DHS METHODOLOGICAL 

REPORTS 18

Hemoglobin Data in DHS

Surveys: Intrinsic Variation 

and Measurement Error

AUGUST 2017

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

Thomas Pullum, Deborah Kortso Collison, Sorrel Namaste, and Dean Garrett.

DHS Methodological Reports No. 18 

Hemoglobin Data in DHS Surveys: 

Intrinsic Variation and Measurement Error 

Thomas Pullum 

Deborah Kortso Collison 

Sorrel Namaste 

Dean Garrett 

ICF 

Rockville, Maryland 

August 2017 

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

Suite 500, Rockville, MD 20850, USA; Telephone: +1 301-407-6500; Fax: +1 301-407-6501; 

E-mail: Tom.Pullum@icf.com 
Acknowledgement: The authors are grateful for the review and input provided by Omar Dary (United 

States Agency for International Development [USAID]), Erin Milner (USAID), and Denish Moorthy 

(Strengthening Partnerships, Results, and Innovations in Nutrition Globally project [SPRING]), and Stan 

Becker (Johns Hopkins University). Special thanks to Courtney Allen for reviewing and formatting tables. 

We would also like to acknowledge the DHS survey managers and others who are responsible for the 

collection and processing of the data used to produce this report. 

Editor: Diane Stoy 

Document Production: Joan Wardell 

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: 

DHS Program, ICF International, 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. 

Recommend citation: 

Pullum, Thomas, Deborah Kortso Collison, Sorrel Namaste, and Dean Garrett. 2017. Hemoglobin Data in 

DHS Surveys: Intrinsic Variation and Measurement Error. DHS Methodological Reports No. 18. 

Rockville, Maryland, USA: ICF. 
iii

Contents 

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

FIGURES ...........................................................................................................................................................vii

PREFACE ..........................................................................................................................................................ix

ABSTRACT ........................................................................................................................................................xi

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

1.1. Background ................................................................................................................................ 1

1.2. Objectives .................................................................................................................................. 1

2. PROCEDURES, DATA, AND METHODS ........................................................................................ 3

2.1. Procedures ................................................................................................................................. 3

2.2. Data ........................................................................................................................................... 5

2.3. Methods ..................................................................................................................................... 7

3. RESULTS: INDICATORS OF DATA QUALITY ..........................................................................11

3.1. Representativeness of subsampling .........................................................................................11

3.2. Data completeness ...................................................................................................................13

3.3. Digit preference .......................................................................................................................14

4. RESULTS: INTRINSIC VARIATION .............................................................................................17

4.1. Variations in the mean, median, and standard deviation .........................................................17

4.2. Association between the mean and standard deviation of the hemoglobin measurement .......22

4.3. Variation in shape ....................................................................................................................23

4.4. Variations in the mean, median, and standard deviation by covariates for children 

and women...............................................................................................................................31

5. SUMMARY AND CONCLUSIONS ..................................................................................................33

REFERENCES..................................................................................................................................................37

APPENDIX 1 .....................................................................................................................................................41

APPENDIX 2 .....................................................................................................................................................43

APPENDIX 3 .....................................................................................................................................................47

APPENDIX 4 .....................................................................................................................................................53

APPENDIX 5 .....................................................................................................................................................61

v 

Tables 

Table 2.1.1 Blood drop used to obtain hemoglobin measurement ............................................................... 3

Table 2.1.2 Hemoglobin cutoffs used in The DHS program to define anemia at sea level. ......................... 4

Table 2.2.1 Summary of variables used in the analysis from the person records (PR) file1 ....................... 7

Table 3.1.1 Tests of the randomness of household selection for hemoglobin measurement, by 

covariates ................................................................................................................................. 12

Table 3.2.1 The percentage of valid, implausible, and missing hemoglobin data ...................................... 14

Table 4.1.1 Summary statistics for hemoglobin concentrations across surveys, not excluding 

implausible values ................................................................................................................... 18

Table 4.1.2 Summary statistics for hemoglobin concentrations across surveys, excluding implausible 

values ....................................................................................................................................... 18

Table 4.1.3 Standard deviations of hemoglobin measurements for 28 countries with two surveys 

that measured children age 6-59 months, adjusted for altitude and excluding values 

outside the range 4-18 g/dL ..................................................................................................... 21

Table 4.3.1 Summary statistics for skew of hemoglobin concentrations across surveys, not excluding 

implausible values ................................................................................................................... 24

Table 4.3.2 Summary statistics for skew of hemoglobin concentrations across surveys, excluding 

implausible values ................................................................................................................... 24

Table 4.3.3. Summary statistics for kurtosis of hemoglobin concentrations across surveys, not 

excluding implausible values ................................................................................................... 28

Table 4.3.4. Summary statistics for kurtosis of hemoglobin concentrations across surveys, excluding 

implausible values ................................................................................................................... 28

Table 4.4.1. Average of the mean, median, standard deviation skew, and kurtosis of hemoglobin 

concentrations for children excluding implausible values, by covariates ................................ 31

Table 4.4.2. Average of the mean, median, standard deviation skew, and kurtosis of hemoglobin 

concentrations for women excluding implausible values, by covariates ................................. 32

Table A1.1 List of surveys for children, nonpregnant women, and men .................................................... 41

Table A2.1 Summary of data completeness among children, by country .................................................. 43

Table A2.2 Summary of data completeness among nonpregnant women, by country ............................... 45

Table A2.3 Summary of data completeness among men, by country ........................................................ 46

Table A3.1 Surveys and subpopulations for which the frequency of final digit 0, for the unadjusted 

hemoglobin concentration, has the greatest deviation from 10% ............................................ 48

Table A3.2 Surveys and subpopulations for which the frequency of final digit 5, if the unadjusted 

hemoglobin concentration, has the greatest deviation from 10% ............................................ 49

Table A3.3 Surveys and sub-populations for which the frequency of final digit 2, for the unadjusted 

hemoglobin concentration, has the greatest deviation from 10% ............................................ 50

Table A3.4 Surveys and subpopulations for which the frequencies of final digits 6, 7, 8, and 9, 

for the unadjusted hemoglobin concentration, have t .............................................................. 51

Table A4.1 The mean, median, standard deviation, skew, and kurtosis of hemoglobin concentrations 

for children after removing values outside the range 4-18 g/dL .............................................. 53

Table A4.2 The mean, standard deviation, median, skew, and kurtosis of hemoglobin concentrations 

for children, including implausible values ............................................................................... 55

Table A4.3 The mean, standard deviation, median, skew, and kurtosis of hemoglobin concentrations 

for nonpregnant women, after removing values outside the range 4-18 g/dL ......................... 57

Table A4.4 The mean, standard deviation, median, skew, and kurtosis of hemoglobin concentrations 

for nonpregnant women, including implausible values ........................................................... 58
vi

Table A4.5 The mean, standard deviation, median, skew, and kurtosis of hemoglobin concentrations 

for men, after removing values outside the range 4-20 g/dL ................................................... 59

Table A4.6 The mean, standard deviation, median, skew, and kurtosis of hemoglobin concentrations 

for men, including implausible values ..................................................................................... 60

Table A.5.1 Mean, standard deviation, and median hemoglobin concentrations g/dL for children, after 

removing values outside the range 4-18 g/dL, by age ............................................................. 61

Table A.5.2 Mean, standard deviation, and median hemoglobin concentrations g/dL for children after 

removing values outside the range 4-18 g/dL, by sex ............................................................. 63

Table A.5.3 Mean, standard deviation, and median hemoglobin concentrations g/dL for children after 

removing values outside the range 4-18 g/dL, by wealth quintile ........................................... 65

Table A.5.4 Mean, standard deviation, and median hemoglobin concentrations g/dL for children after 

removing values outside the range 4-18 g/dL, by residence .................................................... 67

Table A.5.5 Mean, standard deviation, and median hemoglobin concentrations g/dL for nonpregnant 

women after removing values outside the range 4-18 g/dL, by age ........................................ 69

Table A.5.6 Mean, standard deviation, and median hemoglobin concentrations g/dL for nonpregnant 

women after removing values outside the range 4-18 g/dL, wealth quintile ........................... 70

Table A.5.7 Mean, standard deviation, and median adjusted-hemoglobin concentrations g/dL for 

nonpregnant women after removing values outside the range 4-18 g/dL, education .............. 72

Table A.5.8 Mean, standard deviation, and median hemoglobin concentrations g/dL for nonpregnant 

women after removing values outside the range 4-18 g/dL, residence .................................... 73
vii 

Figures 

Figure 2.1.1. Additive adjustment to hemoglobin concentrations g/dL for altitudes greater than 1000 

meters ........................................................................................................................................ 5

Figure 2.2.1. Sample size flow chart ............................................................................................................... 6

Figure 2.3.1. Illustration of the distribution of the hemoglobin level for children age 6-59 months, 

Nigeria 2015 ............................................................................................................................ 10

Figure 3.3.1. Summary statistics for digit preference for hemoglobin data across surveys by 

subpopulations ........................................................................................................................ 15

Figure 4.1.1. Distribution of the standard deviation of the hemoglobin concentration for children, 

excluding implausible values. .................................................................................................. 18

Figure 4.1.2. Distribution of the standard deviation of the hemoglobin concentration for women, 

excluding implausible values. .................................................................................................. 19

Figure 4.1.3. Distribution of the standard deviation of the hemoglobin concentration for men, 

excluding implausible values. .................................................................................................. 19

Figure 4.1.4. The distributions of hemoglobin concentrations in the surveys of children with highest 

dispersion ................................................................................................................................. 20

Figure 4.1.5. The distributions of hemoglobin concentrations in the surveys of children with lowest 

dispersion ................................................................................................................................. 21

Figure 4.2.1. Standard deviation plotted against the mean, for 80 surveys with hemoglobin 

measurements of children age 6-59 months, adjusted for altitude and excluding values 

outside the range 4-18 g/dL. Horizontal lines enclose the expected range of standard 

deviations, from 1.1 to 1.5. ...................................................................................................... 22

Figure 4.2.2. Standard deviation plotted against the mean, for 80 surveys with hemoglobin 

measurements of children age 6-59 months, adjusted for altitude and excluding values 

outside the range 4-18 g/dL. Horizontal lines enclose the expected range of standard 

deviations, from 1.1 to 1.5. The diagonal is the fitted line from a regression of the 

standard deviation on the mean (y=3.16-0.16x). ..................................................................... 23

Figure 4.3.1. Histogram of the skew of the hemoglobin distribution for children in the 80 surveys 

that included hemoglobin measurements for children. ............................................................ 25

Figure 4.3.2. The distributions of hemoglobin for children in the surveys with skew <-0.5 ........................ 25

Figure 4.3.3. Histogram of the skew of the hemoglobin distribution for women in the 65 surveys 

that included hemoglobin measurements for women. ............................................................. 26

Figure 4.3.4. The distributions of hemoglobin for women in the surveys with skew <-0.7. ........................ 27

Figure 4.3.5. Histogram of the kurtosis of the hemoglobin distribution for children in the 80 surveys 

that included hemoglobin measurements for children. ............................................................ 28

Figure 4.3.6. The distributions of hemoglobin for children in the surveys with kurtosis >4. ....................... 29

Figure 4.3.7. Histogram of the kurtosis of the hemoglobin distribution for women in the 65 surveys 

that included hemoglobin measurements for women .............................................................. 30

Figure 4.3.8. The distributions of hemoglobin for women in the surveys with kurtosis >5 ......................... 30

Figure A3.1. Distribution of the prevalence (percentage) of final digit 0 in the hemoglobin measurements for children age 6-59 months (80 surveys), nonpregnant women age 15-49 (65 

surveys), and men (age range varies; 27 surveys). Expected value is 10%. ............................ 47

Figure A3.2. Distribution of the prevalence (percentage) of final digit 5 in the hemoglobin measurements for children age 6-59 months (80 surveys), nonpregnant women age 15-49 (65 

surveys), and men (age range varies; 27 surveys). Expected value is 10%. ............................ 48
viii 

Figure A3.3. Distribution of the prevalence (percentage) of final digit 2 in the hemoglobin measurements for children age 6-59 months (80 surveys), nonpregnant women age 15-49 (65 

surveys), and men (age range varies; 27 surveys). Expected value is 10%. ............................ 49

Figure A3.4. Distribution of the prevalence (percentage) of final digit 6, 7, 8, 9 in the hemoglobin 

measurements for children age 6-59 months (80 surveys), nonpregnant women age 

15-49 (65 surveys), and men (age range varies; 27 surveys). Expected value is 40%. ........... 50
ix

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 

xi

Abstract 

The accurate estimation of anemia is important for tracking and targeting public health interventions. The 

primary source of anemia data in low and middle-income countries is The Demographic and Health Surveys 

Program, in which hemoglobin is assessed with a portable hemoglobinometer. This methodological report 

examines measurement error of hemoglobin assessment and the intrinsic variation in hemoglobin 

concentrations among children (age 6-59 months), nonpregnant women of reproductive age (age 15-49), 

and men (age 15 and above). A total of 80 surveys in The Demographic and Health Surveys Program 

conducted between 2000 and 2016 have been selected, resulting in a total of 1,247,942 hemoglobin 

observations (405,731 children, 607,101 women, and 235,110 men). Data quality was assessed by 

examining potential bias in the sub-sampling strategy, outliers, data completeness, and digit preference. 

Dispersion of the hemoglobin concentrations, which is a combination of measurement error and intrinsic 

variation, was also explored. 

Little bias was found in the situations where hemoglobin measurements are only taken on a sub-sample of 

the population, although in a few surveys there is a slight bias by urban/rural residence, wealth, or the level 

of education of the household head. A small percentage of data are missing (the average percentage of 

missing data was 4.5% for women, 7.1% for children, and 15% for men). There are very few outliers, or 

values outside of plausible ranges (the average percentage across surveys ranged from 0.1% to 0.2%, 

depending on the subpopulation). Some preference is observed for the digit 0 (13% of surveys for children, 

12% for women, and 7% for men) and digit 2 (14% of surveys for children, 6% for women, and 7% for 

men), with an avoidance of digits 6 through 9 (28% of surveys for children, 14% for women, and 22% for 

men). 

Standard deviations are outside the range of 1.1 to 1.5 in many surveys for children (46% of surveys 

excluding implausible values, versus 59% including implausible values); women (71% surveys excluding 

implausible values, versus 82% including implausible values); and men (96% surveys excluding 

implausible values and 96% including implausible values). In many surveys the distributions are not 

normal, especially among adults. Hemoglobin concentrations are higher in urban regions and wealthy 

populations, and in these groups there was less dispersion, skewness, and kurtosis. 

In conclusion, our findings indicate that the overall quality of data is high on some measures, but there are 

exceptions, especially in terms of wide standard deviations. Disentangling measurement error from intrinsic 

variation is difficult. Future research is needed to establish standard parameters that assess measurement 

error in the assessment of hemoglobin and other biomarkers. 

KEY WORDS: Anemia, biomarkers, blood collection, data quality, Demographic and Health Surveys, 

hemoglobin, nutrition 

1 

1. Introduction 

1.1. Background 

Anemia is a widespread public health problem that increases morbidity and mortality, especially among 

women and children (Balarajan, Ramakrishnan, Ozaltin, Shankar, and Subramanian 2011; Stevens et al. 

2013). Anemia, which is defined by a hemoglobin concentration below a certain threshold, is caused by 

factors that affect the morphology, production, turnover, loss, or destruction of red blood cells (Balarajan 

et al. 2011). Although iron deficiency is considered the most common cause of anemia around the globe 

(Engle-Stone et al. 2017; Wirth et al. 2017), there is an growing recognition that iron deficiency is not the 

driving cause of anemia in all settings (Petry et al. 2016). Other major contributors include mineral and 

vitamin deficiencies other than iron deficiency, acute and chronic inflammation, parasitic infections, and 

acquired or inherited disorders (Kassebaum et al. 2014). 

The multifactorial nature of anemia and the challenges in implementing interventions to address these 

factors have resulted in few countries being able to successfully curb the high prevalence of anemia 

(International Food Policy Research Institute 2016). As a result, anemia has been prioritized as one of seven 

nutrition indicators selected for inclusion in the World Health Assembly targets (WHO and 1000 Days 

2014). Anemia data are important for monitoring progress toward meeting international goals and 

advocating for appropriate action in populations at greatest risk. Thus, precise, accurate, and reliable 

measurement is critical to inform the prevention and control of anemia. 

The World Health Organization (WHO) recommends the use of hemoglobin as the biomarker to determine 

the population-based prevalence of anemia (WHO 2011). The reference method for the measurement of 

hemoglobin is the hemoglobincyanide (HiCN) method of the International Council for Standardization in 

Hematology (Zwart et al. 1996). This method, or a method based on the HiCN principle, has been automated 

for use in hospitals, health facilities with large patient populations, and reference laboratories. In 

population-based surveys, the Hemocue (HemoCue AB, Angelholm, Sweden) is routinely used to 

measure hemoglobin and is recommended by the WHO (WHO 2001). This device is relatively inexpensive, 

easily portable, does not require a cold chain, and produces a result within a minute. A Hemocue device 

is generally considered to be capable of providing precise and accurate measurements of hemoglobin 

concentrations (Cohen and Seidl-Friedman 1988). However, results can be compromised during blood 

collection from “milking” of the finger, using the incorrect blood volume in the microcuvette, or not 

properly cleaning the microcuvette (Karakochuk). 

1.2. Objectives 

In July 2016, with the support of USAID, the PATH HealthTech program organized an expert consultation 

meeting on “Hemoglobin testing methods: Research and program implications”. The meeting’s objectives 

were to share experiences performing hemoglobin measurements in large surveys and to discuss challenges 

and opportunities. Data presented at the meeting and discussions among the participants identified multiple 

sources of variation in hemoglobin measurements, which arise from blood sampling procedures and 

conditions (e.g. wicking versus gravity), the use of different HemoCue® models and devices, and biological 

factors (e.g. genetic variations). It was also noted that the extent of variability in hemoglobin measurements 
2 

may be higher for younger children, compared to older children, and there may be less variability of 

hemoglobin measurements among adults, compared to children. 

The findings from the meeting suggested a need to further explore variations in hemoglobin concentration 

and the quality of hemoglobin measurement in population-based surveys. The following two activities were 

proposed, with this report addressing the second: 

1. Create a collaborative group to examine the existing data on hemoglobin assessments from the 

countries discussed at the expert consultation (including Cambodia, Laos, Rwanda, Mexico, and 

Guatemala). 

2. Conduct an in-depth analysis of hemoglobin measurements with data from The Demographic and 

Health Surveys (DHS) Program. 

The first objective of this methodological report is to assess errors of measurement. The second objective 

is to use the large accumulation of DHS survey data to describe intrinsic variation in hemoglobin levels 

across and within a wide range of populations and subpopulations. “Intrinsic” variation refers to the true or 

underlying levels and distributions of hemoglobin. 

Strategies for analyzing data, which assume that errors of measurement are negligible, and strategies for 

detecting errors of measurement, which assume that they are not negligible, are often similar. For an 

individual respondent, the recorded measurement can be expressed as a sum of three terms that include the 

population mean, the individual’s deviation from the population mean, and a disturbance or error, which 

potentially includes measurement error. If there are measurement errors, either random or systematic, they 

will be confounded with true individual-level deviations from the population mean. It is not possible to 

disentangle true variation and measurement error for specific individuals, but if there is measurement error 

then the overall distribution will tend to be over-dispersed, leading to over-estimation of the proportion of 

the population with anemia, particularly with severe anemia. If the distribution is skewed, the median may 

be preferable to the mean as a summary measure, and this report includes medians as well as means. The 

magnitude of error may be associated with some characteristic such as age. We will provide some 

generalizations about the underlying distributions of hemoglobin levels; will identify some surveys that 

probably, if not definitively, had measurement issues; and will use some indicators of data quality that could 

be applied to future surveys, even during data collection. 
3 

2. Procedures, Data, and Methods 

2.1. Procedures 

2.1.2. Hemoglobin measurement in The DHS Program 

DHS surveys frequently include the measurement of hemoglobin. In the DHS surveys, when hemoglobin 

is included, the subpopulation of primary interest is children age 6-59 months. Most surveys that include 

hemoglobin measurement for children also include measurements for women age 15-49, and about onethird of such surveys also include men age 15 and older. Hemoglobin measurement is included in all the 

Malaria Indicator Surveys (MIS), but only for children. The DHS Program generally does not measure 

hemoglobin in children age 5-14. 

The protocol for hemoglobin assessment has remained relatively consistent since testing was first 

introduced into The DHS Program in 1996. Hemoglobin concentrations are measured in a small volume of 

capillary blood with the HemoCue 201+ or the 301+ system1

. Blood is obtained from a finger for adults 

and for children age 12-59 months and from the heel of children age 6-11 months. In early surveys, blood 

was sometimes obtained from the finger of children younger than age 12 months. 

The skin is warmed by rubbing the hands or heel to increase blood flow. The hand or heel is placed below 

the level of the heart, and the finger or heel is then cleaned thoroughly with alcohol. With a finger prick, 

the data collector is trained to select the third or fourth finger, use a rolling movement of the thumb to 

lightly press the finger from the top knuckle toward the tip, and maintain a gentle pressure to trap the blood. 

With a heel prick, the data collector is trained to apply light pressure around the heel. The skin is cleaned 

and then pricked with a sterile, retractable lancet to obtain the blood sample. 

Table 2.1.1 Blood drop used to obtain hemoglobin measurement

Blood drop Biomarkers measured in survey 

Third Hemoglobin only 

Fourth Hemoglobin and malaria 

Fifth Hemoglobin and dried blood spot

In general, after the first two free-flowing blood drops are wiped away with a sterile piece of gauze, the 

third blood drop is sampled with the microcuvette. The fourth or fifth drop of blood is sometimes used for 

hemoglobin measurements when other biomarkers are being tested (see Table 2.1.1.). The blood drop is 

placed in the microcuvette directly from the finger or heel without touching the finger or heel. The data 

collector must ensure that the microcuvette is completely filled with no air bubbles. The outside of the 

microcuvette is cleaned and then inserted into the photometer, which generates a result within a minute and 

allows for the immediate return of results to survey participants. Participants are provided with a referral 

 

1

 The principles used for the HemoCue system to determine hemoglobin concentration are: HemoCue 201+ “The erythrocyte 

membranes are disintegrated by sodium deoxycholate, releasing the hemoglobin. Sodium nitrite converts the hemoglobin iron from 

the ferrous to the ferric state to form methemoglobin, which then combines with azide to form azidmethemoglobin. The photometer 

uses a double wavelength measuring method, 570 nm and 880 nm, for compensation of turbidity.” HemoCue 301+ “Measures the 

absorbance of whole blood at an Hb/HbO2 isobestic point. The analyzer uses a double wavelength measuring method, 506 nm and 

880 nm, for compensation of turbidity.” Obtained from http://hemocuelearningcenter.com/hemoglobin-product-specifications/. 
4 

slip for follow-up medical attention if their hemoglobin concentration is low2. The data collector records 

the hemoglobin concentration in grams per deciliter (g/dL). 

2.1.3. Classifying anemia in DHS surveys 

The cutoffs in Table 2.1.2 are applied to hemoglobin concentrations in DHS surveys to obtain populationlevel estimates of anemia. Hemoglobin concentrations are first adjusted for altitude and cigarette smoking 

because both tend to increase hemoglobin concentrations3

 (World Health Organization and Centers for 

Disease Control and Prevention 2007). For persons living more than 1000 meters above sea level, altitude 

is adjusted by adding 0.32A-0.22A2, where A is the altitude in feet (the altitude in meters multiplied by 

0.0033). The adjustment is negative, as shown in Figure 2.1.1.2 (Nestel P and The INACG Steering 

Committee 2002). Pre-established values are subtracted from observed hemoglobin concentrations among 

smokers, depending on the number of cigarettes smoked per day. If the number is less than 10, there is no 

adjustment; for 10-19, 0.3 g/dL is subtracted; for 20-39 cigarettes, 0.5 is subtracted; and for 40 or more 

cigarettes, 0.7 is subtracted. If the person smokes cigarettes but the number is unknown, 0.3 g/dL is 

subtracted (Nestel P and The INACG Steering Committee 2002)4

. 

Table 2.1.2 Hemoglobin cutoffs used in The DHS program to define anemia at sea 

level.

Anemia measured by hemoglobin (g/dL)*

Any Anemia Mild Moderate Severe

Children age 6- 59 months1 <11.0 10.0- 10.9 7.0- 9.9 <7.0

Nonpregnant women age 15-492 <12.0 10.0- 11.9 7.0- 9.9 <7.0

Men age 15 and above <13.0 11.0- 12. 9 8.0- 10.9 <8.0

1 Malaria Indicator Surveys defines severe anemia as <8.0 g/dL

2 World Health Organization recommends slightly modified hemoglobin cutoffs to define anemia for 

nonpregnant women: mild anemia (11.0-11.9 g/dL), moderate anemia (8.0-10.9 g/dL), and severe 

anemia (<8.0 g/dL).

 

2

 While it can vary between surveys, children and non-pregnant women (or women whose pregnancy status is unknown) are 

generally referred when their hemoglobin concentration is less than 7.0 g/dL in DHS and 8.0 g/dL in MIS. Pregnant women, and 

men, are referred when the hemoglobin concentration is less than 9.0 g/dL. The hemoglobin concentration cutoffs used for referral 

purposes by The DHS Program differ from the WHO classification of severe anemia.

3

 Some early surveys were not adjusted for altitude or were adjusted at altitudes below 1000 meters. 

4

 Until recently, cigarette consumption in some surveys adjusted hemoglobin concentrations by -0.3 g/dL for participants reporting 

cigar and pipe smoking behaviors.
5 

Figure 2.1.1 Additive adjustment to hemoglobin concentrations g/dL for altitudes greater than 1000 meters 

2.2. Data 

2.2.1. Surveys included in the analysis 

The DHS surveys, including MIS surveys, are nationally representative. Eighty surveys have been selected 

for inclusion in this report (Figure 2.2.1). Twenty-four countries are represented with one survey and 28 

countries with two surveys. Of the 80 surveys, 53 surveys are from 33 countries in the African Region, 10

surveys from 6 countries in the Region of the Americas, 6 surveys from 5 countries in the South-East Asia 

Region, 4 surveys from 4 countries in the European Region, 5 surveys from 4 countries in the Eastern 

Mediterranean Region, and 2 surveys from 1 country in the West Pacific Region based on the WHO 

definition of regions. The surveys are listed in Appendix 1. They comprise all DHS and MIS surveys since 

2000 that included hemoglobin measurements, except that if a country had more than two such surveys, 

only the two most recent surveys are included. 

