
lington, Austin, Piano and Wichita Falls, 1987-1999.” Texas
Department of State Health Services, March 2000, Accessed
December 1, 2006 http://www,dshs.state.tx.us/tobacco/pdf/
cleanairord
6. Huang P, McCusker M. “Impact of a Smoking Ban on Res-
taurant and Bar Revenues – El Paso, Texas 2002.” MMWR,
2004;53:150-152.
From the
Chronic Disease Training Opportunities:
A nqmber of training opportunities and resources in different
training modes and accessibility exist. The following is an ab-
breviated list of those opportunities that you can access.
New York and New Jersey Public Health Training Center
Training Title: Move It: A Case Study in Policy Change
and Health Promotion Program Planning
Available at: www.nynj-phtc.org/leaming/default.cfm
Subject Area(s):
Health Promotion and Prevention
Overweight and Obesity
Physical Activity and Fitness
Program Development/Planning
Audience:
Environmental health professionals
Health educators
State or local public health workers
Pacific Public Health Training Center
Online Training: Obesity and Overweight
Available at: http://pphtc.org/training/courselist.htm
South Central Public Health Training Center
Community health and disease & Diversity and cultural com-
petency in public health settings
Available at: http://www.southcentralpartnership.org/training/
train jng. asp? I D=4
School of Public Health University at Albany
Chronic Disease Grand Rounds (Podcast/video/handouts)
• The Diabetes Epidemic: Preventing the Preventable
• Clinical Breast Examination
• Colorectal Cancer
• Healthy Schools Approach: Type 2 diabetes in children
Available at: http://www.albany.edu/sph/coned/webstream.
htmffchronic
Florida DOH Chronic Disease Resources Online
Provider Education Modules
Available at: http://www.onlinece.net/subpages/doh_over-
view.asp
Continuing Education Courses
• CRASH: Cultural Competency Skills for Diabetes Care
• The Insulin Resistance Syndrome—^No Longer Just For
Adults!
• Obesity, Metabolic Syndrome, and Diabetes Prevention
in Adults
• Pre-Hypertension: Diagnosis and Recommendations
• Hypertension ~ Where Are We Now?
• Addressing the Psychosoeial Aspects of Diabetes
Available at: http://www.onlinece.net/courses.asp?discipline=
DOH&action=list
World Health Organization (WHO)
Fact File: Test your knowledge with ten startling facts
about chronic disease.
Videos: Learn about misunderstandings surrounding chronic
disease.
Available at: http://www.who.int/chp/en/
Important Resource!
Fr«nitlie World Healtli Organizitioii IWHO)
Glob.il report on c Ironic disease –
fie venting cAromc diseases: a vAa/ investment
The report makes the case for urgent aotion to halt and turn baok the groWng threat of
chronic diseases, and dispels the iong-heid misunderstandings about heart disease,
stroke, cancer and other chronic diseases, it presents a stateof-the-art guide to effective
and feasible interventions, provides practical suggestions for how countries can
implement these interventions.
To acceso re)>ort go to: httpi/Mww.who Jnt/chptehronio.disease.report/en/indexhtml
TPHA Joumal Volume 58, Issue 3
Visit the TPHTC website www.txphtrainingcenter.org to see
about available and upcoming trainings and news. For ques-
tions or suggestions, you may contact Liz Trevino, TPHTC
Coordinator at 817.735.0311 or etrevino@hsc.unt.edu
On the Hunt for a Reliable
Definition of Chronic Disease
and Other Health Information
Carolyn Medina, M.A., MLIS Librarian
Texas Department of State Health Services
Austin, Texas
Where do people go to get more infonnation about a chronic
health issue? According to the Pew Intemet & American Life
Project October 2006 report,*” 80% of Intemet users go on-
line looking for health information but 75% do not consis-
tently check the source and date of the infomiation they find
online. Where could these people go and know they are get-
ting current expert information? 1 decided to go on a hunt and
see what I could find on the Intemet.
First of all, I wanted to know what exactly is a chronic dis-
ease? A definition was not found in a medical encyclopedia,
nor in a medical dictionary. Going to Google,* ‘̂ typing in
Continued on page 34
33
The Effect of Ordinances Requiring
Smoke-Free Restaurants and Bars
on Revenues: A Follow-Up
A B S T R A C T
Objectives. The purpose of this
study was to extend an earlier evalua-
tion of the economic effects of
ordinances requiring smoke-free res-
taurants and bars.
Methods. Sales tax data for 1
5
cities with smoke-free restaurant ordi-
nances, 5 cities and 2 counties with
smoke-free bar ordinances, and
matched comparison locations were
analyzed by multiple regression, in-
cluding time and a dummy variable
for the ordinance.
Re.sults. Ordinances had no sig-
nificant effect on the fraction of total
retail sales that went to eating and
drinking places or on the ratio
between sales in communities with
ordinances and sales in comparison
communities. Ordinances requiring
smoke-free bars had no significant
effect on the fraction of revenues
going to eating and drinking places
that serve all types of liquor.
Conclusions. Smoke-free ordi-
nances do not adversely affect either
restaurant or bar sales. {Am J Public
Health. 1997;87:1687-1693)
Stanton A. Glantz, PhD, and Lisa R. A. Smith
Introduction
By March 1997, more than 1
50
communities in the United States had
eliminated smoking in public places and
workplaces.’ Califomia law now requires
that all restaurants be smoke-free and that
all bars become smoke-free on January 1,
1998.2 In 1994, using sales tax data, we
evaluated the effects of ordinances requir-
ing smoke-free restaurants on
restaurant
revenues in the first 15 US cities that had
passed ordinances prohibiting smoking in
the enclosed areas of restaurants (not
necessarily including bar areas).^ We
found that restaurant revenues were not
affected, and subsequent studies con-
firmed this result.’*^ The tobacco industry
and its front groups continue to claim that
these ordinances create severe economic
problems for restaurants and bars.^”” We
have added 3 more years of data (through
the second quarter of 1996) for the
original 15 cities (Tahle 1) as well as data
for the first 5 cities and 2 counties to
require bars to be smoke-free (Table 2).
Methods
We conducted two sets of analyses:
(1) an analysis of the effects of smoke-free
restaurant ordinances on restaurant rev-
enues, and (2) an analysis of the effects of
smoke-free bar ordinances on bar rev-
enues.
As before, we obtained data on
taxable restaurant sales and total retail
sales’2-‘3 for communities that had smoke-
free restaurant ordinances in force as well
as for comparison cities (matched on
population, income, smoking prevalence,
and geographic location) that provided
less than 60% of seating for nonsmokers
(Table 1; Point Arena is the comparison
city for Ross, because Tiburon, the
comparison city in our earlier study,
passed a smoke-free ordinance). Analysis
of the restaurants in Califomia compari-
son cities could not go beyond December
1994, after which a state law required all
restaurants to be smoke-free.^ All compari-
son cities were selected before the statisti-
cal analysis was performed.
In the study of smoke-free bar
ordinances, all communities with ordi-
nances that clearly identified bars as
smoke-free and that had been in effect
long enough for us to obtain 1 year of
sales tax data were included. For the five
cities that require bars to be smoke-free,
sales tax data were obtained from the
Research and Statistics Division of the
Califomia Board of Equalization for 199
1
through 1995. Data for the two counties
with smoke-free bar ordinances, pub-
lished in quarterly reports,’^ were avail-
able from 1986 through 1996. So that we
could examine effects on eating and
drinking establishments that sell all types
of liquor, we gathered sales tax data
separately for eating places serving no
alcoholic beverages (category 24), those
serving beer and wine (category 35), and
those serving all types of liquor (category
36). Detailed breakdowns of revenues hy
category number are available only for
1991 and later years; the Board of
Equalization had disposed of the detailed
data from earlier years. For San Luis
Obispo, however, we have these data from
an earlier study.'”* No comparison city is
available for San Luis Obispo because its
ordinance went into effect in August 1990.
