DEMOGRAPHIC

AND

HEALTH

SURVEYS

DHS WORKING PAPERS

2017 No. 128

Charles F. Westoff

July 2017

This document was produced for review by the United States Agency for 

International Development.

Current Pregnancy and Fertility

Current Pregnancy and Fertility 

Charles F. Westoff1

ICF 

Rockville, Maryland, USA 

July 2017 

1 Office of Population Research, Princeton University 

Corresponding author: Charles F. Westoff, Office of Population Research, Princeton University, Princeton, 

NJ 08544-2901; phone 609-258-5867; email: westoff@princeton.edu
Acknowledgments 

The author acknowledges Noreen Goldman and German Rodriguez at the Office of Population 

Research, Princeton University, for numerous suggestions after a careful reading of the 

manuscript. 

Editor: Bryant Robey 

Document Production: Audrey Shenett 

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

Development (USAID) through The DHS Program (#AID-OAA-C-13-00095). 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 International, 530 Gaither Road, Suite 500, Rockville, 

MD 20850, USA; phone: 301-407-6500; fax: 301-407-6501; email: reports@dhsprogram.com; 

Internet: www.dhsprogram.com. 

Recommended citation: 

Westoff, Charles F. 2017. Current Pregnancy and Fertility. DHS Working Paper No. 128. 

Rockville, Maryland, USA: ICF. 
iii 

Abstract 

This study revisits a methodological question pursued in 1980 – whether simple information on 

current pregnancy status can be used to estimate fertility rates reliably. If so, there would be a 

substantial gain in simplicity, convenience, and contemporaneity. The conclusion in 1980, based 

on a study of 15 countries in the World Fertility Survey, was negative. The present analysis using 

data from 148 Demographic and Health Surveys in 65 countries and adding a longitudinal 

perspective in 41 of these countries with repeat surveys, reaches a positive conclusion. An adjusted 

pregnancy rate was developed, based on reported pregnancy durations of 3-8 months, which 

corresponds very closely with national birthrates 1-2 years later. Additional analyses of 

subnational or regional data are much less reliable except for the states of India with much larger 

samples. 

1 

1. Introduction 

This paper revisits the question of whether simple information on current pregnancy status could 

serve as a reliable indicator of the fertility rate. If a strong predictive association could be 

demonstrated, the advantages of simplicity, convenience, and contemporaneity over the more 

complex birth history approach would be considerable. This question was addressed in 1980 with 

data on 15 countries from the World Fertility Survey (Goldman and Westoff 1980). The conclusion 

then was that the current pregnancy estimates, even when adjusted, fell too far short of the fertility 

rates (by an average of 13 percent) to serve as a reliable substitute. 

The same question is addressed here with a much richer data set of 148 surveys from 65 countries 

in The Demographic and Health Surveys (DHS) Program, and a more positive judgment is 

reached. The analysis reported here is richer not only in the number of observations but also with 

the inclusion of some subnational data at the state or province level and with a longitudinal 

perspective focused on the prediction of fertility over time for 41 of the 65 countries, in addition 

to a cross-sectional analysis of all 65 countries. 

2. Measurement Issues 

The pregnancy data used here are based on the responses of women age 15-49 to a direct survey 

question of whether they are currently pregnant and, if so, how many months they think they have 

been pregnant. Table 1, in the first column, shows the simple percentage of women who reply 

“yes” to the question, with a mean unadjusted pregnancy rate of 7.3%, for the most recent surveys 

in the 65 countries. The second column shows an adjusted pregnancy rate, with a mean of 11.9%, 

derived from the number of women 3-8 months pregnant (a 6-month period). This rate is then 

doubled in order to set the rate on a 12-month scale for comparison with annual fertility rates. The 

1980 study used a different adjustment based on a 3-month period (women 5-7 months pregnant), 

and then multiplied by 4 to annualize. 

