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Background: Long COVID symptoms occur for a proportion of acute COVID-19 survivors, with reduced risk among the vaccinated and for Omicron compared with Delta variant infections. The health loss attributed to pre-Omicron long COVID has previously been estimated using only a few major symptoms., Methods: The years lived with disability (YLDs) due to long COVID in Australia during the 2021-22 Omicron BA.1/BA.2 wave were calculated using inputs from previously published case-control, cross-sectional or cohort studies examining the prevalence and duration of individual long COVID symptoms. This estimated health loss was compared with acute SARS-CoV-2 infection YLDs and years of life lost (YLLs) from SARS-CoV-2. The sum of these three components equals COVID-19 disability-adjusted life years (DALYs); this was compared with DALYs from other diseases., Results: A total of 5200 [95% uncertainty interval (UI) 2200-8300] YLDs were attributable to long COVID and 1800 (95% UI 1100-2600) to acute SARS-CoV-2 infection, suggesting long COVID caused 74% of the overall YLDs from SARS-CoV-2 infections in the BA.1/BA.2 wave. Total DALYs attributable to SARS-CoV-2 were 50 900 (95% UI 21 000-80 900), 2.4% of expected DALYs for all diseases in the same period., Conclusion: This study provides a comprehensive approach to estimating the morbidity due to long COVID. Improved data on long COVID symptoms will improve the accuracy of these estimates. As data accumulate on SARS-CoV-2 infection sequelae (e.g. increased cardiovascular disease rates), total health loss is likely to be higher than estimated in this study. Nevertheless, this study demonstrates that long COVID requires consideration in pandemic policy planning, given it is responsible for the majority of direct SARS-CoV-2 morbidity, including during an Omicron wave in a highly vaccinated population., (© The Author(s) 2023. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Measuring population health and costs effects of liberalizing access to electronic nicotine delivery systems (ENDS) is an evolving field with high persisting uncertainty. A critical area of uncertainty for policy-makers are estimates of net harms from ENDS relative to cigarettes, therefore, we model these harms using updated estimates incorporating disease specificity., Methods: We use updated estimates of relative harm of vaping vs smoking, based upon relevant biomarker studies to model the impact of liberalizing access to ENDS in New Zealand (NZ), relative to a ban (where ENDS are not legally available), in an existing proportional multi-state life-table model of 16 tobacco-related diseases., Results: This modeling suggests that ENDS liberalization results in an expected gain of 195 000 quality-adjusted life-years (QALYs) over the remainder of the NZ population's lifespan. There was wide uncertainty in QALYs gained (95% uncertainty interval [UI] = -8000 to 406 000) with a 3.2% probability of net health loss (based upon the number of simulation runs returning positive QALY gains). The average per capita health gain was 0.044 QALYs (equivalent to an extra 16 days of healthy life). Health system cost-savings were expected to be NZ$2.8 billion (US$2.1 billion in 2020 US$; 95%UI: -0.3 to 6.2 billion [2011 NZ$]), with an estimated 3% chance of a net increase in per capita cost., Conclusions: This updated modeling around liberalizing ENDs in NZ, still suggests likely net health and cost-saving benefits-but of lesser magnitude than previous work and with a small possibility of net harm to population health., Implications: This study found evidence using updated biomarker studies that ENDS liberalization could result in QALY gains across the New Zealand population lifespan that are also cost-saving to the health system. Governments should include the information from these types of modeling studies in their decision-making around potentially improving access to ENDS for existing smokers, while at the same further reducing access to tobacco., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Many studies have reported an inferred causal association of income poverty with physical health among children; but making causal inference is challenging due to multiple potential sources of systematic error. We quantified the short-run effect of changes in household poverty status on children's health (asthma and ear infections) and service use (visits to the doctor and parent-reported hospital admissions), using a national longitudinal study of Australian children, with particular attention to potential residual confounding and selection bias due to study attrition., Methods: We use four modelling approaches differing in their capacity to reduce residual confounding (generalized linear, random effects (RE), hybrid and fixed effects (FE) regression modelling) to model the effect of income poverty (<60% of median income) on health for 10 090 children surveyed every 2nd year since 2004. For each method, we simulate the potential impact of selection bias arising due to attrition related to children's health status., Results: Of the 10 090 children included, 20% were in families in poverty at survey baseline (2004). Across subsequent years, ∼25% experienced intermittent and <2% persistent poverty. No substantial associations between poverty and child physical health and service use were observed in the FE models least prone to residual confounding [odds ratio (OR) 0.94, 95% confidence interval (CI) 0.81-1.10 for wheeze], in contrast to RE models that were positive (consistent with previous studies). Selection bias causing null findings was unlikely., Conclusions: While poverty has deleterious causal effects on children's socio-behavioural and educational outcomes, we find little evidence of a short-run causal effect of poverty on asthma, ear infections and health service use in Australia., (© The Author(s) 2021; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential outcomes approaches, often include processing steps before the final estimation step. The purposes of this paper are: (i) to overview the recent emergence of prediction underpinning steps in contemporary causal inference methods as a useful perspective on contemporary causal inference methods, and (ii) explore the role of machine learning (as one approach to 'best prediction') in causal inference. Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum likelihood estimation (TMLE). Machine learning has been used more for propensity scores and TMLE, and there is potential for increased use in G computation and estimation of IPTWs., (© The Author(s) 2019; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Burden of Disease studies-such as the Global Burden of Disease (GBD) Study-quantify health loss in disability-adjusted life-years. However, these studies stop short of quantifying the future impact of interventions that shift risk factor distributions, allowing for trends and time lags. This methodology paper explains how proportional multistate lifetable (PMSLT) modelling quantifies intervention impacts, using comparisons between three tobacco control case studies [eradication of tobacco, tobacco-free generation i.e. the age at which tobacco can be legally purchased is lifted by 1 year of age for each calendar year) and tobacco tax]. We also illustrate the importance of epidemiological specification of business-as-usual in the comparator arm that the intervention acts on, by demonstrating variations in simulated health gains when incorrectly: (i) assuming no decreasing trend in tobacco prevalence; and (ii) not including time lags from quitting tobacco to changing disease incidence. In conjunction with increasing availability of baseline and forecast demographic and epidemiological data, PMSLT modelling is well suited to future multiple country comparisons to better inform national, regional and global prioritization of preventive interventions. To facilitate use of PMSLT, we introduce a Python-based modelling framework and associated tools that facilitate the construction, calibration and analysis of PMSLT models., (© The Author(s) 2020; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: National linked mortality and census data have not previously been available for Australia. We estimated education-based mortality inequalities from linked census and mortality data that are suitable for international comparisons., Methods: We used the Australian Bureau of Statistics Death Registrations to Census file, with data on deaths (2011-2012) linked probabilistically to census data (linkage rate 81%). To assess validity, we compared mortality rates by age group (25-44, 45-64, 65-84 years), sex and area-inequality measures to those based on complete death registration data. We used negative binomial regression to quantify inequalities in all-cause mortality in relation to five levels of education ['Bachelor degree or higher' (highest) to 'no Year 12 and no post-secondary qualification' (lowest)], separately by sex and age group, adjusting for single year of age and correcting for linkage bias and missing education data., Results: Mortality rates and area-based inequality estimates were comparable to published national estimates. Men aged 25-84 years with the lowest education had age-adjusted mortality rates 2.20 [95% confidence interval (CI): 2.08‒2.33] times those of men with the highest education. Among women, the rate ratio was 1.64 (1.55‒1.74). Rate ratios were 3.87 (3.38‒4.44) in men and 2.57 (2.15‒3.07) in women aged 25-44 years, decreasing to 1.68 (1.60‒1.76) in men and 1.44 (1.36‒1.53) in women aged 65-84 years. Absolute education inequalities increased with age. One in three to four deaths (31%) was associated with less than Bachelor level education., Conclusions: These linked national data enabled valid estimates of education inequality in mortality suitable for international comparisons. The magnitude of relative inequality is substantial and similar to that reported for other high-income countries., (© The Author(s) 2019. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Increasing active transport is proposed as a means to address both health and environmental issues. However, the associations between specific modes, such as cycling, walking and public transport, and health outcomes remain unclear. We examined the association between mode of travel to work and mortality., Methods: Cohort studies of the entire New Zealand working population were created using 1996, 2001 and 2006 censuses linked to mortality data. Mode of travel to work was that reported on census day, and causes of death examined were ischaemic heart disease and injury. Main analyses were Poisson regression models adjusted for socio-demographics. Sensitivity analyses included: additional adjustment for smoking in the 1996 and 2006 cohorts, and bias analysis about non-differential misclassification of cycling vs car use., Results: Walking (5%) and cycling (3%) to work were uncommon. Compared with people reporting using motor vehicles to travel to work, those cycling had a reduced all-cause mortality (ACM) in the socio-demographic adjusted models RR 0.87 (0.77-0.98). Those walking (0.97, 0.90-1.04) and taking public transport (0.96, 0.88-1.05) had no substantive difference in ACM. No mode of transport was associated with detectable statistically significant reductions in cause-specific mortality. Sensitivity analyses found weaker associations when adjusting for smoking and stronger associations correcting for likely non-differential misclassification of cycling., Conclusions: This large cohort study supports an association between cycling to work and reduced ACM, but found no association for walking or public-transport use and imprecise cause-specific mortality patterns., (© The Author(s) 2020; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Background: Young people with low education have worse health than those with higher education. This paper examined the extent to which employment and income reduced the adverse effects of low education on mental health among people aged 20-35 years., Methods: We used causal mediation analyses to estimate the total causal effect (TCE) of low education on mental health and to decompose the effect into the natural direct effect (NDE) and the natural indirect effect (NIE) through two mediators examined sequentially: employment (labour-force participation/occupation skill level) and income. Three waves of the Household, Income and Labour Dynamics in Australia (HILDA) survey (2012-14) were used to establish a temporal sequence between low education (not completing high school), mediators and mental health [the Mental Health Inventory (MHI-5)] among participants aged 20-35 years. Among those who were employed, we conducted further analyses examining the effect of job characteristics as a mediator of the relationship between low education and mental health., Results: The TCE of low education on the MHI-5 was -3.61 [95% confidence interval (CI) -5.30 to -1.92]. The NIE through labour force status and occupational skill level was -1.09 (95% CI -2.29 to 0.10) and -1.49 (95% CI -2.79 to -0.19) through both labour-force status/occupational skill level and income, corresponding to a percentage mediated of 41%. Among the employed, education had a much smaller effect on the MHI-5., Conclusions: Improving employment opportunities could reduce nearly half of the adverse effects of low education on the mental health of young people.

