Income Inequality and Cause-Specific Mortality During Economic Development

Income Inequality and Cause-Specific Mortality During Economic Development ELAINE W. LAU, MPH, C. MARY SCHOOLING, PHD, KEITH Y. TIN, MMEDSC, AND GABRIEL M. LEUNG, MD

PURPOSE: Life expectancy is strongly related to national income, whether there is an additional contribution of income inequality is unclear. METHODS: We used negative binomial regression to examine the association of neighborhood-level Gini, adjusted for age, sex, and income, with mortality rates in Hong Kong from 1976 to 2006. RESULTS: The association of neighborhood Gini with all-cause mortality varied over time (p-value for interaction ! .01). Neighborhood Gini was positively associated with nonmedical mortality in 1976 to 1986; incident rate ratio (IRR) 1.09, 95% confidence interval (95% CI) 1.02
1.16 per 0.1 change and in 1991 to 2006, IRR 1.24, 95% CI 1.13
1.36, adjusted for age, sex and absolute income. Similarly adjusted, Gini was not associated with all-cause mortality in 1976 to 1986 (IRR 0.96, 95% CI 0.93
1.00) but was in 1991 to 2006 (IRR 1.25, 95% CI 1.20
1.29), when Gini was also positively associated with death from cardiovascular diseases, respiratory diseases and some cancers. CONCLUSIONS: Independent of income, income inequality was positively associated with nonmedical mortality rates at a low level of spatial aggregation, indicating the consistent harms of social disharmony. However, the impact on medical mortality was less consistent, suggesting the relevance of contextual factors. Ann Epidemiol 2012;22:285–294. Ó 2012 Elsevier Inc. All rights reserved. KEY WORDS:

Absolute Income, All-Cause Mortality, Cause-Specific Mortality, China, Economic Development, Gini, Hong Kong, Income Inequality.

INTRODUCTION Impoverished environments indisputably lead to poor health. Social disparities in health are pervasive and persistent. Life expectancy is strongly related to national income in low- and middle-income countries, although the effect is less marked in high income countries (1). Whether there is an additional contribution to mortality from the level of income inequality, acting via factors such as stress, social capital, or invidious comparisons within a social hierarchy independent of the standard of living (2), remains controversial (3). Mortality related to the level of income inequality could simply be the manifestation of underlying historical, cultural, political, and economic processes that simultaneously generate inequalities in social infrastructure, such as medical, transportation, educational, housing, and parks and recreational systems. Almost all the evidence concerning the health effects of the level of income inequality comes from developed From School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China. Address correspondence to: C. Mary Schooling, PhD, School of Public Health, Li Ka Shing Faculty of Medicine, 21 Sassoon Road, Pokfulam, Hong Kong SAR, China. Tel: 852-3906-2032; Fax: 852-2855-8214. E-mail: [email protected]. Received September 12, 2011. Accepted January 31, 2012. Ó 2012 Elsevier Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010

countries in Europe and North America (3, 4), where contextual or cultural effects may have built up over centuries of economic development. Thus, it is difficult to know whether the observed associations are contextually specific or biologically based. Evidence from other developed sociohistorical settings such as China can be particularly valuable in such situations. Currently, in high-income non-Western settings, such as Japan (5), Singapore (6), South Korea (7), Taiwan (8, 9), and Hong Kong (10), the impact of the level of inequality is sparsely documented and not very clear. In Taiwan, from 1976 to 1995, income inequality affected under-five mortality, but not all-ages mortality (8, 9). Moreover, the effect of income inequality on health is more evident in more developed non-Western countries, such as Chile (11), Ecuador (12), Russia (13), and China (14) than in low income non-Western countries, such as Vietnam (15), suggesting that these associations may change with economic development. Positive associations between income inequality and morbidity or mortality have mainly been reported at relatively high geo-spatial levels such as between countries or states (16), whereas the evidence at lower levels of aggregation is decidedly mixed (17). Thus, it is unclear whether these findings are relevant to the rapidly developing megacities of the developing world. The Hong Kong population was largely formed by migration in the mid-20th century 1047-2797/$ - see front matter doi:10.1016/j.annepidem.2012.01.009

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Selected Abbreviations and Acronyms TPU Z tertiary planning unit IHD Z ischemic heart disease

