DHS METHODOLOGICAL 

REPORTS 21

Comparisons of DHS Estimates 

of Fertility and Mortality 

with Other Estimates

AUGUST 2017

This publication was produced for review by the United States Agency for International Development. It was prepared by 

Thomas Pullum, Shireen Assaf, and Sarah Staveteig.

DHS Methodological Reports No. 21 

Comparisons of DHS Estimates of 

Fertility and Mortality with Other Estimates 

Thomas Pullum 

Shireen Assaf 

Sarah Staveteig1

ICF 

Rockville, Maryland, USA 

August 2017 

1

 Avenir Health 

Corresponding author: Thomas Pullum, International Health and Development, ICF, 530 Gaither Road, 

Suite 500, Rockville, MD 20850, USA; telephone: +1 301–572–0950; fax: +1 301–572–0999; 

email: Tom.Pullum@icf.com
Acknowledgments: Thanks to Amy Tsui, Linnea Zimmerman, Qingfeng Li, and Yoonjoung Choi at Johns 

Hopkins Univeristy and Yared Mekonnen at Mela Research for answering questions about PMA2020 data 

and fertility estimation methods, and to Mahmoud Elkasabi and Trevor Croft for helpful discussions of 

Stata sampling. 

Editor: Diane Stoy 

Document Production: Christopher Gramer 

This study was carried out with support provided by the United States Agency for International 

Development (USAID) through The DHS Program (#GPO–C–00–08–00008–00). The views expressed are 

those of the authors and do not necessarily reflect the views of USAID or the United States Government. 

The DHS Program assists countries worldwide in the collection and use of data to monitor 

and evaluate population, health, and nutrition programs. For additional information about The DHS 

Program, contact: The DHS Program, ICF, 530 Gaither Road, Suite 500, Rockville, MD 20850, USA; 

phone: +1 301-407-6500; fax: +1 301-407-6501; email: reports@dhsprogram.com; Internet: 

www.dhsprogram.com.

Recommended citation: 

Pullum, Thomas, Shireen Assaf, and Sarah Staveteig. 2017. Comparisons of DHS Estimates of Fertility and 

Mortality with Other Estimates. DHS Methodological Reports No. 21. Rockville, Maryland, USA: ICF. 
iii

Contents 

TABLES ............................................................................................................................................................... v

FIGURES ............................................................................................................................................................. v

PREFACE .........................................................................................................................................................vii

ABSTRACT ........................................................................................................................................................ix

1. INTRODUCTION ................................................................................................................................. 1

2. POTENTIAL SOURCES OF DIFFERENCES .................................................................................. 3

2.1. Statistical Issues ............................................................................................................................... 3

2.2. The Reference Period ....................................................................................................................... 6

2.3. Direct versus Indirect Estimates ....................................................................................................... 7

2.4. Unadjusted versus Adjusted Estimates ............................................................................................. 7

2.5. Full Histories versus Truncated Histories ........................................................................................ 7

3. STRATEGIES FOR COMPARISONS ............................................................................................... 9

3.1. Data and Indicators .......................................................................................................................... 9

3.2. Illustrative Comparisons ................................................................................................................11

4. COMPARABILITY OF FERTILITY ESTIMATES FROM DHS AND NON-DHS 

SURVEYS: THE CASE OF PMA2020 DATA .................................................................................17

4.1. Data and Methods ..........................................................................................................................17

4.2. Comparison of PMA2020 and DHS Fertility Estimates ................................................................18

4.3. A Comparison of 2-year 2-birth Adjusted Fertility Rates with 3-year n-birth Rates 

Using DHS Data .............................................................................................................................20

4.4. Simulation of 2-year 2-birth Adjusted Fertility Rates in Ethiopia under Different 

Sampling Conditions ......................................................................................................................23

5. DISCUSSION AND CONCLUSION ................................................................................................. 27

REFERENCES .................................................................................................................................................31

APPENDICES ...................................................................................................................................................33

v 

Tables 

Table 4.1. Fertility rate results from five DHS and PMA2020 surveys.................................................... 19

Table 4.2. Distribution of clusters by strata in Ethiopia DHS 2016, Ethiopia PMA2020 2016 

(Round 4), and DHS subsample simulation ............................................................................ 23

Table A.1. List of surveys included in this report and reference dates for the rates. ................................. 33

Table A.2. TFR estimates from DHS and the UN Population Division .................................................... 49

Table A.3. IMR and U5MR estimates from DHS and IGME ................................................................... 50

Table A.4. Adult mortality probabilities (15q35*1000) from DHS and the UN Population Division ...... 51

Table A.5. MMR estimates from DHS and WHO ..................................................................................... 52

Figures 

Figure 4.1. TFR comparison between DHS and PMA2020 ....................................................................... 19

Figure 4.2. Adolescent fertility rate comparison between DHS and PMA2020 ........................................ 20

Figure 4.3. Comparison of 3-year n-birth and 2-year 2-birth adjusted TFR and Adolescent 

Fertility Rate, DHS surveys 1985-present ............................................................................... 21

Figure 4.4. Distribution of difference between the 2-year 2-birth adjusted TFR and the 3-year 

n-birth TFR estimate and standard error of TFR estimate, DHS surveys 1985-present .......... 21

Figure 4.5. Distribution of difference between the 2-year 2-birth adjusted adolescent fertility rate 

and the 3-year n-birth adolescent fertility rate estimate and standard error of estimate, 

DHS surveys 1985-present ...................................................................................................... 22

Figure 4.6. Percentage of 2-year 2-birth fertility estimates that are within confidence bounds of 

3-year n-birth estimates, DHS surveys 1985-present .............................................................. 22

Figure 4.7. Distribution of difference between the 3-year n-birth TFR from Ethiopia DHS 2016 

and a 2-year 2-birth adjusted TFR computed using 1,000 simulated cluster samples 

similar to the 2016 Ethiopia PMA2020, estimate and standard error of estimate ................... 24

Figure 4.8. Distribution of difference between the 3-year n-birth adolescent fertility rate from 

Ethiopia DHS 2016 and a 2-year 2-birth adjusted adolescent fertility rate computed 

using 1,000 simulated cluster samples similar to the 2016 Ethiopia PMA2020, 

estimate and standard error of estimate ................................................................................... 25

Figure 4.9. Percentage of 2-year 2-birth fertility estimates from simulated PMA2020 Ethiopia 

samples that are within confidence bounds of 3-year n-birth estimates, Ethiopia 

DHS 2016 ................................................................................................................................ 25

Figure A.1. TFR with 95% confidence intervals for 51 DHS surveys and the UN estimate for each 

survey ...................................................................................................................................... 34

Figure A.2. TFR estimates with difference between DHS and UN estimate .............................................. 35

Figure A.3. IMR DHS compared to IGME ................................................................................................. 36

Figure A.4. IMR for DHS compared to IGME with differences ................................................................ 37

Figure A.5. U5MR DHS compared to IGME ............................................................................................. 38

Figure A.6. U5MR DHS compared to IGME with differences .................................................................. 39

Figure A.7. IMR versus U5MR using DHS and IGME estimates .............................................................. 40

Figure A.9. Log of the ratio of DHS and IGME estimates for IMR versus U5MR .................................... 41

Figure A.10. Female probability of dying between age 15 and 50 ............................................................... 41

Figure A.11. Male probability of dying between age 15 and 50 ................................................................... 42

Figure A.12. Male versus Female Adult Mortality ....................................................................................... 42

Figure A.13. Difference in Male Adult Mortality versus Difference in Female Adult Mortality ................. 43
vi

Figure A.14. Adult Female Mortality versus U5MR .................................................................................... 43

Figure A.15. Difference in Adult Female Mortality versus Difference in U5MR ........................................ 44

Figure A.16. MMR using DHS and WHO estimates .................................................................................... 45

Figure A.17. MMR using DHS and WHO estimates and the difference between DHS and WHO .............. 46

Figure A.18. MMR versus Adult Female Mortality ..................................................................................... 47

Figure A.19. Difference of MMR versus difference of Adult Female Mortality .......................................... 47

Figure A.20. U5MR versus MMR ................................................................................................................ 48

Figure A.21. Difference in U5MR versus difference in MMR ..................................................................... 48
vii 

Preface 

The Demographic and Health Surveys (DHS) Program is one of the principal sources of international data 

on fertility, family planning, maternal and child health, nutrition, mortality, environmental health, 

HIV/AIDS, malaria, and provision of health services. 

One of the objectives of The DHS Program is to continually assess and improve the methodology and 

procedures used to carry out national-level surveys as well as to offer additional tools for analysis. 

Improvements in methods used will enhance the accuracy and depth of information collected by The DHS 

Program and relied on by policymakers and program managers in low- and middle-income countries. 

While data quality is a main topic of the DHS Methodological Reports series, the reports also examine 

issues of sampling, questionnaire comparability, survey procedures, and methodological approaches. The 

topics explored in this series are selected by The DHS Program in consultation with the U.S. Agency for 

International Development. 

It is hoped that the DHS Methodological Reports will be useful to researchers, policymakers, and survey 

specialists, particularly those engaged in work in low- and middle-income countries, and will be used to 

enhance the quality and analysis of survey data. 

Sunita Kishor 

Director, The DHS Program 

ix

Abstract 

DHS surveys provide many indicators that are used for program planning and monitoring. Some of these 

are particularly sensitive and attract attention when the results of a new survey are released. This 

methodological report focuses on six demographic indicators of widespread interest: the Total Fertility Rate 

(TFR), Infant Mortality Rate (IMR), Under-5 Mortality Rate (U5MR), the Adult Female Mortality 

Probability (AFMP), the Adult Male Mortality Probability (AMMP) and the Maternal Mortality Ratio 

(MMR). It is not unusual for the results of a new survey to be questioned because they differ from another 

source. The goal of this report is to provide guidance on how to determine whether the estimates of these 

important indicators are plausible and consistent with other sources, or not plausible. 

Determining whether a DHS estimate is consistent with other sources is usually a matter of degree. Some 

differences are expected for a variety of reasons. The report includes a discussion of potential reasons for 

discrepancies. The report then analyzes 51 surveys conducted since 2010. The DHS estimates are 

systematically compared with estimates of the TFR, AFMP, and AMMP from the UN Population Division, 

estimates of the IMR and U5MR from the UN’s Inter Agency Group for Child Mortality Estimation 

(IGME), and MMR estimates from the World Health Organization (WHO). The structure of the 

comparisons can be applied to other sources, although it is always necessary to account for the potential 

kinds of differences discussed earlier. Two specific surveys provide focus for the comparisons. 

The report also illustrates a strategy for comparing fertility rates computed from DHS data with those from 

other sources. We compare DHS fertility rate estimates with those from Performance Monitoring and 

Accountability 2020 (PMA2020) surveys, explore the effect of a slightly different methodology for 

computing rates on DHS estimates, and simulate the effects of a different sampling strategy and rate 

estimation method on fertility rates. Using a standard methodology, fertility rates computed from PMA2020 

data in five countries showed a range of 5% to 22% difference in TFRs and a 4% to 17% difference in 

adolescent fertility rates compared to results from DHS surveys conducted within a three-year timespan. 

To assess the effect of alternate measurement on fertility rates from the exact same survey, we used 256 

DHS datasets to compare the results of a 2-year 2-birth adjusted estimation technique versus a 3-year nbirth technique with the same data. Our results show an average of only about one percentage point 

difference in total fertility and adolescent fertility rates from the same data using the alternate technique. In 

addition to measurement differences, it is important to consider design effects of a different cluster sample. 

In Ethiopia, we simulated subsamples of DHS data with a cluster distribution similar to a corresponding 

PMA2020 survey and recomputed fertility rates with a 2-year 2-birth adjusted estimation technique. These 

combined differences in sampling and methodology produced an average of a 3% to 4% difference in rate 

estimates but could plausibly produce as much as a 10% difference in TFR and a 23% difference in 

adolescent fertility rates. Notably, since the PMA2020 cluster sample sizes are larger than those used by 

DHS, this simulation is likely an overestimate of the effect of sampling differences. 

KEY WORDS: Fertility, PMA2020, under-5 mortality, adult mortality, maternal mortality, sample design, 

data quality 

1 

1. Introduction 

Surveys conducted by The Demographic and Health Surveys Program (DHS) are a source of widely used 

estimates of current fertility, the mortality of children and adults, and maternal mortality. The estimates are 

published in the main reports of each survey, are accessible on STATcompiler (DHS Program 2017), and 

have been analyzed in many DHS publications. The estimates are used by USAID, UNICEF, WHO, and 

other agencies to assess the effectiveness of programs, and by the United Nations Population Division to 

estimate long-term trajectories of vital rates and population growth in many developing countries. 

DHS frequently assesses the quality of these estimates. Recent DHS Methodological Reports have focused 

on the birth histories, which include information about births and child deaths (Pullum and Becker 2014), 

estimates of fertility (Schoumaker 2014), and estimates of maternal mortality (Ahmed et al. 2014). Another 

recent report (Pullum and Staveteig 2017) assesses the quality of reports of ages and dates, which are crucial 

for all rates. These assessments have concluded that most surveys have produced estimates of good quality, 

although a few surveys—often the same surveys for multiple indicators—have been of lower quality. 

Respondents’ incomplete knowledge of age and the dates of vital events is a severe limitation in some 

contexts, but is certainly not the sole source of deficient data. 

There have been a number of apparent inconsistencies between successive DHS surveys, or between DHS 

surveys and other sources. There have also been instances in which a DHS estimate was substantially higher 

or lower than expected, regardless of a specific comparison. This report will review some strategies for 

identifying and understanding such inconsistencies. 

Non-DHS estimates are often based in part on DHS estimates, or derived from procedures that are similar 

to DHS procedures, and cannot be regarded as independent of DHS estimates. It is possible, for example, 

that DHS estimates of the IMR in a particular country are chronically low, because some children who died 

at a very early age were omitted from the birth histories. The estimates produced by the UN agencies, which 

rely to a large extent on DHS data, may have been adjusted upward in an attempt to compensate for those 

omissions. However, if they were not adjusted upward sufficiently, the series of adjusted estimates and 

projections may also be low. When the DHS estimates are compared with the UN estimates, the amount of 

bias in the DHS data will be under-estimated, or perhaps not identified. 

Previous research on differences between estimates has concluded that discrepancies are due to differences 

in the data such as full versus abbreviated birth histories, or to differences in the statistical methods (Alkema 

et al. 2012, Brady and Hill 2017, Hancioglu and Arnold 2013, Masquelier et al. 2014, Silva 2012). 

If a specific estimate is suspicious because it is not consistent with other sources, the analyst cannot 

conclude that it is erroneous. It is possible that some outcomes change abruptly, either temporarily or as 

part of a longer-term trend. However, it is always advisable to include more in-depth analysis in such a 

situation. Strategies for in-depth analysis of an estimate for a specific survey will not be described here. 

The purpose of this report is to provide guidance for judging whether in-depth internal checking, beyond 

what is normal for DHS surveys, is needed. 