Background: Disability among adolescents is associated with both poorer mental health (MH) and higher levels of bullying-victimization. Bullying, therefore, conceivably mediates the association between disability and MH. Quantifying this pathway is challenging as the exposure (disability), mediator (bullying) and outcome (MH) are subjective, and subject to dependent measurement error if the same respondent reports on two or more variables., Methods: Utilizing the counterfactual and potential outcomes approaches to causal mediation, we decomposed the total effect of disability on MH into natural indirect effects (through bullying) and natural direct effects (not through bullying) using a sample of 3409 adolescents. As the study included data from multiple informants (teacher, parent, adolescent) on the outcome (MH, as measured on the Strengths and Difficulties Questionnaire) and two informants (adolescent, parent) on the mediator (bullying), we assessed the influence of dependent measurement error., Results: For preferred analysis (using parent-reported bullying and adolescent-reported MH), the total effect was a 2.18 [95% confidence interval (CI): 0.66-3.40] lower MH score for adolescents with a disability, compared with those with no disability (strength of association equivalent to 37% of the standard deviation for MH). Bullying explained 46% of the total effect. Use of adolescent-reported bullying with adolescent-reported MH produced similar results (37% mediation, 95% CI: 12-74%)., Conclusions: Disability exerts a detrimental effect on adolescent MH, and a large proportion of this appears to operate through bullying. This finding does not appear to be spurious due to dependent measurement error.

