The analysis focuses on 14 high and middle income countries: high income countries were United Kingdom, United States, Japan, Italy, Canada, France, and Germany; low/middle income countries were Argentina, Brazil, Malaysia, Mexico, Nicaragua, Russia and Thailand. The time series for each country was assembled from a variety of historical sources. For high income countries data were obtained from as early as 1915, but records from this time until the mid-1930 s are patchy and inconsistent, resulting in implausible estimates (as seen in Figure 2 below). The series for these high income countries as a whole appears to become more consistent from 1936 and this year is, therefore, used as the starting point for the analysis reported in this paper. Regressions were undertaken using data for the earlier period but, not surprisingly given the quality of the data, there were no statistically significant associations. For most of the low/middle income countries, data are not available until the 1950 s or later. Per capita income (Gross Domestic Product) was obtained for the same period using the Maddison database [14]. The data set permits an examination of the long run effects of GDP fluctuations on maternal and infant mortality. Data were extracted from various sources e.g Demographic Year Books, and were also received directly from contact with the National Statistics Offices (see acknowledgements). Estimates of the maternal mortality ratio - the most commonly used measure (maternal deaths per 100,000 live births) used here only includes direct obstetric causes of death since this enables the longest consistent time-series to be viewed across the selected countries. Indirect causes were not included into most national routine statistics of maternal deaths until after the mid-1960 s.
The substantial negative association between long run GDP per capita and mortality is borne out both by graphical representation (Figure 2) and simple regressions of outcomes (maternal mortality ratio (MMR) and infant mortality rate (IMR)) on GDP per capita (log values) (Table 1). The association with income appears to vary over the years, with the strongest correlation in the 1966 to 1980 period for MMR and 1981 and later for IMR.
Table 1 Association between health outcomes and GDP per capita
The rich country group appears to divide into two distinct sub-groups with regard to maternal mortality. In both Italy and Japan lower maternal mortality is reported from the beginning of the time series, yet their infant mortality remains comparable to other countries. Levels of maternal mortality in the UK, United States and Canada are reported as substantially higher in the 1920 s and early 1930 s and it is only from the late 1930 s and mid-1940 s that levels fall substantially to those at or below those reported in Japan and Italy. The dramatic decline found here in the UK, USA and Canada is consistent with other published series, such as the work of Loudon [15].
Long run time trends mean that any association between the variables using untransformed data will tend to be dominated by the trend itself rather than explaining the effect of deviations from the trend produced by recession or temporary booms. Our paper thus follows other studies of the effect of the macro-economy by transforming outcomes and GDP in order to focus on short term fluctuations [7, 13]. A first difference logarithmic transformation regresses the change in mortality outcomes on changes in GDP per capita (constant prices). Fertility is strongly associated with maternal and infant deaths and so is an important potential confounder. The crude birth rate is added to proxy this effect (the total fertility rate is not available for all observations in the data set). Lagged (logged) values of the mortality and income variable are also included. The format is specified as follows:
Where H
ct
is the health status variable of interest at time t and country c, GDPPC
ct
(time t, country c) is GDP per capita in real (2000) US dollars, CBR is the crude birth rate and u is the lag. A lag of five years is used for all models. The reason is that mortality data are mostly available at five year intervals. Whilst it is possible to impute values between these years, there is no possibility of picking up genuine fluctuations in mortality on a more regular basis. The use of a fixed lag is less restrictive than it seems: in the context of this model, a lag means that the cumulative five year change in GDP is expected to have a cumulative five year association with health outcomes.
We investigate the dataset in two stages. First, both fixed and random effects panel regression were used to explore the effect of changing per capita income on maternal, infant and neonatal mortality. A Hausman test, used to test the consistency of random versus fixed effects, was statistically significant (P < 0.01), implying that random effects are inconsistent and a fixed model should be used for both the upper and middle/low income country groups. Separate estimation was carried out for the (now) upper income countries and low/middle income countries. The dataset for the upper income countries was divided into fifteen year time periods: 1936 to 1950, 1951 to 1965, 1966 to 1980 and 1981 to 2005. The data for low/middle income countries is not divided by periods because of the small number of observations available for the earlier time periods. One of the potential problems with this specification is simultaneity bias if gdp per capita both determines maternal and infant outcomes and is affected by them. The use of change variables should minimise this risk. An instrumental variable (xtivreg in Stata) method that mitigates simultaneity leading to inconsistent estimates was also estimated. A time trend and total fertility rate were used to instrument the change in GDP variable. A Hausman test was also used to compare specifications and test for inconsistency. The tested was rejected (p > 0.5) for both low/middle and upper income groups suggesting no evidence of simultaneity.
A second stage of modelling was to focus on each country individually and the differences in association between per capita income and health during the four post-war time periods. Statistical tests - Durbin Watson (DW) and Breusch-Pagan - indicated that ordinary least squares regression is inefficient as a result of heteroscedastic and autocorrelated (lag 1) errors. Durbin Watson statistics all indicated positive serial correlation with the statistic below the lower bound of significance (Statistics as follows: United Kingdom 0.86, United States 0.26, Japan 0.86, Italy 1.14, Canada 0.98). A generalised least squares model with robust standard errors was therefore used (Prais-Winsten and Cochrane-Orcutt error correction regression, specified as 'Prais' in Stata v10.1). Post-estimated DW statistics suggested that the correction had successfully eliminated serial correlation from the estimation for Canada, Italy and Japan and reduced for United States and UK (the statistic was in the zone of indeterminacy).