View Article PDF

The Pharmaceutical Management Agency (PHARMAC)s statutory role in New Zealand is to achieve the best health outcomes from the use of publicly-subsidised medicines within available funding.1 The health needs of Mori and Pacific people are an important part of PHARMACs decision-making criteria, alongside the health needs of all New Zealanders.2 Assessing health need and identifying medicines usage patterns for populations can provide evidence of disparities and help inform funding decisions and public health activities. Disparities between Mori and non-Mori health outcomes, and likewise for Pacific peoples (Pasifika) who are mostly of Samoan, Tongan, Niuean, or Cook Islands descent in New Zealand, are known to be both large and persistent over multiple issues.3-12 However, data specific to medicines use in the community have been sparse. Despite good quality information on health disparities and usage patterns for some individual diseases, information has still been insufficient to rank potential health gains across medicines overall. Analyses of medicines prescription dispensing rates cannot always address confounding from disease burden,13 where higher needs would be associated with higher use, particularly aggregating for therapeutic groups overall. Such analyses usually require subanalyses comparing proxies for health need (e.g. mortality or hospitalisation) against individual medicines. This is a large task, given there are hundreds of disease entities and medicines, with large overlaps. Moreover, indicators such as hospitalisation, although more relevant for low-mortality / high prevalence diseases such as asthma, can be biased and confounded (see endnote *).14 There has been scope for limited analysis by mapping medicines usage against relevant internally-consistent comprehensive needs data. In New Zealand such data have for the past decade been available from the Ministry of Healths New Zealand Burden of Disease Study (NZBDS), first published in 2001,15 which quantified years of life lost by the New Zealand population in 1996 from premature mortality and disability across a number of individual diseases. The NZBDS included some ethnic-specific data, using prioritised ethnicity Similarly, information in New Zealand on national use of medicines subsidised in the community (listed in the New Zealand Pharmaceutical Schedule)16 has been available, disaggregated by ethnic group, since about 2004, at that time being possible to readily link over 90% of prescriptions dispensed with anonymised age, gender and ethnicity data. The following preliminary analysis therefore provides an overview of medicines dispensed by prescription volumes, category and population dispensing rates for the financial year 2006/07 in Mori, Pasifika and non-Mori/non-Pasifika populations. The data take into account both (1) age differences within each ethnic group, (2) indicators of health need that combine historical morbidity and mortality, and (3) breakdowns by patient numbers vs. proxies for concordance/adherence. Results to date have helped inform PHARMACs policy development for medicines funding and access. Methods Prescription data This analysis used anonymised prescription medicines dispensing claims data for the financial year 1 July 2006 to 30 June 2007 contained in the PharmHouse (now Pharmaceuticals Collection) administrative claims database.17 The PharmHouse/ Pharmaceuticals Collection database links patient-level dispensing of medicines listed on the New Zealand Pharmaceutical Schedule16 with demographic data, including age and ethnicity, by encrypted National Health Index (NHI)18 patient identifier numbers. Encryption is one-way to ensure confidentiality. Endnotes \u0390 and \u2021 provide detail on prescription dispensings data collection, NHI numbers and Practitioners Supply Orders (PSOs). The analysis excluded those medicines dispensed by health practitioners as PSOs and those prescriptions for individual patients otherwise not recording NHI numbers or where the NHI numbering was inconsistent. During 2006/07 93% of prescriptions dispensed in New Zealand in community pharmacies had an NHI number recorded in PharmHouse; 31,935,268 prescriptions were dispensed, most being for individual patients (not PSOs) and containing NHI numbers. However 2,402,723 scripts were PSO, did not contain NHI numbers, or NHI-related information was unavailable for gender, ethnicity or valid age. To reflect true patient burden, we scaled the remaining 29,532,545 true scripts for individual patients containing NHI numbers and known gender, ethnicity and valid age, to account for those with missing information; this gave a synthesised total of 31,889,448 scaled scripts, used thereafter in this analysis. Scaling is described in Appendices 1 and 2 - see all Appendices. Box 1. Method of calculation: total script count We grouped medicines according to clinical indication (based on main usage), using therapeutic groupings in the New Zealand Pharmaceutical Schedule (see Appendix 1). Scaled counts of scripts for these groups were combined with population data (using population estimates categorised by prioritised ethnicity for the 2006/07 year19) to derive ethnic-specific crude and age-standardised incidence rates of scaled prescriptions dispensed (counts of scripts, i.e. prescription items that were dispensed during the year, per 1000 population) for the three prioritised ethnic groups Mori (M), Pasifika (P), and non-Mori/non-Pasifika (nMnP). Similar rates were calculated for Mori and non-Mori (nM, being P+nMnP). Linking prescription with disease burden data We then linked the indication-based medicines groups with relevant disease categories in published burden of disease data for 1996 in the NZBDS.15 For this we calculated age-standardised rates (ASRs) for disability-adjusted life year (DALY) losses for Mori and non-Mori relevant to indication-based pharmaceutical data, using the year 1996 NZBDS-reported rates of DALYs lost by Mori and non-Mori prioritised ethnicity across its five age-groupings of 0-14, 15-24, 25-44, 45-64, and 65+ years.15 The grouper linking indication-based groups with Burden of Disease disease categories is provided in the Annexe to this paper. Pharmaceuticals and DALYs were directly age-standardised to Segis standard world population (as had occurred in the NZBDS), aggregating Segis 18 5-year age groups into the 5 age group categories reported by the NZBDS.15 Gender could not be included in this analysis, as it was not part of the age/ethnic-specific NZBDS 1996 DALY data. [Note: During the production of this paper (in August 2013), the Ministry of Health published the update of the NZBDS for disease burden occurring in 2006.36,37] Differences in the above ASRs allowed us to estimate the numerical differences in scripts dispensed to Mori, given their population size, age structure and disease burden. We used age-standardised rate ratios (ASRRs) for Mori vs. non-Mori for scripts and DALY losses. From these we derived disease burden-adjusted M:nM script ASRRs for each indication-based medicines group. We then calculated gaps in Mori medicines use compared with expected non-Mori usage. These gaps in effect accounted for differences in population size, age structure and disease burden (as DALYL-adjusted shortfall/excess no. scripts in Mori). Box 1 above details the calculations made. Access vs. persistence We estimated the extents to which differential dispensing to Mori could be attributed to access versus persistence (see endnote \u00a7). In the context of this analysis: Access related to differential dispensing to Mori of first prescriptions (index scripts). It was expressed as the variation in numbers of Mori (less or more patients) accessing medicines compared with access in non-Mori after adjusting for population size, age structure and disease burden. We expressed access as the rate ratio of DALYL-adjusted ASRs for 12-month patient period-prevalence (adjASRRaM:nM = adjASRaM \u00f7 adjASRanM); Persistence was the subsequent residual variation in overall numbers of scripts dispensed due to variations in subsequent scripts per index patient, i.e. the individualised frequency of subsequent scripts dispensed to those Mori who had an initial script, expressed as (persistenceM:nM = scripts/patientMori \u00f7 scripts/patientnon-Mori). Total scripts (prescriptions dispensed) were therefore the product of access (number of patients) and persistence (scripts/patient). This metric of access \u00d7 persistence was the basis on which we could estimate gaps in dispensing. The numerical data on prescriptions, patients, and ASRRs allowed us to differentiate between gaps in initial access to scripts and gaps in subsequent persistence with scripts. Gaps with persistence were simply the residual after subtracting gaps in access for total script gaps. Box 2 details these calculations. Further details of calculation methods are available in Appendix 1, including worked examples. Box 2. Method of calculation: access and persistence Results Near parity of script counts (prescriptions dispensed) when adjusted for age During 2006/07 31,935,268 scripts were dispensed in New Zealand, 4,108,107 being PSO scripts and scripts for individuals either without NHI numbers or unknown or invalid age, gender or ethnicity information (comprising 12.9% of all scripts), with non-PSO NHI-containing scripts (including valid gender/ ethnicity/age) scaling to 31,889,448 for this analysis. 3.3 million (scaled) scripts were ascribable to Mori and 1.7 million to Pasifika (detailed in Appendix 2). These script numbers related to 2.7 million patients with individual NHI numbers, which with scaling for missing NHIs became 2.92 million patients (383,000 Mori, 188,000 Pasifika). Age-standardised scaled prescription dispensing (script) rates overall for Mori in 2006/07 were 97% of those for non-Mori/non-Pasifika, and for Pasifika were 123% of those for non-Mori/non-Pasifika (Mori 5919.8 scripts per 1000 age-standardised population, Pasifika 7535.8 per 1000, non-Mori/non-Pasifika 6102.1 per 1000). This contrasted with crude 64% scripts overall per capita in Mori compared with non-Mori/non-Pasifika, and 83% for Pasifika compared with non-Mori/non-Pasifika. The higher usage after adjusting for age is largely explained by the relative youth of Mori and Pasifika; medicine use tends to increase with age and there are proportionately less older Mori and Pasifika (see Appendix 2). There was a large residual variability in scripts by medicine group after adjusting for age. This was often not obviously related to disease burden. For instance when compared with non-Mori/non-Pasifika, Mori and Pasifika showed lower age-standardised script rates for anti-depressants, contraceptives and inhaled corticosteroids, but higher rates for anti-hepatitis B antivirals, short-acting asthma inhalers, and older and depot injection antipsychotics. The differences in therapeutic groups between Mori and Pasifika compared with non-Mori/non-Pasifika were not uniform, as can be seen in Figure A3-3 and Table A3-3 in Appendix 3. For instance, Pasifika were dispensed medicines for attention deficit disorder, Hepatitis C infections and older depot antipsychotics at one fifth the rate of Mori. Asthma medicines and newer antidepressants were relatively under-dispensed in Pasifika compared with Mori. Conversely, Pasifika were dispensed oral hypoglycaemic medicines for type 2 diabetes and blood glucose test strips, older glaucoma medicines, scabies treatments, and hepatitis B medicines at twice the rate of Mori. Mori and Pasifika age-standardised rates were similar for antibiotics, statins, ACE inhibitors, low dose aspirin, and treatments for gout. All of these features are detailed in Figure 1 and in Appendix 3, including tables and further graphs. Lower script counts for Mori when adjusted for health needs Mapping the NZBDS disease categories to medicines listed on the New Zealand Pharmaceutical Schedule, in order to partly relate medicines use to disease impacts (health need), it was possible to link 85% of 2006/07 scripts (prescription dispensings) to relevant NZBDS disease groups. Accordingly, coincidentally 85% of DALY losses in 1996 appeared to be for diseases treatable or preventable by medicines on the Pharmaceutical Schedule. Hence in 1996 perhaps some 480,000 disability-adjusted years of life (DALYs) were lost by the New Zealand population from diseases treatable by medicines on the Pharmaceutical Schedule (out of 563,000 DALYs lost overall for all diseases) see Tables A4-1 and A4-2 in Appendix 4. The generally higher use of medicines by Mori and Pasifika than non-Mori/non-Pasifika must therefore be seen in the context of these populations having general higher health needs. Details of these higher health needs for Mori can be found in Appendix 5. For conditions treated or prevented by medicines on the Pharmaceutical Schedule, differences in burden of disease could be linked to differences between Mori and non-Mori dispensing rates (see endnote **). This mapping suggests that although total Mori script counts were comparable with non-Mori after adjusting for age, actual dispensing for Mori was much lower than needed to overcome their greater disease burden. Hence, although Mori in 2006/07 had 97% age-adjusted script counts relative to non-Mori, after further adjusting for historical 45% higher relative DALY losses in Mori this ratio fell to 81% of what it would be for non-Mori. Moreover, after excluding medicines not covered by the NZBDS diseases the ratio fell further to 63%. Mori had therefore 19-37% lower treatment rates compared with non-Mori (conversely, rates in non-Mori being higher). The total scripts known to be dispensed to Mori in 2006/7 (excluding PSOs and those otherwise without NHIs, but scaled) was 3.3 million (as stated above), of which 2.7 million linked with NZBDS diseases. The overall gap in scripts to Mori after standardising for age and adjusting for historical burden of disease amounted to 977,400 fewer scripts. Most medicines had shortfalls rather than excesses. Key shortfalls are summarised in Table 1. Table 1. Shortfalls in Mori age/DALY-adjusted script counts Medicine Shortfall* Comments antibiotics 181,500 NZBDS categories of bacterial infections, of which 89,100 for amoxicillins antiulcerants 60,500 principally 54,300 for proton pump inhibitors (PPIs); may reflect inappropriately high antiuclerant use in non-Mori statins 53,100 cardiovascular risk (dyslipidaemia); principally simvastatin (45,400) beta blockers 52,900 primarily for cardiovascular risk and disease ACE inhibitors/A2 antagonists 48,800 cardiovascular risk and disease, including diabetes newer antidepressants 46,300 principally selective serotonin reuptake inhibitors (SSRIs) (41,600); also venlafaxine, selective MAOIs low-dose aspirin 40,100 cardiovascular risk inhaled corticosteroids \u00b1 long-acting beta agonists 22,600 asthma oral hypoglycaemics 21,300 primarily cardiovascular risk (type 2 diabetes) diabetes self-testing 19,200 self-management of types 1 and 2 diabetes *Shortfalls are the differences between actual script counts in Mori and numbers expected were Mori to have the same dispensing as non-Mori, after adjusting for population size, age, and disease burden. Access and persistence similarly less in Mori Almost half of the above calculated need-adjusted gap in prescriptions dispensed was due to fewer than expected Mori patients accessing medicines (443,900 absent initial dispensings). We estimated access in Mori to be 67% that of non-Mori. The biggest gap from reduced access was for amoxicillins. The remainder of the gap was due to lower Mori persistence\u2021 with medicines (533,500 absent subsequent dispensings). Persistence in Mori was calculated as 58% of that in non-Mori. The biggest gaps from reduced persistence were for beta-blockers, PPIs, simvastatin, low-dose aspirin for cardiovascular risk, and SSRIs. Conversely, the calculated overall difference in scripts for non-Mori (age and disease burden-adjusted) amounted to at least 12.2 million more scripts. In summary, access and persistence contributed on a similar scale to apparent under-dispensing to Mori. Note however that there were appreciable differences between medicines in the mix of access and persistence. This included examples such as the newer antipsychotics, in which large proportionate shortfall in access was masked by proportionately lesser shortfalls in persistence. These features are evident in the following graphs (Figures 2 to 5) and are detailed in Table A6.2 in Appendix 6. To explain Figures 2-5: Figure 2 shows shortfalls and excesses in scripts for Mori compared with that expected for non-Mori. This reveals the therapeutic areas suggesting the largest gaps in dispensings. Figure 3 shows proportional shortfalls and excesses. This suggests the therapeutic areas with the most divergence in clinical practice from what would be expected in non-Mori, as Mori rates relative to non-Mori. (The data are on a logarithmic scale, so that shortfalls and excesses are distributed symmetrically about a relative rate of 1 (unity), which is the zero line; further explanation is in endnote \u2021\u2021.) Figure 4 suggests numerical shortfalls and excesses broken down by access and persistence. This shows these two factors variable contributions to differential dispensing. Figure 5 shows proportional shortfalls and excesses, broken down by access and persistence. As with figure 3, this suggests the therapeutic areas with the most divergence in clinical practice from what would be expected in non-Mori, as Mori rates relative to non-Mori, but then shows how much is due to differences in access versus differences in persistence. (Again as with figure 3, the data are on a logarithmic scale, see endnote \u2021\u2021). Figures 2 to 5 also include disaggregating of the category NSAIDS/gout/analgesics/ muscl relxnts into component Rx for hyperuricaemia & gout and NSAIDS + muscle relaxants subcategories. Discussion This analysis links patient-level script count data with population-based estimates of health need. This method can give at best broad indications of trends, for what are complex issues. Interpretation of the results may change after more detailed analysis of individual issues. The ability of access to counteract persistence (as seen with some antipsychotics) is an example of more complex effects that may be lost in population-based data. Even so, this work reveals a potentially significant issue with likely differences between ethnic groups, and hence potential for health gain or reduced wastage once shortfalls and excesses are addressed. This is apparent in a majority of disease and disability states, and begs the question of suboptimal or excess treatment elsewhere. Limitations and caveats There are however important limitations and caveats with the analysis: Scripts dispensed are not the same as medicines prescribed. There is evidence that many prescriptions are either not presented or not collected at pharmacies. Reasons for this may include time, cost and transportation. Such factors can affect populations differentially. Mori are more likely to have uncollected prescriptions, their non-collection rate being 45% higher than that of non-Mori aged over 1520,21 (where this statistic stems from 2006/07, when minimum co-payments for the first 20 items were $3 per item; this has since risen to $5). It is not possible to tell from this analysis the extent that failure to dispense represents a systematic failure to prescribe or a systematic failure to ensure that prescriptions are filled. However, this feature may appreciably understate true gaps. Dispensing data are restricted to those prescriptions and patient groups that gain subsidies for publicly-funded community dispensed medicines. The data therefore exclude prescriptions that were not subsidised, or items that fell below the $3-$15 prescription co-payments at that time, where pharmacies would have no need to claim (and hence would not be captured in the PharmHouse claims database data). Non-capture of unclaimed medicines use might undercount appreciably overall medicines use and potentially understate gaps in in populations with poorer access to medicines. Script counts are an imprecise measure of coverage (days) that medicines are actually provided, being confounded by dispensings/script rates and duration (days coverage) of dispensings. With scripts versus dispensings, people living in rural areas tend to get longer dispensings (e.g. 3 months, where 1 month would be standard in non-rural setting). Hence, to the extent that Mori are overrepresented in rural populations, the gaps may be overstated to an uncertain extent. Some PSOs may be used for targeting populations with poor access to medicines. PSOs are more commonly used in rural areas, where the nearest pharmacy may be some distance, and for certain types of medicines, such as antibiotics. PSOs understate true numbers of people receiving medicines, which may mean gaps are overstated to some extent. Gaps in script counts do not necessarily equate with gaps in disease burden and capacity to benefit from effective medicines treatment. Population health gains (expressed for example as quality-adjusted life years (QALYs) gained) reflect not only numbers of patients and script frequencies per patient, but also the effectiveness of medicines in relation to patients' health needs. Hence gaps in health outcomes from patients receiving less medicine are not necessarily the same as gaps in script counts. Linking between script counts and diseases can be imprecise where medicines have multiple clinical indications or disease burden covers a broad range of diseases. Problems linking medicines to single disease groups may have important effects on the analysis results.If these factors were to cause bias that is non-differential, such imprecisions from linking could tend to understate true differences in disease burden-adjusted prescriptions. However, this is not a given; it is possible that differential bias could occur, for instance understating of shortfalls in for one disease category meaning falsely ascribing shortfalls in another disease category, which could overstate net true differences. An example of probable non-differential bias within a disease category (understating true differences) is that of gout and other musculoskeletal conditions. Medicines for gout (e.g. allopurinol) are bundled into wider NSAIDS etc., because the 1996 NZBDS data combined a number of musculoskeletal conditions, meaning the high excess disease burden for Mori for gout (e.g. their age-standardised hospitalisation rates in 2006/07 being 6-7 times that of non-Mori)22 was diluted by other musculoskeletal diseases and hence relative disparities were muted. Overall, Mori had a small observed shortfall of DALYL-adjusted scripts for musculoskeletal diseases (-2,000), but this may well have been due to a large shortfall for allopurinol etc. for gout (-10,700 scripts) masking a similarly-sized excess for NSAIDs (+12,700). Conversely, an example of the potential for differential bias across disease categories (potentially overstating net true differences) is that of carbamazepine and sodium valproate, which are anti-epilepsy medicines. In the analysis, these were matched to the NZBDS Epilepsy category, and there was a shortfall of 5,900 scripts for Mori (out of 208,900 total scripts). However, carbamazepine and sodium valproate are also commonly used for the control of bipolar disorder, inter alia. The lifetime prevalence of bipolar disorder in Mori is double that of the overall population,23 so it is possible that the shortfall for Mori in the Epilepsy category was understated, and the shortfall for Mori in the NZBDS Mental Health category was overstated, to a greater extent than non-Mori. Such possibilities highlight the impact of mismatching of medicine dispensings and disease categories.More specifically, the broad scope of this preliminary analysis does not allow more detailed review of antibiotic use for discrete issues, e.g. the high incidence of acute rheumatic fever in Mori children,11 which relates specifically to childhood penicillin and amoxicillin use in the treatment of acute pharyngotonsillitis (sore throat) where these medicines also treat many other childhood infections. Age-standardised rates and rate ratios are aggregates that can obscure wide variation across age groups. This become particularly important where data are missing (e.g. from prescriptions that lack NHI numbers including PSOs), with the potential to mis-state true gaps.For example, with the amoxicillins (amoxicillin, amoxicillin clavulanate) there was a shortfall of 89,100 scripts for Mori. Much of that shortfall occurred in children aged 0-14 years, whose relative rates were substantially lower than for older age groups. However, 13% of scripts for amoxicillins did not have NHIs and could in theory all have been on PSO (being double the average 7.5% for scripts overall on PSO). If, radically, the many amoxicillin scripts without NHIs were for all Mori children and these in turn had the same relative rates as for older patients, then the shortfall for amoxicillins for Mori children would halve and the overall gap (all ages) would be only 1/5th lower than non-Mori (53,200 script gap). Details including component calculations can be found in endnote \u0390\u0390\u0390. Amoxicillins accounted for 11% of all non-NHI scripts (204,762 scripts without NHIs numbers, out of 1.6 million amoxicillin etc. scripts and 2.4 million scripts without NHIs), so these medicines are an important part of this information gap. Analysis is unavailable for Pasifika. The available Ministry age-specific analysis by ethnic group was confined to comparing Mori with non-Mori, and other relevant disease burden analysis (Ministry of Health 2001b3) provided insufficient detail to enable age-specific and age-standardised disease burden estimates for Pasifika, Mori and non-Mori/non-Pasifika. Pasifika people have needs and underuse at least equal to Mori (see Appendix 3), with two consequences: Important gaps need to be identified and quantified for Pasifika too; Mori vs. non-Mori comparisons if anything may understate the extent of Mori underuse once adjusted for need, as by including Pasifika in the non-Mori group this may dilute the relative effects for Mori. Such deficiencies should be addressable in the Ministrys forthcoming updates of the NZBDS. [Note: The August 2013 published update of the NZBDS36,37 has not included separate results for Pasifika, only comparing Mori with non-Mori.] The use of disease burden estimates based on mortality/morbidity data from 1996 to compare with prescription volumes a decade lat

