Table 1.
Overview of differences in modelling approach and datasets used by UN IGME 2012 and UN IGME 2013, for estimating the U5MR and the number of under-five deaths.
Figure 1.
Illustration of the B-spline regression model for Norway.
From left to right: B-splines and their corresponding spline coefficients (plotted in the same color), observed log(U5MR) and U5MR (black dots) plotted against time, together with the spline estimates (red line). The spline estimate for log(U5MR) in each year is the sum of the non-zero B-splines in that year weighted by their respective spline coefficients.
Figure 2.
UN IGME 2013 and UN IGME 2012 estimates of the U5MR for the years 1990 (left) and 2011 (right).
UN IGME 2013 estimates are plotted against UN IGME 2012 estimates. Gray areas represent relative differences of up to 10%, 20% and 30% respectively. Country-specific U5MR estimates are displayed as green points, or highlighted in red if the estimates differ by more than ten deaths per 1,000 live births. Regions are colored according to the given legend.
Figure 3.
UN IGME 2013 and UN IGME 2012 estimates of the annual rate of reduction for 1990–2011.
UN IGME 2013 estimates are plotted against UN IGME 2012 estimates. Gray areas represent absolute differences of up to 1%, 2% and 3% respectively (absolute difference). Country-specific ARR estimates are plotted in green for high mortality countries (with U5MR in 1990 of at least 40 deaths per 1,000 live births), or in red for a subset of these if the difference is at least 2% and the UN IGME 2013 and UN IGME 2012 estimates disagree with respect to whether the country is on track to meet MDG 4 (4.4% annual rate of reduction). Regions are colored according to the given legend.
Figure 4.
Decomposition of differences in U5MR for 1990 and 2011 into differences due to estimation method and differences due to data. The gray box represents differences up to 10 deaths per 1,000 live births.
Countries with differences of more than 10 deaths per 1,000 deaths due to either factor are highlighted in red.
Figure 5.
Comparison of U5MR estimates for Lao PDR and Burkina Faso where the change in database changed the estimate by more than 10 deaths per 1,000 live births.
Estimates compared are from UN IGME 2013 (solid red line with 90% credible intervals given by the shaded regions), default Loess fit to 2013 database (dashed dark blue line) and UN IGME 2012 (solid dark blue line). Connected dots denote data from the UN IGME 2013 database and gray shaded areas around series of observations represent the sampling variability in the series (quantified by two times of the sampling standard errors). The newly-added/updated series for Lao PDR and Burkina Faso are those shown in dark blue. Excluded data series and detailed information on all data series are displayed in Figure S1.
Figure 6.
Decomposition of differences in U5MR for 1990 and 2011 into differences due to data quality model and differences due to splines model.
The gray box represents differences up to 10 deaths per 1,000 live births. Countries with differences of more than 10 deaths per 1,000 deaths due to either factor are highlighted in red.
Figure 7.
Comparison of U5MR estimates for Afghanistan, Angola, Botswana, Burundi, Central African Republic and South Sudan, where the inclusion of the data quality model changed the estimate by more than 10 deaths per 1,000 live births.
Estimates compared are from UN IGME 2013 (solid red line with 90% credible intervals given by the shaded regions), B2 fit to 2013 database (solid light green line with 90% credible intervals given by the shaded regions), default Loess fit to 2013 database (dashed dark blue line). Connected dots denote data from the UN IGME 2013 database and gray shaded areas around series of observations represent the sampling variability in the series (quantified by two times of the sampling standard errors). Excluded data series and detailed information on all data series are displayed in Figure S1.
Figure 8.
Comparison of U5MR estimates for Algeria, Maldives, Oman and Pakistan, where the inclusion of the data quality model resulted in estimates that are closer to VR data.
Estimates compared are from UN IGME 2013 (solid red line with 90% credible intervals given by the shaded regions), B2 fit to 2013 database (solid light green line with 90% credible intervals given by the shaded regions), default Loess fit to 2013 database (dashed dark blue line). Connected dots denote data from the UN IGME 2013 database and gray shaded areas around series of observations represent the sampling variability in the series (quantified by two times of the sampling standard errors). VR data is denoted by connected black squares. Excluded data series and detailed information on all data series are displayed in Figure S1.
Figure 9.
Comparison of U5MR estimates for Burkina Faso, Mali and Sao Tome and Principe where the change in curve fitting method changed the estimate by more than 10 deaths per 1,000 live births.
Estimates compared are from UN IGME 2013 (solid red line with 90% credible intervals given by the shaded regions), B2 fit to 2013 database (solid light green line with 90% credible intervals given by the shaded regions), default Loess fit to 2013 database (dashed dark blue line). Connected dots denote data from the UN IGME 2013 database and gray shaded areas around series of observations represent the sampling variability in the series (quantified by two times of the sampling standard errors). Excluded data series and detailed information on all data series are displayed in Figure S1.
Figure 10.
Decomposition of differences in under-five deaths in 1990 and 2011 into differences due to the WPP update and differences due to updates in U5MR estimates.
The gray box represents differences up to 10%. Regions with differences of more than 10% due to either factor are highlighted in red.