Population and contact tracer uptake of New Zealand's QR-code-based digital contact tracing app for COVID-19.

This study aimed to understand the population and contact tracer uptake of the quick response (QR)-code-based function of the New Zealand COVID Tracer App (NZCTA) used for digital contact tracing (DCT). We used a retrospective cohort of all COVID-19 cases between August 2020 and February 2022. Cases of Asian and other ethnicities were 2.6 times (adjusted relative risk (aRR) 2.58, 99 per cent confidence interval (95% CI) 2.18, 3.05) and 1.8 times (aRR 1.81, 95% CI 1.58, 2.06) more likely than Maori cases to generate a token during the Delta period, and this persisted during the Omicron period. Contact tracing organization also influenced location token generation with cases handled by National Case Investigation Service (NCIS) staff being 2.03 (95% CI 1.79, 2.30) times more likely to generate a token than cases managed by clinical staff at local Public Health Units (PHUs). Public uptake and participation in the location-based system independent of contact tracer uptake were estimated at 45%. The positive predictive value (PPV) of the QR code system was estimated to be close to nil for detecting close contacts but close to 100% for detecting casual contacts. Our paper shows that the QR-code-based function of the NZCTA likely made a negligible impact on the COVID-19 response in New Zealand (NZ) in relation to isolating potential close contacts of cases but likely was effective at identifying and notifying casual contacts.

Personality disorder prevalence and correlates in a whole of nation dataset.

PURPOSE: The epidemiology of personality disorder is poorly understood. This study aims to describe the population in contact with mental health services with a diagnosis of personality disorder and compare service use between this group and those with a diagnosis of depression. METHODS: Investigation of a routinely collected clinical data set across New Zealand was conducted. We used data from 2008 to 2017 and 1-year data from 2016, the most complete dataset. This allowed for variation over the years and confirmation within a 1-year prevalence. These data were analysed focusing on patients with a primary diagnosis of any personality disorder and the subset with borderline personality disorder (BPD). BPD was chosen as the most common clinical personality disorder diagnosis and that most researched. RESULTS: 8884 (2.8%) of the population in contact with mental health services carried a primary diagnosis of personality disorder. Personality diagnosis other than antisocial personality disorder (ASPD) in men and borderline personality disorder (BPD) in either gender was negligible. Rates of personality pathology increased with social deprivation and were commonest in young adults. Multi-morbidity was the norm, with internalising disorder common. Maori diagnosed with PD were over-represented both in the patient group and by population. CONCLUSION: Borderline personality disorder and antisocial personality disorder are routinely diagnosed in routine clinical practice in New Zealand; however, other categorical diagnoses are not made. Patients with PD are significant users of resources in comparison to depressed patients. Resource utilisation was significantly greater in those with PD, in particular use of inpatient services compared to those with depression.

Early diagnosis of acute rheumatic fever and rheumatic heart disease as part of a secondary prevention strategy: Narrative review.

Acute rheumatic fever (ARF) and its sequela rheumatic heart disease (RHD) remain significant causes of morbidity and mortality. In New Zealand, ARF almost exclusively affects Indigenous Maori and Pacific children. This narrative review aims to present secondary interventions to improve early and accurate diagnosis of ARF and RHD, in order to minimise disease progression in New Zealand. Medline, EMBASE and Scopus databases were searched as well as other electronic publications. Included were 56 publications from 1980 onwards. Diagnosing ARF and RHD as early as possible is central to reducing disease progression. Recent identification of specific ARF biomarkers offer the opportunity to aid initial diagnosis and portable echocardiography has the potential to detect undiagnosed RHD in high-risk areas. However, further research into the benefits and risks to children with subclinical RHD is necessary, as well as an economic evaluation.

Health system costs for individual and comorbid noncommunicable diseases: An analysis of publicly funded health events from New Zealand.

