the prevalence of dsm-iii-r alcohol dependence in two american indian populations
TRANSCRIPT
The Prevalence of DSM-III-R Alcohol Dependence inTwo American Indian Populations
Paul Spicer, Janette Beals, Calvin D. Croy, Christina M. Mitchell, Douglas K. Novins, Laurie Moore, Spero M. Manson,and the American Indian Service Utilization, Psychiatric Epidemiology, Risk and Protective Factors Project Team*
Background: Evidence suggests that American Indian (AI) populations may be at increased risk forproblems with alcohol, but a lack of community-based research using diagnostic criteria has constrained ourability to draw inferences about the extent of severe alcohol problems, such as dependence, in AIpopulations.
Methods: This article draws on data collected by the American Indian Service Utilization, PsychiatricEpidemiology, Risk and Protective Factors Project (AI-SUPERPFP), which involved interviews with 3084AI people living on or near their reservations. The AI-SUPERPFP sample was drawn from two culturallydistinct tribes, which were designated with geographical descriptions: Northern Plains (NP) and Southwest(SW). Comparisons with data collected by the National Comorbidity Survey (NCS) were explored by usingshared measures to situate the findings from AI-SUPERPFP in a national context.
Results: Lifetime rates of DSM-III-R alcohol dependence for men in both AI-SUPERPFP samples were50% higher than those found in the NCS. Rates of lifetime alcohol dependence for women varied bysample, however; NP women had twice the rate of women in the NCS, but SW women had rates quitesimilar to those of NCS women. Patterns for 12-month alcohol dependence in AI-SUPERPFP weregenerally more similar to those found in NCS.
Conclusions: The rates of DSM-III-R alcohol dependence found in AI-SUPERPFP were generallyhigher than US averages and justify continued attention and concern to alcohol problems in AI commu-nities, but they are not nearly as high as those in other reports in the literature that rely on less stringentsampling methods. Furthermore, significant sociocultural influences on the correlates of alcohol depen-dence in AI communities are evident in these data, underscoring the need to appreciate the complex andvarying influences on the patterning of alcohol problems in the diverse cultural contexts of the US.
Key Words: Alcohol Dependence, American Indians, Epidemiology.
THIS ARTICLE DESCRIBES the prevalence and cor-relates of DSM-III-R (American Psychiatric Associa-
tion, 1987) alcohol dependence in two culturally distinct
American Indian (AI) tribes, with explicit comparisons withdata collected in the National Comorbidity Survey (NCS).Recent years have seen increases in community-based ep-idemiological work on alcohol disorders using well defineddiagnostic criteria in samples that permit clear inferencesto known populations (Alderete et al., 2000; Bijl et al.,1998; Grant, 1997; Helzer et al., 1991; Kessler et al., 1994,1997; Kringlen et al., 2001; Teesson et al., 2000; Vega et al.,1998; WHO International Consortium on Psychiatric Epi-demiology, 2000). Given the unique social and culturalcircumstances of contemporary AI communities, it is un-fortunate that comparable work in AI communities hasbeen rare.
Data from the US Census and other sources continue todocument that contemporary AI communities, which tendto be more concentrated in the west and in rural areas thanany other US ethnic group, are disproportionately affectedby poverty, lower rates of educational attainment, unem-ployment, and inadequate resources for most basic services(Snipp, 2000), including a health-care system that continuesto see real declines in per capita funding in the context ofmedical inflation and continued population growth (Dixonet al., 2001). AI peoples and communities have also expe-
From the American Indian and Alaska Native Programs, University ofColorado Health Sciences Center, Aurora, Colorado.
Received for publication April 29, 2002; accepted August 22, 2003.This study was supported by National Institute of Mental Health Grants R01
MH48174 (SMM and JB, principal investigators) and P01 MH42473 (SMM,principal investigator). In addition to these grants, analyses for and the writing ofthis article were partially supported by grants from the NIAAA [R01 AA11932(cofunded by the National Center for Minority Health and Health Disparities; PS,principal investigator) and R01 AA013800 (DKN, principal investigator)]. TheNational Comorbidity Survey was supported by the following grants: R01 MH/DA46376 and R01 MH49098 (NIMH), supplement to R01 MH/DA46376(NIDA), and 90135190 (W.T. Grant Foundation) RC Kessler, PI.
Reprint requests: Paul Spicer, PhD, AIANP, P.O. Box 6508, MS F800,Aurora, CO 80045; Fax: 303-724-1474; E-mail: [email protected].
*The American Indian Service Utilization, Psychiatric Epidemiology, Riskand Protective Factors Project team also includes Cecelia Big Crow, DedraBuchwald, Buck Chambers, Michelle Christensen, Denise Dillard, KarenDuBray, Paula Espinoza, Fay Flame, Candace Fleming, Ann Frederick, JosephGone, Diana Gurley, Lori Jervis, Shirlene Jim, Carol Kaufman, Ellen Keane,Suzell Klein, Denise Lee, Monica McNulty, Denise Middlebrook, ChristinaMitchell, Tilda Nez, Ilena Norton, Theresa O’Nell, Heather Orton, CarletteRandall, Angela Sam, James Shore, Sylvia Simpson, and Lorette Yazzie.
DOI: 10.1097/01.ALC.0000095864.45755.53
0145-6008/03/2711-1785$03.00/0ALCOHOLISM: CLINICAL AND EXPERIMENTAL RESEARCH
Vol. 27, No. 11November 2003
Alcohol Clin Exp Res, Vol 27, No 11, 2003: pp 1785–1797 1785
rienced dramatically disruptive histories over the past 500years that have contributed to these contemporary dispar-ities. Although AI histories vary in important ways depend-ing on local contexts (Spicer, 1962), most tribes have expe-rienced devastating population losses through war anddisease, the appropriation of aboriginal lands by alien gov-ernments and the loss of traditional economies, and sys-tematic efforts to undermine aboriginal worldviews throughreligious conversion and coercive educational experiencesin boarding schools. Despite these, however, contemporaryAI populations remain diverse and vital, with more than300 federally recognized tribes in the lower 48 US statesand a remarkable persistence of aboriginal traditions andlanguages in many AI communities (Kehoe, 1992).
Thus, efforts to address the many and profound healthdisparities in AI communities need to be designed in lightof the cultural diversity of the contemporary AI populationand the often severe levels of economic deprivation expe-rienced by AI people. This report is designed to do this byconsidering rates of alcohol dependence in two diverse AIpopulations presented in the context of explicit nationalcomparisons, in this case, by using data and algorithmsderived from the NCS. The current effort is not the first toexamine the prevalence of alcohol dependence in AI com-munity samples. Indeed, a growing body of work docu-ments the importance of this line of inquiry, includingcontributions from a longitudinal study of psychiatric dis-orders in one Northwest Coastal community (Leung et al.,1993; Shore et al., 1973); important work on the prevalenceof alcohol, drug, and mental health disorders among chil-dren and adolescents (Beals et al., 1997; Costello et al.,1996); and, perhaps most significantly, recent work on theprevalence of alcohol dependence in an extended family ina southwestern tribe and in the service population from twodifferent Indian Health Service (IHS) hospitals on theNavajo Nation (Kunitz and Levy, 2000; Robin et al., 1998).These last two studies, in particular, provide estimates ofthe lifetime prevalence of alcohol dependence that areamong the highest ever reported in the literature (70.4%for men in the Kunitz and Levy study and 83.4% for men inthe Robin et al. study), further contributing to the repre-sentation of alcohol problems as one of the most significanthealth concerns in AI communities (Welty, 2002). Al-though these recent reports provide valuable informationon possible levels of alcohol dependence in AI communi-ties, most have not used standard population-based sam-pling methodologies, raising concerns about possible bias intheir estimates of alcohol dependence at the communitylevel. Furthermore, each of these efforts was conductedwith only one AI cultural group, thus making it difficult toseparate the contributions of potential methodological id-iosyncrasies from the cultures in which the studies havebeen conducted.
