Geographical clustering of cannabis use: Results from the New Zealand Mental Health Survey 2003–2004
Introduction
Epidemiology originated with a tradition of population research on infectious diseases as observed within human populations of rather small local areas in Western Europe and the New World, including communities on small islands, and with case-mapping in relation to city or village blocks and households. Relatively primitive methods to quantify disease clusters within households and local community areas were devised in the 19th century, but a special challenge was encountered when epidemiologists tried to quantify how much local area or household clustering might be left behind, once suspected individual-level causal determinants had been taken into account (Anthony, 2006). Facing this challenge, Carey et al. (1993) developed and refined an alternating logistic regressions approach based upon the generalized linear model with generalized estimating equations (ALR; GLM; GEE). In an initial public health application, Katz et al. used ALR to estimate pairwise odds ratios (PWOR) that directly quantify clustering of diarrheal disease in villages and households of various countries, always with larger estimates of household clustering nested within smaller estimates of village-level clustering, reflecting the prominent influence of individual-level and household-level determinants (e.g., age, presence of a latrine in the household). Nonetheless, even with statistical adjustment for individual-level and household-level covariates of this type, there remained tangible and statistically robust village-level diarrhea clustering, with PWOR ranging upward from 1.03 (95% confidence interval, CI = 1.01, 1.07) in Zambia to 2.2 (95% CI = 1.7, 2.8) in Indonesia (Katz et al., 1993) As in other branches of science, this type of epidemiological research gains strength, and the resulting evidence of epidemiological clustering becomes more credible, when the pattern replicates in different areas and countries of the world.
Later application of the ALR approach in epidemiological research on illegal drug involvement in the United States (US) has produced generally consistent evidence that drug involvement shows an epidemiological patterning akin to what has been observed for diarrheal diseases in villages – that is, modest but statistically robust clustering observed for small local areas such as city block groups and census tracts, also with individual-level covariates held constant. Nonetheless, there is only one published study that quantifies local area clustering of cannabis involvement, based upon cannabis use of neighbourhood residents sampled and assessed for national surveys conducted within US metropolitan areas during the early 1990s (Bobashev and Anthony, 1998, Bobashev and Anthony, 2000, Petronis and Anthony, 2003). The extent and nature of clustering may depend on the structure of drug markets and these differ across time and place so that it is important to investigate such clustering in more recent years and in other countries.
We cannot carry out an exact replication of that one prior cannabis study, because the US Substance Abuse and Mental Health Services Administration (SAMHSA) no longer releases the necessary neighborhood level indicators from its annual national surveys of US residents. For non-exact but systematic replication, we now present evidence from another part of the world, New Zealand (NZ), with PWOR estimates made for the NZ neighbourhood level, nested within larger geopolitical units, also with ALR estimation of the PWOR and with individual-level covariates held constant.
Although NZ is separated from the US by more than 6500 miles of open Pacific Ocean water (> 10,000 km), these two countries share many features of cannabis epidemiology, including many early-onset users and relatively large population prevalence estimates for recent cannabis smoking. To illustrate, based upon a NZ national survey conducted in 2001, 50% of 15–45 year olds had ever used cannabis and 20% had used it in the last year (Wilkins et al., 2002). According to reports compiled by the United Nations Office on Drugs and Crime (http://www.unodc.org/pdf/WDR_2006/wdr2006_volume2.pdf, last accessed 11 March 2008), both NZ and the USA are in the top rank of countries with the highest prevalence of recent cannabis use, along with Canada and Australia (13–17% prevalence proportions for 15–64 year olds). Comparatively, within Western Europe the cannabis prevalence estimates are lower, but there is a wide range from Spain, the Czech Republic, England and Wales at 11%, down to 5% in Ireland, with still lower prevalence estimates observed in Scandinavia and Eastern Europe. The phenomenon of early onset cannabis smoking is well known in the US (e.g., see Degenhardt et al., 2007), and also has been documented in New Zealand. For example, New Zealand research teams studying 1970s birth cohorts estimated that 15% had smoked cannabis by age 15, and that 70–77% had used cannabis by ages 25–26 years (Poulton et al., 2001, Boden et al., 2006).
There has been little reporting of geographic differences within New Zealand, or local characteristics associated with use of cannabis. A comparison of one metropolitan area and one provincial area showed only small differences (Field and Casswell, 2001), but larger regional differences across health districts were apparent in the 2002/03 national health survey, even after age standardisation, with the highest prevalence being 1.7 times that of the lowest prevalence (http://www.phionline.moh.govt.nz/, last accessed 18 September 2007). The use of cannabis, mainly in social situations, often for ‘free’, and evidence of the multiple levels within the cannabis market all suggest that social networks are important in the acquisition and use of cannabis (Wilkins et al., 2005, Wilkins and Sweetsur, 2006). Therefore local area geographic clustering of cannabis use would be expected.
