The authors have declared the following competing interests: Stanton-Hill Research supported this study. Kenneth Hill of Stanton-Hill Research is also the chair of the Technical Advisory Group (TAG) to the UN Inter Agency group of Child Mortality Estimation. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the
Analyzed the data: JP JL. Wrote the first draft of the manuscript: JP. Contributed to the writing of the manuscript: JP JL.
Jon Pedersen and Jing Liu examine the feasibility and potential advantages of using one-year rather than five-year time periods along with calendar year-based estimation when deriving estimates of child mortality.
Child mortality estimates from complete birth histories from Demographic and Health Surveys (DHS) surveys and similar surveys are a chief source of data used to track Millennium Development Goal 4, which aims for a reduction of under-five mortality by two-thirds between 1990 and 2015. Based on the expected sample sizes when the DHS program commenced, the estimates are usually based on 5-y time periods. Recent surveys have had larger sample sizes than early surveys, and here we aimed to explore the benefits of using shorter time periods than 5 y for estimation. We also explore the benefit of changing the estimation procedure from being based on years before the survey, i.e., measured with reference to the date of the interview for each woman, to being based on calendar years.
Jackknife variance estimation was used to calculate standard errors for 207 DHS surveys in order to explore to what extent the large samples in recent surveys can be used to produce estimates based on 1-, 2-, 3-, 4-, and 5-y periods. We also recalculated the estimates for the surveys into calendar-year-based estimates. We demonstrate that estimation for 1-y periods is indeed possible for many recent surveys.
The reduction in bias achieved using 1-y periods and calendar-year-based estimation is worthwhile in some cases. In particular, it allows tracking of the effects of particular events such as droughts, epidemics, or conflict on child mortality in a way not possible with previous estimation procedures. Recommendations to use estimation for short time periods when possible and to use calendar-year-based estimation were adopted in the United Nations 2011 estimates of child mortality.
In 2000, world leaders set, as Millennium Development Goal 4 (MDG 4), a target of reducing global under-five mortality (the number of children who die before their fifth birthday to a third of its 1990 level (12 million deaths per year) by 2015. (The MDGs are designed to alleviate extreme poverty by 2015.) To track progress towards MDG 4, the under-five mortality rate (also shown as 5
Because the DHS originally surveyed samples of 5,000–6,000 women, estimates of under-five mortality are traditionally calculated using data from five-year time periods. Over shorter periods with this sample size, the random errors in under-five mortality estimates become unacceptably large. Nowadays, the average DHS survey sample size is more than 10,000 women, so it should be possible to estimate under-five mortality over shorter time periods. Such estimates should be able to track the effects on under-five mortality of events such as droughts and conflicts better than estimates made over five years. In this study, the researchers determine appropriate time periods for child mortality estimates based on full birth histories, given different sample sizes. Specifically, they ask whether, with the bigger sample sizes that are now available, details about trends in under-five mortality rates are being missed by using the estimation procedures that were developed for smaller samples. They also ask whether calendar-year-based estimates can be calculated; mortality is usually estimated in “years before the survey,” a process that blurs the reference period for the estimate.
The researchers used a statistical method called “jackknife variance estimation” to determine coefficients of variance for child mortality estimates calculated over different time periods using complete birth histories from 207 DHS surveys. Regardless of the estimation period, half of the estimates had a coefficient of variance of less than 10%, a level of random variation that is generally considered acceptable. However, within each time period, some estimates had very high coefficients of variance. These estimates were derived from surveys where there was a small sample size, low fertility (the women surveyed had relatively few babies), or low child mortality. Other analyses show that although the five-year period estimates had lower standard errors than the one-year period estimates, the latter were affected less by bias than the five-year period estimates. Finally, estimates fixed to calendar years rather than to years before the survey were more directly comparable across surveys and brought out variations in child mortality caused by specific events such as conflicts more clearly.
These findings show that although under-five mortality rate estimates based on five-year periods of data have been the norm, the sample sizes currently employed in DHS surveys make it feasible to estimate mortality for shorter periods. The findings also show that using shorter periods of data in estimations of the under-five mortality rate, and using calendar-year-based estimation, reduces bias (makes the estimations more accurate) and allows the effects of events such as droughts, epidemics, or conflict on under-five mortality rates to be tracked in a way that is impossible when using five-year periods of data. Given these findings, the researchers recommend that time periods shorter than five years should be adopted for the estimation of under-five mortality and that estimations should be pegged to calendar years rather than to years before the survey. Both recommendations have already been adopted by the United Nations Inter-agency Group for Child Mortality Estimation (IGME) and were used in their 2011 analysis of under-five mortality.