BACKGROUND: There is little systematic assessment of how total health expenditure is distributed across diseases and comorbidities. The objective of this study was to use statistical methods to disaggregate all publicly funded health expenditure by disease and comorbidities in order to answer three research questions: (1) What is health expenditure by disease phase for noncommunicable diseases (NCDs) in New Zealand? (2) Is the cost of having two NCDs more or less than that expected given the independent costs of each NCD? (3) How is total health spending disaggregated by NCDs across age and by sex? METHODS AND FINDINGS: We used linked data for all adult New Zealanders for publicly funded events, including hospitalisation, outpatient, pharmaceutical, laboratory testing, and primary care from 1 July 2007 to 30 June 2014. These data include 18.9 million person-years and $26.4 billion in spending (US$ 2016). We used case definition algorithms to identify if a person had any of six NCDs (cancer, cardiovascular disease [CVD], diabetes, musculoskeletal, neurological, and a chronic lung/liver/kidney [LLK] disease). Indicator variables were used to identify the presence of any of the 15 possible comorbidity pairings of these six NCDs. Regression was used to estimate excess annual health expenditure per person. Cause deletion methods were used to estimate total population expenditure by disease. A majority (59%) of health expenditure was attributable to NCDs. Expenditure due to diseases was generally highest in the year of diagnosis and year of death. A person having two diseases simultaneously generally had greater health expenditure than the expected sum of having the diseases separately, for all 15 comorbidity pairs except the CVD-cancer pair. For example, a 60-64-year-old female with none of the six NCDs had $633 per annum expenditure. If she had both CVD and chronic LLK, additional expenditure for CVD separately was $6,443/$839/$9,225 for the first year of diagnosis/prevalent years/last year of life if dying of CVD; additional expenditure for chronic LLK separately was $6,443/$1,291/$9,051; and the additional comorbidity expenditure of having both CVD and LLK was $2,456 (95% confidence interval [CI] $2,238-$2,674). The pattern was similar for males (e.g., additional comorbidity expenditure for a 60-64-year-old male with CVD and chronic LLK was $2,498 [95% CI $2,264-$2,632]). In addition to this, the excess comorbidity costs for a person with two diseases was greater at younger ages, e.g., excess expenditure for 45-49-year-old males with CVD and chronic LLK was 10 times higher than for 75-79-year-old males and six times higher for females. At the population level, 23.8% of total health expenditure was attributable to higher costs of having one of the 15 comorbidity pairs over and above the six NCDs separately; of the remaining expenditure, CVD accounted for 18.7%, followed by musculoskeletal (16.2%), neurological (14.4%), cancer (14.1%), chronic LLK disease (7.4%), and diabetes (5.5%). Major limitations included incomplete linkage to all costed events (although these were largely non-NCD events) and missing private expenditure. CONCLUSIONS: The costs of having two NCDs simultaneously is typically superadditive, and more so for younger adults. Neurological and musculoskeletal diseases contributed the largest health system costs, in accord with burden of disease studies finding that they contribute large morbidity. Just as burden of disease methodology has advanced the understanding of disease burden, there is a need to create disease-based costing studies that facilitate the disaggregation of health budgets at a national level.

Socioeconomic and Tobacco Mediation of Ethnic Inequalities in Mortality over Time: Repeated Census-mortality Cohort Studies, 1981 to 2011.

BACKGROUND: Racial/ethnic inequalities in mortality may be reducible by addressing socioeconomic factors and smoking. To our knowledge, this is the first study to estimate trends over multiple decades in (1) mediation of racial/ethnic inequalities in mortality (between Maori and Europeans in New Zealand) by socioeconomic factors, (2) additional mediation through smoking, and (3) inequalities had there never been smoking. METHODS: We estimated natural (1 and 2 above) and controlled mediation effects (3 above) in census-mortality cohorts for 1981-1984 (1.1 million people), 1996-1999 (1.5 million), and 2006-2011 (1.5 million) for 25- to 74-year-olds in New Zealand, using a weighting of regression predicted outcomes. RESULTS: Socioeconomic factors explained 46% of male inequalities in all three cohorts and made an increasing contribution over time among females from 30.4% (95% confidence interval = 18.1%, 42.7%) in 1981-1984 to 41.9% (36.0%, 48.0%). Including smoking with socioeconomic factors only modestly altered the percentage mediated for males, but more substantially increased it for females, for example, 7.7% (5.5%, 10.0%) in 2006-2011. A counterfactual scenario of having eradicated tobacco in the past (but unchanged socioeconomic distribution) lowered mortality for all sex-by-ethnic groups and resulted in a 12.2% (2.9%, 20.8%) and 21.2% (11.6%, 31.0%) reduction in the absolute mortality gap between Maori and Europeans in 2006-2011, for males and females, respectively. CONCLUSIONS: Our study predicts that, in this high-income country, reducing socioeconomic disparities between ethnic groups would greatly reduce ethnic inequalities in mortality over the long run. Eradicating tobacco would notably reduce ethnic inequalities in absolute but not relative mortality.