Data were also obtained for comparison
communities without smoke-free bar ordi-
nances (Table 2).
The analysis was conducted as be-
fore. Briefly, we computed (1) the fraction
of total retail sales at restaurants and (2)
the ratio of restaurant sales in cities with
ordinances to restaurant sales in compa-
rable cities without ordinances. The linear
regression analysis included time; a
dummy variable, L, that indicated whether
The authors are with the Institute for Health
Policy Studies, Department of Medicine, Univer-
sity of Califomia, San Francisco; Dr Glantz is
also with the Division of Cardiology.
Requests for reprints should be sent to
Stanton A, Glantz, PhD. Division of Cardiology.
Box 0124, San Francisco, CA 94143-0124,
This paper was accepted August 8, 1997,
October 1997, Vol. 87. No, 10 American Joumal of Public Health 1687
Public Health Briefs
TABLE 1—Profile of Smoke-Free Restaurant and Comparison Cities
Smoke-Free
City
and Matched ‘
Comparison City
Aspen, Colo
Vail, Colo
Auburn, Calif
Oroville, Calif
Beverly Hills, Calif
Santa Monica, Calif
Bellflower, Calif
Lakewood, Calif
El Cerrito, Calif
San Pablo, Calif
Lodi, Calif
Merced, Calif
Martinez, Calif
Pleasant Hill, Calif
Palo Alto, Calif
Mountain View, Calif
Paradise, Calif
Red Bluff, Calif
Roseville, Calif
Chico, Calif
Ross, Calif
Pt. Arena, Calif
Sacramento, Calif
Fresno, Calif
San Luis Obispo, Calif
Santa Maria, Calif
Snowmass, Colo
Breckenridge, Colo
Telluride, Colo
Steamboat Springs, Colo
1989
Population^
5 049
3 6
59
10 592
11 9
60
31 9
71
86 905
61 8
15
73 000
22 869
25 158
51 87
4
56 2
16
32 038
31 585
55 544
67 460
25 408
12 3
63
44 685
40 076
2 180
428
369 3
65
354 202
41 958
61 284
1 426
1 285
1 292
6 695
Location”
0
0
0
0
0
0
1
1
1
R
1
1
1
1
R
R
R
0
Type of
Smoking
Restriction’^
100
%
Some
100%
Some
100%
Some
100%
Some
100%
Some
100%
Some
100%
Some
100%
Some
100%
None
100%
Some
100%
None
100%
Some
100%
Some
100%
None
100%
None
1989 Median
Household
Income^
37 4
67
41 211
37 272
16614
54 348
35 997
32 711
44 700
39 538
25 479
30 739
24 727
45 964
46 885
55 3
33
42 431
22 954
19 474
39 975
19 005
84 414
21 250
28 183
24 923
25 982
29 492
39 107
33 259
31 968
29 363
=Data are from US Census Bureau.^’
“”O = outside urban area; 1 = inside urban area; R = rural, nonfarm area.
•̂ ”Some” refers to no more than 60% seating area for nonsmokers.
“Data are from Pierce et al.22 (California) and Centers for Disease ControP^ (Colorado).
^Number of months for which data were available for this study.
%0f
Smokers’*
23.5
23.5
24.1
23.6
21.8
21.8
21.8
21.8
22.9
22.9
24.1
25.1
22.0
22.0
19.7
19.7
23.6
23.6
24.1
23.6
21.6
23.6
25.2
25.1
18.9
18.9
23.5
23.5
23.5
23.5
Date
Ordinance
in Effect
8/85
4/91
4/87-7/
87
3/91-6/92
11/91
12/90
3/92
9/92
8/91
10/91
1/90
5/92
8/90
5/89
4/88
No.
Months
in Effect”
119
63
4
16
56
67
52
46
59
57
78
50
71
87
99
or not a smoke-free restaurant law was in
force; and, for Colorado, a dummy
variable for the winter tourist season. The
regression coefficient bi quantifies the
magnitude of the effect of the ordinance.
In addition to analyzing data for each city
separately, we pooled all data on restau-
rant sales as a percentage of total retail
sales for all 15 cities with ordinances for
the entire year in a single analysis,
including dummy variables to allow for
between-city differences.
For analysis of the smoke-free bar
ordinances, in addition to the analyses just
described, we evaluated sales for eating
and drinking establishments with liquor
licenses (category 36) as a fraction of all
retail sales and as a fraction of all sales by
eating and drinking establishments, using
the same procedures just described, by
computing
Bar
Sales
Total Eating and Drinking Places Sales
In a few cases, the residuals showed
evidence of a positive serial correlation
(evidenced by a statistically significant
Durbin-Watson statistic). The residual
plots suggested a long-term nonlinear
relationship that may have reflected the
business cycle in California, which was
strong near the beginning and end of our
study period and in recession in the
middle of the study period (early 1990s).
We reanalyzed the data, including a
quadratic term in time (after centering the
time variable to reduce the structural
multicollinearity’^). P < .05 is considered
statistically significant.
Results
Table 3 summarizes the results for
total restaurant sales as a fraction of all
retail sales and for the ratio between total
restaurant sales in cities with ordinances
and sales in the matched comparison
cities. The first column in the table is the
mean value observed over the study period.
Smoke-free ordinances generally had
no statistically significant effect, either on
the fraction of total retail sales that went to
1688 American Joumal of Public Health October 1997, Vol. 87, No. 10
Public Health Briefs
TABLE 2—Profile of Smoke-Free Bar and Comparison Cities and Counties, California
Smoke-Free City or
County and Comparison
City or County
Anderson’
Red Bluff
Davis
Chico
Redding
Healdsburg
San Luis Obispo
(None available)
Shasta County
Butte County
Santa Clara County
Alameda County
Tiburon
Sausalito
1989
Popuiation^
8 299
12 363
46 209
40 079
66 462
60 471
41 958
72 275
98 6
25
106 183
119 882
7 532
7 152
“Data are from US Census Bureau,2′
•”O = outside urban area; 1 = inside urban area
Location”
0
0
1
1
1
1
1
R
R
1
i
1
1
Type of Restriction’^ : R = rurai, nonfarm area.
1989 Median 22 321 29 044 25 828 41 676
25 581 48 115 75 864 %0f 23,6
23,6 18,9
23,6 19,7
21,7
^”None” refers to locaiities in wiiich bars are specificaiiy exempted from or not mentioned in any existing ordinance.
“uata are trom rierce et ai, Date In Effect
2/93
3/93
2/93 2/94
11/93
No, in Effecf=
35
33 30
42
25 restaurants or on the ratio between sales in The results of the analysis of the bar reported by establishments that sell all The nonlinear model resolved the w
statistically significant changes we esti- Discussion
This study expands and confirms our predominantly agricultural region of Cali- Our earlier work attracted criticism October 1997, Vol. 87, No, 10 American Journal of Public Health 1689 Public Health Briefs TABLE 3—Effect of Smoke-Free Restaurant Ordinances City All combined
Mean
Sales Effect of Ordinance
Change, bi.