Background: Social housing may provide an affordable and secure residential environment, but has also been associated with stigma, poor housing conditions and locational disadvantage. We examined the cumulative effect of additional years, and tenure security (number of transitions in/out), of social housing on mental health in a large cohort of lower-income Australians., Methods: We analysed a longitudinal panel survey that annually collected information on tenure and health from 2001 to 2013. To address the time-varying effect of previous health on social housing occupancy, we used marginal structural models. Stabilized inverse probabilities of treatment weights were generated using ensemble learning to improve prediction. To address remaining residual imbalance across covariates, double adjustment was made by additionally including baseline covariates in models. Mental health was measured using the Mental Health Short-Form summary measure of the SF-36 (MH), and psychological distress was measured using the Kessler Psychological Distress Scale (K10)., Results: People who had continuous exposure to social housing had worse mental health on average than people continuously occupying other tenures. The worst mental health outcomes, however, were observed for people who made multiple transitions. Mental health deteriorated and psychological distress increased with number of transitions: MH -1.04 [95% confidence interval (CI) -2.16; 0.09) and K10 0.56 (95% CI 0.12; 1.00). Estimates are in the order of 6% (MH) and 9% (K10) of one standard deviation for each measure., Conclusions: The more transitions people made in/out of social housing, the greater the impact on mental health and psychological distress, supporting the case for provision of more stable forms of social housing.

Objectives: To estimate the multi-dimensional health impact of radiographic knee OA and quantify the overall health-related quality of life (HRQoL) burden, using a preference-based health utilities measure., Methods: Data on self-reported HRQoL, measured using the SF-12 multi-dimensional health state instrument, were obtained for 2895 patients with radiographic knee OA (Kellgren-Lawrence grade of at least 2) from the Osteoarthritis Initiative and for a general population sample of 3202 from the National Health Measurement Study. The SF-12 was converted to the six-dimensional SF-6D classification to compute preference-based health utilities. Generalized ordinal regression and multinomial regression were used to estimate the health loss on each SF-6D dimension for Osteoarthritis Initiative participants with radiographic knee OA relative to the general population, adjusted for differences in age, BMI, sex, ethnicity and educational level. Predicted SF-6D profiles were then used to compute the average HRQoL loss attributable to radiographic knee OA., Results: Radiographic knee OA was associated with substantial health losses on all dimensions of the SF-6D except for social functioning. Health losses increased with the radiographic severity of OA in dimensions related to physical health, while there was no relationship between worse radiographic disease and worse self-assessed health in mental and emotional dimensions of health. Overall, radiographic knee OA was associated with a HRQoL detriment of 0.040-0.044 at Kellgren-Lawrence grade 2, increasing to 0.045-0.050 at grade 3 and 0.073-0.081 at grade 4., Conclusion: Radiographic knee OA is significantly associated with worse HRQoL across most dimensions of health.