 
2 

Table 1. Estimates of the mean birthrate from the adjusted current 

pregnancy rate 

Country

Survey 

Year

Unadjusted 

Pregnancy 

Rate1

Adjusted 

Pregnancy 

Rate2 Birthrate Difference 

Albania 2008-09 .020 .029 .038 .008 

Armenia 2010 .030 .048 .052 .004 

Azerbaijan 2006 .035 .054 .057 .003 

Bangladesh3 2014 .060 .096 .092 -.004 

Benin 2011-12 .094 .156 .156 .000 

Bolivia 2008 .055 .091 .104 .013 

Burkina Faso 2010 .101 .173 .179 .006 

Burundi 2010 .104 .183 .174 -.009 

Cambodia 2014 .053 .080 .084 .004 

Cameroon 2011 .098 .151 .156 .005 

Chad 2014-15 .135 .214 .200 -.014 

Colombia 2010 .033 .055 .058 .004 

Comoros 2012 .066 .103 .126 .023 

Congo, B 2011-12 .095 .139 .159 .020 

Congo, DR 2013-14 .117 .180 .199 .020 

Cote d'Ivoire 2011-12 .102 .164 .154 -.010 

Dominican Republic 2013 .055 .085 .076 -.009 

Egypt* 2014 .100 .156 .150 -.006 

Ethiopia 2011 .073 .123 .137 .015 

Gabon 2012 .097 .152 .128 -.024 

Gambia 2013 .081 .126 .163 .037 

Ghana 2014 .070 .114 .124 .011 

Guinea 2012 .107 .169 .154 -.015 

Guyana 2009 .043 .062 .080 .018 

Haiti 2012 .058 .090 .101 .010 

Honduras 2011-12 .053 .089 .092 .003 

India 2005-06 .052 .081 .089 .008 

Indonesia* 2012 .043 .065 .075 .010 

Jordan 2012 .095 .153 .169 .016 

Kazakhstan 1999 .029 .045 .057 .012 

Kenya 2014 .071 .110 .124 .015 

Kyrgyz Republic 2012 .067 .106 .106 .000 

Lesotho 2014 .043 .072 .101 .029 

Liberia 2013 .083 .133 .143 .009 

Madagascar 2008-09 .083 .143 .145 .001 

Malawi 2015 .076 .123 .140 -.017 

Maldives 2009 .073 .111 .111 .000 

Mali 2012-13 .115 .188 .193 .005 

Moldova 2005 .024 .040 .045 .005 

Morocco 2003-4 .040 .062 .069 .007 

Mozambique 2011 .110 .184 .176 -.007 

Namibia 2013 .060 .101 .109 .008 

Nepal 2011 .049 .075 .084 .008 

Nicaragua 2001 .049 .083 .100 .017 

Niger 2012 .142 .244 .239 -.005 

Nigeria 2013 .121 .202 .163 -.039 

Pakistan* 2012-13 .108 .171 .173 .002 

Peru 2012 .035 .060 .073 .014 

Philippines 2013 .042 .072 .085 .013 

Rwanda 2014-15 .073 .121 .122 .002 

Sao Tome & Principe 2008-09 .084 .140 .143 .003 

Senegal 2014 .080 .130 .147 .017 

Continued...
3 

Table 1. —Continued

Sierra Leone 2013 .086 .139 .146 .006 

Swaziland 2006-07 .056 .099 .117 .018 

Tajikistan 2012 .076 .126 .116 -.010 

Tanzania 2010 .095 .156 .163 .007 

Timor-Leste 2009-10 .068 .110 .149 .039 

Togo 2013-14 .085 .137 .145 .007 

Turkey 2003 .058 .093 .099 .006 

Uganda 2011 .116 .189 .188 -.001 

Ukraine 2007 .028 .051 .033 -.018 

Vietnam 2002 .047 .077 .077 .001 

Yemen 2013 .084 .130 .195 .065 

Zambia 2013-14 .087 .138 .160 .022 

Zimbabwe 2010-11 .083 .134 .132 -.002 

All Countries .073 .119 .125 .006 

1 Mean number of births in the past 3 years, divided by 3 2 Two times the proportion of women currently pregnant with duration 3-8 months 3 Based on ever-married women 
4 

The objective of adjustment is to take into account the early 

months when pregnancy is less certain and when both 

spontaneous and induced abortions typically occur, and the 9th 

month, which is influenced by previous births. As Table 2 

shows, the average proportion of pregnant women reporting 

the pregnancy in the 9th month is only 5% compared with 15% 

in the 8th month. A variety of such adjustments have been 

tested here, and the correlations between them and the 3-8 

months measure are all well over .90. The 3-8 month interval 

also has the advantage of reducing the sampling error 

associated with shorter intervals. 