In contrast, the existing literature on alcohol use is oftenbased on well defined samples that represent multipletribes. These surveys consistently find that rates of absti-
nence from alcohol in AI communities are equal to and notinfrequently exceed US averages. Data from the StrongHeart Study, for example, found rates of current abstinencein older populations (aged 45–74 years) that ranged from53% in South Dakota to 63% in southwestern Oklahoma(Welty et al., 1995). Reviewing the literature, May (1996)found rates of current abstinence that ranged from 70% inan early study of the Navajo (Levy and Kunitz, 1974) to alow of 16% in a study of an Ojibwe community (Longclawset al., 1980). Similarly, analyses of alcohol use derived fromstudies of AI adolescents have tended, with some notableexceptions (Barnes et al., 1997), to find that rates of alcoholuse for these students were no higher than those of studentsfrom other ethnic groups in the US (Beauvais, 1998; Blumet al., 1992). More recently, May and Gossage (2001) havepublished data on the quantity, frequency, and variability ofalcohol use by using random samples of enrolled membersof four tribes. As the authors note, this study marks the firstto examine patterns of alcohol use by using random com-munity samples of the entire adult population of more thanone tribe. As in the work cited previously, this study foundhigh rates of abstinence, especially among older partici-pants, and evidence for a widespread pattern of bingedrinking, marked by many days without drinking but withhigh-quantity consumption on days when drinkingoccurred.
Absent data on the consequences of alcohol use in thesecommunity surveys, researchers have tended to rely, in-stead, on mortality data to underscore the serious nature ofalcohol problems in many AI communities (Beauvais, 1998;May, 1996). Mortality data are abstracted from the Na-tional Center for Health Statistics by the IHS and consis-tently indicate that AIs are at increased risk of death froma number of causes that may be related to alcohol con-sumption (May, 1989), including accidents, homicides, andsuicides. Although some are concerned about the extent towhich alcohol can be viewed as a necessary and sufficientcause in deaths from accidents, homicides, and suicides(Kunitz and Levy, 1994; Levy and Kunitz, 1974), most agreethat a significant portion of these deaths, all of which occurat dramatically higher rates in AI communities than in thegeneral US population, are alcohol related (May, 1989).Even the most conservative estimate of the extent of alco-hol problems in AI communities, provided by deaths thatMay (1996) calls alcohol specific, i.e., those that have beenattributed directly to alcoholism, are always higher than USaverages: the most recent IHS report, using data from1996–98, indicates that alcohol-specific deaths ranged from25 in 100,000 in the Oklahoma City, OK, service area to87.4 in 100,000 in the Aberdeen, SD, service area, whereasthe 1997 US rate for these causes of mortality was 6.3 in100,000 (IHS, 2002).
Both alcohol use and mortality data have contributed toa consistent representation of alcohol use and its conse-quences in AI communities, i.e., that heavy but episodic useis common among those who do use alcohol and that that
1786 SPICER ET AL.
pattern of use is likely associated with increased mortalityfrom a variety of causes directly and indirectly associatedwith alcohol (Beauvais, 1998; May, 1996). These findingscorroborate the reports of relatively high rates of alcoholdependence reported in select samples (Kunitz and Levy,2000; Robin et al., 1998), and this study takes a logical nextstep in understanding the prevalence of alcohol problemsin AI communities: it provides straightforward population-based inferences about the level of alcohol use and depen-dence among two well defined AI populations—Southwest(SW) and Northern Plains (NP)—living on or near reser-vation communities. Using comparable methods to thoseused in the NCS, this work provides a national contextheretofore lacking in research on alcohol dependence in AIcommunities.
This work is driven by several hypotheses. Given what weknew from the literature, we expected to find comparablelevels of use but higher rates of alcohol dependence forboth men and women in the American Indian ServiceUtilization, Psychiatric Epidemiology Risk and ProtectiveFactors Project (AI-SUPERPFP) samples when comparedwith NCS. Simultaneously, considering the differentialmortality rates due to alcohol-related causes, we expectedhigher rates of alcohol dependence in the NP comparedwith the SW. Given the very different demographic char-acteristics of AI communities, we expected to find differentpatterns in the correlates of this disorder than have beenfound in previous epidemiological work (Bucholz, 1999).Specifically, because of the extent of poverty and unem-ployment in these communities, we did not expect income,formal education, or employment to be significant corre-lates of alcohol dependence. Also, given the widespreadacceptance of living together as a form of marriage in theNP communities where AI-SUPERPFP was conducted, wedid not expect this particular relationship pattern to be arisk factor for alcohol dependence in the NP sample.
METHODS
Data from both the NCS and AI-SUPERPFP studies are presentedhere. The NCS was designed to derive DSM-III-R prevalence rates froma national probability sample. More detail about the NCS is availableelsewhere (Anthony et al., 1994; Kessler et al., 1994, 1996, 1997; Nelson etal., 1996, 1998). The AI-SUPERPFP methods have also been described ingreater detail elsewhere (Beals et al., 2003; Mitchell et al., 2003), and thetraining manual and interview protocol may be found at our Web site(http://www.uchsc.edu/ai).
Samples and Procedures
The NCS was based on a multistage area probability sample of personsin the noninstitutionalized civilian population in the 48 co-terminus states.The sample was stratified by age (15–24, 25–34, 35–44, and 45–54 years)and gender. Fieldwork was performed by the Survey Research Center atthe Institute for Social Research at the University of Michigan betweenSeptember 1990 and February 1992. Trained lay interviewers completedthe data collection. Overall, the response rate was 82.5%, with a total of8098 participants.
Given the practical hurdles to the acquisition of a national sample ofAIs because of their small, dispersed, and diverse populations (Beals et al.,
2003; Eriksen, 1996; Sandefur and Lisper, 1996) and our interest in thepossible effect of culture on both epidemiology and service utilization,AI-SUPERPFP focused on tribally defined reservation populations. Thetwo populations of inference for the AI-SUPERPFP were enrolled mem-bers of two NP tribes (combined into one NP population) and one SWtribe, who were 15 to 54 years old at the time the sampling frame wasdeveloped in 1997 and who lived on or within 20 miles of their respectivereservations. In our work with AI groups, the maintenance of communityconfidentiality is as important as that of individual confidentiality, becauseresearch findings can be as injurious to communities as they are toindividuals (Norton and Manson, 1996). Therefore, NP and SW, ratherthan specific tribal names, are used in this report.
