Cross-national injury mortality differentials by income level: The possible role of age and ageing

Public Health (2008) 122, 1167e1176

www.elsevierhealth.com/journals/pubh

Original Research

Cross-national injury mortality differentials by income level: The possible role of age and ageing S. Moniruzzaman*, R. Andersson Department of Health and Environment, Division of Public Health Sciences, Karlstad University, SE-651 88 Karlstad, Sweden Received 7 March 2007; received in revised form 7 December 2007; accepted 27 February 2008 Available online 29 July 2008

KEYWORDS Homicide; Suicide; Mortality; GNP per capita; Health transition

Summary Objectives: To examine age- and cause-specific injury mortality differentials between low-income (LICs), middle-income (MICs) and high-income countries (HICs), and to discuss their implications in explaining changing injury mortality patterns with economic development against the background of general health transition theory. Study design: Cross-sectional study. Methods: The World Health Organization’s mortality database was used as the source of injury mortality data. The grouping into LICs, MICs and HICs was based on data from World Development Indicator. Results: Unintentional injury mortality (UIM) rates in children and adults are highest in LICs and MICs, respectively. UIM rates in the elderly population, however, increase with higher economic conditions and are highest in HICs. Conclusion: Based on these findings, it is hypothesized that ageing and injury interplay mutually with regard to health transition; declining rates in child UIM with economic development contributes to the ageing process, while increasing UIM among the elderly, in combination with ageing populations, boosts the absolute number of injury deaths in this segment. ª 2008 The Royal Institute of Public Health. Published by Elsevier Ltd. All rights reserved.

Introduction Over the last centuries, most countries, developed and developing, have seen dramatic epidemiologic and demographic changes; mortality and fertility

have declined enormously, the population has grown and disease patterns have shifted. These major changes led to the formulation of two theories: demographic and epidemiologic transition theories. Later, the term ‘health transition’

* Corresponding author. Tel.: þ46 54 700 2535; fax: þ46 54 700 2220. E-mail address: [email protected] (S. Moniruzzaman). 0033-3506/$ - see front matter ª 2008 The Royal Institute of Public Health. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.puhe.2008.02.012

1168 was suggested to encompass the related theories of demographic and epidemiologic transition, and to extend the theories further to encompass the economic, social and cultural aspects of development that drive these shifts in population and health.1e3 The demographic transition theory explains the population growth in terms of a process in which there is a transition from a stage with high mortality and fertility rates to a stage with low mortality and fertility rates.4,5 The demographic component refers to the ageing of populations as a result of declining fertility and declining death rates, particularly in children. According to the health (epidemiologic) transition theory,6,7 as the risk of dying from communicable diseases is reduced for a population, those saved from dying from such causes survive into middle and older ages where they face elevated risk of dying from degenerative and man-made diseases. Since degenerative diseases tend to kill at much older ages than infectious diseases, this transition in causes of death is characterized generally by a redistribution of deaths from the young to the old. Further changes in the mortality profile have been associated with a proposed advanced stage of the transition ‘Age of delayed degenerative diseases’ by Olshansky8 and Ault, which they see as a stage that will propel life expectancy into, and perhaps beyond, the ninth decade. The health transition theory remains a useful framework for the understanding of changing patterns of mortality and health. Nevertheless, little is said about injury in relation to health transition theory. Most analysis and attention is focused on historical shifts from communicable to non-communicable disease patterns. Recent crosssectional studies on injury in a health transition perspective, however, suggest that economic development may play a fundamental role as a driver of changing patterns of injury mortality.9e15 The strength and direction of the association vary considerably with age, sex and type of injury. For example, when analysing national cause-specific injury mortality rates by gross national product (GNP) per capita, unintentional injury mortality (UIM) rates correlated negatively with GNP per capita for all age groups except 75þ years.9e12,14 In a similar study on homicide, there was clear negative association between homicide rates and GNP per capita among all age groups except for 1e4 years, where a weak positive association between homicide and GNP per capita was found.15 Suicide increased slightly in association with nation’s income per capita, especially among women.13

S. Moniruzzaman, R. Andersson Consequently, studies on economic level and injury mortality show complex and deviating associations where demographic transition might serve as a mediating and explanatory link. This study aimed to examine age- and cause-specific injury mortality differentials between low-income (LICs), middle-income (MICs) and high-income countries (HICs), and to discuss their implications in explaining changing injury mortality patterns with economic development against the background of general health transition theory.