from essentially preindustrial southern China to Hong Kong, which was comparatively industrialized and developed (18, 19). Hong Kong subsequently experienced very rapid growth, such that gross domestic product per head in Hong Kong increased from 48% of that of Western Europe in 1950, to 62% in 1973, but 113% in 1998 (18). At the same time income inequality increased substantially from a Gini coefficient of 0.43 in 1971 to 0.53 in 2006 (20), and life expectancy increased by approximately 10 years from 1976 to 2006 (21). Moreover, Hong Kong’s experience may be invaluable in presaging events in other rapidly developing locations. We used this homogeneous non-Western, recently developed population from a geographically compact area to examine several linked questions concerning income inequality and health. First, taking advantage of this unique population history, we examined the association of income inequality with all-cause and cause-specific mortality rates during a period of rapid economic transition from 1976 to 2006. Second, we examined whether the association existed at a low level of spatial disaggregation. Third, to put any associations in perspective we also examined the complementary question concerning the associations of income with mortality. METHODS Data Sources In Hong Kong, indicators of wealth and socioeconomic position, such as income, education, and longest-held occupation or spouse’s occupation, are not routinely collected for deaths. However, residential address is recorded, which combined with small area census information can provide neighborhood socioeconomic characteristics for each death. We obtained details of all deaths in the census or by-census years since death records were computerized, ie, in 1976, 1981, 1986, 1991, 1995, 2001, and 2006, from the Hong Kong Census and Statistics Department. Most deaths in Hong Kong have taken place in hospital (certainly since 1970), thus facilitating accurate ascertainment of cause of death; nevertheless, as in many developed countries, autopsy rates are falling and some misdiagnoses are inevitable (22), although unlikely to be systematic. Deaths for 1995 were used instead of 1996, because in 1996 26% of addresses were missing for technical reasons. We used the residential address to identify the small area of residence (called tertiary planning unit [TPU]) for each

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death. The TPU system was devised by the Planning Department of the Government of the Hong Kong Special Administrative Region for town-planning purposes. TPUs typically represent geographical areas bounded by roads, railway lines, coastlines, contours, waterways, lot boundaries or zoning boundaries of town plans. For the same years, we used the relevant census or by-census to obtain sociodemographic information for each TPU, including income in deciles, median household income, average household size, population by age and sex, the number of households, and the proportion of residents who had moved TPU in the previous 5 years. We also obtained the consumer price index for 1976 to 2006 from the Census and Statistics Department of the Government of the Hong Kong Special Administrative Region (23). There are approximately 280 TPUs in Hong Kong, whose boundaries are regularly updated to reflect population dynamics, although some are sparsely populated. Sparsely populated TPUs are merged with geographically near-by TPUs by the Census and Statistics Department before the data is released to ensure anonymity of the inhabitants. The number of TPUs available for analysis was 190 in 1976, 214 in 1981, 217 in 1986, 194 in 1991, 195 in 1995, 196 in 2001, and 204 in 2006. TPU boundary maps for the census and by-census years were used to check that the TPU code for each death had been assigned to the correct contemporaneous TPU. Exposures We used neighborhood Gini coefficients to represent the level of income inequality (17). Gini coefficients are comparable across Hong Kong because there are no local variations in taxation or social transfers. Gini coefficients are comparable across time in Hong Kong because the taxation system has not changed significantly over the past three decades (24). Gini coefficients display considerable spatial heterogeneity across Hong Kong (25). Gini coefficients were calculated from TPU level income deciles, and multiplied by 10 to give 0.1 Gini units. We used neighborhood median household income per head, calculated as TPU level median household income divided by average household size and by 10,000 to give income units of 10,000 HKD (wUSD 1,282), at 1991 prices, to represent income. We used income in preference to education or occupation because it is possible to standardize income across time. Outcomes Mortality Rates. We used deaths from all-cause as the primary outcome and cause-specific deaths as secondary outcomes. Because of the availability of data, we used 14 sex-specific 5-year age groups from 0-!5 to 65þ for 1976 to 1981 and 16 age groups from 0-!5 to 75þ for 1986 to

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TABLE 1. International Classification of Diseases
8,
9, and
10 codes for each cause of death category Cancer Infection-related (cervix, liver, lymphoma, nasopharynx, and stomach)

Hormone-modulated (breast, endometrium, ovary, and prostate) Lifestyle-related (colorectum and lung) Other cancers Cardiovascular diseases (CVD) Stroke Ischemic heart diseases (IHD) Other CVD Respiratory diseases Chronic obstructive pulmonary disease (COPD) and asthma Pneumonia Other respiratory disease Nonmedical causes (accidents, injuries, homicides, suicides & poisoning)

ICD–8

ICD–9

ICD–10

140–239 147, 151, 155, 180, 200, & 201

140–239 147, 151, 155, 180, 200, & 201

174, 182, 183, & 185

174, 175, 179, 182, 183, & 185

C00–D489 C11, C110–C119, C16, C160–169, C22, C220–C229, C53, C530–539, C81, C810–C819, C82, C820–C829, C83, C830–C839, C84, C840–C845, & C85, C850–C859 C50, C500–C509, C541, C56, & C61

