**2. Method**

There are inherent methodological problems with international comparisons of mortality but the following method has sought to minimise them, as utilised in a number of comparative international studies covering healthcare, suicide, child‐abuse‐related deaths, cancer and neuro‐ logical disease [15–18]. Nonetheless, there can be limitations linked to the accuracy of mortality data in less industrialised nations [1, 8, 19].

## **2.1. Mortality data**

Two types of mortality data have been used: confirmed and estimated figures. The World Health Organisation (WHO) provides *confirmed* annual deaths for babies (<1 year) and infants (1–4 years) in the 21 Western and five of 17 Asian countries [20]. From these figures, under‐five (0–4 years) child mortality rates (CMR) per million (pm) of population have been calculated. Since1968, annual WHO mortality data were collected from member states though the data are invariably 4–5 years behind the year of publication. Whilst probably never entirely accu‐ rate, they are the most consistently reliable available international data for mortality [20].

UN Millennium Goals Indicators (UNMGI) and the UN Statistics Division provide *estimated* levels of child mortality from intra‐country expert committees [8, 18] but have been criticised because of the discrepancies between them [21]. Inevitably, there are variations between WHO, UNICEF and United Nations Millennium Development Goals (UNMDG) data for the same years. For instance, a brief inspection of UNMGI data for the UK in 2010 gives a mortality estimate of 5.2 per thousand live births, equivalent to 5200 pm, but WHO data yield a confirmed rate of 4464 pm [20]. Although WHO rates are invariably lower than UNICEF‐estimated data, UNICEF results are generally closer to WHO figures for the West and the industrialised Asian countries and therefore UNICEF data [8] have been used for societies without WHO information, as indicated in the tables.

As CMR varies on an annual basis, a 3‐year baseline average (1988–1990) is contrasted with a 3‐year index average (2008–2010) and a percentage of change calculated. As also indicated in the tables, WHO data for China were available until 1994, based upon a 10% sample of population (running into the tens of millions), but UNICEF data are used for 2008‐2010. Index data for Canada and New Zealand is only available from 2007–2009 and Germany, Portugal and Spain have slightly later baseline years of 1990–1992 and is noted in the table.

#### **2.2. Poverty data**

Poverty is the context in which child mortality rates (CMR) have been analysed in this chap‐ ter. Therefore, this study assesses the relationship between poverty and CMR in 71 countries from three world regions, the West, Asia and Sub‐Saharan Africa (SSA) and how successful they have been in reducing mortality rates over time relative and comparative to their region. There are, of course, many interrelated social policy factors that influence CMR reflecting dif‐ fering political priorities so included is a comparison of health and military expenditure to

Bringing together markedly different socio‐economic regions has its problems, but it has been argued that in a globalised world the concept of developed and underdeveloped nations is redundant and countries should be seen along a continuum of socio‐economic development [6]. This juxtaposition of three regions provides a comparative perspective of what is hap‐ pening to children, in the context of poverty, within a regional perspective. Although the socio‐political and economic make up of these regions varies considerably, all 71 countries under review are signatory to the United Nations millennium goals aspiration of reducing

The importance of the poverty dimension originates from the seminal work of Wilkinson and Pickett who highlighted the significance of income inequality, a measure of relative poverty relevant to Western societies [4]. Income inequality is linked with a range of negative outcomes such as poorer employment, education, crime, housing and health outcomes as detailed in numerous Western 'clinical' studies [9–14]. International comparisons of CMR are problematic and more so when contrasting three world regions. However, as each nation is assessed, within its own region, against itself over time, it becomes is its own control, enabling us to judge how

In analysing mortality rates, it is easy to forget the emotional impact of the death of a child. This is epitomised in the lament of the octogenarian Elizabeth Barraclough who said '*I've lost a husband, mother and father, brothers and sisters but nothing, nothing is more bitter than losing a bairn*'. This would be true of any parent in any society in any region, so we also consider the impact that disproportionate levels of child mortality have upon societies. This is the first‐ever known comparative study of societies' responses to children in three world regions and draws upon

There are inherent methodological problems with international comparisons of mortality but the following method has sought to minimise them, as utilised in a number of comparative international studies covering healthcare, suicide, child‐abuse‐related deaths, cancer and neuro‐ logical disease [15–18]. Nonetheless, there can be limitations linked to the accuracy of mortality

Two types of mortality data have been used: confirmed and estimated figures. The World Health Organisation (WHO) provides *confirmed* annual deaths for babies (<1 year) and infants (1–4 years) in the 21 Western and five of 17 Asian countries [20]. From these figures, under‐five

reflect what in fiscal terms are competitive concerns [1–5].

successful it has been in reducing CMR relative to its region [12–15].

a range of recent and new research specific to this chapter.

data in less industrialised nations [1, 8, 19].