Methods Data sources and inclusion criteria The World Health Organization’s (WHO) mortality database for the year 2000 was used as the source of age- and sex-specific injury mortality data.16 These statistics provide archival information on numbers of deaths (numerator data) by age, sex and cause, as well as population estimates (denominator data) for all regions of the world. Data for all countries reporting to the WHO mortality database were reviewed. Some countries were excluded due to small population size (<1 million) in order to reduce sampling variability, or lack of information about population subgroups. The three major causes of injury mortality, i.e. UIM, suicide and homicide, were analysed, and mortality rates per 100,000 population were calculated for each age and sex group.

Definitions of injury Intentional and unintentional injuries are defined by a series of external cause codes. Unintentional injuries are subdivided into road traffic injuries, poisoning, falls, fires, drowning and other unintentional injuries.17 Intentional injuries are subdivided into self-inflicted injuries (suicide), interpersonal violence (homicide), war-related injuries and other intentional injuries. The International Classification of Diseases (ICD), 9th and 10th editions, were used to define specific causes of injury mortality in this study:  Unintentional injuries (E800eE949 for ICD-9, V01eX59, Y40eY86, Y88, Y89 for ICD-10).  Self-inflicted injuries, i.e. Suicide (E950eE959 for ICD-9, X60eX84, Y870 for ICD-10).  Interpersonal violence, i.e. Homicide (E960e E969 for ICD-9, X85eY09, Y871 for ICD-10).

Cross-national injury mortality differentials by income level The mortality data were disaggregated into three income levels, i.e. HICs, MICs and LICs, according to the divisions developed by the World Bank.18 Table 1 shows the country groups by income level and their characteristics.

Statistical methods To allow comparisons between income groups (LICs, MICs and HICs), age- and sex-specific injury mortality rates were first calculated for each country. Means and 95% confidence intervals (CI) of mortality rates for the three main causes of injury were then calculated by income groups and age and sex subgroups. Differences in the mean values for income groups (LICs and MICs) were compared with those of the HICs and tested for significance; analysis of variance was performed. To validate the cross-sectional results, age- and sex-specific UIM rates in 1960 and 2000 in HICs were compared to indicate changes over time in the percentage distribution of UIM rates through the age structure.

Results Fig. 1 illustrates the injury mortality rates for UIM, suicide and homicide by age- and income-based groups in the 66 countries. In LICs and MICs, UIM rates increased with age between 5e64 years and in HICs between 5e24 years. The rates in HICs plateaued with age but then increased among older populations. The highest UIM rates were seen among elderly populations in HICs. It is noteworthy

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that for adults aged 15e64 years, UIM rates were highest in MICs, while in children aged 0e4 years, LICs showed the highest rates. Suicide rates, generally, increased with age in MICs and HICs, with higher rates in MICs for all age groups. In LICs, peak rates were seen in adults aged 55e64 years. For homicide rates, the age distribution was similar across all income groups, but with generally higher rates in MICs with a sharp peak in adults aged 25e34 years. Tables 2 and 3 depict the mean and 95% CIs of UIM, suicide and homicide rates in three incomebased groups by age and sex subgroups, including P-values for statistical significance of differences between the country groupings. Age-specific UIM rates varied with sex and income groups. In children, UIM rates were highest in LICs, especially among children aged <1 year (67.8 per 100,000 for males and 43.3 per 100,000 for females). Among adolescents and adults, UIM rates were generally higher for both sexes in MICs than in LICs and HICs, especially for adults aged 65e74 years (116.1 per 100,000 for males and 39.1 per 100,000 for females). It is noteworthy that UIM rates among elderly people (75þ years) were highest for both males (196.2 per 100,000) and females (154.0 per 100,000) in HICs. Suicide was the most common type of intentional injury mortality in all age-specific groups, followed by homicide (Tables 2 and 3). Among adolescents and adults, suicide rates were highest for males in MICs, and were most accentuated among 45e54 year olds (37.6 per 100,000). The lowest suicide rates among adolescents and adults were seen among females in LICs (3.0 per 100,000

Table 1 Income-based country groups, gross national product values used for grouping, countries and mean (standard deviation) gross national income (GNI) per capita for 2000. Income group

GNI per capita (US$)

Country (year of data)