153, 154, & 162

153, 154, & 162

C18, C180–C189, C19, C20, & C34, C340–C349

390–458 344, 430–438 410–414

390–459 342, 344 & 430–438 410–414

I00–I999 I60, I600–698 I20, I200–I259

460–519 490–493

460–519 490–496

J00–J998 J40, J400–J470

480–486

480–487

J10, J100–J189

1800–1999

800–999

2006 because deaths at ages 65þ years in 1976 and 1981 cannot be disaggregated. Age and sex-specific mortality rates in each TPU were calculated as the number of deaths divided by the population. Causes of Death. Causes of death were classified based on the International Classification of Diseases (ICD)-8 for 1976, ICD-9 for 1979-2000, and ICD-10 for 2001 onwards. We classified causes of death as major cardiovascular diseases, ie, stroke, ischemic heart disease (IHD) and other cardiovascular diseases, major respiratory diseases, ie, chronic obstructive pulmonary diseases and asthma, pneumonia and other respiratory diseases, cancer and nonmedical causes (eg, accidents, injuries, homicides, suicides, and poisoning) and all other causes. Consistent with our previous analysis in this population to provide etiological insight (26), we grouped cancer deaths according to current understanding of their major causes, although, as with many multifactorial diseases, these groupings may be an oversimplification. These were infection-related cancers (cervix, liver, lymphoma, nasopharynx, and stomach), hormonally modulated cancers (breast, endometrium, ovary, and prostate), lifestyle-related cancers (colorectum and lung), and other cancers. Table 1 shows the corresponding ICD codes for each cause considered. Statistical Analysis We used negative binomial regression to assess the adjusted associations of neighborhood Gini and income with ageand sex-specific mortality rates because the mortality rates

S000–T983

were overdispersed for a Poisson distribution. We report incident rate ratios with 95% confidence intervals; these give the change in the mortality rate ratio per 0.1 Gini or per HKD 10,000 (wUSD 1282) income. In the analysis of the associations for Gini we also adjusted for income, so as to identify the additional contribution of income inequality beyond that of income. To allow for population mobility, we considered adjusting for the proportion of people who had moved TPU in the previous 5 years. However, this made little difference to the estimates, and we did not use it further. We also examined whether the associations of Gini or income with mortality rates varied over the years (1976 to 2006) or by sex from the heterogeneity across strata and the statistical significance of an interaction term using models including all possible interactions with year or sex. Finally, given that income differentials in health may be less marked at older ages, we also present analysis stratified by age, as !65 years and >65 years. Statistical analyses were implemented in Stata Version 10.0 (StataCorp).

RESULTS Of the 204,084 deaths in all the years considered 193,741 (95%) were included. The remaining 5% of deaths were excluded because of unknown or missing age, sex, cause of death, or place of residence. The associations of Gini and income with all-cause mortality rates varied over the period (p-values for interaction !.01), so we stratified into earlier (1976
1986) and later (1991
2006) periods because the

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TABLE 2. Proportion of population >65, Gini coefficients, and age-standardized mortality rate by major causes in Hong Kong for the years considered Earlier period

Proportion (%) of people >65 All deaths Gini coefficients Age-standardized mortality rate (per 100,000 population)* All-cause Cancer Infection-related cancers Hormone-modulated cancers Lifestyle-related cancers Other cancers Cardiovascular diseases Stroke IHD Other CVD Respiratory diseases COPD and asthma Pneumonia Other respiratory Nonmedical causes Other causes

Later period

1976

1981

1986

1991

1995

2001

2006

5.4 22 628 0.429

6.6 24 822 0.451

7.7 25 902 0.453

8.7 28 429 0.476

9.8 31 468 0.518

11.2 33 378 0.525

12.4 37 457 0.533

695.1

596.9

523.9

501.1

467.1

387.2

363.6

46.7 9.5 50.8 43.5

45.6 8.8 54.1 39.9

44.9 9.2 59.1 42.6

41.9 9.9 59.4 39.2

38.2 9.4 58.7 38.8

35.3 8.6 54.5 38.9

29.6 8.8 50.6 34.2

81.3 49.3 81.3

80.4 52.5 46.1

61.3 52.8 39.5

53.3 53.9 36.7

48.9 48.6 30.5

35.0 35.7 24.2

30.1 35.3 25.2

30.5 70.9 8.6 37.5 185.1

36.5 57.4 7.3 36.9 130.2

41.6 34.8 10.6 26.9 98.8

33.9 33.0 17.7 25.2 93.4

30.3 49.4 6.3 20.9 80.4

23.5 33.1 4.8 21.8 68.7

17.4 38.8 4.5 18.8 67.5

*Standardized by age using the World Standard Population 2000 (27).

associations within these time periods were relatively homogeneous. There was also evidence that some of the associations varied with sex, so we also present sex-stratified analysis. Table 2 shows the proportion of the population >65 years, Gini coefficients, the age-standardized all-cause and cause-specific mortality rates in Hong Kong for the years considered (27). The proportion of people >65 increased from 5.4% in 1976 to 12.4% in 2006. In the earlier period, relatively more deaths were classified as ill-defined or senility, hence the larger proportion attributed to ‘‘other causes.’’ The contribution of hormone-modulated and lifestyle-related cancers remained fairly consistent. The contribution of infection-related cancer, other cancer, all cardiovascular diseases, all respiratory diseases and nonmedical causes decreased. The Gini coefficient increased from 0.429 in 1976 to 0.533 in 2006. Table 3 shows a strong association of income with ageand sex-adjusted all-cause mortality rates in the earlier period, which was weaker in the later period, when the associations also differed by sex. Figure 1 shows that in the later period the age-adjusted association of income with all-cause mortality rates was weaker among women than men. In contrast to income, adjusted for age, sex, and income, there was no clear association of Gini coefficient with all-cause mortality rates in the earlier period, but there was a positive association in the later period, which was more marked among men.