Fraction of total retail saies,
24.9
8.6
1 2.6
13.0
12.8
11.3
11.0
16.2
14.6
6.8
48.9
14.1
13.0 45.3
30.1
18.9 – 0 . 3 2.4
2.0 – 2 . 0 – 1 0 . 9 : 1.1 : – 4 . 2 : 2 . 8 : ± 1.1 P
% .8 20
.809
.865
.805
.000
.000 .564
.794
.964
.703
.954
.896
.021
.182
.988
.896 .015
.009
.006
.012
.070
.609
.066
.076
.953
.789
.337
.506
.488
.011
.964 .879
Model
.650
.650 .6 40
.834
.503
.548
.043
.192
.052
.095
.115
.153
.267
.705
.176
.177
.264
.288
.402
.423
.543
.580
.145
.147
.256
.267 .530
.530 .062
.230
.890
.891
P .000^
.000 .428
.042
.350
.278
.092
.095 .007
.000 .023
.058
.003
.006 .048
.106
.003 .008
.000 .482
.042 Ratio between sales in smoke-free city and sales in comparison city
Aspen, Colo 1.17
.44
.50
.56
1 .32
.86
.44 1.71
.69
.68
.60
1.
10
1 .12
.80
.43
.85
. 0 9 :
. 3 2 :
. 0 3 :
t .12 .04 ± .02 .01 :i .08 d .00 d
.04 d
.04 d .27 d
.07 d .04 1
.09 1 t .02 .04
t .04 .03
: .03 .07
: .09
:.O4 .42
: .62 .08
::03
.03 Note. The first row for each city shows results of linear time ^Significant positive serial correlation of residuals.
.459
.083
.170
.844
.726
.698
.158
.178
.333
.606
.834 .921
.120
.182 .001
.421
.018
.119
.511
.081
.769
.008 .322
.211
.142
.154
.342
.138
.456
.266
.627
.589
model; the
.223
.262
.077
.370
.028
.029
.279
.283
.089
.221
.554
.572
.705 .349
.479
.329
.355
.042 .173
.564 .784
.310
.324
.158 .179
.699
.710
.283 .297
.802
.805 .002
.267^
.002 .629
.814
.005
.015 .214
.148
.000 .001«
.003 .490
.104
.002 .059
.094
.000 .010
.000 second row shows
with no interest in the effects of smoke- As noted above, an exemption for The fact that we did not observe Food service workers enjoy less 1690 American Joumal of Public Health October 1997, Vol. 87, No. 10 I S
10 Aubum – ^
I 9 W ia«6 1990 1992 1994 1996
20 w,i« a
BeDflower
1916 1966 1990 1992 19M 1996
20 15 <
10 5 Beverty Hiils
19S6 1961 1990 1992 1994 1996
20 a.-a. >W ^ a.af^^»^^f**—^^^f*
El Cerrito
1966 1966 1990 1992 1994 1996
15 5 Lodi
1966 1966 1990 1992 1994 1996
15-
10 S Martinez
1966 1966
15- 5 Palo Alto
1966 1966 5 Paradise
1966 1966 Roseville
teJVUeaeee
1966 1966 1990
1990 pnn ft
1990
1992
w 1994
1994 15 (
10 >%,
1966 40 20 1962
6 0 (
40 Sacramento
1966 1990
San Luis Obispo Aspen
1964 1966 1966
Snowmass
1966 1990 Telluride
1966 1990 t^ote. The quarters in which 100% smoke-free restaurant ordinances were in effect are shown as solid points.
FiGURE 1—Restaurant saies as a percentage of total retaii
1992 fut
1992 • tLJt
1992 saies for the 15 communities with smoke-free
1994 1996 Anderson Redding Shasta County
1992 10 T
1966 1969 1990 1992 1994 19
San Luis Obispo 1992 1966 1966 1990 1992 1994 1996
Santa Clara County Note. The quarters in which 100% smoke-free bar ordinances were in effect are shown as solid points.
FIGURE 2—Bar sales as a percentage of total retail sales for the 7 California communities with smoi October 1997, Vol. 87, No. 10 – American Journal of Public Health 1691 Public Health Briefs TABLE 4—Effects of Smoke-Free Bar Ordinances City Anderson
Davis Santa Ciara County
Shasta County Ail combined
i\/1ean
Effect of Ordinance Change, bi. P
Fraction of total retail sales, %
3.1
3.3
2.6 3.8
4.5
2.4 41.3
7.1
– 0 . 7 ± 0.1 ± 0.8 .404
.514
.752
.943
.456 .731
.907
.945
.775
.996
.076 .270
.904
.964 .258
.456 .613
.658
.718
.098
.129
.052 .053
.135
.137
.505
.508
.103
.105
.979
.979 Modei
P .689=
.001 .417
.516
.374
.574
.059 .396
.610
.000 .49
.42 18.05
.31
1.52
1.07
0.70
2.76
– . 1 5 ± .66 + .05 ±
.04 ±
.02 ± .21 ±
.12 .11
.04 .04 .06
.09 .01
.08 .45
.53
.224
.033
.073
.021 .902
.076 .411
.664
.067
.757
.134
.205
.589 .695
Fraction of total restaurant sales, %
19.9
17.9
26.6
29.3
34.8
23.9
76.6
.32 4.8 ± 0.0 ± – 1 . 6 ± 0.6 ± 5.5 .401
.358
.194
.147 .373
.331
.907 .704
.065
.890 .597
.603
.623
.505 .692
.153 .400
.696
.767
.023 .596
.486
.486 .576
.601
.411 .413
.898
.903
.042 .711
.739
.753
.533
.539
.052 .197
.809 .827
.576 .036
.036 .955
.955 .244=
.038
.000 .823=
.002 .032
.000 .046
.000 .000*
.000 .732
.894
.000 Note. For each city, the first row of data shows resuits of the iinear time modei; the second row ^Significant positive serial correiation of residuals.
Acknowledgment References Americans for Nonsmokers Rights; 1997.