When the measurements are made in the field as part of data collection for the entire household, there is no 

distinction between de facto (slept in the household last night) and de jure (usual) residence. In all surveys, 

the vast majority of respondents satisfy both definitions, although in every survey there are some residents 

who satisfy one definition but not the other, usually approximately the same number for each of the two 

possibilities. The DHS main reports generally limit estimates to de facto residents. In this report, however, 

we restrict the analysis to de jure observations because an altitude adjustment based on current location 

could be inappropriate for someone who normally resides elsewhere. This resulted in the removal of 2.5% 

of the total observations for children, 2.9% of women, and 2.3% of men. 

-.25

-.2

-.15

-.1

-.05

0

Adjustment to Hb (g/dL)

1000 2000 3000 4000

Altitude (meters)
6 

Data are included for children age 6-59 completed months, non-pregnant women 15-49 completed years of 

age, and men 15 completed years of age and above (the upper end of the age range varies across the surveys 

of men). Women who state that they are currently pregnant are excluded (9.5% of the total hemoglobin 

observations for women) because hemoglobin concentrations may depend on the trimester of pregnancy. 

No distinction was made between lactating and non-lactating women. Although the age range of eligibility 

for children is age 6-59 months, we found some measurements for children younger than age 6 months. 

These children were excluded from further analysis (5.9% of the total hemoglobin observations for children 

were removed). A total of 1,247,942 hemoglobin observations are included in this analysis (Figure 2.2.1). 

This total reflects all de jure participants selected for hemoglobin measurement that met our subpopulation 

inclusion criteria. 

2.2.2. Definitions of hemoglobin and other variables used in this analysis 

This analysis uses the DHS household standard recode file for each survey, referred to as the “person 

records (PR)” file. Table 2.2.1 summarizes the variables in this analysis. Hemoglobin concentrations, 

whether unadjusted or adjusted for smoking and altitude (when applicable), are presented in g/dL. 

Adjustments for altitude and smoking are made during the preparation of the recode files. In this report, it 

was assumed the adjustments for smoking and altitude were done correctly, using self-reports of tobacco 

use and the altitude at the centroid of the sample cluster measured during data collection. Anemia categories, 

as shown in Table 2.1.2., are defined after adjusting for altitude and smoking, as applicable. Values at a 

Surveys identified for inclusion 

80 surveys in 52 countries

Inclusion criteria

-Surveys from The DHS Program

-Conducted between 2000 and 2017

-Two most recent surveys only if countries ≥2 surveys 

-Children age 6-59 months, nonpregnant women age 15-49, 

men age 15 and above 

-Hemoglobin assessment included in survey

Hemoglobin observations 

n= 607,101 

Children age 6-59 months 

80 surveys in 52 countries 

Non-pregnant women age 15-49 

65 surveys in 46 countries 

Men age 15 and above 

27 surveys in 20 countries 

Hemoglobin observations 

n=405,731 

Hemoglobin observations 

n= 235,110 

Figure 2.2.1 Sample size flow chart
7 

boundary are classified into the category with the higher number. For example, children with a hemoglobin 

concentration of 11 g/dL, along with children who have a higher level, are classified as “not anemic”. In 

this report, the only use of anemia categories will be shown in the shading of categories in figures that show 

the distribution of hemoglobin. 

Table 2.2.1 Summary of variables used in the analysis from the person records (PR) file1

Name Definition Code Notes

HV042 Household selected 

for hemoglobin

1: selected; 0: not selected Some surveys subsampled households for 

hemoglobin testing; some surveys included 

testing in all households.

HA52

HB52

HC52

Read consent 

statement

1: granted; 2: Parent/other 

responsible [adult] refused; 

3: respondent refused; 9: 

Missing; NA: not applicable

Some surveys have other numeric values 

that are also interpreted as “permission not 

granted”.

HA53

HB53

HC53

Hemoglobin 

unadjusted 

g/dL with one implied 

decimal

994: Not Present; 995: 

Refused; 996: Other; 999: 

missing; NA: not applicable

The data files have one implied decimal;

values were divided by 10 to be 

interpretable as g/dL. Missing codes varied 

in some surveys.

HA56

HB56

HC56

Hemoglobin 

adjusted5

g/dL with one implied 

decimal

999: missing; NA: not 

applicable

Adjusted for altitude and smoking among 

women and men and for altitude among

children; adjusted concentrations are in 

g/dL.

HA1

HB1

HC1

Age of respondent Months for children and 

years for adults

HV024 Region of residence Codes are country-specific

HV025 Type of place of 

residence 

1: urban; 2: rural

HV106 Respondent’s

education

0: No education; 1: 

Primary; 2: Secondary; and 

3: Higher

Education of household head is obtained for 

the person in the household with HV101 

(Relation to Household Head) equal to 1 

(Head). Secondary and higher education are

combined when defining education of head 

of household but not when defining women 

respondent’s education in this report.

HV270 Wealth Index 1: lowest; 2: second; 3: 

middle; 4: fourth; 5: highest

A summary scale based on household 

assets, divided into quintiles.

1 HV indicates a household variable; HA refers to women, HB to men, and HC to children; A blank (or “.” in Stata) is used for “not 

applicable” or “not eligible”. Cases receive this code if they are outside the applicable combination of age and sex, or if there is 

subsampling and their household is not included in the hemoglobin subsample.

2.3. Methods 

2.3.1. Methods of analysis 

Estimates in the DHS reports are weighted in order to compensate for over- and under-sampling of the 

various strata in a survey and differential nonresponse. In this report, our focus is on individual 

measurements and not population estimates. Therefore, all calculations are unweighted. DHS does not flag 

and exclude biologically implausible hemoglobin concentrations, although for the purposes of this report, 

biologically implausible values were defined as hemoglobin concentrations outside of the range of 4.0 g/dL 

to 18.0 g/dL for children and women, and 5.0 g/dL to 20.0 g/dL for men (Sullivan, Mei, Grummer-Strawn, 

and Parvanta 2008). 
8 

A. Representative subsampling 

Hemoglobin measurements usually apply to all households, although they are sometimes restricted to a 

subsample of households. In surveys that collected hemoglobin measurements in a subset of households, it 

is important to be confident that those households were randomly selected. To assess this, we first identified 

the surveys where subsampling was undertaken and calculated the fraction of households subsampled. 

Second, we tested whether, when there is subsampling, those households are randomly selected with respect 

to four covariates. We conducted logit regressions in which the outcome was 1 if the household was 

selected, 0 if it was not. The covariates were urban/rural residence, region, education of the household head 

(no education, primary, secondary and above), and wealth quintiles.1

 The logit regression produces a 

maximum likelihood chi-square statistic that can test the statistical significance of the model. If the selection 

was significantly associated with a covariate, we infer that the selection was not random with respect to that 

covariate. 

B. Completeness of data 

The criterion of “completeness” refers to low levels of “missing” observations or measurements that are 

outside a plausible range. The main coded reasons for not being measured, although eligible for 

measurement, are that the person was not present at the time of the measurements, or was present and 

refused consent to be measured. In the case of a child, it is the parent who must provide consent. There is 

also an “other” category, which is not specific as to the reason why the person was not measured. In 

addition, we identified the percentage of participants with values outside the biologically plausible ranges. 

C. Digit preference 

Digit preference was examined to assess whether the distribution of final digits of the hemoglobin 

measurement had a uniform distribution. A tendency for some final digits such as 0 to be reported more 

often than expected (more than 10% of the time), and other digits to be reported less often than expected 

may suggest the improper use of equipment or carelessness. The digit to the right of the decimal place, on 

a g/dL scale, was used to examine digit preference. This part of the analysis used the unadjusted hemoglobin 

measurements and included observations with biologically implausible values. 

D. Distribution of hemoglobin concentrations 

We first examine the distribution of hemoglobin concentrations with the mean, median, range, and standard 

deviation. If measurement errors occur randomly, then the observed variance (the square of the observed 

standard deviation) is equal to the sum of the true variance and the variance of the measurement error. A 

relatively large standard deviation may indicate poorer quality data. A very small standard deviation may 

also suggest inaccurate measurement. Following Sullivan et al. (2008), standard deviations between 1.1 and 

1.5, inclusive, were considered to be acceptable. Standard deviations less than 1.1 and greater than 1.5 were 

identified. Standard deviations are calculated for hemoglobin concentrations adjusted for altitude and 

smoking, as applicable, both including and excluding the hemoglobin concentrations that were biologically 

implausible. We also plotted the standard deviation against the mean, after excluding biologically 

implausible hemoglobin concentrations. 

The shape of the distribution of hemoglobin concentrations is described in terms of skew and kurtosis. The 

reference values for the skew and kurtosis will be those of a normal distribution, 0 and 3, respectively. 

Skew measures asymmetry. It is positive, and the distribution is right-skewed, if there are more extreme 
9 

cases in the right tail than the left tail. It is negative, and the distribution is skewed to the left, if the left tail 

dominates. Kurtosis is greater than 3 if there are many extreme values in the tails, relative to a normal 

distribution; it is less than 3 if the tails are relatively short. A general rule of thumb for an “acceptable” 

range of skew is from -0.5 to 0.5 and an “acceptable” range for kurtosis is from 2 to 4; subpopulations 

outside of this range are identified. We explicitly do not assume that the “true values” are normally 

distributed, and only use a normal distribution as a reference. The skew and kurtosis are calculated with 

and without the inclusion of biologically implausible values. In both cases, hemoglobin concentrations are 

adjusted for altitude and smoking as applicable.

We examined the mean, median, standard deviation, skew, and kurtosis stratified by several covariates for 

women and children, the populations of greatest public health relevance. For women, the covariates include 

(a) age, with intervals 15-19, 20-34, and 35-49; (b) urban and rural residence; (c) level of education (none, 

primary, secondary, higher); and (d) wealth quintiles. For children, the analysis will be repeated for (a) age, 

with intervals 6-11 months, 12-23 months, and 24-59 months; (b) urban and rural residence; (c) boys and 

girls; and (d) wealth quintiles. Differentials for men are not included; it is assumed that any would mirror 

those found for women. Differentials are presented with the exclusion of biologically implausible 

hemoglobin concentrations, and with adjustments for altitude and smoking as applicable. 

The report includes some figures that show the distribution of hemoglobin concentrations in selected 

surveys and subpopulations. Figure 2.3.1 is an example. The Nigeria 2015 survey is the selected example 

because it is a recent survey from one of the largest countries in which DHS works. The top of the figure 

shows the number of cases in the range from 4 to 18 g/dL, as well as the mean, standard deviation, and 

skew of the distribution. The purple, red, orange, and green segments represent severe, moderate, mild, and 

no anemia, respectively5

. The vertical bars have a width of 0.2 g/dL. The conspicuous spikes in the 

histogram are due to heaping at numerical values ending with “.0” in this survey. As noted above, at a 

boundary, those values are classified with the higher category (to the right) rather than with the lower 

category. Figures such as Figure 2.3.1 exclude biologically implausible values and refer to the 

concentrations adjusted for altitude and smoking as applicable. 

 

5

 Standard DHS cutoffs for hemoglobin concentrations are used. 
10

Figure 2.3.1 Illustration of the distribution of the hemoglobin level for children age 6-59 months, Nigeria 2015 
11

3. Results: Indicators of Data Quality 

This chapter presents a review of data quality using three indicators. First, we examine evidence of potential 

selection bias in surveys where hemoglobin measurements were only obtained from a subsample of survey 

participants. Second, we assess the level of data completeness, defined by the percentage of hemoglobin 

measurements that are missing or are outside of a biologically plausible range. Third, we calculate the 

extent of under- or over-reporting of specific final digits.

3.1. Representativeness of subsampling 

Eligibility of individuals for hemoglobin measurement is determined at the level of the household. If the 

household is selected, then all children, women, or men in that household will be selected, so long as they 

are in the appropriate age range and the measurements extend to women and men. In some surveys, all 

households were eligible for hemoglobin measurement, but in some surveys a subsample was selected. Of 

the 80 surveys in this analysis, 35 did not, and 45 did involve subsampling. Subsampling generally takes 

the form of selecting alternate households in the household listing within a cluster, or selecting one in three, 

or (least often) skipping one in three and selecting the other two. Some variation around the target sampling 

fractions (one-third, one-half, and two-thirds) is a normal consequence of sampling6. This strategy should 

be random with respect to any characteristic of the household or its members. 

Table 3.1.1 lists the 45 surveys that involved subsampling and the percentage of the subsample selected for 

hemoglobin measurement (based on variable HV042). Eleven surveys had approximately one-third of 

households selected for hemoglobin measurements from the total sampled population (31.9% to 35.1%); 

30 surveys had approximately one-half of households selected (47.8% to 51.9%); and 4 had two-thirds of 

households selected (64.8% to 66.7%). 

The p-values of the logit regression selected for hemoglobin measurement as the outcome and potential 

selection bias variables as the covariates are shown in Table 3.1.1 The level of statistical significance is 

indicated with adjacent columns including asterisks: * indicates p<.05, ** indicates p<.01, and *** indicates 

p<.001. Based on the large number of tests described in the table, we would expect approximately 9 cells 

with at least one asterisk, 2 cells with at least two asterisks, but no cells with three asterisks. We observed 

7 cells with one asterisk, 6 cells with two asterisks, and 1 cell with three asterisks. 

The greatest evidence of selectivity was related to the education of the household head, especially in the 

Bangladesh 2011 survey, which was large, with 17,141 households, of which 5,754 or 33.6% were selected 

for hemoglobin measurement. The observed sampling fractions were 32.0% if the household head had no 

schooling, 35.0% if he/she had primary schooling, and 33.3% if he/she had secondary or higher education. 

These three sampling fractions are significantly different from one another, although the differences are not 

substantively large and the sampling fractions do not increase or decrease monotonically with increases in 

education. 

It is notable that not a single survey showed significant variation—that is, evidence of nonrandom 

selection—across regions. Only one survey, Cambodia 2011, showed evidence of selectivity on more than 

 

6

 Even a simple even/odd procedure will produce a deviation from exactly half of households being selected, because of 

randomness in whether the number of interviewed households in a cluster is even or odd. 
12

one of the four covariates. The Cambodia 2011 survey had a test statistic that is significant at the 0.01 level 

on both urban/rural residence and education of the household head. This survey had 15,825 households, of 

which 10,275, or 64.9%, were selected for hemoglobin measurement. The observed sampling fractions for 

urban and rural areas were 66.8% and 64.2%, respectively, and were 66.8%, 63.6%, and 65.9%, 

respectively, for the three levels of education of the household head. 

Table 3.1.1 Tests of the randomness of household selection for hemoglobin measurement, by covariates

Survey Sampling 

fraction (%)

Residence pvalueRegion pvalueEducation

p-value

Wealth

p-value

Angola 2011 32.5 0.78 0.95 NA 0.71

Angola 2015-16 50 0.97 1 0.53 0.57

Bangladesh 2011 33.6 0.83 1 0.00*** 0.73

Benin 2006 33.3 0.85 1 0.07 0.17

Benin 2011-12 33.1 0.94 1 0.52 0.08

Bolivia 2003 35.1 0.81 1 0.01** 0.88

Bolivia 2008 34.9 0.89 1 0.00** 0.54

Burkina Faso 2010 49.9 0.86 1 0.51 0.02*

Burundi 2010 50 0.64 1 0.25 0.91

Cambodia 2010 49.9 0.92 1 0.18 0.61

Cambodia 2014 64.9 0.00** 1 0.00** 0.52

Cameroon 2004 50.8 0.47 1 0.49 0.47

Cameroon 2011 50.1 0.97 1 0.89 1

Congo DR 2007 50 0.99 1 0.43 0.71

Congo DR 2013-14 50 0.89 1 0.95 0.7

Congo 2005 50 0.83 1 0.42 0.13

Congo 2011-12 50 0.94 1 0.89 0.82

Cote d'Ivoire 2011-12 50.5 0.41 1 0.85 0.73

Egypt 2014 33.6 0.72 1 0.16 0.21

Gabon 2012 66.6 0.96 1 0.38 0.3

Gambia 2013 47.9 0.76 0.99 0.59 0.18

Ghana 2014 50.1 0.82 1 0.83 0.9

Guinea 2005 49.8 0.91 1 0.88 0.57

Guinea 2012 49.9 0.7 1 0.79 0.83

Haiti 2005-06 49.6 0.88 1 0.6 0.09

Haiti 2012 66.7 0.94 1 0.84 0.82

Jordan 2009 49.5 0.77 0.94 0.68 0.00**

Jordan 2012 64.8 1 0.94 0.81 0.29

Lesotho 2009 51.9 0.77 1 0.96 0.44

Lesotho 2014 49.8 0.57 0.99 0.49 0.38

Malawi 2015-16 33.3 0.78 0.94 0.02* 0.14

Mali 2012-13 49.9 0.72 1 0.5 0.3

Mozambique 2011 50 0.88 1 0.23 0.7

Namibia 2013 49.9 0.45 1 0.93 0.11

Nepal 2011 48.9 0.28 0.96 0.02* 0.67

Niger 2006 49.8 0.86 1 0.17 0.02*

Niger 2012 47.8 0.86 0.98 0** 0.4

Rwanda 2010 50 0.93 1 0.03* 0.15

Rwanda 2014-15 50 0.96 1 0.19 0.05*

Sierra Leone 2008 50 0.78 0.97 0.87 0.14

Sierra Leone 2013 50 0.79 1 0.41 0.23

Timor-Leste 2009-10 33.4 0.94 1 0.22 0.79

Togo 2013-14 49.8 0.9 1 0.15 0.2

Uganda 2011 31.9 0.99 1 0.01* 0.47

Yemen 2013 32 0.96 1 NA 0.59

Note: Congo DR, Congo Democratic Republic; residence defined as urban or rural; region definition varied by survey; education 

defined as head of household received no education, primary, secondary or above; wealth defined as wealth quintiles; significance 

of selectivity in selection is indicated by * for .05 level, ** for .01 level, *** for .001 level.
13

3.2. Data completeness 

Table 3.2.1 presents information on data completeness. Overall the percentage of valid data was high, with 

a small percentage of missing data and hemoglobin measurements outside of the biologically implausible 

ranges. 

The average percentage of missing responses is similar for children and women, 7.1% and 4.5%, 

respectively, and higher for men, 15.0%. There are specific surveys in which the percentage is much higher, 

especially for children. There are some inconsistencies in the codes for incomplete or missing responses. 

There are a few surveys where the consent variable indicates that consent was not obtained but there is a 

hemoglobin measurement. Such inconsistencies are most likely data entry errors or instances in which a 

person was not initially available, a code to that effect was entered, the person became available, and the 

consent code was not updated. There are two surveys in which the unadjusted measurements (H*53) are 

included but the adjusted variables (H*56) are absent. These are surveys in which the smoking questions 

were omitted and altitudes never exceeded 1000 meters. The unadjusted values should have been copied 

directly, with no changes, during data processing, but were not. In addition, there are some variations in the 

use of the standard DHS codes to indicate missing values. A total of 19 of the surveys used the missing 

value codes 994, 995, 996, and 999 for the unadjusted hemoglobin measurement variable (H*53). Of the 

remainder, 29 surveys used 994, 995, 996 and not 999, and 26 surveys used only 999. One survey used 800 

to indicate missing values. In 5 surveys (Angola 2011, Bolivia 2003, Cambodia 2014, Lesotho 2009, and 

Nepal 2006) there were no missing values at all. An examination of the data suggests that the children who 

should have been assigned a “missing” code were actually given the “not applicable” code, which is a blank. 

For example, there are identifiable children whose mother was excluded (and given codes 994, 995, or 996) 

and the child was not measured (probably for the same reason as the mother) and the child was given the 

“not applicable” code (a blank) rather than the same missing value code as the mother. This kind of 

confounding of “missing” and “not applicable” is contrary to normal DHS practice. 

The percentage of measurements that are numeric but outside the plausible ranges is always below 0.8%. 

However, the out-of-range values can have a substantial effect on summary statistics, because they can be 

extremely large. An example from one of the surveys is a single case with a value of 880 (with one implied 

decimal place), which converts to a hemoglobin concentration of 88.0. The value 880 is almost certainly a 

data entry error, for a concentration of either 8.0 or 8.8. Some of the out-of-range values are simply 0. Most 

of the out-of-range values are likely the result of data entry errors, such as entering a spurious leading digit 

or final digit, or dropping the leading digit or final digit. Data entry errors can also lead to an incorrect value 

that is in-range, no matter how the plausible range is defined, and be impossible to detect. 
14

Table 3.2.1 The percentage of valid, implausible, and missing hemoglobin data

Children 

(age 6-59 months)

Women of 

Reproductive Age (15-49)

Men 

(age 15 and above)

Mean % Range Mean % Range Mean % Range

Missing1 7.1% 0.0-53.9% 4.5% 0.0-17.5% 15.0% 0.6-35.3%

Implausible 0.2% 0.0-0.7% 0.1% 0.0-0.8% 0.1% 0.0-0.5%

Valid 92.8% 46.0-100.0% 95.4% 82.4-100.0% 84.9% 64.6-99.3%

Note: Total sample is defined de jure participant selected for hemoglobin measurement; percentage of missing values is based on 

total sample; Final sample is defined as total sample minus respondents with missing hemoglobin measurements available; 

percentage of implausible values is based on final sample. Percentage of valid values reflects the total sample without missing 

and implausible values. Implausible values defined as hemoglobin values outside of 4 g/dL to 18 g/dL for children and nonpregnant 

women and 4 g/dL to 20 g/dL for men after adjusting for altitude and smoking when applicable.

1

In the data file for the Congo 2005 survey, for children only, the missing value code “999” was assigned to children who were not 

measured because their household was not in the subsample selected for hemoglobin measurement. Such children should have 

received a “blank” code for “not applicable”. This error was made during the construction of the public use data files and we could 

not correct it. If that error is taken into account, the correct level of missing was 11.2%, rather than 53.9%.

3.3. Digit preference 

A common indicator of data quality in various contexts is a tendency for certain final digits to appear more 

often than expected by chance. The most familiar example is age heaping, in which ages of adults are 

disproportionately given with final digit 0 and, to a lesser extent, with final digit 5. Heaping of ages is 

usually the result of respondents not knowing their actual ages. In the present context, rounding or other 

forms of unevenness in the distributions of digits would suggest carelessness by the person making the 

readings. 

Figure 3.3.1 summarizes the variation in final digits across surveys and subpopulations. Note that the 

vertical axis is limited to a narrow range between 8.5% and 11.5%. We would expect each of the ten possible 

values (0 through 9) to occur in approximately 10% of the cases. On average, the observed percentage for 

each digit ranges from 9.4% to 11.1%. The distribution is close to uniform, but the results lead to specific 

examination of digits 0, 2, 5, and the range 6-9, and the numbers of surveys in which the observed 

percentages deviate from the expected percentages by 2% or more (deviations of this magnitude are always 

statistically significant). The expectation is 10% for digits 0, 2, and 5, and 40% for the range 6-9. 

Across the 80 surveys of children and the 65 surveys of women, there are 32 times when the percentage 

of final digits 0 or 2 is higher than 12%, almost equally favoring final digit 0 and final digit 2. There are 

only two settings in which the percentage of 0 or 2 is extreme in the lower direction, i.e. is less than 8%. 

Most conspicuously, for women and children in the Albania 2008-09 survey, the observed occurrence of 

0 is much lower than expected, about 5%. This low frequency may be the result of instructions to 

fieldworkers to avoid heaping at 0, with the result that 0 was avoided even when it was appropriate. It is 

likely that a much lower than expected occurrence of final digit 5 in the Armenia 2005 survey has the 

same explanation. Heaping at 5, as well as 0, may have been anticipated during training because of the 

typical pattern of age heaping, but as it happens the only evidence of preference for final digit 5 is found 

for women in the Angola 2006-07 and Benin 2011-12 surveys. 

A deficit of final digits 6, 7, 8, and 9, collectively, is very conspicuous. This pattern is found with a 

negative deviation of 2 percentage points or more in 24 of the 80 surveys of children and 12 of the 67 

surveys of women. It is apparent that there is a tendency in these surveys for readings that should end in 

final digits 6, 7, 8, and 9 to be shifted upwards to the next multiple of 10, a shift that would account for 

the deficit at 6, 7, 8, and 9 and the excess at 0. In the context of age, heaping at multiples of 10 is typically 
15

drawn symmetrically from the neighboring digits. For example, an excess reported at age 50 is usually 

accompanied by deficits at ages 47-49 and 51-53. For the hemoglobin measurements, by contrast, it 

appears that the shift is not symmetric, but is upwards, potentially raising the mean reading. The 

mechanism leading to this asymmetric upward shift in some surveys is worth investigating. 

To summarize, there is a clear tendency for heaping at final digits 0 and 2 and a deficit at digits 6-9, but 

these patterns are only found in a minority of surveys. Even for those surveys, the effect on the 

distributions across categories of anemia will be very small. 

Figure 3.3.1 Summary statistics for digit preference for hemoglobin data across surveys by subpopulations

Note: Hemoglobin concentrations are unadjusted for altitude and/or smoking and include implausible values; n=80 (children), 65

(women), 27 (men) surveys; on average we would expect each digit to occur 10 percent of the time +/- 2 percentage points.