Summary

Abstract

Aim

To describe variations in dispensing of specific medication groups by ethnicity in New Zealand, adjusting for health need.

Method

Preliminary linkage of dispensings of prescription medicines in 2006/07 to age/disease burden proxies of health need for Mori, Pacific peoples (Pasifika) who are mostly of Samoan, Tongan, Niuean, or Cook Islands descent in New Zealand, and non-Mori/non-Pasifika. These disease burden proxies combine differences in prevalence, age, morbidity, and mortality. Variations were disaggregated by patients being first dispensed medicines (access) versus subsequent dispensings (persistence).

Results

Initially, overall age-adjusted incidence of scripts (prescriptions dispensed) to Mori was similar to that of non-Mori. There were differences in therapeutic coverage between Mori and Pasifika, for example greater use of asthma medicines in Mori. However, further adjustments linking with disease burden showed marked variance for a number of diseases. Differences in dispensing included areas of high health need such as heart disease, infections, diabetes, mental health and respiratory disease. Mori had 19-37% lower dispensings overall than non-Mori, with a net difference of nearly 1 million scripts. Mori were both less likely to access medicines, and then after first dispensing had fewer subsequent scripts. Patterns for Pasifika appeared similar, although needs-adjusted analysis is awaited for this population.

Conclusion

Once adjusting for need, there was variable but sizeable differences in medicines dispensed to Mori compared with non-Mori, and likely differences for Pasifika populations. There are however important limitations to this preliminary analysis. Crude and age-standardised metrics may be poor predictors of needs-adjusted gaps in medicines use. In this analysis, solely age-standardised rates tended to underestimate differences once adjusting for burden of disease; future analyses of prescribing patterns should consider better adjusting for disease burden.

Author Information

Scott Metcalfe, Chief Advisor Population Medicine/Deputy Medical Director (Epidemiology), PHARMAC, Wellington; George Laking, Medical Oncologist/Health Economist, Auckland Member, Pharmacology and Therapeutics Advisory Committee (PTAC) Member, Mori Caucus, PHARMAC, Wellington; Jason Arnold, Team Leader Analysis, PHARMAC, Wellington

Acknowledgements

We thank Rico Schoeler (Manager Assessment) and Marama Parore (Manager Access & Optimal Use and Mori Health) of PHARMAC, Wellington; Martin Tobias (Principal Adviser, Public Health Intelligence) of the Ministry of Health, Wellington; and PTAC members in 2008 who both provided advice on earlier analysis and reviewed (1) indication-based medicines groupings and (2) consequent groupings of indication-based medicines with disease categories. The Journals anonymous peer reviewers provided helpful commentary.

Correspondence

Dr Scott Metcalfe, PHARMAC, PO Box 10254, Wellington, New Zealand

Correspondence Email

scott.metcalfe@pharmac.govt.nz

Competing Interests

The authors are PHARMAC staff or advisors.