2. Macdonald H, Glantz S. The political 3. Glantz S, Smith LRA. The effect of 4. The San Luis Obispo Smoking Ordinance: 5. Maroney N, Sherwood D, Stubblebin W. 6. Centers for Disease Control. Assessment of 7. Pope G, Bartosch W. Effect of local 8. Sciacca J, Ratliff M. Prohibiting smoking 9. Samuels B, Glantz S. The politics of local 10. Samuels B, Begay M, Hazan A, Glantz S. 11. Traynor MP, Begay ME, Glantz SA. New 12. Taxable Sales in California (Sates and Use 13. Sales Tax Statistics for Counties and 14. Glantz S, Smith L. The Effect of Ordi- 15. Glantz SA, Slinker BK. Primer of Applied 16. Richards B. Pro-tobacco groups step up 17. Stauber J, Rampton S. Toxic Sludge Is 18. Erratum. In: Glantz S, Smith L. The effect 19. Gerlach K, Shopland D, Hartman A, 1692 American Joumal of Public Health October 1997, Vol. 87, No. 10 Public Health Briefs nationrfl survey ‘of over 100,000 workers. 20. Siegei M. Involuntary smoking in the restau- 21. United States Census of Population and 22. Pierce J, Frakas A, Evans N. Tobacco Use Uptake in Adolescents. Sacramento, Calif: 23. Behavioral Risk Factor Surveillance Sys- Thyroxine Values from Newborn A B S T R A C T Objectives. Severe transient hy- Methods. Congenital hypothy- Results. Thyroxine values were Conclusions. Severe transient M. Lynne Reuss, MD, MPH, Alan Leviton, MD, SM, Nigel Paneth, MD, MPH, Introduction roxine levels postnatally, a condition In this paper we describe thyroxine- tal hypothyroidism-screening programs Methods From January 1991 through Decem- Gestational-age estimates were based At the time of the study, M. Lynne Reuss was Requests for reprints should be sent to M. This paper was accepted November 8, Editor’s Note. Dr Heinz Berendes served Octoberl997,Vol. 87, No. 10 American Journal of Public Health 1693 The Effect of Ordinances Requiring A B S T R A C T StantonA. Glantz, PhD, and Lisa R. A. Smith, BA
Objectives. The cfFcct on restau- Methods. Data were obtained Results. Ordinances had no sig- Conclusions. Smoke-free restau- Introduction
As the evidence that environmental City
Couneil to repeal the first 10()% smoke- This study analyzes sales tax data for Beverly Hills (which amended its ordi- Methods
Data on taxable restaurant sales and 5
eommunities that had smoke-free restau- The authors are with the Institute for Hciilth Requests for reprints should he sent to This paper was accepted February 4, Ju!vl’W4,V()l.K4.No. 7 American Journal of Public Health 1081 Glantz and Smith
TABLE 1—Profile of Smoite-Free and Comparison Cities
Smoke-Free and Aspen, Colo Auburn, Calif Beverly Hills, Calif Bellflower, Calif El Cerrito, Calif Lodi, Calif Martinez, Calif Palo Alto. Calif Paradise, Calif Roseville, Calif Ross, Calif Sacramento, Calif San Luis Obtspo, Calif Snowmass, Colo Telluride, Colo Population 5 049 10 592 31 971 61 815 22 869 51 874 16
32 038 55 544 25 408 63
44 685 2 180 32
369 365 41 958 1 426 1 292 95
(
Inside Area
X X Geographical
Outside Area Nonfarm
X Type of Restriction”
100% 100% 100% Median Income 37 467 37 272 14
54 348 32 711 39 538 30 739 45 964 55 333 22 954 39 975 84 414 28 183 23
25 982 39 107 31 968 %of 23.5
24,1 23.6
21,8
21.6
22.9
24,1 22,0
19,7
23.6 24.1 21,6
25,2 18.9
23,5
23,5 Date 10/85
10/91
4/87-8/87
6/91- 3/92
11/91
1 1/90
3/92 8/91
9/91
1/90 5/92
8/90
5/89
4/88
=1990 US Census of Population and Housing,” No, Of in Effect̂
95 21
5 10
20
32 22
42
14 35
51
63 ordinance was in force or where no more account for late-reported data (written To account for population growth. were also compared with sales in compa- C =
Restaurant Sales in City Restatirant Sales in City’ F = Total Retail Sales’
If an ordinance adversely affected restau- Again, if an ordinance adversely affected Data were analyzed with linear re- y = bt^ + hf + h,L + hJV,
where y is the dependent variable (F or 1082 American Journal of Pubiic Heaith July 1994, Voi. 84, No. 7 Smoke-Free Restaurants
underlying secular trend, and Z. is a 0 If No Ordinance 1 Month of Quarter 2 Months of Quarter Entire Quarter
The coefficient b,, quantifies the magni- Not only were data analyzed for each The variance inllation factors for Results
Table 2 summarizes the results for Smoke-free ordinances generally had TABLE 2—Effect of Smoke-Fre« Restaurant Ordinances on Total Restaurant Saies
City Aspen Aspen Mean
Effect of Ordinance
Change, bi
Fraction of total retail sale:
24.8 13,1 43,5 1,1 ± 1,3 -0.4 ± 0,7 -1.4 ± 0,8 0.9 ± 0,6 -1,3 ± 1,0
P
i.F. %
,408 Ratio of saies with comparison city, C
1.12 1.28 1.69 1.10 .21 ± .12
.03 ± .02 ,04 ± ,03 – 0 7 = ,02 i ,08 :
– ,29 :
,08 : 1 ,07 -.04 ± ,03
.106 Modei
,688 .153 P 000 ,071 fraction of retail sales that went to Beverly Hills is a particularly impor- Discussion
This is the first comprehensive study Juiyl994,VoLHNo.7 American Journai of Public Heaith 1083 Glaniz and Smith
iin ‘HO ini i»i mi
ino Itt’ i«i iMi
INI in; itti it9< oee its' Note. The quarters in which 100% smoke-free ordinances were in effect are represented by solid circles.
FIGURE 1—Restaurant sales as a percentage of total retail sales for the 15 communities included In the study.
t991 19921986
Note. Period of smoke-free ordinance Is indicated by the solid triangles.
FIGURE 2—The 100% smoke-free restaurant ordinance in force in Beverly Hills poses of paying sales taxes has several The communities studied in the 1084 American Journal of Public Health July 1994. Vol. 84, No. 7 Smoke-Free Restaurants nity; Ross is a small affluent San Francisa) This study covers a significant period A common concern is raised about these cities to dine in neighboring cities, Another area of concern is the effect Finally, the fact that the ordinances Acknowledgments References Smoking :ind Heaith. The Health Comc- 2. National Research Council. Committee on 3. Respirautry Health Effects of Passive Smok-
ing: t,img Cancer ami Other Disorders. 4. Giantz SA. Parmley WW. Passive smoking 5. Steeniand K. Passive smoking and the risk 6. Siegel M. Involuntary smoking in the 7. Pithltv Attitiuies toward Cigaretle Smoking 8. Samuels B. Giantz S. The poiitics of kxal 9. Traynor MP, Begay ME, Giantz SA. The 10. Press Alert and Fact Sheet: San Francis- 11. United .Slates Census of Population and 12. Pierce JP. Faritas A, Evans N, et ai. 1.̂ . Behaviomt Risk FactorSurxeithmeeSystem— 14. Taxable Sales in California (Sale.i and Use 15. Sates Tax Statistics for Cotinties and Selected Ih. Mendenhall W. Sincich T.A Second Course 17. Impticts of Local 100% Smoking Bans on 18. Giantz SA. Smith LRA. Tfte Effect of July 1994, Vol. 84, No. 7 American Journal of Public Heaith 1085
Smoi
100%
None
100%
None
100%
None
100%
100%
None
100%
None
100%
None
Household
Income^
19 474
19 005
33 712
22 776
37 544
60 471
Smokers’*
23,6
^Number of months for which data were available for this study.
‘County ordinance is enforced In city.
Ordinance
8/90
2/93
Months
35
65
smoke-free cities and sales in comparison
cities (Table 3 and Figure 1). The linear
model indicates that the fraction of total
retail sales that went to restaurants in-
creased in two cities (Bellflower and
Martinez) and decreased in two cities
(Paradise and Roseville). Restaurant sales
relative to sales in the comparison city
increased in one city (Palo Alto) and
decreased in another (Paradise). These
results are similar to those we previously
reported.^ The nonlinear model produced
similar estimates for the ordinance effects;
two cities showed an increase and one a
significant decrease in terms of restaurant
revenues as a fraction of retail sales, and
one city showed a significant increase in
terms of the ratio between its sales and
those of its comparison city (Table 3).
Analysis of all the data in pooled regres-
sions did not result in significant changes
when either model was used.
data appear in Table 4 and Figure 2, The
linear model indicates that there were no
significant effects of the smoke-free ordi-
nances on bar sales as a fraction of total
retail sales, on the ratio between bar sales
in cities with ordinances and sales in
comparison cities, or on the fraction of
all eating and drinking place revenues
types of liquor (category 36). The nonlin-
ear model indicated that there was one
significant drop in sales (in Davis relative
to its comparison city). Analysis of all the
data in pooled regressions did not result in
significant changes in any variable when
either model was used.
few statistically significant serial correla-
tions we observed, but it had little effect
on the estimates of the ordinance effects.