In this issue of the Journal, Reeves et al. (Am J Epidemiol. 2016;184(6):421-429) present the findings of a natural experiment analyzing the association between reduced housing affordability and mental ill health. Their difference-in-difference analysis of cross-sectional, quarterly population health surveys administered before and after implementation of a policy to reduce Housing Benefit payments in the United Kingdom in April 2011 represents an important way to assess the impact of a national housing policy shift on public health. It is a well-conducted study harnessing a natural experiment and adds to the weight of evidence supporting an association between housing costs and mental health. However, quantitative bias analysis based on the reported findings suggests that a small amount of differential (by unblinded Housing Benefit status) misclassification bias in the outcome may be enough to explain the observed association. Our analysis of possible misclassification bias in the outcome used in the study highlights the need for caution when a difference-in-difference estimate is small, the population is not blinded to its postintervention exposure status, and the outcome measure is subjective and prone to differential (by unblinded exposure or treatment status) misclassification., (© The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

In previous studies, researchers estimated short-term relationships between financial credits and health outcomes using conventional regression analyses, but they did not account for time-varying confounders affected by prior treatment (CAPTs) or the credits' cumulative impacts over time. In this study, we examined the association between total number of years of receiving New Zealand's Family Tax Credit (FTC) and self-rated health (SRH) in 6,900 working-age parents using 7 waves of New Zealand longitudinal data (2002-2009). We conducted conventional linear regression analyses, both unadjusted and adjusted for time-invariant and time-varying confounders measured at baseline, and fitted marginal structural models (MSMs) that more fully adjusted for confounders, including CAPTs. Of all participants, 5.1%-6.8% received the FTC for 1-3 years and 1.8%-3.6% for 4-7 years. In unadjusted and adjusted conventional regression analyses, each additional year of receiving the FTC was associated with 0.033 (95% confidence interval (CI): -0.047, -0.019) and 0.026 (95% CI: -0.041, -0.010) units worse SRH (on a 5-unit scale). In the MSMs, the average causal treatment effect also reflected a small decrease in SRH (unstabilized weights: β = -0.039 unit, 95% CI: -0.058, -0.020; stabilized weights: β = -0.031 unit, 95% CI: -0.050, -0.007). Cumulatively receiving the FTC marginally reduced SRH. Conventional regression analyses and MSMs produced similar estimates, suggesting little bias from CAPTs., (© The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Health behavior takes place within social contexts. In this study, we investigated whether changes in exposure to neighborhood deprivation and smoking prevalence and to household smoking were associated with change in personal smoking behavior. Three waves of biannual data collection (2004-2009) in a New Zealand longitudinal study, the Survey of Family, Income and Employment (SoFIE)-Health, were used, with 13,815 adults (persons aged ≥15 years) contributing to the analyses. Smoking status was dichotomized as current smoking versus never/ex-smoking. Fixed-effects regression analyses removed time-invariant confounding and adjusted for time-varying covariates (neighborhood smoking prevalence and deprivation, household smoking, labor force status, income, household tenure, and family status). A between-wave decile increase in neighborhood deprivation was significantly associated with increased odds of smoking (odds ratio (OR) = 1.08, 95% confidence interval (CI): 1.02, 1.14), but a between-wave increase in neighborhood smoking prevalence was not (OR = 1.04, 95% CI: 0.98, 1.10). Changing household exposures between waves to live with another smoker (compared with a nonsmoker (referent)) increased the odds of smoking (OR = 2.48, 95% CI: 1.84, 3.34), as did changing to living in a sole-adult household (OR = 1.52, 95% CI: 1.07, 2.14). Tobacco control policies and programs should address the broader household and neighborhood circumstances within which individual smoking takes place., (© The Author 2015. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

We investigated whether being in temporary employment, as compared with permanent employment, was associated with a difference in Short Form 36 mental health and whether transitions from permanent employment to temporary employment were associated with mental health changes. We used fixed-effects regression in a nationally representative Australian sample with 10 waves of data collection (2001-2010). Interactions by age and sex were tested. Two forms of temporary employment were studied: "casual" (no paid leave entitlements or fixed hours) and "fixed-term contract" (a defined employment period plus paid leave). There were no significant mental health differences between temporary employment and permanent employment in standard fixed-effects analyses and no significant interactions by sex or age. For all age groups combined, there were no significant changes in mental health following transitions from stable permanent employment to temporary employment, but there was a significant interaction with age (P = 0.03) for the stable-permanent-to-casual employment transition, because of a small transition-associated improvement in mental health for workers aged 55-64 years (β = 1.61, 95% confidence interval: 0.34, 2.87; 16% of the standard deviation of mental health scores). Our analyses suggest that temporary employment is not harmful to mental health in the Australian context and that it may be beneficial for 55- to 64-year-olds transitioning from stable permanent employment to casual employment., (© The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The analysis of repeated measures or panel data allows control of some of the biases which plague other observational studies, particularly unmeasured confounding. When this bias is suspected, and the research question is: 'Does a change in an exposure cause a change in the outcome?', a fixed effects approach can reduce the impact of confounding by time-invariant factors, such as the unmeasured characteristics of individuals. Epidemiologists familiar with using mixed models may initially presume that specifying a random effect (intercept) for every individual in the study is an appropriate method. However, this method uses information from both the within-individual/unit exposure-outcome association and the between-individual/unit exposure-outcome association. Variation between individuals may introduce confounding bias into mixed model estimates, if unmeasured time-invariant factors are associated with both the exposure and the outcome. Fixed effects estimators rely only on variation within individuals and hence are not affected by confounding from unmeasured time-invariant factors. The reduction in bias using a fixed effects model may come at the expense of precision, particularly if there is little change in exposures over time. Neither fixed effects nor mixed models control for unmeasured time-varying confounding or reverse causation.