Figure 1A shows that in the earlier period income, adjusted for age, was negatively associated with most causes of death, apart from hormonally modulated cancers for both sexes and IHD among men, where there were ‘reverse’ associations. Figure 1B shows that in the later period there was no association between income and death from IHD among men. In both periods, income was usually more strongly negatively associated with death from respiratory causes for men than women. Figure 1C shows that, adjusted for age and income, Gini coefficient was positively associated with nonmedical and other causes of death in the earlier period. Figure 1D shows that in the later period Gini was positively associated with most causes of death apart from cancer, with similar associations for both men and women (detailed results are in Appendix 1). Figure 2 shows the associations of income and income inequality with all-cause and cause-specific mortality rates in both periods by age group. In the earlier period income adjusted for age and sex had fairly similar negative associations with most causes of death in both age-groups (Fig. 2A), apart from for hormonally modulated cancers and IHD, where there were ‘‘reverse’’ associations. Figure 2B shows that in the later period income adjusted for age and sex had stronger negative associations with most causes of death among younger than older people. Figure 2C shows that, adjusted for age, sex and income,

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TABLE 3. Adjusted associations of neighborhood income and income inequality with all-cause and cause-specific mortality rates in Hong Kong for 1976–1986 and 1991–2006 Income inequalityy (Neighborhood Gini)

Neighborhood income* (Median household income per capita) Earlier period (1976–1986)

Cause of death Primary outcome All-cause Secondary outcome Cancer: Infection-related Hormone-modulated Lifestyle-related Other cancers Cardiovascular disease: Stroke IHD Other CVD Respiratory diseases: COPD and asthma Pneumonia Other respiratory Nonmedical causes Other causes

IRR

95% CI

Later period (1991–2006)

p value for sex difference IRR

95% CI

Earlier period (1976–1986)

p value for sex difference IRR

95% CI

Later period (1991–2006)

p value for sex difference IRR

95% CI

p value for sex difference

0.39 0.35 to 0.44

.33

0.68 0.66 to 0.71

!.01

0.96 0.93 to 1.00

.90

1.25 1.20 to 1.29

.12

0.33 1.55 0.50 0.49

0.24 to 0.45 0.98 to 2.46 0.38 to 0.66 0.37 to 0.66

.31 .22 .31 .10

0.65 1.09 0.77 0.90

0.58 to 0.72 0.93 to 1.29 0.71 to 0.84 0.83 to 0.99

.01 .11 !.01 !.01

0.96 0.93 0.93 0.90

0.91 to 1.02 0.83 to 1.05 0.87 to 0.99 0.86 to 0.95

.19 .34 .03 .21

1.06 1.01 1.07 1.08

0.99 to 1.14 0.88 to 1.16 1.01 to 1.14 1.01 to 1.16

.43 .69 .95 .14

0.48 0.37 to 0.62 1.65 1.32 to 2.07 0.57 0.43 to 0.76

.92 .01 .27

0.79 0.72 to 0.88 0.89 0.81 to 0.98 0.86 0.77 to 0.96

!.01 .01 .92

0.93 0.88 to 0.98 0.95 0.89 to 1.01 0.97 0.91 to 1.02

.86 .98 .67

1.19 1.10 to 1.28 1.22 1.14 to 1.32 1.17 1.08 to 1.28

.51 .89 .51

0.31 0.52 0.09 0.29 0.17

.01 .03 .03 .06 .50

0.61 0.91 0.62 0.47 0.75

!.01 .02 !.01 !.01 !.01

0.89 0.94 0.94 1.09 1.11

.90 .19 .14 .29 .54

1.15 1.22 1.23 1.24 1.29

.39 .63 .36 .43 .73

0.21 to 0.44 0.38 to 0.72 0.05 to 0.18 0.19 to 0.42 0.13 to 0.21

0.53 to 0.71 0.82 to 1.01 0.51 to 0.77 0.39 to 0.54 0.69 to 0.82

0.83 to 0.96 0.88 to 1.01 0.86 to 1.04 1.02 to 1.16 1.06 to 1.17

1.05 to 1.26 1.12 to 1.34 1.08 to 1.39 1.13 to 1.36 1.21 to 1.37

CI Z confidence interval; COPD Z chronic obstructive pulmonary disease; CVD Z cardiovascular disease; IHD Z ischemic heart diseases; IRR Z incident rate ratio. *Adjusted for age (5 year groups), sex, year. y Additional adjusted for income.

