Department of Biostatistics

655 Huntington Ave

Building II, Room 441

Boston, MA 02115

www.hsph.harvard.edu

TEL 617 432 4908

FAX 617 432 5619

fdominic@hsph.harvard.edu

November	28	2012

From:	Francesca	Dominici,	Scott	L	Zeger,	Holly	Janes,	Sonja	Greven

To:	Bryan	Hubbell,	Jason	Sack,	EPA

Reply	to:	additional	information to	EPA	for	these two studies	

1) Janes	H,	Dominici, Zeger	SL	(2007)	Trends	in	Particulate	Matter	and	Mortality	in	113	U.S.	Counties,	

2000-2002:	Evidence	on	the	Long	Term	Effects	of	Air	Pollution.	Epidemiology,	18:	416-423	(with	

discussion)

2) Greven	S,	Dominici	F,	Zeger	SL (2011):	An	Approach	to	the	Estimation	of	Chronic	Air	Pollution	

Effects	Using	Spatio-Temporal	Information.	Journal	of	the	American	Statistical	Association,

106(494):	396–406.	

These	two	studies targeted	a	very	specific	question:	whether	there	is	an	association	between	month-tomonth	variations	in	mortality	rates	and	month	to	month	variations	in	the	average	PM2.5	for	the	previous	

12	months	(global	effect)	in	the	Medicare	population.	The	fact	that	this	particular	study	does	not	find	an	

association	at	this	specific	time	scale	of	variation	of	exposure	to	air	pollution	and	for	the	study	population	

considered	cannot	lead	to	a	conclusion	that	all	the	other	epidemiological	studies	are	confounded	and	that	

air	pollution	is	not	causally	linked	to	mortality.	In	this	document	we	summarize	our	rationale	for	making	this	

statement.

In	these	studies we	decompose	the	global	effects into	two	parts:	1)	the	association	between	the	national	

average	trend	(NAT)	in	the	monthly	PM2.5	levels	averaged	over	the	previous	12	months	and the	national	

average	trend	(NAT)	in	monthly	mortality	rates	(national	effect);	and	2)	the	association	between	the	

deviation	of	the	community-specific	trend	from	the	NAT	of	PM2.5	and	the	deviation	of	the	communityspecific	trend	from	the	(NAT)	of	mortality rates	(local	effect).	We	decompose	the	global	effect	into	a	

national	effect	plus	a	local	effect	because	we	hypothesize	that	the	national	effect	is	more	likely	to	be	

affected	by	unmeasured	confounding	than	the	local	effect.	We	also	argue	that	if	there	are	large	differences	

between	the	local	and	the	national	effects	then	the	global	effect	should	not	be	reported	without	a	more	in	

depth	investigation	of	confounding.	We	also	acknowledge	in	the	papers	that	differences	between	the	local	

and	national	effects	might be	due	to	measurement	error	and	not	necessarily	unmeasured	confounding.	

Our	results	do	not	invalidate	previous	epidemiological	studies:		In	Janes	et	al	and	Greven	et	al,	both	using	

Medicare	data,	we	did	not	find	evidence	of	a	local	effect	and	we	instead found	evidence	of	a	national	

effect.		Although	these	results	call	for	additional	investigation	of	why	we	found	these	differences	between	

the	local	and	the	national	effects,	these	results	do	not	invalidate	results	of	the	other	cohort	and	multi-site	

time	series	studies.		We	summarize	below	why:

FRANCESCA DOMINICI, PhD

Professor of Biostatistics

Associate Dean for Information Technology
1. We	eliminate	spatial	information,	purposely.	Our	studies,	on	purpose,	discard	all	the	spatial	

variation	in	air	pollution	and	mortality	that	is	available	in	the	data.		This	is	the	main	and	only	

information	used	in	the	cohort	studies.	Also,	our	studies	cannot	invalidate	the	time	series	studies,	

because	they	also	do	not	rely	on	the	day-to-day	information.	Again,	our	studies	only	focus	on	

associations	at	monthly	temporal	scales	and	include	a	fixed	effect	for	county	in	the	regression	

model	which	purposely	eliminate	the	spatial	information	when	estimating	the	global	effect.	At	the	

other	end,	the	cohort	studies	by	Pope	et	al 2009	NEJM,	rely	almost	entirely	on	spatial	information	

and	they	have	developed	approaches	to	adjust	for measured	and	unmeasured	confounding.	In	

Table	1	of	Janes	et	al,	the	percentage	of	the	variability	in	their	PM2.5 variable	is	broken	down	by	

space,	time,	and	space	by	time	components.		We	found	that	over	90%	of	the	variance	in	this	data	is	

attributable	to	spatial	variation	(which	we	ignore),	and	roughly	only	5%	attributable	to	the	space	by	

time	component	(which	is	what	we	used	in	our	papers).	Thus,	when	one	considers	that	this	wealth	

of	information	is	not	accounted	for	in	that	study,	it	is	not	as	surprising that	we	see	vastly	different	

estimates	of	the	PM2.5/mortality	relationship	than	in	other	studies	that	do	exploit	that	variability.	