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Tracking Millennium Development Goal 4, which sets a target of reducing child mortality by two-thirds between 1990 and 2015, requires that the under-five mortality rate (also denoted in the literature as U5MR or 5
The DHS surveys were originally designed for samples of 5,000–6,000 eligible women
Estimates for short time periods have the benefit that trends and deviations from trends may be ascertained with some detail. The mortality effects of particular events, such as a drought, hurricane, or the outbreak of war, may be tracked if the time period is sufficiently short. Moreover, recent major changes in the pace of change of child mortality may be discovered earlier than if long time periods are used for estimation.
The tracking of the Millennium Development Goals is partly global, but the responsibility for meeting the goals lies with each country. Moreover, regardless of international goal setting, countries need to monitor the development of their child mortality level as an important indicator of the performance of their public health system. Global tracking of child mortality can be considered to be served by the current 5-y period of estimation, since short-term and local effects might obscure the overall trend. However, the overall trends for the different countries are constructed by smoothing the data from several surveys
The term “time period” in the context of child mortality estimation actually has two meanings. The first is the duration of the period for which an estimate is derived, 1 y, 2 y, and so on. The second is somewhat subtle. The usual practice in DHS surveys is to estimate mortality based on the concept of “years before the survey.” That is, the estimation period is measured with reference to the time of the interview of each individual woman. As a consequence, the survey estimates for a period will actually cover a longer period than the apparent number of years in the period since the fieldwork may last from 2 mo to nearly a year. An alternative is to measure time in calendar years independent of when each woman was interviewed. The benefit of the traditional DHS approach is that it allows estimates into the fieldwork period of the survey, while the benefit of the calendar year approach is that it is provides a much clearer time reference. Thus, this paper will explore the consequences of using calendar-year-based estimates rather than estimates based on the time of the interview of each woman.
We proceed by considering criteria for when the sampling variability of an estimate is sufficiently small for tracking the levels and trends in child mortality, as well as the theory behind how to select appropriate time periods when sampling phenomena that fluctuated in time. We then give an overview of how DHS surveys are designed and how child mortality estimates and their sampling errors are computed. We then compute child mortality measures and their sampling errors for available DHS surveys in order to ascertain the conditions that allow the use of short time periods.
One way to determine appropriate time periods is to consider the standard errors of the estimates, and then reject any estimate if its standard error is too high. Since the standard error is scaled to what is measured, a common approach used in survey statistics and in other fields is to consider the coefficient of variation, i.e., the standard error divided by the estimate, expressed as a percentage or as the unmodified ratio
It is commonly assumed that an estimate can be accepted for use or publication if its coefficient of variation is less than a given value, usually less than 5% or 10% of the estimate. Nevertheless, the rule of thumb of 5% or 10% is somewhat arbitrary, as is the case with many statistical rules of thumb, such as the 95% confidence interval
Despite the fact that what exact level one should choose as a cutoff may be uncertain, the reason for not accepting high coefficients of variation is clear cut. Like the estimate of child mortality itself, the standard error as observed in a survey is a sample statistic. A large standard error (and correspondingly large coefficient of variation), means that one does not really know where within the confidence interval the true value lies, but only that the confidence interval is wide, because the statistics used to compute it are uncertain. When samples are small, both the estimates and their variance estimates become unstable. This was to a large extent Kish's concern in the quotation above. But when the coefficient of variation is smaller than the level implying instability, there is no obvious statistical reason for choosing between the conventional 5% or 10% or the Eurostat's 8%.
In addition to the purely statistical issue, there is a substantive issue of importance in choosing an acceptable level for the coefficient of variation. For a child mortality rate of 100 deaths per 1,000, a 10% coefficient of variation would give a 95% confidence interval of roughly 80–120, while a 20% coefficient of variation would give a 95% confidence interval of 60–140. The latter is way too large if one wants to estimate change from one period of estimation to the next, or indeed for estimating the level itself. But, ultimately, the question is one that revolves around what is needed for specific purposes.