The AI-SUPERPFP populations were stratified by the same age andgender strata as in the NCS. Unlike that effort, however, AI-SUPERPFPused list-sampling procedures rather than a multistage area probabilitysample. Specifically, tribal rolls, which provide a legal and administrativerecord of all tribal members, served as the basis of the AI-SUPERPFPsamples. Tribal approvals were obtained before the project’s inception,and information from the tribal rolls released to us contained name, dateof birth, tribal enrollment number, and last known address. Many ad-dresses were outdated, and extensive location procedures were developed,which included reference to official records (local and national telephonedirectories, utility records, and other tribal listings) and contact withfamily members and other key informants in these communities. Overall,46.5 and 39.5% of the SW and NP tribal members, respectively, werefound to be living on or near their reservations and, therefore, wereeligible to participate. Of these, 73.7% of the eligible SW members and76.8% of the eligible NP members agreed to participate. The targetsample size was approximately 1500 per tribe, and the final samplestotaled 1446 for the SW sample and 1638 for the NP sample.
All interviews were completed by trained lay interviewers who under-went intensive training in research and interviewing procedures. Informedconsent was obtained from all respondents; for minors, parental or guard-ian consent was acquired before adolescent assent was obtained. UnlikeNCS, AI-SUPERPFP was able to take advantage of computer-assistedpersonal interview technologies: interviewers asked all the questions,reading from a laptop computer, and electronically recorded the answers.Extensive quality-control procedures verified that all portions of the lo-cation, recruitment, and interview procedures were conducted in a stan-dardized, reliable manner. Data collection occurred between July 1997and December 1999.
Table 1 presents demographic characteristics of the three samples: SW,NP, and NCS. In comparison to the NCS, the AI-SUPERPFP sampleswere generally poorer, had less formal education, were less likely to beemployed, and were less likely to be legally married. That the proportionof women in the SW sample was higher than in other samples (56.5%) wasexpected: many of the SW men resided in urban communities wheregreater chances of employment existed. Tribal differences in these demo-graphic correlates often were considerable, with the SW sample typicallyfalling between the NCS and the NP samples.
Assessment
For most variables in these analyses, the AI-SUPERPFP items wereidentical to those of the NCS. For instance, all the demographic correlateswere duplicated between the two datasets, with the exception of thepoverty status variable, which was calculated with the same procedures butwhich was based on the federal poverty definitions for different years(NCS, 1990; AI-SUPERPFP, 1996).
The alcohol section was organized hierarchically. Everyone was asked,“About how old were you the very first time you had more than just a sipof beer, wine, or liquor?” Those responding “never” were designated asnever drank. All those who provided an age of first drink or who were notsure of the age were asked, “In any 1-year period of your entire life, didyou have at least 12 drinks of any kind of alcoholic beverage?” Only thoseanswering “yes” to this item were considered drinkers and asked the fullcomplement of alcohol questions. Among the drinkers, age of first drink
DSM-III-R ALCOHOL DEPENDENCE IN AMERICAN INDIANS 1787
Tab
le1.
Cha
ract
eris
tics
ofth
eA
I-S
UP
ER
PFP
Sam
ple
sC
omp
ared
With
NC
S
Var
iab
le
AI-
SU
PE
RP
FPS
WA
I-S
UP
ER
PFP
-NP
NC
S
%S
ED
iffer
ence
sa%
SE
Diff
eren
ces
%S
ED
iffer
ence
s
Gen
der
Mal
e43
.50.
4N
P,
NC
S49
.50.
3S
W49
.50.
8S
W
Fem
ale
56.5
0.4
50.5
0.3
50.5
0.8
Men
(n�
617)
Wom
en(n
�82
9)M
en(n
�79
0)W
omen
(n�
848)
Men
(n�
3847
)W
omen
(n�
4251
)
%S
ED
iffer
ence
s%
SE
Diff
eren
ces
%S
ED
iffer
ence
s%
SE
Diff
eren
ces
%S
ED
iffer
ence
s%
SE
Diff
eren
ces
Age
(yea
rs)
15–2
425
.70.
8N
M23
.60.
722
.10.
6N
F,S
M26
.30.
6N
M25
.91.
023
.50.
925
–34
26.2
1.2
UF
26.6
1.0
UF
29.6
1.0
29.0
1.0
28.1
1.0
32.1
1.0
SM
,S
F35
–44
25.9
1.2
29.6
1.0
29.9
1.0
25.4
1.0
27.3
1.0
27.1
1.0
�45
22.2
0.8
NM
,U
F20
.20.
818
.40.
6S
M19
.30.
618
.70.
917
.30.
8S
MR
ace
Whi
te0.
00.
00.
00.
076
.51.
074
.20.
9A
fric
anA
mer
ican
0.0
0.0
0.0
0.0
10.1
0.7
12.8
0.7
His
pan
ic0.
00.
00.
00.
010
.00.
79.
40.
6A
sian
0.0
0.0
0.0
0.0
1.9
0.3
1.9
0.3
Am
eric
anIn
dia
n10
0.0
100.
010
0.0
100.
01.
10.
21.
20.
2O
ther
0.0
0.0
0.0
0.0
0.5
0.2
0.6
0.2
Ed
ucat
ion
Less
than
12ye
ars
32.3
1.9
UM
,U
F30
.01.
6U
F28
.71.
6U
F30
.11.
6U
M,
UF
23.5
1.0
SM
,N
F21
.00.
9S
M,
SF,
NM
,N
FH
igh
scho
olgr
adua
teor
Gen
eral
Ed
ucat
iona
lD
evel
opm
ent
43.4
2.0
36.3
1.7
NM
49.8
1.8
SF,
NF,
UM
,U
F39
.31.
8N
M36
.41.
1N
M38
.41.
0N
M
Som
eco
llege
19.8
1.6
27.9
1.6
NM
,U
M17
.41.
4S
F22
.31.
520
.30.
9S
F23
.10.
8C
olle
gegr
adua
te4.
50.
8U
M,
UF
5.9
0.8
UM
,U
F4.
20.
8U
M,
UF
8.4
1.1
UM
,U
F19
.80.
9S
M,
SF,
NM
,N
F17
.50.
8S
M,
SF,
NM
,N
FP
over
tyst
atus
b
Poo
r42
.92.
1N
M,
NF,
UM
,U
F48
.61.
8N
M,
NF,
UM
,U
F59
.02.
0S
M,
SF,
UM
,U
F63
.91.
9S
M,
SF,
UM
,U
F9.
60.
6S
M,
SF,
NM
,N
F,U
F14
.90.
8S
M,
SF,
NM
,N
F,U
ME
mp
loym
ent
Stu
den
t10
.51.
210
.71.
08.
30.
9N
F,U
M15
.11.
2N
M13
.70.
8N
M11
.20.
6W
orki
ngfo
rp
ay62
.51.
9N
F,U
M58
.91.
7U
M62
.61.
8N
F,U
M50
.11.
8S
M,
NM
,U
M,
UF
78.4
1.0
SM
,S
F,N
M,
NF,
UF
65.7
1.0
NF,
UM
Not
wor
king
for
pay
c27
.01.
8U
M30
.41.
6U
M,
UF
29.1
1.7
UM
34.9
1.7
UM
,U
F7.
90.
6S
M,
SF,
NM
,N
F,U
F23
.20.
9S
F,N
F,U
M
Mar
itals
tatu
sM
arrie
d41
.91.
9N
M,
NF,
UM
,U
F43
.21.
7N
M,
NF,
UM
,U
F26
.61.
6S
M,
SF,
UM
,U
F27
.61.
6S
M,
SF,
UM
,U
F57
.01.
1S
M,
SF,
NM
,N
F56
.91.
0S
M,
SF,
NM
,N
FLi
ving
asm
arrie
d15
.51.