Changing socioeconomic inequalities in cancer incidence and mortality: Cohort study with 54 million person-years follow-up 1981-2011.

Cancer is increasingly responsible for the mortality gap between high and low socioeconomic position groups in high-income countries. This study investigates which cancers are contributing more to socioeconomic gaps in mortality and how this changes over time.New Zealand census data from 1981, 1986, 1991, 1996, 2001 and 2006, were linked to three to five years of subsequent mortality and cancer registrations, resulting in 54 and 42 million years of follow-up cancer incidence and mortality, respectively. Age- and ethnicity-standardised cancer mortality rates and the slope index of inequality (SII) by income were calculated.The contribution of cancer to absolute inequalities (SII) in mortality increased from 16 to 27% for men and from 12 to 31% for women from 1981-84 to 2006-11, peaking in 1991-94 for men and in 1996-99 for women and then levelling off, parallel to peaks in lung cancer inequalities. Lung cancer was the largest driver of cancer inequality trends (49% of the cancer mortality gap in 1981-84 to 33% in 2006-11 for men and 32 to 33% for women) followed by colorectal cancer in men (2 to 11%) and breast cancer in women (declining from 44 to 13%). Women in the lowest income quintile experienced no decline in cancer mortality.The contribution of cancer to income inequalities in all-cause mortality has expanded in this high-income country. Action to address socioeconomic inequalities should prioritise equitable tobacco control, obesity control and improved access to cancer screening, early diagnosis and high quality treatment for those with the lowest incomes.

A Typology for Charting Socioeconomic Mortality Gradients: "Go Southwest".

BACKGROUND: Holistic depiction of time-trends in average mortality rates, and absolute and relative inequalities, is challenging. METHODS: We outline a typology for situations with falling average mortality rates (m↓; e.g., cardiovascular disease), rates stable over time (m-; e.g., some cancers), and increasing average mortality rates (m↑; e.g., suicide in some contexts). If we consider inequality trends on both the absolute (a) and relative (r) scales, there are 13 possible combination of m, a, and r trends over time. They can be mapped to graphs with relative inequality (log relative index of inequality [RII]; r) on the y axis, log average mortality rate on the x axis (m), and absolute inequality (slope index of inequality; SII; a) as contour lines. We illustrate this by plotting adult mortality trends: (1) by household income from 1981 to 2011 for New Zealand, and (2) by education for European countries. RESULTS: Types range from the "best" m↓a↓r↓ (average, absolute, and relative inequalities all decreasing; southwest movement in graphs) to the "worst" m↑a↑r↑ (northeast). Mortality typologies in New Zealand (all-cause, cardiovascular disease, nonlung cancer, and unintentional injury) were all m↓r↑ (northwest), but variable with respect to absolute inequality. Most European typologies were m↓r↑ types (northwest; e.g., Finland), but with notable exceptions of m-a↑r↑ (north; e.g., Hungary) and "best" or southwest m↓a↓r↓ for Spain (Barcelona) females. CONCLUSIONS: Our typology and corresponding graphs provide a convenient way to summarize and understand past trends in inequalities in mortality, and hold potential for projecting future trends and target setting.

Changing ethnic inequalities in mortality in New Zealand over 30 years: linked cohort studies with 68.9 million person-years of follow-up.

BACKGROUND: Internationally, ethnic inequalities in mortality within countries are increasingly recognized as a public health concern. But few countries have data to monitor such inequalities. We aimed to provide a detailed description of ethnic inequalities (Maori [indigenous], Pacific, and European/Other) in mortality for a country with high quality ethnicity data, using both standard and novel visualization methods. METHODS: Cohort studies of the entire New Zealand population were conducted, using probabilistically-linked Census and mortality data from 1981 to 2011 (68.9 million person years). Absolute (standardized rate difference) and relative (standardized rate ratio) inequalities were calculated, in 1-74-year-olds, for Maori and Pacific peoples in comparison to European/Other. RESULTS: All-cause mortality rates were highest for Maori, followed by Pacific peoples then European/Other, and declined in all three ethnic groups over time. Pacific peoples experienced the slowest annual percentage fall in mortality rates, then Maori, with European/Other having the highest percentage falls - resulting in widening relative inequalities. Absolute inequalities, however, for both Maori and Pacific males compared to European/Other have been falling since 1996. But for females, only Maori absolute inequalities (compared with European/Other) have been falling. Regarding cause of death, cancer is becoming a more important contributor than cardiovascular disease (CVD) to absolute inequalities, especially for Maori females. CONCLUSIONS: We found declines in all-cause mortality rates, over time, for each ethnic group of interest. Ethnic mortality inequalities are generally stable or even falling in absolute terms, but have increased on a relative scale. The drivers of these inequalities in mortality are transitioning over time, away from CVD to cancer and diabetes; such transitions are likely in other countries, and warrant further research. To address these inequalities, policymakers need to enhance prevention activities and health care delivery, but also support wider improvements in educational achievement and socioeconomic position for highest need populations.