This result suggests that the estimates of
the ordinance effects are not artifacts of
the model specification. The lack of
consistent response suggests that the fe
mated may simply reflect random varia-
tion, given the large number of P values
that were computed, rather than a system-
atic effect of the ordinances.
earlier work showing that smoke-free
restaurant ordinances do not affect restau-
rant revenues. It also shows that the same
is true for smoke-free bar ordinances. The
cities and counties with smoke-free bar
ordinances are diverse. Anderson and
Redding are isolated cities within a
fomia. Davis is a university town. Tiburon
is an affluent suburban community that
enjoys heavy tourist business. San Luis
Obispo is a coastal community that has a
major college as well as substantial
tourism. The two smoke-free counties,
Shasta and Santa Clara, have ordinances
that cover unincorporated areas; Shasta is
rural and Santa Clara is a suburban county
in the San Francisco Bay Area,
from the tobacco industry,’^ acting through
Philip Morris’ National Smokers Alli-
ance.’^ The criticisms have included
claims that there were errors in the
effective dates of the ordinances, that we
mischaracterized the ordinances as smoke-
free when they were not, and that sales tax
data are not accurate (Evans MK, A
review of “The effect of ordinances
requiring smoke-free restaurants on restau-
rant sales” by Stanton A. Glantz and Lisa
R. A. Smith. March 1997, Unpublished,).
Correcting the effective dates of these
ordinances does not affect the conclusions
in our earlier paper.’^ A careful review of
the ordinances for both the smoke-free
and comparison cities shows that they
meet our stated criteria in both cases.’
Sales tax data include all restaurant and
bar sales and are collected by an agency
on Total Restaurant
Aspen, Colo
Auburn, Calif
Bellflower, Calif
Beverly Hills, Calif
El Cerrito, Calif
Lodi, Calif
Martinez, Calif
Palo Alto, Calif
Paradise, Calif
Roseville, Calif
Ross, Calif
Sacramento, Calif
San Luis Obispo, Calif
Snowmass, Colo
Telluride, Colo
– 0 . 4
– 0 . 1
– 0 . 1
0.8
0.3
0.0
0.3
0.0
0.0
2.5
4.4
0.0
0.2
– 2 . 2
– 1 . 1 :
– 1 . 0 :
– 3 . 7 :
1.2:
0 . 0 :
0.1 :
– 3 . 9 :
13.5:
0 . 0 :
0 . 0 :
± 1.7
±0.8
±0.5
±0.4
±0.4
± 1.4
± 1.3
±0.7
±0.7
±0.5
± 0.5
± 1.0
± 3.2
± 1.0
±0.1
±0.8
±0.8
tO.4
±0.4
±5.9
±7.2
±0.6
t 0.6
tO.5
tO.5
t 4 . 3
t 5 . 8
t 3.7
t 5.0
tO.6
tO.6
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
Auburn, Calif
Bellflower, Calif
Beverly Hills, Calif
El Cerrito, Calif
Lodi, Calif
Martinez, Calif
Palo Alto, Calif
Paradise, Calif
Roseville, Calif
Ross, Calif
Sacramento, Calif
San Luis Obispo, Calif
Snowmass, Colo
Telluride, Colo
All combined
t .18
t .02
– . 0 1 j
– . 0 6 d
– . 0 6 d
– . 0 5 d
– . 0 1 d
– . 0 9 d
– . 0 7 d
– . 0 2 d
– . 0 6 d
-.12 i
1.93 d
-.03 d
-.05 d
-.08 d
-.08 d
-.13 :•
-.27 1
-.01 1
-.01 1
t .02
t
:.O8
: .10
:.O3
:.O3
:
:
: .04
: .03
: .03
:
: .03
: .04
:.O5
:.O5
: .14
: .18
: .05
:
results of quadratic time model.
.705
.003
.000
.000
.000
.001
.000
.000
.002
.005
.000
.005
.000
free ordinances. (For a detailed response
to the Evans critique, contact the authors.
This response is also posted on the World
Wide Web at http://www.tobacco.org/
Misc/evansresponse.html.)
the bar area of a restaurant did not
disqualify a smoke-free restaurant ordi-
nance from our study of smoke-free
restaurant ordinances,^ so long as the
eating areas were smoke-free. The present
study shows that smoke-free bar ordi-
nances do not affect bar revenues. It is
important to note that our analysis of the
smoke-free bar ordinances relied on data
for establishments with full liquor licenses
(category 36). This category includes
free-standing bars and bars within restau-
rants. It is not possible to analyze the
effects of these ordinances on these two
subcategories of business separately. Nev-
ertheless, it is important to evaluate the
effects of smoke-free ordinances on this
category of eating and drinking establish-
ments because of claims by the tobacco
industry that smoke-free ordinances par-
ticularly affect sales of establishments that
sell liquor. Moreover, the fact that some of
the restaurant ordinances permitted smok-
ing in bar areas does not support the
tobacco industry’s assertion that the lack
of change in restaurant revenues in the
cities shown in Table 1 is due to a shift of
business to bars and the bar areas of
restaurants.
changes in the fraction of eating and
drinking establishment revenues going to
category 36 businesses is evidence that
these ordinances do not cause shifts
between types of business. It is also
important to emphasize that the purpose
of this study, like that of our earlier study,
was to address the claim that smoke-free
ordinances substantially decrease rev-
enues across the board; the usual claim is
a reduction of 30%. The data do not
support this claim. (Our analytic methods
have a power exceeding .99 to detect a
30% change in restaurant or bar revenues
with a = .05.)
protection from secondhand tobacco
smoke than any other group of employ-
e e s . ” Legislators and govemment offi-
cials can enact health and safety regula-
tions to protect patrons and employees^”
in restaurants and bars from the toxins in
secondhand tobacco smoke without fear
of adverse economic consequences. D
5
15
10
5
15
10
5
10
10
15
10
15
10
S
60
40
20
1966 1966
1990
1990
1992
1992
1992
1992
1994
1994
1994
1996
1996
1996
1996
1996
5
10
5
ft
1966
60
(
20
1966
1966
1966 1990
ordinances inciuded in this study.
1992
1990
1994 1996
1992 1994 1996
1994 1996
1994 1996
restaurant
Davis
1990
Tiburon
1966 1966 1990 1992 1994 1996
on Total Bar Sales, California
Redding
San Luis Obispo
Tiburon
– 0 . 4 ±
0.0 ±
0.1 ±
0.0 ±
0.0 ±
0.0 ±
0.0 ±
0.0 ±
0.2 ±
0.2 ±
0.6 ±
0.3 ±
0.5 ±
0.3 ±
0.5
0.8
0.2
0.2
0.2
0.3
0.3
0.2
0.2
0.1
0.1
5.1
0.6
0.4
0.5
.043
.000
.000
.129
.000
.000
.000
Ratio between sales in smoke-free city and sales in comparison city
Anderson
Davis
Redding
San Luis Obispo
Santa Clara County
Shasta County
Tiburon
Ail combined
Anderson
Davis
Redding
San Luis Obispo
Santa Clara County
Shasta County
Tiburon
Ail combined
– . 2 7 ±
– . 0 8 ±
– . 1 0 +
-7.41 ±
– . 0 2 ±
– . 1 0 ±
– . 0 9 ±
– . 2 4 ±
5.3
3.91
.06
.07
– 3 . 2 ±
– 1 . 5 ±
– 1 . 6 ±
– 0 . 9 ±
– 1 . 1 ±
1.0 ±
0.9 +
0.1 ±
1.5 +
0.5 ±
– 1 . 7 ±
0.4 ±
3.4
1.1
1.1
0.9
1.1
0.3
2.6
0.5
0.7
0.8
1.0
3.1
3.4
0.8
1.0
.067
.603
.000
.000
.000
.000
.000
.011
.000
.692
.000
.000
.000
.002
.005
.374
.000
.000
shows results of the quadratic time modei.