The appropriate time period for analysis is not simply the smallest that the sample size can support. If one assumes the absence of bias in a given child mortality measure estimate, in principle the appropriate time period is determined by the level of child mortality, the level of fertility, the sample size (and the sample design), and the variation in child mortality over time. The Shannon-Nyquist sampling theorem
The Shannon-Nyquist sampling theorem assumes measurements at instants. In the case of child mortality measurements, that is not possible, as child mortality estimates are, again for sample size reasons, not measured at instants, but for periods that are long compared to possible fluctuations in mortality level. If child mortality varies regularly with 5 y between peaks, then a series of 1-y periods of estimation is able to reconstruct the curve form relatively well, as shown by the step curve in
The figure shows a cosine curve (green lines) with a 5-y periodicity and average estimates based on 1- to 5-y periods (blue lines).
Even with a 1-y estimation period there is some distortion in terms of peaks and troughs, if one considers that child mortality is a continuous smooth function of time. With a 2-y period more major distortions appear, because the periodicity of the measurements does not match the periodicity of the variation. This is even more pronounced with 3- and 4-y periods, and with 5-y periods the original variation is not visible at all. The scenario shown in
The Rwanda 2000 DHS survey provides an empirical example of the issues at hand. The birth history recorded in the survey covers the 1994 genocide. Although the massacres took place mainly from April to July, effects on child mortality may have lasted longer. As can be seen in
Estimates based on years before the survey.
The distortion of the estimates is a problem that pertains not only to the shape of the trend lines of the mortality measures, but also—as the literature within economics indicates—to the issue that the coefficients of models based on the long time periods may be biased
A few observations follow from this exposition. First, sample sizes and mortality levels constrain how short a period can be used for estimation of child mortality measures. Second, having a large sample and using it to estimate child mortality measures with long observation periods is not optimal, because if mortality fluctuates, then significant bias may be a result. Thus, it may be better to trade lower precision (higher variance) in the estimate for higher accuracy (reduced bias) than to assume that a 5-y estimation period is always preferable. The problem remains, however, how that trade-off should be calibrated, and that will be addressed in the rest of this article. The purpose of this article is to determine appropriate time periods for child mortality estimates from full birth histories, given various sample sizes. Thus, it tries to answer the question of whether, given the increased sample sizes in recent years, we are wasting information by sticking to the estimation procedures that were developed for smaller samples.
The data used in this analysis were datasets that were publicly available from DHS, i.e., they could be downloaded from the Measure DHS website (
Rather than using exact dates in the estimation, DHS uses so-called century month coding, in which dates are converted into the number of months since January 1900. Some surveys, such as the Nepali and Ethiopian ones, use national calendars. These were recalculated according to the standard DHS coding and the Gregorian calendar. DHS data files do not contain the day part of dates, and therefore the conversion can be only approximate.
The DHS surveys generally employ a two-stage stratified sampling design
Year | All Surveys | Surveys with Samples<20,000 Households | ||||||||||
Sampled Households | Eligible Women | Sampled Households | Eligible Women | |||||||||
Mean | Median | Count | Mean | Median | Count | Mean | Median | Count | Mean | Median | Count | |
|
6,824 | 6,268 | 28 | 6,057 | 5,532 | 28 | 6,824 | 6,268 | 27 | 6,057 | 5,532 | 27 |
|
11,640 | 7,354 | 34 | 11,024 | 6,929 | 34 | 7,662 | 6,709 | 31 | 7,391 | 6,864 | 31 |
|
11,003 | 8,016 | 54 | 10,947 | 8,298 | 54 | 8,079 | 7,922 | 46 | 8,287 | 8,048 | 46 |
|
12,640 | 10,259 | 49 | 11,964 | 10,505 | 49 | 10,549 | 9,884 | 38 | 10,388 | 9,157 | 38 |
|
17,903 | 10,310 | 61 | 15,662 | 10,401 | 61 | 10,010 | 9,720 | 44 | 9,969 | 9,326 | 44 |
|
12,728 | 8,975 | 226 | 11,781 | 8,337 | 226 | 8,789 | 8,106 | 186 | 8,641 | 7,958 | 186 |
Based on output from the Measure DHS STATcompiler, and relevant survey reports when data were unavailable from the compiler.
Sampling weights are calculated so that they account for the details of the design. Although the DHS design is in principle self-weighting within the explicit strata, weight variations do occur because the estimate of the PSU sizes from the sampling frame may differ from the actual number found in the PSUs during the mapping and listing. Moreover, weight variation naturally occurs between strata that have different overall sampling rates.