In summary, aim of this paper is to use data from a national survey to examine whether there is geographical clustering of cannabis use in New Zealand, to investigate possible reasons for this clustering, and to see if any geographic clustering remains once individual-level correlates are taken into account. Because one of the issues in ‘neighborhood’ analyses is what constitutes a neighborhood (Diez-Roux, 1998) we will use two levels of clustering, one larger than what would be expected to be a neighborhood, and the other smaller. We will examine:
- 1.
geographic clustering of cannabis use, at a very localised area level (the smallest census area called a ‘meshblock’) and the local government level;
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associations between cannabis use and individual-level characteristics (age, sex, ethnicity, education, and income);
- 3.
the extent to which geographic clustering might be explained by individual-level characteristics;
The approach of this paper fits within a perspective from infectious disease epidemiology, with all three standard components of a public health model: agents (here the drug cannabis), hosts (humans with individual-level susceptibility/resistance traits), and environments (including drug availability). Evidence on geographic clustering of health conditions is important in this public health perspective; it often points toward environmental or contextual targets for more probing research on mechanisms or targets for preventive activities.
Think about the perspective of a national policy-maker or a local health officer with decisions to be made about whether and how to respond to expressions of public concern about emerging health issues. For illustration, consider the decision-making implications if Katz et al. had found no statistically robust village-level clustering of diarrheal diseases, once the ALR approach was used to take into account the underlying influence of individual-level covariates (e.g., age, frequency of hand-washing) and household-level covariates (e.g., presence of a latrine for the household). For the policy-maker or health officer, decision-making about effective prevention and control of these diarrheal diseases would shift in the direction of fundamental individual-level and household-level interventions (e.g., promotion of hand-washing; improvements in household-level human waste disposal), though perhaps not to the exclusion of village-level interventions (e.g., improved sanitary engineering for the public water supply). In contrast, Katz et al. actually found statistically robust and tangible PWOR estimates for village-level clustering, even when individual-level and household-level covariates were held constant. From this public health perspective, tangible local community-level clustering sheds light on the potential benefit of village-level interventions, even when the lower level influences have been taken into account. We will return to this topic in Section 4, after we have described the materials and methods of this study and presented our findings.
Section snippets
Materials and methods
Ethics approval was obtained from all 14 regional health ethics committees and written informed consent was obtained from each participant. Parental consent to participate was not required for 16–17 year olds; the age of sexual consent in New Zealand is 16 years and this often is used as an age for consent to survey participation. A chapter in a report to the New Zealand Ministry of Health provides full details of materials and methods (//www.moh.govt.nz/moh.nsf/pagesmh/5223/$File/mental-health-survey-2006-methods.pdf
Sample characteristics
The characteristics of the sample are shown in Table 1. The most notable discrepancy between the unweighted numbers and the weighted percentages is seen for ethnicity. To provide more precision for estimates for Māori and for Pacific people, the number of Māori was doubled and the number of Pacific was quadrupled from that expected from their numbers in the population; with weighting, the percentage in each ethnic group corresponds to that in the 2001 census.
Cannabis use
Table 1 shows the numbers who
Discussion
The clustering of cannabis use seen in the US (Bobashev and Anthony, 1998, Bobashev and Anthony, 2000) is also seen in New Zealand, a country in which, like the US, about 13% of the adult population have used cannabis in the past year. In New Zealand, clustering occurred only at the small area of a census meshblock, not at the larger local authority area. Clustering was reduced but still remained at the meshblock level when a standard set of socio-demographic correlates were taken into account.
Role of funding sources
Te Rau Hinengaro: The New Zealand Mental Health Survey (NZMHS) was funded by the Ministry of Health, Alcohol Advisory Council and Health Research Council of New Zealand. The work of the MSU-based authors (LD, KMB, JCA) was supported by the National Institute on Drug Abuse (K05DA015799; R01DA016558). The New Zealand Ministry of Health was provided with monitoring information during the collection of the data. This paper was submitted for comment but without any right to veto. Other funders had
Contributors
Authors JEW and KMS were involved in design of the survey and overseeing of data collection by a contracted agency. JCA conceived this paper, which was developed by JEW, LD and KMB. KMB undertook the statistical analysis in consultation with JEW, LD and JCA. JEW wrote most of the first draft of the manuscript, assisted by LD. All authors contributed to and have approved the final manuscript.
Conflict of interest
None declared.
Acknowledgements
We acknowledge the other members of the NZMHS Research Team: M.A. Oakley Browne, M.A. McGee, J. Baxter, J. Kokaua, T.K. Kingi, R. Tapsell, S. Foliaki, D. Schaaf, M.H. Durie, C. Tukuitonga and C. Gale. The NZMHS was carried out in conjunction with the World Health Organization World Mental Health (WMH) Survey Initiative. We thank the WMH staff for assistance with instrumentation, fieldwork, and data analysis. These activities were supported by the United States National Institute of Mental
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