5N
F,U
M,
UF
19.0
1.4
UM
,U
F22
.41.
6U
M,
UF
26.1
1.6
SM
,U
M,
UF
5.4
0.5
SM
,S
F,N
M,
NF
6.5
0.5
SM
,S
F,N
M,
NF
Sep
arat
ed,
div
orce
d,
wid
owed
8.9
1.1
NF
10.9
1.0
NF
13.9
1.3
UM
17.8
1.3
SM
,S
F,U
M7.
20.
5N
M,
NF,
UF
12.9
0.7
UM
Nev
erm
arrie
d33
.61.
8U
F27
.01.
5N
M37
.11.
7S
F,N
F,U
F28
.61.
5N
M30
.51.
0U
F23
.80.
9S
M,
NM
,U
M
aG
roup
abb
revi
atio
nsd
enot
esi
gnifi
cant
pai
rwis
eco
mp
aris
ons
(p�
0.01
):S
W,S
outh
wes
t;N
P,N
orth
ern
Pla
ins;
NC
S,N
atio
nalC
omor
bid
ityS
urve
y(U
Sp
opul
atio
n);S
M,S
outh
wes
tmal
es;S
F,S
outh
wes
tfem
ales
;N
M,
Nor
ther
nP
lain
sm
ales
;N
F,N
orth
ern
Pla
ins
fem
ales
;U
M,
US
mal
es;
UF,
US
fem
ales
.b
Ind
eter
min
ates
incl
uded
inth
eno
npoo
rin
the
AI-
SU
PE
RP
FPsa
mp
le.
cIn
clud
esho
mem
aker
,lo
okin
gfo
rw
ork,
unem
plo
yed
,re
tired
,p
erm
anen
tlyd
isab
led
,an
dot
her.
1788 SPICER ET AL.
was derived from the first item. Age of heaviest drinking was derived fromthe following item: “Think back on the period in your life when you weredrinking most. How old were you when you began that period?” Thedichotomous variable 12-month drinking and the continuous variablesmost drinks in day (12-month) were both derived from the following items:“Think about the past 12 months. What is the largest number of drinks youhad on any single day during that period?” (NCS) and “During the pastyear (the last 12 months, including this past month), what is the most youhad to drink in any one day?” (AI-SUPERPFP).
For both studies, the alcohol-dependence questions were asked withinthe NCS version of the Composite International Diagnostic Instrument.As part of a previous effort (Beals et al., 2002; National Center forPost-Traumatic Stress Disorder, 1996), the NCS version of the CompositeInternational Diagnostic Instrument was carefully reviewed for culturalappropriateness and comprehensibility in AI populations. As a conse-quence, two types of modifications were made to the alcohol-dependencesection. The first, and most common, were clarifications. For instance, inthe item “Have you ever felt such a strong desire or urge to use alcohol thatyou could not resist it or could not think of anything else?” the synonymurge was added because desire was not commonly used in these commu-nities in the context of alcohol usage. The second type of modification wasculturally mandated, as seen in the item “Did alcohol ever cause youconsiderable problems with your family, friends, at work, or the police?”Because alcohol is legally prohibited on many AI reservations (includingthose in this study), persons with alcohol in their possession are necessarilysubject to arrest. In this context, inclusion of problems with the police hadthe potential to artificially inflate the rates of this symptom of alcoholdependence, so the question was separated into two items to allow aninvestigation of the hypothesis. Although the rates reported here includethese two items as a single indicator, later articles are planned thatexamine the implications of this item and other modified items. Overall, ofthe 18 questions in this section, clarifying changes were made to 4 items,and culturally mandated changes were made to 2 items.
Our use of alcohol dependence reflects our awareness, informed by thediscussions that led to the 4th edition of the DSM, of problems in theDSM-III-R formulation of alcohol abuse as a residual category that lackeda clear conceptual framework (American Psychiatric Association, 1994).Thus, we used alcohol dependence to provide a clearer representation ofmore serious problems with alcohol, as was also done in the recent reportsby Kunitz and Levy (2000) and Robin et al. (1998). To facilitate compar-ison, alcohol dependence was derived by using the NCS algorithms, whichwere also based on DSM-III-R. A separate report on this study thatexamines rates of alcohol and drug abuse and dependence in these AIpopulations by using DSM-IV criteria is now available (Mitchell et al.,2003), but the use of these different criteria and algorithms obviates theexplicit national comparison that is the focus here. The nine criteria forDSM-III-R alcohol dependence (American Psychiatric Association, 1987)are (1) drinking more than intended, (2) persistent desire to cut down, (3)spending a great deal of time in getting, using, and recovering from the useof alcohol, (4) frequent intoxication or withdrawal when expected to fulfillmajor obligations or when use is hazardous, (5) giving up other importantactivities to use alcohol, (6) continued use despite problems from alcohol,(7) tolerance, (8) withdrawal, and (9) drinking to alleviate withdrawalsymptoms. A diagnosis of alcohol dependence is indicated if a personreports having three or more of these symptoms persisting for a month ormore or occurring repeatedly in the past.
Data Analyses
Variable construction was completed with SPSS (SPSS Inc., Chicago,IL) and SAS (SAS Institute, Cary, NC); all inferential analyses wereconducted in Stata (Stata Corp., College Station, TX) by using sample andnonresponse weights (Cochran, 1977). Because of the differential genderdistributions, data from each sample are presented separately by sex. Wepresent estimates, SEs, and significant pairwise comparisons for sixgroups: SW men, SW women, NP men, NP women, US men, and USwomen; data on the last two groups were derived from the NCS. For
categorical variables, Pearson’s �2, corrected for the survey design andconverted into an F statistic (Stata), was used to determine where signif-icant differences existed across groups. For continuous variables, an om-nibus F statistic was used (Stata). Nonoverlapping confidence intervalsindicate significant differences. Because of the multiple comparisons, wediscuss only differences at the level of p � 0.01 for the subgroup compar-isons of prevalence rates. Superscripts denote significant pairwise com-parisons in the tables.
In addition to prevalence rates of use and disorder, the demographiccorrelates of lifetime and 12-month alcohol dependence were evaluated byusing multinomial logistic regression. We included six indicator variablesin logistic regression models by dummy coding the individual categories:gender (with female as the referent group), age group (25–34, 35–44, or45–54 years, with 15–24 years serving as the referent group), educationlevel (high school diploma/General Educational Development, some col-lege, or college graduate, with those having less than a high schooleducation as the referent group), poverty status [poor (falling below theUS level of poverty), with nonpoor as the referent group], employment(student or not working for pay, with working for pay as the referentgroup), and marital status (living as married, separated/widowed/divorced,or never married, with married as the referent group). In these analyses,all variables were entered in one step, so that results indicate odds ratios(ORs) after controlling for all other variables in the model.