The estimation of fertility rates also has measurement issues associated with underreporting due 

to omission and dating problems (Schoumaker 2014). Different measures are evaluated here. The 

third column of Table 1 lists the fertility rate reported in the past 3 years (divided by 3 to annualize 

the rate for comparison with the pregnancy rates) for each country. Although this birthrate seems 

to be a more reliable estimate than births in the past 12 months, it loses some of the time sensitivity 

with current pregnancy rates. 

The basic logic of the central question here is whether near-future birthrates can be predicted 

accurately from the adjusted current pregnancy rates. Such an analysis requires at least two surveys 

for a country. This paper begins with a cross-sectional view of all 65 countries, including those 

with only one survey as well as those with multiple surveys. This cross-sectional picture is 

followed by the longitudinal perspective, which is based on the 41 countries with at least two DHS 

surveys. 

3. Results 

3.1 Cross-Sectional Comparisons 

As Figure 1 shows, there is a strong cross-sectional association between the adjusted pregnancy 

rate and the birthrate for the most recent surveys in the 65 countries. The correlation is .948. Two 

outliers are Yemen, where the pregnancy rate appears under-reported, and Nigeria, where fertility 

may be underestimated. In the earlier Nigeria 2008 DHS survey, the two measures are similar, 

with a difference of only .004. The mean difference between the two measures across the 65 

countries is .006 (see Table 1), with an average birthrate of .125 and an estimated pregnancy rate 

of .119. Most of the differences show birthrates higher than the pregnancy rates. 

Although the statistical association is strong, relative differences cannot be ignored. A leading 

example is Ukraine (another outlier in Figure 1), with a pregnancy rate of .051 and a birthrate of 

.033. If this birthrate is accurate, the pregnancy rate would imply a serious over-estimate. Gambia, 

Guyana, Lesotho, and Timor-Leste have the opposite problem, where the pregnancy rate estimates 

are below the birthrate. 

 

Table 2. Mean reported duration of 

current pregnancy in 65 countries. 

Month 

Mean 

Proportion 

Standard 

Deviation 

1 .042 .018 

2 .101 .024 

3 .128 .016 

4 .129 .015 

5 .132 .016 

6 .131 .020 

7 .134 .021 

8 .152 .031 

9 .050 .023 

 
5 

Figure 1. Association between the adjusted current pregnancy rate and the mean birthrate 

in the past 3 years 

The perspective has focused thus far on a cross-sectional analysis in which fertility was measured 

by an average of births in the 3 years before the survey and the current pregnancy rate was 

measured at the time of the survey. In the following longitudinal analysis, a different measure of 

fertility is used that follows the pregnancy measure in time, rather than preceding it. 

3.2 Longitudinal Analysis 

The longitudinal analysis is based on data from 41 countries that have conducted more than one 

DHS survey, typically at 5-year intervals. Table 3 shows the statistical association between the 

adjusted current pregnancy rate and the birthrate 1-2 years later, on average. The two measures are 

derived from different surveys linked across time. The early survey is used to estimate the 

pregnancy rate using the same adjusted current status measure from the earlier analysis. The later 

survey is used to estimate the birthrate 1-2 years after the preceding survey, calculated as the 

difference between the mean number of births in the past 5 years minus the mean number in the 

past 3 years, divided by 2. This measure reflects the birthrate 4-5 years before the later survey and 

thus 1-2 years after the previous one, helping to line up the pregnancy rates and birthrates. 

Current pregnancy rate (adjusted) 

Mean birthrate in last 3 years 
6 

Table 3. Current pregnancy rate (adjusted) and birth 

rates 1-2 years later. 