- New Zealand Public Health and Disability Act 2000 http://www.legislation.govt.nz/act/public/2000/0091/latest/DLM80051.html section 47 Objectives of Pharmac PHARMAC Decision Criterion 2 The particular health needs of Mori & Pacific peoples http://www.pharmac.govt.nz/DecisionCriteria. In: PHARMAC. Operating policies and procedures of the Pharmaceutical Management Agency ( PHARMAC ), Third Edition, 2006. http://www.pharmac.govt.nz/2005/12/22/231205.pdf Priorities for Maori and Pacific health: evidence from epidemiology. Public Health Intelligence Occasional Bulletin No. 3. Wellington: Ministry of Health, 2001.http://www.moh.govt.nz/notebook/nbbooks.nsf/0/A31842D91480064FCC256A55007A980A/$file/PrioritiesForMaoriandPacificHealth.pdf Durie M. Understanding health and illness: research at the interface between science and indigenous knowledge. Int J Epidemiol. 2004;33:1138-43. http://ije.oxfordjournals.org/content/33/5/1138.long Bramley D, Riddell T, Crengle S, Curtis E, Harwood M, Nehua D, Reid P. A call to action on Maori cardiovascular health. N Z Med J. 2004;117:U957. http://journal.nzma.org.nz/journal/117-1197/957/ Blakely T, Ajwani S, Robson B, Tobias M, Bonn\u00e9 M. Decades of disparity: widening ethnic mortality gaps from 1980 to 1999. N Z Med J. 2004;117:U995. http://journal.nzma.org.nz/journal/117-1199/995/ Robson B, Purdie G, Cormack D. Unequal impact: Mori and non-Mori cancer statistics 1996-2001. Wellington: Ministry of Health, 2006. Harris R, Tobias M, Jeffreys M, Waldegrave K, Karlsen S, Nazroo J. Effects of self reported racial discrimination and deprivation on Mori health and inequalities in New Zealand: cross-sectional study. Lancet 2006;367: 2005-9 Blakely T, Tobias M, Atkinson J, et al. Tracking disparity: trends in ethnic and socioeconomic inequalities in mortality, 1981-2004. Wellington: Ministry of Health, 2007. Rumball-Smith JM. Not in my hospital? Ethnic disparities in quality of hospital care in New Zealand: a narrative review of the evidence. N Z Med J. 2009;122:68-83.http://journal.nzma.org.nz/journal/122-1297/3662/ Hale M, Sharpe N. Persistent rheumatic fever in New Zealand--a shameful indicator of child health. N Z Med J. 2011;124:6-8. http://journal.nzma.org.nz/journal/124-1329/4530/ Soeberg M, Blakely T, Sarfati D, Tobias M, Costilla R, et al. Cancer trends: trends in cancer survival by ethnic and socioeconomic group, New Zealand 1991-2004. Wellington: University of Otago and Ministry of Health, 2012. http://www.health.govt.nz/publication/cancer-trends-trends-cancer-survival-ethnic-and-socioeconomic-group-new-zealand-1991-2004 Crengle S, Lay-Yee R, Davis P, Pearson J. A comparison of Mori and non-Mori patient visits to doctors: the National Primary Medical Care Survey (NatMedCa): 2001/02. Report 6. Wellington: Ministry of Health, 2005. http://www.health.govt.nz/publication/comparison-maori-and-non-maori-patient-visits-doctors Smith MW. Hospital discharge diagnoses: how accurate are they and their international classification of diseases (ICD) codes? N Z Med J 1989;102:507-8. The Burden of Disease and Injury in New Zealand. Public Health Intelligence Occasional Bulletin No. 1. Wellington: Ministry of Health, 2001.http://www.moh.govt.nz/notebook/nbbooks.nsf/0/E916C53D599AE126CC2569F0007AAA23/$file/BurdenofDisease.pdf http://www.pharmac.health.nz/tools-resources/pharmaceutical-schedule Ministry of Health. Pharmaceutical Collection. http://www.health.govt.nz/nz-health-statistics/national-collections-and-surveys/collections/pharmaceutical-collection Ministry of Health. National Health Index (NHI). http://www.health.govt.nz/our-work/preventative-health-wellness/immunisation/national-immunisation-register/national-health-index-nhi Statistics New Zealand, Census and population projections. supplied annually to Ministry of Health. Projections produced by Statistics New Zealand according to assumptions specified by the Ministry of Health. Ministry of Health. Tatau Kahukura: Mori Health Chart Book 2010, 2nd Edition. Wellington: Ministry of Health, 2010. http://www.health.govt.nz/publication/tatau-kahukura-maori-health-chart-book-2010-2nd-edition p.57. Ministry of Health. A portrait of health: key results of the 2006/07 New Zealand Health Survey. Wellington: Ministry of Health, 2008. http://www.health.govt.nz/publication/portrait-health-key-results-2006-07-new-zealand-health-survey Table 1.4 p.15, pp 277-8, Table A1.1 pp.339,343. PHARMAC age-standardised analysis of data provided in: Publicly funded hospital discharges - July 2006 to 30 June 2007. Wellington: Ministry of Health, 2010.http://www.health.govt.nz/publication/publicly-funded-hospital-discharges-1-july-2006-30-june-2007 (using Segis world population standard) Oakley Browne MA, Wells JE, Scott KM (eds). Te Rau Hinengaro: The New Zealand Mental Health Survey. Wellington: Ministry of Health, 2006. http://www.health.govt.nz/publication/te-rau-hinengaro-new-zealand-mental-health-survey Tobias M, Blakely T, Matheson D, Rasanathan K, Atkinson J. Changing trends in indigenous inequalities in mortality: lessons from New Zealand. Int J Epidemiol. 2009;38:1711-22.http://ije.oxfordjournals.org/content/38/6/1711.full.pdf+html Murray CJL. Rethinking DALYs. In: Murray CJL, Lopez AD (eds). The Global Burden of Disease: a comprehensive assessment of mortality and disability from diseases, injuries and risk factors in 1990 and projected to 2020. Cambridge, MA: Harvard University Press, on behalf of the World Health Organization and the World Bank; 1996. Chapter 1 pp.1-98. A key objective of PHARMAC is to fund pharmaceuticals that are cost effective in meeting the health needs of the population, writes Australian health economist Professor Anthony Harris. in: PHARMAC. Annual Review 2011. Wellington: Pharmaceutical Management Agency (PHARMAC), 2011. http://www.pharmac.govt.nz/2011/12/13/Ann%20Rev%202011.pdf pp.12-13. Prescription for Pharmacoeconomic Analysis: methods for cost-utility analysis, Version 2.1. PHARMAC: Wellington, New Zealand, 2012. http://www.pharmac.govt.nz/2012/06/26/PFPAFinal.pdf Didham R, Callister P. The effect of ethnic prioritisation on ethnic health analysis: a research note. N Z Med J 2012;125:U5278. http://journal.nzma.org.nz/journal/125-1359/5278/ Robson B, Purdie G, Cram F, Simmonds S. Age standardisation - an indigenous standard? Emerg Themes Epidemiol. 2007 May 14;4:3. http://www.ete-online.com/content/4/1/3 Winnard D, Wright C, Taylor WJ, Jackson G, Te Karu L, et al. National prevalence of gout derived from administrative health data in Aotearoa New Zealand. Rheumatology (Oxford). 2012;51:901-9. http://rheumatology.oxfordjournals.org/content/early/2012/01/16/rheumatology.ker361.full Tahana Y. Professor bows out on a high note. The New Zealand Herald, 21 July 2012. http://www.nzherald.co.nz/education/news/article.cfm?c_id=35&objectid=10821126 Wheeler A, Humberstone V, Robinson E. Ethnic comparisons of antipsychotic use in schizophrenia. Aust N Z J Psychiatry. 2008;42:863-73. http://anp.sagepub.com/content/42/10/863.long State Services Commission. EEO Policy to 2010: future directions of EEO in the New Zealand Public Service. Wellington: SSC, 1997. http://www.ssc.govt.nz/eeo-policy-to-2010 New Zealand Health and Disability Ethics Committees. Guidance on Ethical Research Review. Ethical Guidelines for Observational Studies: observational research, audits and related activities, 2007.http://www.neac.health.govt.nz/moh.nsf/indexcm/neac-resources-publications-ethicalresearchguidelines, http://www.neac.health.govt.nz/moh.nsf/Files/neac-resources/$file/ethical-guidelines-for-observational-studies-2012.pdf Guidelines paragraphs 2.1-2.7, 11.1-11.11. Narrowing gap between Mori and non-Mori life expectancy. Statistics New Zealand, 2013. http://www.stats.govt.nz/browse_for_stats/health/life_expectancy/NZLifeTables_MR10-12.aspx Ministry of Health. Health Loss in New Zealand: A report from the New Zealand Burden of Diseases, Injuries and Risk Factors Study, 2006-2016. Wellington: Ministry of Health, 2013.http://www.health.govt.nz/publication/health-loss-new-zealand-report-new-zealand-burden-diseases-injuries-and-risk-factors-study-2006-2016 New Zealand Burden of Diseases Statistical Annexehttp://www.health.govt.nz/publication/new-zealand-burden-diseases-statistical-annexe Ministry of Health. Ways and Means: a report on methodology from the New Zealand Burden of Diseases, Injuries and Risk Factors Study, 2006-2016. Wellington: Ministry of Health, 2012.http://www.health.govt.nz/publication/ways-and-means-report-methodology-new-zealand-burden-disease-injury-and-risk-study-2006-2016 Disparities in the use of medicines for Mori. Best Practice Journal 2012;45:12-13. http://www.bpac.org.nz/magazine/2012/august/disparities.asp-