To test whether logistic regressions should be run separately for eachsample, preliminary logistic regressions were conducted for lifetime and12-month dependence that included the dummy-coded demographic cat-egories, two dummy-coded variables for sample membership, and addi-tional dummy-coded variables that represented potential interactions be-tween the demographics and sample membership. For both the lifetimeand 12-month dependence models, coefficients for categories of gender,age, education, employment, and marital status were significant at the 0.05level even though the sample membership and interactions were included.This indicated that the correlations of the demographics with alcoholdependence remained after controlling for sample membership. Addition-ally, interactions with categories of gender, age, education, employment,and marital status were significant at the 0.05 level. For interested readers,we present the ORs for the significant interactions in “Results.” Thesemirror the findings from the separate equations presented in Tables 4 and5 in a number of ways. In fact, the ORs from the separate equations foreach sample can be derived directly from the interaction terms in thesingle logistic equation. However, given the number of significant inter-actions, which indicated that the correlations between demographic vari-ables and alcohol dependence varied across samples, we believed that asingle logistic equation would not accurately represent the relationship ofthese variables with either lifetime or 12-month alcohol dependence.Thus, in the main report of the results, we present separate logisticregression models for the SW, NP, and NCS samples. In these models, wediscuss significant associations at the p � 0.05 level.
RESULTS
Table 2 shows estimates, SEs, and pairwise comparisonsof the key alcohol-use variables. The SW women were morelikely than any of the other groups to have never consumedalcohol (62.4%); the NCS men were least likely to havenever consumed alcohol (18.6%). Generally, women weremore likely to be lifetime nondrinkers than men were, and,especially among men, AIs were more likely to be lifetimenondrinkers than were others in the US population, asrepresented by NCS. Similar patterns were found for 12-month drinking. On average, those from the NP, and par-ticularly NP women, typically took their first drinks at ayounger age than their SW and US counterparts. However,differences in the age that the heaviest drinking began were
DSM-III-R ALCOHOL DEPENDENCE IN AMERICAN INDIANS 1789
less common. Finally, among past-year drinkers, SWwomen reported drinking the fewest drinks in the heaviest-drinking episode in the past month.
Table 3 presents the lifetime and 12-month prevalencerates of DSM-III-R alcohol dependence. More than 30% ofboth samples of AI men qualified for lifetime alcohol de-pendence, compared with 20.1% for US men. Amongwomen, those from the NP sample (20.5%) were morelikely to be alcohol dependent than were either SW or USwomen. Considering 12-month rates, the gender differ-ences remained; however, many of the sample differenceswere no longer apparent. Both lifetime and 12-month prev-alence rates varied by age. As might be expected, 12-monthprevalence rates were lower for the older cohorts. Age andlifetime rates also seemed to differ by sample. A graphicalrepresentation of the lifetime rates is found in Fig. 1.
The older NCS cohorts had lower rates of lifetime de-pendence. Among the NP cohorts, however, lifetime ratesincreased across age groups. Once again, the SW patternfell somewhere between the NCS and NP samples.
The rates of lifetime dependence among men andwomen in the AI samples conditional on alcohol use werediverse but always higher than for the US population. Therates of lifetime dependence among nonabstaining SW andNP men were not significantly different from one anotherat the 0.01 level (SW: 47.6%, n � 383; NP: 41.4%, n � 576),but both of these rates were significantly higher than forNCS male nonabstainers (24.7%; n � 3189; p � 0.01). Thelifetime dependence rate among SW nonabstaining women(22.7%; n � 289) was not significantly different from thatfor NP nonabstaining women (31.0%; n � 529) at the 0.01level; yet both rates were significantly higher than amongNCS women drinkers (13.4%; n � 2712; p � 0.01).
Considering the 12-month dependence rates conditionalon alcohol use, stronger differences were found amongwomen than among men. NP nonabstaining women had a12-month dependence rate (11.5%; n � 531) that wassignificantly higher than among SW women nonabstainers(3.3%; n � 290; p � 0.01) and among NCS women non-abstainers (6.0%; n � 2712; p � 0.01); however, the 12-month dependence rates of nonabstaining SW and NCSwomen were not significantly different from one another atthe p � 0.01 level. Among nonabstaining men, no differ-ences in 12-month dependence rates were significant at the0.01 level (SW: 18.7%, n � 383; NP: 17.7%, n � 578; NCS:13.1%, n � 3189).
Additionally, only small differences existed in the overallnumber of alcohol-dependence criteria endorsed byalcohol-dependent individuals in the AI-SUPERPFP andNCS samples (alcohol-dependent individuals in the SWmet an average of 5.1 criteria; in the NP, an average of 5.4criteria; and in the NCS, an average of 4.8 criteria), butthese differences were not significant when considered inlight of the additional requirement that the symptom hadexisted for more than a month or occurred on a recurrentbasis in the past. More detailed analyses of the structure of
Tab
le2.
AI-
SU
PE
RP
FP/N
CS
Alc
ohol
Use
Com
par
ison
s
Var
iab
le
AI-
SU
PE
RP
FPS
WA
I-S
UP
ER
PFP
NP
NC
S
Men
Wom
enM
enW
omen
Men
Wom
en
Res
ult
SE
Diff
eren
cesa
Res
ult
SE
Diff
eren
ces
Res
ult
SE
Diff
eren
ces
Res
ult
SE
Diff
eren
ces
Res
ult
SE
Diff
eren
ces
Res
ult
SE
Diff
eren
ces
%W
hone
ver
dra
nk34
.9%
2.0
SF,
UM
62.4
%1.
8S
M,
NM
,N
F,U
M,
UF
26.6
%1.
7S
F,U
M,
UF
34.5
%1.
7S
F,U
M18
.6%
0.9
SM
,S
F,N
M,
NF,
UF
38.5
%1.
0S
F,N
M,
UM
%W
hod
idno
td
rink
inth
ep
ast
year
54.6
%2.
1S
F,N
M,
UM
,U
F79
.3%
1.5
SM
,N
M,
NF,
UM
,U
F
39.9
%1.
9S
M,
SF,
NF,
UM
53.1
%1.
8S
F,N
M,
UM
,U
F27
.6%
1.0
SM
,S
F,N
M,
NF,
UF
44.9
%1.
1S
M,
SF,
NF,
UM
Mea
nag
e,in
year
s,of
first
drin
k(a
mon
gd
rinke
rs)
15.0
0.2
SF,
UF
16.5
0.2
SM
,N
M,
NF,
UM
14.3
0.1
SF,
NF,
UM
,U
F15
.30.
1S
F,N
M,
UF
15.2
0.1
SF,
NM
,U
F16
.60.
1S
M,
NM
,N
F,U
M
Mea
nag
e,in
year
s,b
egan
heav
iest
drin
king
(am
ong
drin
kers
)
20.5
0.3
20.7
0.3
19.9
0.2
UM
,U
F20
.90.
321
.40.
2N
M21
.50.
2N
M
Mea
nm
ost
drin
ksin
day
,p
ast
year
(am
ong
pas
t-ye
ard
rinke
rs)
12.6
0.7
SF
6.8
0.5
SM
,N
M,
NF,
UM
,U
F
15.2
0.4
SF,
NF,
UF
11.5
0.4
SF,
NM
13.2
0.6
SF
10.9
0.7
SF,
NM
aG
roup
abb
revi
atio
nsd
enot
esi
gnifi
cant
pai
rwis
eco
mp
aris
ons
(p�
0.01
):S
M,S
outh
wes
tmal
es;S
F,S
outh
wes
tfem
ales
;NM
,Nor
ther
nP
lain
sm
ales
;NF,
Nor
ther
nP
lain
sfe
mal
es;U
M,U
Sm
ales
;UF,
US
fem
ales
.
1790 SPICER ET AL.
Tab
le3.
