Geographical clustering of cannabis use: results from the New Zealand Mental Health Survey 2003-2004.
BACKGROUND
In epidemiology, it always has been important to study local area patterns of disease occurrence. New methods to quantify local area and household clustering of disease emerged late in the 19th century and were refined during the 20th century. Nonetheless, multi-level models to estimate local area clustering of illegal drug use did not appear until the 1990s, and to date, there is just one study with estimates of local neighbourhood clustering of cannabis use, based on a United States sample. Here, seeking the first replication of that single prior study, we estimate the degree to which cannabis use might cluster within neighbourhoods of New Zealand (NZ), and we also study higher level clustering and suspected individual-level determinants of recent cannabis use.


METHODS
A national probability community sample (n=12,992) of adults aged 16 years or more with standardized assessment of cannabis use. Alternating logistic regression produced estimates for cannabis clustering.


RESULTS
In NZ, use of cannabis was common: 41.6% had ever used it and 13.1% had used it in the past year. There was clustering within the smallest local areas (pairwise odds ratio=1.3-1.5) but not within larger government districts (PWOR=1.02). Age, male sex, ethnicity, education, and marital status were all associated with cannabis use, but did not account for observed clustering.


CONCLUSIONS
Neighborhood clustering of recent cannabis use has emerged in New Zealand, as in the US. Standard individual-level characteristics explain only some of this clustering. Other explanations must be sought, perhaps including personal networks and local supply.
Wells
J Elisabeth
JE
Department of Public Health and General Practice, University of Otago, Christchurch, PO Box 4345, Christchurch 8140, New Zealand. elisabeth.wells@otago.ac.nz
Degenhardt
Louisa
L
Bohnert
Kipling M
KM
Anthony
James C
JC
Scott
Kate M
KM
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D004493:Education; D005006:Ethnicity; D005260:Female; D005843:Geography; D006306:Health Surveys; D006801:Humans; D007182:Income; D008297:Male; D002189:Marijuana Abuse / Q000453:epidemiology*; D008603:Mental Health / Q000706:statistics & numerical data*; D008875:Middle Aged; D015233:Models, Statistical; D009520:New Zealand / Q000453:epidemiology; D011336:Probability; D012959:Socioeconomic Factors; D055815:Young Adult