Tony Blakely

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). ResultsContribution 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.

Background: Socioeconomic inequalities in child mortality are known to exist; however the trends in these inequalities have not been well examined. This study examines the trends in child mortality inequality between 1981 and 1999 against the background of the rapid and dramatic social and economic restructuring in New Zealand during this time period. Methods: Record linkage studies of census and mortality records of all New Zealand children aged 0–14 years on census night 1981, 1986, 1991, 1996, each followed up for three years for mortality between ages 1–14 years. Socioeconomic position was measured using maternal education, household income, and highest occupational class in the household. Standardised mortality rates, rate ratios, and rates differences as well as regression based measures of inequality were calculated. Results: Mortality in all socioeconomic groups fell between 1981 and 1999. Socioeconomic inequality in child mortality existed by all measures of socioeconomic position, however only trends by income suggested a change over time: the relative index of inequality increased from 1.5 in 1981–84 to 1.8 in 1996–99 (p trend 0.06), but absolute inequality remained stable (slope index of inequality 15/100 000 in 1981–84 and 14/100 000 in 1996–99. Conclusions: Dramatic changes in income in New Zealand possibly translated into increasing relative inequality in child mortality by income, but not by education or occupational class. The a priori hypothesis that socioeconomic inequalities in child mortality would have increased in New Zealand during a period of rapid structural reform and widening income inequalities was only partly supported.

Socioeconomic differences in mortality in New Zealand have traditionally been measured using occupational class from mortality data (based on usual or last occupation) as the numerator, and class from census data (current occupation on census night) as the denominator. Such analyses are prone to numerator-denominator bias. Record linkage of census and mortality data in the New Zealand Census-Mortality Study (NZCMS) allows analyses of 'linked' data that will avoid numerator-denominator bias, but may be prone to other biases. To determine differences in the assignment of occupational class between census and mortality data; to investigate biases in the observed association of class with mortality using linked census-mortality data; and to compare the class-mortality association using unlinked versus linked census-mortality data. Census records for males aged 25-64 years on census night 1991 were anonymously and probabilistically linked to 5,844 out of 8,145 eligible deaths occurring in the second and third years following census night. (by objective) Only 47% of linked deaths had an occupation recorded on census data, compared to 84% on mortality data - a census to mortality ratio of 0.56. Relatively fewer deaths were identified as class 4 on census data (census to mortality ratio of 0.45) compared to other classes (ratios 0.55 to 0.64). Linkage bias: A lower likelihood of 25-44 year old deaths (but not 45-64 year olds) from lower socioeconomic classes being successfully linked to a census record meant that analyses using linked census-mortality data underestimated the class-mortality association. Bias due to exclusion of economically inactive: Analyses on linked-census data (using current occupational class) considerably underestimated the estimated association of usual occupational class with mortality. The strength of the association of class with mortality according to linked census-mortality data (and adjusted for the above two biases) and unlinked data were roughly comparable. Differences in the recording of occupational class on census and mortality data in New Zealand mean measuring mortality differences by class is thwart with difficulty. If one assumes that biases for any particular method of analysis are similar over time, or one carefully adjusts where possible for bias, using occupational class to monitor trends in socioeconomic mortality gradients may be valid.

We used a New Zealand census mortality cohort of 758 367 men and 798 828 women aged 25 to 74 years at the 1996 census followed up for mortality for three years to compare mortality in current and never smokers (see http://www.wnmeds.ac.nz/nzcms-info.html for details). We used a New Zealand specific measure of neighbourhood socioeconomic deprivation, constructed using proportions of people at the 1996 census with/in: no telephone access; no car access; receipt of a means tested benefit; unemployment; low household income; single parent families; nil qualifications; non-tenured homes; and household crowding.⁴ The specification of other socioeconomic covariates is detailed in the footnote to table 1. Rate ratios were measured using Poisson regression (SAS version 8.2).

Maori and Pacific deaths were severely undercounted in the mid-1980s and first half of 1990s, resulting in numerator-denominator bias when calculating mortality rates by ethnicity. We used the New Zealand Census-Mortality Study to adjust for this bias and calculate corrected ethnic-specific mortality rates from 1980 to 1999. Age-specific adjusters were calculated for the period 1980-99. They were applied to mortality data to obtain a corrected number of deaths. Mortality rates (by age and gender) were calculated by dividing the total number of adjusted deaths by the respective census counts. Contrary to unadjusted rates, corrected Maori and Pacific mortality rates were clearly higher than non- Maori non-Pacific rates during the 1980s and early 1990s. From 1980-84 (1361 per 100,000 for males and 965 per 100,000 for females) to 1996-99 (1258 and 894), there was only a modest decrease in Maori 1 to 74 year old mortality rates. Pacific mortality rates changed little from 1980-84 (1264 and 672) to 1996-99 (1144 and 696 per 100,000 for males and females respectively). Non-Maori non-Pacific mortality rates, however, decreased by about 30% from 1980-84 (919 and 553) to 1996-99 (641 and 407 per 100,000 for males and females, respectively). Cancer (lung, prostate, breast, colorectal) mortality rates tended to increase over time among Maori compared to steadily decreasing among non-Maori non-Pacific. Of note, Pacific colorectal cancer mortality rates have increased by about ten-fold during the 1980s and 1990s. All ethnic groups experienced falls in cardiovascular disease mortality rates, but the decreases were much greater among non-Maori non-Pacific. The gaps between Maori and non-Maori non-Pacific mortality widened over the 1980s and 1990s mainly due to steadily declining non-Maori non-Pacific mortality rates and stagnant Maori mortality rates. Likewise, the gaps between Pacific and non-Maori non-Pacific mortality also widened during that period.

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.