Expected

8.5

9

9.5

10

10.5

11

11.5

0 1 2 3 4 5 6 7 8 9

Mean percentage of recorded digit

Final digit of unadjusted hemoglobin measurement

Children Women Men

17

4. Results: Intrinsic Variation 

This chapter of the report examines the distributions of the hemoglobin measurements within each survey 

and provides an overview of their variability in terms of central tendency, dispersion, and shape. Central 

tendency is described with the mean and median. If a distribution is symmetric, these are equal or nearly 

equal; if a distribution is skewed, the mean will shift away from the median, in the direction of the longer 

tail (to the left for negative skew or to the right for positive skew). Dispersion is measured with the standard 

deviation and shape with the skew and kurtosis. For surveys and subpopulations with very low or very high 

values of these statistics, relative to other surveys and subpopulations, it is reasonable to question whether 

there may be some distortion due to measurement error. Unusual distributions may occur because a 

population is unusually healthy, unhealthy, homogeneous, or heterogeneous, with no issues of data quality. 

Alternatively, distributions may be unusual because of measurement error, which could cause high 

dispersion or nonrandom measurement error that could cause extreme levels of skew or kurtosis. Because 

of the possibility of measurement error, we hesitate to state definitively that what is described here—at the 

extremes—is intrinsic variation. This chapter will allow for an interpretation that the most unusual 

distributions have been at least somewhat distorted by measurement error, although that inference cannot 

be conclusive. The contexts in which data collection is most difficult are likely to be the contexts in which 

anemia is most prevalent. All of the hemoglobin concentrations in this chapter have been adjusted for 

altitude and smoking as appropriate. 

4.1. Variations in the mean, median, and standard deviation 

We now describe the central tendency and dispersion of hemoglobin concentrations in the surveys. Highly 

dispersed measurements can result either from a genuinely high level of dispersion in the population or 

from the addition of errors to the correct values. Errors of measurement are easiest to detect in the tails, 

although such errors can occur anywhere within the distribution. If the errors of measurement are random, 

and approximately equally likely to be upward or downward, then both tails will be spuriously extended. 

If, for example, the left tail of the distribution is extended and the right tail is not, and it is believed that 

measurement error is responsible, then a mechanism for displacement that is disproportionately downward 

may be indicated. It is important not to jump to a conclusion that unusually high or low dispersions are 

incorrect, because populations may indeed include severely anemic subpopulations, or may be uniformly 

healthy. 

The averages of the mean, median, standard deviation across surveys are shown in Table 4.1.1 and Table 

4.1.2, with implausible values included and excluded, respectively. In addition, the percentage of surveys 

with standard deviation below 1.1 or above 1.5 are presented. Figures 4.1.1, 4.1.2, and 4.1.3 present the 

distribution of the standard deviation of the hemoglobin concentration (excluding implausible values) for 

children, women, and men. There are almost no surveys with a standard deviation below 1.1, but a large 

percentage of surveys are above 1.5 even when implausible values are excluded. Standard deviations above 

1.5 are most common for surveys of men. 
18

Table 4.1.1 Summary statistics for hemoglobin concentrations across surveys, not excluding implausible 

values

Mean

of Mean

Mean

of Median

Mean 

of SD

Surveys with hb 

SD<1.1 (%)

Surveys with hb 

SD>1.5 (%)

Population

Children 10.58 10.67 1.79 0.0% 58.8%

Women (age 15-49) 12.34 12.46 1.85 0.0% 81.5%

Men (age 15 and above) 14.23 14.32 2.01 0.0% 96.3%

Note: Hb, hemoglobin; Adjusted for smoking and altitude when applicable

Table 4.1.2 Summary statistics for hemoglobin concentrations across surveys, excluding implausible values

Mean

of Mean

Mean

of Median

Mean 

of SD

Surveys with hb 

SD<1.1 (%)

Surveys with hb 

SD>1.5 (%)

Population

Children 10.57 10.67 1.48 0.0% 46.3%

Women (age 15-49) 12.33 12.46 1.58 0.0% 70.8%

Men (age 15 and above) 14.21 14.32 1.79 0.0% 96.3%

Note: Hb, hemoglobin; Implausible values defined as hemoglobin values outside of 4 g/dL to 18 g/dL for children and 

nonpregnant women and 4 g/dL to 20 g/dL for men after adjusting for altitude and smoking when applicable.

Figure 4.1.1 Distribution of the standard deviation of the hemoglobin concentration for children, excluding 

implausible values. 

Note: Implausible values defined as hemoglobin values outside of 4 g/dL to 18 g/dL after adjustments to altitude when applicable 
19

Figure 4.1.2 Distribution of the standard deviation of the hemoglobin concentration for women, excluding 

implausible values. 

Note: Implausible values defined as hemoglobin values outside of 4 g/dL to 18 g/dL after adjustments to smoking and altitude when 

applicable 

Figure 4.1.3 Distribution of the standard deviation of the hemoglobin concentration for men, excluding 

implausible values. 

Note: Implausible values defined as hemoglobin values outside of 4 g/dL to 20 g/dL after adjustments to smoking and altitude when 

applicable 
20

Figure 4.1.4 shows the distribution of hemoglobin concentrations for the 8 surveys of children with the 

greatest dispersion: Guinea 2012 (1.72), Niger 2006 (1.73), Cameroon 2004 (1.73), Ethiopia 2016 (1.73), 

Mali 2012-13 (1.74), Burkina Faso 2014 (1.74), Ethiopia 2011 (1.77), and Yemen 2013 (1.79). In all of 

these surveys the standard deviation is greater than 1.7 (but less than 1.8). Figure 4.1.5 shows the 

distribution of hemoglobin for children for two of the surveys that are most concentrated: Albania 2008-09

(1.15) and Egypt 2014 (1.17). 

Figure 4.1.4 The distributions of hemoglobin concentrations in the surveys of children with highest

dispersion 
21

Figure 4.1.5 The distributions of hemoglobin concentrations in the surveys of children with lowest 

dispersion 

The distributions for women tend to be more dispersed than the distributions for children (after excluding 

implausible values). The surveys with the largest standard deviations are Lesotho 2014 (1.80), Zimbabwe 

2010-11 (1.83), Haiti 2005-06 (1.83), Niger 2006 (1.87), and Yemen 2013 (1.91). The surveys with the 

lowest standard deviations are Egypt 2014 (1.12) and Albania 2008-09 (1.21). Among men, the surveys 

with the highest standard deviations are Benin 2006 (1.91), Ethiopia 2016 (1.91), Niger 2006 (1.94), and 

Congo Democratic Republic 2007 (2.02). Albania 2008-09 has the least dispersion (1.29). 

For the 28 countries that had two surveys with hemoglobin measurements of children, Table 4.1.3 compares

the standard deviations of the two surveys. The average absolute value of the difference is only 0.08. For 

17 surveys, the second survey had a larger standard deviation. For 10 surveys, there was a decrease. For 

one survey there was no change. 

Table 4.1.3 Standard deviations of hemoglobin measurements for 28 countries with

two surveys that measured children age 6-59 months, adjusted for 

altitude and excluding values outside the range 4-18 g/dL

Survey 1 Survey 2 SD Difference

Angola 2011 1.42 Angola 2015-16 1.42 0

Benin 2006 1.69 Benin 2011-12 1.56 -0.13

Bolivia 2003 1.56 Bolivia 2008 1.60 0.04

Burkina Faso 2010 1.66 Burkina Faso 2014 1.74 0.08

Burundi 2010 1.36 Burundi 2012 1.56 0.2

Cambodia 2010 1.33 Cambodia 2014 1.28 -0.05

Cameroon 2004 1.73 Cameroon 2011 1.52 -0.21

Congo 2005 1.48 Congo 2011-12 1.33 -0.15

Congo DR 2007 1.68 Congo DR 2013-14 1.70 0.02

Ethiopia 2011 1.77 Ethiopia 2016 1.73 -0.04

Ghana 2014 1.55 Ghana 2016 1.49 -0.06

Guinea 2005 1.64 Guinea 2012 1.72 0.08

Haiti 2005-06 1.56 Haiti 2012 1.32 -0.24

Honduras 2005-06 1.30 Honduras 2011-12 1.24 -0.06

Jordan 2009 1.37 Jordan 2012 1.38 0.01

Lesotho 2009 1.48 Lesotho 2014 1.61 0.13

Madagascar 2013 1.47 Madagascar 2016 1.41 -0.06

Malawi 2014 1.50 Malawi 2015-16 1.47 -0.03

Mali 2012-13 1.74 Mali 2015 1.64 -0.1

Nepal 2006 1.37 Nepal 2011 1.35 -0.02

Niger 2006 1.73 Niger 2012 1.50 -0.23

Peru 2011 1.30 Peru 2012 1.26 -0.04

Rwanda 2010 1.35 Rwanda 2014-15 1.39 0.04
22

Senegal 2014 1.58 Senegal 2015 1.51 -0.07

Sierra Leone 2008 1.52 Sierra Leone 2013 1.61 0.09

Tanzania 2010 1.43 Tanzania 2015-16 1.47 0.04

Uganda 2011 1.62 Uganda 2014-15 1.60 -0.02

Zimbabwe 2010-11 1.43 Zimbabwe 2015 1.33 -0.1

Note: Congo DR, Congo Democratic Republic; SD, Standard deviation; Hemoglobin adjusted for 

altitude and smoking prior to removing implausible values

To summarize, at the national level, children tend to have more concentrated distributions than women, and 

women tend to have more concentrated distributions than men. Surveys that have higher dispersion may be 

less accurately measured. Nevertheless, it is also possible that wider distributions arise from genuinely high 

levels of heterogeneity in the true hemoglobin levels. There is relative stability of dispersion in successive 

surveys in the same country. It is difficult to make inferences about data quality based on unusually low

levels of dispersion but there are no surveys that fall below our threshold of 1.1. 

4.2. Association between the mean and standard deviation of the hemoglobin measurement 

We have investigated possible reasons for why so many surveys have more dispersion than expected. One 

conspicuous pattern is a strong negative association between the mean and the standard deviation. For the 

hemoglobin measurements for children, as just described, the correlation between the mean and the standard 

deviation is -0.68. 

Figure 4.2.1 Standard deviation plotted against the mean, for 80 surveys with hemoglobin measurements of 

children age 6-59 months, adjusted for altitude and excluding values outside the range 4-18

g/dL. Horizontal lines enclose the expected range of standard deviations, from 1.1 to 1.5 

Figure 4.2.1 shows a scatterplot for the 80 surveys, with the mean on the horizontal axis and the standard 

deviation on the vertical axis. The 38 surveys with a standard deviation greater than 1.5 (but never greater 

than 1.8) are indicated by points above the upper horizontal red line. There is a clear pattern, in which a 

higher standard deviation tends to correspond with a lower mean. Figure 4.2.2 is identical to Figure 4.2.1, 

but with the addition of a regression line. 
23

Surveys with low means, especially below 10 or so, indicate that the prevalence of any anemia is very high. 

When a low mean is accompanied by a high standard deviation, the prevalence of severe anemia is usually 

even greater than it would be with a smaller standard deviation, because there will be more cases in the tails 

of the distribution. Conversely, a high mean corresponds with a low prevalence of any anemia. Because a 

high mean tends to be accompanied by a low standard deviation, the left tail tends to be particularly short 

and the prevalence of severe anemia tends to be particularly low. 

Figure 4.2.2 Standard deviation plotted against the mean, for 80 surveys with hemoglobin measurements of 

children age 6-59 months, adjusted for altitude and excluding values outside the range 4-18

g/dL. Horizontal lines enclose the expected range of standard deviations, from 1.1 to 1.5. The 

diagonal is the fitted line from a regression of the standard deviation on the mean (y=3.16-0.16x) 

It is possible that this strong negative association between the mean and the standard deviation of the 

distribution is a byproduct of measurement error, but it may also suggest a real association among the 

prevalences of mild, moderate, and severe anemia, regardless of the specific cutoffs assigned to those 

categories. The suggested implication is that if the prevalence of any anemia is high, the prevalence of 

severe anemia will be especially high; if the prevalence of any anemia is low, the prevalence of severe 

anemia will be especially low. 

4.3. Variation in shape 

Next, we consider the shape of the distribution of hemoglobin measurements, and more specifically, skew 

and kurtosis. The coefficient that measures skew is 0 for a symmetric distribution, negative if extreme 

values tend to be on the left, and positive if extreme values tend to be on the right. The mean skew is

presented in Tables 4.31 and 4.3.2, not excluding and excluding implausible values, respectively. The 

positive skew in Table 4.3.1 is due to a small number of out-of-range codes with high numeric values. After 

excluding the out-of-range values, Table 4.3.2 shows that the hemoglobin distributions tend to be skewed 

slightly to the left, with the greatest skew among women and the lowest skew among children.
24

Table 4.3.1 Summary statistics for skew of hemoglobin concentrations across surveys, 

not excluding implausible values

Mean of Skew

Surveys with skew 

<- 0.5 (%)

Surveys with skew 

>0.5 (%)

Population

Children 3.01 33.8% 23.8%

Women (age 15-49) 2.46 63.1% 27.7%

Men (age 15 and above) 1.95 48.1% 22.2%

Note: Adjusted for smoking and altitude when applicable

Table 4.3.2 Summary statistics for skew of hemoglobin concentrations across surveys, 

excluding implausible values

Mean of Skew

Surveys with skew

<-0.5 (%)

Surveys with skew 

>0.5 (%)

Population

Children -0.39 20.0% 0.0%

Women (age 15-49) -0.61 75.4% 0.0%

Men (age 15 and above) -0.51 44.4% 0.0%

Note: Implausible values defined as hemoglobin values outside of 4 g/dL to 18 g/dL for children and 

nonpregnant women and 4 g/dL to 20 g/dL for men after adjusting for altitude and smoking when applicable

Figure 4.3.1 is a histogram that shows the distribution of the skew across the hemoglobin distributions for 

children excluding implausible values. Although the distributions are much more often skewed to the left, 

rather than the right, the magnitude of the skew is relatively small. Seventeen surveys have a coefficient 

below -0.5. No surveys have a coefficient greater than 0.5. 

The greatest skew is found in the following 7 surveys: Burundi 2012 (-0.75), Armenia 2005 (-0.72), Kyrgyz 

Republic 2012 (-0.70), Albania 2008-09 (-0.66), Rwanda 2014-15 (-0.64), and Peru 2011 (-0.60). The 

distributions of hemoglobin for children in these surveys are shown in Figure 4.3.2. Visually, the 

distributions appear close to normal, although a slightly exaggerated tail extending to the left is visible. The 

far left of each distribution includes a few cases with hemoglobin levels below 7 (colored purple), the cutoff 

for extreme anemia. 
25

Figure 4.3.1 Histogram of the skew of the hemoglobin distribution for children in the 80 surveys that 

included hemoglobin measurements for children 

Figure 4.3.2 The distributions of hemoglobin for children in the surveys with skew <-0.5 
26

The skew for the hemoglobin distributions for women ranges from -0.99 to -0.12. No distributions are even 

mildly skewed to the right, and many more distributions are noticeably skewed to the left. A total of 50

surveys deviate from the normal standard by more than 0.5 units. These surveys represent 83% of the 

surveys of women, whereas only 20% of the surveys of children showed that amount of skew. The 

distribution of the skew is shown in Figure 4.3.3. 

Rather than list all 50 surveys with a skew below -0.5, we list and show the 7 with a value below -0.8: 

Ethiopia 2011 (-0.99), Azerbaijan 2006 (-0.95), Armenia 2005 (-0.95), Kyrgyz Republic 2012 (-0.95), Peru 

2011 (-0.89), Ethiopia 2016 (-0.88), and Uganda 2011 (-0.81).The distributions for these seven surveys 

with greatest (negative) skew are shown in Figure 4.3.4. The extended tails on the left are indeed more 

conspicuous, and include most cases of severe and moderate anemia. 

Figure 4.3.3 Histogram of the skew of the hemoglobin distribution for women in the 65 surveys that included 

hemoglobin measurements for women 
27

Figure 4.3.4 The distributions of hemoglobin for women in the surveys with skew <-0.7 

The second indicator of departure from the shape of a normal distribution is the kurtosis. The coefficient 

for kurtosis is 3 for a normal distribution. It is greater than 3 if the distribution tends to have longer tails 

than a normal distribution (with the same mean and standard deviation), and less than 3 if the distribution 

has shorter tails than a normal distribution. One marker is a deviation of one unit below or above the 

reference value of 3. 

The mean kurtosis is presented in Tables 4.3.3 and 4.3.4, including and excluding implausible values, 

respectively. As with the skew, the inclusion of out-of-range or implausible values leads to very high but 

misleading coefficients. When those values or excluded, the kurtosis is still almost always greater than 3, 

but is typically in the vicinity of 4—usually less than 4 for children but usually more than 4 for women and 

men. 

The distribution of kurtosis across the 80 surveys is shown in Figure 4.3.5. The distributions for children 

have a kurtosis in the range of 2.59 to 5.09. Several distributions are slightly more concentrated than the 

standard of 3, although there are no distributions more than one unit below 3, i.e. below 2. The following 5 

surveys have a kurtosis that is more than one unit above 3, i.e. more than 4.0: Albania 2008-09 (5.09),

Rwanda 2010 (4.94), Rwanda 2014-15 (4.26), Honduras 2005-06 (4.17), and Niger 2006 (4.07).

These 5 distributions are shown in Figure 4.3.6. The figures do not include the value of the kurtosis, but it 

is listed above. There is visual evidence of extreme observations in both directions. Three of these surveys 
28

with relatively high kurtosis also had relatively high skew to the left—that is, the tails are long on both 

directions but especially on the left: Albania 2008-09; Rwanda 2014-15; and Honduras 2005-06. Most of 

these distributions do not show unusually high levels of anemia.

Table 4.3.3. Summary statistics for kurtosis of hemoglobin concentrations across 

surveys, not excluding implausible values

Mean of kurtosis

Surveys with kurtosis 

<2 (%)

Surveys with 

kurtosis >4 (%)

Population

Children 112.33 0.0% 62.5%

Women (age 15-49) 103.56 0.0% 93.8%

Men (age 15 and above) 80.95 0.0% 88.9%

Note: Adjusted for smoking and altitude when applicable

Table 4.3.4. Summary statistics for kurtosis of hemoglobin concentrations across 

surveys, excluding implausible values

Mean of kurtosis

Surveys with kurtosis 

<2 (%)

Surveys with 

kurtosis >4 (%)

Population

Children 3.54 0.0% 6.3%

Women (age 15-49) 4.25 0.0% 61.5%

Men (age 15 and above) 4.16 0.0% 55.6%

Note: Implausible values defined as hemoglobin values outside of 4 g/dL to 18 g/dL for children and 

nonpregnant women and 4 g/dL to 20 g/dL for men after adjusting for altitude and smoking when applicable

Figure 4.3.5 Histogram of the kurtosis of the hemoglobin distribution for children in the 80 surveys that 

included hemoglobin measurements for children 
29

Figure 4.3.6 The distributions of hemoglobin for children in the surveys with kurtosis >4 

Next we apply the same standards to the distributions for women, which show some conspicuous 

differences from children. The distribution of kurtosis across the 65 surveys that included measurements of 

women is shown in Figure 4.3.7. The kurtosis of the hemoglobin distributions for women is shifted in the 

direction of even more cases in the tails than was observed in the distributions for children, with a range 

from 2.91 to 5.89. The lowest values match almost exactly with the standard of a normal distribution. A 

total of 40 surveys—62% of the surveys of women—have a kurtosis that exceeds 4, compared with 6% of 

the surveys of children. 

Rather than list all 40 surveys, we set a higher threshold and list the 9 surveys with kurtosis greater than 5: 

Armenia 2005 (5.89), Albania 2008-09 (5.72), Peru 2011 (5.59), Timor-Leste 2009-10 (5.31), Azerbaijan 

2006 (5.24), Ethiopia 2011 (5.23), Peru 2012 (5.22), Egypt 2014 (5.14), and Guatemala 2014-15 (5.08). 

The distributions for these surveys are shown in Figure 4.3.8. 

Surveys with high kurtosis tend to have high levels of skew. Four surveys—Armenia 2005, Albania 2008-

09, Peru 2011, and Ethiopia 2011—appeared on both the lists of the greatest skew and the greatest kurtosis. 
30

Figure 4.3.7 Histogram of the kurtosis of the hemoglobin distribution for women in the 65 surveys that 

included hemoglobin measurements for women 

4

Figure 4.3.8 The distributions of hemoglobin for women in the surveys with kurtosis >5 
31

Because of the way they are formally defined, skew and kurtosis are necessarily associated with each other 

and with the standard deviation Among the 80 distributions of hemoglobin for children, the correlation 

between abs(skew) and kurtosis is 0.38. Among the 65 distributions for women, the same correlation is 

0.70. One should therefore be cautious about over-interpreting the overlap between the surveys identified 

by large standard deviations, skew, and kurtosis. 

4.4. Variations in the mean, median, and standard deviation by covariates for children and 

women 

Table 4.4.1 and Table 4.4.2 present the mean values of the mean, median, and standard deviation stratified 

by covariates for children and women, respectively. In addition, information is provided in these tables on 

the percentage of surveys that are below 1.1 standard deviations and above 1.5 standard deviations. On 

average, the mean, median, and standard deviation of hemoglobin concentrations increase slightly as wealth 

increases from the bottom quintile to the top quintile. Stratifying children by age showed slight increases 

in the mean and median hemoglobin concentrations with increasing age. The lowest level of dispersion was 

for children age 6 to 11 months. For girls, compared with boys, the mean and median tend to be slightly 

higher and the dispersion is lower. Overall, the mean and median are higher in urban areas, compared to 

rural areas; dispersion is higher in rural areas. A similar pattern is seen for urban and rural residence and 

wealth quintiles in women. Hemoglobin concentrations slightly decrease and the dispersion increases on 

average as age increases in women. As respondent’s education increases, the mean and median hemoglobin 

concentrations increase and the dispersion decreases in women. 

Table 4.4.1. Average of the mean, median, standard deviation skew, and kurtosis of hemoglobin 

concentrations for children excluding implausible values, by covariates

Mean 

of Mean

Mean 

of Median

Mean 

of SD

Surveys with 

Hb SD<1.1 (%)

Surveys with

Hb SD>1.5 (%)

Residence

Urban 10.72 10.83 1.44 1.3% 32.5%

Rural 10.50 10.60 1.49 0.0% 50.0%

Wealth

Quintile 1 10.39 10.48 1.50 0.0% 53.8%

Quintile 2 10.47 10.59 1.48 0.0% 50.0%

Quintile 3 10.56 10.66 1.47 0.0% 40.0%

Quintile 4 10.68 10.77 1.44 1.3% 36.3%

Quintile 5 10.91 11.01 1.39 3.8% 27.5%

Age (months)

6-11 9.98 10.06 1.42 1.3% 28.8%

12-23 10.09 10.19 1.46 0.0% 38.8%

24-59 10.82 10.92 1.42 3.8% 36.3%

Sex

Male 10.52 10.63 1.50 0.0% 53.8%

Female 10.61 10.72 1.46 1.3% 40.0%

Note: Implausible values defined as hemoglobin values outside of 4 g/dL to 18 g/dL after adjusting for altitude when applicable
32

Table 4.4.2. Average of the mean, median, standard deviation skew, and kurtosis of hemoglobin 

concentrations for women excluding implausible values, by covariates

Mean

of Mean

Mean

of Median

Mean 

of SD

Surveys with 

Hb SD<1.1 (%)

Surveys with 

Hb SD>1.5 (%)

Residence

Urban 12.36 12.50 1.57 1.5% 67.7%

Rural 12.31 12.44 1.59 0.0% 72.3%

Wealth

Quintile 1 12.25 12.38 1.59 0.0% 73.8%

Quintile 2 12.29 12.42 1.59 0.0% 78.5%

Quintile 3 12.31 12.44 1.58 0.0% 72.3%

Quintile 4 12.35 12.47 1.57 1.5% 64.6%

Quintile 5 12.41 12.53 1.53 3.1% 60.0%

Age (year)

15-19 12.34 12.46 1.51 1.5% 53.8%

20-34 12.35 12.48 1.56 1.5% 64.6%

35-49 12.29 12.45 1.64 0.0% 83.1%

Education

No education 12.27 12.40 1.63 0.0% 78.1%

Primary 12.38 12.51 1.58 1.6% 70.3%

Secondary 12.41 12.52 1.54 0.0% 60.9%

Higher 12.46 12.59 1.51 6.3% 50.0%

Note: Implausible values defined as hemoglobin values outside of 4 g/dL to 18 g/dL after adjusting for altitude and smoking when 

applicable
33

5. Summary and Conclusions 

This report has had two goals: to assess the quality of DHS measurements of hemoglobin concentrations, 

and to make inferences about how the underlying or true distributions of such concentrations may 

systematically vary across different populations. There has inevitably been some confounding of those two 

goals, but this chapter will summarize our conclusions. The report uses all the hemoglobin data collected 

by DHS since 2000, except that only the two most recent surveys were used from countries that had more 

than two surveys in this time interval. The analysis includes data from children age 6-59 months (80 

surveys), nonpregnant women age 15-49 (65 surveys), and men age 15 and over (27 surveys; the upper end 

of the age range is usually 59 but varies somewhat). Measurements are generally comparable between DHS 

surveys and over time because standard collection and analysis procedures are used. 

Despite the rigorous standards of training and supervision in DHS surveys, the inclusion of biomarker 

specialists on most teams of interviewers, and the use of high quality equipment, there is room for 

measurement error. Our approaches to assessing the quality of hemoglobin data included checking for 

potential bias when subsampling was used, examining the relative frequency of missing values and 

biologically implausible values, checking the measurements for potential digit preference, and identifying 

potential over-dispersion in the distributions. Similar methods have been applied elsewhere to assess data 

quality for anthropometric measurements (Assaf, Kothari, and Pullum 2015) and ages and dates (Pullum 

2006). Overall, we find that in most surveys the hemoglobin data are of high quality. 

In the surveys with subsampling, the selection of participants is generally applied consistently across 

geographic units and household characteristics. There are some statistically significant relationships 

between the selection for hemoglobin measurement and the education of the head of household, wealth 

quintile, and urban/rural residence, but the differences in observed sampling fractions, across 

subpopulations, were at most 2 to 3 percentage points even when statistically significant. Re-weighting to 

compensate for this kind of variation would have a negligible effect. Based on this information, we conclude 

that the national estimates from the surveys are not biased toward identifiable subpopulations. 