Ethnic inequalities in cancer incidence and mortality: census-linked cohort studies with 87 million years of person-time follow-up.

BACKGROUND: Cancer makes up a large and increasing proportion of excess mortality for indigenous, marginalised and socioeconomically deprived populations, and much of this inequality is preventable. This study aimed to determine which cancers give rise to changing ethnic inequalities over time. METHODS: New Zealand census data from 1981, 1986, 1991, 1996, 2001, and 2006, were all probabilistically linked to three to five subsequent years of mortality (68 million person-years) and cancer registrations (87 million person years) and weighted for linkage bias. Age-standardised rate differences (SRDs) for Maori (indigenous) and Pacific peoples, each compared to European/Other, were decomposed by cancer type. RESULTS: The absolute size and percentage of the cancer contribution to excess mortality increased from 1981-86 to 2006-11 in Maori males (SRD 72.5 to 102.0 per 100,000) and females (SRD 72.2 to 109.4), and Pacific females (SRD -9.8 to 42.2) each compared to European/Other. Specifically, excess mortality (SRDs) increased for breast cancer in Maori females (linear trend p < 0.01) and prostate (p < 0.01) and colorectal cancers (p < 0.01) in Maori males. The incidence gap (SRDs) increased for breast (Maori and Pacific females p < 0.01), endometrial (Pacific females p < 0.01) and liver cancers (Maori males p = 0.04), and for cervical cancer it decreased (Maori females p = 0.03). The colorectal cancer incidence gap which formerly favoured Maori, decreased for Maori males and females (p < 0.01). The greatest contributors to absolute inequalities (SRDs) in mortality in 2006-11 were lung cancer (Maori males 50 %, Maori females 44 %, Pacific males 81 %), breast cancer (Maori females 18 %, Pacific females 23 %) and stomach cancers (Maori males 9 %, Pacific males 16 %, Pacific females 20 %). The top contributors to the ethnic gap in cancer incidence were lung, breast, stomach, endometrial and liver cancer. CONCLUSIONS: A transition is occurring in what diseases contribute to inequalities. The increasing excess incidence and mortality rates in several obesity- and health care access-related cancers provide a sentinel warning of the emerging drivers of ethnic inequalities. Action to further address inequalities in cancer burden needs to be multi-pronged with attention to enhanced control of tobacco, obesity, and carcinogenic infectious agents, and focus on addressing access to effective screening and quality health care.

Home safety assessment and modification to reduce injurious falls in community-dwelling older adults: cost-utility and equity analysis.

BACKGROUND: This study aimed to improve on previous modelling work to determine the health gain, cost-utility and health equity impacts from home safety assessment and modification (HSAM) for reducing injurious falls in older people. METHODS: The model was a Markov macrosimulation one that estimated quality-adjusted life-years (QALYs) gained. The setting was a country with detailed epidemiological and cost data (New Zealand (NZ)) for 2011. A health system perspective was taken and a discount rate of 3% was used (for both health gain and costs). Intervention effectiveness estimates came from a Cochrane systematic review and NZ-specific intervention costs were from a randomised controlled trial. RESULTS: In the 65 years and above age group, the HSAM programme cost a total of US$98 million (95% uncertainty interval (UI) US$65 to US$139 million) to implement nationally and the accrued net health system costs were US$74 million (95% UI: cost saving to US$132 million). Health gains were 34 000 QALYs (95% UI: 5000 to 65 000). The incremental cost-effectiveness ratio (ICER) was US$6000 (95% UI: cost saving to US$13 000), suggesting that HSAM is highly cost-effective. Targeting HSAM only to older people with previous injurious falls and to older people aged 75 years and above were also cost-effective (ICERs=US$1000 and US$11 000, respectively). There was no evidence for differential cost-effectiveness by gender or by ethnicity (Indigenous New Zealanders: Maori vs non-Maori). CONCLUSIONS: As per other studies, this modelling study indicates that the provision of an HSAM intervention produces considerable health gain and is highly cost-effective among older people. Targeting this intervention to older people with previous injurious falls is a promising initial approach before any scale up. TRIAL REGISTRATION NUMBER: ACTRN12609000779279.