The report will also not review methods for adjusting data, since DHS has a policy of not adjusting 

estimates. There are several reasons for this policy. First, the main reports on DHS surveys are very 

comprehensive, with a wide variety of demographic and health indicators, and their publication cannot be 

delayed by a thorough analysis. Second, the data files are available at the same time as the main report is 

released, and these must be consistent with the report so that users can replicate and extend the information 

from the main report. If the national IMR were adjusted upwards, for example, then the microdata would 

not be consistent with the adjusted rate. Third, users may choose to make adjustments, and the DHS 

estimates in the reports that are consistent with the data files serve as a continuous reference or baseline 
2 

against which a variety of alternative adjustments can be made. Thus, decisions about whether and how to 

adjust an estimate are made by the users, which include agencies such as the UN Population Division. 

This report is intended primarily for users of the main reports that are published after each survey and for 

users of STATcompiler. For the benefit of users of DHS microdata files, some details will describe a 

specific statistical package, Stata, although they can be applied in other packages, such as SPSS, SAS, and 

R. These details are relevant to users of the data files and can be bypassed by other readers. 

Data and methods will be described in the relevant chapters. 
3 

2. Potential Sources of Differences 

This chapter will provide a brief review of reasons why two indicators from different sources may 

unexpectedly differ, but not actually be incompatible. The chapter serves as a checklist of potential issues 

that may be the source of the apparent difference. A more in-depth analysis is required when a difference 

remains even after the effect of these issues has been considered. 

2.1. Statistical Issues 

Many of the apparent discrepancies in DHS estimates can be attributed to sampling error in the DHS 

estimate, or sampling error or uncertainty in the number to which the DHS estimate is being compared. We 

will briefly review the statistical properties of DHS estimates, including how to interpret the standard error 

of an estimate, the relevance of denominators and numerators, the role of the sample design and how to 

take it into account, and the interpretation of confidence intervals, and how they differ from tests. 

The standard error of an estimate 

Virtually every number in DHS reports is either a description of the sample—for example, the sample 

size—or an estimate of a population quantity, such as a mean or median, a proportion or percentage, or a 

rate or ratio. Sample estimates of a population quantity, or parameter, are statistics, and are subject to 

sampling variability. 

If we were to draw repeated samples from the population, using the same sampling frame, sampling design, 

and sample size, the estimates of the same parameter would vary from one sample to another. The sampling 

error for a specific estimate is the deviation of that estimate from the “true” value in the population, or the 

statistic minus the parameter. Because we do not know the value of the parameter, the sampling error for 

the sample is not known. If the statistic is unbiased, then (by definition) the expected sampling error is zero 

and the average deviation over all possible repeated samples is zero. 

The standard error can be interpreted as approximately the average of the absolute values of these 

deviations, over all possible samples. Statistical theory enables us to estimate the standard error, although 

we have only one sample and we do not know the true value of the parameter. The reports of DHS surveys 

include an Appendix B, which provides the estimated standard error for about 50 indicators in the surveys 

of women and of men, nationally and within the sampling strata. Standard errors are not included in 

STATcompiler, although a confidence interval for the MMR is included. 

The role of the sample design 

With few exceptions, DHS samples are obtained in the following steps—skipping over the role of surveyspecific considerations such as the budget and the desired statistical power. 

The DHS samples are stratified, with strata generally corresponding to the urban and rural areas of each 

region (the level 1 administrative unit). The sample must satisfy some minimal criteria, which is specified 

in terms of the anticipated standard errors within each stratum. Strata with relatively small populations tend 

to be over-sampled, while strata with relatively large populations are often under-sampled. The sample thus 

has a distribution that is somewhat more equally allocated across strata than the population distribution, in 

order to optimize the standard errors of within-stratum estimates. The use of strata and different sampling 

fractions for the strata requires adjustments during analysis with sampling weights. 

The sampling frame is generally a list of all enumeration areas (EAs) in the most recent census, with an 

estimate of the number of households in each EA. Within each stratum, the EAs are sampled with 
4 

probability proportional to size, where “size” is measured by the number of households in the sampling 

frame. The constant of proportionality in each stratum is determined by the desired sample size within that 

stratum. The EAs are also described as primary sampling units (PSUs) or clusters. Within each selected 

cluster, a constant number of households (such as 30) are selected during the household listing. 

Households within the same cluster tend to resemble one another more closely than households selected at 

random from different clusters. This overlap of information from the same cluster requires other 

adjustments to the standard errors of stratum-level estimates or national-level estimates during analysis. 

Adjustments to the weights are also required because of the variation in nonresponse. 

DHS recommends three types of adjustments during the analysis. The first is the use of sample weights that 

are required to produce unbiased estimates of population quantities. Weights can also alter the standard 

errors. When weights are used, the standard errors tend to increase. The second adjustment is for clustering. 

If this adjustment is not made, the estimates of the standard errors will be too small. That is, if the cluster 

adjustment is used, the standard errors tend to increase. The third adjustment is for stratification. If this 

adjustment is not made, the estimates of the standard errors will be too large. The adjustment tends to reduce 

them. The adjustments for clustering and stratification do not alter the point estimates, only the standard 

errors.1

The typical DHS sample design reduces the cost of fieldwork, relative to a simple random sample, but it 

also effectively reduces the sample size. Appendix B of the survey reports provides the actual sample size, 

or denominator, for each indicator and the design effect (DEFT). The design effect can be interpreted as 

the ratio of the actual sample size to the size of an equivalent simple random sample. If, for example, 

N=10,000 and DEFT=2, then the adjusted standard error will be equivalent to the unadjusted standard error 

of a sample of size 10,000/2 = 5,000 cases. The DEFT is virtually always greater than 1, although it can be 

less than 1 if the increase in efficiency due to stratification has more impact than the reduction of efficiency 

due to clustering. Adjusting the standard errors is very relevant to this report, because a difference that is 

statistically significant without the adjustment can easily become insignificant with the adjustment, which 

increases the standard error. 

The relevance of denominators and numerators 

The sample size for an indicator, typically the denominator, is very important for conclusions about 

statistical significance. Appendix B of the survey reports makes this clear. In a simple random sample, a 

standard error is inversely proportional to the square root of the sample size. In a more complex sample, a 

standard error is inversely proportional to the square root of the effective sample size. A chi-square test 

statistic is directly proportional to the effective sample size. And, a z or t test statistic is directly proportional 

to the square root of the effective sample size. For rare events, the number of cases in the numerator is also 

important. For example, an infant mortality rate can be written as r=d/b, where b is the size of a cohort of 

births and d is the number of deaths to that cohort before the first birthday. That is, d is the numerator and 

b is the denominator. For a simple random sample, d will have a Poisson sampling distribution and the 

standard error of the rate is approximately [sqrt(d)]/b. This estimate can be written in two different ways. 

The first is [sqrt(r]]/[sqrt(b)], which confirms that the standard error is inversely proportional to the square 

 

1

 Researchers using Stata can make these adjustments with the svyset and svy commands. All the estimates in the 

reports, including Appendix B, and on STATcompiler, have made the same adjustments but with different software, 

so they may differ slightly from the estimates from Stata or another package. The adjustments currently possible with 

DHS data are incomplete. Ideally, DHS datasets would provide two weights, one that is the inverse of the sampling 

fraction for the selection of clusters within strata, and one that is the inverse of the sampling fraction for the selection 

of households with clusters. It is not possible for DHS to separate these two components, because knowledge of the 

cluster-level weight could enable the identification of the actual cluster by anyone with access to the sampling frame. 

The weight variable included in DHS data files is the normalized product of those two weights. 
5 

root of the denominator. Alternatively, the standard error is r/[sqrt(d)], which shows that the standard error 

is also inversely proportional to the square root of the numerator. The second relationship is particularly 

relevant for analysis of the MMR, because even in large surveys, the number of maternal deaths to sisters 

in the standard time interval (the 7 years before the survey) is often quite small and less than 100. 

Tests of differences 

Many statistical tests of hypotheses about DHS rates can be constructed without using the data files, if the 

standard error can be located in STATcompiler or in Appendix B of the reports. These include tests of 

differences between surveys, including two surveys conducted in the same country, or differences from 

specific values from another source. Some estimates from other sources, such as the UN Population 

Division, are not accompanied by standard errors or confidence intervals.2

 Tests can be one-tailed or twotailed. 

Many users of DHS data make a judgment about whether a difference between two estimates is significant 

by checking whether the 95% confidence intervals for the two estimates overlap. This technique is not 

equivalent to a test, except when there is a good separation between the confidence intervals. In that case, 

the user lacks a specific p-value for the test. Another weakness of the technique is that there is often a clear 

one-sided nature to the research hypothesis—for example, that the MMR has declined between two surveys, 

whereas confidence intervals are always two-sided. 

To illustrate how to test whether a change in the MMR from one survey to the next was statistically 

significant, we will use two successive surveys from Zimbabwe, conducted in 2010 and 2015. In this 

example, the confidence intervals around the two estimates overlap, but there was a highly significant 

decline. An inference that there was not a significant decline, based on the overlap of the confidence 

intervals, would have been incorrect.3

If we go to STATcompiler and download the MMR estimates for the two surveys,4 we find estimates of 

960 and 651 maternal deaths per 100,000 births, respectively, for the 2010 and 2015 surveys. These 

numbers imply that there was a substantial decline from the first survey to the second, but was it statistically 

significant and can it be interpreted as a real change in the population? 

STATcompiler provides confidence intervals to accompany the point estimates of the MMR. In the first 

survey, the interval extends from 778 to 1142. The second interval extends from 473 to 829. Each interval 

was calculated by adding and subtracting 2*se to each point estimate, where “se” is the standard error. 

Testing is conceptually different from estimation, although both involve the estimate and the standard error 

of the estimate. The information required to conduct the relevant tests is contained in the estimates just 

given. We can conduct the test of a difference with five steps. Refer to the two surveys as #1 and #2, in 

chronological order. The goal is to determine the significance of the change from #1 to #2. 

Step 1. Find the standard error (se) of each MMR estimate by dividing the width of each confidence interval 

by 4. Thus, the se for the 2010 estimate is (1142-778)/4= 91 and the se for the 2015 estimate is (829-473)/4 

= 89. 

 

2

 Uncertainty intervals, which may be provided by sources other than DHS, are not equivalent to confidence intervals. 

3

 There is always a probability (given by the p-value) that any inference from a test is incorrect. 

4

 There is approximately a two-year overlap in the reference periods, because the reference period is 7 years and the 

surveys are 5 years apart. The only way to avoid overlap would be to re-estimate with the data files. The effect of 

overlap is to bias the test toward the null hypothesis of no change. 
6 

Step 2. Calculate the differences between the pairs of estimates by subtracting the earlier estimate from the 

later estimate. If there was a decline over time, the difference is negative. If there was an increase over time, 

the difference is positive. The difference is 651-960=-309.

Step 3. Calculate the standard error of the difference. This is a simple calculation because the successive 

surveys are statistically independent. The standard error of the difference calculated in Step 2 is just the 

square root of the sum of the squares of the standard errors from the two surveys. Here, the standard error 

of the difference between surveys #1 and #2 is the square root of the sum of 91 squared and 89 squared. 

This is 127.29. (Rounding is deferred until the end of the calculations.) 

Step 4. Divide the difference by its standard error. This will produce a z test statistic, which will have an 

approximately normal sampling distribution with expected value 0 and standard deviation 1 under the null 

hypothesis that there was no change in the population MMR. The z score is -309/127.29 =-2.43. 

Step 5. Compare the test statistics with the critical values of the z distribution. The standard criterion is the 

.05 level of significance. If the research hypothesis is that the population MMR changed between the two 

surveys, moving either up or down, and the null hypothesis is that it did not change, then the null hypothesis 

will be rejected if z is outside the range from -1.96 to +1.96. Clearly, the calculated z value is outside this 

range. We reject the null hypothesis that there was no change. 

The research hypothesis, however, is actually that the MMR declined between two surveys. That is, there 

is a direction to our expectation for change in the MMR, and the test should be one-sided rather than twosided. The critical value for this test is -1.65. If the calculated z value is less than -1.65, then we infer that 

the decline was significant with p<.05. If z is less than -2.33, then we infer that the decline was significant 

with p<.01. The calculated z value, -2.43, is therefore highly significant. The p-value is .008. 

This approach is more accurate than checking whether the two confidence intervals overlap but is itself a 

shortcut. A more accurate approach would require going to the data files and using a statistical model with 

a logit link function and adjustments for clusters and strata, as well as sampling weights, in both surveys. 

If the approach described here produces a borderline result, then it would be advisable to use the more 

accurate approach. 

2.2. The Reference Period 

DHS estimates of fertility and mortality are always calculated for a reference period of time, such as the 3, 

5, or 7 years before the survey, because they come from the retrospective birth histories or sibling histories. 

In the main reports, some national estimates are published with one reference period and another estimate 

for subpopulations. With adjustments to software, it is possible to construct estimates for intervals 

expressed in calendar years, rather than years ago, but these estimates must refer to an interval of time. 

A common misinterpretation of the rates from DHS surveys is that they refer to the date of the survey. For 

example, the estimates of the MMR from the 2010 and 2015 surveys of Zimbabwe could be misinterpreted 

as applying to 2010 and 2015. They actually refer to the 7 years prior to those surveys—an interval of 1 to 

84 months prior to the month of interview for each woman in the survey. The best choice of a time point 

would be the midpoint of that time interval, which would be approximately 3.5 years before the mean date 

of interview. 

Estimates from UN agencies may refer to an interval of time, expressed as calendar years, or to points in 

time. For example, WHO provides estimates for 2015 that can be interpreted as a point in time, specifically 

the midpoint of 2015 or July 1, 2015. This manner of dating the estimates is possible because WHO applies 

statistical models for the MMR, which synthesize many data sources (including DHS) with varying 

intervals for each source. The models fit a continuous trend line through these sources, and the points on 
7 

that line can be interpreted as estimates for specific time points. The best comparison with a DHS estimate 

would be for a point in time that was 3.5 years before the DHS survey. 

2.3. Direct versus Indirect Estimates 

Another potential source of discrepancies between estimates from DHS and other sources is that the other 

sources may have used indirect estimation. This is of particular relevance for comparisons with estimates 

from a census or survey that did not collect a birth history. Indirect estimation techniques were developed 

for such sources primarily during the 1950s and 1960s, before it had been demonstrated that it was possible 

to collect birth histories of adequate quality in most settings. Methods were developed to estimate a total 

fertility rate using the number of children ever born for women in successive 5-year age groups, and 

infant/child mortality rates using the number of children ever born and the number still alive for women in 

successive 5-year age groups. These methods obtain their leverage from a combination of assumptions, 

such as an assumption that fertility or mortality has not changed or has changed linearly in recent years, 

and synthetic data sources, such as model life tables or model fertility schedules (see UN Manual on Indirect 

Estimation (United Nations 1983)). There is a general, although not universal, consensus that direct 

estimates are more accurate than indirect estimates, except if the birth histories have very high levels of 

omission or displacement. When there is omission of births and/or deaths, reports of the numbers of children 

used for the indirect estimates will also be low. If there appears to be a discrepancy between a DHS direct 

estimate and an indirect estimate from another source, it is quite possible that the assumptions and synthetic 

data used in the construction of the indirect estimate were inappropriate. 