Gini coefficient was positively associated with nonmedical causes among younger people in the earlier period and with other causes among older people. Figure 2D shows that in the later period among younger people Gini was positively associated with almost all causes of death, which was similar for the older age group (Fig. 1D) apart from a lack of association of Gini with nonmedical causes. DISCUSSION In a developed non-Western population with a uniquely rapid history of economic development, we found that income inequality, independent of age, sex, and income, was positively associated with premature death from nonmedical causes throughout a period of rapid economic development at a low level of geo-spatial disaggregation. In the more recent period, income inequality was also clearly associated with greater rates of mortality from most causes particularly for younger people; however, this association was not evident in the earlier period. In contrast, income, independent of age and sex, was consistently negatively associated with mortality rates, but with a larger impact for younger people and in the earlier period, although there were also reverse associations in the earlier period for IHD among men and perhaps for hormonally modulated cancers in both sexes.

Despite these intriguing findings, our study has limitations. First, longitudinal data on individual income or education are not available, so a hierarchical, prospective study could not be implemented. The recent history of economic development in our, and similar, settings precludes longrunning cohort studies, and potentially thereby evidence from most of the global population during a period of immense economic and social transition. However, the relation between income inequality and health may be independent of the income-health relation at the individual level (17). Moreover, our study considered two different time periods and is internally consistent making the notable changes over time interpretable. However, as with many other similar studies, we cannot distinguish whether the observed associations of income and income equality with mortality rates are attributable to contemporaneous or lifelong influences, nor can we be sure whether the generally weaker associations at older ages are real or artifactual. Second, the ecological nature of our study (at the neighborhood level) may contain residual confounding. Environmental factors affecting health, such as pollution, population density, safety, open spaces and access to facilities, could have changed their spatial patterning over the period. However, it is not obvious how such a change could have reduced the impact of income whilst increasing the impact of income inequality. Hong

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Kong is very compact and densely populated, with good transport links and little school zoning, which reduces the scope for neighborhood segregation. Moreover, there was an expansion of social welfare in Hong Kong with the introduction of the subsidized home ownership scheme in 1976, universal free 9-year education in 1979, and the expansion of medical facilities with universal access (28). Third, pretax household income was used for neighborhood Gini coefficients; however, there is no difference in taxes or transfer payments within Hong Kong. Fourth, the categorization of cancer into etiological groups may be an oversimplification because the causes of many cancers are multifactorial. Fifth, inevitably there have been improvements in disease diagnosis during the period, particular for cancer and cardiovascular disease; however, there is no reason to think that these varied by Gini or income as most hospital care has always been provided in the public sector. Sixth, we were unable to examine the association of income inequality with mortality throughout the period of rapid economic development in Hong Kong, because relevant records are

not available. There was no census between 1931 and 1961 and no small area census information before 1976. Seventh, analysis at the neighborhood level demonstrates the effect of income inequality in a changing context, ie, of rapid economic growth, not of changing levels of income inequality. Specifically, rapid economic growth and consequent improvements in lifetime living conditions in Hong Kong may have contributed to the large reductions in mortality rates from 1976 to 2006 despite the high and growing level of relative inequality shown by the Hong Kong Gini coefficients. However, we did consider the period 1976 to 2006 as an earlier and later period, which did have differing Gini coefficients. A sustained contribution of income inequality to nonmedical mortality rates in a non-Western setting is consistent with much of the early work on income inequality that focused on violence (29), which is most likely more relevant among younger people. Our study adds by demonstrating this association at a low level of spatial aggregation in a non-western mega-city. Wide differentials in income

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0.03125

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IRR per 10,000 HKD at 1991 prices

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IRR per 10,000 HKD at 1991 prices

291

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FIGURE 2. (A-D) Incidence rate ratio and 95% confidence intervals for all-cause and cause-specific mortality rates by age group in Hong Kong in two periods.

promote exclusion and weaken social connections (30), which may set the scene for destructive behavior. An inconsistent contribution of income inequality to all-cause mortality with economic development is consistent with a study in Taiwan in which the authors found that income inequality impacted under-five mortality during rapid economic development, but not all-ages mortality (8). Nevertheless, the clear impact of income inequality on medical mortality in the later period is consistent with many studies from long-term developed countries (2). One interpretation of our study is a consistent positive effect of income inequality on medical, as well as nonmedical, mortality rates which was obscured by a threshold effect (4) in the earlier period (1976 to 1986), because the detrimental effect of Gini is not usually evident below Gini values of approximately 0.3 (4). However, it is not obvious why there should be a different threshold effect for different causes of death in different age groups. Alternatively, our study could be interpreted as an empirical demonstration of how moving up the curve between income and mortality from a straight to a curvilinear

association generates a ‘‘concavity induced income inequality effect’’ (31, 32) when aggregated data are used because additional income raises individual health by a decreasing amount at greater levels of income. As such, we cannot distinguish whether the observed positive association of income inequality with all-cause mortality rates in the later period is a function of aggregation effects or represents an effect of income inequality on mortality rates. The lack of consistency over time suggests the positive association might be artifactual or limited to specific causes of death, which showed more consistent associations. However, apart from nonmedical mortality the only major medical cause of death associated with income inequality in both periods was ‘‘other causes,’’ suggesting an artifactual rather than an etiological explanation. Our observations concerning income are consistent with the well-known curvilinear and asymptotic association between national income and mortality rates (31), and the negative associations of material conditions with mortality in a high income post-industrial settings such as Europe and North America (33, 34). The changing association of