Patterns of cancer care costs in a country with detailed individual data.

OBJECTIVE: To determine health system expenditure on cancers by time since diagnosis using data for an entire country. METHODS: New Zealand cancer registry data was linked to hospitalization, pharmaceutical, outpatient, general practice, laboratory, and other datasets, with costs ascribed to each event occurring in 2006-2011. "Excess" cancer costs were estimated by subtracting "expected costs" for citizens without cancer from the "total cost" for cancer patients ($2011 inflation-adjusted). Gamma regressions were used to estimate costs per person-month. RESULTS: For first adult cancer diagnosed that the excess cost per person was between US$3400 and US$4300 in the first month postdiagnosis (varied by sex and age), fell to US$50-US$150 per month at 2 or more years postdiagnosis (excluding those within a year of death), but increased again if dying from their cancer (US$3800-US$8300 in the last month of life). Such patterns varied by cancer, for example, in the first month postdiagnosis for 65 year olds it varied 20-fold from US$800 for prostate to US$15,900 for brain cancer. Per diagnosed case, total excess costs varied from US$5000 (melanoma) to US$66,000 (bone and connective tissue) [Corrected]. Excess cancer costs made up 6.5% of total Vote:Health expenditure in 2010-2011, with colorectal (14.7%), breast (14.4%) being the top 2 contributors, and prostate, non-Hodgkin lymphoma, leukemia, and lung each contributing about 6%. CONCLUSIONS: Costs vary substantially by time since diagnosis and cancer type. The results and regression equations reported in this paper can be used in modeling requiring cancer costs by time since diagnosis and proximity to death.

Examining the impact of COVID-19 on Maori:non-Maori health inequities in Aotearoa, New Zealand: an observational study protocol.

INTRODUCTION: The COVID-19 pandemic has had both direct and indirect impacts on the health of populations worldwide. While racial/ethnic health inequities in COVID-19 infection are now well known (and ongoing), knowledge about the impact of COVID-19 pandemic management on non-COVID-19-related outcomes for Indigenous peoples is less well understood. This article presents the study protocol for the Health Research Council of New Zealand funded project 'Ma te Mohio ka Marama: Impact of COVID-19 on Maori:non-Maori inequities'. The study aims to explore changes in access to healthcare, quality of healthcare and health outcomes for Maori, the Indigenous peoples of Aotearoa New Zealand (NZ) and non-Maori during the COVID-19 outbreak period across NZ. METHODS AND ANALYSIS: This observational study is framed within a Kaupapa Maori research positioning that includes Kaupapa Maori epidemiology. National datasets will be used to report on access to healthcare, quality of healthcare and health outcomes between Maori and non-Maori during the COVID-19 pandemic in NZ. Study periods are defined as (a) prepandemic period (2015-2019), (b) first pandemic year without COVID-19 vaccines (2020) and (c) pandemic period with COVID-19 vaccines (2021 onwards). Regional and national differences between Maori and non-Maori will be explored in two phases focused on identified health priority areas for NZ including (1) mortality, cancer, long-term conditions, first 1000 days, mental health and (2) rheumatic fever. ETHICS AND DISSEMINATION: This study has ethical approval from the Auckland Health Research Ethics Committee (AHREC AH26253). An advisory group will work with the project team to disseminate the findings of this project via project-specific meetings, peer-reviewed publications and a project-specific website. The overall intention of the project is to highlight areas requiring health policy and practice interventions to address Indigenous inequities in health resulting from COVID-19 pandemic management (both historical and in the future).

The association of active smoking with multiple cancers: national census-cancer registry cohorts with quantitative bias analysis.