A high occurrence of incomplete data results in a reduced effective sample size, wasted time and money, 

wider confidence intervals for estimates, and lower power for tests. In addition, values outside of the 

plausible range can result in an over or underestimation of anemia. Hemoglobin measurements were 

successfully obtained for the great majority of selected respondents. The codes to describe the reasons for 

missing observations are not uniformly applied across surveys. In particular, there are some surveys in 

which household members who were not available or who refused to be measured were apparently given a 

“not applicable” code. Such deviations from normal practice can result in users of DHS data potentially 

misinterpreting the data when conducting secondary analyses. Most instances of inconsistent codes were in 

older surveys. 

When DHS converts the hemoglobin measurements to anemia levels in the main survey reports, there is no 

flagging or removal of values that are highly improbable and are almost certainly data entry errors. There 

are no international guidelines for removing such cases, and because there are not many of them, they have 

little net effect on the estimates of anemia. However, values outside of a plausible range can have a 

substantial effect on the statistics to describe the distribution, particularly the standard deviation, skew, and 

kurtosis. In this analysis we identified and then removed the values that were outside a plausible range. 
34

Many summary statistics were presented twice, with and without the inclusion of those cases. The range 

selected is somewhat arbitrary, but has been used elsewhere (Sullivan et al. 2008). There is a need for a 

consensus about plausible ranges for children, women, and men. Until that consensus is reached, DHS could 

consider adopting the ranges defined in this report rather than assigning extremely low hemoglobin 

concentrations, such as “0.0 g/dL”, into the category of “severe anemia” or assigning extremely high 

hemoglobin concentrations, such as “88.0 g/dL”, into the “no anemia” category. 

A check for digit preference showed evidence of heaping at final digits 0 and 2 and a deficits for digits 6-

9, although these patterns are found in only a minority of surveys. The pattern and level of heaping has a 

negligible effect on the calculation of means and proportions, but in some surveys could result in a slight 

underestimation of anemia because of an apparent upward transfer from 6-9 into 0, and the lower boundaries 

for all of the anemia categories end in final digit 0. A greater concern is that digit preference is considered 

to be a symptom of carelessness and can suggest more serious measurement errors. In a few surveys, final 

digits 0 and 5 occur much less often than expected, almost certainly because of an over-reaction to special 

efforts to avoid those digits. Training of interviewers and supervisors that focuses on the symptoms of 

careless fieldwork, such as potential over-reporting of final digit 0, may not actually lead to better data. 

We examined the distributions of hemoglobin measurements in terms of the mean, median, standard 

deviation, skew, and kurtosis. There is currently no established guideline for the standard deviation, but it 

has been suggested, based on empirical experience, that an acceptable range is between 1.1 and 1.5 g/dL 

(Sullivan et al. 2008). We found almost no national-level standard deviations below 1.1. A majority of the 

standard deviations were higher than this range, mostly between 1.5 and 2.0. 

For skew and kurtosis, the shape parameters, the values for normal distribution (0 and 3, respectively) were 

taken as reference values. In the majority of surveys the data is skewed to the left, which we would generally 

assume to be the case in low- and middle-income countries, where anemia is common. The degree to which 

the skew and kurtosis of the distribution differ from those of a normal distribution, especially by large 

amounts, can indicate unevenness in the quality of the measurements, not just in the true distribution. 

In a heterogeneous population, especially at the national level, or if there are serious inequalities in health, 

it is quite likely that the true distribution of hemoglobin is not normal. Therefore, we also looked at 

distributions stratified by residence, education, sex, wealth, and age. It was found that the standard 

deviation, skew, and kurtosis were greater for women than for children. We would have hypothesized more 

measurement error for children. It is more difficult to obtain a blood sample for a child, partly due to the 

small size of a child’s finger. A possible explanation is that the prevalence of anemia is lower for adults 

than for children. We also found that the standard deviations tend to be smaller among the urban, wealthier, 

and more educated sub-populations but the standard deviations still remained high within sub-populations. 

An important component of most data quality assessments is consistency with an external standard. Such 

comparisons have been conducted for anthropometry (Corsi, Perkins, and Subramanian 2017). External 

consistency is not included in this report because DHS is the principal source of hemoglobin measurements 

in the countries represented here. Future studies may be able to compare DHS data with other sources, such 

as national micronutrient surveys, albeit not in the same countries and time periods. 

Many factors have the potential to influence the variability in hemoglobin concentrations other than 

improper and inadequate training of hemoglobin measurement and data management. Little is known about 
35

these factors although some studies have found a difference between capillary versus venous blood samples 

(Neufeld et al. 2002), use of different HemoCue® devices and/or models (Rappaport, Barr, Green, and 

Karakochuk 2017; Rappaport, Karakochuk, Whitfield, Kheang, and Green 2017), humidity (Nguyen 2002), 

inherent drop-to-drop variability (Bond and Richards-Kortum 2015; Conway, Hinchliffe, Earland, and 

Anderson 1998) and other biological variations (age, sex, anthropometry, clinical factors) (Karakochuk). 

The HEmoglobin MEasurement (HEME) Working Group is addressing some of these issues using 

secondary data, but more studies specifically designed to address these issues are needed. In addition, 

changes to the anemia cutoff values may have an even greater impact on the prevalence of anemia than 

measurement errors or inherent variations in hemoglobin concentrations. The current cutoffs recommended 

by WHO to define anemia are based on a 1968 report with limited data (World Health Organization 1968)

and urgently need to be reviewed. 

A better understanding of the factors that influence the accurate measurement and interpretation of 

hemoglobin concentrations would inform DHS data collection procedures and post-data collection 

adjustments to hemoglobin concentrations. Feasibility is central to the adoption of new approaches. For 

example, it has been proposed that pooling more than one drop of blood would provide greater withinsubject reliability (Bond and Richards-Kortum 2015; Conway et al. 1998), although this will not be feasible 

in DHS surveys where several blood samples are needed to assess additional biomarkers. It may be feasible 

to apply further adjustments, such as those for altitude and smoking, if new factors are found to consistently

alter hemoglobin concentrations. 

Historically, anemia has been considered an indicator of iron deficiency. However, hemoglobin is neither 

a sensitive nor a specific biomarker for iron deficiency (Petry et al. 2016; World Health Organization and 

Centers for Disease Control and Prevention 2007). As a result, there is an increasing recognition that 

collecting micronutrient biomarkers, especially iron, and biomarkers of infections will better inform 

programming. Nevertheless, anemia is one of the seven World Health Assembly target indicators being 

tracked globally and remains an important indicator of overall well-being, analogous to stunting. Low and 

middle-income countries’ primary source of anemia data comes from surveys conducted by The DHS 

Program. This report is an important step forward in better understanding the quality of data used to assess 

anemia and inform country and global policies. 

37

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41

Appendix 1 

Table A1.1 List of surveys for children, nonpregnant women, and men

Survey Children Women Men

Albania 2008-09 X X X

Angola 2011 X NA NA

Angola 2015-16 X NA NA

Armenia 2005 X X NA

Azerbaijan 2006 X X NA

Bangladesh 2011 X X NA

Benin 2006 X X X

Benin 2011-12 X X NA

Bolivia 2003 X X NA

Bolivia 2008 X X NA

Burkina Faso 2010 X X X

Burkina Faso 2014 X NA NA

Burundi 2010 X X X

Burundi 2012 X NA NA

Cambodia 2010 X X NA

Cambodia 2014 X X NA

Cameroon 2004 X X NA

Cameroon 2011 X X NA

Congo 2005 X X NA

Congo 2011-12 X X NA

Congo Democratic Republic 2007 X X X

Congo Democratic Republic 2013-14 X X X

Cote d'Ivoire 2011-12 X X X

Egypt 2014 X X NA

Ethiopia 2011 X X X

Ethiopia 2016 X X X

Gabon 2012 X X X

Gambia 2013 X X NA

Ghana 2014 X X NA

Ghana 2016 X NA NA

Guatemala 2014-15 X X NA

Guinea 2005 X X NA

Guinea 2012 X X X

Guyana 2009 X X X

Haiti 2005-06 X X X

Haiti 2012 X X X

Honduras 2005-06 X X NA

Honduras 2011-12 X X NA

India 2005-06 X X X

Jordan 2009 X X NA

Jordan 2012 X X NA

Kenya 2015 X NA NA

Kyrgyz Republic 2012 X X NA

Lesotho 2009 X X X

Lesotho 2014 X X X

Liberia 2011 X NA NA

Madagascar 2013 X NA NA

Madagascar 2016 X NA NA

Malawi 2014 X NA NA

Malawi 2015-16 X X NA

Mali 2012-13 X X NA

Mali 2015 X NA NA

Moldova 2005 X X NA

Mozambique 2011 X X NA

Myanmar 2015-16 X X NA

Namibia 2013 X X X

Nepal 2006 X X NA

Nepal 2011 X X NA

Niger 2006 X X X

Niger 2012 X X X

Nigeria 2015 X NA NA

Peru 2011 X X NA

Peru 2012 X X NA
42

Rwanda 2010 X X NA

Rwanda 2014-15 X X NA

Sao Tome and Principe 2008-09 X X X

Senegal 2014 X NA NA

Senegal 2015 X NA NA

Sierra Leone 2008 X X X

Sierra Leone 2013 X X X

Swaziland 2006-07 X X X

Tanzania 2010 X X NA

Tanzania 2015-16 X X NA

Timor-Leste 2009-10 X X NA

Togo 2013-14 X X X

Uganda 2011 X X NA

Uganda 2014-15 X NA NA

Yemen 2013 X X NA

Zimbabwe 2010-11 X X X

Zimbabwe 2015 X X X

Total 80 65 27

Note: X = Survey measured, NA = Not measured
43

Appendix 2 

Table A2.1 Summary of data completeness among children, by country

Country

Total 

Sample

Missing

(%)

Final 

Sample

Hb<4

g/dL

Hb>18

g/dL

Implausible 

(%)

Valid

(%)

Albania 2008-09 1,490 6.4% 1,394 0 1 0.1% 93%

Angola 2011 3,234 0.0% 3,234 1 0 0.0% 100%

Angola 2015-16 6,915 2.6% 6,734 44 4 0.7% 97%

Armenia 2005 1,267 18.8% 1,029 3 0 0.3% 81%

Azerbaijan 2006 1,963 8.1% 1,804 2 0 0.1% 92%

Bangladesh 2011 2,507 10.6% 2,242 1 0 0.0% 89%

Benin 2006 5,021 14.7% 4,282 9 2 0.3% 85%

Benin 2011-12 4,329 13.6% 3,739 15 0 0.4% 86%

Bolivia 2003 2,978 0.0% 2,978 2 0 0.1% 100%

Bolivia 2008 2,895 12.4% 2,535 6 1 0.3% 87%

Burkina Faso 2010 6,468 3.6% 6,234 13 0 0.2% 96%

Burkina Faso 2014 6,208 2.0% 6,083 6 0 0.1% 98%

Burundi 2010 3,473 6.6% 3,244 0 0 0.0% 93%

Burundi 2012 3,795 2.1% 3,715 8 1 0.2% 98%

Cambodia 2010 3,937 5.2% 3,734 1 0 0.0% 95%

Cambodia 2014 4,465 0.0% 4,465 0 0 0.0% 100%

Cameroon 2004 3,418 0.5% 3,401 5 1 0.2% 99%

Cameroon 2011 5,353 1.6% 5,268 5 3 0.2% 98%

Congo 2005 4,171 53.9%1 1,923 3 1 0.2% 46%

Congo 2011-12 4,436 4.9% 4,217 3 0 0.1% 95%

Congo DR 2007 3,952 10.7% 3,531 11 6 0.5% 89%

Congo DR 2013-14 8,395 2.6% 8,179 5 1 0.1% 97%

Cote d'Ivoire 2011-12 3,661 8.4% 3,353 3 1 0.1% 91%

Egypt 2014 4,568 1.8% 4,487 0 0 0.0% 98%

Ethiopia 2011 10,357 11.7% 9,142 26 3 0.3% 88%

Ethiopia 2016 9,010 6.2% 8,451 14 2 0.2% 94%

Gabon 2012 3,873 3.4% 3,741 2 3 0.1% 96%

Gambia 2013 3,889 15.5% 3,288 7 0 0.2% 84%

Ghana 2014 2,802 4.3% 2,681 1 0 0.0% 96%

Ghana 2016 3,063 1.5% 3,017 1 0 0.0% 98%

Guatemala 2014-15 11,265 4.1% 10,803 1 3 0.0% 96%

Guinea 2005 2,754 8.1% 2,530 3 5 0.3% 92%

Guinea 2012 3,256 2.2% 3,183 6 0 0.2% 98%

Guyana 2009 2,171 26.3% 1,601 2 0 0.1% 74%

Haiti 2005-06 2,701 1.6% 2,658 0 2 0.1% 98%

Haiti 2012 4,315 2.9% 4,190 1 0 0.0% 97%

Honduras 2005-06 9,119 0.1% 9,109 32 1 0.4% 100%

Honduras 2011-12 10,217 9.2% 9,279 4 27 0.3% 91%

India 2005-06 37,825 5.2% 35,870 14 6 0.1% 95%

Jordan 2009 4,274 12.4% 3,743 0 0 0.0% 88%

Jordan 2012 6,081 10.1% 5,468 4 1 0.1% 90%

Kenya 2015 3,635 6.6% 3,394 1 1 0.1% 93%

Kyrgyz Republic 2012 4,121 3.0% 3,998 3 3 0.2% 97%

Lesotho 2009 1,991 0.0% 1,991 2 1 0.2% 100%

Lesotho 2014 1,706 1.0% 1,689 2 1 0.2% 99%

Liberia 2011 3,267 3.2% 3,163 0 0 0.0% 97%

Madagascar 2013 5,558 3.3% 5,374 10 0 0.2% 97%

Madagascar 2016 7,199 2.4% 7,024 2 1 0.0% 98%

Malawi 2014 1,966 2.0% 1,927 1 0 0.1% 98%

Malawi 2015-16 5,363 2.4% 5,233 5 0 0.1% 97%

Mali 2012-13 5,044 6.9% 4,696 10 2 0.3% 93%

Mali 2015 7,297 1.0% 7,225 19 4 0.3% 99%

Moldova 2005 1,521 16.1% 1,276 0 12 0.9% 83%

Mozambique 2011 4,904 1.8% 4,815 4 1 0.1% 98%

Myanmar 2015-16 4,519 13.8% 3,896 1 0 0.0% 86%

Namibia 2013 2,388 4.3% 2,286 2 0 0.1% 96%

Nepal 2006 4,742 0.0% 4,742 0 1 0.0% 100%

Nepal 2011 2,227 5.0% 2,116 1 0 0.0% 95%

Niger 2006 4,048 12.4% 3,548 7 1 0.2% 87%

Niger 2012 5,602 14.1% 4,812 1 1 0.0% 86%

Nigeria 2015 6,274 4.9% 5,968 2 0 0.0% 95%

Peru 2011 8,488 4.5% 8,105 2 1 0.0% 95%
44

Peru 2012 9,029 4.5% 8,626 0 0 0.0% 96%

Rwanda 2010 4,068 1.1% 4,025 0 4 0.1% 99%

Rwanda 2014-15 3,501 0.4% 3,488 1 1 0.1% 100%

ST and Principe 2008-09 1,885 6.1% 1,770 5 0 0.3% 94%

Senegal 2014 6,274 3.7% 6,043 5 5 0.2% 96%

Senegal 2015 6,283 3.0% 6,097 2 0 0.0% 97%

Sierra Leone 2008 2,899 13.3% 2,512 6 2 0.3% 86%

Sierra Leone 2013 5,706 7.5% 5,280 5 2 0.1% 92%

Swaziland 2006-07 2,741 10.0% 2,466 0 0 0.0% 90%

Tanzania 2010 7,160 7.8% 6,603 7 0 0.1% 92%

Tanzania 2015-16 9,386 4.8% 8,931 7 0 0.1% 95%

Timor-Leste 2009-10 3,040 15.4% 2,572 17 0 0.7% 84%

Togo 2013-14 3,283 3.5% 3,169 1 0 0.0% 96%

Uganda 2011 2,337 9.2% 2,123 2 0 0.1% 91%

Uganda 2014-15 4,590 4.0% 4,407 2 0 0.0% 96%

Yemen 2013 4,669 18.0% 3,827 0 5 0.1% 82%

Zimbabwe 2010-11 5,292 19.7% 4,251 4 3 0.2% 80%

Zimbabwe 2015 5,857 12.3% 5,138 0 0 0.0% 88%

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; Total sample is defined de jure 

participant selected for hemoglobin measurement and is age 6-59 months; percentage of missing 

values is based on total sample; Final sample is defined as total sample minus hemoglobin 

measurement available; percentage of implausible values is based on final sample. Percentage of 

valid values is 100 minus the percentage of missing and implausible values.

1

In the data file for the Congo 2005 survey, for children only, the missing value code “999” was 

assigned to children who were not measured because their household was not in the subsample 

selected for hemoglobin measurement. Such children should have received a “blank” code for “not 

applicable”. This error was made during the construction of the public use data files and we could 

not correct it. If that error is taken into account, the correct level of missing was 11.2%, rather than 

53.9%.
45

Table A2.2 Summary of data completeness among nonpregnant women, by country

Total 

Sample

Missing 

(%)

Final 

Sample

Hb<4 g/dL Hb>18 

g/dL

Implausible 

(%)

Valid (%)

Albania 2008-09 7,422 0.4% 7,396 0 1 0.0% 100%

Armenia 2005 6,008 0.4% 5,986 6 3 0.2% 99%

Azerbaijan 2006 7,813 0.7% 7,760 4 2 0.1% 99%

Bangladesh 2011 5,295 2.7% 5,150 0 0 0.0% 97%

Benin 2006 4,523 0.8% 4,486 5 2 0.2% 99%

Benin 2011-12 5,447 14.7% 4,644 18 0 0.4% 85%

Bolivia 2003 5,608 0.0% 5,608 2 3 0.1% 100%

Bolivia 2008 5,486 0.0% 5,486 2 3 0.1% 100%

Burkina Faso 2010 7,916 4.1% 7,595 1 2 0.0% 96%

Burundi 2010 4,533 10.1% 4,074 0 2 0.0% 90%

Cambodia 2010 9,565 7.4% 8,860 8 5 0.1% 92%

Cambodia 2014 11,025 1.3% 10,880 0 0 0.0% 99%

Cameroon 2004 4,516 0.1% 4,513 4 0 0.1% 100%

Cameroon 2011 7,146 2.4% 6,974 2 1 0.0% 98%

Congo 2005 2,850 0.2% 2,843 3 1 0.1% 100%

Congo 2011-12 5,037 4.6% 4,806 0 1 0.0% 95%

Congo DR 2007 3,971 0.9% 3,934 13 17 0.8% 98%

Congo DR 2013-14 8,552 4.3% 8,182 3 6 0.1% 96%

Cote d'Ivoire 2011-12 5,130 14.7% 4,374 4 3 0.2% 85%

Egypt 2014 6,318 0.9% 6,263 0 0 0.0% 99%

Ethiopia 2011 16,237 12.1% 14,268 9 9 0.1% 88%

Ethiopia 2016 14,552 7.6% 13,440 3 5 0.1% 92%

Gabon 2012 5,012 3.0% 4,864 3 1 0.1% 97%

Gambia 2013 4,617 8.1% 4,243 4 2 0.1% 92%

Ghana 2014 4,668 7.1% 4,337 2 0 0.0% 93%

Guatemala 2014-15 24,876 3.1% 24,106 0 8 0.0% 97%

Guinea 2005 3,364 0.1% 3,361 5 1 0.2% 100%

Guinea 2012 4,300 3.4% 4,155 1 0 0.0% 97%

Guyana 2009 4,284 0.7% 4,254 1 3 0.1% 99%

Haiti 2005-06 4,781 0.0% 4,779 2 3 0.1% 100%

Haiti 2012 9,039 2.7% 8,792 9 1 0.1% 97%

Honduras 2005-06 17,458 0.0% 17,450 35 12 0.3% 100%

Honduras 2011-12 21,661 4.7% 20,648 0 79 0.4% 95%

India 2005-06 108,213 3.5% 104,474 68 20 0.1% 96%

Jordan 2009 7,378 7.7% 6,813 0 1 0.0% 92%

Jordan 2012 12,428 15.2% 10,538 0 5 0.0% 85%

Kyrgyz Republic 2012 7,481 3.2% 7,245 3 4 0.1% 97%

Lesotho 2009 3,605 0.0% 3,605 1 2 0.1% 100%

Lesotho 2014 3,151 1.3% 3,109 3 1 0.1% 99%

Malawi 2015-16 7,719 3.9% 7,421 5 3 0.1% 96%

Mali 2012-13 5,112 9.7% 4,618 4 0 0.1% 90%

Moldova 2005 6,778 0.4% 6,750 4 22 0.4% 99%

Mozambique 2011 12,446 2.4% 12,144 8 2 0.1% 97%

Myanmar 2015-16 12,164 0.7% 12,081 2 2 0.0% 99%

Namibia 2013 5,464 9.5% 4,944 1 4 0.1% 90%

Nepal 2006 9,835 0.0% 9,835 1 0 0.0% 100%

Nepal 2011 6,014 4.8% 5,724 1 1 0.0% 95%

Niger 2006 3,540 1.4% 3,489 3 3 0.2% 98%

Niger 2012 5,141 13.8% 4,432 2 1 0.1% 86%

Peru 2011 21,599 1.5% 21,279 12 2 0.1% 98%

Peru 2012 23,143 1.5% 22,807 2 6 0.0% 99%

Rwanda 2010 6,500 1.2% 6,425 2 1 0.0% 99%

Rwanda 2014-15 6,251 0.9% 6,197 0 3 0.0% 99%

ST and Principe 2008-09 2,356 1.1% 2,330 0 0 0.0% 99%

Sierra Leone 2008 3,102 2.8% 3,014 5 2 0.2% 97%

Sierra Leone 2013 7,688 5.1% 7,299 2 1 0.0% 95%

Swaziland 2006-07 4,761 10.2% 4,275 0 5 0.1% 90%

Tanzania 2010 9,806 10.2% 8,803 5 2 0.1% 90%

Tanzania 2015-16 12,514 7.1% 11,623 11 2 0.1% 93%

Timor-Leste 2009-10 4,216 9.3% 3,824 3 2 0.1% 91%

Togo 2013-14 4,433 2.2% 4,334 0 1 0.0% 98%

Uganda 2011 2,705 12.0% 2,381 1 0 0.0% 88%

Yemen 2013 4,315 0.8% 4,280 3 6 0.2% 99%

Zimbabwe 2010-11 9,005 17.5% 7,433 8 5 0.2% 82%

Zimbabwe 2015 9,228 6.7% 8,608 7 2 0.1% 93%

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; Total sample is defined de jure participant selected for 

hemoglobin measurement and is nonpregnant; percentage of missing values is based on total sample; Final sample is 

defined as total sample minus hemoglobin measurement available; percentage of implausible values is based on final 

sample. Percentage of valid values is 100 minus the percentage of missing and implausible values.
46

Table A2.3 Summary of data completeness among men, by country

Total Sample Missing 

(%)

Final Sample Hb<4

g/dL

Hb>20 

g/dL

Implausible

(%)

Valid

(%)

Albania 2008-09 3,193 6.4% 2,989 0 0 0.0% 94%

Benin 2006 6,043 26.6% 4,436 1 5 0.1% 73%

Burkina Faso 2010 7,677 6.5% 7,181 1 0 0.0% 94%

Burundi 2010 4,800 14.1% 4,125 1 1 0.0% 86%

Congo DR 2007 5,213 14.6% 4,453 4 20 0.5% 85%

Congo DR 2013-14 9,249 6.6% 8,643 2 8 0.1% 93%

Cote d'Ivoire 2011-12 5,607 20.0% 4,485 0 1 0.0% 80%

Ethiopia 2011 16,746 19.2% 13,525 8 17 0.2% 81%

Ethiopia 2016 15,517 24.1% 11,770 5 12 0.1% 76%

Gabon 2012 5,884 6.7% 5,488 0 2 0.0% 93%

Guinea 2012 3,850 4.4% 3,679 2 4 0.2% 95%

Guyana 2009 4,976 35.3% 3,217 0 2 0.1% 65%

Haiti 2005-06 5,100 5.0% 4,846 1 0 0.0% 95%

Haiti 2012 9,838 5.4% 9,305 2 1 0.0% 95%

India 2005-06 69,405 6.4% 64,933 20 29 0.1% 93%

Lesotho 2009 3,095 0.6% 3,077 0 5 0.2% 99%

Lesotho 2014 2,867 2.4% 2,799 3 1 0.1% 97%

Namibia 2013 4,942 16.3% 4,138 1 5 0.1% 84%

Niger 2006 4,068 23.1% 3,128 4 3 0.2% 77%

Niger 2012 4,975 28.3% 3,569 0 3 0.1% 72%

ST and Principe 2008-09 3,112 29.0% 2,210 0 0 0.0% 71%

Sierra Leone 2008 3,763 19.8% 3,019 3 3 0.2% 80%

Sierra Leone 2013 7,653 10.5% 6,853 0 1 0.0% 90%

Swaziland 2006-07 4,642 21.8% 3,628 0 3 0.1% 78%

Togo 2013-14 4,736 7.7% 4,373 1 4 0.1% 92%

Zimbabwe 2010-11 8,862 28.0% 6,379 1 11 0.2% 72%

Zimbabwe 2015 9,297 17.5% 7,669 2 1 0.0% 82%

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; Total sample is defined de jure participant selected for hemoglobin 

measurement; percentage of missing values is based on total sample; final sample is defined as total sample minus hemoglobin 

measurement available; percentage of implausible values is based on final sample. Percentage of valid values is 100 minus the 

percentage of missing and implausible values.
47

Appendix 3. Digit preferences 

Figure A3.1 Distribution of the prevalence (percentage) of final digit 0 in the hemoglobin measurements for 

children age 6-59 months (80 surveys)1, nonpregnant women age 15-49 (65 surveys)2, and men 

(age range varies; 27 surveys)2. Expected value is 10%.