Objective: To compare the quality of hospital care for New Zealand (NZ) Māori and NZ European adult patients, using the rate of unplanned readmission or death within 30 days of discharge as an indicator of quality., Design: Retrospective cohort study., Setting: NZ public hospitals., Participants: Data from 89 658 patients who were admitted for one of a defined set of surgical procedures at NZ public hospitals 2002-8 were obtained from the NZ Ministry of Health. Outcome The odds of readmission for NZ Māori when compared with NZ European patients were calculated using logistic regression, incorporating variables for age, sex, comorbidity, index procedure, hospital volume and socioeconomic position., Results: NZ Māori had 16% higher odds of readmission or death when compared with NZ European patients (OR = 1.16; 95% CI 1.08-1.24) after adjusting for all covariates. Readmission or death was also associated with being female (OR = 1.09; 1.03-1.15), older age (OR = 1.33; 1.19-1.48, for >79 years compared with 18-39 years), higher comorbidity (OR = 2.08; 1.89-2.31, for Charlson score 3+ compared with 0) and higher hospital volume (OR = 0.81; 0.76-0.86, for lowest volume compared with highest)., Conclusions: This study suggests ethnic disparities in the quality of hospital care in NZ using unplanned readmission rate as an indicator of quality. There are well-documented differences in health outcomes between Māori and NZ Europeans, and it is possible that differential treatment within the health system contributes to these health status inequalities.

Background: This article on the state of epidemiology in the WHO Western Pacific Region (WPR) is the first in a series of eight articles commissioned by the International Epidemiological Association (IEA) to identify global opportunities to promote the development of epidemiology., Methods: Global mortality and disease data were used to summarize the burden of mortality, disease, risk factor and patterns of inequalities in the region. Medline bibliometrics were used to estimate epidemiological publication output by country. Key informant surveys, Internet and literature searches and author knowledge and networks were used to elicit perspectives on epidemiological training, research, funding and workforce. Findings The WPR has the lowest age-standardized disability-adjusted life-years (DALY) rate per 1000 of the six WHO regions, with non-communicable disease making the largest percentage contributions in both low- and middle-income countries (LMICs, 68%) and high-income countries (HICs, 84%) in the WPR. The number of Medline-indexed epidemiological research publications per year was greatest for Japan, Australia and China. However, the rate per head of population was greatest for Micronesia and New Zealand. The substantive focus of research roughly equated with burden of disease patterns. Research capacity (staff, funding, infrastructure) varies hugely between countries. Epidemiology training embedded within academic Masters of Public Health programmes is the dominant vehicle for training in most countries. Field epidemiology and in-service training are also common. The Pacific Island countries and territories, because of sparse populations over large distances and chronic workforce and funding capacity problems, rely on outside agencies (e.g. WHO, universities) for provision of training. Cross-national networks and collaborations are increasing., Conclusion: Communicable disease surveillance and research need consolidation (especially in eastern Asian WPR countries), and non-communicable disease epidemiological capacity requires strengthening to match disease trends. Capacity and sustainability of both training and research within LMICs in WPR are ongoing priorities. China in particular is advancing quickly. One role for the IEA in building capacity is facilitating collaborative networks within WPR.

Background: Two main methods of quantifying cancer patient survival are generally used: cancer-specific survival and relative survival. Both techniques are used to estimate survival in a single population, or to estimate differences in survival between populations. Arguments have been made that the relative survival approach is the only valid choice for population-based cancer survival studies because cancer-specific survival estimates may be invalid if there is misclassification of the cause of death. However, there has been little discussion, or evidence, as to how strong such biases may be, or of the potential biases that may result using relative survival techniques, particularly bias arising from the requirement for an external comparison group., Methods: In this article we investigate the assumptions underlying both methods of survival analysis. We provide simulations relating to the impact of misclassification of death and non-comparability of expected survival for cause-specific and relative survival approaches, respectively., Results: For cause-specific analyses, bias through misclassification of cause of death resulted in error in descriptive analyses particularly of cancers with moderate or poor survival, but had smaller impact in analyses involving group comparisons. Relative survival ratio (RSR) estimations were robust in relation to non-comparability of comparison populations for single RSR but were less so in group comparisons where there was large variation in survival., Conclusions: Both cause-specific survival and relative survival are potentially valid epidemiological methods in population-based cancer survival studies, and the choice of method is dependent on the likely magnitude and direction of the biases in the specific analyses to be conducted.