Com
par
ison
Bet
wee
nLi
fetim
ean
d12
-Mon
thP
reva
lenc
eof
DS
M-I
II-R
Alc
ohol
Dep
end
ence
inA
I-S
UP
ER
PFP
and
NC
S,
by
Age
and
Gen
der
Var
iab
le
AI-
SU
PE
RP
FPS
WA
I-S
UP
ER
PFP
NP
NC
S
Men
Wom
enM
enW
omen
Men
Wom
en
%S
ED
iffer
ence
sa%
SE
Diff
eren
ces
%S
ED
iffer
ence
s%
SE
Diff
eren
ces
%S
ED
iffer
ence
s%
SE
Diff
eren
ces
Life
time
alco
hol
dep
end
ence
Tota
l31
.11.
9S
F,N
F,U
M,
UF
8.7
1.0
SM
,N
M,
NF,
UM
30.5
1.8
SF,
NF,
UM
,U
F20
.51.
5S
M,
SF,
NM
,U
F20
.10.
9S
M,
SF,
NM
,U
F8.
20.
6S
M,
NM
,N
F,U
M15
–24
year
s22
.53.
4S
F,U
F7.
51.
9S
M,
UM
20.2
2.9
UF
14.5
2.4
18.3
1.8
SF,
UF
8.3
1.2
SM
,N
M,
UM
25–3
4ye
ars
23.6
3.7
SF,
UF
7.9
2.1
SM
,N
M,
UM
26.8
3.2
SF,
UF
21.1
3.1
UF
21.7
1.5
SF,
UF
10.9
1.1
SM
,N
M,
NF,
UM
35–4
4ye
ars
44.9
4.3
SF,
NF,
UM
,U
F9.
72.
1S
M,
NM
,U
M37
.53.
7S
F,U
F21
.63.
3S
M,
UF
23.7
1.9
SM
,S
F,U
F7.
00.
9S
M,
NM
,N
F,U
M�
45ye
ars
33.8
3.8
SF,
UM
,U
F9.
72.
1S
M,
NM
,N
F37
.33.
9S
F,U
M,
UF
25.9
3.4
SF,
UF
15.0
1.9
SM
,N
M,
UF
5.1
1.2
SM
,N
M,
NF,
UM
12-m
onth
alco
hol
dep
end
ence
Tota
l12
.21.
4S
F,U
F1.
30.
4S
M,
NM
,N
F,U
M13
.01.
3S
F,U
F7.
61.
0S
F,U
F10
.70.
7S
F,U
F3.
70.
4S
M,
NM
,N
F,U
M15
–24
year
s12
.62.
73.
01.
2U
M11
.12.
39.
72.
015
.11.
6S
F,U
F5.
61.
0U
M25
–34
year
s13
.93.
0S
F,U
F1.
50.
9S
M,
NM
,U
M11
.42.
3S
F8.
82.
211
.21.
1S
F,U
F5.
00.
8S
M,
UM
35–4
4ye
ars
13.9
3.0
SF,
UF
0.5
0.5
SM
,N
M,
UM
18.5
3.0
SF,
UF
6.0
1.9
10.0
1.4
SF,
UF
2.5
0.6
SM
,N
M,
UM
�45
year
s7.
52.
1U
F0.
00.
09.
12.
2U
F5.
31.
8U
F4.
81.
0U
F0.
70.
3S
M,
NM
,N
F,U
M
aG
roup
abb
revi
atio
nsd
enot
esi
gnifi
cant
pai
rwis
eco
mp
aris
ons
(p�
0.01
):S
M,S
outh
wes
tmal
es;S
F,S
outh
wes
tfem
ales
;NM
,Nor
ther
nP
lain
sm
ales
;NF,
Nor
ther
nP
lain
sfe
mal
es;U
M,U
Sm
ales
;UF,
US
fem
ales
.
DSM-III-R ALCOHOL DEPENDENCE IN AMERICAN INDIANS 1791
alcohol dependence and the role of various criteria insegregating classes of individuals with alcohol dependenceawait later analyses, but these results provide a preliminaryindication that alcohol-dependent individuals in all samplesmet the criteria for the disorder in numerous ways, meetingmore than half of the nine criteria for alcohol dependencewhen only three were required for the diagnosis.
Table 4 presents lifetime alcohol dependence in the con-text of gender, age, education, poverty, employment, andmarital status. These demographic correlates have beenpreviously found to be important in other epidemiologicalresearch on alcohol abuse or dependence (Bucholz, 1999).In examining lifetime dependence, men continued to be atincreased risk for lifetime alcohol dependence even aftercontrolling for all other demographic correlates. The ORsfor men ranged from 2.03 in the NP sample to 6.08 in theSW sample (the latter of which is likely higher because ofthe very low prevalence of the disorder among women inthat sample). Age seemed to be differentially related tolifetime alcohol dependence across samples: for both AIsamples, those in the two older age cohorts were at in-creased risk for lifetime alcohol dependence comparedwith those 15 to 24 years of age. In the NCS, however, theoldest age group (45–54 years) was at decreased risk forlifetime alcohol dependence compared with the youngestage group. Education was related to lifetime alcohol de-
pendence only in the NCS, in which college graduates wereless likely to qualify for lifetime alcohol dependence thanwere others. Poverty status was not related to lifetimealcohol dependence in any sample. However, those notworking for pay were at increased risk among the NPsample, whereas students in NCS were at decreased risk.Marital status also functioned differently across samples:for the SW and NCS, those who were married were atdecreased risk compared with those who were living asmarried (SW and NCS), separated/divorced/widowed(NCS), or never married (SW). Marital status was unre-lated to lifetime alcohol dependence among the NP sample,however.
In the single-equation model of lifetime alcohol depen-dence, there were significant (p � 0.05) interactions be-tween the NP sample and male gender (OR, 0.65), age 45to 54 years (OR, 2.84), being a college graduate (OR, 2.05),being a student (OR, 3.52), and never having been married(OR, 0.59). There were also significant interactions be-tween the SW sample and male gender (OR, 1.96), age 35to 44 years (OR, 2.72), age 45 to 54 years (OR, 3.56), beinga high school graduate or the equivalent (OR, 1.68), havingsome college (OR, 1.97), being a college graduate (OR,2.30), and being a student (OR, 2.30).
Table 5, which examines the correlations for 12-monthalcohol dependence, shows that men in the SW were at
Fig. 1. Lifetime dependence rates by sample, gender, and age cohort.
1792 SPICER ET AL.
remarkably higher risk for 12-month alcohol dependencethan were women from the same sample (OR, 20.68), againlargely as a function of the low prevalence of the disorder
among SW women. The relationship with age remainedevident for NCS, with the older cohorts at less risk, butagain that relationship was not apparent for either AI
Table 4. Multivariate Analyses of the Demographic Correlates of Lifetime Alcohol Dependence in AI-SUPERPFP and NCS
Variable
AI-SUPERPFP SW AI-SUPERPFP NP NCS
OR SE OR SE OR SE
GenderFemale 1.00 — 1.00 — 1.00 —Male 6.08 1.07* 2.03 0.28* 3.12 0.31*
Age (years)15–24 1.00 — 1.00 — 1.00 —25–34 1.12 0.30 1.25 0.26 1.10 0.1735–44 2.91 0.77* 1.71 0.38* 1.05 0.17�45 2.16 0.59* 1.71 0.39* 0.60 0.11*
EducationLess than 12 years 1.00 — 1.00 — 1.00 —High school graduate or GeneralEducational Development
1.26 0.26 0.74 0.13 0.78 0.11
Some college 1.44 0.34 1.18 0.24 0.77 0.12College graduate 1.14 0.41 1.11 0.34 0.52 0.08*
Poverty statusa
Nonpoor 1.00 — 1.00 — 1.00 —Poor 1.29 0.22 1.02 0.15 1.16 0.18
EmploymentWorking for pay 1.00 — 1.00 — 1.00 —Student 0.79 0.27 1.18 0.30 0.34 0.08*Not working for payb 1.18 0.22 1.38 0.21* 1.05 0.14
Marital statusMarried 1.00 — 1.00 — 1.00 —Living as married 1.92 0.43* 1.28 0.24 1.94 0.33*Separated, widowed, divorced 1.04 0.27 1.18 0.24 1.65 0.22*Never married 1.70 0.39* 0.73 0.14 1.23 0.17
a Indeterminates included in nonpoor in the AI-SUPERPFP sample.b Includes homemaker, looking for work, unemployed, retired, permanently disabled, and other.* p � 0.05.