Full birth histories are taken from all women aged 15–49 y in most surveys, although some, principally in the Middle East and southern Asia, define only married women in the 15- to 49-y-old group as eligible. Both usual residents of the households and visitors are used in the estimation.
The DHS estimates of child mortality are calculated using a synthetic cohort approach
Estimates of variance for child mortality estimates from full birth histories collected through a typical household survey are somewhat involved. That is both because the various mortality ratios are computed in a complex fashion as described above, so that working out their variance estimator would be practically impossible, and because the sampling design is complex, i.e., includes unequal weights, clustering, and stratification, resulting in estimators that are not linear.
The variance estimates in the DHS reports have been calculated with a jackknife procedure. The basic premise of this method is that the realized sample can be seen as a good approximation of the population distribution, and that repeated samples from the sample can be made. The distribution of the estimates made from these subsamples will then reflect the sampling variability. While several methods based on this premise exist, the unique characteristic of the jackknife approach is that subsamples are formed by deleting one sampling unit from the sample, calculating the estimate, replacing the deleted unit, and then repeating the procedure with the next sampling unit. Thus, a number of replicate estimates equal to the number of sampling units is formed, and the variance is basically the variance of the distribution of replicates.
In jackknife cluster samples, the ultimate clusters, rather than the last stage sampling units, are used. Thus, for example, with a sample of 368 clusters and 24,000 births, 368 replicates are computed, not 24,000.
According to the DHS sampling manual
A characteristic of the variance estimator as employed by DHS is that it disregards stratification. Therefore, it likely overestimates variance, at least in samples where strata allocations are more or less proportional to the stratum sizes. A test on the 2002 DHS survey for Eritrea confirms that it was indeed the unstratified estimator that was used.
An alternative variance estimator is the JKn variance estimator used by Westat's WesVar software
The differences in standard errors between the estimates that DHS presents and those taking stratification into account are on the order of 10% or less, since the variances of the child mortality measures usually do not differ all that much between strata.
The mortality estimates and standard errors were produced by a program written in Delphi (Embarcadero RAD Studio XE). For each data source, estimates for 1-, 2-, 3-, 4-, and 5-y periods were calculated as far back in time as was possible for each survey, given the length of the birth histories. The estimates of childhood mortality for 5-y periods before the survey match exactly those produced by the Measure DHS online statistics compiler (STATcompiler) or the DHS final reports for datasets that are not represented in the compiler output. Delphi was chosen because the system produces compiled code. It is therefore much faster than the interpreted code used by R, Stata, or SPSS. The program reads comma-separated or Excel files exported from the original DHS data files. It is available from the authors on request. Computed estimates from the DHS surveys used in our analysis are found in
Regardless of the length of the estimation period, 75% of the estimates of 5
Only estimates for periods 20 y or less before the survey were included. Two extreme outliers have been excluded from the graph.
Period of Estimate | Mean | 25th Percentile | Median | 75th Percentile |
1 y | 0.13 | 0.07 | 0.10 | 0.14 |
2 y | 0.10 | 0.06 | 0.08 | 0.11 |
3 y | 0.09 | 0.05 | 0.07 | 0.10 |
4 y | 0.08 | 0.05 | 0.06 | 0.09 |
5 y | 0.07 | 0.04 | 0.06 | 0.08 |
Total | 0.10 | 0.06 | 0.08 | 0.12 |
Calendar-year estimates. Only estimates 20 or fewer years before the survey included.
The results are highly determined by sample size, mortality level, and fertility (
Estimates based on years before the survey. The number of surveys used for the estimates in each panel is indicated in the upper left corner of each.
Estimates based on years before the survey. Estimates based on calendar years. The number of surveys used for the estimates in each panel is indicated in the upper left corner of each.
One should note, however, that there are exceptions to the general rules of sample size and fertility. Sierra Leone 2008 has, for example, no estimate with a coefficient of variation lower than 10%. It is unclear why this is so, since Sierra Leone 2008 has a standard DHS sample design, with a sample size of 7,572 households and a total fertility rate of 5.1 births per woman.