Background: We describe trends from 1951 to 2006 in inequalities in mortality between the indigenous (Māori) and non-indigenous (non-Māori, mainly European-descended) populations of New Zealand. We relate these trends to the historical context in which they occurred, including major structural adjustment of the economy from the mid 1980s to the mid 1990s, followed by a retreat from neoliberal social and economic policies from the late 1990s onwards. This was accompanied by economic recovery and the introduction of health reforms, including a reorientation of the health system towards primary health care., Methods: Abridged period lifetables for Māori and non-Māori from 1951 to 2006 were constructed using standard demographic methods. Absolute [standardized rate difference (SRD)] and relative [standardized rate ratio (SRR)] mortality inequalities for Māori compared with European/Other ethnic groups (aged 1-74 years) were measured using the New Zealand Census-Mortality Study (an ongoing data linkage study that links mortality to census records) from 1981-84 to 2001-04. The SRDs were decomposed into their contributions from major causes of death. Poisson regression modelling was used to estimate the extent of socio-economic mediation of the ethnic mortality inequality over time., Results: Life expectancy gaps and relative inequalities in mortality rates (aged 1-74 years) widened and then narrowed again, in tandem with the trends in social inequalities (allowing for a short lag). Among females, the contribution of cardiovascular disease to absolute mortality inequalities steadily decreased, but was partly offset by an increasing contribution from cancer. Among males, the contribution of CVD increased from the early 1980s to the 1990s, then decreased again. The extent of socio-economic mediation of the ethnic mortality inequality peaked in 1991-94, again more notably among males., Conclusion: Our results are consistent with a causal association between changing economic inequalities and changing health inequalities between ethnic groups. However, causality cannot be established from a historical analysis alone. Three lessons nevertheless emerge from the New Zealand experience: the lag between changes in ethnic social inequality and ethnic health inequality may be short (<5 years); both changes in the distribution of the social determinants of health and an appropriate health system response may be required to address ethnic health inequalities; and timely monitoring of ethnic health inequalities, based on high-quality ethnicity data, may help to sustain political commitment to pro-equity health and social policies.

Background: Recent work in a number of countries has identified growing geographical inequalities in health between deprived and non-deprived neighbourhoods. The health gaps observed cannot be entirely explained by differences in the characteristics of individuals living in those neighbourhoods, which has led to a concerted international public health research effort to determine what contextual features of neighbourhoods matter. This article reports on access to potentially health-promoting community resources across all neighbourhoods in New Zealand. Prevailing international opinion is that access to community resources is worse in deprived neighbourhoods., Methods: Geographical Information Systems were used to calculate geographical access to 16 types of community resources (including recreational amenities, and shopping, educational and health facilities) in 38,350 small census areas across the country. The distribution of these access measures by neighbourhood socioeconomic deprivation was determined., Results: For 15 out of 16 measures of community resources, access was clearly better in more deprived neighbourhoods. For example, the travel time to large supermarkets was approximately 80% greater in the least deprived quintile of neighbourhoods compared with the most deprived quintile., Conclusions: These results challenge the widely held, but largely untested, view that areas of high social disadvantage have poorer access to community resources. Poor locational access to community resources among deprived neighbourhoods in New Zealand does not appear to be an explanation of poorer health in these neighbourhoods. If anything, a pro-equity distribution of community resources may be preventing even wider disparities in neighbourhood inequalities in health.

Evidence is growing that secondhand smoke can cause death from several diseases. The association between household exposure to secondhand smoke and disease-specific mortality was examined in two New Zealand cohorts of lifelong nonsmokers ("never smokers") aged 45-77 years. Individual census records from 1981 and 1996 were anonymously and probabilistically linked with mortality records from the 3 years that followed each census. Age- and ethnicity-standardized mortality rates were compared for never smokers with and without home exposure to secondhand smoke (based on the reported smoking behavior of other household members). Relative risk estimates adjusted for age, ethnicity, marital status, and socioeconomic position showed a significantly greater mortality risk for never smokers living in households with smokers, with excess mortality attributed to tobacco-related diseases, particularly ischemic heart disease and cerebrovascular disease, but not lung cancer. Adjusted relative risk estimates for all cardiovascular diseases were 1.19 (95% confidence interval: 1.04, 1.38) for men and 1.01 (95% confidence interval: 0.88, 1.16) for women from the 1981-1984 cohort, and 1.25 (95% confidence interval: 1.06, 1.47) for men and 1.35 (95% confidence interval: 1.11, 1.64) for women from the 1996-1999 cohort. Passive smokers also had nonsignificantly increased mortality from respiratory disease. Sensitivity analyses indicate that these findings are not due to misclassification bias.