Table 5. Multivariate Analyses of the Demographic Correlates of 12-Month Alcohol Dependence in AI-SUPERPFP and NCS
Variable
AI-SUPERPFP SW AI-SUPERPFP NP NCS
OR SE OR SE OR SE
GenderFemale 1.00 — 1.00 — 1.00 —Male 20.68 9.17* 2.09 0.41* 3.51 0.50*
Age (years)15–24 1.00 — 1.00 — 1.00 —25–34 1.49 0.57 0.94 0.26 0.77 0.1435–44 1.89 0.78 1.41 0.41 0.63 0.13*�45 0.99 0.46 0.69 0.24 0.25 0.06*
EducationLess than 12 years 1.00 — 1.00 — 1.00 —High school graduate or GeneralEducational Development
1.78 0.60 0.50 0.12* 0.98 0.19
Some college 1.31 0.55 0.89 0.26 0.95 0.21College graduate 0.99 0.82 0.30 0.17* 0.55 0.12*
Poverty statusa
Nonpoor 1.00 — 1.00 — 1.00 —Poor 2.08 0.60* 0.95 0.20 1.23 0.24
EmploymentWorking for pay 1.00 — 1.00 — 1.00 —Student 1.33 0.64 0.83 0.28 0.44 0.12*Not working for payb 1.01 0.32 1.14 0.25 1.09 0.20
Marital statusMarried 1.00 — 1.00 — 1.00 —Living as married 4.71 1.82* 1.61 0.45 2.88 0.65*Separated, widowed, divorced 2.28 1.15 1.50 0.45 2.04 0.38*Never married 4.41 1.68* 1.12 0.32 1.64 0.30*
a Indeterminates included in nonpoor in the AI-SUPERPFP sample.b Includes homemaker, looking for work, unemployed, retired, permanently disabled, and other.* p � 0.05.
DSM-III-R ALCOHOL DEPENDENCE IN AMERICAN INDIANS 1793
sample. In terms of educational attainment, those in the NPwho did not finish high school were generally at increasedrisk for 12-month alcohol dependence. The only significantcorrelation with income was found in the SW, with thosebelow the federal poverty level at increased risk. Employ-ment status continued to be relatively unimportant for bothAI samples. Finally, the relationship between marital statusand current alcohol dependence further underscores that,at least in the NP communities, marital status was notrelated to alcohol problems in the same way as it was forthe SW or NCS.
In the single-equation model for 12-month alcohol de-pendence, there were significant (p � 0.05) interactionsbetween the NP sample and male gender (OR, 0.59), age 35to 44 years (OR, 2.27), and age 45 to 54 years (OR, 2.77).There were also significant interactions between the SWsample and male gender (OR, 5.78), age 35 to 44 years(OR, 3.11), age 45 to 54 years (OR, 4.16), being a student(OR, 3.10), and never having been married (OR, 2.61).
DISCUSSION
Prevalence
This effort is among the first to document the prevalenceof DSM-III-R alcohol dependence in community samplesof AIs, as opposed to clinic- or family-based samples. Fur-thermore, by using standardized measures from a recentstudy of the US population and through the inclusion oftwo tribal groups, these data can simultaneously be placedin a national context while underscoring the cultural diver-sity among contemporary AI populations. This work under-scores significant levels of alcohol dependence, especiallyamong AI men, providing additional confirmation of dataderived from mortality statistics but providing additionalspecificity as to those segments of the population in whichproblematic drinking (as measured by alcohol dependence)is concentrated. Furthermore, the results reported hereconstitute an important corrective to previous claims forthe prevalence of alcohol dependence by using nonstand-ard sampling methods, which may have generated artifi-cially high prevalence rates of alcohol dependence. Therates in this study are indeed quite high and of seriousconcern to health policy makers and planners, but theprevalence of alcohol dependence is not nearly as high asstereotypes of the drunken Indian (Westermeyer, 1974)may lead people to believe.
Alcohol Use and Alcohol Dependence
This study was explicitly intended to fill the gap betweenknowledge generated by surveys on alcohol use and data onalcohol-related mortality reported by the IHS (2002). Dataon alcohol use collected in this context, although limited byour use of only those AI-SUPERPFP items that were iden-tical to those used in the NCS, nevertheless contributed tothe consistent representation that emerges from other stud-
ies of alcohol use: rates of lifetime and past-year abstinenceare generally higher in these AI communities than in theNCS, indicating that where lifetime and past-year problemswith alcohol occur, they are likely concentrated in a smallersegment of these AI populations than in the NCS. This isalso reflected in our analyses of the conditional probabili-ties of dependence given use, presented previously, whichare significantly higher in the AI samples than in the NCSfor lifetime dependence, although, interestingly, not formen with regard to 12-month alcohol dependence.
The use of the criteria of alcohol dependence, as definedby the DSM-III-R, represents a principled choice to exam-ine clinically significant levels of alcohol-related problems,as has been done, for example, by Kunitz and Levy (2000)in their recent report on Navajo alcohol problems or Robinet al. (1998) in another southwestern AI group, and thesecriteria permit explicit comparisons with national work,which has not previously been done. Nevertheless, it wouldbe premature to conclude that the diagnosis of alcoholdependence provides the best index of alcohol problems inAI communities, especially insofar as many of the criteriamay be a consequence of a binge-drinking style that hasbeen demonstrated to be more common in AI communities(Levy and Kunitz, 1974; May and Gossage, 2001; Robin etal., 1998). Rather, the analyses presented here are designedto provide a preliminary indication of levels of alcoholproblems by using diagnostic criteria that are well estab-lished in clinical and research settings, both nationally andinternationally, and that include such clinically significantindices as withdrawal, tolerance, drinking more than in-tended, efforts to quit or cut down, giving up importantactivities to drink, and continued use despite awareness ofproblems. Future research will explore whether these indi-vidual criteria function in these AI samples in ways thatdiffer from the way they work in the NCS, although theresults discussed previously indicate no significant differ-ences in the number of criteria met among alcohol-dependent individuals in the AI-SUPERPFP and NCS.
