1 Department of Public Health, Wellington School of Medicine and Health Sciences, University of Otago, PO Box 7343, Wellington, New Zealand.
2 Centre for Public Health Research, Level 6, 90 Symonds Street, Massey University, Auckland, New Zealand.
3 Auckland Regional Public Health Service, Auckland District Health Board, Box 41200 St Lukes, Auckland, New Zealand.
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Abstract |
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Methods In all, 480 of 693 (69%) 014 year old deaths during 19911994 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 (14, 59, and 1014 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.
Accepted 18 February 2003
Lower socioeconomic position is associated with increasedchild mortality,17 but the pattern is variable by cause of death, socioeconomic factor, and age group. By cause of death, socioeconomic differences for unintentional injury deaths are often more marked.2,810 Some studies have found that the all-cause mortality gradient by parental occupational class among school age children is particularly weak, or even absent,8,11,12 then reappears for own socioeconomic position in early adulthood.11 Such variation by age might be due to social mobility during childhood and adolescence as parental socioeconomic position wanes in importance as a predictor of health and a young adults own socioeconomic trajectory becomes more important. On examining the possible theoretical processes of equalization in early youth, West11 argues that the:
... effects associated with the secondary (high) school, the peer group and youth culture cut across those of the family, home background, and neighbourhood in such a way as to reduce or remove class differences in health. (ref. 11, p. 833)
However, this variation by age is far from conclusive. First, some of the UK evidence relies on unlinked census and mortality data that are prone to numeratordenominator bias,11 and the studies based on linked censusmortality data are prone to imprecision.12 Second, occupational class is just one measure of socioeconomic position and many children are often unable to be assigned a class.3 Third, some of the argument for smaller occupational class differences in mortality is based on smaller absolute differences in mortality during school age.11 However, 19911993 mortality data for England and Wales demonstrates social class mortality gradients in relative risk terms during all ages of childhood, although arguably more so among 14 year olds than 59 and 1014 year olds.1
Children in one-parent families have also been found to have elevated mortality compared with other children3,9,13 for which the underlying causal mechanisms might be material deprivation, unsafe living environments, and social isolation that may accompany sole parenting.14 Interestingly, Ostberg9 simultaneously analyses one-parent families, social class, and other structural factors and finds an independent association of one-parent families with injury deaths but not with non-injury deaths. The reduction in the differential in British infant mortality between the babies of lone mothers and couples has been confined to the neonatal period which suggests the improvement has been more to do with healthcare factors than changes in social and economic factors.5
Disentangling the socioeconomic determinants of child mortality is difficult due to death among children being relatively uncommon in developed countries and limited data on child mortality by socioeconomic factors. Few studies measure the association of income with child mortality. The independent effects of socioeconomic position and one-parent families on child mortality are unclear. The New Zealand census collects information on a range of social factors such as income, education, occupational class, car access, small-area deprivation, and family type (i.e. one- and two-parent families). The record linkage of 1991 census and 19911994 mortality data, therefore, allows a statistically powerful and unique opportunity to examine social differences in child mortality for the entire New Zealand population. In 1993 child mortality in New Zealand ranked high at 17 out of 21 for OECD countries, in contrast to previously favourable rankings during the 1960s. During the 1980s and 1990s there were rapid and extensive neo-liberal reforms implemented by successive governments in New Zealand, accompanied by a dramatic increase in various measures of inequality among children.15
The objectives of this paper are:
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Methods |
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Socioeconomic variables
The highest educational qualification of any adult in the household was categorized in the following hierarchical order: tertiary (e.g. university degree, nursing), trade (e.g. technical certificates), school (i.e. any school-based qualification), and nil qualifications. Total household income was equivalised using the Jensen Index, a New Zealand-specific equivalisation scale that allows for economies of scale in a household and the differential impact on household expenditure of children versus adults.20 The highest occupational class of any adult in the household was assigned using the New Zealand Socio-Economic Index (NZSEI).21 The NZSEI uses educational and income values for each occupation to assign a scaled occupational socioeconomic status and six occupational classes. Household car access was categorized as 0,1, or 2 cars. Household labour force status was classified as: employed, if one or more adults were employed; unemployed, if one or more adults were unemployed (i.e. actively seeking work, and available to start work) and no adult was employed; or was otherwise classified as non-active labour force. Two household composition variables were calculated: crowding (number of people per bedroom) and family type (one parent, and two parent and other). Neighbourhood socioeconomic deprivation was assigned using the New Zealand Index of Deprivation (NZDep91). The components of this index measured for areas of approximately 100 people include telephone access, car access, means-tested benefits, unemployment, income, one-parent families, qualifications, tenure, and crowding.22,23
We were able to assign categories of household education, car access, crowding, and family type to at least 98% of 014 year old census respondents at a usual and private residence on census night. However, a household income could only be reliably assigned if all adults were at their usual residence on census night, resulting in 19% of children having a missing household income value. Some 23% of children lived in a household where none of the adults had an occupation during the 4 weeks preceding census night, and therefore were not assigned a household occupational class.
Analyses
To be included in the analyses children had to be at their usual and private residence on census night with at least one adult in the household. This resulted in 742 587 children (94.7% of 014 year old census respondents) and 2 013 871 person years of follow-up. This full cohort included 435 linked deaths that corresponded to 627 weighted deaths. The distribution of age and cause of death for the 435 linked deaths within the full cohort are presented in Table 1. Multivariable analyses were restricted to the 566 673 children (76.3% of full cohort; 1 537 824 person years of follow-up) with complete data for all the above socioeconomic factors excluding occupational class. This restricted cohort for multivariable analyses included 309 linked deaths corresponding to 444 weighted deaths.