To review research evidence on the effectiveness of monetary incentives in modifying dietary behavior, we conducted a systematic review of randomized, controlled trials (RCTs) identified from electronic bibliographic databases and reference lists of retrieved relevant articles. Studies eligible for inclusion met the following criteria: RCT comparing a form of monetary incentive with a comparative intervention or control; incentives were a central component of the study intervention and their effect was able to be disaggregated from other intervention components; study participants were community-based; and outcome variables included anthropometric or dietary assessment measures. Data were extracted on study populations, setting, interventions, outcome variables, trial duration, and follow-up. Appraisal of trial methodological quality was undertaken based on comparability of baseline characteristics, randomization method, allocation concealment, blinding, follow-up, and use of intention-to-treat analysis. Four RCTs were identified as meeting the inclusion criteria. All four trials demonstrated a positive effect of monetary incentives on food purchases, food consumption, or weight loss. However, the trials had some methodological limitations including small sample sizes and short durations. In addition, no studies to date have assessed effects according to socioeconomic or ethnic group or measured the cost-effectiveness of such schemes. Monetary incentives are a promising strategy to modify dietary behavior, but more research is needed to address the gaps in evidence. In particular, larger, long-term RCTs are needed with population groups at high risk of nutrition-related diseases.

Background: The association of social capital with health and mortality is contentious, and empirical findings are inconsistent. This study tests the association of neighbourhood-level volunteerism with mortality., Methods: Cohort study of 1996 New Zealand census respondents aged 25-74 years (4.75 million person years) using multilevel Poisson regression analyses. Neighbourhood (average population 2,034) measures included indices of social capital (volunteering activities for all census respondents) and deprivation., Results: Adjusting for just age and marital status, the mortality rate ratios for people living in the quintile of neighbourhoods with the lowest compared with highest volunteerism were 1.16 (95% confidence interval 1.08-1.24) and 1.09 (1.01-1.18), for males and females, respectively. Adjusting for potential individual-level and neighbourhood-level socioeconomic confounders reduced the rate ratios to 0.94 (0.88-1.01) and 0.92 (0.85-1.01), respectively. There was no significant association with any cause of death, including suicide [rate ratios 0.89 (0.64-1.22) and 0.57 (0.31-1.05), respectively]. Restricting the analyses to only those census respondents living at their census night address for five or more years, and therefore 'exposed' to that level of volunteerism for a longer period, did not substantially alter findings., Conclusions: This study, one of the largest multilevel studies yet, found no statistically significant independent association of a structural measure of neighbourhood social capital with mortality-including suicide. Assuming social features of neighbourhoods are important determinants of health, future research should examine other features (e.g. social fragmentation) and other outcomes (e.g. behaviour).

Ethnic variation in mortality and whether this variation can be explained by socioeconomic status are of substantive interest to social epidemiologists. The authors consider the analysis of mortality data for a mixture of majority and minority ethnic groups. Such data are likely to be coarsely cross-classified by age and socioeconomic status and yet, even then, in some cells of this cross-classification the observed mortality rate will be an imprecise estimate of the underlying rate. The authors illustrate conventional and Bayesian approaches to analysis with data from the 1996 census used by the New Zealand Census-Mortality Study. A conventional approach is exploratory data analysis first followed by Poisson regression. The authors use spline smoothing within a generalized additive model framework as an exploratory data analysis, following a strategy of adding just enough model structure to gain a sensible picture. A Bayesian approach is modeling first and then a description of posterior estimates using exploratory data analysis techniques. The authors use hierarchical Poisson regression and then illustrate their posterior estimates of the mortality rate using the same spline smoothing as before. The advantage of the hierarchical Bayesian approach is that it assesses uncertainty about a Poisson regression model proposed a priori; the conventional approach assumes that the fitted Poisson regression model is correct. All analyses use software that is available at no cost.

Background: The contributions of tobacco smoking to overall mortality and socioeconomic inequalities in mortality vary between populations and over time. We determined how these contributions varied by sex and over time in two national New Zealand cohort studies., Methods: Poisson regression and modelling were conducted on linked census-mortality cohorts for people aged 45-74 years in 1981-84 and 1996-99 (2.0 and 2.7 million person-years, respectively)., Results: Contribution to socioeconomic inequalities in mortality. Adjusting for current and former smoking reduced the all-cause mortality rate ratios for men with nil educational qualifications compared with men with post-school qualifications from 1.34 to 1.29 in 1981-84 and from 1.31 to 1.25 in 1996-99, or 16 and 21% reductions in relative inequalities. Equivalent results for women were 1.42-1.41 in 1981-84 and 1.42-1.37 in 1996-99, or 3 and 11% reductions in relative inequalities. Contribution to overall mortality. Using 1996-99 data, we estimated that if all current smokers quit and became ex-smokers, mortality rates would reduce by 11% for men and 5% for women. If everyone was a never smoker (i.e. a historically smoke-free society), mortality rates would have been 26% lower for men and 25% lower for women., Conclusions: The contribution of smoking to educational inequalities in mortality was greater for males, and increased over time for both males and females, reflecting the historically differential phasing of the tobacco epidemic by sex and socioeconomic position. Complete cessation of smoking in contemporary New Zealand would reduce both overall mortality and educational inequalities in mortality.