292

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IHD with income among men is consistent with the reversal in the social patterning of IHD specifically among men previously observed in long-term developed western populations (35) and is consistent with a sociobiological hypothesis suggesting up-regulation of the gonadotropic axis with economic development may increase vulnerability to IHD among men (36) and vulnerability to hormone-modulated cancer in both sexes (37), as has been seen in other similar settings (38, 39). In contrast, the stronger negative association of income with respiratory mortality and more recently with lifestyle-related cancers among men than women may be attributable to smoking, which in Hong Kong is more common among men than women (40), or to more harmful occupational exposures among men than women. As with all observational analyses our study is not definitive and needs to be considered in the context of all the relevant evidence concerning income and mortality. Nevertheless, our study showed that in a non-Western developed context there was a consistent impact of income on death from almost all diseases, apart from diseases of affluence, but not a consistent impact of income inequality on death from most diseases although income inequality contributed to deaths from nonmedical causes. This would suggest that societal efforts to improve health in similar settings should focus on economic growth rather than on income distribution, although this study cannot disaggregate the relative impacts of contemporaneous from life time income.

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3. Lynch J, Smith DG, Harper S, Hillemeier M, Ross N, Kaplan GA, et al. Is income inequality a determinant of population health? Part 1. A systematic review. Milbank Q. 2004;82:5–99. 4. Kondo N, Sembajwe G, Kawachi I, Van Dam RM, Subramanian SV, Yamagata Z. Income inequality, mortality, and self rated health: Meta-analysis of multilevel studies. BMJ. 2009;339:1–9. 5. Kagamimori S, Gaina A, Nasermoaddeli A. Socioeconomic status and health in the Japanese population. Soc Sci Med. 2009;68:2152–2160. 6. Sabanayagam C, Shankar A, Wong TY, Saw SM, Foster PJ. Socioeconomic status and overweight/obesity in an adult Chinese population in Singapore. J Epidemiol. 2007;17:161–168. 7. Khang YH, Lynch J, Yun S, Lee SI. Trends in socioeconomic health inequalities in Korea: use of mortality and morbidity measures. J Epidemiol Community Health. 2004;58:308–314. 8. Chiang TL. Economic transition and changing relation between income inequality and mortality in Taiwan: Regression analysis. BMJ. 1999;319: 1162–1165. 9. Leon-Gonzalez R, Tseng FM. Socio-economic determinants of mortality in Taiwan: Combining individual and aggregate data. Health Policy. 2010; 99:23–36. 10. Schooling CM, Lau EWL, Tin KYK, Leung GM. Social disparities and cause-specific mortality during economic development. Soc Sci Med. 2010;70:1550–1557. 11. Subramanian SV, Delgado I, Jadue L, Vega J, Kawachi I. Income inequality and health: Multilevel analysis of Chilean communities. J Epidemiol Community Health. 2003;57:844–848. 12. Larrea C, Kawachi I. Does economic inequality affect child malnutrition? The case of Ecuador. Soc Sci Med. 2005;60:165–178. 13. Walberg P, McKee M, Shkolnikov V, Chenet L, Leon DA. Economic change, crime, and mortality crisis in Russia: Regional analysis. BMJ. 1998;317:312–318. 14. Pei X, Rodriguez E. Provincial income inequality and self-reported health status in China during 1991–7. J Epidemiol Community Health. 2006;60: 1065–1069.

CONCLUSION Income inequality at a low level of spatial aggregation had a consistent positive association with nonmedical premature mortality rates during a period of rapid economic development in a non-Western setting, suggesting a key role of income inequality for nonmedical causes of death. However, at the same time, income inequality was less consistently positively associated with nonmedical mortality both over time and by cause of death, suggested that the observed associations may be contextually specific rather than biologically based. The authors thank the Census and Statistics Department of the Government of the Hong Kong Special Administrative Region for their help with the data collection. Elaine W. Lau had full access to all of the data and the accuracy of the data analysis.