Contact diana@nzma.org.nz
for the PDF of this article

View Article PDF

The Pharmaceutical Management Agency (PHARMAC)s statutory role in New Zealand is to achieve the best health outcomes from the use of publicly-subsidised medicines within available funding.1 The health needs of Mori and Pacific people are an important part of PHARMACs decision-making criteria, alongside the health needs of all New Zealanders.2 Assessing health need and identifying medicines usage patterns for populations can provide evidence of disparities and help inform funding decisions and public health activities. Disparities between Mori and non-Mori health outcomes, and likewise for Pacific peoples (Pasifika) who are mostly of Samoan, Tongan, Niuean, or Cook Islands descent in New Zealand, are known to be both large and persistent over multiple issues.3-12 However, data specific to medicines use in the community have been sparse. Despite good quality information on health disparities and usage patterns for some individual diseases, information has still been insufficient to rank potential health gains across medicines overall. Analyses of medicines prescription dispensing rates cannot always address confounding from disease burden,13 where higher needs would be associated with higher use, particularly aggregating for therapeutic groups overall. Such analyses usually require subanalyses comparing proxies for health need (e.g. mortality or hospitalisation) against individual medicines. This is a large task, given there are hundreds of disease entities and medicines, with large overlaps. Moreover, indicators such as hospitalisation, although more relevant for low-mortality / high prevalence diseases such as asthma, can be biased and confounded (see endnote *).14 There has been scope for limited analysis by mapping medicines usage against relevant internally-consistent comprehensive needs data. In New Zealand such data have for the past decade been available from the Ministry of Healths New Zealand Burden of Disease Study (NZBDS), first published in 2001,15 which quantified years of life lost by the New Zealand population in 1996 from premature mortality and disability across a number of individual diseases. The NZBDS included some ethnic-specific data, using prioritised ethnicity Similarly, information in New Zealand on national use of medicines subsidised in the community (listed in the New Zealand Pharmaceutical Schedule)16 has been available, disaggregated by ethnic group, since about 2004, at that time being possible to readily link over 90% of prescriptions dispensed with anonymised age, gender and ethnicity data. The following preliminary analysis therefore provides an overview of medicines dispensed by prescription volumes, category and population dispensing rates for the financial year 2006/07 in Mori, Pasifika and non-Mori/non-Pasifika populations. The data take into account both (1) age differences within each ethnic group, (2) indicators of health need that combine historical morbidity and mortality, and (3) breakdowns by patient numbers vs. proxies for concordance/adherence. Results to date have helped inform PHARMACs policy development for medicines funding and access. Methods Prescription data This analysis used anonymised prescription medicines dispensing claims data for the financial year 1 July 2006 to 30 June 2007 contained in the PharmHouse (now Pharmaceuticals Collection) administrative claims database.17 The PharmHouse/ Pharmaceuticals Collection database links patient-level dispensing of medicines listed on the New Zealand Pharmaceutical Schedule16 with demographic data, including age and ethnicity, by encrypted National Health Index (NHI)18 patient identifier numbers. Encryption is one-way to ensure confidentiality. Endnotes \u0390 and \u2021 provide detail on prescription dispensings data collection, NHI numbers and Practitioners Supply Orders (PSOs). The analysis excluded those medicines dispensed by health practitioners as PSOs and those prescriptions for individual patients otherwise not recording NHI numbers or where the NHI numbering was inconsistent. During 2006/07 93% of prescriptions dispensed in New Zealand in community pharmacies had an NHI number recorded in PharmHouse; 31,935,268 prescriptions were dispensed, most being for individual patients (not PSOs) and containing NHI numbers. However 2,402,723 scripts were PSO, did not contain NHI numbers, or NHI-related information was unavailable for gender, ethnicity or valid age. To reflect true patient burden, we scaled the remaining 29,532,545 true scripts for individual patients containing NHI numbers and known gender, ethnicity and valid age, to account for those with missing information; this gave a synthesised total of 31,889,448 scaled scripts, used thereafter in this analysis. Scaling is described in Appendices 1 and 2 - see all Appendices. Box 1. Method of calculation: total script count We grouped medicines according to clinical indication (based on main usage), using therapeutic groupings in the New Zealand Pharmaceutical Schedule (see Appendix 1). Scaled counts of scripts for these groups were combined with population data (using population estimates categorised by prioritised ethnicity for the 2006/07 year19) to derive ethnic-specific crude and age-standardised incidence rates of scaled prescriptions dispensed (counts of scripts, i.e. prescription items that were dispensed during the year, per 1000 population) for the three prioritised ethnic groups Mori (M), Pasifika (P), and non-Mori/non-Pasifika (nMnP). Similar rates were calculated for Mori and non-Mori (nM, being P+nMnP). Linking prescription with disease burden data We then linked the indication-based medicines groups with relevant disease categories in published burden of disease data for 1996 in the NZBDS.15 For this we calculated age-standardised rates (ASRs) for disability-adjusted life year (DALY) losses for Mori and non-Mori relevant to indication-based pharmaceutical data, using the year 1996 NZBDS-reported rates of DALYs lost by Mori and non-Mori prioritised ethnicity across its five age-groupings of 0-14, 15-24, 25-44, 45-64, and 65+ years.15 The grouper linking indication-based groups with Burden of Disease disease categories is provided in the Annexe to this paper. Pharmaceuticals and DALYs were directly age-standardised to Segis standard world population (as had occurred in the NZBDS), aggregating Segis 18 5-year age groups into the 5 age group categories reported by the NZBDS.15 Gender could not be included in this analysis, as it was not part of the age/ethnic-specific NZBDS 1996 DALY data. [Note: During the production of this paper (in August 2013), the Ministry of Health published the update of the NZBDS for disease burden occurring in 2006.36,37] Differences in the above ASRs allowed us to estimate the numerical differences in scripts dispensed to Mori, given their population size, age structure and disease burden. We used age-standardised rate ratios (ASRRs) for Mori vs. non-Mori for scripts and DALY losses. From these we derived disease burden-adjusted M:nM script ASRRs for each indication-based medicines group. We then calculated gaps in Mori medicines use compared with expected non-Mori usage. These gaps in effect accounted for differences in population size, age structure and disease burden (as DALYL-adjusted shortfall/excess no. scripts in Mori). Box 1 above details the calculations made. Access vs. persistence We estimated the extents to which differential dispensing to Mori could be attributed to access versus persistence (see endnote \u00a7). In the context of this analysis: Access related to differential dispensing to Mori of first prescriptions (index scripts). It was expressed as the variation in numbers of Mori (less or more patients) accessing medicines compared with access in non-Mori after adjusting for population size, age structure and disease burden. We expressed access as the rate ratio of DALYL-adjusted ASRs for 12-month patient period-prevalence (adjASRRaM:nM = adjASRaM \u00f7 adjASRanM); Persistence was the subsequent residual variation in overall numbers of scripts dispensed due to variations in subsequent scripts per index patient, i.e. the individualised frequency of subsequent scripts dispensed to those Mori who had an initial script, expressed as (persistenceM:nM = scripts/patientMori \u00f7 scripts/patientnon-Mori). Total scripts (prescriptions dispensed) were therefore the product of access (number of patients) and persistence (scripts/patient). This metric of access \u00d7 persistence was the basis on which we could estimate gaps in dispensing. The numerical data on prescriptions, patients, and ASRRs allowed us to differentiate between gaps in initial access to scripts and gaps in subsequent persistence with scripts. Gaps with persistence were simply the residual after subtracting gaps in access for total script gaps. Box 2 details these calculations. Further details of calculation methods are available in Appendix 1, including worked examples. Box 2. Method of calculation: access and persistence Results Near parity of script counts (prescriptions dispensed) when adjusted for age During 2006/07 31,935,268 scripts were dispensed in New Zealand, 4,108,107 being PSO scripts and scripts for individuals either without NHI numbers or unknown or invalid age, gender or ethnicity information (comprising 12.9% of all scripts), with non-PSO NHI-containing scripts (including valid gender/ ethnicity/age) scaling to 31,889,448 for this analysis. 3.3 million (scaled) scripts were ascribable to Mori and 1.7 million to Pasifika (detailed in Appendix 2). These script numbers related to 2.7 million patients with individual NHI numbers, which with scaling for missing NHIs became 2.92 million patients (383,000 Mori, 188,000 Pasifika). Age-standardised scaled prescription dispensing (script) rates overall for Mori in 2006/07 were 97% of those for non-Mori/non-Pasifika, and for Pasifika were 123% of those for non-Mori/non-Pasifika (Mori 5919.8 scripts per 1000 age-standardised population, Pasifika 7535.8 per 1000, non-Mori/non-Pasifika 6102.1 per 1000). This contrasted with crude 64% scripts overall per capita in Mori compared with non-Mori/non-Pasifika, and 83% for Pasifika compared with non-Mori/non-Pasifika. The higher usage after adjusting for age is largely explained by the relative youth of Mori and Pasifika; medicine use tends to increase with age and there are proportionately less older Mori and Pasifika (see Appendix 2). There was a large residual variability in scripts by medicine group after adjusting for age. This was often not obviously related to disease burden. For instance when compared with non-Mori/non-Pasifika, Mori and Pasifika showed lower age-standardised script rates for anti-depressants, contraceptives and inhaled corticosteroids, but higher rates for anti-hepatitis B antivirals, short-acting asthma inhalers, and older and depot injection antipsychotics. The differences in therapeutic groups between Mori and Pasifika compared with non-Mori/non-Pasifika were not uniform, as can be seen in Figure A3-3 and Table A3-3 in Appendix 3. For instance, Pasifika were dispensed medicines for attention deficit disorder, Hepatitis C infections and older depot antipsychotics at one fifth the rate of Mori. Asthma medicines and newer antidepressants were relatively under-dispensed in Pasifika compared with Mori. Conversely, Pasifika were dispensed oral hypoglycaemic medicines for type 2 diabetes and blood glucose test strips, older glaucoma medicines, scabies treatments, and hepatitis B medicines at twice the rate of Mori. Mori and Pasifika age-standardised rates were similar for antibiotics, statins, ACE inhibitors, low dose aspirin, and treatments for gout. All of these features are detailed in Figure 1 and in Appendix 3, including tables and further graphs. Lower script counts for Mori when adjusted for health needs Mapping the NZBDS disease categories to medicines listed on the New Zealand Pharmaceutical Schedule, in order to partly relate medicines use to disease impacts (health need), it was possible to link 85% of 2006/07 scripts (prescription dispensings) to relevant NZBDS disease groups. Accordingly, coincidentally 85% of DALY losses in 1996 appeared to be for diseases treatable or preventable by medicines on the Pharmaceutical Schedule. Hence in 1996 perhaps some 480,000 disability-adjusted years of life (DALYs) were lost by the New Zealand population from diseases treatable by medicines on the Pharmaceutical Schedule (out of 563,000 DALYs lost overall for all diseases) see Tables A4-1 and A4-2 in Appendix 4. The generally higher use of medicines by Mori and Pasifika than non-Mori/non-Pasifika must therefore be seen in the context of these populations having general higher health needs. Details of these higher health needs for Mori can be found in Appendix 5. For conditions treated or prevented by medicines on the Pharmaceutical Schedule, differences in burden of disease could be linked to differences between Mori and non-Mori dispensing rates (see endnote **). This mapping suggests that although total Mori script counts were comparable with non-Mori after adjusting for age, actual dispensing for Mori was much lower than needed to overcome their greater disease burden. Hence, although Mori in 2006/07 had 97% age-adjusted script counts relative to non-Mori, after further adjusting for historical 45% higher relative DALY losses in Mori this ratio fell to 81% of what it would be for non-Mori. Moreover, after excluding medicines not covered by the NZBDS diseases the ratio fell further to 63%. Mori had therefore 19-37% lower treatment rates compared with non-Mori (conversely, rates in non-Mori being higher). The total scripts known to be dispensed to Mori in 2006/7 (excluding PSOs and those otherwise without NHIs, but scaled) was 3.3 million (as stated above), of which 2.7 million linked with NZBDS diseases. The overall gap in scripts to Mori after standardising for age and adjusting for historical burden of disease amounted to 977,400 fewer scripts. Most medicines had shortfalls rather than excesses. Key shortfalls are summarised in Table 1. Table 1. Shortfalls in Mori age/DALY-adjusted script counts Medicine Shortfall* Comments antibiotics 181,500 NZBDS categories of bacterial infections, of which 89,100 for amoxicillins antiulcerants 60,500 principally 54,300 for proton pump inhibitors (PPIs); may reflect inappropriately high antiuclerant use in non-Mori statins 53,100 cardiovascular risk (dyslipidaemia); principally simvastatin (45,400) beta blockers 52,900 primarily for cardiovascular risk and disease ACE inhibitors/A2 antagonists 48,800 cardiovascular risk and disease, including diabetes newer antidepressants 46,300 principally selective serotonin reuptake inhibitors (SSRIs) (41,600); also venlafaxine, selective MAOIs low-dose aspirin 40,100 cardiovascular risk inhaled corticosteroids \u00b1 long-acting beta agonists 22,600 asthma oral hypoglycaemics 21,300 primarily cardiovascular risk (type 2 diabetes) diabetes self-testing 19,200 self-management of types 1 and 2 diabetes *Shortfalls are the differences between actual script counts in Mori and numbers expected were Mori to have the same dispensing as non-Mori, after adjusting for population size, age, and disease burden. Access and persistence similarly less in Mori Almost half of the above calculated need-adjusted gap in prescriptions dispensed was due to fewer than expected Mori patients accessing medicines (443,900 absent initial dispensings). We estimated access in Mori to be 67% that of non-Mori. The biggest gap from reduced access was for amoxicillins. The remainder of the gap was due to lower Mori persistence\u2021 with medicines (533,500 absent subsequent dispensings). Persistence in Mori was calculated as 58% of that in non-Mori. The biggest gaps from reduced persistence were for beta-blockers, PPIs, simvastatin, low-dose aspirin for cardiovascular risk, and SSRIs. Conversely, the calculated overall difference in scripts for non-Mori (age and disease burden-adjusted) amounted to at least 12.2 million more scripts. In summary, access and persistence contributed on a similar scale to apparent under-dispensing to Mori. Note however that there were appreciable differences between medicines in the mix of access and persistence. This included examples such as the newer antipsychotics, in which large proportionate shortfall in access was masked by proportionately lesser shortfalls in persistence. These features are evident in the following graphs (Figures 2 to 5) and are detailed in Table A6.2 in Appendix 6. To explain Figures 2-5: Figure 2 shows shortfalls and excesses in scripts for Mori compared with that expected for non-Mori. This reveals the therapeutic areas suggesting the largest gaps in dispensings. Figure 3 shows proportional shortfalls and excesses. This suggests the therapeutic areas with the most divergence in clinical practice from what would be expected in non-Mori, as Mori rates relative to non-Mori. (The data are on a logarithmic scale, so that shortfalls and excesses are distributed symmetrically about a relative rate of 1 (unity), which is the zero line; further explanation is in endnote \u2021\u2021.) Figure 4 suggests numerical shortfalls and excesses broken down by access and persistence. This shows these two factors variable contributions to differential dispensing. Figure 5 shows proportional shortfalls and excesses, broken down by access and persistence. As with figure 3, this suggests the therapeutic areas with the most divergence in clinical practice from what would be expected in non-Mori, as Mori rates relative to non-Mori, but then shows how much is due to differences in access versus differences in persistence. (Again as with figure 3, the data are on a logarithmic scale, see endnote \u2021\u2021). Figures 2 to 5 also include disaggregating of the category NSAIDS/gout/analgesics/ muscl relxnts into component Rx for hyperuricaemia & gout and NSAIDS + muscle relaxants subcategories. Discussion This analysis links patient-level script count data with population-based estimates of health need. This method can give at best broad indications of trends, for what are complex issues. Interpretation of the results may change after more detailed analysis of individual issues. The ability of access to counteract persistence (as seen with some antipsychotics) is an example of more complex effects that may be lost in population-based data. Even so, this work reveals a potentially significant issue with likely differences between ethnic groups, and hence potential for health gain or reduced wastage once shortfalls and excesses are addressed. This is apparent in a majority of disease and disability states, and begs the question of suboptimal or excess treatment elsewhere. Limitations and caveats There are however important limitations and caveats with the analysis: Scripts dispensed are not the same as medicines prescribed. There is evidence that many prescriptions are either not presented or not collected at pharmacies. Reasons for this may include time, cost and transportation. Such factors can affect populations differentially. Mori are more likely to have uncollected prescriptions, their non-collection rate being 45% higher than that of non-Mori aged over 1520,21 (where this statistic stems from 2006/07, when minimum co-payments for the first 20 items were $3 per item; this has since risen to $5). It is not possible to tell from this analysis the extent that failure to dispense represents a systematic failure to prescribe or a systematic failure to ensure that prescriptions are filled. However, this feature may appreciably understate true gaps. Dispensing data are restricted to those prescriptions and patient groups that gain subsidies for publicly-funded community dispensed medicines. The data therefore exclude prescriptions that were not subsidised, or items that fell below the $3-$15 prescription co-payments at that time, where pharmacies would have no need to claim (and hence would not be captured in the PharmHouse claims database data). Non-capture of unclaimed medicines use might undercount appreciably overall medicines use and potentially understate gaps in in populations with poorer access to medicines. Script counts are an imprecise measure of coverage (days) that medicines are actually provided, being confounded by dispensings/script rates and duration (days coverage) of dispensings. With scripts versus dispensings, people living in rural areas tend to get longer dispensings (e.g. 3 months, where 1 month would be standard in non-rural setting). Hence, to the extent that Mori are overrepresented in rural populations, the gaps may be overstated to an uncertain extent. Some PSOs may be used for targeting populations with poor access to medicines. PSOs are more commonly used in rural areas, where the nearest pharmacy may be some distance, and for certain types of medicines, such as antibiotics. PSOs understate true numbers of people receiving medicines, which may mean gaps are overstated to some extent. Gaps in script counts do not necessarily equate with gaps in disease burden and capacity to benefit from effective medicines treatment. Population health gains (expressed for example as quality-adjusted life years (QALYs) gained) reflect not only numbers of patients and script frequencies per patient, but also the effectiveness of medicines in relation to patients' health needs. Hence gaps in health outcomes from patients receiving less medicine are not necessarily the same as gaps in script counts. Linking between script counts and diseases can be imprecise where medicines have multiple clinical indications or disease burden covers a broad range of diseases. Problems linking medicines to single disease groups may have important effects on the analysis results.If these factors were to cause bias that is non-differential, such imprecisions from linking could tend to understate true differences in disease burden-adjusted prescriptions. However, this is not a given; it is possible that differential bias could occur, for instance understating of shortfalls in for one disease category meaning falsely ascribing shortfalls in another disease category, which could overstate net true differences. An example of probable non-differential bias within a disease category (understating true differences) is that of gout and other musculoskeletal conditions. Medicines for gout (e.g. allopurinol) are bundled into wider NSAIDS etc., because the 1996 NZBDS data combined a number of musculoskeletal conditions, meaning the high excess disease burden for Mori for gout (e.g. their age-standardised hospitalisation rates in 2006/07 being 6-7 times that of non-Mori)22 was diluted by other musculoskeletal diseases and hence relative disparities were muted. Overall, Mori had a small observed shortfall of DALYL-adjusted scripts for musculoskeletal diseases (-2,000), but this may well have been due to a large shortfall for allopurinol etc. for gout (-10,700 scripts) masking a similarly-sized excess for NSAIDs (+12,700). Conversely, an example of the potential for differential bias across disease categories (potentially overstating net true differences) is that of carbamazepine and sodium valproate, which are anti-epilepsy medicines. In the analysis, these were matched to the NZBDS Epilepsy category, and there was a shortfall of 5,900 scripts for Mori (out of 208,900 total scripts). However, carbamazepine and sodium valproate are also commonly used for the control of bipolar disorder, inter alia. The lifetime prevalence of bipolar disorder in Mori is double that of the overall population,23 so it is possible that the shortfall for Mori in the Epilepsy category was understated, and the shortfall for Mori in the NZBDS Mental Health category was overstated, to a greater extent than non-Mori. Such possibilities highlight the impact of mismatching of medicine dispensings and disease categories.More specifically, the broad scope of this preliminary analysis does not allow more detailed review of antibiotic use for discrete issues, e.g. the high incidence of acute rheumatic fever in Mori children,11 which relates specifically to childhood penicillin and amoxicillin use in the treatment of acute pharyngotonsillitis (sore throat) where these medicines also treat many other childhood infections. Age-standardised rates and rate ratios are aggregates that can obscure wide variation across age groups. This become particularly important where data are missing (e.g. from prescriptions that lack NHI numbers including PSOs), with the potential to mis-state true gaps.For example, with the amoxicillins (amoxicillin, amoxicillin clavulanate) there was a shortfall of 89,100 scripts for Mori. Much of that shortfall occurred in children aged 0-14 years, whose relative rates were substantially lower than for older age groups. However, 13% of scripts for amoxicillins did not have NHIs and could in theory all have been on PSO (being double the average 7.5% for scripts overall on PSO). If, radically, the many amoxicillin scripts without NHIs were for all Mori children and these in turn had the same relative rates as for older patients, then the shortfall for amoxicillins for Mori children would halve and the overall gap (all ages) would be only 1/5th lower than non-Mori (53,200 script gap). Details including component calculations can be found in endnote \u0390\u0390\u0390. Amoxicillins accounted for 11% of all non-NHI scripts (204,762 scripts without NHIs numbers, out of 1.6 million amoxicillin etc. scripts and 2.4 million scripts without NHIs), so these medicines are an important part of this information gap. Analysis is unavailable for Pasifika. The available Ministry age-specific analysis by ethnic group was confined to comparing Mori with non-Mori, and other relevant disease burden analysis (Ministry of Health 2001b3) provided insufficient detail to enable age-specific and age-standardised disease burden estimates for Pasifika, Mori and non-Mori/non-Pasifika. Pasifika people have needs and underuse at least equal to Mori (see Appendix 3), with two consequences: Important gaps need to be identified and quantified for Pasifika too; Mori vs. non-Mori comparisons if anything may understate the extent of Mori underuse once adjusted for need, as by including Pasifika in the non-Mori group this may dilute the relative effects for Mori. Such deficiencies should be addressable in the Ministrys forthcoming updates of the NZBDS. [Note: The August 2013 published update of the NZBDS36,37 has not included separate results for Pasifika, only comparing Mori with non-Mori.] The use of disease burden estimates based on mortality/morbidity data from 1996 to compare with prescription volumes a decade lat