Background: The strength of the smoking-mortality association may vary over time and by ethnic group., Methods: Cohort studies of 1.6 million (1981-84) and 1.9 million (1996-99) New Zealanders aged 25-74 years were formed by the linkage of census and mortality data. Comparing current smokers with never smokers, standardized rate ratios (RRs) and rate differences (RDs) were calculated for all-cause and ischaemic heart disease (IHD) mortality., Results: Between 1981-84 and 1996-99 the all-cause mortality RR increased from 1.59 (95% CI 1.53-1.66) to 2.05 (1.97-2.14) for men and from 1.49 (1.42-1.56) to 2.01 (1.91-2.12) for women. All-cause RRs were significantly greater among non-Ma-ori non-Pacific than Ma-ori: 2.22 (2.12-2.33) compared with 1.51 (1.35-1.69) in men and 2.20 (2.09-2.33) compared with 1.45 in women (1.27-1.66), respectively, in 1996-99. This RR heterogeneity remained after adjusting for socio-economic factors and was similar for IHD. The RDs demonstrated less heterogeneity. For example, in 1996-99 the RDs were 627 per 100,000 (452-802) for Ma-ori compared with 464 (427-502) for non-Ma-ori non-Pacific among men, and 368 (228-509) compared with 340 (311-370) among women., Conclusions: In New Zealand the relative effect of smoking on mortality differs over time and by ethnicity. We expect that such heterogeneity exists in other countries where the background mortality rates vary over time or between social groups. Information on this heterogeneity, including ethnicity-specific data, is needed to accurately determine the mortality burden owing to tobacco. The size of the RR estimates should be interpreted in the context of absolute mortality and effect measures.

Objective: To determine the shape of the income-mortality association, before and after adjusting for confounding by other socioeconomic variables., Methods: Poisson regression analyses were conducted on 11.7 million years of follow-up of 25-59 year old New Zealand census respondents spanning four separate cohort studies (1981-1984, 1986-1989, 1991-1994, and 1996-1999)., Results: Mortality among low-income people was approximately two times that among high-income people. Adjustment for potential socioeconomic confounders (marital status, education, car access, and neighbourhood socioeconomic deprivation) halved the strength of the income-mortality association, but did not appreciably change the shape of the association. Further adjustment for labour force status largely removed the income-mortality association. The association of non-transformed income with mortality was non-linear, with a flattening out of the slope at higher incomes. Both the logarithm and rank of income appeared to have a better linear fit with the mortality rate, although the association of mortality with the logarithm of income flattened out notably at low incomes., Conclusions: Much, but not all, of the crude association of income with mortality could be due to confounding. Adjusting income-mortality associations for labour force status (also a proxy for health status) is problematic: on the one hand, it over-adjusts the association as poor health will be on the pathway from income to mortality; on the other hand, it appropriately adjusts for both confounding by labour force status and reverse causation whereby income changes as a result of poor health. Both logarithmic and rank transformations of income have a reasonable linear fit with income.

Background: Although the association between child mortality and socioeconomic status is well established, it is unclear whether child mortality differences by socioeconomic position are present at all ages. The association of one-parent families with mortality, and whether any such association is due to associated low socioeconomic position, is also not clear., Methods: In all, 480 of 693 (69%) 0-14 year old deaths during 1991-1994 were linked to 1991 census records. Analyses were weighted to adjust for potential linkage bias., Results: There was approximately twofold higher mortality among the lowest compared with the highest socioeconomic categories of education, income, car access, and neighbourhood deprivation. Occupational class differences were weaker. These socioeconomic differences in mortality were strongest among infants (particularly sudden infant death syndrome [SIDS] mortality), but similar across other age groups (1-4, 5-9, and 10-14 years). The socioeconomic differences were of a similar magnitude for unintentional injury, cancer, congenital, and other deaths. Multivariable analyses demonstrated persistent independent associations of education, income, car access, and neighbourhood deprivation with mortality. Rate ratios (adjusted for age and ethnicity) for one-parent families compared with two-parent or other families were 1.2 (95% CI: 1.0, 1.5) and 1.8 (95% CI: 1.2, 2.5) for all-cause and unintentional injury mortality, respectively. Further adjustment for socioeconomic factors reduced these associations to 0.8 (95% CI: 0.6, 1.2) and 1.2 (95% CI: 0.7, 2.2), respectively., Conclusions: There does not appear to be notable variation in relative risk terms of socioeconomic differences in child mortality by age or cause of death. Any association of one-parent families with child mortality is due to associated low socioeconomic position.

Background: Computerized record linkage is commonly used in cohort studies to ascertain the study outcome, and as such its accuracy classifying the outcome can be described using the standard epidemiological terms of sensitivity and positive predictive value (PPV)., Method: We describe a 'duplicate method' to calculate the PPV of record linkage when each record can only be involved in one match (e.g. linking population files to death files). The method does not require a validation subset of records from both files with detailed personal information (e.g. name and address), and is therefore ideal for linkage projects using anonymous data. The duplicate method assumes that the number of records from one file with zero, one, two, etc., links from the other file is distributed in a manner predicted by combinatorial probabilities. Having made this assumption, the number of false positive links, and hence the PPV, are estimable. We demonstrate this duplicate method using output from anonymous and probabilistic record linkage of census and mortality records in New Zealand., Results: The PPV estimates conform to the pattern expected based on the underlying theory of probabilistic record linkage, and were robust to sensitivity analyses. We encourage other researchers to further assess the accuracy of this method.