RESEARCH ARTICLE
Malaria, malnutrition, and birthweight:
A meta-analysis using individual participant
data
Jordan E. Cates1*, Holger W. Unger2,3, Valerie Briand4, Nadine Fievet4,
Innocent Valea5,6, Halidou Tinto5,6, Umberto D’Alessandro7, Sarah H. Landis8,
Seth Adu-Afarwuah9, Kathryn G. Dewey10, Feiko O. ter Kuile11, Meghna Desai12,
Stephanie Dellicour11, Peter Ouma13, Julie Gutman12, Martina Oneko13,
Laurence Slutsker14, Dianne J. Terlouw11,15, Simon Kariuki13, John Ayisi13,
a1111111111 Mwayiwawo Madanitsa
11,16, Victor Mwapasa16, Per Ashorn17, Kenneth Maleta16,
a1111111111 Ivo Mueller
18, Danielle Stanisic19, Christentze Schmiegelow20, John P. A. Lusingu20,21,
a1111111111 Anna Maria van Eijk
11, Melissa Bauserman22,23, Linda Adair23, Stephen R. Cole1,
Daniel Westreich1‡a1111111111 , Steven Meshnick
1‡, Stephen Rogerson3‡
a1111111111
1 Department of Epidemiology, UNC-Chapel Hill, Chapel Hill, North Carolina, United States of America,
2 Department of Obstetrics and Gynaecology, Edinburgh Royal Infirmary, Edinburgh, United Kingdom,
3 Department of Medicine at the Doherty Institute, The University of Melbourne, Parkville, Victoria, Australia,
4 UMR216-MERIT, French National Research Institute for Sustainable Development (IRD), Paris Descartes
University, Paris, France, 5 Unite de Recherche Clinique de Nanoro, Institut de Recherche en Sciences de la
OPENACCESS Santé-DRO, Bobo-Dioulasso, Burkina Faso, 6 Departement de Recherche Clinique, Centre Muraz, Bobo-
Dioulasso, Burkina Faso, 7 Medical Research Council Unit, The Gambia; London School of Hygiene and
Citation: Cates JE, Unger HW, Briand V, Fievet N, Tropical Medicine, London, United Kingdom, 8 Worldwide Epidemiology, GlaxoSmithKline, Uxbridge, United
Valea I, Tinto H, et al. (2017) Malaria, malnutrition, Kingdom, 9 Department of Nutrition and Food Science, University of Ghana, Legon, Accra, Ghana,
and birthweight: A meta-analysis using individual 10 Department of Nutrition, University of California, Davis, California, United States of America,
participant data. PLoS Med 14(8): e1002373. 11 Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, United Kingdom,
12 Malaria Branch, Division of Parasitic Diseases and Malaria, Center for Global Health, Centers for Disease
https://doi.org/10.1371/journal.pmed.1002373
Control and Prevention, Atlanta, Georgia, United States of America, 13 Kenya Medical Research Institute
Academic Editor: Lorenz von Seidlein, Mahidol- (KEMRI)/ Centre for Global Health Research, Kisumu, Kenya, 14 Malaria and Neglected Tropical Diseases,
Oxford Tropical Medicine Research Unit, Center for Malaria Control and Elimination, PATH, Seattle, Washington, United States of America,
THAILAND 15 Malawi-Liverpool-Wellcome Trust Clinical Research Programme, Blantyre, Malawi, 16 School of Public
Health and Family Medicine, College of Medicine, University of Malawi, Blantyre, Malawi, 17 Center for Child
Received: March 6, 2017 Health Research University of Tampere School of Medicine and Tampere University Hospital, Tampere,
Finland, 18 Walter and Eliza Hall Institute, Parkville, Victoria, Australia, 19 Institute for Glycomics, Griffith
Accepted: July 11, 2017 University, Gold Coast, Queensland, Australia, 20 Centre for Medical Parasitology, Depart. Of Immunology
and Microbiology, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark, 21 National
Published: August 8, 2017
Institute for Medical Research, Tanga Centre, Tanga, Tanzania, 22 Department of Pediatrics, Division of
Copyright: This is an open access article, free of all Neonatal-Perinatal Medicine, School of Medicine, UNC-Chapel Hill, Chapel Hill, North Carolina, United States
copyright, and may be freely reproduced, of America, 23 Department of Nutrition, UNC-Chapel Hill, Chapel Hill, North Carolina, United States of
distributed, transmitted, modified, built upon, or America
otherwise used by anyone for any lawful purpose.
‡These authors are joint senior authors on this work.
The work is made available under the Creative
* jordanecates@gmail.com
Commons CC0 public domain dedication.
Data Availability Statement: Data are available
from the WWARN data repository (http://www. Abstract
wwarn.org/working-together/sharing-data/
accessing-data) for researchers who meet the
criteria for access to confidential data.