Low-income countries, n ¼ 7 Middle-income countries, n ¼ 34

765

Armenia, Azerbaijan, Georgia, Kyrgyzstan, Rep Moldova, Ukraine, Uzbekistan Albania, Argentina, Belarus, Brazil, Bulgaria, Chile, Colombia, Costa Rica, Croatia, Cuba, Czech Rep, Ecuador, Egypt, El Salvador, Estonia, Guatemala, Hungary, Kazakhstan, Latvia, Lithuania, Macedonia, Mauritius, Mexico, Panama, Paraguay, Philippines, Poland, Romania, Rush Fed, Slovakia, Slovenia, Thailand, Uruguay, Venezuela Australia, Austria, China (HK), Denmark, Finland, Germany, Greece, Ireland, Israel, Italy, Japan, Kuwait, Netherlands, New Zealand, Norway, Portugal, Puerto Rico, Rep Korea, Singapore, Spain, Sweden, Switzerland, UK, USA

High-income countries, n ¼ 24

766e9385

9386

Mean (SD) GNI per capita (US$) 570.0 (167.2) 3281.5 (2058.7)

22891.7 (8645.2)

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S. Moniruzzaman, R. Andersson

200

Unintentional injury mortality

rates/100 000

160

120

LIC MIC HIC

80

40

0

<1

1-4

5-14

15-24

35-44

25-34

45-54

55-64

65-74

75+

age-groups 30

Suicide

rates/100 000

24

18

LIC MIC HIC

12

6

0 15-24

25-34

35-44

45-54

55-64

65-74

75+

age-group 30

Homicide

rates/100 000

24

18

LIC MIC HIC

12

6

0

<1

1-4

5-14

15-24

25-34

35-44

45-54

55-64

65-74

75+

Figure 1 Rates of injury-related deaths in males and females by age and income group in 2000. LIC, low-income country; MIC, middle-income country; HIC, high-income country.

Cross-national injury mortality differentials by income level

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Table 2 Means and 95% confidence intervals (CI) of the injury mortality rates for unintentional injury, suicide and homicide (per 100,000 population) by age and sex in three income-based country groups in 2000: males. Injury types

HIC

MIC

LIC

P-value

Mean (95% CI) UIM Age group (years) <1 1e4 5e14 15e24 25e34 35e44 45e54 55e64 65e74 75þ

13.4 9.6 6.7 40.3 35.4 34.8 39.1 45.6 71.0 196.2

Suicide Age group (years) 15e24 25e34 35e44 45e54 55e64 65e74 75þ

13.8 19.7 21.1 22.2 22.7 25.1 39.3

(10.0e17.5) (15.0e24.3) (16.4e25.8) (16.6e27.7) (16.8e28.6) (19.3e30.6) (28.9e49.7)

17.0 25.1 32.0 37.6 33.5 35.8 46.7

(12.3e21.6) (17.7e32.5) (22.2e41.7) (24.4e50.7) (22.2e44.8) (24.7e46.9) (32.7e60.6)

Homicide Age group (years) <1 1e4 5e14 15e24 25e34 35e44 45e54 55e64 65e74 75þ

2.1 0.8 0.3 6.0 5.9 3.8 2.8 2.1 1.8 3.1

(1.1e3.1) (0.2e1.3) (0.2e0.5) (0.7e12.6) (0.1e11.9) (1.0e6.6) (0.9e4.6) (0.9e3.3) (0.8e2.9) (0.2e6.0)

4.3 0.7 1.0 26.5 33.1 29.3 25.5 20.3 16.7 19.1

(2.4e6.0) (0.4e0.9) (0.5e1.3) (12.2e40.8) (18.0e48.2) (17.3e41.2) (16.0e35.1) (12.5e27.1) (10.10e23.2) (11.85e26.4)

(9.8e16.8) (7.0e12.1) (5.5e7.9) (33.5e46.9) (28.5e42.3) (27.4e42.2) (29.8e48.43) (34.4e56.7) (53.6e88.3) (159.8e232.6)

40.3 19.6 15.4 56.1 72.4 89.0 105.6 110.1 116.1 193.8

(27.3e53.1) (15.7e23.5) (13.2e17.5) (41.7e70.4) (48.9e95.8) (59.3e118.5) (68.5e142.6) (75.6e144.5) (94.1e138.0) (163.0e224.7)

67.8 37.0 16.7 37.4 60.4 83.5 103.5 103.0 82.7 75.0

(15.6e119.9) (15.3e58.5) (7.3e26.0) (18.1e56.5) (26.5e94.1) (26.9e140.0) (22.5e184.3) (27.5e178.4) (37.5e127.8) (38.1e111.6)