Summary

Abstract

Aim

To describe variations in dispensing of specific medication groups by ethnicity in New Zealand, adjusting for health need.

Method

Preliminary linkage of dispensings of prescription medicines in 2006/07 to age/disease burden proxies of health need for Mori, Pacific peoples (Pasifika) who are mostly of Samoan, Tongan, Niuean, or Cook Islands descent in New Zealand, and non-Mori/non-Pasifika. These disease burden proxies combine differences in prevalence, age, morbidity, and mortality. Variations were disaggregated by patients being first dispensed medicines (access) versus subsequent dispensings (persistence).

Results

Initially, overall age-adjusted incidence of scripts (prescriptions dispensed) to Mori was similar to that of non-Mori. There were differences in therapeutic coverage between Mori and Pasifika, for example greater use of asthma medicines in Mori. However, further adjustments linking with disease burden showed marked variance for a number of diseases. Differences in dispensing included areas of high health need such as heart disease, infections, diabetes, mental health and respiratory disease. Mori had 19-37% lower dispensings overall than non-Mori, with a net difference of nearly 1 million scripts. Mori were both less likely to access medicines, and then after first dispensing had fewer subsequent scripts. Patterns for Pasifika appeared similar, although needs-adjusted analysis is awaited for this population.

Conclusion

Once adjusting for need, there was variable but sizeable differences in medicines dispensed to Mori compared with non-Mori, and likely differences for Pasifika populations. There are however important limitations to this preliminary analysis. Crude and age-standardised metrics may be poor predictors of needs-adjusted gaps in medicines use. In this analysis, solely age-standardised rates tended to underestimate differences once adjusting for burden of disease; future analyses of prescribing patterns should consider better adjusting for disease burden.

Author Information

Scott Metcalfe, Chief Advisor Population Medicine/Deputy Medical Director (Epidemiology), PHARMAC, Wellington; George Laking, Medical Oncologist/Health Economist, Auckland Member, Pharmacology and Therapeutics Advisory Committee (PTAC) Member, Mori Caucus, PHARMAC, Wellington; Jason Arnold, Team Leader Analysis, PHARMAC, Wellington

Acknowledgements

We thank Rico Schoeler (Manager Assessment) and Marama Parore (Manager Access & Optimal Use and Mori Health) of PHARMAC, Wellington; Martin Tobias (Principal Adviser, Public Health Intelligence) of the Ministry of Health, Wellington; and PTAC members in 2008 who both provided advice on earlier analysis and reviewed (1) indication-based medicines groupings and (2) consequent groupings of indication-based medicines with disease categories. The Journals anonymous peer reviewers provided helpful commentary.

Correspondence

Dr Scott Metcalfe, PHARMAC, PO Box 10254, Wellington, New Zealand

Correspondence Email

scott.metcalfe@pharmac.govt.nz

Competing Interests

The authors are PHARMAC staff or advisors.

- New Zealand Public Health and Disability Act 2000 http://www.legislation.govt.nz/act/public/2000/0091/latest/DLM80051.html section 47 Objectives of Pharmac PHARMAC Decision Criterion 2 The particular health needs of Mori & Pacific peoples http://www.pharmac.govt.nz/DecisionCriteria. In: PHARMAC. Operating policies and procedures of the Pharmaceutical Management Agency ( PHARMAC ), Third Edition, 2006. http://www.pharmac.govt.nz/2005/12/22/231205.pdf Priorities for Maori and Pacific health: evidence from epidemiology. Public Health Intelligence Occasional Bulletin No. 3. Wellington: Ministry of Health, 2001.http://www.moh.govt.nz/notebook/nbbooks.nsf/0/A31842D91480064FCC256A55007A980A/$file/PrioritiesForMaoriandPacificHealth.pdf Durie M. Understanding health and illness: research at the interface between science and indigenous knowledge. Int J Epidemiol. 2004;33:1138-43. http://ije.oxfordjournals.org/content/33/5/1138.long Bramley D, Riddell T, Crengle S, Curtis E, Harwood M, Nehua D, Reid P. A call to action on Maori cardiovascular health. N Z Med J. 2004;117:U957. http://journal.nzma.org.nz/journal/117-1197/957/ Blakely T, Ajwani S, Robson B, Tobias M, Bonn\u00e9 M. Decades of disparity: widening ethnic mortality gaps from 1980 to 1999. N Z Med J. 2004;117:U995. http://journal.nzma.org.nz/journal/117-1199/995/ Robson B, Purdie G, Cormack D. Unequal impact: Mori and non-Mori cancer statistics 1996-2001. Wellington: Ministry of Health, 2006. Harris R, Tobias M, Jeffreys M, Waldegrave K, Karlsen S, Nazroo J. Effects of self reported racial discrimination and deprivation on Mori health and inequalities in New Zealand: cross-sectional study. Lancet 2006;367: 2005-9 Blakely T, Tobias M, Atkinson J, et al. Tracking disparity: trends in ethnic and socioeconomic inequalities in mortality, 1981-2004. Wellington: Ministry of Health, 2007. Rumball-Smith JM. Not in my hospital? Ethnic disparities in quality of hospital care in New Zealand: a narrative review of the evidence. N Z Med J. 2009;122:68-83.http://journal.nzma.org.nz/journal/122-1297/3662/ Hale M, Sharpe N. Persistent rheumatic fever in New Zealand--a shameful indicator of child health. N Z Med J. 2011;124:6-8. http://journal.nzma.org.nz/journal/124-1329/4530/ Soeberg M, Blakely T, Sarfati D, Tobias M, Costilla R, et al. Cancer trends: trends in cancer survival by ethnic and socioeconomic group, New Zealand 1991-2004. Wellington: University of Otago and Ministry of Health, 2012. http://www.health.govt.nz/publication/cancer-trends-trends-cancer-survival-ethnic-and-socioeconomic-group-new-zealand-1991-2004 Crengle S, Lay-Yee R, Davis P, Pearson J. A comparison of Mori and non-Mori patient visits to doctors: the National Primary Medical Care Survey (NatMedCa): 2001/02. Report 6. Wellington: Ministry of Health, 2005. http://www.health.govt.nz/publication/comparison-maori-and-non-maori-patient-visits-doctors Smith MW. Hospital discharge diagnoses: how accurate are they and their international classification of diseases (ICD) codes? N Z Med J 1989;102:507-8. The Burden of Disease and Injury in New Zealand. Public Health Intelligence Occasional Bulletin No. 1. Wellington: Ministry of Health, 2001.http://www.moh.govt.nz/notebook/nbbooks.nsf/0/E916C53D599AE126CC2569F0007AAA23/$file/BurdenofDisease.pdf http://www.pharmac.health.nz/tools-resources/pharmaceutical-schedule Ministry of Health. Pharmaceutical Collection. http://www.health.govt.nz/nz-health-statistics/national-collections-and-surveys/collections/pharmaceutical-collection Ministry of Health. National Health Index (NHI). http://www.health.govt.nz/our-work/preventative-health-wellness/immunisation/national-immunisation-register/national-health-index-nhi Statistics New Zealand, Census and population projections. supplied annually to Ministry of Health. Projections produced by Statistics New Zealand according to assumptions specified by the Ministry of Health. Ministry of Health. Tatau Kahukura: Mori Health Chart Book 2010, 2nd Edition. Wellington: Ministry of Health, 2010. http://www.health.govt.nz/publication/tatau-kahukura-maori-health-chart-book-2010-2nd-edition p.57. Ministry of Health. A portrait of health: key results of the 2006/07 New Zealand Health Survey. Wellington: Ministry of Health, 2008. http://www.health.govt.nz/publication/portrait-health-key-results-2006-07-new-zealand-health-survey Table 1.4 p.15, pp 277-8, Table A1.1 pp.339,343. PHARMAC age-standardised analysis of data provided in: Publicly funded hospital discharges - July 2006 to 30 June 2007. Wellington: Ministry of Health, 2010.http://www.health.govt.nz/publication/publicly-funded-hospital-discharges-1-july-2006-30-june-2007 (using Segis world population standard) Oakley Browne MA, Wells JE, Scott KM (eds). Te Rau Hinengaro: The New Zealand Mental Health Survey. Wellington: Ministry of Health, 2006. http://www.health.govt.nz/publication/te-rau-hinengaro-new-zealand-mental-health-survey Tobias M, Blakely T, Matheson D, Rasanathan K, Atkinson J. Changing trends in indigenous inequalities in mortality: lessons from New Zealand. Int J Epidemiol. 2009;38:1711-22.http://ije.oxfordjournals.org/content/38/6/1711.full.pdf+html Murray CJL. Rethinking DALYs. In: Murray CJL, Lopez AD (eds). The Global Burden of Disease: a comprehensive assessment of mortality and disability from diseases, injuries and risk factors in 1990 and projected to 2020. Cambridge, MA: Harvard University Press, on behalf of the World Health Organization and the World Bank; 1996. Chapter 1 pp.1-98. A key objective of PHARMAC is to fund pharmaceuticals that are cost effective in meeting the health needs of the population, writes Australian health economist Professor Anthony Harris. in: PHARMAC. Annual Review 2011. Wellington: Pharmaceutical Management Agency (PHARMAC), 2011. http://www.pharmac.govt.nz/2011/12/13/Ann%20Rev%202011.pdf pp.12-13. Prescription for Pharmacoeconomic Analysis: methods for cost-utility analysis, Version 2.1. PHARMAC: Wellington, New Zealand, 2012. http://www.pharmac.govt.nz/2012/06/26/PFPAFinal.pdf Didham R, Callister P. The effect of ethnic prioritisation on ethnic health analysis: a research note. N Z Med J 2012;125:U5278. http://journal.nzma.org.nz/journal/125-1359/5278/ Robson B, Purdie G, Cram F, Simmonds S. Age standardisation - an indigenous standard? Emerg Themes Epidemiol. 2007 May 14;4:3. http://www.ete-online.com/content/4/1/3 Winnard D, Wright C, Taylor WJ, Jackson G, Te Karu L, et al. National prevalence of gout derived from administrative health data in Aotearoa New Zealand. Rheumatology (Oxford). 2012;51:901-9. http://rheumatology.oxfordjournals.org/content/early/2012/01/16/rheumatology.ker361.full Tahana Y. Professor bows out on a high note. The New Zealand Herald, 21 July 2012. http://www.nzherald.co.nz/education/news/article.cfm?c_id=35&objectid=10821126 Wheeler A, Humberstone V, Robinson E. Ethnic comparisons of antipsychotic use in schizophrenia. Aust N Z J Psychiatry. 2008;42:863-73. http://anp.sagepub.com/content/42/10/863.long State Services Commission. EEO Policy to 2010: future directions of EEO in the New Zealand Public Service. Wellington: SSC, 1997. http://www.ssc.govt.nz/eeo-policy-to-2010 New Zealand Health and Disability Ethics Committees. Guidance on Ethical Research Review. Ethical Guidelines for Observational Studies: observational research, audits and related activities, 2007.http://www.neac.health.govt.nz/moh.nsf/indexcm/neac-resources-publications-ethicalresearchguidelines, http://www.neac.health.govt.nz/moh.nsf/Files/neac-resources/$file/ethical-guidelines-for-observational-studies-2012.pdf Guidelines paragraphs 2.1-2.7, 11.1-11.11. Narrowing gap between Mori and non-Mori life expectancy. Statistics New Zealand, 2013. http://www.stats.govt.nz/browse_for_stats/health/life_expectancy/NZLifeTables_MR10-12.aspx Ministry of Health. Health Loss in New Zealand: A report from the New Zealand Burden of Diseases, Injuries and Risk Factors Study, 2006-2016. Wellington: Ministry of Health, 2013.http://www.health.govt.nz/publication/health-loss-new-zealand-report-new-zealand-burden-diseases-injuries-and-risk-factors-study-2006-2016 New Zealand Burden of Diseases Statistical Annexehttp://www.health.govt.nz/publication/new-zealand-burden-diseases-statistical-annexe Ministry of Health. Ways and Means: a report on methodology from the New Zealand Burden of Diseases, Injuries and Risk Factors Study, 2006-2016. Wellington: Ministry of Health, 2012.http://www.health.govt.nz/publication/ways-and-means-report-methodology-new-zealand-burden-disease-injury-and-risk-study-2006-2016 Disparities in the use of medicines for Mori. Best Practice Journal 2012;45:12-13. http://www.bpac.org.nz/magazine/2012/august/disparities.asp-