1Hemoglobin concentrations not adjusted for altitude 

2Hemoglobin concentrations not adjusted for altitude and smoking 
48

Table A3.1 Surveys and subpopulations for which the frequency of final digit 0, for the unadjusted 

hemoglobin concentration, has the greatest deviation from 10% 

Frequency of digit 0 is less than 8%:

Albania 2008-09 Men 3.3

Albania 2008-09 Women 5.1

Albania 2008-09 Children 5.2

Frequency of digit 0 is more than 12%:

Niger 2012 Men 12.1

Cote d'Ivoire 2011-12 Women 12.2

Gambia 2013 Children 12.2

Yemen 2013 Women 12.4

Yemen 2013 Children 12.5

Sierra Leone 2013 Women 12.6

Egypt 2014 Women 12.7

Mozambique 2011 Women 12.8

Egypt 2014 Children 13.0

Nigeria 2015 Children 13.0

Benin 2011-12 Children 13.5

Niger 2012 Children 13.5

Timor-Leste 2009-10 Children 13.7

Timor-Leste 2009-10 Women 13.9

Benin 2011-12 Women 14.2

Sierra Leone 2013 Children 14.5

Angola 2011 Children 17.5

Figure A3.2 Distribution of the prevalence (percentage) of final digit 5 in the hemoglobin measurements for 

children age 6-59 months (80 surveys)1, nonpregnant women age 15-49 (65 surveys)2, and men 

(age range varies; 27 surveys)2. Expected value is 10%.

1Hemoglobin concentrations not adjusted for altitude >1000 meters 

2Hemoglobin concentrations not adjusted for altitude >1000 meters and smoking >1 cigarette 
49

Table A3.2 Surveys and subpopulations for which the frequency of final digit 

5, if the unadjusted hemoglobin concentration, has the greatest 

deviation from 10%

Frequency of digit 5 is less than 8%:

Armenia 2005 Children 7.5

Frequency of digit 5 is more than 12%:

Benin 2011-12 Women 13.9

Figure A3.3 Distribution of the prevalence (percentage) of final digit 2 in the hemoglobin measurements for 

children age 6-59 months (80 surveys)1, nonpregnant women age 15-49 (65 surveys)2, and men 

(age range varies; 27 surveys)2. Expected value is 10%.

1Hemoglobin concentrations not adjusted for altitude 

2Hemoglobin concentrations not adjusted for altitude and smoking 
50

Table A3.3 Surveys and sub-populations for which the frequency of final digit 2, for the unadjusted 

hemoglobin concentration, has the greatest deviation from 10%

Frequency of digit 2 is less than 8%

No surveys 

Frequency of digit 2 is more than 12%: 

Armenia 2005 Children 12.1

Sierra Leone 2008 Women 12.1

Guinea 2012 Children 12.1

Bangladesh 2011 Children 12.1

Niger 2012 Men 12.2

India 2005-06 Children 12.2

Lesotho 2014 Children 12.3

Benin 2006 Children 12.4

Guyana 2009 Children 12.4

Mali 2012-13 Children 12.5

Niger 2012 Women 12.6

Sierra Leone 2008 Children 12.6

Albania 2008-09 Women 12.7

Benin 2006 Men 12.8

Sierra Leone 2013 Children 13

Timor-Leste 2009-10 Women 13

Sierra Leone 2008 Men 13.8

Albania 2008-09 Children 13.9

Figure A3.4 Distribution of the prevalence (percentage) of final digit 6, 7, 8, 9 in the hemoglobin 

measurements for children age 6-59 months (80 surveys)1, nonpregnant women age 15-49 (65 

surveys)2, and men (age range varies; 27 surveys)2. Expected value is 40%.

1Hemoglobin concentrations not adjusted for altitude 

2Hemoglobin concentrations not adjusted for altitude and smoking 
51

Table A3.4 Surveys and subpopulations for which the frequencies of final digits 6, 7, 8, and 9, for the 

unadjusted hemoglobin concentration, have t

Frequency of digits 6, 7, 8, and 9 is less than 38%: 

Timor-Leste 2009-10 Children 31.4

Timor-Leste 2009-10 Women 32.4

Sierra Leone 2013 Children 33.7

Sierra Leone 2008 Men 35.1

Sierra Leone 2008 Children 35.2

Moldova 2005 Children 35.3

Benin 2006 Men 35.7

Yemen 2013 Women 35.8

Benin 2011-12 Women 36

Niger 2012 Men 36

Sierra Leone 2008 Women 36.3

Egypt 2014 Women 36.4

Benin 2006 Children 36.5

Lesotho 2009 Children 36.6

Lesotho 2014 Children 36.6

Benin 2006 Women 37.1

Haiti 2012 Men 37.1

Guinea 2012 Children 37.2

Angola 2015-16 Children 37.2

Niger 2006 Men 37.2

Sierra Leone 2013 Women 37.3

Nigeria 2015 Children 37.4

Niger 2006 Children 37.4

Angola 2011 Children 37.4

Guyana 2009 Children 37.5

Haiti 2005-06 Children 37.5

Swaziland 2006-07 Children 37.6

Malawi 2014 Children 37.6

Yemen 2013 Children 37.6

Benin 2011-12 Children 37.7

Mozambique 2011 Women 37.8

Mali 2012-13 Children 37.8

Madagascar 2016 Children 37.8

Niger 2012 Women 37.9

Mozambique 2011 Children 37.9

Niger 2012 Children 37.9

Guinea 2012 Men 38

Frequency of digits 6, 7, 8, and 9 is more than 42%: 

Armenia 2005 Children 42.1

53

Appendix 4 

Table A4.1 The mean, median, standard deviation, skew, and kurtosis of hemoglobin concentrations for 

children after removing values outside the range 4-18 g/dL

Mean Median SD Min Max Skew Kurtosis

Albania 2008-09 11.81 12.00 1.15 6.30 17.20 -0.66 5.09

Angola 2011 10.74 10.80 1.42 4.00 15.50 -0.48 3.98

Angola 2015-16 10.40 10.50 1.42 4.10 15.70 -0.53 3.91

Armenia 2005 11.53 11.80 1.57 5.70 16.00 -0.72 3.78

Azerbaijan 2006 11.27 11.40 1.27 5.80 16.20 -0.42 3.92

Bangladesh 2011 10.80 10.90 1.23 4.70 14.70 -0.48 3.84

Benin 2006 9.64 9.80 1.69 4.00 17.60 -0.35 3.28

Benin 2011-12 10.50 10.60 1.56 4.10 17.70 -0.50 3.67

Bolivia 2003 10.85 11.00 1.56 4.30 16.20 -0.51 3.82

Bolivia 2008 10.51 10.60 1.60 4.40 16.60 -0.34 3.39

Burkina Faso 2010 9.04 9.10 1.66 4.00 15.90 -0.11 2.95

Burkina Faso 2014 9.02 9.10 1.74 4.10 15.20 -0.05 2.67

Burundi 2010 11.03 11.20 1.36 4.70 15.60 -0.58 3.90

Burundi 2012 10.79 11.00 1.56 4.40 15.10 -0.75 3.83

Cambodia 2010 10.74 10.80 1.33 4.70 15.10 -0.42 3.47

Cambodia 2014 10.72 10.80 1.28 4.50 15.20 -0.41 3.51

Cameroon 2004 10.07 10.20 1.73 4.00 15.40 -0.33 3.14

Cameroon 2011 10.43 10.50 1.52 4.60 15.10 -0.41 3.38

Congo 2005 10.34 10.40 1.48 4.40 15.50 -0.32 3.45

Congo 2011-12 10.38 10.50 1.33 4.20 14.90 -0.47 3.76

Congo DR 2007 10.09 10.20 1.68 4.10 17.30 -0.23 3.39

Congo DR 2013-14 10.29 10.40 1.70 4.00 17.60 -0.41 3.25

Cote d'Ivoire 2011-12 9.87 10.00 1.55 4.20 16.80 -0.20 3.09

Egypt 2014 11.45 11.50 1.17 5.40 15.90 -0.31 3.95

Ethiopia 2011 10.74 11.00 1.77 4.00 16.00 -0.64 3.49

Ethiopia 2016 10.38 10.50 1.73 4.10 18.00 -0.49 3.45

Gabon 2012 10.40 10.50 1.40 4.10 15.70 -0.52 3.55

Gambia 2013 9.81 10.00 1.57 4.00 14.70 -0.40 3.16

Ghana 2014 10.14 10.30 1.55 4.30 14.10 -0.37 3.01

Ghana 2016 10.24 10.40 1.49 4.00 14.80 -0.58 3.52

Guatemala 2014-15 11.42 11.50 1.31 4.20 15.80 -0.39 3.68

Guinea 2005 9.78 9.90 1.64 4.00 14.90 -0.42 3.25

Guinea 2012 9.73 9.90 1.72 4.00 14.50 -0.36 2.91

Guyana 2009 11.21 11.30 1.35 5.20 16.20 -0.43 3.92

Haiti 2005-06 10.47 10.60 1.56 4.00 16.80 -0.29 3.39

Haiti 2012 10.41 10.50 1.32 4.50 15.10 -0.30 3.24

Honduras 2005-06 11.29 11.40 1.30 4.00 16.80 -0.52 4.17

Honduras 2011-12 11.50 11.60 1.24 4.10 16.30 -0.41 3.84

India 2005-06 10.29 10.40 1.56 4.00 17.60 -0.41 3.39

Jordan 2009 11.41 11.50 1.37 6.00 15.60 -0.33 3.11

Jordan 2012 11.52 11.60 1.38 5.30 18.00 -0.36 3.45

Kenya 2015 11.24 11.30 1.51 4.90 16.50 -0.50 3.65

Kyrgyz Republic 2012 11.02 11.20 1.50 4.60 17.00 -0.70 3.96

Lesotho 2009 10.93 11.00 1.48 4.90 16.00 -0.52 3.73

Lesotho 2014 10.91 10.90 1.61 4.00 17.40 -0.05 3.72

Liberia 2011 9.95 10.00 1.49 4.60 14.90 -0.19 3.18

Madagascar 2013 10.91 11.00 1.47 4.10 15.70 -0.36 3.50

Madagascar 2016 11.08 11.10 1.41 4.90 16.40 -0.28 3.49

Malawi 2014 10.73 10.90 1.50 5.00 15.20 -0.54 3.50

Malawi 2015-16 10.40 10.50 1.47 4.20 17.10 -0.34 3.44

Mali 2012-13 9.43 9.50 1.74 4.00 15.80 -0.23 3.02

Mali 2015 9.28 9.40 1.64 4.00 16.90 -0.28 3.04

Moldova 2005 11.41 11.50 1.16 7.10 14.80 -0.31 3.54

Mozambique 2011 10.25 10.30 1.59 4.00 16.80 -0.34 3.38

Myanmar 2015-16 10.72 10.80 1.36 5.10 15.10 -0.42 3.48

Namibia 2013 10.91 11.00 1.43 5.30 16.50 -0.29 3.50

Nepal 2006 10.95 11.00 1.37 4.80 15.50 -0.42 3.50

Nepal 2011 11.03 11.10 1.35 5.00 15.40 -0.27 3.51

Niger 2006 9.47 9.50 1.73 4.00 17.40 0.17 4.07

Niger 2012 9.92 10.00 1.50 4.30 17.50 -0.22 3.54

Nigeria 2015 10.12 10.30 1.61 4.00 15.70 -0.41 3.25

Peru 2011 11.38 11.50 1.30 4.50 15.90 -0.60 3.99

Peru 2012 11.32 11.40 1.26 4.50 15.80 -0.56 3.86
54

Rwanda 2010 11.31 11.40 1.35 4.70 18.00 -0.23 4.94

Rwanda 2014-15 11.28 11.40 1.39 4.70 16.80 -0.64 4.26

ST and Principe 2008-09 10.62 10.70 1.27 4.90 15.30 -0.36 3.85

Senegal 2014 10.47 10.60 1.58 4.00 18.00 -0.41 3.60

Senegal 2015 10.08 10.20 1.51 4.00 15.10 -0.52 3.39

Sierra Leone 2008 9.94 10.10 1.52 4.00 15.30 -0.37 3.74

Sierra Leone 2013 9.59 9.80 1.61 4.00 17.00 -0.32 3.06

Swaziland 2006-07 11.19 11.30 1.53 4.10 15.80 -0.45 3.70

Tanzania 2010 10.53 10.60 1.43 4.10 15.40 -0.40 3.59

Tanzania 2015-16 10.59 10.70 1.47 4.00 17.40 -0.41 3.63

Timor-Leste 2009-10 11.14 11.20 1.23 4.40 16.50 -0.32 3.51

Togo 2013-14 10.10 10.10 1.51 4.70 14.80 -0.22 2.98

Uganda 2011 10.88 11.00 1.62 5.00 17.60 -0.34 3.24

Uganda 2014-15 10.71 10.80 1.60 4.30 15.80 -0.44 3.40

Yemen 2013 8.72 8.70 1.79 4.00 15.60 0.20 2.72

Zimbabwe 2010-11 10.63 10.70 1.43 4.50 14.80 -0.37 3.26

Zimbabwe 2015 11.29 11.40 1.33 5.60 16.70 -0.34 3.83

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude prior to 

removing implausible values 
55

Table A4.2 The mean, standard deviation, median, skew, and kurtosis of hemoglobin concentrations for 

children, including implausible values

Mean Median SD Min Max Skew Kurtosis

Albania 2008-09 11.82 12.00 1.17 6.30 21.40 -0.24 7.82

Angola 2011 10.74 10.80 1.43 3.80 15.50 -0.50 4.09

Angola 2015-16 10.35 10.50 1.60 1.70 24.80 -0.96 9.99

Armenia 2005 11.51 11.70 1.63 1.80 16.00 -1.04 5.60

Azerbaijan 2006 11.26 11.40 1.30 2.40 16.20 -0.71 5.86

Bangladesh 2011 10.79 10.90 1.24 3.10 14.70 -0.57 4.36

Benin 2006 9.67 9.80 2.31 3.30 88.00 13.99 451.49

Benin 2011-12 10.46 10.60 1.67 0.00 17.70 -1.05 6.77

Bolivia 2003 10.84 11.00 1.57 2.70 16.20 -0.57 4.11

Bolivia 2008 10.49 10.60 1.65 0.00 18.80 -0.52 4.75

Burkina Faso 2010 9.02 9.10 1.68 2.70 15.90 -0.16 3.07

Burkina Faso 2014 9.02 9.00 1.75 3.10 15.20 -0.07 2.72

Burundi 2010 11.03 11.20 1.36 4.70 15.60 -0.58 3.90

Burundi 2012 10.77 11.00 1.60 2.80 18.50 -0.84 4.50

Cambodia 2010 10.73 10.80 1.34 3.70 15.10 -0.45 3.63

Cambodia 2014 10.72 10.80 1.28 4.50 15.20 -0.41 3.51

Cameroon 2004 10.08 10.20 2.00 2.30 67.20 6.53 195.92

Cameroon 2011 10.46 10.50 2.28 0.00 97.20 20.18 768.98

Congo 2005 10.37 10.40 2.29 0.50 85.50 18.19 604.40

Congo 2011-12 10.37 10.50 1.35 0.20 14.90 -0.60 4.64

Congo DR 2007 10.19 10.20 3.78 0.00 99.90 18.39 435.29

Congo DR 2013-14 10.29 10.40 1.71 2.40 19.80 -0.42 3.45

Cote d'Ivoire 2011-12 9.88 10.00 1.95 0.70 77.80 12.38 437.73

Egypt 2014 11.45 11.50 1.17 5.40 15.90 -0.31 3.95

Ethiopia 2011 10.74 10.90 2.16 0.50 94.20 10.14 392.28

Ethiopia 2016 10.37 10.50 1.76 0.90 19.50 -0.55 3.88

Gabon 2012 10.41 10.50 1.43 3.40 19.80 -0.38 4.69

Gambia 2013 9.79 9.90 1.60 1.00 14.70 -0.53 3.71

Ghana 2014 10.13 10.30 1.55 3.90 14.10 -0.38 3.07

Ghana 2016 10.24 10.40 1.50 2.00 14.80 -0.62 3.75

Guatemala 2014-15 11.42 11.50 1.32 3.80 22.80 -0.30 4.50

Guinea 2005 9.91 9.90 3.67 1.10 95.00 16.32 344.13

Guinea 2012 9.72 9.90 1.75 1.90 14.50 -0.43 3.14

Guyana 2009 11.20 11.30 1.39 0.80 16.20 -0.86 7.12

Haiti 2005-06 10.48 10.60 1.58 4.00 19.90 -0.16 4.03

Haiti 2012 10.41 10.50 1.32 3.60 15.10 -0.33 3.37

Honduras 2005-06 11.25 11.40 1.41 0.00 19.20 -1.39 10.32

Honduras 2011-12 11.75 11.60 4.92 0.70 99.90 16.67 298.85

India 2005-06 10.29 10.40 1.57 2.00 19.90 -0.40 3.65

Jordan 2009 11.41 11.50 1.37 6.00 15.60 -0.33 3.11

Jordan 2012 11.51 11.60 1.40 2.00 18.30 -0.51 4.64

Kenya 2015 11.24 11.30 1.52 3.00 18.50 -0.50 3.95

Kyrgyz Republic 2012 11.02 11.20 1.54 2.10 23.10 -0.58 5.56

Lesotho 2009 10.93 11.00 1.50 2.60 18.30 -0.56 4.46

Lesotho 2014 10.91 10.90 1.65 2.40 18.30 -0.15 4.48

Liberia 2011 9.95 10.00 1.49 4.60 14.90 -0.19 3.18

Madagascar 2013 10.90 11.00 1.52 0.90 15.70 -0.71 5.61

Madagascar 2016 11.07 11.10 1.42 2.90 18.50 -0.30 3.80

Malawi 2014 10.73 10.90 1.51 3.80 15.20 -0.57 3.65

Malawi 2015-16 10.39 10.50 1.50 1.80 17.10 -0.48 4.14

Mali 2012-13 9.42 9.50 1.79 0.60 29.80 0.07 7.01

Mali 2015 9.30 9.40 2.49 3.00 99.70 19.82 725.47

Moldova 2005 12.15 11.50 7.78 7.10 98.00 9.93 102.58

Mozambique 2011 10.26 10.30 1.88 0.80 78.00 9.49 354.21

Myanmar 2015-16 10.72 10.80 1.36 3.40 15.10 -0.45 3.64

Namibia 2013 10.90 11.00 1.45 3.20 16.50 -0.38 3.96

Nepal 2006 10.96 11.00 1.63 4.80 72.00 10.73 412.26

Nepal 2011 11.02 11.10 1.36 3.40 15.40 -0.34 3.87

Niger 2006 9.46 9.50 1.76 1.10 18.30 0.11 4.37

Niger 2012 9.92 10.00 1.51 2.90 20.20 -0.17 3.98

Nigeria 2015 10.12 10.30 1.61 3.60 15.70 -0.42 3.29

Peru 2011 11.38 11.50 1.32 -2.30 18.30 -0.75 5.72

Peru 2012 11.32 11.40 1.26 4.50 15.80 -0.56 3.86

Rwanda 2010 11.32 11.40 1.37 4.70 18.40 -0.10 5.38

Rwanda 2014-15 11.28 11.40 1.40 2.60 18.40 -0.65 4.71

ST and Principe 2008-09 10.59 10.70 1.36 0.30 15.30 -1.12 8.98

Senegal 2014 10.49 10.60 2.26 1.20 98.00 19.07 746.34
56

Senegal 2015 10.08 10.20 1.51 3.80 15.10 -0.53 3.44

Sierra Leone 2008 9.96 10.10 2.33 0.00 96.00 19.88 741.73

Sierra Leone 2013 9.61 9.80 2.39 2.10 99.70 20.21 768.22

Swaziland 2006-07 11.19 11.30 1.53 4.10 15.80 -0.45 3.70

Tanzania 2010 10.52 10.60 1.46 0.40 15.40 -0.59 4.70

Tanzania 2015-16 10.59 10.70 1.49 2.20 17.40 -0.49 4.02

Timor-Leste 2009-10 11.07 11.20 1.49 -0.30 16.50 -2.42 18.39

Togo 2013-14 10.09 10.10 1.52 2.20 14.80 -0.26 3.16

Uganda 2011 10.88 11.00 1.64 1.20 17.60 -0.48 4.02

Uganda 2014-15 10.70 10.80 1.60 3.60 15.80 -0.47 3.51

Yemen 2013 8.80 8.70 3.13 4.00 99.70 19.33 561.29

Zimbabwe 2010-11 10.65 10.70 1.88 3.30 67.90 11.62 348.90

Zimbabwe 2015 11.29 11.40 1.33 5.60 16.70 -0.34 3.83

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude 
57

Table A4.3 The mean, standard deviation, median, skew, and kurtosis of hemoglobin concentrations for 

nonpregnant women, after removing values outside the range 4-18 g/dL

Mean Median SD Min Max Skew Kurtosis

Albania 2008-09 12.82 12.90 1.21 5.90 17.70 -0.71 5.72

Armenia 2005 12.86 13.00 1.46 4.00 17.20 -0.95 5.89

Azerbaijan 2006 12.26 12.40 1.55 4.00 17.20 -0.95 5.24

Bangladesh 2011 12.17 12.20 1.35 5.30 17.90 -0.38 3.80

Benin 2006 11.45 11.50 1.59 4.40 16.20 -0.45 3.68

Benin 2011-12 12.20 12.30 1.53 5.40 18.00 -0.22 3.55

Bolivia 2003 12.56 12.70 1.59 4.40 17.50 -0.57 4.12

Bolivia 2008 12.35 12.50 1.63 4.20 17.40 -0.54 4.14

Burkina Faso 2010 11.89 12.10 1.75 4.10 17.60 -0.56 3.69

Burundi 2010 13.28 13.40 1.50 5.40 17.70 -0.65 4.29

Cambodia 2010 12.10 12.20 1.42 4.00 17.40 -0.48 4.24

Cambodia 2014 12.07 12.10 1.34 4.00 17.30 -0.55 4.46

Cameroon 2004 12.08 12.20 1.73 4.40 17.80 -0.47 3.80

Cameroon 2011 12.24 12.40 1.63 4.60 17.10 -0.57 3.99

Congo 2005 11.57 11.70 1.50 4.50 16.00 -0.54 3.78

Congo 2011-12 11.79 11.90 1.41 5.70 16.50 -0.31 3.53

Congo DR 2007 11.87 11.95 1.77 4.10 17.80 -0.37 3.62

Congo DR 2013-14 12.24 12.30 1.57 4.30 17.70 -0.35 3.67

Cote d'Ivoire 2011-12 11.74 11.80 1.62 4.00 16.80 -0.44 3.70

Egypt 2014 12.58 12.60 1.12 6.20 18.00 -0.43 5.14

Ethiopia 2011 13.17 13.40 1.73 4.20 18.00 -0.99 5.23

Ethiopia 2016 12.78 13.00 1.77 4.30 18.00 -0.88 4.57

Gabon 2012 11.49 11.60 1.59 4.50 18.00 -0.51 3.71

Gambia 2013 11.33 11.50 1.73 4.10 16.60 -0.59 3.72

Ghana 2014 12.10 12.20 1.48 4.70 17.10 -0.55 3.88

Guatemala 2014-15 13.31 13.40 1.41 4.40 18.00 -0.74 5.08

Guinea 2005 11.78 11.90 1.72 5.20 17.90 -0.48 3.50

Guinea 2012 11.87 12.00 1.61 4.20 16.60 -0.63 4.17

Guyana 2009 12.30 12.50 1.57 5.40 17.60 -0.70 4.10

Haiti 2005-06 11.99 12.20 1.83 4.00 16.90 -0.75 3.87

Haiti 2012 11.86 12.00 1.60 4.00 16.70 -0.73 4.44

Honduras 2005-06 13.11 13.20 1.40 4.00 17.80 -0.70 4.96

Honduras 2011-12 13.21 13.30 1.36 4.60 17.90 -0.65 4.82

India 2005-06 11.69 11.90 1.73 4.00 18.00 -0.71 4.09

Jordan 2009 12.72 12.90 1.48 5.70 17.20 -0.67 4.03

Jordan 2012 12.42 12.60 1.59 4.60 17.80 -0.62 3.76

Kyrgyz Republic 2012 12.31 12.50 1.65 4.10 17.50 -0.95 4.69

Lesotho 2009 12.87 13.10 1.73 4.20 18.00 -0.78 4.26

Lesotho 2014 12.87 13.00 1.80 4.20 17.90 -0.69 4.09

Malawi 2015-16 12.50 12.60 1.65 4.30 17.70 -0.63 4.28

Mali 2012-13 11.78 11.90 1.62 4.20 17.10 -0.56 3.97

Moldova 2005 12.58 12.70 1.36 4.90 16.90 -0.74 4.77

Mozambique 2011 11.72 11.90 1.75 4.00 17.80 -0.52 4.05

Myanmar 2015-16 12.05 12.10 1.52 4.10 17.40 -0.55 4.22

Namibia 2013 13.08 13.20 1.61 4.50 17.70 -0.64 4.40

Nepal 2006 12.46 12.60 1.55 4.40 17.60 -0.56 4.08

Nepal 2011 12.57 12.70 1.53 4.00 17.40 -0.53 4.08

Niger 2006 12.15 12.30 1.87 4.10 17.70 -0.47 3.95

Niger 2012 12.07 12.20 1.60 4.10 17.00 -0.62 4.17

Peru 2011 13.05 13.20 1.37 4.10 17.90 -0.89 5.59

Peru 2012 12.98 13.10 1.33 4.60 18.00 -0.77 5.23

Rwanda 2010 13.31 13.50 1.53 4.70 17.90 -0.74 4.39

Rwanda 2014-15 13.10 13.20 1.50 4.80 17.70 -0.68 4.52

ST and Principe 2008-09 12.13 12.20 1.52 5.90 16.30 -0.47 3.86

Sierra Leone 2008 11.97 12.10 1.64 4.40 17.40 -0.50 3.57

Sierra Leone 2013 12.01 12.10 1.58 4.10 17.80 -0.39 3.93

Swaziland 2006-07 12.65 12.80 1.69 5.20 17.60 -0.61 3.83

Tanzania 2010 12.11 12.30 1.73 4.10 17.40 -0.69 4.17

Tanzania 2015-16 11.99 12.10 1.68 4.20 17.40 -0.61 4.13

Timor-Leste 2009-10 12.79 12.90 1.41 4.40 17.80 -0.76 5.31

Togo 2013-14 12.06 12.20 1.60 4.20 17.30 -0.54 4.19

Uganda 2011 13.00 13.10 1.67 4.00 17.60 -0.81 4.97

Yemen 2013 10.66 10.80 1.91 4.20 17.00 -0.12 2.91

Zimbabwe 2010-11 12.72 12.90 1.83 4.50 17.90 -0.68 3.90

Zimbabwe 2015 12.74 12.90 1.72 4.00 17.80 -0.80 4.49

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude and smoking prior 

to removing implausible values 
58

Table A4.4 The mean, standard deviation, median, skew, and kurtosis of hemoglobin concentrations for 