PURPOSES: (1) Determine the association of multiple cancers with smoking, focusing on cancers with an uncertain association; and (2) illustrate quantitative bias analysis as applied to registry data, to adjust for misclassification of smoking and residual confounding by alcohol and obesity. METHODS: New Zealand 1981 and 1996 censuses, including smoking questions, were linked to cancer registry data giving 14.8 million person-years of follow-up. Rate ratios (RR) for current versus never smokers, adjusting for age, sex, ethnicity and socioeconomic factors were calculated and then subjected to quantitative bias analysis. RESULTS: RR estimates for lung, larynx (including ear and nasosinus), and bladder cancers adjusted for measured confounders and exposure misclassification were 9.28 (95 % uncertainty interval 8.31-10.4), 6.14 (4.55-8.30), and 2.22 (1.94-2.55), respectively. Moderate associations were found for cervical (1.82; 1.51-2.20), kidney (1.29; 1.07-1.56), liver cancer (1.75; 1.37-2.24; European only), esophageal (2.14; 1.73-2.65), oropharyngeal (2.30; 1.94-2.72), pancreatic (1.68; 1.44-1.96), and stomach cancers (1.42; 1.22-1.66). Protective associations were found for endometrial (0.67; 0.56-0.79) and melanoma (0.72; 0.65-0.81), and borderline association for thyroid (0.76; 0.58-1.00), colon (0.89; 0.81-0.98), and CML (0.66; 0.44-0.99). Remaining cancers had near null associations. Adjustment for residual confounding suggested little impact, except the RRs for endometrial, kidney, and esophageal cancers were slightly increased, and the oropharyngeal and liver (European/other) RRs were decreased. CONCLUSIONS: Our large study confirms the strong association of smoking with many cancers and strengthens the evidence for protective associations with thyroid cancer and melanoma. With large data sets, considering and adjusting for residual systematic error is as important as quantifying random error.

Predicting high health-cost users among people with cardiovascular disease using machine learning and nationwide linked social administrative datasets.

OBJECTIVES: To optimise planning of public health services, the impact of high-cost users needs to be considered. However, most of the existing statistical models for costs do not include many clinical and social variables from administrative data that are associated with elevated health care resource use, and are increasingly available. This study aimed to use machine learning approaches and big data to predict high-cost users among people with cardiovascular disease (CVD). METHODS: We used nationally representative linked datasets in New Zealand to predict CVD prevalent cases with the most expensive cost belonging to the top quintiles by cost. We compared the performance of four popular machine learning models (L1-regularised logistic regression, classification trees, k-nearest neighbourhood (KNN) and random forest) with the traditional regression models. RESULTS: The machine learning models had far better accuracy in predicting high health-cost users compared with the logistic models. The harmony score F1 (combining sensitivity and positive predictive value) of the machine learning models ranged from 30.6% to 41.2% (compared with 8.6-9.1% for the logistic models). Previous health costs, income, age, chronic health conditions, deprivation, and receiving a social security benefit were among the most important predictors of the CVD high-cost users. CONCLUSIONS: This study provides additional evidence that machine learning can be used as a tool together with big data in health economics for identification of new risk factors and prediction of high-cost users with CVD. As such, machine learning may potentially assist with health services planning and preventive measures to improve population health while potentially saving healthcare costs.

Trends in penicillin dispensing during an acute rheumatic fever prevention programme.

AIM: Acute rheumatic fever (ARF), a serious inflammatory condition, often leads to rheumatic heart disease (RHD). Between 2011 and 2016, Aotearoa New Zealand implemented a rheumatic fever prevention programme (RFPP) to reduce high rates of ARF through improved community access to timely diagnosis and early treatment of group A streptococcal (GAS) pharyngitis, which has been shown to prevent subsequent ARF. This study aimed to quantify the change in penicillin antibiotic dispensing rates among children aged 18 years or younger during the RFPP. METHOD: This retrospective analysis utilised administrative data from the National Pharmaceutical Collection. Using a controlled, interrupted time series analysis, the effect of the RFPP on antibiotic dispensing rates was explored. Poisson regression models were used to assess the change in dispensing rates during the RFPP among control regions (those not in the RFPP) and regions participating in the RFPP. The primary measure was rate ratio (RR) for the difference between the observed versus counterfactual rates of penicillin dispensing. RESULT: A total of 12,154,872 dispensing records between 2005 and 2018 were included. Amoxicillin was the most frequently dispensed penicillin (57.7%), followed by amoxicillin-clavulanate (23.4%). Amoxicillin dispensing increased by 4.3% in regions operating the RFPP compared to the increase in control regions (p<0.001). The overall rate of penicillin dispensing decreased, driven by a rapid decline in amoxicillin-clavulanate dispensing. CONCLUSION: During the RFPP an increase in amoxicillin dispensing was seen in regions participating in the programme and regions outside of the programme, indicating the programmatic approach led to improved adherence to recommended first-line antibiotics.