This work was supported by National Cancer
Institute grant CA-61021.
1. Local ordinance database. Berkeley, Calif:
realities of statewide smoking legislation:
the passage of California’s Assembly Bill
13. Tobacco Control. 1997;6:41-54.
ordinances requiring smoke-free restau-
rants on restaurant sales. Am J Public
Health. 1994;84:1O81-IO85.
A Study of the Economic Impacts on San
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Luis Obispo, Calif: Taylor Consulting
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The Impact of Tobacco Control Ordinances
on Restaurant Revenues in California.
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for Economic Policy Studies and the
Claremont Graduate School; January 1994.
the impact of a 100% smoke-free ordi-
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Wkly Rep. 1995;44:370-372.
smoke-free restaurant policies on restau-
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Mass: Center for Health Economics Re-
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in restaurants: effects on restaurant sales.
Am J Health Promotion. In press.
tobacco control. JAMA. 199I;266:211O-
2117.
Philip Morris’ failed experiment in Pitts-
burgh. J Health Polit Policy Law. 1992; 17:
329-351.
tobacco industry strategy to prevent local
tobacco control. JAMA. 1993;270:479-
486.
Tax) (Table 5). Sacramento, Calif: State of
Califomia Board of Equalization; 1986-
1996.
Selected Cities and Detailed Industry
Reports by County. Denver, Colo: State of
Colorado Department of Revenue; January
1982-June 1996.
nances Requiring Smoke-Free Restaurants
on Restaurant Sates in California. San
Francisco, Calif: University of Califomia,
San Francisco, Institute for Health Policy
Studies; 1992. Monograph series.
Regression and Analysis of Variance. New
York, NY: McGraw-Hill; 1990.
attacks on a longtime foe. Wall Street J.
July 23, 1997:B1.
Good for You. Monroe, Me: Common
Courage Press; 1995:14,29-31.
of ordinances requiring smoke-free restau-
rants on restaurant sales. Am J Public
Health. 1997;87:1729-1730.
Gibson J, Pechacek T. Workplace smoking
policies in the United States: results from a
Tobacco Control. In press.
rant workplace: a review of employee expo-
sure and health effects. JAMA. 1993;270:490-
493.
Housing 1990: Summary Tape File 3A.
Washington, DC: US Dept of Commerce,
Bureau ofthe Census; 1990.
in Califomia 1992: A Focus on Preventing
Califomia Department of Health Services;
1993.
tem—Summary Prevalence Report. Atlanta,
Ga: Centers for Disease Control; 1991.
Screening of 919 Infants Bom before
29 Weeks’ Gestation
pothyroxinemia in premature infants
is associated with cerebral palsy and
mental retardation; this study as-
sessed its prevalence in very prema-
ture infants.
roidism screening programs in three
states provided thyroxine values for
919 newborn infants younger than 29
weeks who were enrolled in a
multicenter study.
lower than 4.0 |ig/dL in 2 1 % of
survivors and increased each week
by 0.6 |ag/dL (95% confidence inter-
val [CI] = 0.4, 0.7). At tests done 1
to 2 days after birth, levels were 2.5
|ig/dL higher (95% CI = 1.8, 3.3)
than at tests done at 8 to 14 days. In
New York, levels were 1.0 (ig/dL
higher (95% CI = 0.3,1.6) than else-
where. The levels of infants who died
were 1.3 ng/dL lower (95% CI = 0.6,
2.0) than those of survivors.
hypothyroxinemia is common in very
premature infants and deserves fur-
ther study. (Am J Public Health.
1997;87:1693-1697)
and Mervyn Susser, MB, BCh, FRCP(E), DrPH
Preterm infants often have low thy-
referred to as transient hypothyroxinemia
of prematurity.'”‘^ Transient hypothyrox-
inemia of prematurity is a self-limited
phenomenon thought to be caused by
immaturity of the hypothalamic-pituitary-
thyroid system and by changes in thyroid
function that accompany severe illness,
that is, nonthyroidal illness. Congenital
hypothyroidism is not thought to explain
why transient hypothyroxinemia is de-
tected at newbom screening of premature
infants because thyrotropin levels are
normal. However, recent studies of pre-
term infants have linked very low thyrox-
ine levels with abnormal cognitive and
neurological development at ages 2
through 9 years.'”*”‘* It has been difficult
to establish what represents a very low
thyroxine level at any given gestational
age because little is known about the
gestational age-specific distribution of
thyroxine values in very preterm infants.
State screening programs tend to collect
and report infonnation classified by birth-
weight, not by gestational age,’^ and they
rarely report quantitative results.
screening findings in 919 preterm infants
bom before 29 weeks’ gestation and
enrolled in a multicenter study of cranial
ultrasonographic abnormalities, the Devel-
opmental Epidemiology Network Study.
These infants, whose gestational ages
were established according to a study
protocol, received intensive neonatal care
in one of four nurseries in three states:
Massachusetts, New Jersey, and New
York. Quantitative thyroxine-screening
results were obtained from state congeni-
and were assessed in relation to survival,
postmenstrual and postnatal age at the
time of screening, and site of care.
ber 1993, 1662 infants weighing 500
through 1500 g were systematically en-
rolled in a multicenter study of neonatal
brain injury, the Developmental Epidemi-
ology Network Study. Study infants were
bom in four hospitals in Massachusetts,
New Jersey, and New York (two hospi-
tals). Of the 919 bom at less than 29
weeks’ gestation, and therefore at high
risk for severe transient hypothyrox-
inemia of prematurity, 746 survived to
discharge from the intensive care nursery.
on fetal ultrasound obtained before the
14th week of gestation (32%), dates in the
prenatal record (62%), matemal postpartum
interview (4%), and the admission logbook
ofthe neonatal intensive care unit (2%).
with the Sergievsky Center, Columbia Univer-
sity, New York, NY. She is now with the Believue
Research Foundation, Niskayuna, NY. Alan
Leviton is with Harvard Medical School, Boston,
Mass. Nigel Paneth Is with the College of Human
Medicine, Michigan State University, East Lan-
sing. Mervyn Susser is with Columbia Univer-
sity, New York, NY.
Lynne Reuss, MD, MPH, Believue Research
Foundation, 2210 Schenectady Troy Rd, Niska-
yuna, NY 12309.
1996.
as the responsible editor and Dr Mary Northridge
as editor for this paper. As is our practice,
Dr Mervyn Susser had no part in the review and
decision process.