2.4. Unadjusted versus Adjusted Estimates 

As stated earlier, DHS estimates are not adjusted, but comparisons will be affected by any adjustments to 

non-DHS rates that are being compared. For example, WHO estimates of the MMR for countries that have 

conducted DHS surveys make extensive use of DHS data but they apply several adjustments, including a 

deduction for deaths that were pregnancy-related (in terms of timing) but not obstetric, and an increase to 

compensate for likely omission. Both adjustments are based on limited evidence. The model is Bayesian, 

and borrows information from the UN regional level. The model uses covariates, including GDP estimates 

from the World Bank, and it applies the DHS estimate of the proportion of deaths to women that were 

maternal to an estimate of the number of deaths to women that comes from the UN Population Division. 

The use of UN Population Division adjusted estimates of age-specific death rates for adults, rather than 

DHS unadjusted estimates of these rates, as well as the UNPD adjusted estimate of the General Fertility 

Rate for the denominator of the MMR, account for the differences between DHS and WHO estimates. 

The IGME estimates of infant and child mortality rates involve a blending of many sources. The model for 

those estimates does not include any covariates. Detailed documentation on the adjustments made to the 

alternative estimates are readily available at the websites provided elsewhere in this report. 

2.5. Full Histories versus Truncated Histories 

An additional source of difference in published survey results relates to how the data are collected and 

processed. All PMA2020 and most MICS surveys, for example, simplify data collection by using 

abbreviated birth histories for specified intervals before the interview that range from 1 to 5 years and by 

not collecting data on multiple births. When a DHS estimate is compared with an estimate from a truncated 

birth history, there will be obvious differences such as fewer births per respondent and a shorter reference 

period. There may be other limitations as well. For example, when respondents are asked to include events 

in a time interval, such as the past year, it is not clear if the nominal interval of 1 year is consistently 

interpreted by all respondents and interviewers. However, the focus on very recent events may reduce 

omission. Alternate methods for imputing incomplete or unknown dates may also cause variation in 
8 

published fertility rates. Chapter 4 of this report will focus on a strategy for comparisons of this sort that 

uses microdata files and simulation from PMA2020 and DHS data. 
9 

3. Strategies for Comparisons 

This chapter will illustrate methods for comparing DHS estimates with one another, or comparing a DHS 

estimate with another estimate, making a judgement about consistency and plausibility, and taking into 

account several potential reasons why the estimates could differ, including sampling variation. In some 

situations, the alternative estimates can be judged to be consistent. In other situations, the DHS estimate—

or perhaps the alternative estimate—may appear to be biased in one direction or another. If reconciliation 

is not possible, then the user must decide whether to blend them with some statistical procedure or to 

prioritize one estimate over another. 

3.1. Data and Indicators 

Data 

This chapter will use data from 51 DHS surveys. These are surveys for which fieldwork began in 2010 or 

later and for which the data had been released by the closing date for this report. If a country had conducted 

more than one survey in that interval, only the most recent survey was used. A list of these surveys and the 

reference dates of the indicators is shown in Table A.1 of the Appendix. All estimates are at the national 

level. The DHS estimates used in this chapter are available on STATcompiler, with the exception of some 

confidence intervals as described below. 

Comparisons will be made with estimates from three other sources, described with acronyms: IGME (UN 

Inter-Agency Group for Child Mortality Estimation); UNPD (UN Population Division); and WHO (World 

Health Organization). These acronyms are convenient but misleading, because the three sources are part of 

the United Nations and are inter-related. The IGME estimates are associated with UNICEF (the UN 

Children’s Fund), and the WHO estimates involve an inter-agency group known as the MMEIG (Maternal 

Mortality Estimation Inter-Agency Group). The websites are: 

IGME: https://data.unicef.org/topic/child-survival/ (UNICEF 2017) 

UNPD: https://esa.un.org/unpd/wpp/ (United Nations 2017) 

WHO: http://www.who.int/reproductivehealth/publications/monitoring/maternal-mortality-2015 (WHO 

2015)

All the IGME, UNPD, and WHO data used in this report are freely available on these websites. 

This chapter also single out two specific DHS surveys: the survey conducted in Afghanistan in 2015 and 

the survey in Angola in 2015-16. The results from both surveys became available in 2017. There have been 

some data quality concerns for both surveys, but they are selected mainly to provide more specificity to a 

strategy for identifying deviations from what could have been expected. 

Indicators 

Six indicators will be used: the Total Fertility Rate (TFR), the Infant Mortality Rate (IMR), the Under-5 

Mortality Rate (U5MR), the Adult Female Mortality Probability (AFMP), the Adult Male Mortality 

Probability (AMMP), and the Maternal Mortality Ratio (MMR). All are synthetic (that is, they are 

calculated with recent data but have a cohort interpretation) and involve complex calculations, as described 

below. 
10

Total Fertility Rate (TFR): The sum of the age-specific fertility rates for ages 15-49 (multiplied by five if 

these are 5-year rates), using data from the birth histories for the 3 years before the survey. The TFR can 

be interpreted as the average number of births that a woman would have if she survived from the 15th

birthday to the 50th birthday and had children according to current age-specific rates. The TFR estimates 

used for this chapter are summarized in Table A.2. 

Infant Mortality Rate (IMR): The probability that a child will die before the first birthday (1q0), multiplied 

by 1000. DHS uses data from the birth histories for the 5 years before the survey to calculate the IMR. See 

Table A.3. 

Under-5 Mortality Rate (U5MR): The probability that a child will die before the fifth birthday (5q0), 

multiplied by 1000. DHS uses data from the birth histories for the 5 years before the survey to calculate the 

U5MR. See Table A.3. 

Adult Female Mortality Probability (AFMP): The probability that a woman who has survived to her 15th

birthday will die before the 50th birthday (35q15), multiplied by 1000. DHS uses data from the sibling 

histories for the 7 years before the survey to calculate the AFMP. See Table A.4. 

Adult Male Mortality Probability (AMMP): The probability that a man who has survived to his 15th birthday 

will die before the 50th birthday (35q15), multiplied by 1000. DHS uses data from the sibling histories for 

the 7 years before the survey to calculate the AMMP. See Table A.4. 

Maternal Mortality Ratio (MMR): The number of maternal deaths per 100,000 births. DHS obtains maternal 

deaths from the sibling histories to calculate a MMR and births from the birth histories to calculate a General 

Fertility Rate (GFR), by using the 7 years before the survey, dividing the MMR by the GFR, and multiplying 

by 100,000. The MMR can be interpreted roughly as the probability that a pregnancy will result in a 

woman’s death (multiplied by 100,000). Births, rather than pregnancies, are used in the denominator 

because the number of pregnancies is not available. See Table A.5. 

The IMR and U5MR have “rate” in their names but are actually probabilities. The AFMP and AMMP are 

also probabilities, not rates. As a group, these four probabilities are preferred over rates, because most rates 

are affected by the age distribution, while the probabilities are not. The TFR is a rate but is not affected by 

the age distribution. The MMR is a ratio of two rates, both of which are affected by the current age 

distribution of women age 15-49.

All six of these indicators are provided in STATcompiler. The upper and lower bounds of a confidence 

interval for the MMR is given in Stat Compiler. The estimates of all the indicators, shown in Tables A.2 – 

A.5 of the Appendix, and the confidence interval for the MMR, are available in STATcompiler. At this 

time, STATcompiler does not include confidence intervals for the other indicators, although it is likely that 

confidence intervals will be added in the future. Appendix B of the main reports includes confidence 

intervals for the TFR, IMR, and U5MR. Special computer runs for this chapter re-calculated those 

confidence intervals, using adjustments for sampling weights, clustering, and stratification. Confidence 

intervals for the AFMP and AMMP were not calculated. 

The DHS estimates will be compared with IGME estimates of the IMR and U5MR; UNPD estimates of the 

TFR, AFMP, and AMMP; and WHO estimates of the MMR. The analysis is based on a set of 21 figures 

provided in the Appendix, which in turn are based on tables also provided in the Appendix that follows the 

figures. 
11

3.2. Illustrative Comparisons 

We begin with an overview of the structure of the figures. The following list specifies which figures apply 

to each of the six indicators: 

Indicator Figure number 

TFR A.1, A.2 

IMR A.3, A.4, A.7, A.8, A.9 

U5MR A.5, A.6, A.7, A.8, A.9, A.14, A.15, A.20, A.21

AFMP A.10, A.12, A.13, A.14, A.15, A.18, A.19

AMMP A.11, A.12, A.13

MMR A.16, A.17, A.18, A.19, A.20, A.21

Many figures are paired with another, in such a way that the first figure focuses on the estimated levels of 

the DHS and UN indicator, and the second on the difference between the two estimates. Figures A.7, A.10, 

and A.11 are the only figures that are not part of such a pairing. 

About half of the figures involve a pairing of indicators. The following list identifies such pairs: 

Indicators Figure number 

IMR and U5MR A.7, A.8, A.9 

AFMP and AMMP A.12, A.13

U5MR and AFMP A.14, A.15

MMR and AFMP A.18, A.19

U5MR and MMR A.20, A.21

This pairing of indicators is based on empirical correspondences. The IMR and U5MR have a logical or 

definitional association because they are the probabilities of dying before age 1 and the probability of dying 

before age 5, respectively. It is a formal requirement, therefore, that IMR<U5MR. Beyond that, the two 

probabilities are highly correlated empirically. Figures A.7, A.8, and A.9 describe this pairing. The adult 

probabilities of dying between age 15 and 50 (AFMP and AMMP) will be associated because many causes 

of death for women and men are the same. This pairing is examined in Figures A.12-A.13.

There are also definitional constraints on the relationship between the MMR and the AFMP, because a 

maternal cause of death is one of many possible causes of death. This pairing is described in figures A.18-

A.19. The MMR and AFMP are on different scales, and there is not a simple inequality, although there is 

a relationship. 

Two pairings of indicators are based on the commonality of many influences on the mortality of children 

and women. We thus examine the pairing of the U5MR with the AFMP, in Figures A.14-A.15, and with 

the MMR, in Figures A.20-A.21.

Some relationships are clearer on a log scale than on the original scales of rates and probabilities. Figures 

A.8 and A.9 use the same data as Figure A.7, but on a log scale. Figure A.9 is a differencing of Figure A.8. 

Logarithms have a base of 10 (10 and 100 on the original scalar, for example, correspond with 1 and 2 on 

the log scale). 

We will now demonstrate how these figures can be interpreted and used to make decisions about 

plausibility, potential data quality issues, or a need for further analysis. For each figure, we will make some 

general comments and will comment specifically on the Afghanistan and Angola surveys. Since there is 
12

only one survey per country, it will be sufficient to identify each survey with just the name of the country, 

although it must be understood that the reference is to a specific survey. We will show confidence intervals 

for many of the DHS estimates but will not conduct statistical tests. However, if the other estimate is outside 

the DHS 95% confidence interval, and we treat the other estimate as a fixed point, it will be safe to say that 

there is a significant difference with p<.05. Numerical values can be made more precise by referring to the 

tables in the Appendix. 

Total Fertility Rate 

Figure A.1 compares the TFR estimate with the TFR estimate from the UN Population Division (identified 

as the “UN” estimate). The UN estimate is for July 1 of the calendar year that is closest to the reference 

date (the midpoint of the reference period) for the DHS estimate. The DHS estimate is represented by a 

blue interval—the 95% confidence interval. The DHS point estimate would be at approximately the 

midpoint of the confidence interval. The countries are sorted by the DHS point estimate. The UN point 

estimate is shown with a red dot. 

The DHS and UN estimates are not independent. Some of the UN trajectories for the TFR were updated 

with the most recent survey. The UN estimates come from the 2017 revision of World Population Prospects, 

which was issued in June 2017. For every country, the UN Population Division does a careful analysis of 

the plausibility of new estimates. When a country trajectory is revised on the basis of new data, the new 

estimate has only a limited effect. 

For most countries, the UN estimate is within the DHS confidence interval. It is easy to identify the 

countries in which the DHS interval is completely below or completely above the UN estimate. The 

numbers of these two types of displacement are approximately equal. The agreement appears to be strong, 

in general and specifically for both Afghanistan and Angola. 

Figure A.2 maintains the same ordering of countries, on the basis of the DHS estimate of the TFR, but 

provides green bars that show the DHS estimate minus the UN estimate on an exaggerated scale. For all 

countries, the DHS and UN estimates are within half a child of each other. The largest differences are for 

the Kyrgyz Republic, Haiti, and Mozambique for which the DHS estimate was higher than the UN estimate, 

and Ethiopia and Malawi, for which the DHS estimate was lower than the UN estimate. With the 

Afghanistan and Angola surveys, the agreement is good for both countries. 

Infant Mortality Rate 

Figure A.3 is a similar comparison that uses the IMR. The non-DHS source is IGME. The majority of 

IGME estimates are within the DHS confidence interval, although there are a number of surveys for which 

the IGME estimate is completely above the DHS confidence interval. For most countries where the DHS 

estimate of the IMR is above 50, the IGME estimate is significantly higher. The greatest weakness of 

estimates of infant and child mortality from a birth history is the potential omission of children who died. 

These omissions have a minor effect on fertility rates but a major effect on mortality rates. Omissions are 

almost certainly greatest in the countries with the highest prevalence of early deaths. 

In Figure A.3, the IGME estimate for Angola is higher than the IGME estimate for any other country, with 

the exception of Sierra Leone. Extreme values of any indicator, from any source, should be treated with 

caution. It is possible that the high deviation for Angola is partially due to a potential IGME over-estimate 

of the true IMR. 

The IMR estimates for Angola are further apart than for any other country. The IGME estimate is more 

than twice the DHS estimate. The deviation for Afghanistan is not as great as that for Angola, but is among 

the six largest deviations. 
13

Figure A.4 gives the “DHS minus IGME” differences on their own scale, with more clarity than in Figure 

A.3. As in Figure A.3, the countries are sequenced by the DHS estimate of the IMR. Six countries have a 

deviation of more than 20 points, including Afghanistan and especially Angola. Apart from the possibility 

that the IGME estimates for these two countries, particularly Angola, are too high, it appears that the DHS 

estimate of the IMR for Angola is much too low, by perhaps 50 points, and the estimate for Afghanistan is 

somewhat low, by perhaps 20 points. 

Under-5 Mortality Rate 

The U5MR is highly correlated with the IMR. There is a close correspondence between Figures A.5 and 

A.3 and between Figures A.6 and A.4, although a comparison of Figures A.5 and A.3 shows a better 

correspondence between DHS and IGME for the U5MR than for the IMR. Only six of the 51 countries 

have conspicuous deviations. In addition to Afghanistan and, especially, Angola, these are Comoros, 

Gambia, Benin, and Mali. For these six countries, the IGME estimate is at least 28 points higher than the 

DHS estimate. For Angola, there is a 100 point difference. 