Bias in relative survival methods when using incorrect life-tables: lung and bladder cancer by smoking status and ethnicity in New Zealand.

Relative survival and excess mortality approaches are commonly used to estimate and compare net survival from cancer. These approaches are based on the assumption that the underlying (non-cancer) mortality rate of cancer patients is the same as that of the general population. This assumption is likely to be violated particularly in the context of smoking-related cancers. The magnitude of this bias has not been estimated. The objective of this article is to estimate the bias in relative survival ratios (RSRs) and excess mortality rate ratios (EMRRs) from using total population compared to correct subpopulation specific life-tables. Analyses were conducted on 1996-2001 linked census-cancer data (including smoking status) for people with lung and bladder cancer, using sex-specific (standard practice), sex- and ethnic-specific, sex- and smoking-specific and sex-, ethnic- and smoking-specific life-tables. Five-year RSRs using sex-specific life-tables, compared to fully stratified life-tables, were underestimated by 10-25% for current smoking and Maori populations. For example, the current smoker male bladder cancer RSR was 0.700 for sex-specific life-tables, compared to 0.838 for fully stratified life-tables. Similarly, EMRRs comparing current to never smokers and Maori to non-Maori were overestimated using sex-specific life-tables only: modestly only for lung cancer, but markedly for bladder cancer. For example, the EMRR comparing current to never smokers with bladder cancer in a fully adjusted regression model was 1.475 when using sex-specific life-tables only, but reduced to 1.098 when using fully stratified life-tables. Substantial bias can occur when estimating relative cancer survival across subpopulations if non-matching life-tables are used.

High rates of endometrial cancer among Pacific women in New Zealand: the role of diabetes, physical inactivity, and obesity.

PURPOSE: To determine the endometrial cancer rates, and the proportion attributable to diabetes mellitus (DM), physical inactivity, and overweight/obesity, by ethnicity with a focus on Pacific women in New Zealand. METHODS: Linked census-cancer records (1981-2004) were used to determine incidence rates of endometrial cancer by ethnicity. Health survey data (2006-2007) were used to determine risk factor prevalence by ethnicity. Relative risks for the association between diabetes, obesity, physical inactivity and endometrial cancer were sourced from published studies. Population attributable risk (PAR) methods, with Monte Carlo simulation, were used to estimate the PAR% by ethnicity and applied to 2001-2004 cancer rates. RESULTS: Pacific women had 2.61 (95 % confidence interval 2.22-3.05) times the endometrial cancer rate of European/Other women pooled over time, and the most rapidly increasing rates over time with the rate ratio increasing from 1.96 (1.14-3.37) in 1981/1986 to 3.78 (3.03-4.71) in 2001/2004 (p for trend = 0.14). Pacific women had the highest PAR% for DM, physical inactivity, and overweight/obesity (63.1 %), followed by Maori (58.6 %) and European/Other (48.6 %). Applying these PAR% to 2001-2004 endometrial cancer rates, the rate ratio comparing Pacific to European/Other endometrial cancer reduced from 3.8 for total cancer (attributable plus non-attributable) to 2.3 for non-attributable cancer, and the rate difference reduced by 79 % from 51 to 11 per 100,000. CONCLUSIONS: Pacific women have high endometrial cancer rates in New Zealand. Some, but not all, of the ethnic inequalities were explained by measured differences in obesity/overweight, DM, and physical inactivity.

A decade of tobacco control efforts in New Zealand (1996-2006): impacts on inequalities in census-derived smoking prevalence.