 

Country and 

Survey Year 

Pregnancy 

Rate 

Adjusted 

Birth Rate 

1-2 Years 

Later 

Birth Rate 

Minus 

Pregnancy 

Rate 

Armenia 

2000 .043 .041 -.002 

2005 .044 .045 .001 

2010 .040 

 

Bangladesh* 

1999 .113 .126 .013 

2004 .102 .112 .010 

2007 .107 .108 .001 

2011 .099 .090 -.009 

2014 .091 

 

Benin 

2001 .185 .177 -.008 

2006 .173 .167 -.006 

2012 .156 

 

Bolivia 

1998 .094 .126 .032 

2003 .088 .101 .013 

2008 .090 

 

Burkina Faso 

1999 .160 .183 .023 

2003 .170 .181 .011 

2010 .173 

 

Cambodia 

2000 .103 .114 .011 

2005 .087 .086 -.001 

2010 .073 .080 .007 

2014 .084 

 

Cameroon 

1998 .140 .150 .010 

2004 .163 .147 -.016 

2011 .151 

 

Colombia 

2000 .081 .068 -.013 

2005 .066 .060 -.006 

2010 .061 

 

Congo, B. 

2005 .132 .138 .006 

2011 .150 

 

Congo, D.R. 

2007 .180 .189 .009 

2013 .180 

 

Continued... 

 
7 

Table 3.—Continued

 

Country and 

Survey Year 

Pregnancy 

Rate 

Adjusted 

Birth Rate 

1-2 Years 

Later 

Birth Rate 

Minus 

Pregnancy 

Rate 

Dominican Republic

1999 .082 .091 .009

2002 .086 .081 -.005

2007 .077 .079 .002

2013 .085

 

Egypt* 

2003 .147 .117 -.030

2008 .151 .135 -.016

2014 .135

 

Ethiopia 

2000 .154 .164 .010

2005 .140 .153 .013

2011 .123

 

Ghana 

1998 .138 .136 -.002

2003 .122 .119 -.003

2008 .117 .116 -.001

2014 .116

 

Guinea 

1999 .148 .152 .004

2005 .152 .153 .001

2012 .168

 

Haiti 

2000 .108 .131 .023

2006 .101 .095 -.006

2012 .094

 

Honduras 

2006 .094 .092 -.002

2012 .094

 

India* 

1993 .129 .129 .000

1999 .113 .118 .005

2005 .110

 

Indonesia* 

2002 .098 .099 .001

2007 .097 .093 -.004

2012 .090

 

Jordan 

2002 .185 .182 -.003

2007 .204 .180 -.024

2012 .153

 

Kenya 

1998 .133 .146 .013

2003 .128 .134 .006

2008 .119 .128 .009

2014 .110

Continued... 
8 

Table 3.—Continued

 

Country and 

Survey Year 

Pregnancy 

Rate 

Adjusted 

Birth Rate 

1-2 Years 

Later 

Birth Rate 

Minus 

Pregnancy 

Rate 

Lesotho 

2004 .101 .091 -.010

2009 .073 .083 .010

2014 .070

 

Liberia 

2007 .168 .163 -.005

2012 .145

 

Madagascar

1997 .164 .159 -.005

2004 .139 .148 .009

2009 .144

 

Malawi 

2000 .197 .176 -.021

2004 .208 .166 -.042

2010 .159 .144 -.015

2015 .123

 

Mali

2001 .209 .194 -.015

2006 .201 .207 .006

2013 NA 

Mozambique 

1997 .179 .167 -.012

2003 .161 .152 -.009

2011 .169 

Namibia 

2000 .099 .095 -.004

2008 .098 .099 .001

2013 .099

 

Nepal 

2001 .134 .106 -.028

2006 .089 .087 -.002

2011 .074

 

Niger

1998 .209 .211 .002

2006 .209 .239 .030

2012 .227

 

Nigeria 

2003 .177 .167 -.010

2008 .168 .164 -.004

2013 .193

 

Pakistan* 

2007 .200 .182 -.018

2012 .175

 

Peru 

2000 .073 .072 -.001

2005 .067 .075 .008

2010 .070

 

Continued... 
9 

Table 3.—Continued

 

Country and 

Survey Year 

Pregnancy 

Rate 

Adjusted 

Birth Rate 

1-2 Years 

Later 

Birth Rate 

Minus 

Pregnancy 

Rate 

Philippines 

1998 .101 .103 .002

2003 .096 .093 -.003

2008 .087 .088 .001

2013 .076 

Rwanda 

2000 .152 .140 -.012

2005 .136 .141 .005

2010 .114 .113 -.001

2015 .113

 