According to the IGME estimates, the true U5MR for Angola is the highest of 51 countries. Chad and Sierra 

Leone are the only countries included here for which the IGME estimate is higher than 140. If correct, the 

level of omission in the Angola survey was enormous, with more than half of under-5 deaths omitted. 

Comparing the Infant and Under-5 Mortality Rates 

Figure A.7 is a scatterplot with the IMR on the horizontal axis, the U5MR on the vertical axis, and countries 

(or surveys) represented by dots. Blue dots represent the DHS estimates and red dots the IGME estimates. 

Figure A.8 matches exactly with Figure A.7, except that in Figure A.8, both rates have been logged to 

reduce the compression of the lower rates in Figure A.7. The figures include regression lines, which were 

calculated separately for the DHS and IGME estimates. 

As stated above, the IMR and U5MR are highly correlated. The correlation squared on the original scales 

of the rates, in Figure A.7, is 0.88 for the DHS estimates and 0.96 for the IGME estimates. It is even higher 

on the log scales. In Figure A.8, the correlation squared is 0.94 for the DHS estimates and 0.98 for the 

IGME estimates. In both figures, the IGME correlation is somewhat greater than the DHS correlation, 

probably because the IGME adjustments are designed, in part, to improve the correspondence between the 

IMR and U5MR. 

DHS surveys that deviate substantially from either the blue line or the red line, in either of Figures A.7 or 

A.8, may merit further investigation. For example, in Figure A.7, a blue dot stands out, with the greatest 

deviation above the blue regression line for a survey with an IMR that is substantially lower than would be 

expected, given its U5MR. This point represents the Niger 2012 survey. A possible explanation for such a 

deviation is that a high proportion of infant deaths were misclassified by being reported for age 1 rather 

than age 0. According to Table A.3 in the Appendix, the DHS estimates of the IMR and U5MR were 51 

and 127, respectively. The IGME estimates are 69 and 132 respectively, with the main difference a reallocation of deaths from age 1-4 into age 0. 

Another blue dot represents the greatest deviation below the blue line in Figure A.7, with a U5MR that is 

substantially lower than expected, given its IMR. This dot represents Pakistan, which has DHS estimates 

for the IMR and U5MR of 74 and 89, respectively. The IGME estimates are 74 and 92, which does not 

change the IMR estimate but moves the point upward toward the red line, which is below the blue line. 

Figures A.7 and A.8 also include labels for the blue dots for the DHS estimates for Afghanistan and Angola 

and red dots for the IGME estimates for those two countries. The blue dots for those two countries do not 

conspicuously deviate from the regression lines. Thus, the combination of the IMR and U5MR is plausible. 
14

However, the red dots for those two countries are substantially above the corresponding blue dots, on both 

scales, especially for Angola. 

In Figures A.7 and A.8, it would be helpful to be able to pair the blue and red dots for specific countries—

that is, to know how the difference between the DHS and IGME estimates of the U5MR relates to the 

difference between the DHS and IGME estimates of the IMR for the same country. Figure A.9 describes 

those differences, working from Figure A.8, which is on a log-log scale. In Figure A.9, the horizontal axis 

is the difference between the values of corresponding blue and red dots on the horizontal axis of Figure 

A.8. The vertical axis is the difference between the values of corresponding blue and red dots on the vertical 

axis of Figure A.8.

Female and Male Probabilities of Dying Between 15 and 50 

The remaining DHS estimates are limited to 33 surveys/countries that included sibling histories and 

produced adult mortality estimates. They are compared with other estimates of adult mortality for women 

and men in those countries at approximately the midpoint of the reference period produced by the UN 

Population Division, referred to as the UN. Figure A.10 compares the UN and DHS estimates for women 

with a scatterplot. There is high correlation. The square of the correlation is 0.68, and the regression line is 

shown. The figure also includes a 45 degree line for equality of the DHS and UN estimates. Figure A.10 

identifies countries for which there is a high deviation from either the regression line or the line of equality. 

Most points are above the line, which indicates that the UN estimate is higher than the DHS estimate, 

because of a UN adjustment for omission, in the sibling histories, of sisters who died between the age 15 

and 50. There are a number of upward adjustments of .10 (10 percentage points) or more, which will not 

be discussed. There is a surprising deviation for Zambia, for which the UN estimate is negative by about 

10 percentage points. A downward adjustment, especially by this magnitude, is unusual. Afghanistan and 

Angola have very similar estimates from both DHS and the UN, with the points for both almost exactly on 

the line of equality. 

Figure A.11 is analogous to Figure A.10 but for men. The DHS estimates are based on the brothers in the 

sibling histories. The interpretations are almost exactly the same as for women in Figure A.10. Most of the 

deviations from the line of equality are in the direction of a higher estimate from the UN than from DHS. 

Zambia stands out with a large deviation in the opposite direction, while the dots for Afghanistan and 

Angola are almost exactly on the line of equality of the DHS and UN estimates. 

Figures A.12 and A.13 compare the probabilities of dying between age 15 and 50 for women and men, as 

estimated by DHS (blue dots and line) or the UN (red dots and line). In this figure, a diagonal line 

corresponds with equal probabilities for women and men. The DHS and UN regression lines are very close 

to each other. The correlation squared is very high, at 0.86 for DHS and 0.90 for the UN. Both lines have a 

slope close to 1 and are just slightly above the line of equality. That is, female mortality is higher than male 

mortality about as often as it is lower, but is a little more likely to be lower. There is a great deal of variation 

in these probabilities, although the rates for women and men are usually quite close. 

With Afghanistan and Angola, the figure shows a relatively large deviation for the Afghanistan DHS, which 

is below all lines. The male rate is low, given the female rate or, conversely, the female rate is high, given 

the male rate. Since under-reporting of deaths is much more likely than over-reporting, there appears to be 

omission of male deaths. The corresponding red dots, for the UN estimates for Afghanistan and Angola, 

are closer to all lines. 

The differences between the DHS and UN estimates are more easily seen with Figure A.13, which plots the 

differences for women on the horizontal axis and for men on the vertical axis. Afghanistan is represented 

by a point at approximately (-.05,-.01); the DHS estimate is below the UN estimate by .05 for women and 

below by .01 for men. That is, the DHS estimates are low (relative to the UN estimates) but only by a 
15

negligible amount (.01) for women and a larger amount (.05) for men. In Figure A.13, the point for Angola 

is very close to the origin (0,0), which implies that it almost perfectly matches the UN estimate. There are 

many other surveys for which the DHS and UN estimates of adult mortality differ by much more than those 

for Afghanistan and Angola. 

Comparing Adult Female Mortality and Under-5 Mortality 

Figure A.14 is another scatterplot with blue dots for DHS estimates and red dots for UN estimates. The 

horizontal axis is the U5MR and the vertical axis is the AFMP. Two regression lines are shown. The lines 

are very close to each other but the fit is relatively poor, compared with the regression lines in earlier 

figures. These two estimated probabilities are not strongly associated. There are several outliers from the 

general pattern, but these would be identifiable in the previous univariate figures for the U5MR and the 

AFMP. Figure A.15 plots the differences between the DHS and UN estimates, with the U5MR difference 

on the vertical axis and the AFMP difference on the horizontal axis. Afghanistan and Angola stand out 

because the DHS estimates of the U5MR are much lower than the UN estimates. The two points are very 

close to the vertical axis because the DHS and UN estimates of the AFMP agree closely. Other outliers in 

this figure would have been identified earlier. 

Maternal Mortality Ratio 

Figure A.16 compares 31 DHS estimates of the MMR, including confidence intervals, with the WHO point 

estimates. WHO provides uncertainty intervals, but these are model-based and cannot be interpreted as 

confidence intervals. The figure includes the 31 DHS surveys with sibling histories. The dates of the WHO 

estimates are approximately at the midpoints of the reference intervals for the DHS seven-year estimates, 

and not at the dates of the surveys. 

Again, the WHO estimates are not independent of the DHS estimates, because DHS data are used in the 

construction of the WHO estimates. The WHO estimates have had multiple adjustments that are not applied 

to the DHS estimates. 

For 16 surveys, the WHO estimate is within the confidence interval for the DHS estimate. For 10 surveys, 

the WHO estimate is above the DHS confidence interval and for 5 it is below. The differences between the 

two estimates, expressed as the DHS estimate minus the WHO estimate, are shown in Figure A.17. In this 

figure, a majority of the differences are within the DHS confidence interval. 

The Angola survey has one of the most serious under-estimates, relative to the WHO estimate. There are 

eight surveys in which the DHS point estimate is at least 200 points below the WHO point estimate. The 

negative deviation for Angola is the same as for Nigeria, and is exceeded only by Gambia and Burundi. 

It is less common for the DHS estimate to be greater than the WHO estimate. A positive deviation greater 

than 200 points is observed for only three countries—Zimbabwe, Lesotho, and, largest of all, Afghanistan. 

The deviation for Afghanistan is the greatest, by far, among the 31 countries. 

Figures A.18 and A.19 compare the DHS and UN/WHO estimates of adult female mortality and maternal 

mortality. This kind of comparison focuses on the balance of adult female deaths that are classified as 

maternal or not maternal. Figure A.18 is a scatterplot in which the blue dots represent the combination of 

the two for DHS and the red dots the combination for UN/WHO estimates. The horizontal axis is the AFMP 

for women, which is a probability, and the vertical axis is the MMR, which is a complex ratio. The 

horizontal and vertical scales are quite different, but are used because of the measures that are readily 

available from general websites. The correlations between the two indicators are moderately high for both 

the DHS estimates and the UN/WHO estimates, and the two fitted lines are almost perfectly aligned with 

each other, although the blue and red scatterplots have a number of differences. 
16

The interpretation of Figure A.19, which plots the differences between the two sets of estimates, with one 

dot for each county, is easier to interpret. There are several outliers on this scatterplot. Points that are 

relatively far to the left or the right identify countries with the most substantial DHS versus UN differences 

in the AFMP estimates. Points that are relatively far upwards or downwards identify countries that have 

substantial DHS versus WHO differences in MMR estimates. Figure 19 identifies countries with all four 

possible combinations of deviations. 

Comparing Adult Female Mortality and Maternal Mortality 

With Afghanistan, the AFMP estimates from DHS and the UN are very close, although the MMR estimate 

is much higher for DHS than WHO. This combination of differences implies that a substantial number of 

deaths to women were misclassified as maternal. This interpretation is much more easily reached with 

Figure A.19 than with the separate figures for the AFMP and the MMR. 

Angola is somewhat low for both the AFMP and the MMR estimates, but more for the MMR estimate than 

the AFMP estimate. If these differences are interpreted as errors, DHS missed some deaths for women, and 

did not correctly classify some of the women’s deaths that were actually maternal. 

Comparing Under-5 Mortality and Maternal Mortality 

A final comparison between child mortality and maternal mortality is shown in Figures A.20 and A.21.

Figure A.20 is another overlay of two scatterplots, one with blue dots for DHS data and one with red dots 

for IGME/WHO data. In this figure, the fitted lines are quite different. The fitted line based on IGME/WHO 

data has a much better fit than the DHS line. Figure A.21 plots the differences in the U5MR dimension (the 

vertical axis) against the differences in the MMR dimension (the horizontal axis). Most of the points are in 

a relatively narrow range, although about a third could be described as outliers that warrant further analysis. 

The two most serious outliers, by far, are Afghanistan and Angola. Both of these countries have low 

estimates of the U5MR, relative to the IGME estimates. Angola has the most extreme deviation. Angola 

has a lower MMR than expected, by comparison with the WHO estimate, while Afghanistan is very high. 

Figures A.1-A.21 show some redundancies. Some are more helpful than others, but together they provide 

a strategy for comparing DHS estimates with the various IGME, UN Population Division, and WHO 

estimates, calibrated with reference dates. 
17

4. Comparability of Fertility Estimates from DHS and non-DHS 

Surveys: The Case of PMA2020 Data 

In countries that lack comprehensive vital registration systems, fertility rates are often computed from 

nationwide surveys that ask women of reproductive age about their birth history. DHS surveys are the 

largest such worldwide source of survey data on fertility in low- and middle-income countries. In recent 

years, a number of independent surveys have also produced nationwide fertility estimates. When rates differ 

by survey source, it may be difficult to know which is more accurate or which should be used for trend 

analysis. This chapter explores variation in extant and simulated fertility rate estimates between DHS and 

non-DHS surveys, and uses Performance Monitoring and Accountability 2020 (PMA2020) as a reference 

point for estimates and methodology. 

As with any survey indicator, a number of factors affect fertility estimates. First and foremost, there is 

natural variation in sampling and response rates that would be expected to produce slightly different 

estimates even if a survey was conducted in the exact same way at the exact same time. Added to this 

natural variation are the facts that DHS and non-DHS surveys are rarely conducted in the exact same 

months, and tend to employ slightly different sampling strategies. Non-DHS surveys may phrase questions 

about birth history in a slightly different way, gather only a truncated birth history, omit information about 

multiple births (twins), or follow different imputation procedures when dates of children’s birth are 

unknown. Even if the data gathered by two surveys are similar, the methodology to compute fertility rates 

may be slightly different. DHS surveys compute fertility based on a full birth history and a 3-year reference 

period, while other surveys may ask for a truncated birth history and/or employ a shorter reference period. 

Two examples of this difference are MICS surveys, which vary in the level of birth history they gather and 

in their reference period for computing rates, and the PMA2020 surveys, which gather the two most recent 

births and use a 2-year period of exposure in computing fertility. In this analysis, we explore the real and 

simulated differences between the DHS and PMA2020 fertility estimates. 

4.1. Data and Methods 

The data for this chapter come from 256 DHS surveys fielded from 1985 until the present, plus five recent 

nationwide PMA2020 surveys whose timeframe most closely corresponds with a published DHS survey. 

The PMA2020 surveys are multistage cluster surveys of households, women, and service delivery points 

that employ a standard methodology and have been conducted in ten countries to date (Zimmerman et al. 

2017). In comparing DHS and PMA2020 data and methods, we focus on two key fertility measures: the 

adolescent fertility rate and the total fertility rate (TFR). The adolescent fertility rate is measured by births 

per 1,000 women age 15 to 19, and corresponds to the number of average annual live births per woman age 

15 to 19 during a specified reference period before the date of the survey. The TFR is a synthetic measure 

of the average number of children a woman would have between age 15 and 49 if she gave birth at prevailing 

5-year age-specific rates.

We begin by comparing PMA2020 adolescent fertility rates and total fertility rates to corresponding DHS 

estimates for five countries. The PMA2020 data on Ghana were excluded in response to concerns about the 

plausibility of their fertility estimates.5

 DHS fertility rates were computed with the tfr2 package in Stata 

(Schoumaker 2013) that replicates DHS methodology, including the standard 3-year reference period. We 

refer to these standard DHS estimates as a “3-year n-birth estimate” because they use a window of 3 years 

prior to interview and allow as many births as were reported in those 3 years, including multiple births.