Cohort Effects
Also significant is the high prevalence of lifetime alcoholdependence in both of the oldest age groups (35–44 yearsand 45–54 years) in the AI-SUPERPFP samples, which wasnot evident in NCS or, indeed, in other national datasets(Bucholz, 1999). The meaning of this pattern is currentlyunclear, but it may indicate different cohort effects in theseAI communities, especially because the oldest cohort inAI-SUPERPFP was born later than the same-aged partic-ipants in either the Epidemiologic Catchment Area study,which was conducted in the early 1980s, or the NCS, whichwas conducted from 1990 to 1992. These findings mightalso reflect important changes in the drinking patterns inthe US population, as reflected by the NCS sample: al-though it seems that the oldest participants in AI-SUPERPFP were heavier drinkers than others of compa-
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rable age at earlier times in the US, differences betweenAI-SUPERPFP and NCS may have been attenuated in theyounger cohorts as problem drinking increased in the USpopulation (Nelson et al., 1998). Additional analyses toexplore these questions, which will involve linking partici-pants in AI-SUPERPFP and NCS by date of birth, will helpto answer some of these important questions, includingcloser attention to the fact that these age differences are nolonger significant in the 12-month models for AI-SUPERPFP, which is consistent with previous work docu-menting high rates of remission among AI populations inlater life (Kunitz and Levy, 1994; Leung et al., 1993).
Cultural Differences in the Correlates of Lifetime and 12-Month Alcohol Dependence
A number of important cultural differences also emergein these analyses. Our hypothesis about differential levelsof alcohol dependence by tribe was partially borne out:although the prevalence rates did not differ for men, theNP women were at markedly higher risk for disorder whencompared with their SW counterparts. NP women were atincreased risk for both lifetime and 12-month alcohol de-pendence (when compared with NCS), whereas SW womenwere at no greater risk for either—largely because compar-atively few used alcohol either in their lifetimes or in thepast year. Whereas the probability of lifetime dependenceconditional on use was significantly higher for both menand women in the AI samples than it was in the NCS, thiswas not true for the conditional probability of 12-monthdependence for most groups in our samples, although NPfemale drinkers did have a greater probability of 12-monthdependence (compared with NCS women drinkers). Suchincreased conditional risk for 12-month dependence wasnot found for SW women drinkers or any AI men drinkers,suggesting that NP female drinkers are at uniquely in-creased risk for 12-month alcohol dependence given alco-hol use.
Given the extent of unemployment and poverty in AI-SUPERPFP communities, as hypothesized, neither of thesefactors was a particularly consistent correlate of alcoholdependence, although poverty status was significant in pre-dicting 12-month alcohol dependence in the SW sampleand unemployment was significant in predicting lifetimealcohol dependence in the NP sample. Similarly, educa-tional attainment was not a significant correlate of lifetimealcohol dependence in any sample described here (with theexception of a lower risk for college graduates in the NCS).However, the pattern for 12-month alcohol dependence inthe NP sample was quite close to that found in the Epide-miologic Catchment Area study, with both high school andcollege graduates at decreased risk (Bucholz, 1999). Thefact that education was not a significant correlate of eitherlifetime or 12-month alcohol dependence in the SW sampledeserves some special discussion. This finding may makesense in light of the relative persistence of the aboriginal
economic and traditional educational systems in this soci-ety, such that routes to prestige are still to be found outsideof the formal educational system of the US. Insofar as thisis the case, one would not predict the same correlates of alack of educational attainment in the SW society as wouldbe found in the US more generally.
We were also aware that, especially in the NP commu-nities, nonmarital unions were quite common, as confirmedin Table 1. Therefore, it is not surprising to find thatindividuals living together as married in the NP were not atsignificantly greater risk for alcohol dependence than werethose who were formally married. In the cultural context ofthe NP, many of these nonmarital unions are the culturalequivalents of marriage and therefore would not be ex-pected to evidence the correlations with alcohol depen-dence that are seen in other samples.
Limitations
The study reported here used standardized methodologyin community samples of AI people living on or near theirreservations to estimate the prevalence of alcohol depen-dence. However, a number of differences between thisstudy and the NCS may limit the accuracy of the cross-studycontrasts examined here. First, the AI-SUPERPFP andNCS used different sampling strategies—the AI-SUPERPFP used tribal rolls, whereas the NCS relied onhousehold enumeration. Second, the response rates for theAI-SUPERPFP were lower than they were for the NCS.Third, the AI-SUPERPFP interviews were conducted 5 to7 years after the NCS interviews, necessitating differentdefinitions of poverty for the two samples and making itdifficult to draw firm conclusions when comparing the agecohorts across studies. Fourth, although the AI-SUPERPFP and NCS interviews correspond closely witheach other, the modifications to the NCS interview torender it more comprehensible, acceptable, and appropri-ate for the AI-SUPERPFP participants may also limit theaccuracy of these cross-study contrasts.
Significance of the AI-SUPERPFP/NCS Comparison
As the first study specifically designed to place data usingdiagnostic criteria obtained from randomly selected com-munity samples of AI reservation communities in a com-parative context, these results carry special weight for ourunderstandings of AI problems with alcohol. The ratesreported here are generally quite high, but not uniformlyso: important cultural differences in the prevalence andcorrelates of alcohol dependence have emerged in theanalysis of the two AI-SUPERPFP samples and NCS. Also,insofar as this effort has used a methodology that is sharedwith the NCS and other recent studies (Vega et al., 1998),these arguments gain even greater force than they havepreviously had in the literature on AI drinking, in whichexplicit comparisons of alcohol disorders were not possible(Levy and Kunitz, 1974; May, 1996; Stratton et al., 1978).
DSM-III-R ALCOHOL DEPENDENCE IN AMERICAN INDIANS 1795
This study thus contributes increased knowledge on theextent and distribution of problems with alcohol in AIcommunities at the same time that it adds to the growingbody of sophisticated epidemiological studies that haverecently begun to lay the groundwork for articulating someof the ways in which social and cultural factors may shapethe experiences of alcohol in diverse US ethnic groups(National Institute of Alcohol Abuse and Alcoholism,2000).
ACKNOWLEDGMENTS
AI-SUPERPFP would not have been possible without the sig-nificant contributions of many people. The following interviewers,computer/data management staff, and administrative staff sup-plied energy and enthusiasm for an often difficult job: Anna E.Barün, Antonita Begay, Amelia T. Begay, Cathy A. E. Bell, PhyllisBrewer, Nelson Chee, Mary Cook, Helen J. Curley, Mary C.Davenport, Rhonda Wiegman Dick, Marvine D. Douville, PearlDull Knife, Geneva Emhoolah, Fay Flame, Roslyn Green, BillieK. Greene, Jack Herman, Tamara Holmes, Shelly Hubing, Cam-eron R. Joe, Louise F. Joe, Cheryl L. Martin, Jeff Miller, RobertH. Moran, Jr, Natalie K. Murphy, Melissa Nixon, Ralph L. Roan-horse, Margo Schwab, Jennifer Settlemire, Donna M. Shang-reaux, Matilda J. Shorty, Selena S. S. Simmons, Wileen Smith,Tina Standing Soldier, Jennifer Truel, Lori Trullinger, ArnoldTsinajinnie, Jennifer M. Warren, Intriga Wounded Head, Theresa(Dawn) Wright, Jenny J. Yazzie, and Sheila A. Young. We wouldalso like to acknowledge the contributions of the Methods Advi-sory Group: Margarita Alegria, Evelyn J. Bromet, Dedra Buch-wald, Peter Guarnaccia, Steven G. Heeringa, Ronald Kessler, R.Jay Turner, and William A. Vega. Finally, we thank the NationalComorbidity Survey for their generous policies on data dissemi-nation and the tribal members who so generously answered all thequestions asked of them.
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