INTRODUCTION: Smoking prevalence in New Zealand is highly related to socioeconomic disadvantage and ethnicity, with particularly high smoking prevalence among the Maori population. In the 10-year period from 1996 to 2006, a range of tobacco control activities were implemented in New Zealand. Uniquely in New Zealand, the national census has regularly included questions on smoking status. The purpose of this paper is to inform policy by examining the relationships between smoking prevalence and age, sex, socioeconomic position, and ethnicity, comparing data from the 1996 and 2006 national censuses. METHODS: Socioeconomic deprivation was measured using the year-specific NZDep index of socioeconomic deprivation for small areas, based on 9 variables from the relevant census. Ethnicity (Maori, Pacific, and European & Other) was assessed using self-definition. Smoking information from each census is stratified by age group, sex, NZDep, and ethnicity. RESULTS: The strong relationship between small-area socioeconomic deprivation and smoking prevalence remained unchanged in New Zealand over the decade 1996-2006. Smoking prevalence continued to be associated with Maori ethnicity independently of small-area socioeconomic deprivation. Smoking prevalence reduced modestly between 1996 and 2006 but increased in some age/sex/ethnic/deprivation groups. CONCLUSIONS: The findings of this analysis provide information to support the design and implementation of tobacco control policies in New Zealand over the next 10 years and suggest that current tobacco control policies need to be strengthened and additional, more carefully targeted, measures implemented.

Setting a good example? Changes in smoking prevalence among key occupational groups in New Zealand: evidence from the 1981 and 2006 censuses.

INTRODUCTION: Role modeling by smokers may influence smoking among children and young adults. Most work on smoking by occupation has focused on health workers. A unique opportunity to explore smoking by occupation is presented in New Zealand (NZ) due to inclusion of a smoking status question in most national censuses since 1976. Our aim was to assess trends in smoking prevalence among potential role model occupational groups in NZ. METHODS: Adult smoking status by occupation was obtained from the 1981 census (N = 1,321,323) and 2006 census (N = 1,744,479). Subjects were aggregated into 5 broad groups of potential role-model occupations: teachers, uniformed services, health-related occupations, public figures, and sportspeople/entertainers. Age and sex-standardized current smoking prevalences were calculated using the 2006 NZ employed population as the reference standard. RESULTS: Standardized smoking prevalence among the employed population was 34.5% in 1981 and had declined 37% in relative terms and 12.8% in absolute terms to 21.7% in 2006. Relative declines in smoking prevalence between 1981 and 2006 ranged from 35% to 60% among the role model occupational groups and absolute declines from 8.3% to 19.9%. Maori had higher smoking prevalence and lower relative declines in prevalence in each occupational group from 1981 to 2006, compared with non-Maori. Specific occupations mostly had low smoking prevalences--particularly doctors and teachers. But some role model occupations had high crude smoking prevalences in 2006 (up to 47%). CONCLUSIONS: Persisting high smoking prevalence among some occupational groups suggest that additional targeted smoking cessation support for role model occupational groups may be justified.

Where Are We Dying? Ethnic Differences in Place of Death Among New Zealanders Dying of Cancer.

PURPOSE: Around a third of people with cancer will die outside of their preferred place of death, with substantial variation occurring between and within countries in terms of place of death. Here, we examine place of death within the New Zealand cancer context, with specific focus on differences between Indigenous Maori and other ethnic groups. METHODS: Using national-level data, we identified all those who died in New Zealand between 2007 and 2018 of cancer (N = 107,373), stratified by ethnicity and cancer type, and linked these patients to national health and mortality records. We then described the crude and age-standardized proportions of cancer deaths by location separately by ethnic group, and conducted logistic regression to compare odds of death within a given location between ethnic groups. RESULTS: After adjusting for age, sex, and deprivation, we found that Maori people with cancer are more likely to die in a private residence than Europeans (46% v 26%; odds ratio [OR] 2.45; 95% CI, 2.36 to 2.55), and also somewhat more likely to die in hospital (27% v 23%; OR 1.26; 95% CI, 1.21 to 1.32). Commensurately, Maori are less likely to die in either hospice inpatient unit (14% v 27%; OR 0.48; 95% CI, 0.45 to 0.51) or residential care (12% v 30%; OR 0.56; 95% CI, 0.52 to 0.59). Pacific patients generally follow the same pattern as Maori patients. These findings were largely repeated across cancer types, with some variation in the magnitude not overall pattern. CONCLUSION: It remains unclear whether these differences reflect differences in preferences for place of death between ethnic groups, or whether they reflect differences in access to appropriate supportive care. Further research is required to examine these differences in greater detail.