 

5

 Authors’ discussion with PMA2020 staff, August 2017. 
18

PMA2020 collects up to the two most recent births, does not record whether the births are multiples or not, 

and uses a post-estimate inflation factor to adjust fertility rates for multiple births. To compute PMA2020 

fertility rates, we used the tfr2 package and then added an age-specific adjustment factor for multiple births 

derived from DHS data on children born in the past 5 years. Specifically, we examined all single or the first 

of multiple live births reported to DHS in the 5 years before the survey, and computed the percentage of 

births that were the first of multiple births by 5-year age group. This method, as employed by PMA2020, 

does not consider whether a multiple birth was a triplet or more. Multiple births greater than two are rare, 

and are not expected to influence the number greatly. We used the age-specific percentage of live births to 

women age 15 to 49 in the past 5 years that were multiple births to inflate the age-specific fertility rates. 

These age-specific adjusted rates were then used to compute a new TFR and to adjust the standard errors 

for all rates. Fertility rates computed by this method are referred to as “2-year 2-birth adjusted rates”

because they consider up to two births and a reference period of the past 2 years, and also adjust for multiple 

births. All results in this chapter were computed with survey-specific weights provided with the dataset.

Next, because the extant PMA2020 data offer only five points of comparison, we compare DHS results 

from the standard “3-year n-birth” method to the simulated “2-year 2-birth adjusted fertility rate” estimates 

using all 256 available standard, continuous, and interim DHS datasets from 1985 onward. In order to make 

the DHS data comparable to PMA2020, we dropped any birth that was more than the first of a multiple set, 

kept the two most recent births, and used a 2-year reference period for fertility rates. We added an agespecific adjustment factor for multiple births as described above and proceeded with DHS as with 

PMA2020 data to adjust the fertility rates and standard errors. How do the rates and standard errors using 

a 2-year 2-birth adjusted method compare to the traditional 3-year n-birth TFR published by DHS? Do these 

estimates always overlap?

Finally, it is important to consider that non-DHS surveys use a different methodology for computing fertility 

rates, and may use a different sampling strategy. In particular, estimates of fertility from non-DHS surveys 

may be based on a smaller number of clusters. In the third section of this chapter we simulate 2-year 2-birth 

adjusted fertility rates with 2016 Ethiopia DHS data 1,000 times using a sub-sampling strategy that follows 

from a recent PMA2020 survey in Ethiopia (Round 4). For the sampling strategy, we compared PMA2020 

cluster selection to the DHS cluster selection by strata—urban or rural residence within region with some 

regions grouped—and randomly sampled the number of PMA2020 clusters from among the DHS clusters 

in the same strata. In cases where PMA2020 had selected a greater number of clusters in the urban area of 

a region, all corresponding DHS clusters were selected and a compensatory number of clusters in the 

corresponding rural area were selected. Data were re-weighted to equal the original number of DHS 

respondents in the stratum. For each subsample, we computed 2-year 2-birth adjusted fertility rates and 

compared the resulting point estimate and standard error to the nationwide sample. One thousand sample 

simulations were run. We examine how these estimates of rates and standard errors compare to the 3-year 

n-birth estimate provided by the full 2016 Ethiopia DHS sample, and discuss the limitations of this 

approach.

4.2. Comparison of PMA2020 and DHS Fertility Estimates 

We examined data from five national surveys conducted by PMA2020 in Ethiopia, Indonesia, Kenya, 

Nigeria, and Uganda and compared their fertility results to DHS surveys that were conducted within 3 years 

of the PMA2020 survey. The resulting estimates, standard errors, and sample sizes are shown in Table 4.1. 

As the table indicates, the TFR estimates from PMA2020 surveys tend to be lower than those from DHS 

by 5% (Uganda) to 22% (Nigeria), while adolescent fertility rates range from 17% lower (Nigeria) to 4% 

higher (Kenya) than corresponding DHS estimates. 
19

Table 4.1. Fertility rate results from five DHS and PMA2020 surveys 

DHS PMA2020

Country Year TFR

SE of 

TFR

Adolescent 

FR

SE of 

Adolescent 

FR

Sample 

size

Year and 

round number TFR

SE of 

TFR

Adolescent 

FR

SE of 

Adolescent 

FR

Sample 

size

Ethiopia 2016 4.6 0.06 79.5 2.8 15,683 2016 (Round 4) 4.2 0.10 77.3 4.8 7,481

Indonesia 2012 2.6 0.03 48.5 1.6 45,607 2015 (Round 1) 2.3 0.06 48.0 4.1 10,455

Kenya 2014 3.9 0.04 96.3 2.4 31,079 2014 (Round 2) 3.4 0.11 99.7 7.6 4,329

Nigeria 2013 5.5 0.04 121.7 2.3 38,948 2016 (Round 3) 4.3 0.08 101.5 4.8 11,054

Uganda 2011 6.2 0.09 134.5 4.9 8,674 2014 (Round 1) 5.9 0.17 139.9 9.1 3,716

Figure 4.1 shows a chart of comparative TFRs and their confidence intervals from PMA2020 and 

corresponding DHS surveys. Although—with the exception of Nigeria—the TFRs are generally close, their 

confidence intervals do not overlap in any study country. Adolescent fertility rates and their confidence 

intervals are shown in Figure 4.2. Confidence intervals for the corresponding estimates overlap in every 

country except Nigeria. 

Figure 4.1. TFR comparison between DHS and PMA2020 

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

2016

DHS

2016

PMA

(Rd 4)

2012

DHS

2015

PMA

(Rd 1)

2014

DHS

2014

PMA

(Rd 2)

2013

DHS

2016

PMA

(Rd 3)

2011

DHS

2014

PMA

(Rd 1)

Ethiopia Indonesia Kenya Nigeria Uganda TFR

Note: Bars indicate 95% confidence intervals
20

Figure 4.2. Adolescent fertility rate comparison between DHS and PMA2020 

4.3. A Comparison of 2-year 2-birth Adjusted Fertility Rates with 3-year n-birth Rates using DHS 

Data 

As discussed in the introduction, differences in survey fertility results may be driven by a number of factors 

such as questionnaire design and sampling strategy that are quite separate from questionnaire and 

imputation effects. Retrospective fertility rates produced by surveys are subject to errors of omission and 

displacement (Pullum and Becker 2014; Schoumaker 2014) that may vary by survey. Imputation of births 

that appear to be heaped on a particular month may affect fertility rates by a few percentage points (Choi et 

al. 2017). To independently assess the difference in fertility rates expected by a 2-year 2-birth adjusted 

method versus a 3-year n-birth method, we used all 256 available standard, continuous, and interim DHS 

datasets to calculate both estimates on the same data. Employing a different methodology with the same 

data enables us to assess the effect of computational and collection differences apart from fieldwork factors, 

imputation, or sampling design. A comparison of TFRs and adolescent fertility rates computed with each 

method for the same survey are shown in Figure 4.3. The results show a 99.8% correlation in TFRs and a 

99.5% correlation in adolescent fertility rates. The differences in adolescent fertility rates appear to be larger 

when the rates are higher.

To gauge the specific differences between estimates, we produced histograms of the results. Figure 4.4 

shows a distribution of the percentage difference in TFR estimates between the 2-year 2-birth adjusted 

method and the 3-year n-birth method on the exact same data and a distribution of the percentage difference 

in standard errors between the two estimates. The results show a fairly close match: TFR estimates from 

the 2-year 2-birth adjusted method range from -7% to +7% different from the 3-year n-birth estimates with 

a mean difference of less than one percentage point. As expected, the method that uses a truncated birth 

history produces higher standard errors that range from 17% to 27% higher than their 3-year n-birth 

counterparts, with an average difference of 22%.

0

20

40

60

80

100

120

140

160

2016

DHS

2016

PMA

(Rd 4)

2012

DHS

2015

PMA

(Rd 1)

2014

DHS

2014

PMA

(Rd 2)

2013

DHS

2016

PMA

(Rd 3)

2011

DHS

2014

PMA

(Rd 1)

Ethiopia Indonesia Kenya Nigeria Uganda

Adolescent Fertility Rate (per 1,000)

Note: Bars indicate 95% confidence intervals
21

Figure 4.3. Comparison of 3-year n-birth and 2-year 2-birth adjusted TFR and Adolescent Fertility Rate, DHS 

surveys 1985-present 

Figure 4.4. Distribution of difference between the 2-year 2-birth adjusted TFR and the 3-year n-birth TFR 

estimate and standard error of TFR estimate, DHS surveys 1985-present 

The corresponding results for the difference in adolescent fertility rates and standard errors of those rates 

between the two computation methods are shown in Figure 4.5. Here we see more variability in rates, from 

a -29% to a +16% difference in rate estimates and a mean also near zero (less than one percentage point). 

The difference in standard errors of these estimates ranges from 1% higher to 35% higher, with an average 

difference in standard errors of 22%. 
22

Figure 4.5. Distribution of difference between the 2-year 2-birth adjusted adolescent fertility rate and the 

3-year n-birth adolescent fertility rate estimate and standard error of estimate, DHS surveys 

1985-present 

Figure 4.6. Percentage of 2-year 2-birth fertility estimates that are within confidence bounds of 3-year 

n-birth estimates, DHS surveys 1985-present 

We also assessed the percentage of 2-year 2-birth adjusted estimates that fall within the confidence intervals 

of the DHS estimates and the percentage of the confidence intervals between the two estimates that overlap. 

While overlapping confidence intervals may still be statistically significantly different, this comparison 

provides a reasonable gauge the robustness of these alternate estimates. The results of the comparison are 
23

shown in Figure 4.6. In all, 93% of the adolescent fertility rate estimates and 84% of the TFR estimates 

using the 2-year 2-birth adjusted method fell within the confidence interval of the original DHS 3-year nbirth estimate. All alternate rates produced confidence intervals that overlapped with DHS estimates. 

4.4. Simulation of 2-year 2-birth Adjusted Fertility Rates in Ethiopia under Different Sampling 

Conditions 

In addition to alternate methods of estimating fertility between DHS and non-DHS surveys, the surveys 

may also have different sampling strategies. In particular, surveys that rely on a smaller number of clusters 

and respondents may produce different estimates of rates, particularly when a slightly different 

methodology is used to compute rates. In this section, we randomly simulate subsamples of the 2016 

Ethiopia DHS that are more comparable to the number of clusters sampled in the comparable PMA2020 

survey to explore how the use of a subsample of clusters combined with a different methodology might 

affect the rate estimates. 

In order to produce subsamples of DHS data, we compared the number of clusters sampled by the Ethiopia 

2016 DHS survey and the corresponding PMA2020 survey and chose the same stratified cluster 

composition as PMA2020. In the 2016 Ethiopia survey, DHS sampled 643 clusters, while the corresponding 

PMA2020 survey (Ethiopia Round 4) sampled 221 clusters. As shown in Table 4.2, in four regions, the 

Ethiopia Round 4 PMA2020 survey sampled more clusters in the urban area of the region than DHS; in 

these regions, a compensatory number of rural clusters were drawn. The resulting selection parameters are 

shown in the table; 221 DHS clusters were randomly selected from among the strata as indicated. 

Table 4.2. Distribution of clusters by strata in Ethiopia DHS 2016, Ethiopia PMA2020 2016 (Round 4), and 

DHS subsample simulation 

DHS Cluster Sample - Ethiopia 2016 PMA2020 Cluster Sample - Ethiopia Round 4 Resulting Simulated Cluster Sample

Urban Rural Total Urban Rural Total Urban Rural Total

Addis Ababa 56 - 56 Addis Ababa 22 - 22 Addis Ababa 22 - 22

Amhara 11 60 71 Amhara 14 26 40 Amhara 11 29 40

Oromia 10 64 74 Oromia 18 34 52 Oromia 10 42 52

SNNPR 8 63 71 SNNPR 28 19 47 SNNPR 8 39 47

Tigray 15 48 63 Tigray 21 13 34 Tigray 15 19 34

All others 102 206 308 All others 6 20 26 All others 6 20 26

202 441 643 Total 109 112 221 72 149 221

We randomly simulated this subsample of DHS clusters 1,000 times, used a 2-year 2-birth adjusted method 

to compute the resulting TFR and adolescent fertility rate, and then compared the results to the overall 

fertility estimates from the standard 3-year n-birth full sample. The distribution of percentage differences 

in rates and standard errors of TFR is shown in Figure 4.5. There is fairly good consistency between rates: 

the 2-year 2-birth adjusted estimates of TFR using a subsample of DHS data range from a -7% to a +10% 

difference and average less than one percentage point difference with 3-year n-birth estimates using the full 

sample. However, the standard errors shown in Figure 4.7 tend to be about twice as high as those from DHS 

data, and range from 89% to 111% higher, with an average of 102% higher. These results compare favorably 

to those shown using actual PMA2020 data in Table 4.1, where the TFR was 8% lower than in the 2016 

Ethiopia DHS, but appear to show a higher inflation of standard errors than in actual PMA2020 data, where 

the standard error of the TFR was only 65% higher. 
24

Figure 4.7. Distribution of difference between the 3-year n-birth TFR from Ethiopia DHS 2016 and a 2-year

2-birth adjusted TFR computed using 1,000 simulated cluster samples similar to the 2016 

Ethiopia PMA2020, estimate and standard error of estimate 

Using the same 1,000 simulation subsamples, we computed the adolescent fertility rate using the 2-year 2-

birth adjusted method and compared it to the standard 3-year n-birth adolescent fertility rate from the entire 

DHS sample. The results are shown in Figure 4.8. The percentage difference in adolescent fertility rate 

estimates ranges from -23% to +12% with an average of -4%. These results are consistent with the finding 

from the actual Round 4 PMA2020 Ethiopia survey, in which the adolescent fertility rate was 3% lower 

than in DHS. As with the TFR, the standard errors for adolescent fertility tend to be about twice as high in 

the subsample as in the corresponding DHS full sample. The errors average 97% higher, with a range of 

76% to 115% higher. This estimated range is higher than the actual percentage difference in standard errors 

of adolescent fertility rates between PMA2020 and DHS data for Ethiopia, which were only 68% higher in 

the PMA2020 survey. 
25

Figure 4.8. Distribution of difference between the 3-year n-birth adolescent fertility rate from Ethiopia DHS 

2016 and a 2-year 2-birth adjusted adolescent fertility rate computed using 1,000 simulated 

cluster samples similar to the 2016 Ethiopia PMA2020, estimate and standard error of estimate 

Figure 4.9. Percentage of 2-year 2-birth fertility estimates from simulated PMA2020 Ethiopia samples that 

are within confidence bounds of 3-year n-birth estimates, Ethiopia DHS 2016 

As with the prior set of DHS simulations, the percentage of estimates that overlapped actual DHS values 

was computed. The results are shown in Figure 4.9. Only 69% of TFR point estimates from the simulated 

subsample fell within the confidence interval of the DHS estimate, although 99.9% of the confidence 

intervals between the two estimates overlapped. In addition, while only 67% of adolescent fertility rate 
26

estimates were within confidence estimates of the DHS estimates, 99.5% produced confidence intervals 

that overlapped with DHS.