**2. Method**

**2.1. Mortality data**

under‐five CMR by 2% per annum [7, 8].

66 International Development

There is a long‐standing debate about definitions of poverty, crucially between 'relative' poverty in Western countries and 'absolute' poverty in the developing world [22–25]. Recently, the World Bank highlights that whilst there is no internationally agreed definition of poverty, in effect each country determines a 'relevant welfare measure' juxtaposed against a selected poverty line for that country to report poverty in relation to its total population [26]. The Western concept of rela‐ tive poverty is usually proportionate to national average income, so a family income 60% below the average is designated as in relative poverty [26–28].

For Western countries, a ratio of income inequality is used, that is, the gap between the top and bottom 20% of incomes used by Wilkinson & Pickett [4], alongside gross national income (GNI) data [29] as indicated in the tables. The benefit of using this ratio is that it is country specific, thereby reflecting the relative positions of poorer families within that society but avoiding the blurring of average incomes. As previously noted, income inequalities have been found to be associated with a wide range of poorer outcomes in education, crime, unemploy‐ ment and health [2, 4, 30–33].

As no comparable income inequality data exist for Asian countries, GNI figures by purchasing power parity (PPP) have been used [34]. PPP is the estimated value of the local currency con‐ verted into US dollars sufficient to obtain basic foodstuffs but does not demonstrate the income gaps that exist in that society. Absolute poverty relates to an individual surviving on \$1–2 a day [24, 25]. GNI is the total national income divided by total population, adjusted for PPP and so provides a global indication of parity of income to show relative gaps between the West and other regions [29]. The problem of an average income figure is that it obscures variations between groups. For example, the UK's average income is £28,000, yet 60% of the population receive under £18,000 p.a. indicating the mode income is far lower than the average [35].

Recent World Bank data has been published that includes 30 of the 33 SSA countries (Anglo, Congo (Kinshasa) and Somalia were not available) and so matching GNI data are reported for 2010 [28]. SSA data are available for 2015, but over 5 years, there was virtually no difference between the countries ranking, hence CMR and 2010 GNI are also correlated to explore any link between CMR and poverty.

#### **2.3. Socio‐economic, health and military expenditure**

The different socio‐economic backgrounds of these regions are recognised but to an extent both Asian and SSA societies from the former British Empire have faced similar postcolonial struggles [36]. Comparisons of countries since their independence *within*, not *between* regions, are therefore considered reasonable.

Although Angola, China, Nigeria, Somalia, South Africa and Yemen are considered developing countries, they are among the world's top 20 producers of minerals and oil [34]. It is also noted that 14 of the 33 SSA countries have endured serious civil conflict over the period under review.

An important policy priority context is what percentage countries spend of their national wealth (gross domestic product, GDP) on health and military. World Bank data are extrapolated as a percentage of GDP for health and military expenditure from which a military to health expen‐ diture ratio is calculated [37]. This ratio reflects national priorities and is likely to be influenced by local/regional political history as regimes change over time and respond to their sense of threat from their regional perspective. This is exemplified by the long‐standing tension between India and Pakistan, Greece and turkey. Hence the military and health ratios can be sen as broad indicators of policy proirities.

#### **2.4. Statistical analysis**

Spearman rank order (Rho) correlations have been used to determine any association between regional CMR and poverty, that is, GNI, military and health data. Standard deviations (SD) of CMR in each of the regions have been calculated and 1 SD above or below the regional average is the measure used to assess whether a nation merits a *reproach*, using the words of William Penn (1693), or a *commendation*. 'It is a reproach to Government and Religion t Suffer such Poverty and Excess' [38]
