Economic development as a determinant of injury mortality – A longitudinal approach
Introduction
Economic development has long been considered an important determinant of changing patterns of disease, disability, and mortality in populations (McKeowan et al., 1975, Preston, 1975, Preston, 1978, Rogers and Wofford, 1989). Throughout the last century, important epidemiological changes accompanied economic development in most countries, especially those known today as high-income countries (HICs). Further, both mortality and fertility rates declined enormously. Rising standards of living in a population make a major contribution to the shift in main causes of death, from infectious and communicable diseases such as malaria, diarrhea, and tuberculosis to non-communicable and man-made conditions such as cancers, cardiovascular diseases, and injuries resulting from road traffic accidents (van Beeck et al., 1998, Murray and Chen, 1993, Murray and Lopez, 1997, Oppe, 1991, Thom et al., 1985). These major changes in disease patterns led to the formulation of a theory called the ‘health (epidemiologic) transition’ theory (Omran, 1971). This ‘health transition’ may have been completed in all high-income countries, whereas in developing countries it is still underway (Omran, 1971, Omran, 1983).
In high-income countries, the rising trends in cardiovascular diseases of the 1950s and 1960s were later reversed in spite of growing economic development (Uemura & Pisa, 1988). In the 1970s, such observations inspired the graphic modeling of the health transition process in the form of ‘waves’ of health problems replacing each other through the course of historical development: first a declining wave of communicable diseases (diseases of poverty), to be followed by a wave of non-communicable diseases (diseases of civilization or well-being), and thirdly (and tentatively) an upswing in psycho-social health problems (Hjort, 1994).
Discerning secular trends and understanding health transition processes are essential for the adoption of adequate health policies and health planning in all countries. The relationship between socio-economic development and injury as a cause of mortality and morbidity needs further clarification (Beaglehole & Bonita, 2004). In an attempt to examine the relationship between economic development and injury mortality, cross-sectional studies have produced diverging results. Early studies on road traffic injury in a global perspective showed increasing injury mortality rates with growing national economies (Söderlund and Zwi, 1995, Wintemute, 1985). However, more recent and comprehensive studies on unintentional injury mortality (UIM) in the span from low to high-income countries have yielded more complex findings. These analyses have consistently shown an inverted U-shaped pattern in relation to economic development (Ahmed and Andersson, 2000, Ahmed and Andersson, 2002, Moniruzzaman and Andersson, 2005a, Plitponkarnpim et al., 1999, Plitponkarnpim et al., 1999). In parallel cross-sectional studies on intentional injury mortality (suicide and homicide), similar inverted U-shaped patterns are also found in association with GNP per capita (Moniruzzaman and Andersson, 2004, Moniruzzaman and Andersson, 2005b).
The main problem in previous cross-sectional studies where the association between injury mortality and economic development was examined is that even if cross-sectional studies produce clear inverted U-shaped curves for mortality from low to high-income countries at a specific point of time, this does not necessarily mean that specific countries follow similar curves as they grow richer over time. This is why further analysis is required to examine whether previous cross-sectional findings can be verified using a longitudinal approach.
Since high-income countries have generally advanced through most stages of epidemiological transition, as far is known today, this paper aims to explore the longitudinal relationship between injury mortality and economic development in high-income countries over a long time span.
Section snippets
Data sources and inclusion criteria
Mortality and GDP per capita – US$ adjusted with purchasing power parity (PPP) data – were derived from the official health database for the member countries of the Organization for Economic Cooperation and Development (OECD) for the period of 1960–1999 (OECD, 2006). PPP-adjusted values are the rates of currency conversion that equalize the purchasing power of different currencies. Thus, PPP-adjusted values eliminate differences in price levels between countries. Data for all OECD countries
Results
Injury mortality (all causes) and cause-specific injury mortality (i.e., RTA, fall injury, suicide, and homicide) rates over time, for the three income categories of countries, are shown in Fig. 2. In recent decades, substantial improvements in injury mortality have been observed in all income categories. More specifically, for income categories 1 and 2, injury mortality rates (all causes) first increased up until 1972, with rates of mortality peaking in 1972 – at 73 per 100,000 population –
Discussion
Data from 21 member countries of the OECD were analyzed in this study. These countries are referred to as the ‘established market economies’ (Murray & Lopez, 1997) or the ‘industrialized countries’ (Söderlund & Zwi, 1995). There were two major shifts of countries between the income categories since 1960; New Zealand's GDP per capita was similar to that of Australia, Canada, and Denmark (category 1) in 1960 and Ireland's GDP per capita was close to the average for category 3 (Greece, Portugal,
Conclusion
Longitudinal analyses among high-income countries confirm earlier cross-sectional findings, where most injury categories seem to follow inverted U-shaped trend lines, with declining trends after peaking at various stages of temporal and economic development. Injury mortality rates increased up to a certain level of GDP per capita in all income country categories, and then decreased significantly up to the highest level of GDP per capita. The comparison of injury mortality between time and
Acknowledgements
We wish to thank Karen Leander for English checking and the Swedish Rescue Services Agency, for their financial support.
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