Smoke-Free Restaurants on
Restaurant Sales
rant revenues of kKal ordinances
requiring smoke-free rcstiuinmts is
an impiirtant consideration for res-
t;iur:iteurs themselves and the cities
thai depend on sales tax revenues to
provide services.
from the California State Board of
Hiiualization and Colorado State
Department of Revenue on taxable
restaurant sales from I486 (1982 for
Aspen) through 1993 for all 15 eities
where ordinances were in force, as
well as tor 15 similar control commu-
nities without smoke-free ordinances
during this period. These data were
analyzed using multiple regression.
including lime and a dummy variable
foi whether an ordinance was in
loree. Total restaurant sales were
analyzed as a fraction of lotal retail
sales and restaurant sales in smoke-
free eities vs the eomparison cities
similar in population, median in-
come, and other factors.
nificant etlcet on ihe fraction of total
retail sales ihal went lo restaurants
or on Ihe ratio of restaurant sales in
eommunities with ordinances eom-
parcd wilh those in the matched
control ciimniunities.
rant ordinances do noi adversely
aflect restaurant sales. {Am J Public
Health. |9’M;84:inSl-I085)
tobacco smoke endangers nonsmokers'”^
has aeeumulated. more and more eommu-
nities have restricted or eliminated smok-
ing in public places and workplaces.
Several communities have enaeted legisla-
tion that requires smoke-free restaurants,
thereby protcctingthe public and, particu-
larly, restaurant employees” from the
toxie chemicals in seeondhand tobaeeo
smoke. Such legislation, however, is not in
the interests of the tobacco industry
beeause creation of smoke-free restau-
rants is a highly visible statement that
tobacco use is no longer socially aecept-
able.’ Thus, tobaeeo companies have
sponsored front organizations like the
Beverly Hills Restaurant Assoeiation, Res-
taurants for a Sensible Voluntary Policy
on Smoking. Californians for Fair Busi-
ness Policy, and the California Business
and Restaurant Alliance to mobilize res-
taurants against local smoke-free ordi-
nances.*”’ This strategy achieved its first
success in 1987, when the tobacco indus-
try eonvineed the Beverly Hills
free restaurant ordinance in California on
the basis of undocumented claims that
business dropped 30% beeause of the
ordinance.”•^ Because similar predictions
for other cities have been published
nationally, voieed repeatedly through pub-
lie testimony, and regularly printed in
news reports, we tested the hypothesis
that the passage of a smoke-free restau-
rant ordinance is aeeompanied by an
immediate significant drop in restaurant
sales.'”
the first 15 US eities to enact smoke-free
ordinances affecting restaurants. The Cali-
fornia eities of Auburn. BellHowcr (which
repealed its ordinanee in Mareh 1992),
nance 4 months after it went into foree),
E! Cerrito, Lodi, Martinez, Palo Alto,
Paradise. Roscville. Ross. Sacramento,
and San Luis Obispo, and the Colorado
cities of Aspen, Snowmass Village, and
Telluride have had such UK)% smoke-free
restaurant ordinances in force long enough
to assess their effects. We also examined
sales tax data from 15 comparison cities
similar to the smoke-free cities in popula-
tion, ineomc, smoking prevalenee, and
other factors.”-” An analysis of restau-
rant sales as a fraction of total retail sales,
and of restaurant sales in eities witb
smoke-free restaurant ordinances eom-
pared with those in similar cities that do
not have smoke-free ordinances, shows no
significant effects on business.
total retail sales were obtained from the
California State Board of Equalization”
and Colorado State Department of Rev-
enue’^ from the first quarter of 1986
through the first or second quarter of 1993
(depending on data availability) for the 1
rant ordinanees in foree. Included were
cities whose ordinances were in force for
at least four quarters during this period,
plus Beverly Hills and Bellflower, Calif,
whose ordinanees were repealed. Data
were also obtained for 15 eomparison
eommunities where no sueh smoke-free
Policy Studies, Department nf Medicine, al the
University of California. San Francisco.
Stant
1994.
Comparison Cities
Vail. Colo
Oroviile, Calif
Santa Monica, Calif
Lakewood, Calif
San Pablo, Calif
Merced, Calif
Pleasant Hill, Calif
Mountain View, Calif
Red Bluff, Calif
Chico, Calif
Tiburon, Calif
Fresno, Calif
Santa Maria, Calif
Breckenridge, Colo
Steamboat Springs, Colo
(1989)3
3 659
11 960
86 905
73 000
25 158
56 2
31 585
67 460
12 3
40 076
7 5
354 202
61 284
1285
6 6
Urbanized
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
Urbanized Rural
X
X
X
X
X
X
X
X
X
Smoking
Some
None
Some
100%
None
100%
Some
100%
Some
100%
Some
100%
None
100%
None
100%
Some
100%
None
100%
Some
100%
Some
100%
Some
100%
Some
Household
(1989)=
41 211
166
35 997
44 700
25 479
24 727
46 885
42 431
19 474
19 005
75 864
24 9
29 492
33 259
29 363
Smokers^
25.1
23.6
25,1
Ordinance
in Effect
11/91
“”Some” refers to no more than 60% seating areas for nonsmokers,
^Tobacco Use in California (reported by county) ‘̂ for California and Behavioral Risk Factor Surveillanoe Study for Cobrado (statewide! 1 9 9 r ̂
“NumbGf of months for which data were available for this study, i=«<"oy.iuo, i ^ . ,
Months
16
20
23
than 60% seating availabiiity for nonsmok-
ers occurred as a part of an existing
ordinance (Table 1). Sales data for Aspen
and its comparison city were collected
from the first quarter of 1982 because
Aspen’s ordinance was passed in 1985.
Data were recorded for “Eating and
Drinking Places” and “Total Retail Sales.”
Published data for restaurant sales and
total retail sales in the city of Paradise for
the second, third, and fourth quarters of
1990 and in the city of San Luis Obispo for
the fourth quarter of 1990 and first
quarter of 1991 were corrected as in-
structed by the Board of Equalization to
communications from Robert Rossi, June
15,1992, and July 20,1993).
inflation, and changes in underlying eco-
nomic conditions, the fraction (F) of total
retail sales at restaurants was computed
as follows:
rable cities without ordinances as follows;
with Ordinance
without Ordinance
Restaurant Sales
rants, this fraction would be expected to
drop when the ordinance was in force.
Restaurant sales in cities with ordinances
sales, this ratio would be expected to
drop.
gression’^:
C), / is time needed to represent the
dummy variable that indicates whether a
smokc-lrce restaurant law is in force. The
estimate nf the ctwtficient b, quantifies the
annual rate of increase (or decrease) in
the dependent variable, _v, each year. The
dummy variable /. quantifies the presence
ol a smoke-free restaurant ordinance as
follows:
W If Ordinance in Force for
V^ If Ordinance in Force for
1 If Ordinance in Force for
tude of the ctfect of the ordinance on the
dependent variable. Because all of the
Colorado cities under study are ski cen-
ters, the restaurant business is much
stronger during the winter tourist season.