One limitation of this simulation was the sample size of the clusters, which tends to be higher in 

PMA2020 than in DHS. The simulated subsamples of DHS data produced an unweighted sample size that 

averaged around 5,800 versus over 7,400 in the PMA2020 survey. A larger sample within each cluster 

would reduce the standard errors of the estimates and may explain the smaller difference between 

PMA2020 and DHS standard errors in actuality and those predicted by the simulation. An additional 

limitation of the sampling technique was that we had to choose from among DHS clusters in a stratum 

without regard to their ‘true’ relative size, which may be considered at the survey design stage but is not 

data available to the end user. While most countries’ statistical offices endeavor to classify clusters based 

on similar size, cluster population may vary slightly. Knowledge of relative cluster size would have 

produced slightly different samples that may have been closer to DHS values.
27

5. Discussion and Conclusion 

DHS Surveys provide many indicators that are used for program planning and monitoring. Some of these 

are particularly sensitive and noteworthy when the results from a new survey are released. This 

methodological report focused on six demographic indicators of widespread interest: the Total Fertility 

Rate (TFR), Infant Mortality Rate (IMR), Under-5 Mortality Rate (U5MR), the Adult Female Mortality 

Probability (AFMP), the Adult Male Mortality Probability (AMMP) and the Maternal Mortality Ratio 

(MMR). The IMR, U5MR, AFMP, and AMMP are all probabilities (1q0, 5q0, and 35q15 for women and 

men, respectively), although the first two are commonly described as rates. Demographers also calculate 

mortality rates for adult women and men, but here we use the probability of dying between age 15 and 50, 

partly to be consistent between children and adults and partly because the adult probabilities are more 

readily available from multiple sources. The MMR is a much more complex indicator that is interpretable 

as the number of maternal deaths per 100,000 births. DHS calculates the TFR, IMR, and U5MR from the 

birth histories and the AFMP, AMMP, and MMR from the sibling histories. 

It is not unusual for the results of a new survey to be questioned when they differ from some other source. 

The goal of this report has been to provide guidance on determining whether the estimates of these 

important indicators are plausible, in the sense of being consistent with other sources, or are not plausible. 

If the latter conclusion is reached, then further in-depth investigation is required. We do not discuss 

strategies for such investigations, because they are very specific to the indicator and the survey. 

Determining whether a DHS estimate is consistent with other sources is usually a matter of degree. Some 

differences are expected for a variety of reasons. This report includes a discussion of potential reasons for 

discrepancies. An important source of variation, even under the best of circumstances, is sampling error. 

The ramifications of the sample design and sampling error are discussed. Other possible reasons for 

discrepancies include different reference periods of time. For example, the usual TFR refers to the past 3 

years, and is centered 1.5 years before the mean date of interview. The IMR and U5MR refer to the past 5 

years, and are centered 2.5 years before the mean date of interview. The AFMP, AMMP, and MMR refer 

to the past 7 years, and are centered 3.5 years before the mean date of interview. When comparing sources, 

it is important to consider the reference period. Differences can also arise from the use of indirect versus 

direct estimates (DHS estimates are always direct), the adjustment of data (DHS does not make 

adjustments), and the use of complete versus truncated birth histories (DHS birth histories are always 

complete). All comparisons must consider these differences. 

Chapter 3 of the report analyzed 51 surveys conducted since 2010 in terms of the estimates of the TFR, 

IMR, and U5MR; 31 of those surveys included sibling histories, in terms of the estimates of the AFMP, 

AMMP, and MMR. These estimates were systematically compared with estimates of the TFR, AFMP, and 

AMMP from the UN Population Division, estimates of the IMR and U5MR from the UN’s Inter Agency 

Group for Child Mortality Estimation (IGME), and MMR estimates from the World Health Organization 

(WHO). These official, adjusted estimates were selected for the dates or time intervals closest to the 

reference dates for the corresponding DHS estimates. All estimates, from both DHS and the UN agencies, 

were downloaded from publicly accessible websites. 

To provide more focus, the analysis centered on the Afghanistan 2015 and Angola 2015-16 surveys. A set 

of 21 figures guided the comparison of the DHS estimates and the UN estimates. The structure of the 

comparisons could be applied to other sources, although it is always necessary to consider the potential 

kinds of differences discussed earlier. The UN estimates are not independent of the DHS estimates, because 

DHS data are among the many ingredients of the UN estimates, but they have been carefully adjusted with 

statistical and demographic models. The figures used for the comparisons are accompanied by tables that 

provide specific numerical values, and in many cases, confidence intervals or uncertainty intervals. The 
28

findings from these comparisons will not be repeated here, because they were solely intended to be 

illustrative of the strategy used for making the comparisons. 

In countries that lack comprehensive and accurate vital registration, surveys estimate vital rates through 

retrospective data. Differing fertility estimates produced by DHS and non-DHS surveys such as MICS and 

PMA2020 that conduct fieldwork within the same timeframe may cause confusion in interpretation and 

trend analysis. Chapter 4 compared DHS fertility rate estimates with those from an alternative source, 

PMA2020, explored the effect of a slightly different methodology for computing rates on DHS estimates, 

and simulated subsamples of Ethiopia 2016 DHS data that are similar to PMA2020’s Round 4 Ethiopia 

survey to assess the effect of a different sampling strategy and rate estimation method on fertility rate 

results. 

Chapter 4 shows that PMA2020 fertility rates estimated with a standard methodology have a range of 5% 

to 22% difference in TFRs and a 4% to 17% difference in adolescent fertility rates compared to those 

published by The DHS Program. The standard errors in PMA2020 fertility estimates also range from 65% 

to 222% higher than corresponding DHS estimates. Beyond survey factors such as questionnaire design 

and date imputation, these estimates are produced by techniques that slightly differ in methodology. DHS 

publishes 3-year n-birth fertility rates while published PMA2020 fertility estimates tend to be 2-year 2-birth 

adjusted rates.6

 In addition, survey design effects (sample frames and sizes) may play a role in differences. 

In Ethiopia, for example, both surveys used a two-stage stratified random sample, although PMA2020 

grouped several regions together as one, sampled a smaller number of clusters in each region, and sampled 

a larger number of women per cluster. 

To assess the effect of alternate measurement on fertility rates from the exact same survey, Chapter 4 used 

all 256 available standard, continuous, and interim DHS datasets to compare the results of a 2-year 2-birth 

adjusted estimation technique versus a 3-year n-birth technique with the same data. Our results show that 

fertility rates calculated with a 2-year 2-birth adjusted fertility rate method should typically average only a 

one percentage point difference in total fertility and adolescent fertility rates. However, there may be as 

much as a 7 percentage point difference in total fertility rates and a 29 percentage point in adolescent fertility 

rates produced by these two different methods when using the same data. 

In addition to the differences produced by alternate techniques of fertility measurement, it is important to 

consider the overall design effect of a different sample. In Ethiopia, we simulated 1,000 subsamples of 2016 

DHS data with a cluster distribution similar to PMA2020 and computed rates with a 2-year 2-birth adjusted 

estimation technique. We found that these combined differences in sampling and methodology typically 

produce an average 3% to 4% difference but may plausibly produce as much as a 10% difference in TFR, 

a 23% difference in adolescent fertility rate in Ethiopia, and typically around double the standard errors of 

DHS estimates. Notably, since the PMA2020 cluster sample sizes are larger than those used by DHS, this 

simulation is likely to overestimate the effect of sampling difference on results from Ethiopia. 

When comparing fertility estimates from different surveys, it is important to recognize that differences in 

sampling, sample size, and overall fertility rate computation methods may—even with the same data—

drive apparent differences in fertility rates. However, we find that measurement and sampling factors on 

the same survey data typically produce results that have overlapping confidence intervals. When results 

from alternate surveys are unusually different from DHS, it may also be worth considering differences in 

the survey timeframe, wording of questionnaires, and date imputation that could help account for the 

observed difference. Such factors are beyond what we can assess from DHS data alone. A recent report by 

Choi et al. finds that random redistribution of excess January births in PMA2020 data would increase TFR 

estimates by an average of 3%. Surveys such as MICS, which may employ a truncated birth history, base 

 

6

 Authors’ correspondence with PMA2020 staff, August 2017. 
29

fertility on a different reference period, or do not employ a PMA2020-type adjustment for multiple births 

should also be expected to produce variable, and lower fertility rates than the published DHS estimates. 

31

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33

Appendices 

Table A.1. List of surveys included in this report and reference dates for the rates 

Reference date

Survey

Mean Date 

of Interview TFR

Child 

mortality

Adult 

Mortality

Afghanistan 2015 2015.8 2014.3 2013.3 2012.3

Angola 2015-16 2016.0 2014.5 2013.5 2012.5

Armenia 2010 2010.9 2009.4 2008.4 2007.4

Bangladesh 2014 2014.7 2013.2 2012.2 2011.2

Benin 2011-12 2012.1 2010.6 2009.6 2008.6

Burkina Faso 2010 2010.7 2009.2 2008.2 2007.2

Burundi 2010 2010.8 2009.3 2008.3 2007.3

Cambodia 2014 2014.6 2013.1 2012.1 2011.1

Cameroon 2011 2011.3 2009.8 2008.8 2007.8

Chad 2014-15 2015.1 2013.6 2012.6 2011.6

Colombia 2015 2015.6 2014.1 2013.1 2012.1

Comoros 2012 2012.8 2011.3 2010.3 2009.3

Congo 2011-12 2011.9 2010.4 2009.4 2008.4

Cote d'Ivoire 2011-12 2012.1 2010.6 2009.6 2008.6

Dominican Republic 2013 2013.6 2012.1 2011.1 2010.1

DRC 2013-14 2013.9 2012.4 2011.4 2010.4

Egypt 2014 2014.4 2012.9 2011.9 2010.9

Ethiopia 2011 2011.2 2009.7 2008.7 2007.7

Gabon 2012 2012.2 2010.7 2009.7 2008.7

Gambia 2013 2013.2 2011.7 2010.7 2009.7

Ghana 2014 2014.8 2013.3 2012.3 2011.3

Guatemala 2014-15 2015.2 2013.7 2012.7 2011.7

Guinea 2012 2012.6 2011.1 2010.1 2009.1

Haiti 2012 2012.3 2010.8 2009.8 2008.8

Honduras 2011-12 2012.1 2010.6 2009.6 2008.6

Indonesia 2012 2012.4 2010.9 2009.9 2008.9

Jordan 2012 2012.8 2011.3 2010.3 2009.3

Kenya 2014 2014.6 2013.1 2012.1 2011.1

Kyrgyz Republic 2012 2012.8 2011.3 2010.3 2009.3

Lesotho 2014 2014.8 2013.3 2012.3 2011.3

Liberia 2013 2013.4 2011.9 2010.9 2009.9

Malawi 2015-16 2015.9 2014.4 2013.4 2012.4

Mali 2012-13 2013.0 2011.5 2010.5 2009.5

Mozambique 2011 2011.6 2010.1 2009.1 2008.1

Myanmar 2015-16 2016.1 2014.6 2013.6 2012.6

Namibia 2013 2013.6 2012.1 2011.1 2010.1

Nepal 2011 2011.3 2009.8 2008.8 2007.8

Niger 2012 2012.3 2010.8 2009.8 2008.8

Nigeria 2013 2013.3 2011.8 2010.8 2009.8

Pakistan 2012-13 2012.9 2011.4 2010.4 2009.4

Philippines 2013 2013.7 2012.2 2011.2 2010.2

Rwanda 2014-15 2015.1 2013.6 2012.6 2011.6

Senegal 2010-11 2011.0 2009.5 2008.5 2007.5

Sierra Leone 2013 2013.6 2012.1 2011.1 2010.1

Tajikistan 2012 2012.6 2011.1 2010.1 2009.1

Tanzania 2015-16 2015.9 2014.4 2013.4 2012.4

Togo 2013-14 2014.1 2012.6 2011.6 2010.6

Uganda 2011 2011.7 2010.2 2009.2 2008.2

Yemen 2013 2013.8 2012.3 2011.3 2010.3

Zambia 2013-14 2013.9 2012.4 2011.4 2010.4

Zimbabwe 2015 2015.7 2014.2 2013.2 2012.2
34

Figure A.1. TFR with 95% confidence intervals for 51 DHS surveys and the UN estimate for each survey 