Similar considerations pertain to neonatal mortality (formally, the number of deaths during the first 28 d after birth per 1,000 births, but estimated in DHS surveys and here as the number of deaths during the first month) and infant mortality (the number of deaths during the first year after birth per 1,000 births) estimates. Since the number of deaths observed in a period is smaller for these estimates, the number of years with low coefficients of variation is also fewer than for the 5
Not surprisingly in the case of neonatal mortality, the number of continuous years before the survey that can provide estimates with acceptable coefficients of variation is quite small regardless of the period used. For 1-y estimation periods, one needs samples of more than 15,000 households to be able to estimate back much in time.
If a coefficient of variation of less than 5% is the standard, then samples of more than 15,000 households are needed in order to make estimates for 1-y periods for 5
The results described above can also be seen graphically for each individual country survey. The general trend, regardless of sample size, is that the confidence intervals around the curves are quite narrow close to the survey date, and they then become wider the further away from the survey date the estimate is located. Mali 2001 (
Sample size 12,439 households; total fertility rate 6.8 (births per woman). Estimates based on years before the survey.
In contrast to Mali 2001, the Kazakhstan 1999 survey had a small sample, and the country had relatively low mortality and quite low fertility (
Sample size 5,841households; total fertility rate 2.0 (births per woman). Estimates based on years before the survey.
Moldova 2005 (
Sample size 11,095 households; total fertility rate 1.7 (births per woman). Estimates based on calendar years.
The Zimbabwe 1999 data show a characteristic trumpet shape of the confidence bands, which is seen in many surveys: the further the estimate is from the survey date, the wider the estimate of the confidence band (
Sample size 6,372 households; total fertility rate 4.0 (births per woman). Estimates based on calendar years.
As noted in the Introduction, focusing on the coefficient of variation overlooks the combined effects of bias due to long periods and sampling error. In principle, one can consider the mean square error, rather than the sampling error in isolation. If the bias and sampling error are uncorrelated, then the mean square error (MSE) is given by:
The problem is to estimate the bias. A simple approach is to assume that the difference between two estimates, e.g., for a 5-y period and a 1-y period, constitutes the bias of the longer period. That is certainly inaccurate, not the least because both estimates are subject to sampling error, and the 1-y estimate also may have some long-period bias. Moreover, the 1-y estimate is also biased because of selection effects, as noted before. With these caveats, one may reconsider the calculation of the root mean square error for the 5
The root mean square error for the 5-y estimate (using the difference between the 1-y estimate for February 1994 and the 5-y period centered on February 1993 as a proxy for bias) is 51.2, while that for the February 1994 1-y period estimate remains 10.4, as the bias is assumed to be 0 (
Midpoint of Reference Period | Single-Year Estimate | 5-y Estimate | ||||
5
|
SE | CV |
5
|
SE | CV | |
1986 February | 167.3 | 11.8 | 7.0% | |||
1987 February | 136.2 | 10.7 | 7.9% | |||
1988 February | 131.5 | 9.4 | 7.2% | 140.5 | 6.0 | 4.3% |
1989 February | 135.4 | 9.6 | 7.1% | |||
1990 February | 136.9 | 8.9 | 6.5% | |||
1991 February | 162.2 | 9.2 | 5.7% | |||
1992 February | 177.1 | 12.9 | 7.3% | |||
1993 February | 204.8 | 10.1 | 4.9% | 218.6 | 6.7 | 3.0% |
1994 February | 269.3 | 10.4 | 3.9% | |||
1995 February | 265.2 | 11.9 | 4.5% | |||
1996 February | 191.9 | 9.9 | 5.1% | |||
1997 February | 194.1 | 10.2 | 5.3% | |||
1998 February | 219.8 | 10.1 | 4.6% | 196.2 | 6.5 | 3.2% |
1999 February | 195.6 | 11.4 | 5.8% | |||
2000 February | 179.9 | 11.0 | 6.1% |
Estimates based on years before survey.
CV, coefficient of variation; SE, standard error.
The large root mean square error of the 5-y estimate appears credible when considered together with a graphical view of the development of 5
The preceding analysis implicitly makes three points. First, even though the 5-y period estimates in general have lower standard errors than the 1-y period estimates, the 1-y period estimates may be preferable, because of the potential bias that may occur in using the 5-y estimates. Second, one possible decision criterion is that when the mean square error (as calculated above) is larger for the 5-y estimate than for the 1-y estimate, the 1-y estimate is preferable. Third, if the coefficient of variation of the 1-y estimate is sufficiently low (i.e., at least lower than 10%), then the 1-y estimate should be preferred. In cases where the differences between the 5-y period estimates and the 1-y estimates are slight, the choice is largely irrelevant.