0.000 0.000 0.000 0.131 0.034 0.011 0.013 0.011 0.010 0.003

11.2 18.0 25.7 31.1 32.7 29.7 25.1

(1.5e20.1) (1.7e34.2) (2.9e48.5) (1.6e60.6) (3.0e62.4) (2.5e56.9) (2.0e48.1)

0.377 0.419 0.214 0.177 0.322 0.318 0.293

3.5 0.6 0.4 7.4 14.4 17.0 17.4 12.5 10.8 11.5

(0.2e9.2) (0.1e1.3) (0.0e0.8) (3.1e11.6) (7.0e21.1) (6.6e27.7) (5.2e29.5) (2.1e22.8) (4.6e17.0) (6.7e16.2)

0.195 0.838 0.030 0.044 0.011 0.002 0.001 0.001 0.001 0.002

UIM, unintentional injury mortality; HIC, high-income countries, MIC, middle-income countries; LIC, low-income countries.

for 15e24 year olds). For both sexes, suicide rates were highest among elderly people in MICs (46.7 per 100,000 for males and 12.1 per 100,000 for females). Homicide rates varied greatly age, sex and income groups (Tables 2 and 3). In children, there were small differences between male and female infanticide (i.e. homicide in children aged <1 year) rates across income-based country groups. For men, age-specific homicide rates were highest among adults in MICs (33.1 per 100,000 for 25e34 year olds). For women, the highest homicide rate was seen among elderly people in LICs (8.0 per 100,000 for adults aged 75þ years). There were significant differences in age-specific homicide

rates between income groups in older adults and elderly populations. Fig. 2 shows the percentage distribution of UIM rates for males and females of all ages in HICs for 1960 and 2000. According to this figure, between 1960 and 2000, deaths that had previously occurred at younger ages were replaced by a larger proportion of deaths at advanced ages for both males and females.

Discussion This was an explorative study that did not aim to determine causal mechanisms between age-related

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Table 3 Means and 95% confidence intervals (CIs) of the injury mortality rates for unintentional injury, suicide and homicide (per 100,000 population) by age and sex in three income-based country groups in 2000: females. Injury types

HIC

MIC

LIC

P-value

Mean (95% CI) UIM Age group (years) <1 1e4 5e14 15e24 25e34 35e44 45e54 55e64 65e74 75þ

10.6 6.5 3.6 10.3 7.9 9.1 11.1 15.7 32.5 154.0

Suicide Age group (years) 15e24 25e34 35e44 45e54 55e64 65e74 75þ

4.6 5.8 6.6 7.6 8.4 8.9 11.8

(3.1e5.9) (4.5e7.1) (4.9e8.2) (5.7e9.6) (5.8e11.0) (6.2e11.5) (7.3e16.2)

Homicide Age group (years) <1 1e4 5e14 15e24 25e34 35e44 45e54 55e64 65e74 75þ

3.0 0.7 0.4 1.2 1.3 1.3 0.8 0.6 0.8 1.1

(1.5e4.5) (0.3e1.1) (0.2e0.7) (0.5e1.8) (0.7e1.7) (0.8e1.8) (0.5e1.1) (0.4e0.8) (0.4e1.1) (0.7e1.3)

(7.2e13.8) (4.7e8.2) (2.8e4.3) (8.3e12.3) (6.1e9.6) (7.1e11.1) (8.7e13.3) (12.3e18.9) (26.8e38.3) (121.7e186.3)

29.1 13.6 7.2 12.6 12.3 16.1 22.4 27.8 39.1 142.2

(20.7e37.5) (10.8e16.3) (6.1e8.2) (9.2e15.8) (8.1e16.5) (10.5e21.6) (14.5e30.2) (19.5e36.1) (32.8e45.4) (105.6e178.6)

43.3 27.4 6.3 7.8 10.9 15.7 21.5 28.3 30.0 46.0

(8.1e78.4) (10.5e44.2) (2.8e9.6) (3.6e11.9) (5.0e16.6) (5.5e25.8) (5.8e37.2) (10.1e46.3) (15.8e44.5) (27.2e64.6)

0.001 0.000 0.000 0.264 0.226 0.105 0.057 0.053 0.204 0.023

4.3 4.8 6.0 7.4 7.7 9.7 12.1

(3.3e5.1) (3.5e5.9) (4.3e7.6) (5.1e9.6) (5.2e10.3) (6.2e13.3) (7.5e16.7)