Senegal 

2005 .142 .143 .001

2010 .133 .143 .010

2014 .142 

Sierra Leone 

2008 .129 .147 .018

2013 .139

 

Tanzania 

1998 .154 .161 .007

2004 .173 .158 -.015

2010 .152 .143 -.009

2015 .143

 

Uganda 

2001 .195 .195 .000

2006 .187 .183 -.004

2011 .180

 

Zambia 

2002 .165 .169 .004

2007 .174 .167 -.007

2014 .137

 

Zimbabwe 

1999 .129 .113 -.016

2006 .107 .107 .000

2014 .123

All country surveys .133** .132 -.001

* Based on ever-married women

** Excludes most recent survey 

The results summarized in Table 3 show the two measures for a total of 86 comparisons across the 

41 countries. Overall, as Figure 2 shows, there is a close association between the two measures, 

with a strong correlation of .964 for the 41 countries based on their most recent surveys. If all 86 

surveys are included, the correlation is almost identical, at .961. 

 
10 

Figure 2. Association between the adjusted current pregnancy rate and the mean birthrate 

1 – 2 years later 

A close examination of Table 3 focusing on the differences between the two rates shows this close 

pattern, with many of the differences around zero. The number of surveys in which the current 

pregnancy estimates exceed the birthrate is the same as the number with the reverse association. 

There are numerous possible explanations for these differences, including sampling error as well 

as measurement error. The sampling error depends mainly on the size of the sample and design 

effects. To take the Rwanda 2014-15 DHS as an example, the estimated sampling error for the 

total sample of 13,497 women for the proportion currently pregnant (.073) yields a 95% confidence 

range between .068 and .078. In addition, there are uncertainties about under-reporting especially 

in the early months of pregnancy, which led to the need for adjustment. There is also sampling 

error associated with the estimation of the birthrates calculated for the comparison, as well as other 

types of measurement problems involving the omission of births and the recall of dates. Also, the 

birthrates are the average of 2 years rather than a single-year estimate. 

Overall, both the pregnancy rate and the birthrate have the same average value for the 86 

comparisons and a net difference of .001. Despite the strong association, there are several countries 

with relatively large differences. These include Egypt in the 2003 survey, Jordan in 2007, Malawi 

in both earlier surveys, Nepal in 2001, and Niger in 2006. In five of these six instances the 

estimated pregnancy rate exceeds the birthrate. The exception is Niger, where the calculated 

birthrate is higher than the pregnancy rate. Birthrate 1 – 2 years later 

Adjusted current pregnancy rate in last survey 
11 

3.3 Subnational Level 

In the interest of further evaluating this general method of estimating recent fertility from current 

pregnancy data, regional or other subnational data have been used in several countries with large 

samples of women surveyed. 

Nigeria 

Nigeria has had several recent DHS surveys, in 2013, 2008, and 2003, with large samples in each 

of 37 states. Two analyses were conducted. The adjusted current pregnancy rate was estimated for 

each state in 2008 and compared with the birthrate 1-2 years later calculated from the 2013 survey. 

Although the correspondence is high across the 37 states, in several states the difference is 

unacceptably large. In this comparison, 12 states show “errors” mostly where the pregnancy 

estimate is more than 20 percent higher than the calculated birthrate. A similar pattern appears in 

the 2003 pregnancy estimates of the later birthrates. 

Rwanda 

Rwanda has 30 districts in both its 2010 and 2015 surveys, ranging in sample size from 400 to 650 

women. The estimated current pregnancy rates in 2010 for each district and the corresponding 

birthrates 1-2 years later show a very poor relationship. Half of the districts have pregnancy rates 

and birthrates with differences greater than 20%, equally divided between those under-estimating 

and those over-estimating the subsequent birthrate. Only a third of the districts fall below a 10% 

difference range. 

Ghana 

There are 10 regions of Ghana and three surveys in 2003, 2008, and 2014 with samples of women 

ranging from 350 to 1,040. A comparison of the adjusted pregnancy rate in each region in 2008 

with the birthrate 1-2 years later shows a fairly close fit, with an average difference of .015 and 

only one region with a large difference (.037), the Upper West, the region with the smallest number 

of women surveyed. Comparing the pregnancy rate in 2003 with the later birthrate shows a similar 

picture, with an average difference of .016. 