2. Both	the	local	and	the	national	effects	can	be	affected	by	unmeasured	confounding.	We	do	

hypothesize	that	the	national	effect	is	more	likely	to	be	affected	by	unmeasured	confounding	than	

the	local	effect.	However	the	unmeasured	confounding	for	the	local	scale	cannot	be	excluded	

either.	For	example,	the	national	association	can	be	confounded	by	a	national	downward	trend	in	

smoking	that	causes	a	reduction	in	mortality.		As	PM2.5	and	mortality	are	both	trending	

downwards	nationally,	not	having	a	good	measure	of	the	effect	of	smoking	in	the	model	would	

cause	an	upward	bias	in	the	national	association.	An	example	that	would	cause	the	local	association	

to	be	biased	upwards	would	be	if	communities	that	were	very	'health	conscious	and	

environmentally	friendly'	and	thus	reduced	their	air	pollution	more	than	the	average	community	

would	also	improve	on	other	health-related	measures	such	as	smoking	and	obesity,	causing	drops	

in	mortality	larger	than	in	the	average	community,	even	without	the	effects	of	the	reduce	in	air	

pollution. On	the	other	hand,	an	example	that	would	cause	a	downward	bias	in	the	local	

association	would	be	local	manufacturing	activity.	For	example,	suppose	a	new	plant	is	built	that	

employs	3,000	workers	and	has	a	positive	influence	on	the	economic	productivity	of	its	local	area.	

The	improved	economy	causes	reduced	mortality	relative	to	the	regional	trend,	but	also	increases	

pollution	relative	to	the	regional trend. Such	a	phenomena	would	shift	the	local	association	toward	

zero.	As	individual- and	location-level	information,	in	contrast	to	cohort	studies,	is	very	limited	for	

the	Medicare	data,	we	cannot	exclude	any	of	these potential	sources	of	confounding	in	our	studies.

3. Another	important	driver	of	the	potential	differences	between	the	local	and	the	national	effects	

could	be	measurement	error.	We	expect	that	measurement	error	could	affect	both	these	

associations	but	it	is	hard	to	anticipate	which	one	is	more	biased.	If	the	exposure	is	measured	with	

error,	as	we	know	is	the	case,	one	of	the	two	components	might	be	more	precise	than	the	other.	

Because	the	national	scale	component	is	an	average	of	more	independent	monitors	across	a	

broader	geographic	extent,	its	"measurement	error"	(including	local	geographic	variations	and	pure	

device	measurement	error)	might	be	expected	to	be	smaller,	relative	to	the	size	of	the	PM	signal.	

The	local	component	might	have	greater	measurement	error	as	a	proportion	of	its	signal,	and	this	

might	bias	its	coefficients,	probably	toward	the	null.	More	work	is	needed	in	this	area.
4. There	might	not	be	an	association	at	the	time	scale	and	lag	investigated	in	our	studies. Further,	it	is	

an	open	question	as to	what	lag	the	“monthly”	PM2.5 levels	should	be	calculated	at.		In	the	Janes	

paper	this	is	calculated	as	the	12	and	24-month	averages	of	PM2.5 up	to	and	including	the	current	

month.		It	is	then	assumed	that	that	calculated	exposure	is	associated	with	the current	month’s	

observed	deaths.		It	may	be	the	case	that	the	exposure	period	does	not	stretch	that	far	back,	and	is	

in	fact	over	a	shorter	window.		In	Janes	et	al,	the	authors	acknowledge	that	this	is	a	question	that	

warrants	further	research	(see	also	Schwartz	et	al	EHP	2008).	Thus,	it	is	entirely	possible	that	these	

papers	are	looking	for	an	association	at	a	timescale	for	which	no	association	truly	exists.

In	summary,	in	Janes	et	al	2007	and	Greven	et	al	2011,	we examined in	unbiased	fashion,	the	epidemiologic	

evidence	relevant	to	estimating	the	effect	of	chronic	air	pollution	exposure	on	survival. We	have	published	

all	of	our	findings	without	regard	to	the	outcome. None	of	the	investigators	have	a	financial	conflict	of	

interest.

Francesca	Dominici,	PhD

Associate	Dean	of	Information	Technology

Professor	of	Biostatistics

Harvard	School	of	Public	Health,	Boston,	MA

Scott	L.	Zeger,	PhD

Vice	Provost	for	Research

The	Johns	Hopkins University,	Baltimore,	MD

Sonja	Greven,	PhD

Assistant	Professor

Department	of	Statistics

Ludwig-Maximilians-Universität	München

Ludwigstraße	33

Munich,	Germany

Holly	Janes,	PhD

Affiliate	Assistant	Professor,	Biostatistics

Fred	Hutchinson	Cancer	Research Center

University	of	Washington,	Seattle,	WA