3.0 3.2 3.4 4.0 6.8 6.6 10.8

(0.2e5.6) (0.6e5.8) (0.4e6.3) (0.1e7.7) (1.2e12.4) (1.4e11.7) (3.4e18.1)

0.453 0.154 0.239 0.302 0.837 0.667 0.960

4.2 0.6 0.6 3.2 4.0 4.2 4.6 3.6 4.0 5.6

(1.8e6.4) (0.3e0.8) (0.4e0.7) (2.1e4.1) (2.7e5.1) (2.7e5.5) (3.1e6.1) (2.3e4.9) (2.7e5.2) (3.8e7.3)

3.5 0.9 0.5 1.9 3.5 4.1 4.5 4.3 6.4 8.0

(0.8e7.9) (0.1e1.7) (0e0.9) (0.4e3.4) (1.4e5.6) (0.8e7.3) (1.5e7.3) (1.3e7.2) (2.3e10.4) (3.0e13.0)

0.735 0.548 0.434 0.016 0.003 0.004 0.000 0.001 0.000 0.000

UIM, unintentional injury mortality; HIC, high-income countries, MIC, middle-income countries; LIC, low-income countries.

injuries and economic development; therefore, the scope of the study is limited to specific relationships between these variables. This study on injury mortality by country’s income level is part of a broader research programme on economic development as a determinant of injury mortality.13e15 Findings in this study are mainly in line with those of earlier studies dealing with national economic differentials in injury risks. There are, indeed, considerable economic gradients in injury risks across nations. However, some findings are new and help to throw light on the dynamics of injury mortality by age and economy. In LICs, many leading health problems such as malaria, diarrhoea, tuberculosis, malnutrition,

new emergence of human immunodeficiency virus/acquired immunodeficiency syndrome and other infectious diseases are directly linked to lack of resources, and are often termed ‘diseases of poverty’.19e21 However, in wealthy societies, most of these major killers of the past have declined, coupled with high mortality and morbidity in non-communicable degenerative diseases, such as different types of cancer, diabetes and cardiovascular diseases; often termed ‘diseases of affluence’.19,22 According to the present findings, injury seems to fit into both of these extremes, depending on which age group and injury category is being considered (Fig. 3). Moreover, the present results indicate an intermediate and

Cross-national injury mortality differentials by income level

% distribution of deaths

55

1173

Male

44

33

1960 2000

22

11

0 <1

1-4

5-14

15-24 25-34 35-44 45-54 55-64 65-74

75+

age-group

% distribution of deaths

55

Female 1960

44

2000 33

22

11

0

<1

1-4

5-14

15-24 25-34 35-44 45-54 55-64 65-74

75+

age-group

Figure 2 Percentage distributions of deaths from unintentional injury for high-income countries among age groups by sex for 1960 and 2000.

transitory stage of health development. This study also found a dramatic redistribution of injury deaths by economic level from the young to the elderly, which may appear as both

Injury as a disease of modernization

Injury as a disease of affluence

Unintentional injury mortality

Injury as a disease of poverty

a consequence of demographic transition and as a driver of the same process. Relative to other countries, LICs have the highest and HICs have the lowest UIM rates in children,

LIC MIC HIC

Age

Figure 3 Unintentional injury mortality in different model by country’s income group in 2000 (schematic). LIC, lowincome country; MIC, middle-income country; HIC, high-income country.

1174 which places child injury as a typical example of a ‘disease of poverty’. The combination of a wide range of childhood safety hazards with the lack of healthcare facilities results in high levels of child injury mortality in LICs. While infectious diseases have not yet been eliminated as a significant cause of child death, they have been reduced significantly. This reduction has allowed injury to become more visible as a major cause of child death in LICs. In elderly people, the highest UIM rates were seen in HICs and the lowest rates were seen in LICs, which makes UIM among the elderly a clear example of a ‘disease of affluence’. While varying patterns exist in the age distribution of unintentional injury death, the general trend in HICs from 1960 to 2000 was a shift in the distribution in the direction of older ages. This shift in UIM is likely to have considerable impacts on two major demographic variables: the size and relative proportion of the population at advanced ages; and the health and vitality of the elderly. The authors’ earlier studies on UIM indicated that UIM among the elderly increases with better economic conditions, while the opposite is seen for all other age groups.14 In the adult age groups, UIM rates were highest in MICs which indicates the existence of a possible third intermediate stage of health development. This stage has previously been described as a shift from traditional poverty-related health patterns to modern wealth-related health patterns: the ‘age of receding pandemics’.6 However, the present study identified a specific health problem (adult unintentional injury) which seems to peak in this particular stage. The general reason for higher rates of unintentional injuries among adults in LICs and especially in MICs compared with HICs could be attributed to rapid motorization, industrialization and urbanization. These are all very significant and intertwined processes in early phases of ‘modernization’,23e25 which qualify adult unintentional injury to be categorized as a group of its own, here tentatively termed as ‘disease of modernization’. In HICs, these developments occurred over more than 50 years, while in many LICs and MICs, these changes now generally occur over much shorter periods.26 Therefore, UIM in elderly people tends to increase with economic level. Several studies have estimated prospective changes in the size and relative distributions of the population at advanced ages for developed nations.27e29 For example, Siegel27 projected that by the year 2020, the population aged 65þ years in the USA will double from 23 million in 1976 to approximately 45