Contact diana@nzma.org.nz
for the PDF of this article

View Article PDF

The Pharmaceutical Management Agency (PHARMAC)s statutory role in New Zealand is to achieve the best health outcomes from the use of publicly-subsidised medicines within available funding.1 The health needs of Mori and Pacific people are an important part of PHARMACs decision-making criteria, alongside the health needs of all New Zealanders.2 Assessing health need and identifying medicines usage patterns for populations can provide evidence of disparities and help inform funding decisions and public health activities. Disparities between Mori and non-Mori health outcomes, and likewise for Pacific peoples (Pasifika) who are mostly of Samoan, Tongan, Niuean, or Cook Islands descent in New Zealand, are known to be both large and persistent over multiple issues.3-12 However, data specific to medicines use in the community have been sparse. Despite good quality information on health disparities and usage patterns for some individual diseases, information has still been insufficient to rank potential health gains across medicines overall. Analyses of medicines prescription dispensing rates cannot always address confounding from disease burden,13 where higher needs would be associated with higher use, particularly aggregating for therapeutic groups overall. Such analyses usually require subanalyses comparing proxies for health need (e.g. mortality or hospitalisation) against individual medicines. This is a large task, given there are hundreds of disease entities and medicines, with large overlaps. Moreover, indicators such as hospitalisation, although more relevant for low-mortality / high prevalence diseases such as asthma, can be biased and confounded (see endnote *).14 There has been scope for limited analysis by mapping medicines usage against relevant internally-consistent comprehensive needs data. In New Zealand such data have for the past decade been available from the Ministry of Healths New Zealand Burden of Disease Study (NZBDS), first published in 2001,15 which quantified years of life lost by the New Zealand population in 1996 from premature mortality and disability across a number of individual diseases. The NZBDS included some ethnic-specific data, using prioritised ethnicity Similarly, information in New Zealand on national use of medicines subsidised in the community (listed in the New Zealand Pharmaceutical Schedule)16 has been available, disaggregated by ethnic group, since about 2004, at that time being possible to readily link over 90% of prescriptions dispensed with anonymised age, gender and ethnicity data. The following preliminary analysis therefore provides an overview of medicines dispensed by prescription volumes, category and population dispensing rates for the financial year 2006/07 in Mori, Pasifika and non-Mori/non-Pasifika populations. The data take into account both (1) age differences within each ethnic group, (2) indicators of health need that combine historical morbidity and mortality, and (3) breakdowns by patient numbers vs. proxies for concordance/adherence. Results to date have helped inform PHARMACs policy development for medicines funding and access. Methods Prescription data This analysis used anonymised prescription medicines dispensing claims data for the financial year 1 July 2006 to 30 June 2007 contained in the PharmHouse (now Pharmaceuticals Collection) administrative claims database.17 The PharmHouse/ Pharmaceuticals Collection database links patient-level dispensing of medicines listed on the New Zealand Pharmaceutical Schedule16 with demographic data, including age and ethnicity, by encrypted National Health Index (NHI)18 patient identifier numbers. Encryption is one-way to ensure confidentiality. Endnotes \u0390 and \u2021 provide detail on prescription dispensings data collection, NHI numbers and Practitioners Supply Orders (PSOs). The analysis excluded those medicines dispensed by health practitioners as PSOs and those prescriptions for individual patients otherwise not recording NHI numbers or where the NHI numbering was inconsistent. During 2006/07 93% of prescriptions dispensed in New Zealand in community pharmacies had an NHI number recorded in PharmHouse; 31,935,268 prescriptions were dispensed, most being for individual patients (not PSOs) and containing NHI numbers. However 2,402,723 scripts were PSO, did not contain NHI numbers, or NHI-related information was unavailable for gender, ethnicity or valid age. To reflect true patient burden, we scaled the remaining 29,532,545 true scripts for individual patients containing NHI numbers and known gender, ethnicity and valid age, to account for those with missing information; this gave a synthesised total of 31,889,448 scaled scripts, used thereafter in this analysis. Scaling is described in Appendices 1 and 2 - see all Appendices. Box 1. Method of calculation: total script count We grouped medicines according to clinical indication (based on main usage), using therapeutic groupings in the New Zealand Pharmaceutical Schedule (see Appendix 1). Scaled counts of scripts for these groups were combined with population data (using population estimates categorised by prioritised ethnicity for the 2006/07 year19) to derive ethnic-specific crude and age-standardised incidence rates of scaled prescriptions dispensed (counts of scripts, i.e. prescription items that were dispensed during the year, per 1000 population) for the three prioritised ethnic groups Mori (M), Pasifika (P), and non-Mori/non-Pasifika (nMnP). Similar rates were calculated for Mori and non-Mori (nM, being P+nMnP). Linking prescription with disease burden data We then linked the indication-based medicines groups with relevant disease categories in published burden of disease data for 1996 in the NZBDS.15 For this we calculated age-standardised rates (ASRs) for disability-adjusted life year (DALY) losses for Mori and non-Mori relevant to indication-based pharmaceutical data, using the year 1996 NZBDS-reported rates of DALYs lost by Mori and non-Mori prioritised ethnicity across its five age-groupings of 0-14, 15-24, 25-44, 45-64, and 65+ years.15 The grouper linking indication-based groups with Burden of Disease disease categories is provided in the Annexe to this paper. Pharmaceuticals and DALYs were directly age-standardised to Segis standard world population (as had occurred in the NZBDS), aggregating Segis 18 5-year age groups into the 5 age group categories reported by the NZBDS.15 Gender could not be included in this analysis, as it was not part of the age/ethnic-specific NZBDS 1996 DALY data. [Note: During the production of this paper (in August 2013), the Ministry of Health published the update of the NZBDS for disease burden occurring in 2006.36,37] Differences in the above ASRs allowed us to estimate the numerical differences in scripts dispensed to Mori, given their population size, age structure and disease burden. We used age-standardised rate ratios (ASRRs) for Mori vs. non-Mori for scripts and DALY losses. From these we derived disease burden-adjusted M:nM script ASRRs for each indication-based medicines group. We then calculated gaps in Mori medicines use compared with expected non-Mori usage. These gaps in effect accounted for differences in population size, age structure and disease burden (as DALYL-adjusted shortfall/excess no. scripts in Mori). Box 1 above details the calculations made. Access vs. persistence We estimated the extents to which differential dispensing to Mori could be attributed to access versus persistence (see endnote \u00a7). In the context of this analysis: Access related to differential dispensing to Mori of first prescriptions (index scripts). It was expressed as the variation in numbers of Mori (less or more patients) accessing medicines compared with access in non-Mori after adjusting for population size, age structure and disease burden. We expressed access as the rate ratio of DALYL-adjusted ASRs for 12-month patient period-prevalence (adjASRRaM:nM = adjASRaM \u00f7 adjASRanM); Persistence was the subsequent residual variation in overall numbers of scripts dispensed due to variations in subsequent scripts per index patient, i.e. the individualised frequency of subsequent scripts dispensed to those Mori who had an initial script, expressed as (persistenceM:nM = scripts/patientMori \u00f7 scripts/patientnon-Mori). Total scripts (prescriptions dispensed) were therefore the product of access (number of patients) and persistence (scripts/patient). This metric of access \u00d7 persistence was the basis on which we could estimate gaps in dispensing. The numerical data on prescriptions, patients, and ASRRs allowed us to differentiate between gaps in initial access to scripts and gaps in subsequent persistence with scripts. Gaps with persistence were simply the residual after subtracting gaps in access for total script gaps. Box 2 details these calculations. Further details of calculation methods are available in Appendix 1, including worked examples. Box 2. Method of calculation: access and persistence Results Near parity of script counts (prescriptions dispensed) when adjusted for age During 2006/07 31,935,268 scripts were dispensed in New Zealand, 4,108,107 being PSO scripts and scripts for individuals either without NHI numbers or unknown or invalid age, gender or ethnicity information (comprising 12.9% of all scripts), with non-PSO NHI-containing scripts (including valid gender/ ethnicity/age) scaling to 31,889,448 for this analysis. 3.3 million (scaled) scripts were ascribable to Mori and 1.7 million to Pasifika (detailed in Appendix 2). These script numbers related to 2.7 million patients with individual NHI numbers, which with scaling for missing NHIs became 2.92 million patients (383,000 Mori, 188,000 Pasifika). Age-standardised scaled prescription dispensing (script) rates overall for Mori in 2006/07 were 97% of those for non-Mori/non-Pasifika, and for Pasifika were 123% of those for non-Mori/non-Pasifika (Mori 5919.8 scripts per 1000 age-standardised population, Pasifika 7535.8 per 1000, non-Mori/non-Pasifika 6102.1 per 1000). This contrasted with crude 64% scripts overall per capita in Mori compared with non-Mori/non-Pasifika, and 83% for Pasifika compared with non-Mori/non-Pasifika. The higher usage after adjusting for age is largely explained by the relative youth of Mori and Pasifika; medicine use tends to increase with age and there are proportionately less older Mori and Pasifika (see Appendix 2). There was a large residual variability in scripts by medicine group after adjusting for age. This was often not obviously related to disease burden. For instance when compared with non-Mori/non-Pasifika, Mori and Pasifika showed lower age-standardised script rates for anti-depressants, contraceptives and inhaled corticosteroids, but higher rates for anti-hepatitis B antivirals, short-acting asthma inhalers, and older and depot injection antipsychotics. The differences in therapeutic groups between Mori and Pasifika compared with non-Mori/non-Pasifika were not uniform, as can be seen in Figure A3-3 and Table A3-3 in Appendix 3. For instance, Pasifika were dispensed medicines for attention deficit disorder, Hepatitis C infections and older depot antipsychotics at one fifth the rate of Mori. Asthma medicines and newer antidepressants were relatively under-dispensed in Pasifika compared with Mori. Conversely, Pasifika were dispensed oral hypoglycaemic medicines for type 2 diabetes and blood glucose test strips, older glaucoma medicines, scabies treatments, and hepatitis B medicines at twice the rate of Mori. Mori and Pasifika age-standardised rates were similar for antibiotics, statins, ACE inhibitors, low dose aspirin, and treatments for gout. All of these features are detailed in Figure 1 and in Appendix 3, including tables and further graphs. Lower script counts for Mori when adjusted for health needs Mapping the NZBDS disease categories to medicines listed on the New Zealand Pharmaceutical Schedule, in order to partly relate medicines use to disease impacts (health need), it was possible to link 85% of 2006/07 scripts (prescription dispensings) to relevant NZBDS disease groups. Accordingly, coincidentally 85% of DALY losses in 1996 appeared to be for diseases treatable or preventable by medicines on the Pharmaceutical Schedule. Hence in 1996 perhaps some 480,000 disability-adjusted years of life (DALYs) were lost by the New Zealand population from diseases treatable by medicines on the Pharmaceutical Schedule (out of 563,000 DALYs lost overall for all diseases) see Tables A4-1 and A4-2 in Appendix 4. The generally higher use of medicines by Mori and Pasifika than non-Mori/non-Pasifika must therefore be seen in the context of these populations having general higher health needs. Details of these higher health needs for Mori can be found in Appendix 5. For conditions treated or prevented by medicines on the Pharmaceutical Schedule, differences in burden of disease could be linked to differences between Mori and non-Mori dispensing rates (see endnote **). This mapping suggests that although total Mori script counts were comparable with non-Mori after adjusting for age, actual dispensing for Mori was much lower than needed to overcome their greater disease burden. Hence, although Mori in 2006/07 had 97% age-adjusted script counts relative to non-Mori, after further adjusting for historical 45% higher relative DALY losses in Mori this ratio fell