0

1

2

3

4

5

6

7

8

9

Armenia 2010

Colombia 2015

Myanmar 2015-16

Bangladesh 2014

Dominican Republic 2013

Indonesia 2012

Nepal 2011

Cambodia 2014

Honduras 2011-12

Philippines 2013

Guatemala 2014-15

Lesotho 2014

Egypt 2014

Jordan 2012

Haiti 2012

Kyrgyz Republic 2012

Namibia 2013

Tajikistan 2012

Pakistan 2012-13

Kenya 2014

Zimbabwe 2015

Gabon 2012

Rwanda 2014-15

Ghana 2014

Comoros 2012

Yemen 2013

Malawi 2015-16

Liberia 2013

Togo 2013-14

Ethiopia 2011

Benin 2011-12

Sierra Leone 2013

Cote d'Ivoire 2011-12

Senegal 2010-11

Cameroon 2011

Guinea 2012

Congo 2011-12

Tanzania 2015-16

Zambia 2013-14

Afghanistan 2015

Nigeria 2013

Gambia 2013

Mozambique 2011

Burkina Faso 2010

Mali 2012-13

Uganda 2011

Angola 2015-16

Burundi 2010

Chad 2014-15

DRC 2013-14

Niger 2012

DHS estimate

UN estimate
35

Figure A.2. TFR estimates with difference between DHS and UN estimate 

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Armenia 2010

Colombia 2015

Myanmar 2015-16

Bangladesh 2014

Dominican Republic 2013

Indonesia 2012

Nepal 2011

Cambodia 2014

Honduras 2011-12

Philippines 2013

Guatemala 2014-15

Lesotho 2014

Egypt 2014

Jordan 2012

Haiti 2012

Kyrgyz Republic 2012

Namibia 2013

Tajikistan 2012

Pakistan 2012-13

Kenya 2014

Zimbabwe 2015

Gabon 2012

Rwanda 2014-15

Ghana 2014

Comoros 2012

Yemen 2013

Malawi 2015-16

Liberia 2013

Togo 2013-14

Ethiopia 2011

Benin 2011-12

Sierra Leone 2013

Cote d'Ivoire 2011-12

Senegal 2010-11

Cameroon 2011

Guinea 2012

Congo 2011-12

Tanzania 2015-16

Zambia 2013-14

Afghanistan 2015

Nigeria 2013

Gambia 2013

Mozambique 2011

Burkina Faso 2010

Mali 2012-13

Uganda 2011

Angola 2015-16

Burundi 2010

Chad 2014-15

DRC 2013-14

Niger 2012
36

Figure A.3. IMR DHS compared to IGME 

0

20

40

60

80

100

120

Armenia 2010

Colombia 2015

Jordan 2012

Egypt 2014

Philippines 2013

Honduras 2011-12

Kyrgyz Republic 2012

Dominican Republic 2013

Guatemala 2014-15

Cambodia 2014

Indonesia 2012

Rwanda 2014-15

Gambia 2013

Tajikistan 2012

Comoros 2012

Bangladesh 2014

Kenya 2014

Namibia 2013

Congo 2011-12

Myanmar 2015-16

Ghana 2014

Malawi 2015-16

Benin 2011-12

Gabon 2012

Tanzania 2015-16

Yemen 2013

Angola 2015-16

Zambia 2013-14

Afghanistan 2015

Nepal 2011

Senegal 2010-11

Togo 2013-14

Zimbabwe 2015

Niger 2012

Liberia 2013

Uganda 2011

Mali 2012-13

DRC 2013-14

Burundi 2010

Ethiopia 2011

Haiti 2012

Lesotho 2014

Cameroon 2011

Mozambique 2011

Burkina Faso 2010

Guinea 2012

Cote d'Ivoire 2011-12

Nigeria 2013

Chad 2014-15

Pakistan 2012-13

Sierra Leone 2013

DHS estimate

IGME estimate
37

Figure A.4. IMR for DHS compared to IGME with differences 

-65

-60

-55

-50

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

5

10

Armenia 2010

Colombia 2015

Jordan 2012

Egypt 2014

Philippines 2013

Honduras 2011-12

Kyrgyz Republic 2012

Dominican Republic 2013

Guatemala 2014-15

Cambodia 2014

Indonesia 2012

Rwanda 2014-15

Gambia 2013

Tajikistan 2012

Comoros 2012

Bangladesh 2014

Kenya 2014

Namibia 2013

Congo 2011-12

Myanmar 2015-16

Ghana 2014

Malawi 2015-16

Benin 2011-12

Gabon 2012

Tanzania 2015-16

Yemen 2013

Angola 2015-16

Zambia 2013-14

Afghanistan 2015

Nepal 2011

Senegal 2010-11

Togo 2013-14

Zimbabwe 2015

Niger 2012

Liberia 2013

Uganda 2011

Mali 2012-13

DRC 2013-14

Burundi 2010

Ethiopia 2011

Haiti 2012

Lesotho 2014

Cameroon 2011

Mozambique 2011

Burkina Faso 2010

Guinea 2012

Cote d'Ivoire 2011-12

Nigeria 2013

Chad 2014-15

Pakistan 2012-13

Sierra Leone 2013
38

Figure A.5. U5MR DHS compared to IGME 

0

20

40

60

80

100

120

140

160

180

Colombia 2015

Armenia 2010

Jordan 2012

Egypt 2014

Honduras 2011-12

Dominican...

Kyrgyz Republic...

Philippines 2013

Cambodia 2014

Guatemala 2014-...

Indonesia 2012

Tajikistan 2012

Bangladesh 2014

Comoros 2012

Myanmar 2015-16

Rwanda 2014-15

Kenya 2014

Yemen 2013

Gambia 2013

Nepal 2011

Namibia 2013

Afghanistan 2015

Ghana 2014

Malawi 2015-16

Gabon 2012

Tanzania 2015-16

Congo 2011-12

Angola 2015-16

Zimbabwe 2015

Benin 2011-12

Senegal 2010-11

Zambia 2013-14

Lesotho 2014

Ethiopia 2011

Togo 2013-14

Haiti 2012

Pakistan 2012-13

Uganda 2011

Liberia 2013

Mali 2012-13

Burundi 2010

Mozambique 2011

DRC 2013-14

Cote d'Ivoire...

Cameroon 2011

Guinea 2012

Niger 2012

Nigeria 2013

Burkina Faso 2010

Chad 2014-15

Sierra Leone 2013

DHS estimate

IGME estimate
39

Figure A.6. U5MR DHS compared to IGME with differences 

-110

-100

-90

-80

-70

-60

-50

-40

-30

-20

-10

0

10

20

Colombia 2015

Armenia 2010

Jordan 2012

Egypt 2014

Honduras 2011-12

Dominican Republic 2013

Kyrgyz Republic 2012

Philippines 2013

Cambodia 2014

Guatemala 2014-15

Indonesia 2012

Tajikistan 2012

Bangladesh 2014

Comoros 2012

Myanmar 2015-16

Rwanda 2014-15

Kenya 2014

Yemen 2013

Gambia 2013

Nepal 2011

Namibia 2013

Afghanistan 2015

Ghana 2014

Malawi 2015-16

Gabon 2012

Tanzania 2015-16

Congo 2011-12

Angola 2015-16

Zimbabwe 2015

Benin 2011-12

Senegal 2010-11

Zambia 2013-14

Lesotho 2014

Ethiopia 2011

Togo 2013-14

Haiti 2012

Pakistan 2012-13

Uganda 2011

Liberia 2013

Mali 2012-13

Burundi 2010

Mozambique 2011

DRC 2013-14

Cote d'Ivoire 2011-12

Cameroon 2011

Guinea 2012

Niger 2012

Nigeria 2013

Burkina Faso 2010

Chad 2014-15

Sierra Leone 2013
40

Figure A.7. IMR versus U5MR using DHS and IGME estimates 

Figure A.8. Log of IMR versus log of U5MR using DHS and IGME estimates 

Afghanistan DHS

Angola DHS

Niger DHS

Pakistan DHS

Afghanistan IGME

Angola y = 1.90x - 15.70

R2 = 0.88

y = 1.68x - 11.53

R2 = 0.96

0

20

40

60

80

100

120

140

160

180

0 20 40 60 80 100 120

U5MR

IMR

DHS

IGME

Angola DHS

Afghanistan DHS

Afghanistan IGME

y = 1.26x - 0.25 Angola IGME

R2 = 0.94

y = 1.19x - 0.18

R2 = 0.98

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

1.0 1.2 1.4 1.6 1.8 2.0 2.2

log(U5MR)

log(IMR)

DHS

IGME
41

Figure A.9. Log of the ratio of DHS and IGME estimates for IMR versus U5MR 

Figure A.10. Female probability of dying between age 15 and 50 

Angola

Comoros

Benin

Afghanistan

Gambia

DRC

Niger

Armenia

Tajikistan

y = 0.94x + 0.02

R2 = 0.81

-0.50

-0.40

-0.30

-0.20

-0.10

0.00

0.10

0.20

-0.40 -0.35 -0.30 -0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10

log( U5MR DHS/ U5MR IGME)

log(IMR DHS/IMR IGME)

Afghanistan

Angola

Zimbabwe

Zambia

Lesotho

y = 0.78x + 0.06

R2 = 0.68

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

UN estimate

DHS estimate
42

Figure A.11. Male probability of dying between age 15 and 50 

Figure A.12. Male versus Female Adult Mortality 

Afghanistan

Angola

Zimbabwe

Zambia

Lesotho

y = 0.74x + 0.08

R2 = 0.70

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

UN estimate

DHS estimate

Afghanistan DHS

Angola ...

Afghanistan UN

Angola UN

y = 0.95x + 0.03

R2 = 0.86

y = 0.91x + 0.05

R2 = 0.90

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

Male Adult Mortality

Female Adult Mortality

DHS estimates

UN estimates
43

Figure A.13. Difference in Male Adult Mortality versus Difference in Female Adult Mortality 

Figure A.14. Adult Female Mortality versus U5MR 

Afghanistan

Angola

y = 0.88x + 0.00

R2 = 0.73

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10

Difference in Male Adult Mortality

Difference in Female Adult Mortality

Afghanistan DHS

Angola ...

Afghanistan

Angola

y = 0.001x + 0.08

R2 = 0.15

y = 0.00x + 0.10

R2 = 0.22

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0 20 40 60 80 100 120 140 160 180

Adult Female Mortality

U5MR

DHS estimates

UN Female Adult Mortality and IGME U5MR
44

Figure A.15. Difference in Adult Female Mortality versus Difference in U5MR 

Afghanistan

Angola

y = 61.81x - 6.91

R2 = 0.02

-120

-100

-80

-60

-40

-20

0

20

-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15

Difference in U5MR

Difference in Adult Female Mortality
45

Figure A.16. MMR using DHS and WHO estimates 

0

200

400

600

800

1000

1200

1400

1600

Colombia 2015

Guatemala 2014-15

Cambodia 2014

Comoros 2012

Angola 2015-16

Rwanda 2014-15

Indonesia 2012

Gabon 2012

Burkina Faso 2010

Namibia 2013

Kenya 2014

Mali 2012-13

Zambia 2013-14

Togo 2013-14

Congo 2011-12

Gambia 2013

Uganda 2011

Mozambique 2011

Senegal 2010-11

Burundi 2010

Tanzania 2015-16

Niger 2012

Nigeria 2013

Cote d'Ivoire 2011-12

Zimbabwe 2015

Ethiopia 2011

Guinea 2012

Cameroon 2011

Chad 2014-15

Lesotho 2014

Afghanistan 2015

DHS estimate

WHO estimate
46

Figure A.17. MMR using DHS and WHO estimates and the difference between DHS and WHO 

-500

-400

-300

-200

-100

0

100

200

300

400

500

600

700

800

900

Colombia 2015

Guatemala 2014-15

Cambodia 2014

Comoros 2012

Angola 2015-16

Rwanda 2014-15

Indonesia 2012

Gabon 2012

Burkina Faso 2010

Namibia 2013

Kenya 2014

Mali 2012-13

Zambia 2013-14

Togo 2013-14

Congo 2011-12

Gambia 2013

Uganda 2011

Mozambique 2011

Senegal 2010-11

Burundi 2010

Tanzania 2015-16

Niger 2012

Nigeria 2013

Cote d'Ivoire 2011-12

Zimbabwe 2015

Ethiopia 2011

Guinea 2012

Cameroon 2011

Chad 2014-15

Lesotho 2014

Afghanistan 2015
47

Figure A.18. MMR versus Adult Female Mortality 

Figure A.19. Difference of MMR versus difference of Adult Female Mortality 

Afghanistan DHS

Angola DHS

Afghanistan

Angola

y = 1,758.61x + 207.12

R2 = 0.29

y = 1,720.92x + 182.78

R2 = 0.33

0

200

400

600

800

1000

1200

1400

0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50

MMR

Adult Female Mortality

DHS estimates

UN Female Adult Mortality and WHO MMR

Afghanistan

Angola

y = 2,863.43x + 63.79

R2 = 0.32

-600

-400

-200

0

200

400

600

800

1000

-0.15 -0.10 -0.05 0.00 0.05 0.10 0.15

Difference in MMR

Difference in Adult Female Morality
48

Figure A.20. U5MR versus MMR 

Figure A.21. Difference in U5MR versus difference in MMR 

Afghanistan DHS

Angola DHS

Afghanistan

Angola

y = 0.06x + 51.24

R2 = 0.21

y = 0.13x + 24.35

R2 = 0.58

0

20

40

60

80

100

120

140

160

180

0 200 400 600 800 1000 1200 1400

U5MR

MMR

DHS estimates

WHO MMR and IGME U5MR

Afghanistan

Angola

y = 0.02x - 8.56

R2 = 0.03

-120

-100

-80

-60

-40

-20

0

20

-600 -400 -200 0 200 400 600 800 1000

Difference in U5MR

Difference in MMR
49

Table A.2. TFR estimates from DHS and the UN Population Division 

Survey TFR [C.I.] DHS TFR UN Difference

Afghanistan 2015 5.29 [5.13,5.44] 5.26 0.03

Angola 2015-16 6.22 [6.02,6.42] 5.95 0.27

Armenia 2010 1.70 [1.58,1.84] 1.72 -0.02

Bangladesh 2014 2.28 [2.19,2.38] 2.22 0.06

Benin 2011-12 4.90 [4.79,5.02] 5.22 -0.32

Burkina Faso 2010 5.99 [5.85,6.13] 6.08 -0.09

Burundi 2010 6.38 [6.20,6.57] 6.50 -0.12

Cambodia 2014 2.72 [2.63,2.82] 2.70 0.02

Cameroon 2011 5.09 [4.94,5.24] 5.25 -0.16

Chad 2014-15 6.45 [6.28,6.61] 6.31 0.14

Colombia 2015 1.97 [1.88,2.07] 1.93 0.04

Comoros 2012 4.32 [4.07,4.59] 4.60 -0.28

Congo 2011-12 5.11 [4.93,5.30] 4.86 0.25

Cote d'Ivoire 2011-12 4.96 [4.73,5.19] 5.14 -0.18

Dominican Republic 2013 2.48 [2.36,2.61] 2.53 -0.05

DRC 2013-14 6.57 [6.37,6.76] 6.40 0.17

Egypt 2014 3.47 [3.39,3.54] 3.38 0.09

Ethiopia 2011 4.80 [4.55,5.07] 5.26 -0.46

Gabon 2012 4.10 [3.89,4.33] 4.00 0.10

Gambia 2013 5.60 [5.40,5.82] 5.62 -0.02

Ghana 2014 4.19 [4.00,4.40] 4.18 0.01

Guatemala 2014-15 3.13 [3.03,3.23] 3.19 -0.06

Guinea 2012 5.10 [4.92,5.29] 5.13 -0.03

Haiti 2012 3.53 [3.38,3.69] 3.13 0.40

Honduras 2011-12 2.94 [2.85,3.02] 2.65 0.29

Indonesia 2012 2.60 [2.53,2.67] 2.45 0.15

Jordan 2012 3.51 [3.38,3.64] 3.60 -0.09

Kenya 2014 3.90 [3.79,4.02] 4.10 -0.20

Kyrgyz Republic 2012 3.63 [3.46,3.82] 3.12 0.52

Lesotho 2014 3.26 [3.09,3.44] 3.26 0.00

Liberia 2013 4.73 [4.53,4.94] 4.83 -0.10

Malawi 2015-16 4.43 [4.30,4.58] 4.88 -0.44

Mali 2012-13 6.10 [5.92,6.28] 6.35 -0.25

Mozambique 2011 5.92 [5.76,6.09] 5.45 0.47

Myanmar 2015-16 2.28 [2.16,2.41] 2.30 -0.02

Namibia 2013 3.65 [3.50,3.80] 3.60 0.05

Nepal 2011 2.60 [2.42,2.80] 2.96 -0.36

Niger 2012 7.64 [7.46,7.82] 7.40 0.24

Nigeria 2013 5.55 [5.41,5.69] 5.74 -0.19

Pakistan 2012-13 3.83 [3.72,3.95] 3.72 0.11

Philippines 2013 3.04 [2.94,3.15] 3.05 -0.01

Rwanda 2014-15 4.16 [4.04,4.29] 4.20 -0.04

Senegal 2010-11 4.98 [4.82,5.16] 5.10 -0.12

Sierra Leone 2013 4.91 [4.71,5.12] 4.84 0.07

Tajikistan 2012 3.76 [3.60,3.92] 3.50 0.26

Tanzania 2015-16 5.20 [5.01,5.40] 5.24 -0.04

Togo 2013-14 4.78 [4.60,4.97] 4.69 0.09

Uganda 2011 6.20 [5.99,6.42] 5.91 0.29

Yemen 2013 4.43 [4.27,4.59] 4.40 0.03

Zambia 2013-14 5.26 [5.12,5.41] 5.20 0.06

Zimbabwe 2015 4.02 [3.88,4.17] 4.00 0.02

Notes: Difference is DHS – UN estimate. UN estimate source (United 

Nations 2017).
50

Table A.3. IMR and U5MR estimates from DHS and IGME 

Survey

IMR [C.I.]