S. Moniruzzaman, R. Andersson million. All segments of the elderly population are expected to grow dramatically, particularly the extreme aged (85þ years). The proportion of elderly people in relation to the total population is also expected to increase by the second decade of this century. While the major demographic component that will bring forth increases in absolute number and proportions of people at advanced ages is different size cohorts moving through the age structure, rapid mortality declines in lower ages tend to accelerate the ageing of the population by allowing larger proportions of successive birth cohorts to survive to advanced ages. In this regard, it is important to recognize that even a minor difference in projection assumptions about mortality can produce rather large differences in the absolute number of people expected to survive to advanced ages in the future. This phenomenon of population ageing is a demographic process currently being experienced by both developed and developing nations throughout the world.30 The contribution from intentional injury mortality (homicide and suicide) to these developments is less clear due to smaller and more uncertain numbers. The present results suggest that intentional injury also largely falls into the ‘diseases of modernization’ category, but again caution should be attributed to possible impact from situational conditions in states related to the former Soviet Union. Although WHO mortality data are the best estimates available at the present time, they do suffer from a number of limitations.31 The first is missing information. Whereas vital registration systems capture around 17 million deaths annually, this represents just less than three-quarters of the total estimated global mortality. Some regions of the world are especially poorly represented in this regard; for example, national vital registration data were only available for 19% of the countries in the African region. In countries where such data are missing, information from other sources (e.g. survey data) coupled with indirect demographic techniques are used to estimate mortality and disability. However, extrapolations of this type should be interpreted with caution. Other problems in this study, related to country income grouping, are the very small sample size for LICs, and the regional distribution of countries in the three income-based groups, especially for MICs. Thirtynine European countries (14 of which are newly independent former USSR countries), 16 Latin American and Caribbean countries, two North American countries, and seven countries representing Western Pacific regions were included, but only one African country and one South-East

Cross-national injury mortality differentials by income level Asian country. This causes difficulties regarding the generalizibility of the results. Countries with small populations were excluded to eliminate possible impact from natural variation due to small numbers. Also, there were a number of countries for which data were not available for population subgroups, cause-specific injury mortality rates (particularly in LICs) or GNP per capita; consequently, it was not possible to include these countries in the analyses. The main methodological weakness of this study is its cross-sectional approach. Cross-sectional data analysis does not permit conclusions on the complex changes in mortality over time, but can generate hypotheses that can later be tested in more rigorous efforts,32 as has been done by Plitponkarnpim et al.9,10 Further, cross-sectional findings from this study together with previous studies on complex changes in mortality over time provide support for hypotheses on such changes of mortality.33e37 However, longitudinal studies that use age- and sex-specific data are needed to further validate the relationships described here, when interpreted in terms of changes over time with economic development.

Conclusion Age largely determines whether a certain injury category is seen as a ‘disease of poverty’ or a ‘disease of affluence’. While unintentional injury in children and homicide (all ages) appear to be ‘diseases of poverty’, unintentional injury among elderly people and suicide (especially in women) appear to be ‘diseases of affluence’. Moreover, for the adult population, UIM rates peak for MICs, possibly pointing to a third intermediate and transitory stage, here tentatively denoted ‘disease of modernization’. Based on these findings, it is hypothesized that ageing and injury interplay mutually with regard to health transition; declining rates in child UIM with economic development contributes to the ageing process, while increasing UIM among the elderly, in combination with ageing populations, boosts the absolute number of injury deaths in this segment.

Ethical approval Data used was from a published WHO dataset.

Funding Swedish Rescue Services Agency.

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Competing interests None declared.

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