A further consideration is the time location of the estimates. As noted, the DHS estimates cover a longer calendar period than the estimation period, because the time reference is with respect to each individual interview date. This is a relatively minor consideration for 5-y periods, especially if the fieldwork period is short, but becomes an important consideration for the short estimation periods, such as 1- and 2-y periods, especially in the context of rapidly changing mortality. The consideration is also becoming increasingly important as samples increase in size, since large surveys require longer fieldwork periods.
The effect of the time location can easily be seen by recomputing the estimates for calendar years rather than years before the survey. The midyear estimates of 5
Since the Rwanda massacres took place within a calendar year, it is likely that estimates based on calendar year are more accurate than estimation based on years before the survey. But what works best for sudden changes in mortality is dependent on the actual time location of the sudden change. Nevertheless, fixing the estimates to calendar years rather than to years before the survey, which is anchored in the individual interview dates of the women providing the birth histories, has the benefit of making the estimates more directly comparable across surveys. The apparent inconsistencies between the 1-y estimates from the two Rwanda surveys, as well as between the 5-y period estimates, disappear with calendar-year-based estimation.
Our analysis shows that whereas childhood mortality estimates based on 5-y periods of data have previously been the norm, the sample sizes presently employed in DHS surveys make it feasible to base the estimates on shorter periods. It also shows that using calendar years rather than years before the survey as a basis for estimation is useful because it brings out variation in child mortality in a clearer and more realistic manner.
Apart from the benefit in relation to tracking the Millennium Development Goals and the general benefit of avoidance of bias, there are three main benefits of this approach in relation to monitoring of public health. The first is that changes in mortality rates may be detected earlier. The second is that the relationship between child mortality rates and other events may be elucidated. One example is that of Rwanda, where the use of 5-y rates based on years before the survey seriously distorts the effect of the genocide on child mortality. Another example is the relationship between child mortality and outbreaks of some epidemic diseases, such as measles, which may be detected with the use of 1-y rates, but which will be undetectable with the use of 5-y rates. Finally, using short time periods for estimates, survey data may be used to establish consistency with other sources of child mortality estimates. Vital registration is often incomplete in developing countries, but one often assumes that the trends in vital registration data can be used for tracking fluctuation in child mortality
For surveys with sample sizes larger than 8,000 households, it is possible in most cases to estimate child mortality for single years about 10 y back before the survey, although for low-fertility countries this cannot be done. For really large sample sizes, i.e., 20,000 or more, the factor limiting estimation for single-year periods is more the inherent selection bias when estimating back in time, than the sample size itself.
How short should the estimation period be? The results for countries such as Rwanda, with a very abrupt mortality change, suggest that 2-y periods do not bring large improvement to the estimation compared to 5-y periods, because somewhat unpredictable misestimation may still occur.
A practical rule for when short time periods should be chosen is when the mean square error (as calculated above) for the 5-y estimates becomes large compared to that of the short-period estimates. Large mean square errors are likely to occur when there are rapid surges or drops in mortality. In practice, one can use the same decision rule for the relative mean square error as for the coefficient of variation. A simpler decision rule is to use estimates with a coefficient of variation lower than 10%.
Some of the variation in child mortality estimates between different surveys in a population with changing rates of change in mortality stems from the way time periods are calculated. Both for 5-y periods and shorter ones, there is merit in changing estimation to fixed calendar years, rather than years before the survey. This is particularly the case for short time periods, but may likely also resolve some of the differences between survey estimates from different surveys in the same country, even for 5-y periods.
The two main recommendations given here—adopting shorter time periods than 5 y for estimation and using calendar years—have been adopted by the United Nations Inter-agency Group for Child Mortality Estimation in the 2011 estimates of child mortality
Number of contiguous years from survey date allowing estimates of mortality with coefficient of variation less than 10%.
(XLS)
Estimates of mortality and standard errors, part 1: Albania to Jordan.
(XLS)
Estimates of mortality and standard errors, part 2: Jordan to Zimbabwe.
(XLS)
The authors would like to thank the members of the United Nations Inter-agency Group for Child Mortality Estimation and its Technical Advisory Group for comments on initial versions of the paper.
under-five mortality rate
Demographic and Health Surveys
primary sampling unit