To iilluw for this effect, the dummy
variable H-‘was included for the Colorado
cities, set to I for Ihe lirsi quarter (the
winter tourist season) and 0 otherwise.
city separately, but nil the data on
restaunmt sales as a percentage of total
rcliiil saies for all 15 cities with ordinanees
for the entire year period were pooled in a
single analysis, including 29 additional
dummy variables, to allow for between-
city differences in the mean values of the
fraction of total retail sales going to
restaurants.
each variable were computed to assess
multicollincarity, and the Durbin-Watson
statistic was computed to test for autocor-
relatltin iimong the residuals. The vari-
imcc inflation iactors were always well
below 2, and the Durbin-Watson statistic
never reached statistical significance. A
change is considered statistically signifi-
cant when/* < .05.
total restaurant sales as a fraction of all
retail sales (F), and total restaurant sales
in cities with ordinances compared with
tliose in the matched comparison cities
(( ). The first column in the table is the
mc:m v:ilue observed from I98fi (1982 for
Aspen) to the second quarter of 1993 to
provide n comparison with the magnitude
nf the change associated with the ordi-
nance.
no statistieally significant effect on the
Auburn
Belifiower
Beveriy Hills
El Cerrito
Lodi
Martinez
Palo Alto
Paradise
Roseville
Ross
Sacramento
San Luis Obispo
Snowmass
Telluride
All combined
Auburn
Beltflower
Beverly Hills
El Cerrito
Lodi
Martinez
Palo Alto
Paradise
Roseville
Ross
Sacramento
San Luis Obispo
Snowmass
Telluride
All combined
7.5
12,8
12.7
11.7
10,3
15,8
14.9
7.1
13,9
12-7
49,2
29,6
18,4
1.0 ± 0.5
1,5 ±0,6
0,6 ± 1,2
0.1 ± 0.6
2,9 ± 1,0
0,7 ± 1,1
-0.9 ± 0.4
-3,3 ± 9.1
0.2 ± 0,6
6.0 ± 5.7
9.4 ± 4.7
,092
,025
,633
,637
,902
,008
,520
,078
,039
.715
-102
,764
,301
,055
.210
.44
.50
,56
.90
.41
.71
.68
,05
1.12
,95
,42
.82
-.02 ± ,02
– ,06 ± ,04
– 0 0 ± .08
-,01 ± .03
.23 d
-,02 =
– . 0 5 –
–
t ,03
t .03
b.01
t ,03
!:0,6
t .20
t ,07
,186
,347
,171
.998
.742
,194
,004
.049
.562
,196
,091
.177
-193
,282
,166
,319
.313
,033
,100
,005
.404
,115
,181
,156
,132
.102
,082
,374
,197
.611
,082
,036
.238
.053
,270
,329
,416
.144
.089
,302
,403
,154
,584
,372
,828
,007
,008
,646
.255
,939
,007
,204
.075
111
,243
,233
.327
,006
,120
,000
.327
,621
,029
,495
.017
.001
,001
.132
,300
,028
.001
,113
,000
,006
.000
restaurants or on total restaurant sales in
cities with ordinances compared with
those in cities without smoke-free ordi-
nances (Table 2 and Figure I). There is
marginal evidence that the fraction of
total retail sales to restaurants increased
in two cities (Bellflower, P = .025; Mar-
tinez. P – .008) and decreased in one city
(Rosevilie, P = .039). In a comparison of
restaurant sales in one city with an
ordinance versus one city without an
ordinanee, sales increased in one city
{Palo Alto. P = .(X)4) and decreased in
another (Paradise, P = .049). The iaek of
consistent response suggests that these
results may simply reflect random varia-
tion, given the large number of P values
that were computed. Analysis of ail the
data in pooled regressions did not detect
significant changes in the percentage of
retail sales or sales in cities with smoke-
free ordinances compared with those in
cities without ordinances.
tant ease because it has been used by the
tobacco industry to support the claim that
smoke-frcc restaurant ordinances are as-
sociated with a 30% drop in business
(Figure 2). However, data reveal that no
such drop in sales occurred upon enact-
ment, and that no inerease in sales
followed repeal 4 months later. Likewise,
despite the fact that the Bcllflower ordi-
nance was repealed because of claims that
business dropped, the ordinance was
actually associated with a marginally sig-
nificant (P – ,025) increase in business.
that examines taxable sales data to deter-
mine the eoinomic impact of smoke-free
restaurant ordinances on restaurant sales.
Using data from the California State
Board of Equalization and the Colorado
State Department of Revenue for pur-
did not reduce saies by 30% (dashed iine with “TI [tobacco industry]
claim”), as the tobacco industry had suggested; rather, it had no
significant effect on saies.
advantages. First, the numbers rctlect alt
restaurant sales in a community, not just
those of a small sample of restaurants.
Second, the numbers arc objective; they
were collected through consistent meth-
ods by agencies with no interest in the
effects of smoking restrictions on restau-
rant sales. Third, sales tax data can be
expected to be reasonably accurate since
it is a crime lo lie in reporting the figures.
report are different from each other and
represent a cross-section of communiiics
that might enact legislation controlling
smoking in restaurants; Auburn is a small
Sierra foothills community: Beverly Hills
is a well-to-do urban city; Bcliflowcr is a
middlc-c!;issbcdnx>m community; FJCcr-
rito and Martinez lie wilhin highly indus-
trial areas; Ltidi is a rural agricultural
center; Palo Alto is a large suburban
university community; Paradise is a small,
scmiagricultural community; Sacramento
is a large city and the state capital; Sun
Luis Obispo is a college town; Roscvillc
represents a semirural bcdrixim commu-
Bay community; and the three Colorado
cities arc mountainous, tourist resort
areas. The fact Ihat there were no adverse
eifects on business in these communities
supports the conclusion that the results
generalize broadly. Further, these 15
cities represent every ciiy that has passed
smoke-free ordinances that have been in
cfTect long enough to study.
of time. It is important to take into
account long-term (secular) trends as well
as the quartcr-by-quarter random varia-
tion and short-term economic changes.
We avoided short-term analyses because
it is generally possible to reach any
conclusion desired by seleetively picking
the “corrccl” two quarters for analysis.’^
To avoid such hiases and increase tbe
power of the statistical analysis to detect
an effect of the ordinances, we used data
for a 7-ycar period (12 years for Aspen
Lind Vail). This length of time allowed us
to obtain gixKi estimates of secular trends
before evaluating any cllecls of the ordi-
nances.
the possibility that patrons will dine in
adjacent communities without such restric-
tions. Our data address this concern
because the cities examined in this study
are not isolated communities. Auburn,
Lodi. Martinez. Paradise. Roscville. and
San Luis Obispo, while not in large urban
centers, are all surrounded by unincorpo-
rated areas that contain restaurants. Bev-
erly Hills and Bellflower and their com-
parison cities, Santa Monica and
Lakewood, are all located in Los Angeles
County, a major metropolitan area in
which all communities directly abut other
comnuinilies. El Ccrrito. Palo Alto. Ross,
and their comparison cities all lie within
I he San Francisai Bay region. Sacra-
mento and its comparison city, Fresno,
both large urban eenters, face competi-
tion from several neighboring communi-
ties. Although the skiing communities of
Aspen, Telluride, and Snowmass Village
are relatively secluded, other resort towns
nearby that allow smoking would repre-
sent viable tourist alternatives to these
smoke-free cities. If people were leaving
our analysis would have detected it.
on b:irs since smoking and drinking are
thought to go together. Revenues from
bars and “full-service” restaurants are
included in the sales tax data we used.
The ordinances examined in this study
contain different provisions governing
bars independently and bars in relation to
restaurants. Had there been a significant
effect on sales in such restaurants, our
analysis would have detected it. Further-
more, an analysis of individual classes of
restaurants (based on whether they sell
different types of alcohol) for four cities in
Calif()rnia previously showed no effect
when full-service restaurants were ana-
lyzed separately.”*
in Beverly Hills and Beilflower were
repealed adds to the strength of our
conclusions. Had the ordinances affected
sales negatively, we would have expected
to see an increase in sales following
repeal. However, there was no increase in
Beverly Hills, and sales dropped in Bell-
flower after the ordinance was repealed.
Thus, legislators and government officials
can enact such health and safety require-
ments to protect patrons and employees
in restaurants from the toxins in seeond-
hand tobacco smoke without the fear of
adverse economic a>nsequences. •
This research was supported by funds provided
by the Cigarette and Tobaccti Surtax Fund of
ihe State of California through the Tobacco-
Reialed Disease Research Program of the
University ol California (award 1RTS20).
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