DHS

IMR [U.I.]1

IGME

Difference 

IMR

U5MR [C.I.]

DHS

U5MR [U.I.]1

IGME

Difference 

U5MR

Afghanistan 2015 44.8 [39.4,50.8] 69.9 [58.7,83.5] -25.1 55.1 [49.4,61.5] 96.7 [79.1,118.2] -41.6

Angola 2015-16 44.3 [39.4,49.6] 101.4 [68.8,149.2] -57.2 68.3 [61.5,75.8] 167.1 [107.7,252.2] -98.8

Armenia 2010 13.4 [7.8,22.9] 17.9 [15.4,20.5] -4.5 16.4 [10.3,26.1] 20.0 [17.2,23.0] -3.6

Bangladesh 2014 38.1 [33.6,43.2] 35.3 [32.7,38.0] 2.8 46.1 [41.0,51.8] 44.0 [40.3,47.8] 2.1

Benin 2011-12 41.8 [38.0,46.0] 72.3 [63.8,80.3] -30.5 70.1 [64.8,75.8] 113.9 [98.7,128.2] -43.8

Burkina Faso 2010 65.1 [60.3,70.2] 75.8 [71.2,80.8] -10.7 128.4 [121.2,136.1] 130.4 [117.9,144.3] -2.0

Burundi 2010 58.7 [52.5,65.6] 69.3 [57.0,84.5] -10.6 96.1 [88.2,104.6] 108.6 [86.8,135.8] -12.5

Cambodia 2014 28.0 [22.8,34.2] 30.7 [25.2,37.2] -2.7 34.7 [28.8,41.8] 35.8 [29.3,43.7] -1.1

Cameroon 2011 62.5 [57.0,68.4] 70.8 [55.7,89.0] -8.3 121.8 [114.3,129.6] 112.8 [85.4,146.1] 9.0

Chad 2014-15 72.3 [66.7,78.2] 90.2 [75.6,109.7] -18.0 132.9 [125.4,140.7] 151.6 [114.3,199.9] -18.7

Colombia 2015 13.9 [11.4,17.0] 14.5 [10.7,19.9] -0.6 16.3 [13.6,19.6] 16.9 [12.4,23.4] -0.6

Comoros 2012 35.8 [28.1,45.6] 63.1 [41.4,98.6] -27.3 49.7 [39.5,62.3] 86 [52.8,142.1] -36.3

Congo 2011-12 39.4 [33.5,46.2] 45.3 [39.5,52.1] -5.9 67.8 [59.9,76.6] 66.1 [55.8,78.1] 1.7

Cote d'Ivoire 2011-12 67.9 [59.6,77.4] 79.0 [71.3,87.7] -11.1 108 [98.4,118.4] 112.7 [100.4,126.6] -4.7

Dominican Republic 2013 27.2 [20.6,35.8] 27.5 [23.6,32.3] -0.3 30.9 [24.0,39.8] 33.3 [28.1,39.8] -2.4

DRC 2013-14 58.3 [53.7,63.4] 82.6 [70.2,94.8] -24.3 104.2 [97.5,111.3] 112.2 [91.4,135.4] -8.0

Egypt 2014 22.2 [19.4,25.5] 23.4 [20.5,26.6] -1.2 27.5 [24.3,31.0] 27.9 [24.2,32.0] -0.4

Ethiopia 2011 59.2 [53.0,65.9] 57.1 [50.4,64.4] 2.1 88.0 [79.8,97.0] 86.9 [75.0,99.8] 1.1

Gabon 2012 42.5 [35.7,50.5] 44.2 [38.5,50.7] -1.7 64.6 [56.5,73.8] 66.1 [55.6,78.1] -1.5

Gambia 2013 34.3 [29.1,40.5] 51.7 [45.9,59.2] -17.4 53.6 [45.8,62.6] 81.4 [62.0,105.6] -27.8

Ghana 2014 41.2 [35.1,48.4] 47.0 [41.0,53.8] -5.8 59.9 [52.6,68.0] 69.2 [58.5,81.1] -9.4

Guatemala 2014-15 27.8 [24.3,31.8] 26.6 [19.9,35.6] 1.2 35.4 [31.4,39.8] 32.1 [23.4,44.5] 3.3

Guinea 2012 66.8 [59.5,75.0] 71.2 [62.6,81.2] -4.4 122.9 [112.8,133.8] 111.9 [96.6,129.8] 11.0

Haiti 2012 59.2 [52.7,66.6] 60.0 [54.5,66.6] -0.8 88.4 [79.6,98.2] 81.2 [72.5,91.6] 7.2

Honduras 2011-12 23.6 [20.3,27.4] 21.4 [18.9,24.3] 2.2 29.0 [25.5,33.0] 25.4 [22.2,29.1] 3.6

Indonesia 2012 31.5 [27.7,35.8] 28.5 [26.3,30.8] 3.0 39.9 [35.6,44.6] 34.7 [31.8,37.7] 5.2

Jordan 2012 17.2 [13.0,22.7] 17.8 [15.1,21.0] -0.6 21.0 [16.5,26.7] 20.8 [17.5,24.8] 0.2

Kenya 2014 38.7 [35.0,42.8] 39.2 [33.5,45.7] -0.5 52.3 [48.2,56.8] 55.6 [45.8,67.2] -3.3

Kyrgyz Republic 2012 26.9 [21.1,34.1] 26.5 [25.8,27.1] 0.4 31.0 [24.9,38.4] 30.0 [29.2,30.8] 1.0

Lesotho 2014 59.5 [50.3,70.3] 72.3 [62.2,83.9] -12.8 85.3 [74.3,97.7] 94.1 [79.4,111.3] -8.8

Liberia 2013 53.8 [47.0,61.4] 65.2 [58.6,72.8] -11.4 93.8 [84.5,104.0] 89.3 [78.9,101.2] 4.5

Malawi 2015-16 41.7 [37.8,45.9] 47.3 [39.9,58.8] -5.6 63.8 [58.9,69.0] 71.3 [57.8,92.4] -7.5

Mali 2012-13 56.0 [49.8,62.9] 82.9 [71.0,97.7] -26.9 95.1 [86.6,104.3] 136.6 [105.3,174.3] -41.5

Mozambique 2011 64.1 [58.3,70.4] 74.3 [67.8,81.4] -10.2 96.9 [89.8,104.4] 107.6 [97.0,119.3] -10.7

Myanmar 2015-16 40.3 [33.2,48.9] 41.9 [34.2,50.6] -1.6 50.0 [41.9,59.6] 53.5 [42.5,66.5] -3.5

Namibia 2013 38.9 [32.7,46.1] 36.0 [30.5,42.9] 2.9 54.5 [47.1,62.9] 51.6 [42.3,64.2] 2.9

Nepal 2011 45.9 [39.2,53.7] 40.0 [36.2,44.1] 5.9 54.4 [47.1,62.7] 50.7 [45.2,56.7] 3.7

Niger 2012 50.6 [45.4,56.5] 68.9 [64.1,74.3] -18.3 127.2 [119.3,135.5] 132.1 [117.3,148.8] -4.9

Nigeria 2013 68.5 [64.6,72.7] 81.5 [73.4,90.3] -13.0 128.0 [121.7,134.5] 130.3 [115.8,146.4] -2.3

Pakistan 2012-13 73.6 [66.8,81.0] 73.5 [66.9,81.4] 0.1 88.9 [80.8,97.8] 91.8 [82.6,103.1] -2.9

Philippines 2013 22.7 [19.3,26.6] 24.4 [21.1,27.9] -1.7 31.1 [27.0,35.8] 31.2 [26.5,36.5] -0.1

Rwanda 2014-15 32.3 [28.2,37.0] 37.1 [32.1,42.9] -4.8 50.4 [45.0,56.4] 52.1 [43.6,62.3] -1.7

Senegal 2010-11 46.7 [42.3,51.6] 50.0 [47.8,52.4] -3.3 71.6 [65.6,78.1] 75.8 [68.5,83.7] -4.2

Sierra Leone 2013 92.3 [85.6,99.6] 102.3 [94.5,110.9] -10.0 155.8 [146.7,165.3] 150.6 [134.8,168.3] 5.2

Tajikistan 2012 34.5 [28.9,41.2] 44.7 [36.4,54.6] -10.2 155.8 [146.7,165.3] 150.6 [134.8,168.3] 5.2

Tanzania 2015-16 43.2 [38.5,48.5] 37.6 [29.6,47.6] 5.6 43.4 [36.8,51.1] 52.6 [42.2,65.8] -9.2

Togo 2013-14 48.5 [42.6,55.2] 57.9 [52.2,64.0] -9.4 67.1 [60.7,74.1] 53.3 [39.8,71.2] 13.8

Uganda 2011 53.8 [48.2,60.0] 52.3 [48.0,57.1] 1.5 88.3 [79.8,97.7] 88.3 [78.3,99.1] 0.0

Yemen 2013 43.2 [38.8,48.1] 40.3 [35.1,46.4] 2.9 90 [81.9,98.8] 80.1 [72.2,88.9] 9.9

Zambia 2013-14 44.6 [40.2,49.6] 51.1 [46.9,56.1] -6.5 52.7 [47.7,58.3] 51.1 [43.7,60.1] 1.6

Zimbabwe 2015 50.1 [44.4,56.7] 48.8 [40.4,59.9] 1.3 69 [61.1,77.7] 74.5 [59.1,95.1] -5.5

190% uncertainty intervals. Differences are the DHS – IGME estimates. IGME source (UNICEF 2017).
51

Table A.4. Adult mortality probabilities (15q35*1000) from DHS and the UN Population Division 

Survey

Female Adult 

Mortality DHS

Female Adult 

Mortality UN

Difference 

Female

Male Adult 

Mortality DHS

Male Adult 

Mortality UN

Difference 

Male

Afghanistan 2015 119 125 -6 84 140 -56

Angola 2015-16 110 131 -21 182 180 2

Burkina Faso 2010 146 180 -34 145 186 -41

Burundi 2010 145 222 -77 179 250 -71

Cambodia 2014 75 84 -9 129 126 3

Cameroon 2011 228 256 -28 232 262 -30

Chad 2014-15 163 252 -89 189 273 -84

Colombia 2015 50 42 8 105 126 -21

Comoros 2012 66 133 -67 66 155 -89

Congo 2011-12 201 235 -34 173 239 -66

Cote d'Ivoire 2011-12 227 290 -63 205 303 -98

Ethiopia 2011 157 197 -40 181 221 -40

Gabon 2012 148 221 -73 171 200 -29

Gambia 2013 99 162 -63 102 185 -83

Guatemala 2014-15 51 62 -11 115 144 -29

Guinea 2012 173 183 -10 173 197 -24

Indonesia 2012 80 82 -2 97 105 -8

Kenya 2014 138 121 17 183 179 4

Lesotho 2014 436 360 76 476 429 47

Malawi 2015-16 184 193 -9 218 252 -34

Mali 2012-13 101 183 -82 105 188 -83

Mozambique 2011 199 270 -71 241 309 -68

Myanmar 2015-16 72 89 -17 163 118 45

Namibia 2013 164 221 -57 244 265 -21

Niger 2012 128 167 -39 136 176 -40

Nigeria 2013 124 256 -132 123 263 -140

Rwanda 2014-15 88 117 -29 124 151 -27

Senegal 2010-11 114 122 -8 115 143 -28

Tanzania 2015-16 173 165 8 181 217 -36

Togo 2013-14 170 170 0 176 176 0

Uganda 2011 201 265 -64 252 299 -47

Zambia 2013-14 294 192 102 330 246 84

Zimbabwe 2015 282 293 -11 300 315 -15

Notes: Difference is DHS – UN estimate. UN source (United Nations 2017). 
52

Table A.5. MMR estimates from DHS and WHO

Survey MMR [C.I.] DHS MMR [U.I.]1 WHO Difference

Afghanistan 2015 1291 [1071,1512] 496 [325,752] 795

Angola 2015-16 239 [164,313] 526 [251,1048] -287

Burkina Faso 2010 341 [275,406] 447 [350,563] -106

Burundi 2010 500 [376,624] 849 [622,1145] -349

Cambodia 2014 170 [95,246] 188 [148,240] -18

Cameroon 2011 782 [647,916] 707 [558,924] 75

Chad 2014-15 860 [728,993] 931 [635,1382] -71

Colombia 2015 132 [66,198] 66 [59,79] 66

Comoros 2012 172 [60,284] 396 [259,610] -224

Congo 2011-12 426 [274,579] 537 [398,718] -111

Cote d'Ivoire 2011-12 614 [445,783] 732 [551,952] -118

Ethiopia 2011 676 [541,810] 608 [470,824] 68

Gabon 2012 316 [178,454] 327 [236,451] -11

Gambia 2013 433 [299,567] 753 [542,1030] -320

Guatemala 2014-15 140 [94,186] 105 [99,112] 35

Guinea 2012 724 [531,916] 731 [574,935] -7

Indonesia 2012 313 [220,406] 173 [135,223] 140

Kenya 2014 362 [254,471] 584 [423,817] -222

Lesotho 2014 1024 [731,1318] 555 [366,935] 469

Mali 2012-13 368 [259,478] 652 [532,820] -284

Mozambique 2011 443 [328,559] 682 [539,864] -239

Namibia 2013 358 [222,495] 319 [230,437] 39

Niger 2012 535 [425,645] 661 [531,830] -126

Nigeria 2013 576 [500,652] 867 [673,1128] -291

Rwanda 2014-15 253 [181,326] 336 [256,430] -83

Senegal 2010-11 484 [337,631] 398 [286,551] 86

Tanzania 2015-16 530 [405,655] 464 [342,638] 66

Togo 2013-14 401 [290,512] 396 [285,540] 5

Uganda 2011 438 [368,507] 451 [354,583] -13

Zambia 2013-14 398 [323,474] 262 [204,330] 136

Zimbabwe 2015 651 [473,829] 379 [324,454] 272

Notes: 180% uncertainty intervals. Difference is DHS – WHO estimate. WHO 

source (WHO 2015).