15. Granlund D, Chuc NT, Phuc HD, Lindholm L. Inequality in mortality in Vietnam during a period of rapid transition. Soc Sci Med. 2010;70:232–239. 16. Wilkinson RG, Pickett KE. Income inequality and population health: A review and explanation of the evidence. Soc Sci Med. 2006;62:1768–1784. 17. Subramanian SV, Kawachi I. Income inequality and health: What have we learned so far? Epidemiol Rev. 2004;26:78–91. 18. Maddison A. Development Centre Studies. The World Economy: A Millennial Perspective. Paris: OECD; 2001. 19. Tsang SA. Modern History of Hong Kong. Hong Kong: Hong Kong University Press; 2004. 20. The Government of the Hong Kong Special Administrative Region. Household income and gini coefficient. Information note. Available from: http://www.legco.gov.hk/yr01–02/english/panels/fa/papers/fa1108cb1– 346–01e.pdf. Accessd February 10, 2012. p. 1–10. 21. Census & Statistic Department. Hong Kong Life Tables for 1971–2007. Available from http://www.censtatd.gov.hk/products_and_services/products/ individual_statistical_tables/index/jsp. Accessd February 10, 2012. 22. Tse GM, Lee JC. A 12-month review of autopsies performed at a universityaffiliated teaching hospital in Hong Kong. Hong Kong MJ. 2000;6:190–194.

REFERENCES 1. Wilkinson RG. Unhealthy Societies: The Afflication of Inequality. London: Routledge; 1996. 2. Kennedy BP, Kawachi I, Prothrow-Stith D. Income distribution and mortality: cross sectional ecological study of the Robin Hood index in the United States [Erratum in: BMJ. 1996; 312: 1194]. BMJ. 1996;312: 1004–1007.

23. Census & Statistic Department. (2010).[homepage on the internet].[cited 2010 Jun 7] Consumer Price Index. Available from: http://www.censtatd. gov.hk/hong_kong_statistics/statistics_by_subject/index_t.jsp?subjectIDZ 12&charsetIDZ1&displayModeZT. Accessd February 10, 2012. 24. Ho HCY. The Fiscal System of Hong Kong. London: Croom Helm Ltd; 1979. 25. Wong IOL, Cowling BJ, Lo SV, Leung GM. A multilevel analysis of the effects of neighborhood income inequality on individual self-rated health in Hong Kong. Soc Sci Med. 2009;68:124–132.

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26. Wong IOL, Cowling BJ, Law SCK, Mang OWK, Schooling CM, Leung GM. Understanding socio-historical imprint on cancer risk by ageperiod-cohort decomposition in Hong Kong. J Epidemiol Community Health. 2010;64:596–603. 27. Ahmad OB, Boschi-Pinto C, Lopez AD, Murray CJL, Lozano R, Inoue M. Age Standardization of Rates: A New WHO Standard. (GPE Discussion Paper no. 31). Geneva: WHO; 2001. 28. Carroll JM. A concise history of Hong Kong. Hong Kong: Hong Kong University Press; 2007. 29. Mclsaac SJ, Wilkinson RG. Income distribution and cause-specific mortality. Eur J Public Health. 1997;7:45–53. 30. Wilkinson RG, Kawachi I, Kennedy BP. Mortality, the social environment, crime and violence. Sociol Health Illness. 1998;20:578–597. 31. Ellison GT. Letting the Gini out of the bottle? Challenges facing the relative income hypothesis. Soc Sci Med. 2002;54:561–576. 32. Wagstaff A, Van Doorslaer E. Income inequality and health: What does the literature tell us? Annu Rev Public Health. 2000;21:543–567. 33. Mackenbach JP, Stirbu I, Roskam AJ, Schaap MM, Menvielle G, Leinsalu M, et al. Socioeconomic inequalities in health in 22 European countries. N Engl J Med. 2008;358:2468–2481.

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34. Krieger N, Rehkopf DH, Chen JT, Waterman PD, Marcelli E, Kennedy M. The fall and rise of US inequities in premature mortality: 1960–2002. PLoS Med. 2008;5:227–241. 35. Davey Smith G. Life-course appraoches in inequalitties in adult chronic dieseas risk. Proc Nutr Soc. 2007;66:216–236. 36. Schooling CM, Leung GM. A socio-biological explanation for social disparities in non-communicable chronic diseasesdthe product of history? J Epidemiol Community Health. 2010;64:941–949. 37. Schooling CM, Jiang C, Zhang W, Lam TH, Cheng KK, Leung GM. Size does matter: adolescent build and male reproductive success in the guangzhou biobank cohort study. Ann Epidemiol. 2011;21:56–60. 38. Shimizu H, Ross RK, Bernstein L, Yatani R, Henderson BE, Mack TM. Cancers of the prostate and breast among Japanese and white immigrants in Los Angeles County. Br J Cancer. 1991;63:963–966. 39. Deapen D, Liu L, Perkins C, Bernstein L, Ross RK. Rapidly rising breast cancer incidence rates among Asian-American women. Int J Cancer. 2002;99:747–750. 40. The Government of the Hong Kong Special Administrative Region. Tobacco Control Office, Department of Health. Statistics: pattern of smoking in Hong Kong. Available at: http://www.tco.gov.hk/english/ infostation/infostation_sta_01.html. Accessd February 10, 2012