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Results |
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All-cause mortality by age group
Table 3 shows rate ratios by aggregated levels of each socioeconomic factor across four age groups (<1 year, 14 years, 59 years, and 1014 years). The education gradient for infant deaths appears stronger than for the three other age groups. To further investigate a possible differential association of socioeconomic position with child mortality according to age, we examined for possible heterogeneity in the rate ratios across age groups. Our null hypothesis that the education rate ratios are homogeneous across these four age groups was rejected (P = 0.01) for the nil compared with tertiary qualification rate ratios, but not rejected for the trade and school compared to tertiary rate ratios (P = 0.23). For other socioeconomic factors, there was a possible tendency towards stronger differences for infant mortality and weaker differences for 59 year old mortality. However, of all levels of the remaining socioeconomic factors, only the rate ratios for children in the $10 000$29 999 household income group compared with the reference household income group (
$30 000) were statistically significantly different (P = 0.02). Whilst not included elsewhere in this paper, additional results for 1519 year olds are presented in the final column of Table 3
for comparison. The 1519 year old rate ratios tended to be intermediary between those for 59 and 1014 year olds, except for car access and equivalized household income, where the 1519 year old association was null.
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Discussion |
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Second, our findings are consistent with a strong association of socioeconomic factors with mortality at all ages in childhood supporting recent findings in the UK,1 although the association was probably strongest for infant mortality (Table 3). Third, we found strong associations of a range of socioeconomic factors with unintentional injury and SIDS consistent with previous research.27 We also found higher risks of childhood cancer and congenital death among lower socioeconomic households according to most socioeconomic factorsalthough 95% CI often included 1.0. It is unclear whether these findings for congenital and cancer deaths are due to varying incidence or case fatality rates by socioeconomic position.
Fourth, households with no adult in employment and one-parent families tended to be associated with increased child mortality adjusting for just age and ethnicity (Table 2), but controlling for socioeconomic position largely removed these associations (Table 5
). Our analyses for household labour force status and one-parent families should be interpreted cautiously due to small effect sizes, statistical imprecision, possible residual linkage bias, and some apparent selection bias for the multivariable analyses of one-parent families and injury. Regarding linkage bias, all analyses in this paper used weights that allow for linkage of child deaths by small-area deprivation and demographic factors. We are confident that this weighting performs well to adjust for the (small) linkage bias by socioeconomic position.17,19 However, we need to be more cautious regarding one-parent families as they are a residentially mobile population and our linkage success was highly dependent on geocodes. It is possible that we underestimated the association of one-parent families (and households with no adult in employment) with child mortality. However, the notable relative reductions in the rate ratios for one-parent families when controlling for other socioeconomic factors is a valid observationeven if we might have underestimated the actual rate ratios. Our two main conclusions are, first, that there is a modest association of one-parent families and parental unemployment with child mortality when controlling for just age and ethnicity. Second, any increased child mortality in these households and families appears to be due to correlated socioeconomic factors. For example, our multivariable analyses for one-parent families suggest that education was a notable confounder, however (somewhat surprisingly) income did not appear to explain much of this association over and above education.
Fifth, household crowding is a major independent risk factor for infectious disease morbidity,28 but given that infection is not a major cause of death among children it does not emerge as an independent risk factor for mortality analyses.
The final major finding of our study is the importance of small area socioeconomic deprivation on childhood mortality over and above individual or household level socioeconomic position as a predictor of childhood mortality. The association of small area socioeconomic deprivation with all-cause mortality was halved, but not removed, after adjusting for household socioeconomic position (Table 5). For example, the rate ratio for the most deprived quintile reduced from 2.3 (95% CI: 1.6, 3.2) to 1.8 (95% CI: 1.2, 2.6). While suggestive of an independent ecological or contextual effect of neighbourhood socioeconomic deprivation on childhood mortality, this finding is difficult to interpret due to possible residual confounding by household socioeconomic position.29 Interestingly, the notable association of small area socioeconomic deprivation with unintentional injury death in the multivariable analyses (Table 5
), taken together with other research demonstrating a differential distribution of environmental causes of childhood injury by socioeconomic position,14,30 points to a true contextual effect of neighbourhood socioeconomic deprivation on child injury mortality.
It seems likely that the marked social inequalities in child mortality in New Zealand, accompanied by increasing social inequalities, are key explanations for the low ranking of child health status in New Zealand relative to other industrialized countries. Broad population-based policies (including education, social assistance, labour market, and taxation policies) are required to reduce social inequalities that, in turn, should reduce the health gradient.31
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Acknowledgements |
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SUMMARY STATISTICS NEW ZEALAND SECURITY STATEMENT
The New Zealand Census-Mortality Study (NZCMS) is a study of the relationship between socioeconomic factors and mortality in New Zealand, based on the integration of anonymized population census data from Statistics New Zealand and mortality data from the New Zealand Health Information Service. The project was approved by Statistics New Zealand as a Data Laboratory project under the Microdata Access Protocols in 1997. The data-sets created by the integration process are covered by the Statistics Act and can be used for statistical purposes only. Only approved researchers who have signed Statistics New Zealands declaration of secrecy can access the integrated data in the Data Laboratory. (A full security statement is in a technical report at http://www.wnmeds.ac.nz/nzcms-info.html.) For further information about confidentiality matters in regard to this study please contact Statistics New Zealand.
KEY MESSAGES
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