Haiti 

Two sets of comparisons are possible for nine regions of Haiti – between 2006 and 2012 and 

between 2000 and 2006. Between 2006 and 2012, four of the nine regions have poor fits, as do 

three different regions of the nine between 2000 and 2006. The average difference between the 

pregnancy rates and the “predicted” birthrates in the later surveys comes close to 30% of the 

birthrate.
12 

India 

In India the large samples of 124,385 women in the 2005-06 survey and 89,199 women in the 

1998-99 survey increase confidence in the reliability of estimates for the 26 individual states in 

the country. The comparison of the adjusted current pregnancy rate in the 1998-99 survey with the 

birthrate 1-2 years later (Table 4) shows a close correspondence, with a few exceptions. The overall 

averages are .116, for both rates. The three main exceptions are Meghalaya, Tamil Nadu, and Uttar 

Pradesh. In Meghalaya the birthrate is overestimated by 25%, and by 30% in Tamil Nadu, while 

in Uttar Pradesh the reverse discrepancy appears, with a similar difference. Overall, the number of 

states with underestimates of the birthrate is the same as the number with overestimates. 

Table 4. The association of the estimated current 

pregnancy rate in India in 1999 with the birth 

rate 1-2 years later, by state. 

 

States 

1999 

Current 

Pregnancy 

Rate 

Birth Rate 

1-2 Years 

Later Difference 

Andhra Pradesh .084 .085 .001 

Arunachal Pradesh .182 .160 -.022 

Assam .118 .117 -.001 

Bihar .130 .160 .030 

Delhi .099 .100 .001 

Goa .093 .084 -.009 

Gujarat .088 .113 .025 

Haryana .095 .112 .017 

Himachal Pradesh .092 .089 -.003 

Jammu & Kashmir .109 .122 .013 

Karnataka .108 .095 -.013 

Kerala .082 .073 -.009 

Madhya Pradesh .135 .139 .004 

Maharashtra .109 .092 -.017 

Manipur .132 .132 .000 

Meghalaya .220 .175 -.045 

Mizoram .113 .138 .025 

Nagaland .156 .176 .020 

Orissa .108 .108 .000 

Punjab .121 .099 -.022 

Rajasthan .139 .133 -.006 

Sikkim .105 .095 -.010 

Tamil Nadu .099 .076 -.023 

Tripura .098 .085 -.013 

Uttar Pradesh .122 .162 .040 

West Bengal .084 .101 .017 

 

All States .116 .116 .000 
13 

These subnational analyses of the relationship between the current adjusted pregnancy rate and the 

subsequent birthrate show mixed results. The evidence supporting the use of pregnancy data is 

fairly strong for India and Ghana but is not encouraging for Nigeria, Rwanda, and Haiti. The 

reasons reflect both the smaller samples and also the unknown mobility of populations across local 

borders, which is clearly more of an issue within countries than at the national level. 

4. Summary and Conclusion. 

The main focus of this research has been to revisit the question of whether information on current 

pregnancy can be used to estimate fertility reliably. If so, the gains in simplicity and in 

contemporaneity would be considerable. The earlier evidence in 1980, based on cross-sectional 

data from 15 countries in the World Fertility Survey program, concluded that the pregnancy data 

were not useful because they underestimated birthrates. The present analysis, based on 148 DHS 

surveys, reaches a more positive conclusion, although the evidence does not support the value of 

pregnancy data at subnational levels. The strong support at the national level is based not only on 

the cross-sectional evidence for 148 surveys but also on a longitudinal design that examines 

repeated surveys in 41 countries of the association of current pregnancy rates and birthrates 1-2 

years after the survey. 

15 

References 

Goldman, Noreen, and Charles F. Westoff. 1980. “Can Fertility be Estimated from Current 

Pregnancy Data?” Population Studies 34(3):535-550. 

Schoumaker, Bruno. 2014. Quality and Consistency of DHS Fertility Estimates, 1990 to 2012.

DHS Methodological Reports No. 12. Rockville, MD: ICF.