nonpregnant women, including implausible values

Mean Median SD Min Max Skew Kurtosis

Albania 2008-09 12.83 12.90 1.21 5.90 19.90 -0.68 5.82

Armenia 2005 12.86 13.00 1.83 1.20 92.90 13.37 619.80

Azerbaijan 2006 12.25 12.40 1.57 1.70 18.40 -1.03 5.84

Bangladesh 2011 12.17 12.20 1.35 5.30 17.90 -0.38 3.80

Benin 2006 11.46 11.50 2.07 0.20 98.00 15.88 677.37

Benin 2011-12 12.16 12.30 1.67 0.00 18.00 -1.19 9.62

Bolivia 2003 12.56 12.70 1.61 1.50 18.50 -0.63 4.72

Bolivia 2008 12.35 12.50 1.65 1.20 19.30 -0.58 4.75

Burkina Faso 2010 11.89 12.10 1.75 3.80 19.70 -0.55 3.79

Burundi 2010 13.28 13.40 1.51 5.40 19.40 -0.62 4.33

Cambodia 2010 12.10 12.20 1.72 0.20 85.00 10.01 410.93

Cambodia 2014 12.07 12.10 1.34 4.00 17.30 -0.55 4.46

Cameroon 2004 12.08 12.20 1.74 3.20 17.80 -0.54 4.14

Cameroon 2011 12.24 12.40 1.64 0.00 18.80 -0.63 4.47

Congo 2005 11.56 11.70 1.54 0.50 19.90 -0.74 5.76

Congo 2011-12 11.80 11.90 1.48 5.70 43.20 1.73 45.49

Congo DR 2007 12.11 12.00 5.04 -0.40 99.90 14.23 242.78

Congo DR 2013-14 12.26 12.30 2.10 2.10 99.70 17.49 737.61

Cote d'Ivoire 2011-12 11.74 11.80 1.66 0.20 18.70 -0.54 4.67

Egypt 2014 12.58 12.60 1.12 6.20 18.00 -0.43 5.14

Ethiopia 2011 13.17 13.40 1.87 2.10 90.50 4.10 210.36

Ethiopia 2016 12.78 13.00 1.78 3.40 19.40 -0.88 4.68

Gabon 2012 11.50 11.60 2.00 3.30 95.00 14.64 625.39

Gambia 2013 11.34 11.50 1.96 2.70 69.10 5.56 180.24

Ghana 2014 12.09 12.20 1.49 3.10 17.10 -0.62 4.32

Guatemala 2014-15 13.31 13.40 1.42 4.40 24.90 -0.68 5.34

Guinea 2005 11.76 11.90 1.76 1.10 18.80 -0.65 4.54

Guinea 2012 11.87 12.00 1.62 3.90 16.60 -0.65 4.26

Guyana 2009 12.34 12.50 2.30 3.90 96.00 15.89 521.93

Haiti 2005-06 11.99 12.20 1.85 2.80 19.90 -0.74 4.12

Haiti 2012 11.86 12.00 1.63 0.80 18.80 -0.87 5.37

Honduras 2005-06 13.09 13.20 1.52 0.80 48.80 -0.25 28.52

Honduras 2011-12 13.54 13.30 5.46 4.60 99.90 14.78 234.26

India 2005-06 11.69 11.90 1.74 2.00 22.90 -0.74 4.37

Jordan 2009 12.72 12.90 1.49 5.70 30.80 -0.39 7.02

Jordan 2012 12.42 12.60 1.60 4.60 31.20 -0.43 5.56

Kyrgyz Republic 2012 12.31 12.50 1.67 2.60 21.80 -0.91 5.16

Lesotho 2009 12.87 13.10 1.74 3.80 18.70 -0.78 4.42

Lesotho 2014 12.86 13.00 1.83 2.40 18.10 -0.81 4.84

Malawi 2015-16 12.50 12.60 1.68 2.20 22.80 -0.65 5.06

Mali 2012-13 11.77 11.90 1.65 1.00 17.10 -0.72 4.95

Moldova 2005 12.82 12.70 4.52 0.00 98.00 15.62 273.95

Mozambique 2011 11.71 11.85 1.76 2.10 18.30 -0.57 4.32

Myanmar 2015-16 12.04 12.10 1.53 2.50 21.10 -0.56 4.47

Namibia 2013 13.08 13.20 1.62 1.30 18.40 -0.68 4.89

Nepal 2006 12.46 12.60 1.56 3.20 17.60 -0.58 4.18

Nepal 2011 12.57 12.70 1.54 3.60 18.70 -0.54 4.26

Niger 2006 12.14 12.30 1.90 0.20 20.00 -0.52 4.58

Niger 2012 12.08 12.20 1.82 3.40 69.00 6.41 217.99

Peru 2011 13.05 13.20 1.40 1.00 41.70 -0.65 15.00

Peru 2012 12.98 13.10 1.34 2.20 21.20 -0.77 5.53

Rwanda 2010 13.31 13.50 1.54 2.20 18.30 -0.81 4.95

Rwanda 2014-15 13.10 13.20 1.50 4.80 18.80 -0.66 4.55

ST and Principe 2008-09 12.13 12.20 1.52 5.90 16.30 -0.47 3.86

Sierra Leone 2008 11.97 12.10 1.83 -0.30 49.30 2.17 61.89

Sierra Leone 2013 12.02 12.10 1.89 3.10 99.70 13.49 641.51

Swaziland 2006-07 12.66 12.80 1.70 5.20 19.40 -0.55 3.92

Tanzania 2010 12.11 12.30 1.84 3.20 68.00 2.55 100.25

Tanzania 2015-16 11.98 12.10 1.70 1.90 24.00 -0.67 4.78

Timor-Leste 2009-10 12.79 12.90 1.44 3.60 22.20 -0.81 6.62

Togo 2013-14 12.06 12.20 1.60 4.20 19.10 -0.52 4.24

Uganda 2011 13.00 13.10 1.68 3.80 17.60 -0.85 5.22

Yemen 2013 10.74 10.80 3.33 2.00 99.70 17.71 474.31

Zimbabwe 2010-11 12.72 12.90 2.05 0.90 83.10 4.96 191.13

Zimbabwe 2015 12.74 12.90 1.74 2.90 19.00 -0.87 4.92

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude and 

smoking 
59

Table A4.5 The mean, standard deviation, median, skew, and kurtosis of hemoglobin concentrations for 

men, after removing values outside the range 4-20 g/dL

Mean Median SD Min Max Skew Kurtosis

Albania 2008-09 14.08 14.10 1.29 9.10 19.80 0.15 3.42

Benin 2006 13.50 13.60 1.91 5.00 19.80 -0.43 3.89

Burkina Faso 2010 13.84 14.00 1.89 4.00 19.80 -0.46 3.71

Burundi 2010 15.05 15.10 1.72 5.20 19.90 -0.48 3.87

Congo DR 2007 13.37 13.40 2.02 4.20 19.80 -0.29 3.57

Congo DR 2013-14 14.07 14.20 1.86 4.00 19.90 -0.39 3.66

Cote d'Ivoire 2011-12 13.91 14.00 1.90 5.30 19.90 -0.39 3.42

Ethiopia 2011 14.98 15.10 1.86 4.00 20.00 -0.77 4.88

Ethiopia 2016 14.66 14.80 1.91 4.30 20.00 -0.82 4.79

Gabon 2012 13.78 13.80 1.73 4.00 19.90 -0.40 4.15

Guinea 2012 13.91 14.00 1.74 5.80 20.00 -0.51 4.12

Guyana 2009 14.37 14.40 1.60 5.30 19.90 -0.53 4.72

Haiti 2005-06 13.95 14.20 1.90 4.10 19.70 -0.90 4.94

Haiti 2012 13.88 14.00 1.63 4.10 19.90 -0.64 4.96

India 2005-06 14.22 14.40 1.82 4.00 20.00 -0.72 4.77

Lesotho 2009 14.90 15.00 1.77 5.80 20.00 -0.71 4.51

Lesotho 2014 14.79 14.90 1.84 5.80 19.90 -0.60 3.99

Namibia 2013 14.91 15.00 1.73 6.80 19.70 -0.39 3.66

Niger 2006 14.28 14.40 1.94 4.50 19.80 -0.49 3.89

Niger 2012 13.99 14.10 1.69 5.50 19.40 -0.48 4.06

ST and Principe 2008-09 13.93 14.00 1.76 4.90 19.60 -0.48 4.38

Sierra Leone 2008 13.36 13.50 1.89 4.60 19.30 -0.39 3.56

Sierra Leone 2013 13.56 13.60 1.81 4.40 19.40 -0.41 3.96

Swaziland 2006-07 14.84 15.00 1.70 5.40 20.00 -0.65 4.64

Togo 2013-14 14.28 14.40 1.81 4.10 19.90 -0.58 4.57

Zimbabwe 2010-11 14.70 14.90 1.86 4.20 19.90 -0.65 4.22

Zimbabwe 2015 14.61 14.70 1.70 4.50 20.00 -0.46 4.02

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude and 

smoking prior to removing implausible values 
60

Table A4.6 The mean, standard deviation, median, skew, and kurtosis of hemoglobin concentrations for 

men, including implausible values

Mean Median SD Min Max Skew Kurtosis

Albania 2008-09 14.08 14.10 1.29 9.10 19.80 0.15 3.42

Benin 2006 13.54 13.60 2.58 3.70 98.00 13.84 444.25

Burkina Faso 2010 13.83 14.00 1.89 3.30 19.80 -0.48 3.81

Burundi 2010 15.04 15.10 1.74 1.90 20.10 -0.56 4.56

Congo DR 2007 13.68 13.40 5.55 -0.40 99.90 13.09 202.04

Congo DR 2013-14 14.09 14.20 2.28 3.20 99.70 11.97 459.18

Cote d'Ivoire 2011-12 13.91 14.00 1.90 5.30 20.40 -0.38 3.44

Ethiopia 2011 14.98 15.10 1.89 2.60 26.10 -0.83 5.60

Ethiopia 2016 14.66 14.80 1.94 1.80 22.40 -0.85 5.24

Gabon 2012 13.80 13.80 2.06 4.00 95.50 11.17 455.91

Guinea 2012 13.91 14.00 1.78 3.10 23.80 -0.48 5.04

Guyana 2009 14.38 14.40 1.62 5.30 23.60 -0.46 4.98

Haiti 2005-06 13.95 14.20 1.90 3.70 19.70 -0.92 5.06

Haiti 2012 13.88 14.00 1.63 3.80 20.10 -0.67 5.19

India 2005-06 14.22 14.40 1.84 2.20 23.80 -0.75 5.10

Lesotho 2009 14.91 15.00 1.78 5.80 22.30 -0.64 4.60

Lesotho 2014 14.78 14.90 1.89 2.10 20.20 -0.84 5.63

Namibia 2013 14.91 15.00 1.75 3.90 21.80 -0.41 4.04

Niger 2006 14.27 14.40 1.99 1.40 22.20 -0.65 5.14

Niger 2012 14.00 14.10 1.70 5.50 23.40 -0.38 4.39

ST and Principe 2008-09 13.93 14.00 1.76 4.90 19.60 -0.48 4.38

Sierra Leone 2008 13.36 13.50 1.96 -0.50 22.40 -0.57 5.53

Sierra Leone 2013 13.57 13.60 2.09 4.40 99.70 9.96 424.74

Swaziland 2006-07 14.84 15.00 1.71 5.40 20.70 -0.62 4.67

Togo 2013-14 14.28 14.40 1.83 2.10 21.30 -0.59 4.97

Zimbabwe 2010-11 14.74 14.90 2.20 2.80 63.30 4.65 100.33

Zimbabwe 2015 14.61 14.70 1.71 2.10 21.90 -0.52 4.53

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; Standard deviation; Hemoglobin adjusted for altitude and smoking 
61

Appendix 5 

Table A.5.1 Mean, standard deviation, and median hemoglobin concentrations g/dL for children, after 

removing values outside the range 4-18 g/dL, by age

6-11 months 12-23 months 24-59 months

Mean Median SD Mean Median SD Mean Median SD

Albania 2008-09 11.49 11.70 1.21 11.40 11.60 1.21 11.96 12.10 1.08

Angola 2011 10.12 10.10 1.39 10.36 10.40 1.35 10.99 11.10 1.39

Angola 2015-16 9.85 10.00 1.24 10.05 10.20 1.38 10.60 10.70 1.42

Armenia 2005 10.39 10.50 1.76 11.06 11.20 1.57 11.87 12.00 1.40

Azerbaijan 2006 10.77 10.70 1.22 10.80 11.00 1.31 11.52 11.60 1.19

Bangladesh 2011 10.10 10.20 1.24 10.27 10.30 1.24 11.07 11.20 1.13

Benin 2006 9.12 9.30 1.83 9.24 9.40 1.69 9.88 10.00 1.62

Benin 2011-12 10.15 10.30 1.66 10.20 10.40 1.62 10.64 10.80 1.51

Bolivia 2003 9.86 10.00 1.45 10.07 10.20 1.61 11.18 11.30 1.42

Bolivia 2008 9.86 9.90 1.55 9.76 9.80 1.59 10.85 11.00 1.49

Burkina Faso 2010 8.48 8.50 1.63 8.45 8.50 1.62 9.33 9.40 1.60

Burkina Faso 2014 8.66 8.70 1.61 8.42 8.40 1.67 9.27 9.30 1.72

Burundi 2010 10.21 10.20 1.29 10.83 10.90 1.28 11.24 11.40 1.34

Burundi 2012 9.89 10.00 1.47 10.59 10.80 1.49 11.00 11.20 1.55

Cambodia 2010 9.80 9.90 1.22 10.15 10.20 1.34 11.09 11.20 1.19

Cambodia 2014 10.00 10.00 1.22 10.21 10.30 1.34 11.02 11.10 1.16

Cameroon 2004 9.44 9.50 1.83 9.50 9.60 1.74 10.41 10.50 1.62

Cameroon 2011 9.88 10.00 1.42 10.12 10.30 1.49 10.65 10.80 1.50

Congo 2005 9.86 10.10 1.50 10.04 10.20 1.54 10.54 10.50 1.41

Congo 2011-12 9.79 9.80 1.19 10.02 10.10 1.26 10.61 10.70 1.33

Congo DR 2007 9.27 9.40 1.70 9.82 9.90 1.66 10.32 10.40 1.62

Congo DR 2013-14 9.84 10.00 1.61 10.15 10.30 1.71 10.41 10.60 1.69

Cote d'Ivoire 2011-12 9.30 9.40 1.47 9.43 9.50 1.50 10.13 10.20 1.52

Egypt 2014 10.95 11.00 1.25 11.07 11.20 1.19 11.69 11.70 1.07

Ethiopia 2011 10.08 10.20 1.63 10.18 10.40 1.75 11.00 11.20 1.74

Ethiopia 2016 9.83 10.00 1.51 9.90 10.00 1.67 10.63 10.80 1.73

Gabon 2012 10.19 10.20 1.37 10.10 10.20 1.37 10.56 10.70 1.39

Gambia 2013 9.57 9.70 1.30 9.20 9.30 1.47 10.07 10.20 1.59

Ghana 2014 9.73 9.80 1.49 9.70 9.80 1.55 10.34 10.40 1.51

Ghana 2016 9.82 9.90 1.46 9.77 9.90 1.49 10.47 10.60 1.45

Guatemala 2014-15 10.25 10.30 1.25 10.90 10.90 1.31 11.76 11.80 1.16

Guinea 2005 9.41 9.60 1.78 9.11 9.20 1.66 10.06 10.10 1.53

Guinea 2012 9.40 9.50 1.67 9.24 9.40 1.71 9.94 10.20 1.70

Guyana 2009 10.57 10.50 1.37 10.82 10.90 1.40 11.44 11.50 1.26

Haiti 2005-06 9.94 9.90 1.51 9.93 10.00 1.52 10.75 10.90 1.51

Haiti 2012 9.72 9.70 1.22 9.96 10.10 1.31 10.69 10.80 1.25

Honduras 2005-06 10.45 10.50 1.21 10.80 10.90 1.29 11.56 11.60 1.21

Honduras 2011-12 10.73 10.80 1.22 11.07 11.20 1.26 11.78 11.80 1.13

India 2005-06 9.94 10.00 1.38 9.72 9.80 1.53 10.52 10.70 1.54

Jordan 2009 10.74 10.80 1.35 10.87 11.00 1.31 11.72 11.80 1.29

Jordan 2012 10.90 11.00 1.27 11.00 11.10 1.39 11.78 11.90 1.31

Kenya 2015 10.74 10.80 1.45 10.61 10.70 1.40 11.50 11.60 1.47

Kyrgyz Republic 2012 10.56 10.70 1.41 10.47 10.70 1.59 11.30 11.40 1.41

Lesotho 2009 10.37 10.60 1.57 10.46 10.50 1.51 11.16 11.30 1.39

Lesotho 2014 10.63 10.60 1.61 10.47 10.50 1.71 11.08 11.10 1.55

Liberia 2011 9.75 9.80 1.30 9.71 9.70 1.50 10.05 10.10 1.50

Madagascar 2013 10.02 10.00 1.34 10.37 10.40 1.47 11.21 11.30 1.39

Madagascar 2016 10.18 10.20 1.30 10.51 10.50 1.38 11.37 11.40 1.32

Malawi 2014 10.15 10.30 1.47 10.26 10.40 1.44 11.00 11.20 1.46

Malawi 2015-16 9.57 9.60 1.40 9.92 10.00 1.40 10.68 10.80 1.42

Mali 2012-13 9.38 9.50 1.46 8.96 9.00 1.69 9.58 9.70 1.76

Mali 2015 9.35 9.50 1.45 8.81 8.90 1.55 9.41 9.60 1.67

Moldova 2005 10.91 10.90 1.02 11.01 11.10 1.13 11.64 11.70 1.12

Mozambique 2011 9.72 9.90 1.53 9.80 9.90 1.58 10.51 10.60 1.54

Myanmar 2015-16 10.11 10.20 1.36 10.12 10.20 1.36 10.99 11.10 1.27

Namibia 2013 10.39 10.50 1.54 10.32 10.40 1.43 11.19 11.30 1.33

Nepal 2006 9.97 10.00 1.29 10.30 10.40 1.28 11.29 11.40 1.27

Nepal 2011 10.10 10.20 1.15 10.45 10.50 1.22 11.33 11.30 1.30

Niger 2006 9.30 9.30 1.47 8.85 8.90 1.61 9.70 9.80 1.75

Niger 2012 9.52 9.60 1.44 9.28 9.30 1.39 10.17 10.20 1.48

Nigeria 2015 9.87 10.00 1.54 9.78 9.90 1.59 10.27 10.40 1.61

Peru 2011 10.26 10.40 1.33 10.76 10.90 1.34 11.73 11.80 1.12
62

Peru 2012 10.35 10.40 1.35 10.70 10.80 1.29 11.65 11.70 1.09

Rwanda 2010 10.21 10.30 1.28 10.95 10.90 1.30 11.58 11.60 1.26

Rwanda 2014-15 10.34 10.50 1.42 11.03 11.20 1.32 11.53 11.60 1.32

ST and Principe 2008-09 9.97 10.10 1.23 10.08 10.20 1.31 10.93 11.00 1.15

Senegal 2014 10.31 10.30 1.26 9.94 10.00 1.49 10.66 10.80 1.60

Senegal 2015 9.96 10.00 1.23 9.44 9.50 1.42 10.30 10.50 1.52

Sierra Leone 2008 9.64 9.90 1.66 9.62 9.80 1.55 10.09 10.10 1.47

Sierra Leone 2013 9.33 9.50 1.56 9.31 9.50 1.63 9.70 9.90 1.60

Swaziland 2006-07 10.37 10.50 1.38 10.35 10.50 1.58 11.60 11.70 1.36

Tanzania 2010 9.92 10.00 1.31 10.11 10.10 1.41 10.76 10.90 1.39

Tanzania 2015-16 9.88 10.00 1.42 10.14 10.20 1.40 10.88 11.00 1.42

Timor-Leste 2009-10 10.45 10.40 1.21 10.79 10.90 1.31 11.35 11.40 1.14

Togo 2013-14 9.46 9.60 1.32 9.62 9.70 1.46 10.37 10.50 1.49

Uganda 2011 10.22 10.30 1.50 10.41 10.60 1.51 11.17 11.30 1.61

Uganda 2014-15 9.69 9.90 1.61 10.26 10.30 1.50 10.99 11.10 1.53

Yemen 2013 8.40 8.30 1.71 8.28 8.20 1.72 8.92 8.90 1.80

Zimbabwe 2010-11 10.00 10.00 1.49 10.15 10.20 1.38 10.91 11.00 1.36

Zimbabwe 2015 10.55 10.70 1.27 10.79 10.90 1.39 11.54 11.60 1.24

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude prior to 

removing implausible values 
63

Table A.5.2 Mean, standard deviation, and median hemoglobin concentrations g/dL for children after 

removing values outside the range 4-18 g/dL, by sex

Male Female

Mean Median SD Mean Median SD

Albania 2008-09 11.79 12.00 1.19 11.83 12.00 1.10

Angola 2011 10.65 10.80 1.43 10.82 11.00 1.42

Angola 2015-16 10.34 10.40 1.43 10.45 10.60 1.41

Armenia 2005 11.54 11.80 1.60 11.52 11.70 1.52

Azerbaijan 2006 11.22 11.30 1.25 11.34 11.40 1.29

Bangladesh 2011 10.76 10.90 1.26 10.83 10.90 1.21

Benin 2006 9.56 9.70 1.71 9.73 9.90 1.67

Benin 2011-12 10.47 10.60 1.63 10.53 10.70 1.49

Bolivia 2003 10.80 11.00 1.58 10.90 11.00 1.53

Bolivia 2008 10.46 10.60 1.61 10.56 10.70 1.58

Burkina Faso 2010 8.96 9.10 1.66 9.11 9.20 1.66

Burkina Faso 2014 8.93 9.00 1.75 9.12 9.20 1.72

Burundi 2010 10.97 11.10 1.37 11.08 11.20 1.35

Burundi 2012 10.72 10.90 1.53 10.85 11.10 1.60

Cambodia 2010 10.68 10.80 1.37 10.80 10.90 1.29

Cambodia 2014 10.67 10.80 1.32 10.77 10.80 1.23

Cameroon 2004 9.98 10.10 1.71 10.17 10.30 1.75

Cameroon 2011 10.37 10.40 1.50 10.50 10.60 1.53

Congo 2005 10.30 10.30 1.53 10.39 10.50 1.42

Congo 2011-12 10.33 10.50 1.35 10.43 10.50 1.32

Congo DR 2007 10.02 10.10 1.66 10.17 10.30 1.69

Congo DR 2013-14 10.25 10.40 1.70 10.33 10.50 1.69

Cote d'Ivoire 2011-12 9.81 10.00 1.57 9.93 10.00 1.52

Egypt 2014 11.48 11.50 1.24 11.42 11.50 1.09

Ethiopia 2011 10.71 10.90 1.79 10.78 11.00 1.76

Ethiopia 2016 10.35 10.50 1.75 10.42 10.60 1.72

Gabon 2012 10.30 10.40 1.43 10.51 10.60 1.36

Gambia 2013 9.76 9.90 1.58 9.86 10.00 1.56

Ghana 2014 10.08 10.20 1.57 10.19 10.30 1.52

Ghana 2016 10.14 10.30 1.51 10.35 10.50 1.47

Guatemala 2014-15 11.42 11.50 1.32 11.42 11.50 1.30

Guinea 2005 9.75 9.90 1.67 9.81 9.90 1.61

Guinea 2012 9.68 9.80 1.72 9.78 10.00 1.73

Guyana 2009 11.21 11.30 1.39 11.22 11.30 1.30

Haiti 2005-06 10.40 10.50 1.56 10.55 10.70 1.57

Haiti 2012 10.40 10.50 1.36 10.43 10.50 1.27

Honduras 2005-06 11.25 11.30 1.32 11.33 11.40 1.27

Honduras 2011-12 11.47 11.60 1.27 11.53 11.60 1.20

India 2005-06 10.27 10.40 1.58 10.31 10.40 1.54

Jordan 2009 11.37 11.50 1.39 11.45 11.60 1.34

Jordan 2012 11.47 11.50 1.43 11.58 11.60 1.32

Kenya 2015 11.15 11.30 1.51 11.33 11.40 1.50

Kyrgyz Republic 2012 11.01 11.20 1.51 11.04 11.20 1.50

Lesotho 2009 10.94 11.00 1.44 10.92 11.00 1.51

Lesotho 2014 10.90 10.90 1.61 10.93 11.00 1.62

Liberia 2011 9.88 9.90 1.50 10.01 10.10 1.47

Madagascar 2013 10.88 11.00 1.51 10.95 11.00 1.42

Madagascar 2016 11.04 11.10 1.43 11.12 11.20 1.38

Malawi 2014 10.71 10.80 1.53 10.76 10.90 1.47

Malawi 2015-16 10.35 10.50 1.52 10.45 10.50 1.42

Mali 2012-13 9.38 9.50 1.73 9.49 9.60 1.74

Mali 2015 9.22 9.30 1.62 9.34 9.50 1.66

Moldova 2005 11.42 11.50 1.18 11.39 11.40 1.14

Mozambique 2011 10.22 10.30 1.62 10.28 10.40 1.56

Myanmar 2015-16 10.66 10.80 1.41 10.79 10.90 1.30

Namibia 2013 10.84 10.90 1.43 10.97 11.10 1.43

Nepal 2006 10.95 11.10 1.40 10.95 11.00 1.35

Nepal 2011 11.12 11.20 1.32 10.93 11.00 1.38

Niger 2006 9.45 9.50 1.76 9.49 9.60 1.70

Niger 2012 9.87 10.00 1.54 9.97 10.00 1.46

Nigeria 2015 10.05 10.20 1.60 10.20 10.30 1.61

Peru 2011 11.32 11.40 1.33 11.45 11.50 1.28

Peru 2012 11.28 11.40 1.32 11.35 11.40 1.21

Rwanda 2010 11.24 11.30 1.40 11.39 11.40 1.28

Rwanda 2014-15 11.23 11.40 1.41 11.34 11.40 1.37

ST and Principe 2008-09 10.57 10.60 1.33 10.66 10.80 1.21
64

Senegal 2014 10.42 10.50 1.60 10.52 10.60 1.55

Senegal 2015 10.00 10.20 1.53 10.15 10.30 1.49

Sierra Leone 2008 9.86 10.00 1.59 10.01 10.10 1.45

Sierra Leone 2013 9.56 9.80 1.60 9.62 9.90 1.62

Swaziland 2006-07 11.13 11.20 1.55 11.24 11.40 1.50

Tanzania 2010 10.46 10.50 1.45 10.60 10.70 1.41

Tanzania 2015-16 10.53 10.60 1.50 10.66 10.80 1.44

Timor-Leste 2009-10 11.13 11.20 1.21 11.16 11.20 1.24

Togo 2013-14 10.03 10.10 1.52 10.16 10.20 1.50

Uganda 2011 10.86 10.90 1.57 10.91 11.00 1.67

Uganda 2014-15 10.64 10.80 1.62 10.77 10.90 1.57

Yemen 2013 8.72 8.70 1.81 8.72 8.70 1.78

Zimbabwe 2010-11 10.58 10.70 1.47 10.68 10.80 1.40

Zimbabwe 2015 11.25 11.30 1.35 11.34 11.40 1.30

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude prior to 

removing implausible values 
65

Table A.5.3 Mean, standard deviation, and median hemoglobin concentrations g/dL for children after removing values outside 

wealth quintile

Lowest Second Middle Fourth 

Mean Median SD Mean Median SD Mean Median SD Mean Median SD 

Albania 2008-09 11.65 11.80 1.17 11.68 11.90 1.21 11.79 12.00 1.24 11.86 12.10 1.09 