to 81% of what it would be for non-Mori. Moreover, after excluding medicines not covered by the NZBDS diseases the ratio fell further to 63%. Mori had therefore 19-37% lower treatment rates compared with non-Mori (conversely, rates in non-Mori being higher). The total scripts known to be dispensed to Mori in 2006/7 (excluding PSOs and those otherwise without NHIs, but scaled) was 3.3 million (as stated above), of which 2.7 million linked with NZBDS diseases. The overall gap in scripts to Mori after standardising for age and adjusting for historical burden of disease amounted to 977,400 fewer scripts. Most medicines had shortfalls rather than excesses. Key shortfalls are summarised in Table 1. Table 1. Shortfalls in Mori age/DALY-adjusted script counts Medicine Shortfall* Comments antibiotics 181,500 NZBDS categories of bacterial infections, of which 89,100 for amoxicillins antiulcerants 60,500 principally 54,300 for proton pump inhibitors (PPIs); may reflect inappropriately high antiuclerant use in non-Mori statins 53,100 cardiovascular risk (dyslipidaemia); principally simvastatin (45,400) beta blockers 52,900 primarily for cardiovascular risk and disease ACE inhibitors/A2 antagonists 48,800 cardiovascular risk and disease, including diabetes newer antidepressants 46,300 principally selective serotonin reuptake inhibitors (SSRIs) (41,600); also venlafaxine, selective MAOIs low-dose aspirin 40,100 cardiovascular risk inhaled corticosteroids \u00b1 long-acting beta agonists 22,600 asthma oral hypoglycaemics 21,300 primarily cardiovascular risk (type 2 diabetes) diabetes self-testing 19,200 self-management of types 1 and 2 diabetes *Shortfalls are the differences between actual script counts in Mori and numbers expected were Mori to have the same dispensing as non-Mori, after adjusting for population size, age, and disease burden. Access and persistence similarly less in Mori Almost half of the above calculated need-adjusted gap in prescriptions dispensed was due to fewer than expected Mori patients accessing medicines (443,900 absent initial dispensings). We estimated access in Mori to be 67% that of non-Mori. The biggest gap from reduced access was for amoxicillins. The remainder of the gap was due to lower Mori persistence\u2021 with medicines (533,500 absent subsequent dispensings). Persistence in Mori was calculated as 58% of that in non-Mori. The biggest gaps from reduced persistence were for beta-blockers, PPIs, simvastatin, low-dose aspirin for cardiovascular risk, and SSRIs. Conversely, the calculated overall difference in scripts for non-Mori (age and disease burden-adjusted) amounted to at least 12.2 million more scripts. In summary, access and persistence contributed on a similar scale to apparent under-dispensing to Mori. Note however that there were appreciable differences between medicines in the mix of access and persistence. This included examples such as the newer antipsychotics, in which large proportionate shortfall in access was masked by proportionately lesser shortfalls in persistence. These features are evident in the following graphs (Figures 2 to 5) and are detailed in Table A6.2 in Appendix 6. To explain Figures 2-5: Figure 2 shows shortfalls and excesses in scripts for Mori compared with that expected for non-Mori. This reveals the therapeutic areas suggesting the largest gaps in dispensings. Figure 3 shows proportional shortfalls and excesses. This suggests the therapeutic areas with the most divergence in clinical practice from what would be expected in non-Mori, as Mori rates relative to non-Mori. (The data are on a logarithmic scale, so that shortfalls and excesses are distributed symmetrically about a relative rate of 1 (unity), which is the zero line; further explanation is in endnote \u2021\u2021.) Figure 4 suggests numerical shortfalls and excesses broken down by access and persistence. This shows these two factors variable contributions to differential dispensing. Figure 5 shows proportional shortfalls and excesses, broken down by access and persistence. As with figure 3, this suggests the therapeutic areas with the most divergence in clinical practice from what would be expected in non-Mori, as Mori rates relative to non-Mori, but then shows how much is due to differences in access versus differences in persistence. (Again as with figure 3, the data are on a logarithmic scale, see endnote \u2021\u2021). Figures 2 to 5 also include disaggregating of the category NSAIDS/gout/analgesics/ muscl relxnts into component Rx for hyperuricaemia & gout and NSAIDS + muscle relaxants subcategories. Discussion This analysis links patient-level script count data with population-based estimates of health need. This method can give at best broad indications of trends, for what are complex issues. Interpretation of the results may change after more detailed analysis of individual issues. The ability of access to counteract persistence (as seen with some antipsychotics) is an example of more complex effects that may be lost in population-based data. Even so, this work reveals a potentially significant issue with likely differences between ethnic groups, and hence potential for health gain or reduced wastage once shortfalls and excesses are addressed. This is apparent in a majority of disease and disability states, and begs the question of suboptimal or excess treatment elsewhere. Limitations and caveats There are however important limitations and caveats with the analysis: Scripts dispensed are not the same as medicines prescribed. There is evidence that many prescriptions are either not presented or not collected at pharmacies. Reasons for this may include time, cost and transportation. Such factors can affect populations differentially. Mori are more likely to have uncollected prescriptions, their non-collection rate being 45% higher than that of non-Mori aged over 1520,21 (where this statistic stems from 2006/07, when minimum co-payments for the first 20 items were $3 per item; this has since risen to $5). It is not possible to tell from this analysis the extent that failure to dispense represents a systematic failure to prescribe or a systematic failure to ensure that prescriptions are filled. However, this feature may appreciably understate true gaps. Dispensing data are restricted to those prescriptions and patient groups that gain subsidies for publicly-funded community dispensed medicines. The data therefore exclude prescriptions that were not subsidised, or items that fell below the $3-$15 prescription co-payments at that time, where pharmacies would have no need to claim (and hence would not be captured in the PharmHouse claims database data). Non-capture of unclaimed medicines use might undercount appreciably overall medicines use and potentially understate gaps in in populations with poorer access to medicines. Script counts are an imprecise measure of coverage (days) that medicines are actually provided, being confounded by dispensings/script rates and duration (days coverage) of dispensings. With scripts versus dispensings, people living in rural areas tend to get longer dispensings (e.g. 3 months, where 1 month would be standard in non-rural setting). Hence, to the extent that Mori are overrepresented in rural populations, the gaps may be overstated to an uncertain extent. Some PSOs may be used for targeting populations with poor access to medicines. PSOs are more commonly used in rural areas, where the nearest pharmacy may be some distance, and for certain types of medicines, such as antibiotics. PSOs understate true numbers of people receiving medicines, which may mean gaps are overstated to some extent. Gaps in script counts do not necessarily equate with gaps in disease burden and capacity to benefit from effective medicines treatment. Population health gains (expressed for example as quality-adjusted life years (QALYs) gained) reflect not only numbers of patients and script frequencies per patient, but also the effectiveness of medicines in relation to patients' health needs. Hence gaps in health outcomes from patients receiving less medicine are not necessarily the same as gaps in script counts. Linking between script counts and diseases can be imprecise where medicines have multiple clinical indications or disease burden covers a broad range of diseases. Problems linking medicines to single disease groups may have important effects on the analysis results.If these factors were to cause bias that is non-differential, such imprecisions from linking could tend to understate true differences in disease burden-adjusted prescriptions. However, this is not a given; it is possible that differential bias could occur, for instance understating of shortfalls in for one disease category meaning falsely ascribing shortfalls in another disease category, which could overstate net true differences. An example of probable non-differential bias within a disease category (understating true differences) is that of gout and other musculoskeletal conditions. Medicines for gout (e.g. allopurinol) are bundled into wider NSAIDS etc., because the 1996 NZBDS data combined a number of musculoskeletal conditions, meaning the high excess disease burden for Mori for gout (e.g. their age-standardised hospitalisation rates in 2006/07 being 6-7 times that of non-Mori)22 was diluted by other musculoskeletal diseases and hence relative disparities were muted. Overall, Mori had a small observed shortfall of DALYL-adjusted scripts for musculoskeletal diseases (-2,000), but this may well have been due to a large shortfall for allopurinol etc. for gout (-10,700 scripts) masking a similarly-sized excess for NSAIDs (+12,700). Conversely, an example of the potential for differential bias across disease categories (potentially overstating net true differences) is that of carbamazepine and sodium valproate, which are anti-epilepsy medicines. In the analysis, these were matched to the NZBDS Epilepsy category, and there was a shortfall of 5,900 scripts for Mori (out of 208,900 total scripts). However, carbamazepine and sodium valproate are also commonly used for the control of bipolar disorder, inter alia. The lifetime prevalence of bipolar disorder in Mori is double that of the overall population,23 so it is possible that the shortfall for Mori in the Epilepsy category was understated, and the shortfall for Mori in the NZBDS Mental Health category was overstated, to a greater extent than non-Mori. Such possibilities highlight the impact of mismatching of medicine dispensings and disease categories.More specifically, the broad scope of this preliminary analysis does not allow more detailed review of antibiotic use for discrete issues, e.g. the high incidence of acute rheumatic fever in Mori children,11 which relates specifically to childhood penicillin and amoxicillin use in the treatment of acute pharyngotonsillitis (sore throat) where these medicines also treat many other childhood infections. Age-standardised rates and rate ratios are aggregates that can obscure wide variation across age groups. This become particularly important where data are missing (e.g. from prescriptions that lack NHI numbers including PSOs), with the potential to mis-state true gaps.For example, with the amoxicillins (amoxicillin, amoxicillin clavulanate) there was a shortfall of 89,100 scripts for Mori. Much of that shortfall occurred in children aged 0-14 years, whose relative rates were substantially lower than for older age groups. However, 13% of scripts for amoxicillins did not have NHIs and could in theory all have been on PSO (being double the average 7.5% for scripts overall on PSO). If, radically, the many amoxicillin scripts without NHIs were for all Mori children and these in turn had the same relative rates as for older patients, then the shortfall for amoxicillins for Mori children would halve and the overall gap (all ages) would be only 1/5th lower than non-Mori (53,200 script gap). Details including component calculations can be found in endnote \u0390\u0390\u0390. Amoxicillins accounted for 11% of all non-NHI scripts (204,762 scripts without NHIs numbers, out of 1.6 million amoxicillin etc. scripts and 2.4 million scripts without NHIs), so these medicines are an important part of this information gap. Analysis is unavailable for Pasifika. The available Ministry age-specific analysis by ethnic group was confined to comparing Mori with non-Mori, and other relevant disease burden analysis (Ministry of Health 2001b3) provided insufficient detail to enable age-specific and age-standardised disease burden estimates for Pasifika, Mori and non-Mori/non-Pasifika. Pasifika people have needs and underuse at least equal to Mori (see Appendix 3), with two consequences: Important gaps need to be identified and quantified for Pasifika too; Mori vs. non-Mori comparisons if anything may understate the extent of Mori underuse once adjusted for need, as by including Pasifika in the non-Mori group this may dilute the relative effects for Mori. Such deficiencies should be addressable in the Ministrys forthcoming updates of the NZBDS. [Note: The August 2013 published update of the NZBDS36,37 has not included separate results for Pasifika, only comparing Mori with non-Mori.] The use of disease burden estimates based on mortality/morbidity data from 1996 to compare with prescription volumes a decade lat

Summary

Abstract

Aim

To describe variations in dispensing of specific medication groups by ethnicity in New Zealand, adjusting for health need.