294

Neighborhood income* (Median household income per capita) Male Earlier period 1976–1986

Income inequalityy (Neighborhood Gini)

Female Later period 1991–2006

Earlier period 1976–1986

Male Later period 1991–2006

Earlier period 1976–1986

Female Later period 1991–2006

Earlier period 1976–1986

Later period 1991–2006

Cause of death

IRR

95% CI

IRR

95% CI

IRR

95% CI

IRR

95% CI

IRR

95% CI

IRR

95% CI

IRR

95% CI

IRR

95% CI

All-cause Cancer: Infection-related Hormone-modulated Lifestyle-related Other cancers Cardiovascular disease: Stroke IHD Other CVD Respiratory diseases: COPD and asthma Pneumonia Other respiratory Non-medical causes Other causes

0.37

0.32 to 0.44

0.63

0.59 to 0.67

0.41

0.35 to 0.49

0.75

0.71 to 0.80

0.96

0.92 to 0.99

1.27

1.21 to 1.33

0.96

0.92 to 1.01

1.21

1.14 to 1.27

0.29 2.86 0.46 0.39

0.19 to 0.44 0.89 to 4.83 0.32 to 0.65 0.26 to 0.59

0.58 1.32 0.69 0.76

0.51 to 0.67 0.97 to 1.79 0.62 to 0.78 0.67 to 0.86

0.41 1.39 0.54 0.65

0.25 to 0.66 0.85 to 2.29 0.35 to 0.83 0.42 to 0.99

0.76 1.08 0.89 1.11

0.65 to 0.90 0.91 to 1.29 0.78 to 1.01 0.97 to 1.26

0.93 0.82 0.90 0.88

0.87 to 1.01 0.59 to 1.14 0.84 to 0.97 0.82 to 0.94

1.04 1.06 1.06 1.03

0.95 to 1.14 0.81 to 1.39 0.99 to 1.15 0.95 to 1.13

1.03 0.95 1.01 0.95

0.94 to 1.13 0.83 to 1.07 0.90 to 1.12 0.87 to 1.03

1.11 0.99 1.07 1.16

0.98 to 1.25 0.85 to 1.17 0.97 to 1.18 1.04 to 1.30

0.47 2.19 0.68

0.32 to 0.69 1.64 to 2.93 0.46 to 1.01

0.66 1.02 0.88

0.56 to 0.77 0.90 to 1.14 0.75 to 1.03

0.49 1.18 0.49

0.34 to 0.69 0.83 to 1.68 0.33 to 0.75

0.92 0.77 0.85

0.80 to 1.05 0.64 to 0.89 0.73 to 0.99

0.93 0.95 0.95

0.87 to 1.01 0.87 to 1.03 0.88 to 1.03

1.22 1.22 1.19

1.09 to 1.35 1.12 to 1.34 1.07 to 1.35

0.92 0.95 0.98

0.85 to 0.99 0.86 to 1.05 0.90 to 1.06

1.16 1.21 1.13

1.04 to 1.29 1.08 to 1.35 1.00 to 1.29

0.19 0.36 0.05 0.21 0.18

0.12 to 0.33 0.22 to 0.59 0.02 to 0.13 0.12 to 0.36 0.13 to 0.26

0.50 0.82 0.48 0.33 0.68

0.42 to 0.61 0.70 to 0.95 0.36 to 0.64 0.26 to 0.41 0.61 to 0.76

0.53 0.74 0.22 0.44 0.15

0.31 to 0.90 0.49 to 1.14 0.09 to 0.55 0.25 to 0.74 0.11 to 0.22

0.84 1.03 0.86 0.72 0.84

0.68 to 1.04 0.88 to 1.20 0.64 to 1.16 0.58 to 0.88 0.75 to 0.94

0.89 0.98 0.99 1.06 1.09

0.82 to 0.98 0.89 to 1.08 0.88 to 1.11 0.98 to 1.15 1.02 to 1.16

1.12 1.24 1.26 1.28 1.29

1.01 to 1.25 1.10 to 1.38 1.07 to 1.47 1.14 to 1.43 1.19 to 1.39

0.89 0.90 0.86 1.14 0.12

0.79 to 1.01 0.82 to 0.99 0.74 to 0.99 1.02 to 1.27 1.04 to 1.21

1.22 1.19 1.15 1.18 1.27

1.04 to 1.43 1.03 to 1.37 0.93 to 1.42 1.01 to 1.38 1.16 to 1.39

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APPENDIX 1. Adjusted associations of neighborhood income and income inequality with all-cause and cause-specific rates in Hong Kong for 1976–1986 and 1991–2006

CI Z confidence interval; COPD Z chronic obstructive pulmonary disease; CVD Z cardiovascular disease; IHD Z ischemic heart diseases; IRR Z incident rate ratio. *Adjusted for age (5 year groups), sex, year. y Additional adjusted for income.

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