Angola 2011 10.78 11.00 1.55 10.75 10.80 1.51 10.67 10.80 1.37 10.63 10.60 1.31 

Angola 2015-16 10.31 10.40 1.49 10.36 10.50 1.52 10.37 10.40 1.33 10.52 10.60 1.30 

Armenia 2005 11.51 11.80 1.65 11.66 11.90 1.43 11.72 11.90 1.44 11.46 11.60 1.56 

Azerbaijan 2006 11.14 11.20 1.34 11.22 11.30 1.28 11.24 11.30 1.22 11.42 11.50 1.28 

Bangladesh 2011 10.62 10.70 1.19 10.62 10.70 1.22 10.85 10.90 1.25 10.94 11.10 1.12 

Benin 2006 9.40 9.50 1.71 9.48 9.70 1.73 9.44 9.50 1.68 9.80 10.00 1.61 

Benin 2011-12 10.28 10.40 1.62 10.38 10.50 1.56 10.42 10.50 1.52 10.67 10.80 1.53 

Bolivia 2003 10.67 10.80 1.46 10.73 10.90 1.45 10.84 11.10 1.57 10.99 11.20 1.75 

Bolivia 2008 10.37 10.40 1.50 10.48 10.60 1.52 10.33 10.40 1.70 10.66 10.80 1.65 

Burkina Faso 2010 8.82 8.90 1.71 8.92 9.00 1.61 8.85 8.90 1.66 9.08 9.10 1.59 

Burkina Faso 2014 8.69 8.70 1.75 8.91 9.00 1.76 9.06 9.10 1.71 9.09 9.10 1.66 

Burundi 2010 10.84 11.00 1.38 10.99 11.20 1.41 10.96 11.10 1.37 11.03 11.20 1.33 

Burundi 2012 10.42 10.60 1.67 10.65 10.80 1.59 10.84 11.10 1.53 10.86 11.10 1.60 

Cambodia 2010 10.50 10.60 1.34 10.60 10.70 1.33 10.69 10.80 1.31 10.85 10.90 1.34 

Cambodia 2014 10.42 10.50 1.30 10.59 10.60 1.23 10.70 10.80 1.27 10.81 10.80 1.25 

Cameroon 2004 9.87 10.00 1.72 9.94 10.10 1.77 9.97 10.10 1.77 10.28 10.30 1.70 

Cameroon 2011 10.21 10.20 1.47 10.40 10.50 1.59 10.36 10.50 1.55 10.51 10.60 1.50 

Congo 2005 10.18 10.30 1.56 10.28 10.30 1.41 10.22 10.30 1.50 10.43 10.50 1.41 

Congo 2011-12 10.37 10.50 1.35 10.31 10.40 1.37 10.42 10.50 1.34 10.43 10.50 1.20 

Congo DR 2007 9.91 9.90 1.73 9.81 9.80 1.66 10.09 10.10 1.70 10.31 10.40 1.70 

Congo DR 2013-14 10.13 10.30 1.70 10.15 10.40 1.78 10.27 10.30 1.68 10.42 10.50 1.69 

Cote d'Ivoire 2011-12 9.68 9.70 1.51 9.61 9.70 1.51 9.86 10.00 1.49 10.11 10.20 1.60 

Egypt 2014 11.27 11.30 1.26 11.33 11.40 1.22 11.51 11.60 1.13 11.49 11.50 1.13 

Ethiopia 2011 10.42 10.60 1.85 10.76 10.90 1.75 10.88 11.10 1.75 10.91 11.10 1.67 

Ethiopia 2016 9.92 10.10 1.80 10.44 10.60 1.68 10.75 10.90 1.59 10.65 10.80 1.67 

Gabon 2012 10.28 10.40 1.46 10.35 10.50 1.42 10.57 10.70 1.21 10.59 10.70 1.31 

Gambia 2013 9.61 9.70 1.57 9.75 9.90 1.49 9.62 9.80 1.66 10.02 10.20 1.52 

Ghana 2014 9.71 9.80 1.54 9.89 10.10 1.55 10.21 10.30 1.52 10.54 10.60 1.37 

Ghana 2016 9.84 10.00 1.51 10.06 10.30 1.58 10.42 10.60 1.42 10.67 10.80 1.36 

Guatemala 2014-15 11.23 11.30 1.34 11.36 11.40 1.29 11.36 11.40 1.32 11.56 11.60 1.29 

Guinea 2005 9.66 9.70 1.56 9.64 9.80 1.67 9.74 9.80 1.71 9.89 10.00 1.69 

Guinea 2012 9.35 9.40 1.81 9.51 9.60 1.70 9.65 9.80 1.70 10.04 10.20 1.72 

Guyana 2009 11.23 11.30 1.33 11.11 11.30 1.32 11.09 11.20 1.42 11.27 11.40 1.42 

Haiti 2005-06 10.54 10.70 1.61 10.59 10.70 1.50 10.50 10.70 1.51 10.13 10.20 1.63 

Haiti 2012 10.49 10.50 1.29 10.44 10.50 1.32 10.18 10.20 1.27 10.36 10.40 1.32 

Honduras 2005-06 11.23 11.30 1.28 11.19 11.30 1.34 11.22 11.30 1.33 11.45 11.50 1.23 

Honduras 2011-12 11.41 11.50 1.29 11.49 11.60 1.23 11.47 11.50 1.25 11.60 11.60 1.19 

India 2005-06 9.95 10.10 1.47 10.07 10.20 1.55 10.26 10.40 1.56 10.40 10.50 1.57 

Jordan 2009 11.18 11.30 1.42 11.43 11.60 1.35 11.50 11.60 1.35 11.62 11.70 1.26 

Jordan 2012 11.31 11.40 1.45 11.54 11.60 1.32 11.57 11.60 1.37 11.65 11.70 1.35 

Kenya 2015 10.97 11.10 1.54 11.13 11.30 1.55 11.28 11.30 1.45 11.51 11.60 1.35 

Kyrgyz Republic 2012 10.93 11.10 1.53 10.96 11.20 1.50 11.08 11.30 1.55 11.05 11.10 1.42 

Lesotho 2009 10.90 11.00 1.50 10.89 11.00 1.38 10.95 11.10 1.45 10.99 11.10 1.49 

Lesotho 2014 10.81 10.80 1.56 10.88 10.90 1.63 10.92 10.90 1.68 10.94 11.10 1.63 
66

Liberia 2011 9.99 10.10 1.53 9.86 9.80 1.48 9.75 9.80 1.51 10.11 10.20 1.38 

Madagascar 2013 10.78 10.80 1.49 10.88 10.90 1.40 10.97 11.00 1.41 10.99 11.10 1.57 

Madagascar 2016 10.98 11.10 1.38 11.12 11.20 1.36 11.22 11.30 1.39 11.10 11.10 1.45 

Malawi 2014 10.41 10.60 1.61 10.61 10.80 1.51 10.78 11.00 1.46 10.89 10.90 1.47 

Malawi 2015-16 10.18 10.20 1.51 10.27 10.40 1.53 10.48 10.60 1.44 10.46 10.50 1.41 

Mali 2012-13 9.05 9.20 1.77 9.10 9.20 1.72 9.23 9.20 1.69 9.53 9.60 1.64 

Mali 2015 8.94 9.00 1.66 9.02 9.10 1.66 9.15 9.20 1.66 9.49 9.60 1.54 

Moldova 2005 11.18 11.30 1.21 11.31 11.30 1.15 11.41 11.50 1.13 11.45 11.50 1.19 

Mozambique 2011 9.69 9.80 1.63 9.93 10.00 1.60 10.23 10.30 1.56 10.45 10.50 1.49 

Myanmar 2015-16 10.62 10.70 1.34 10.71 10.90 1.39 10.74 10.80 1.41 10.83 10.90 1.31 

Namibia 2013 10.81 10.80 1.44 10.85 10.90 1.35 10.89 11.00 1.42 10.93 11.10 1.50 

Nepal 2006 11.01 11.10 1.43 10.85 10.90 1.35 10.79 10.90 1.33 10.96 11.10 1.35 

Nepal 2011 11.03 11.10 1.32 10.89 11.00 1.37 10.93 11.00 1.38 11.01 11.10 1.36 

Niger 2006 9.21 9.20 1.76 9.24 9.30 1.59 9.42 9.50 1.85 9.52 9.50 1.81 

Niger 2012 9.79 9.90 1.48 9.93 10.00 1.51 9.98 10.10 1.43 9.87 10.00 1.47 

Nigeria 2015 9.49 9.60 1.71 9.74 9.80 1.64 10.09 10.20 1.55 10.42 10.50 1.44 

Peru 2011 11.25 11.30 1.29 11.26 11.40 1.33 11.41 11.50 1.31 11.63 11.70 1.26 

Peru 2012 11.14 11.20 1.24 11.19 11.30 1.24 11.33 11.50 1.31 11.64 11.70 1.21 

Rwanda 2010 11.25 11.20 1.42 11.27 11.30 1.27 11.37 11.40 1.32 11.33 11.40 1.30 

Rwanda 2014-15 11.06 11.20 1.51 11.22 11.40 1.43 11.25 11.40 1.37 11.42 11.40 1.26 

ST and Principe 2008-09 10.49 10.50 1.21 10.51 10.60 1.32 10.65 10.80 1.31 10.79 10.90 1.27 

Senegal 2014 10.13 10.20 1.64 10.41 10.50 1.55 10.74 10.80 1.48 10.94 11.00 1.38 

Senegal 2015 9.72 9.80 1.59 9.93 10.10 1.53 10.28 10.40 1.36 10.50 10.60 1.31 

Sierra Leone 2008 9.86 10.00 1.51 9.80 10.00 1.56 9.80 9.90 1.48 9.92 10.10 1.57 

Sierra Leone 2013 9.54 9.80 1.62 9.47 9.80 1.58 9.46 9.70 1.54 9.61 9.80 1.59 

Swaziland 2006-07 11.12 11.20 1.57 11.33 11.40 1.55 11.11 11.20 1.51 11.14 11.20 1.46 

Tanzania 2010 10.43 10.50 1.52 10.48 10.50 1.41 10.63 10.70 1.41 10.55 10.60 1.39 

Tanzania 2015-16 10.44 10.60 1.52 10.47 10.60 1.56 10.63 10.70 1.42 10.70 10.70 1.42 

Timor-Leste 2009-10 11.07 11.20 1.21 11.26 11.30 1.14 11.12 11.20 1.22 11.13 11.30 1.28 

Togo 2013-14 10.03 10.10 1.61 9.86 10.00 1.56 10.01 10.00 1.49 10.25 10.30 1.30 

Uganda 2011 10.60 10.70 1.68 10.78 10.90 1.61 10.90 11.00 1.61 11.00 11.10 1.65 

Uganda 2014-15 10.56 10.70 1.61 10.57 10.80 1.59 10.66 10.80 1.62 10.96 11.10 1.55 

Yemen 2013 8.37 8.30 1.69 8.56 8.50 1.68 8.67 8.60 1.88 8.90 8.80 1.77 

Zimbabwe 2010-11 10.63 10.70 1.48 10.60 10.60 1.40 10.66 10.80 1.43 10.59 10.70 1.50 

Zimbabwe 2015 11.26 11.30 1.33 11.33 11.40 1.31 11.28 11.30 1.21 11.24 11.30 1.36 

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude prior to removing implausible va
67

Table A.5.4 Mean, standard deviation, and median hemoglobin concentrations g/dL for children after 

removing values outside the range 4-18 g/dL, by residence

Urban Rural

Mean Median SD Mean Median SD

Albania 2008-09 11.95 12.10 1.01 11.70 11.90 1.23

Angola 2011 10.70 10.80 1.32 10.76 10.90 1.48

Angola 2015-16 10.43 10.50 1.37 10.35 10.40 1.48

Armenia 2005 11.52 11.80 1.59 11.56 11.70 1.52

Azerbaijan 2006 11.37 11.50 1.25 11.19 11.30 1.28

Bangladesh 2011 10.86 11.00 1.29 10.77 10.90 1.21

Benin 2006 9.95 10.10 1.64 9.48 9.60 1.70

Benin 2011-12 10.63 10.70 1.54 10.42 10.50 1.57

Bolivia 2003 10.93 11.10 1.63 10.75 10.80 1.47

Bolivia 2008 10.59 10.80 1.65 10.42 10.50 1.53

Burkina Faso 2010 9.49 9.60 1.61 8.91 9.00 1.65

Burkina Faso 2014 9.55 9.60 1.62 8.93 8.90 1.74

Burundi 2010 11.21 11.30 1.34 10.99 11.10 1.36

Burundi 2012 11.23 11.30 1.30 10.70 10.90 1.60

Cambodia 2010 11.02 11.20 1.32 10.64 10.70 1.32

Cambodia 2014 10.97 11.10 1.27 10.63 10.70 1.27

Cameroon 2004 10.34 10.40 1.62 9.92 10.00 1.77

Cameroon 2011 10.63 10.70 1.43 10.30 10.30 1.56

Congo 2005 10.37 10.50 1.46 10.31 10.30 1.50

Congo 2011-12 10.37 10.40 1.28 10.38 10.50 1.35

Congo DR 2007 10.27 10.40 1.53 9.97 10.00 1.76

Congo DR 2013-14 10.41 10.50 1.63 10.25 10.40 1.72

Cote d'Ivoire 2011-12 10.20 10.40 1.55 9.70 9.70 1.51

Egypt 2014 11.55 11.60 1.10 11.38 11.50 1.21

Ethiopia 2011 10.98 11.20 1.74 10.70 10.90 1.78

Ethiopia 2016 10.66 10.80 1.64 10.32 10.50 1.74

Gabon 2012 10.46 10.60 1.38 10.33 10.40 1.42

Gambia 2013 10.18 10.30 1.50 9.64 9.80 1.58

Ghana 2014 10.48 10.60 1.47 9.89 10.00 1.55

Ghana 2016 10.60 10.70 1.33 10.03 10.20 1.55

Guatemala 2014-15 11.56 11.60 1.29 11.34 11.40 1.32

Guinea 2005 10.05 10.10 1.60 9.70 9.80 1.64

Guinea 2012 10.23 10.30 1.50 9.52 9.60 1.77

Guyana 2009 11.17 11.30 1.42 11.22 11.30 1.33

Haiti 2005-06 10.30 10.40 1.55 10.56 10.70 1.56

Haiti 2012 10.33 10.40 1.36 10.45 10.50 1.30

Honduras 2005-06 11.42 11.50 1.27 11.23 11.30 1.30

Honduras 2011-12 11.53 11.60 1.21 11.48 11.60 1.25

India 2005-06 10.44 10.50 1.59 10.20 10.30 1.54

Jordan 2009 11.44 11.60 1.35 11.35 11.40 1.41

Jordan 2012 11.53 11.60 1.37 11.51 11.60 1.39

Kenya 2015 11.37 11.50 1.46 11.15 11.30 1.53

Kyrgyz Republic 2012 11.06 11.10 1.46 11.01 11.20 1.51

Lesotho 2009 10.75 10.90 1.65 10.95 11.00 1.45

Lesotho 2014 10.95 11.00 1.59 10.90 10.90 1.62

Liberia 2011 9.92 10.00 1.45 9.96 10.00 1.51

Madagascar 2013 10.86 11.00 1.50 10.92 11.00 1.46

Madagascar 2016 10.88 11.00 1.46 11.11 11.20 1.40

Malawi 2014 10.88 11.00 1.40 10.67 10.80 1.54

Malawi 2015-16 10.64 10.70 1.45 10.36 10.50 1.47

Mali 2012-13 10.19 10.30 1.54 9.19 9.30 1.73

Mali 2015 9.97 10.10 1.43 9.11 9.20 1.65

Moldova 2005 11.50 11.50 1.16 11.32 11.40 1.15

Mozambique 2011 10.57 10.60 1.51 10.10 10.20 1.60

Myanmar 2015-16 10.77 10.80 1.32 10.71 10.80 1.37

Namibia 2013 10.90 11.00 1.46 10.91 11.00 1.42

Nepal 2006 11.10 11.20 1.37 10.91 11.00 1.37

Nepal 2011 11.08 11.10 1.37 11.01 11.10 1.35

Niger 2006 9.74 9.90 1.66 9.36 9.40 1.74

Niger 2012 10.02 10.00 1.57 9.89 10.00 1.48

Nigeria 2015 10.62 10.80 1.48 9.87 10.00 1.62

Peru 2011 11.50 11.60 1.29 11.24 11.40 1.31

Peru 2012 11.43 11.60 1.26 11.17 11.20 1.26

Rwanda 2010 11.39 11.50 1.45 11.30 11.30 1.33

Rwanda 2014-15 11.47 11.50 1.27 11.23 11.40 1.42
68

ST and Principe 2008-09 10.49 10.60 1.26 10.70 10.80 1.27

Senegal 2014 10.79 10.90 1.46 10.33 10.40 1.61

Senegal 2015 10.42 10.50 1.37 9.95 10.10 1.54

Sierra Leone 2008 10.13 10.20 1.54 9.84 10.00 1.51

Sierra Leone 2013 9.74 10.00 1.70 9.52 9.80 1.56

Swaziland 2006-07 10.96 11.00 1.49 11.23 11.40 1.53

Tanzania 2010 10.54 10.60 1.38 10.52 10.60 1.44

Tanzania 2015-16 10.75 10.80 1.41 10.55 10.60 1.49

Timor-Leste 2009-10 11.21 11.30 1.28 11.13 11.20 1.21

Togo 2013-14 10.41 10.50 1.33 9.98 10.10 1.56

Uganda 2011 11.15 11.30 1.43 10.82 10.90 1.66

Uganda 2014-15 11.03 11.10 1.57 10.65 10.80 1.59

Yemen 2013 9.07 9.00 1.77 8.61 8.60 1.79

Zimbabwe 2010-11 10.53 10.70 1.46 10.66 10.70 1.42

Zimbabwe 2015 11.31 11.40 1.41 11.29 11.30 1.29

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude prior to 

removing implausible values 
69

Table A.5.5 Mean, standard deviation, and median hemoglobin concentrations g/dL for nonpregnant women 

after removing values outside the range 4-18 g/dL, by age

15-19 20-24 35-49

Mean Median SD Mean Median SD Mean Median SD

Albania 2008-09 12.83 12.80 1.11 12.85 12.90 1.15 12.80 12.90 1.30

Armenia 2005 13.01 13.10 1.40 12.94 13.00 1.34 12.72 12.90 1.58

Azerbaijan 2006 12.52 12.60 1.37 12.28 12.40 1.47 12.13 12.40 1.69

Bangladesh 2011 12.16 12.20 1.28 12.24 12.30 1.35 12.08 12.20 1.38

Benin 2006 11.53 11.60 1.53 11.46 11.50 1.58 11.39 11.50 1.64

Benin 2011-12 12.18 12.30 1.48 12.21 12.30 1.52 12.20 12.30 1.56

Bolivia 2003 12.63 12.70 1.43 12.55 12.70 1.59 12.52 12.70 1.68

Bolivia 2008 12.43 12.50 1.63 12.31 12.50 1.59 12.34 12.40 1.68

Burkina Faso 2010 11.86 12.10 1.74 11.90 12.10 1.74 11.90 12.10 1.77

Burundi 2010 13.36 13.40 1.42 13.27 13.40 1.53 13.21 13.30 1.54

Cambodia 2010 12.00 12.10 1.32 12.19 12.30 1.37 12.03 12.20 1.53

Cambodia 2014 11.97 12.10 1.31 12.14 12.20 1.28 12.02 12.10 1.43

Cameroon 2004 12.14 12.20 1.63 12.08 12.20 1.74 12.04 12.20 1.79

Cameroon 2011 12.22 12.40 1.63 12.26 12.40 1.58 12.22 12.40 1.70

Congo 2005 11.66 11.70 1.44 11.57 11.70 1.50 11.48 11.60 1.53

Congo 2011-12 11.80 11.90 1.36 11.82 11.90 1.41 11.74 11.80 1.43

Congo DR 2007 12.01 12.10 1.74 11.86 11.90 1.78 11.79 11.90 1.78

Congo DR 2013-14 12.23 12.30 1.53 12.24 12.30 1.57 12.25 12.30 1.62

Cote d'Ivoire 2011-12 11.82 11.90 1.54 11.70 11.80 1.63 11.75 11.90 1.66

Egypt 2014 12.50 12.50 1.00 12.57 12.60 1.09 12.59 12.60 1.17

Ethiopia 2011 13.37 13.50 1.69 13.15 13.40 1.77 13.03 13.20 1.68

Ethiopia 2016 12.92 13.10 1.69 12.73 13.00 1.84 12.75 12.90 1.71

Gabon 2012 11.43 11.60 1.54 11.54 11.60 1.55 11.46 11.70 1.67

Gambia 2013 11.50 11.70 1.61 11.29 11.40 1.75 11.26 11.50 1.78

Ghana 2014 11.85 12.00 1.36 12.12 12.30 1.49 12.21 12.30 1.52

Guatemala 2014-15 13.37 13.40 1.30 13.34 13.40 1.37 13.22 13.40 1.54

Guinea 2005 11.78 12.00 1.82 11.73 11.80 1.68 11.83 12.00 1.69

Guinea 2012 11.91 12.10 1.55 11.91 12.00 1.58 11.78 12.00 1.69

Guyana 2009 12.30 12.50 1.49 12.41 12.50 1.47 12.18 12.40 1.70

Haiti 2005-06 11.91 12.20 1.85 12.01 12.20 1.80 12.05 12.30 1.88

Haiti 2012 11.68 11.80 1.56 11.91 12.10 1.58 11.95 12.10 1.64

Honduras 2005-06 13.14 13.20 1.33 13.14 13.20 1.37 13.02 13.10 1.49

Honduras 2011-12 13.24 13.30 1.25 13.27 13.30 1.32 13.09 13.20 1.47

India 2005-06 11.66 11.90 1.69 11.69 11.90 1.71 11.70 11.90 1.77

Jordan 2009 12.94 13.00 1.36 12.77 12.90 1.42 12.49 12.70 1.59

Jordan 2012 12.67 12.80 1.50 12.51 12.60 1.48 12.13 12.30 1.72

Kyrgyz Republic 2012 12.38 12.50 1.48 12.19 12.40 1.58 12.42 12.70 1.81

Lesotho 2009 13.07 13.20 1.53 12.74 13.00 1.80 12.91 13.10 1.75

Lesotho 2014 12.85 13.00 1.79 12.85 13.00 1.77 12.91 13.10 1.87

Malawi 2015-16 12.45 12.50 1.52 12.59 12.70 1.64 12.36 12.50 1.75

Mali 2012-13 11.87 12.00 1.57 11.78 12.00 1.61 11.72 11.80 1.67

Moldova 2005 12.70 12.80 1.22 12.61 12.70 1.33 12.50 12.60 1.45

Mozambique 2011 11.73 11.80 1.72 11.71 11.80 1.76 11.72 11.90 1.75

Myanmar 2015-16 11.99 12.10 1.42 12.13 12.20 1.45 11.98 12.10 1.62

Namibia 2013 13.12 13.30 1.56 13.17 13.30 1.56 12.96 13.10 1.66

Nepal 2006 12.33 12.50 1.45 12.45 12.60 1.56 12.57 12.70 1.61

Nepal 2011 12.46 12.60 1.45 12.61 12.70 1.52 12.59 12.70 1.60

Niger 2006 12.08 12.20 1.82 12.21 12.30 1.87 12.09 12.20 1.89

Niger 2012 12.04 12.20 1.57 12.14 12.30 1.63 11.96 12.10 1.56

Peru 2011 13.05 13.20 1.26 13.08 13.20 1.33 13.02 13.20 1.45

Peru 2012 12.99 13.10 1.20 13.00 13.10 1.30 12.96 13.10 1.42

Rwanda 2010 13.33 13.50 1.46 13.37 13.50 1.52 13.20 13.40 1.60

Rwanda 2014-15 13.06 13.10 1.36 13.12 13.20 1.49 13.10 13.20 1.60

ST and Principe 2008-09 11.79 11.90 1.45 12.13 12.20 1.50 12.34 12.40 1.54

Sierra Leone 2008 11.80 11.90 1.62 12.01 12.20 1.60 11.99 12.20 1.71

Sierra Leone 2013 11.90 12.00 1.55 12.05 12.10 1.58 12.05 12.20 1.60

Swaziland 2006-07 12.68 12.80 1.58 12.60 12.70 1.72 12.71 12.90 1.74

Tanzania 2010 12.09 12.20 1.56 12.14 12.30 1.75 12.09 12.30 1.81

Tanzania 2015-16 11.95 12.10 1.59 12.05 12.20 1.67 11.93 12.10 1.74

Timor-Leste 2009-10 12.70 12.80 1.30 12.84 13.00 1.39 12.79 13.00 1.50

Togo 2013-14 11.79 11.90 1.56 12.11 12.20 1.52 12.14 12.30 1.70

Uganda 2011 13.06 13.20 1.63 13.08 13.20 1.65 12.81 13.00 1.71

Yemen 2013 10.74 10.70 1.80 10.68 10.80 1.88 10.63 10.70 1.96

Zimbabwe 2010-11 12.77 13.00 1.73 12.76 13.00 1.83 12.61 12.90 1.91

Zimbabwe 2015 12.75 12.90 1.62 12.81 13.00 1.71 12.63 12.80 1.80

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude and smoking prior 

to removing implausible values 
70

Table A.5.6 Mean, standard deviation, and median hemoglobin concentrations g/dL for nonpregnant women after removing va