Method

Preliminary linkage of dispensings of prescription medicines in 2006/07 to age/disease burden proxies of health need for Mori, Pacific peoples (Pasifika) who are mostly of Samoan, Tongan, Niuean, or Cook Islands descent in New Zealand, and non-Mori/non-Pasifika. These disease burden proxies combine differences in prevalence, age, morbidity, and mortality. Variations were disaggregated by patients being first dispensed medicines (access) versus subsequent dispensings (persistence).

Results

Initially, overall age-adjusted incidence of scripts (prescriptions dispensed) to Mori was similar to that of non-Mori. There were differences in therapeutic coverage between Mori and Pasifika, for example greater use of asthma medicines in Mori. However, further adjustments linking with disease burden showed marked variance for a number of diseases. Differences in dispensing included areas of high health need such as heart disease, infections, diabetes, mental health and respiratory disease. Mori had 19-37% lower dispensings overall than non-Mori, with a net difference of nearly 1 million scripts. Mori were both less likely to access medicines, and then after first dispensing had fewer subsequent scripts. Patterns for Pasifika appeared similar, although needs-adjusted analysis is awaited for this population.

Conclusion

Once adjusting for need, there was variable but sizeable differences in medicines dispensed to Mori compared with non-Mori, and likely differences for Pasifika populations. There are however important limitations to this preliminary analysis. Crude and age-standardised metrics may be poor predictors of needs-adjusted gaps in medicines use. In this analysis, solely age-standardised rates tended to underestimate differences once adjusting for burden of disease; future analyses of prescribing patterns should consider better adjusting for disease burden.

Author Information

Scott Metcalfe, Chief Advisor Population Medicine/Deputy Medical Director (Epidemiology), PHARMAC, Wellington; George Laking, Medical Oncologist/Health Economist, Auckland Member, Pharmacology and Therapeutics Advisory Committee (PTAC) Member, Mori Caucus, PHARMAC, Wellington; Jason Arnold, Team Leader Analysis, PHARMAC, Wellington

Acknowledgements

We thank Rico Schoeler (Manager Assessment) and Marama Parore (Manager Access & Optimal Use and Mori Health) of PHARMAC, Wellington; Martin Tobias (Principal Adviser, Public Health Intelligence) of the Ministry of Health, Wellington; and PTAC members in 2008 who both provided advice on earlier analysis and reviewed (1) indication-based medicines groupings and (2) consequent groupings of indication-based medicines with disease categories. The Journals anonymous peer reviewers provided helpful commentary.

Correspondence

Dr Scott Metcalfe, PHARMAC, PO Box 10254, Wellington, New Zealand

Correspondence Email

scott.metcalfe@pharmac.govt.nz

Competing Interests

The authors are PHARMAC staff or advisors.

- New Zealand Public Health and Disability Act 2000 http://www.legislation.govt.nz/act/public/2000/0091/latest/DLM80051.html section 47 Objectives of Pharmac PHARMAC Decision Criterion 2 The particular health needs of Mori & Pacific peoples http://www.pharmac.govt.nz/DecisionCriteria. In: PHARMAC. Operating policies and procedures of the Pharmaceutical Management Agency ( PHARMAC ), Third Edition, 2006. http://www.pharmac.govt.nz/2005/12/22/231205.pdf Priorities for Maori and Pacific health: evidence from epidemiology. Public Health Intelligence Occasional Bulletin No. 3. Wellington: Ministry of Health, 2001.http://www.moh.govt.nz/notebook/nbbooks.nsf/0/A31842D91480064FCC256A55007A980A/$file/PrioritiesForMaoriandPacificHealth.pdf Durie M. Understanding health and illness: research at the interface between science and indigenous knowledge. Int J Epidemiol. 2004;33:1138-43. http://ije.oxfordjournals.org/content/33/5/1138.long Bramley D, Riddell T, Crengle S, Curtis E, Harwood M, Nehua D, Reid P. A call to action on Maori cardiovascular health. N Z Med J. 2004;117:U957. http://journal.nzma.org.nz/journal/117-1197/957/ Blakely T, Ajwani S, Robson B, Tobias M, Bonn\u00e9 M. Decades of disparity: widening ethnic mortality gaps from 1980 to 1999. N Z Med J. 2004;117:U995. http://journal.nzma.org.nz/journal/117-1199/995/ Robson B, Purdie G, Cormack D. Unequal impact: Mori and non-Mori cancer statistics 1996-2001. Wellington: Ministry of Health, 2006. Harris R, Tobias M, Jeffreys M, Waldegrave K, Karlsen S, Nazroo J. Effects of self reported racial discrimination and deprivation on Mori health and inequalities in New Zealand: cross-sectional study. Lancet 2006;367: 2005-9 Blakely T, Tobias M, Atkinson J, et al. Tracking disparity: trends in ethnic and socioeconomic inequalities in mortality, 1981-2004. Wellington: Ministry of Health, 2007. Rumball-Smith JM. Not in my hospital? Ethnic disparities in quality of hospital care in New Zealand: a narrative review of the evidence. N Z Med J. 2009;122:68-83.http://journal.nzma.org.nz/journal/122-1297/3662/ Hale M, Sharpe N. Persistent rheumatic fever in New Zealand--a shameful indicator of child health. N Z Med J. 2011;124:6-8. http://journal.nzma.org.nz/journal/124-1329/4530/ Soeberg M, Blakely T, Sarfati D, Tobias M, Costilla R, et al. Cancer trends: trends in cancer survival by ethnic and socioeconomic group, New Zealand 1991-2004. Wellington: University of Otago and Ministry of Health, 2012. http://www.health.govt.nz/publication/cancer-trends-trends-cancer-survival-ethnic-and-socioeconomic-group-new-zealand-1991-2004 Crengle S, Lay-Yee R, Davis P, Pearson J. A comparison of Mori and non-Mori patient visits to doctors: the National Primary Medical Care Survey (NatMedCa): 2001/02. Report 6. Wellington: Ministry of Health, 2005. http://www.health.govt.nz/publication/comparison-maori-and-non-maori-patient-visits-doctors Smith MW. Hospital discharge diagnoses: how accurate are they and their international classification of diseases (ICD) codes? N Z Med J 1989;102:507-8. The Burden of Disease and Injury in New Zealand. Public Health Intelligence Occasional Bulletin No. 1. Wellington: Ministry of Health, 2001.http://www.moh.govt.nz/notebook/nbbooks.nsf/0/E916C53D599AE126CC2569F0007AAA23/$file/BurdenofDisease.pdf http://www.pharmac.health.nz/tools-resources/pharmaceutical-schedule Ministry of Health. Pharmaceutical Collection. http://www.health.govt.nz/nz-health-statistics/national-collections-and-surveys/collections/pharmaceutical-collection Ministry of Health. National Health Index (NHI). http://www.health.govt.nz/our-work/preventative-health-wellness/immunisation/national-immunisation-register/national-health-index-nhi Statistics New Zealand, Census and population projections. supplied annually to Ministry of Health. Projections produced by Statistics New Zealand according to assumptions specified by the Ministry of Health. Ministry of Health. Tatau Kahukura: Mori Health Chart Book 2010, 2nd Edition. Wellington: Ministry of Health, 2010. http://www.health.govt.nz/publication/tatau-kahukura-maori-health-chart-book-2010-2nd-edition p.57. Ministry of Health. A portrait of health: key results of the 2006/07 New Zealand Health Survey. Wellington: Ministry of Health, 2008. http://www.health.govt.nz/publication/portrait-health-key-results-2006-07-new-zealand-health-survey Table 1.4 p.15, pp 277-8, Table A1.1 pp.339,343. PHARMAC age-standardised analysis of data provided in: Publicly funded hospital discharges - July 2006 to 30 June 2007. Wellington: Ministry of Health, 2010.http://www.health.govt.nz/publication/publicly-funded-hospital-discharges-1-july-2006-30-june-2007 (using Segis world population standard) Oakley Browne MA, Wells JE, Scott KM (eds). Te Rau Hinengaro: The New Zealand Mental Health Survey. Wellington: Ministry of Health, 2006. http://www.health.govt.nz/publication/te-rau-hinengaro-new-zealand-mental-health-survey Tobias M, Blakely T, Matheson D, Rasanathan K, Atkinson J. Changing trends in indigenous inequalities in mortality: lessons from New Zealand. Int J Epidemiol. 2009;38:1711-22.http://ije.oxfordjournals.org/content/38/6/1711.full.pdf+html Murray CJL. Rethinking DALYs. In: Murray CJL, Lopez AD (eds). The Global Burden of Disease: a comprehensive assessment of mortality and disability from diseases, injuries and risk factors in 1990 and projected to 2020. Cambridge, MA: Harvard University Press, on behalf of the World Health Organization and the World Bank; 1996. Chapter 1 pp.1-98. A key objective of PHARMAC is to fund pharmaceuticals that are cost effective in meeting the health needs of the population, writes Australian health economist Professor Anthony Harris. in: PHARMAC. Annual Review 2011. Wellington: Pharmaceutical Management Agency (PHARMAC), 2011. http://www.pharmac.govt.nz/2011/12/13/Ann%20Rev%202011.pdf pp.12-13. Prescription for Pharmacoeconomic Analysis: methods for cost-utility analysis, Version 2.1. PHARMAC: Wellington, New Zealand, 2012. http://www.pharmac.govt.nz/2012/06/26/PFPAFinal.pdf Didham R, Callister P. The effect of ethnic prioritisation on ethnic health analysis: a research note. N Z Med J 2012;125:U5278. http://journal.nzma.org.nz/journal/125-1359/5278/ Robson B, Purdie G, Cram F, Simmonds S. Age standardisation - an indigenous standard? Emerg Themes Epidemiol. 2007 May 14;4:3. http://www.ete-online.com/content/4/1/3 Winnard D, Wright C, Taylor WJ, Jackson G, Te Karu L, et al. National prevalence of gout derived from administrative health data in Aotearoa New Zealand. Rheumatology (Oxford). 2012;51:901-9. http://rheumatology.oxfordjournals.org/content/early/2012/01/16/rheumatology.ker361.full Tahana Y. Professor bows out on a high note. The New Zealand Herald, 21 July 2012. http://www.nzherald.co.nz/education/news/article.cfm?c_id=35&objectid=10821126 Wheeler A, Humberstone V, Robinson E. Ethnic comparisons of antipsychotic use in schizophrenia. Aust N Z J Psychiatry. 2008;42:863-73. http://anp.sagepub.com/content/42/10/863.long State Services Commission. EEO Policy to 2010: future directions of EEO in the New Zealand Public Service. Wellington: SSC, 1997. http://www.ssc.govt.nz/eeo-policy-to-2010 New Zealand Health and Disability Ethics Committees. Guidance on Ethical Research Review. Ethical Guidelines for Observational Studies: observational research, audits and related activities, 2007.http://www.neac.health.govt.nz/moh.nsf/indexcm/neac-resources-publications-ethicalresearchguidelines, http://www.neac.health.govt.nz/moh.nsf/Files/neac-resources/$file/ethical-guidelines-for-observational-studies-2012.pdf Guidelines paragraphs 2.1-2.7, 11.1-11.11. Narrowing gap between Mori and non-Mori life expectancy. Statistics New Zealand, 2013. http://www.stats.govt.nz/browse_for_stats/health/life_expectancy/NZLifeTables_MR10-12.aspx Ministry of Health. Health Loss in New Zealand: A report from the New Zealand Burden of Diseases, Injuries and Risk Factors Study, 2006-2016. Wellington: Ministry of Health, 2013.http://www.health.govt.nz/publication/health-loss-new-zealand-report-new-zealand-burden-diseases-injuries-and-risk-factors-study-2006-2016 New Zealand Burden of Diseases Statistical Annexehttp://www.health.govt.nz/publication/new-zealand-burden-diseases-statistical-annexe Ministry of Health. Ways and Means: a report on methodology from the New Zealand Burden of Diseases, Injuries and Risk Factors Study, 2006-2016. Wellington: Ministry of Health, 2012.http://www.health.govt.nz/publication/ways-and-means-report-methodology-new-zealand-burden-disease-injury-and-risk-study-2006-2016 Disparities in the use of medicines for Mori. Best Practice Journal 2012;45:12-13. http://www.bpac.org.nz/magazine/2012/august/disparities.asp-

Contact diana@nzma.org.nz
for the PDF of this article

Subscriber Content

The full contents of this pages only available to subscribers.

LOGINSUBSCRIBE