4-18 g/dL, wealth quintile

Lowest Second Middle Fourth 

Mean Median SD Mean Median SD Mean Median SD Mean Median SD 

Albania 2008-09 12.74 12.80 1.24 12.79 12.90 1.34 12.82 12.90 1.27 12.89 12.90 1.1

Armenia 2005 12.83 12.90 1.43 12.87 12.90 1.52 12.88 13.00 1.47 12.90 13.10 1.4

Azerbaijan 2006 12.08 12.30 1.65 12.21 12.40 1.61 12.32 12.50 1.55 12.34 12.50 1.5

Bangladesh 2011 11.99 12.00 1.39 12.03 12.10 1.36 12.12 12.20 1.29 12.22 12.20 1.3

Benin 2006 11.45 11.60 1.63 11.48 11.60 1.53 11.43 11.50 1.62 11.42 11.50 1.6

Benin 2011-12 12.20 12.30 1.48 12.25 12.40 1.59 12.21 12.30 1.58 12.22 12.40 1.5

Bolivia 2003 12.18 12.30 1.54 12.29 12.40 1.54 12.47 12.50 1.59 12.77 12.90 1.5

Bolivia 2008 12.03 12.20 1.58 12.28 12.40 1.64 12.32 12.40 1.61 12.41 12.50 1.6

Burkina Faso 2010 11.65 11.80 1.82 11.77 11.90 1.76 11.83 12.00 1.79 12.00 12.20 1.6

Burundi 2010 13.12 13.20 1.51 13.12 13.20 1.51 13.19 13.30 1.48 13.34 13.40 1.3

Cambodia 2010 11.79 11.90 1.53 11.93 12.00 1.52 11.99 12.10 1.43 12.17 12.30 1.3

Cambodia 2014 11.80 11.90 1.43 11.96 12.00 1.33 12.03 12.10 1.31 12.18 12.20 1.3

Cameroon 2004 12.26 12.30 1.80 12.06 12.20 1.78 12.14 12.30 1.71 12.05 12.20 1.6

Cameroon 2011 12.38 12.50 1.61 12.35 12.50 1.70 12.25 12.40 1.64 12.19 12.30 1.6

Congo 2005 11.52 11.70 1.41 11.51 11.60 1.48 11.49 11.60 1.58 11.45 11.60 1.5

Congo 2011-12 11.90 12.00 1.43 11.76 11.90 1.48 11.70 11.80 1.35 11.73 11.70 1.3

Congo DR 2007 11.88 12.00 1.80 11.78 11.80 1.78 11.82 11.90 1.84 12.15 12.20 1.8

Congo DR 2013-14 12.24 12.40 1.62 12.24 12.30 1.61 12.34 12.40 1.53 12.26 12.30 1.5

Cote d'Ivoire 2011-12 11.75 11.90 1.67 11.63 11.70 1.62 11.73 11.80 1.61 11.81 11.90 1.6

Egypt 2014 12.49 12.50 1.27 12.61 12.70 1.17 12.64 12.70 1.15 12.64 12.60 1.0

Ethiopia 2011 12.77 13.00 1.91 13.12 13.30 1.71 13.13 13.30 1.74 13.22 13.40 1.6

Ethiopia 2016 12.12 12.40 2.00 12.73 13.00 1.75 12.85 13.00 1.71 12.99 13.20 1.6

Gabon 2012 11.72 11.80 1.56 11.38 11.50 1.68 11.36 11.50 1.53 11.24 11.40 1.6

Gambia 2013 11.14 11.40 1.71 11.12 11.30 1.72 11.12 11.30 1.72 11.43 11.60 1.7

Ghana 2014 12.12 12.30 1.42 11.91 12.00 1.52 12.03 12.20 1.54 12.21 12.30 1.4

Guatemala 2014-15 13.19 13.30 1.48 13.34 13.50 1.47 13.33 13.40 1.41 13.30 13.40 1.3

Guinea 2005 11.70 11.80 1.63 11.66 11.80 1.78 11.71 11.80 1.82 12.01 12.10 1.6

Guinea 2012 11.65 11.80 1.74 11.59 11.80 1.65 11.84 12.00 1.62 12.10 12.30 1.5

Guyana 2009 12.52 12.70 1.59 12.19 12.30 1.62 12.16 12.30 1.59 12.14 12.30 1.5

Haiti 2005-06 12.14 12.30 1.76 12.22 12.40 1.79 11.94 12.20 1.89 11.84 12.10 1.8

Haiti 2012 12.20 12.30 1.51 11.96 12.10 1.61 11.68 11.90 1.62 11.77 11.90 1.6

Honduras 2005-06 13.09 13.20 1.41 13.19 13.30 1.45 13.13 13.20 1.40 13.05 13.10 1.3

Honduras 2011-12 13.28 13.40 1.36 13.36 13.40 1.37 13.21 13.30 1.35 13.08 13.10 1.3

India 2005-06 11.26 11.40 1.73 11.46 11.60 1.78 11.62 11.80 1.76 11.74 11.90 1.7

Jordan 2009 12.68 12.80 1.48 12.61 12.80 1.56 12.75 12.90 1.39 12.78 12.90 1.4

Jordan 2012 12.42 12.60 1.61 12.37 12.50 1.59 12.35 12.50 1.63 12.49 12.70 1.5

Kyrgyz Republic 2012 12.17 12.50 1.71 12.31 12.50 1.67 12.27 12.50 1.69 12.32 12.60 1.7

Lesotho 2009 12.98 13.20 1.61 12.96 13.10 1.60 12.83 12.90 1.68 12.77 13.00 1.8

Lesotho 2014 13.12 13.30 1.77 13.06 13.30 1.74 12.81 13.00 1.82 12.66 12.80 1.8

Malawi 2015-16 12.52 12.60 1.57 12.58 12.70 1.59 12.55 12.70 1.61 12.46 12.60 1.7

Mali 2012-13 11.51 11.60 1.66 11.67 11.80 1.59 11.70 11.80 1.57 11.74 11.90 1.6

Moldova 2005 12.42 12.50 1.41 12.43 12.60 1.34 12.48 12.60 1.42 12.64 12.70 1.3

Mozambique 2011 11.39 11.60 1.60 11.66 11.70 1.64 11.83 12.00 1.66 11.83 11.90 1.7

Myanmar 2015-16 11.93 12.00 1.59 12.01 12.10 1.57 12.06 12.20 1.50 12.12 12.20 1.5

Namibia 2013 12.84 13.00 1.68 13.08 13.20 1.55 13.11 13.20 1.62 13.11 13.20 1.6
71

Nepal 2006 12.64 12.80 1.59 12.25 12.40 1.56 12.33 12.50 1.59 12.49 12.60 1.5

Nepal 2011 12.61 12.70 1.54 12.55 12.60 1.58 12.45 12.60 1.53 12.52 12.60 1.4

Niger 2006 11.69 11.80 1.83 12.00 12.10 1.90 12.14 12.30 1.90 12.07 12.30 1.9

Niger 2012 11.74 12.00 1.73 12.08 12.20 1.55 11.97 12.00 1.60 12.19 12.30 1.5

Peru 2011 13.03 13.10 1.39 13.06 13.20 1.36 13.01 13.10 1.38 13.09 13.20 1.3

Peru 2012 12.87 12.90 1.38 12.98 13.10 1.32 13.02 13.10 1.31 13.03 13.10 1.3

Rwanda 2010 13.25 13.40 1.56 13.20 13.30 1.52 13.33 13.50 1.52 13.37 13.50 1.4

Rwanda 2014-15 12.81 13.00 1.56 13.06 13.20 1.48 13.18 13.30 1.47 13.23 13.30 1.4

ST and Principe 2008-09 12.12 12.10 1.53 12.08 12.20 1.52 12.01 12.20 1.57 12.18 12.20 1.4

Sierra Leone 2008 11.96 12.10 1.65 11.91 12.10 1.67 11.94 12.10 1.56 11.95 12.20 1.7

Sierra Leone 2013 11.84 11.90 1.51 11.88 12.00 1.57 11.82 12.00 1.56 12.01 12.10 1.5

Swaziland 2006-07 12.84 13.00 1.57 12.80 12.90 1.64 12.61 12.80 1.78 12.56 12.70 1.7

Tanzania 2010 12.14 12.30 1.74 12.16 12.30 1.72 12.28 12.40 1.70 12.12 12.20 1.7

Tanzania 2015-16 11.89 12.00 1.66 11.95 12.10 1.62 12.03 12.20 1.71 12.08 12.20 1.7

Timor-Leste 2009-10 12.80 13.00 1.47 12.84 13.00 1.39 12.86 13.00 1.33 12.74 12.90 1.4

Togo 2013-14 12.29 12.40 1.54 12.33 12.40 1.54 12.10 12.20 1.64 11.81 11.90 1.6

Uganda 2011 12.69 12.80 1.54 12.87 13.00 1.55 13.06 13.20 1.52 12.99 13.10 1.6

Yemen 2013 10.11 10.20 1.86 10.64 10.70 1.93 10.66 10.80 1.90 10.86 10.90 1.9

Zimbabwe 2010-11 12.78 13.00 1.85 12.78 13.00 1.83 12.79 13.00 1.80 12.66 12.90 1.8

Zimbabwe 2015 12.69 12.80 1.68 12.86 13.00 1.67 12.71 12.90 1.71 12.77 13.00 1.8

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude and smoking prior to removing im
72

Table A.5.7 Mean, standard deviation, and median adjusted-hemoglobin concentrations g/dL for 

nonpregnant women after removing values outside the range 4-18 g/dL, education

No Education Primary Secondary Higher

Mean Median SD Mean Median SD Mean Median SD Mean Median SD

Albania 2008-09 12.36 12.40 1.49 12.80 12.90 1.26 12.83 12.90 1.22 12.90 12.90 0.91

Armenia 2005 11.94 11.90 1.91 13.00 13.20 1.50 12.84 12.90 1.48 12.93 13.10 1.41

Azerbaijan 2006 12.06 12.20 1.50 12.02 12.40 1.79 12.24 12.40 1.57 12.43 12.60 1.37

Bangladesh 2011 12.02 12.10 1.40 12.11 12.20 1.37 12.31 12.30 1.29 12.31 12.50 1.37

Benin 2006 11.44 11.50 1.60 11.48 11.60 1.60 11.45 11.50 1.52 11.73 11.90 1.90

Benin 2011-12 12.21 12.30 1.54 12.10 12.20 1.53 12.28 12.40 1.49 11.88 12.00 1.34

Bolivia 2003 12.22 12.40 1.75 12.47 12.60 1.59 12.62 12.70 1.54 12.89 13.00 1.57

Bolivia 2008 12.01 12.10 1.68 12.28 12.40 1.62 12.38 12.50 1.63 12.52 12.60 1.63

Burkina Faso 2010 11.82 12.00 1.77 12.10 12.20 1.61 12.07 12.30 1.73 12.16 12.50 1.52

Burundi 2010 13.17 13.30 1.50 13.27 13.40 1.49 13.61 13.70 1.47 13.30 13.80 1.88

Cambodia 2010 11.94 12.00 1.59 12.06 12.10 1.43 12.20 12.30 1.33 12.37 12.50 1.08

Cambodia 2014 11.99 12.10 1.49 12.02 12.10 1.36 12.14 12.20 1.26 12.23 12.30 1.18

Cameroon 2004 12.38 12.60 1.91 12.03 12.10 1.70 11.99 12.10 1.64 11.86 12.00 1.62

Cameroon 2011 12.23 12.40 1.74 12.35 12.40 1.61 12.13 12.20 1.61 12.42 12.50 1.38

Congo 2005 11.59 11.70 1.24 11.50 11.60 1.52 11.59 11.70 1.52 11.71 12.00 1.48

Congo 2011-12 11.93 12.00 1.35 11.80 11.90 1.44 11.77 11.80 1.39 11.73 11.70 1.36

Congo DR 2007 11.83 11.90 1.86 11.86 11.90 1.82 11.93 12.00 1.67 11.71 11.70 1.61

Congo DR 2013-14 12.37 12.40 1.61 12.23 12.30 1.59 12.19 12.30 1.55 12.34 12.50 1.59

Cote d'Ivoire 2011-12 11.69 11.80 1.63 11.73 11.80 1.64 11.89 12.00 1.56 11.81 12.00 1.61

Egypt 2014 12.57 12.60 1.18 12.68 12.70 1.20 12.56 12.60 1.12 12.58 12.60 0.99

Ethiopia 2011 12.90 13.10 1.84 13.40 13.50 1.60 13.59 13.70 1.45 13.56 13.60 1.47

Ethiopia 2016 12.46 12.70 1.91 12.94 13.10 1.66 13.13 13.30 1.58 13.24 13.40 1.46

Gabon 2012 11.80 11.90 1.55 11.58 11.70 1.58 11.42 11.60 1.59 11.23 11.60 1.63

Gambia 2013 11.13 11.30 1.75 11.30 11.50 1.66 11.60 11.70 1.70 11.76 11.80 1.65

Ghana 2014 12.10 12.30 1.46 12.09 12.20 1.42 12.07 12.20 1.52 12.39 12.40 1.42

Guatemala 2014-15 13.22 13.40 1.54 13.30 13.40 1.45 13.33 13.40 1.31 13.41 13.50 1.30

Guinea 2005 11.77 11.90 1.69 11.75 12.00 1.78 11.85 12.00 1.81 11.96 12.00 1.56

Guinea 2012 11.77 12.00 1.66 11.97 12.10 1.53 12.12 12.30 1.49 12.37 12.30 1.34

Guyana 2009 12.41 12.60 1.62 12.25 12.50 1.66 12.30 12.50 1.57 12.39 12.40 1.31

Haiti 2005-06 12.06 12.30 1.89 11.93 12.10 1.82 12.00 12.20 1.80 12.15 12.50 1.83

Haiti 2012 12.03 12.20 1.62 11.89 12.10 1.62 11.75 11.90 1.57 12.02 12.00 1.49

Honduras 2005-06 13.03 13.20 1.48 13.12 13.20 1.43 13.09 13.10 1.31 13.09 13.20 1.21

Honduras 2011-12 13.23 13.40 1.51 13.25 13.30 1.38 13.16 13.20 1.29 13.11 13.20 1.30

India 2005-06 11.47 11.60 1.79 11.60 11.80 1.75 11.80 12.00 1.68 12.05 12.20 1.57

Jordan 2009 12.56 12.70 1.68 12.57 12.70 1.55 12.71 12.80 1.47 12.80 12.90 1.42

Jordan 2012 12.34 12.50 1.74 12.37 12.50 1.73 12.38 12.50 1.57 12.49 12.70 1.56

Kyrgyz Republic 2012 11.74 12.00 2.14 12.54 12.50 1.50 12.33 12.60 1.64 12.29 12.50 1.66

Lesotho 2009 12.76 13.10 1.85 12.88 13.10 1.67 12.86 13.10 1.79 12.87 13.10 1.85

Lesotho 2014 12.94 13.00 1.27 12.93 13.10 1.80 12.82 13.00 1.82 12.81 13.00 1.80

Malawi 2015-16 12.36 12.50 1.68 12.53 12.70 1.63 12.49 12.60 1.67 12.39 12.40 1.70

Mali 2012-13 11.66 11.80 1.63 11.92 12.00 1.61 12.19 12.30 1.50 12.32 12.40 1.61

Moldova 2005 12.82 12.80 1.54 13.12 13.20 1.08 12.55 12.70 1.36 12.63 12.70 1.38

Mozambique 2011 11.62 11.70 1.69 11.72 11.90 1.72 11.79 11.90 1.87 12.06 12.30 1.68

Myanmar 2015-16 12.03 12.10 1.63 12.01 12.10 1.56 12.09 12.20 1.47 12.02 12.10 1.43

Namibia 2013 12.94 13.00 1.61 12.97 13.20 1.70 13.12 13.20 1.57 13.35 13.40 1.54

Nepal 2006 12.38 12.50 1.61 12.58 12.70 1.54 12.51 12.60 1.45 12.63 12.70 1.44

Nepal 2011 12.51 12.60 1.58 12.65 12.70 1.55 12.57 12.70 1.48 12.68 12.80 1.50

Niger 2006 12.06 12.20 1.91 12.45 12.60 1.65 12.45 12.50 1.68 12.42 12.40 1.87

Niger 2012 12.04 12.20 1.61 12.11 12.20 1.62 12.21 12.40 1.47 12.26 12.00 1.40

Peru 2011 13.07 13.20 1.42 13.02 13.20 1.43 13.05 13.20 1.32 13.09 13.20 1.36

Peru 2012 12.84 13.00 1.48 12.97 13.10 1.39 12.96 13.00 1.29 13.05 13.20 1.32

Rwanda 2010 13.09 13.20 1.58 13.34 13.50 1.50 13.41 13.60 1.56 13.34 13.70 1.60

Rwanda 2014-15 12.97 13.10 1.65 13.12 13.20 1.49 13.14 13.20 1.40 12.93 13.20 1.71

ST and Principe 2008-09 11.87 11.90 1.51 12.19 12.20 1.51 12.07 12.20 1.52 11.67 11.60 1.38

Sierra Leone 2008 11.96 12.10 1.63 11.95 12.20 1.70 11.98 12.10 1.64 12.22 12.50 1.57

Sierra Leone 2013 11.94 12.00 1.56 11.91 12.00 1.59 12.14 12.30 1.57 12.71 12.80 1.67

Swaziland 2006-07 12.63 12.80 1.66 12.68 12.90 1.73 12.62 12.70 1.68 12.76 12.90 1.67

Tanzania 2010 11.93 12.10 1.78 12.26 12.40 1.73 11.89 12.00 1.64 11.99 12.10 1.44

Tanzania 2015-16 11.75 11.90 1.70 12.06 12.20 1.70 11.98 12.10 1.63 12.25 12.20 1.47

Timor-Leste 2009-10 12.75 13.00 1.49 12.84 13.00 1.44 12.78 12.90 1.34 12.90 13.00 1.07

Togo 2013-14 12.13 12.20 1.62 12.11 12.30 1.60 11.93 12.00 1.56 11.86 11.90 1.62

Uganda 2011 12.76 12.90 1.68 12.97 13.10 1.63 13.20 13.30 1.59 13.06 13.30 2.12

Yemen 2013

NA

Zimbabwe 2010-11 12.82 13.10 1.92 12.75 13.00 1.89 12.72 12.90 1.81 12.60 12.90 1.76

Zimbabwe 2015 12.87 13.00 1.63 12.69 12.80 1.76 12.76 12.90 1.71 12.72 12.90 1.72

Note: Congo DR, Congo Democratic Republic; NA, not available; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude 

and smoking prior to removing implausible values 
73

Table A.5.8 Mean, standard deviation, and median hemoglobin concentrations g/dL for nonpregnant women 

after removing values outside the range 4-18 g/dL, residence

Urban Rural

Mean Median SD Mean Median SD

Albania 2008-09 12.87 12.90 1.13 12.78 12.80 1.29

Armenia 2005 12.82 12.90 1.46 12.93 13.00 1.46

Azerbaijan 2006 12.31 12.50 1.51 12.19 12.40 1.60

Bangladesh 2011 12.31 12.30 1.36 12.10 12.20 1.34

Benin 2006 11.46 11.50 1.59 11.44 11.50 1.59

Benin 2011-12 12.18 12.30 1.48 12.22 12.40 1.56

Bolivia 2003 12.67 12.80 1.60 12.35 12.50 1.55

Bolivia 2008 12.41 12.50 1.63 12.23 12.40 1.63

Burkina Faso 2010 12.07 12.30 1.70 11.81 12.00 1.76

Burundi 2010 13.43 13.50 1.59 13.24 13.40 1.47

Cambodia 2010 12.35 12.40 1.28 11.97 12.00 1.47

Cambodia 2014 12.21 12.30 1.30 12.01 12.10 1.35

Cameroon 2004 11.96 12.10 1.70 12.20 12.30 1.74

Cameroon 2011 12.18 12.30 1.60 12.31 12.40 1.66

Congo 2005 11.59 11.70 1.51 11.51 11.60 1.48

Congo 2011-12 11.67 11.80 1.35 11.85 11.90 1.43

Congo DR 2007 11.93 12.00 1.70 11.81 11.90 1.84

Congo DR 2013-14 12.17 12.30 1.57 12.28 12.40 1.58

Cote d'Ivoire 2011-12 11.80 11.90 1.62 11.68 11.80 1.63

Egypt 2014 12.55 12.60 1.04 12.60 12.60 1.19

Ethiopia 2011 13.45 13.60 1.56 13.04 13.30 1.79

Ethiopia 2016 13.12 13.30 1.59 12.61 12.80 1.84

Gabon 2012 11.42 11.60 1.58 11.64 11.80 1.60

Gambia 2013 11.69 11.80 1.66 11.06 11.30 1.73

Ghana 2014 12.11 12.30 1.50 12.08 12.20 1.46

Guatemala 2014-15 13.31 13.40 1.36 13.31 13.40 1.45

Guinea 2005 11.87 12.00 1.69 11.74 11.80 1.72

Guinea 2012 12.07 12.20 1.50 11.75 11.90 1.67

Guyana 2009 12.23 12.40 1.50 12.32 12.50 1.60

Haiti 2005-06 11.84 12.10 1.85 12.14 12.40 1.81

Haiti 2012 11.68 11.80 1.61 12.01 12.20 1.57

Honduras 2005-06 13.07 13.20 1.36 13.13 13.20 1.42

Honduras 2011-12 13.12 13.20 1.34 13.28 13.40 1.36

India 2005-06 11.79 12.00 1.69 11.61 11.80 1.75

Jordan 2009 12.71 12.80 1.46 12.73 12.90 1.51

Jordan 2012 12.41 12.60 1.58 12.43 12.60 1.60

Kyrgyz Republic 2012 12.41 12.60 1.60 12.26 12.50 1.67

Lesotho 2009 12.80 13.10 1.88 12.89 13.10 1.68

Lesotho 2014 12.63 12.90 1.84 12.98 13.10 1.77

Malawi 2015-16 12.42 12.50 1.71 12.52 12.70 1.63

Mali 2012-13 12.03 12.20 1.58 11.67 11.80 1.63

Moldova 2005 12.67 12.80 1.34 12.46 12.60 1.38

Mozambique 2011 11.72 11.90 1.85 11.71 11.80 1.67

Myanmar 2015-16 12.08 12.20 1.47 12.03 12.10 1.54

Namibia 2013 13.14 13.30 1.63 13.03 13.20 1.59

Nepal 2006 12.53 12.60 1.55 12.43 12.60 1.55

Nepal 2011 12.63 12.80 1.54 12.55 12.60 1.53

Niger 2006 12.37 12.50 1.81 12.03 12.10 1.88

Niger 2012 12.16 12.30 1.56 12.03 12.20 1.61

Peru 2011 13.06 13.20 1.36 13.04 13.10 1.39

Peru 2012 13.01 13.10 1.32 12.92 13.00 1.35

Rwanda 2010 13.37 13.60 1.58 13.30 13.40 1.52

Rwanda 2014-15 13.17 13.30 1.47 13.08 13.20 1.51

ST and Principe 2008-09 12.00 12.10 1.55 12.23 12.30 1.48

Sierra Leone 2008 12.00 12.20 1.63 11.95 12.10 1.65

Sierra Leone 2013 12.23 12.30 1.58 11.86 12.00 1.56

Swaziland 2006-07 12.42 12.60 1.76 12.75 12.90 1.65

Tanzania 2010 11.95 12.10 1.76 12.17 12.30 1.71

Tanzania 2015-16 12.05 12.20 1.70 11.97 12.10 1.67

Timor-Leste 2009-10 12.76 12.90 1.40 12.80 12.90 1.41

Togo 2013-14 11.77 11.90 1.60 12.23 12.30 1.57

Uganda 2011 13.23 13.40 1.73 12.90 13.00 1.63

Yemen 2013 10.93 11.00 1.82 10.56 10.60 1.93

Zimbabwe 2010-11 12.59 12.80 1.84 12.79 13.00 1.82

Zimbabwe 2015 12.71 12.90 1.75 12.77 12.90 1.70

Note: Congo DR, Congo Democratic Republic; ST, Sao Tome; SD, Standard deviation; Hemoglobin adjusted for altitude and smoking prior to 

removing implausible values