Background
Funding: JC was funded by the National Institute of
Allergy and Infectious Diseases at the National Four studies previously indicated that the effect of malaria infection during pregnancy on the
Institutes of Health (Pre-doctoral Training in
risk of low birthweight (LBW; <2,500 g) may depend upon maternal nutritional status. We
Infectious Disease Epidemiology grant #5 T32
AI070114). The STOPPAM project, "Strategies To investigated this dependence further using a large, diverse study population.
Prevent Pregnancy-Associated Malaria," was
PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002373 August 8, 2017 1 / 20
Malaria, malnutrition, and birthweight
supported by the European Union’s Seventh Methods and findings
Framework Programme (EU FP7); STOPPAM
contract number: 200889. STOPPAM I (Benin) and We evaluated the interaction between maternal malaria infection and maternal anthropo-
STOPPAM II (Tanzania). The FSP/MISAME study metric status on the risk of LBW using pooled data from 14,633 pregnancies from 13 studies
(Burkina Faso) was funded by Nutrition Third (6 cohort studies and 7 randomized controlled trials) conducted in Africa and the Western
World, The Belgium Ministry of Development,
Pacific from 1996–2015. Studies were identified by the Maternal Malaria and Malnutrition
Flemish Interuniversity Council, and French
Ministry of Development. The ECHO study (M3) initiative using a convenience sampling approach and were eligible for pooling given
(Democratic Republic of the Congo) was funded by adequate ethical approval and availability of essential variables. Study-specific adjusted
the Department of Epidemiology, University of
effect estimates were calculated using inverse probability of treatment-weighted linear and
North Carolina Chapel Hill, UNC Gillings School of
log-binomial regression models and pooled using a random-effects model. The adjusted
Global Public Health. The iLiNS-DYAD (Ghana) trial
was funded by a grant to the University of risk of delivering a baby with LBW was 8.8% among women with malaria infection at antena-
California, Davis from the Bill & Melinda Gates tal enrollment compared to 7.7% among uninfected women (adjusted risk ratio [aRR] 1.14
Foundation. EMEP was partly supported by the
[95% confidence interval (CI): 0.91, 1.42]; N = 13,613), 10.5% among women with malaria
Malaria in Pregnancy (MiP) Consortium, which is
funded through a grant from the Bill & Melinda infection at delivery compared to 7.9% among uninfected women (aRR 1.32 [95% CI: 1.08,
Gates Foundation to the Liverpool School of 1.62]; N = 11,826), and 15.3% among women with low mid-upper arm circumference
Tropical Medicine, UK and partly by the US Centers (MUAC <23 cm) at enrollment compared to 9.5% among women with MUAC 23 cm (aRR
for Disease Control and Prevention (CDC), Division
1.60 [95% CI: 1.36, 1.87]; N = 9,008). The risk of delivering a baby with LBW was 17.8%
of Parasitic Diseases and Malaria through a
cooperative agreement with Kenya Medical among women with both malaria infection and low MUAC at enrollment compared to 8.4%
Research Institute (KEMRI), Center for Global among uninfected women with MUAC 23 cm (joint aRR 2.13 [95% CI: 1.21, 3.73];
Health Research (CGHR), Kisumu, Kenya. The
N = 8,152). There was no evidence of synergism (i.e., excess risk due to interaction)
IPTp-MON study (Kenya) was partly supported by
between malaria infection and MUAC on the multiplicative (p = 0.5) or additive scale
the MiP Consortium, which is funded through a
grant from the Bill & Melinda Gates Foundation to (p = 0.9). Results were similar using body mass index (BMI) as an anthropometric indicator
the Liverpool School of Tropical Medicine, UK and of nutritional status. Meta-regression results indicated that there may be multiplicative inter-
partly supported by the CDC. The ITN project
action between malaria infection at enrollment and low MUAC within studies conducted in
(Kenya) was funded by the US Agency for
International Development. The Special Health Africa; however, this finding was not consistent on the additive scale, when accounting for
Support Fund from the Royal Netherlands multiple comparisons, or when using other definitions of malaria and malnutrition. The major
Embassy (Nairobi, Kenya) provided additional limitations of the study included availability of only 2 cross-sectional measurements of
support for the study of the impact of ITN in
malaria and the limited availability of ultrasound-based pregnancy dating to assess impacts
pregnancy. The Kisumu study (Kenya) was funded
by US Agency for International Development on preterm birth and fetal growth in all studies.
(grants AOT0483-PH1-2171 and HRN-A-00-04-
00010-02) and the Netherlands Foundation for the
Advancement of Tropical Research. The STOPMIP Conclusions
study (Kenya) was funded by the Malaria in Pregnant women with malnutrition and malaria infection are at increased risk of LBW com-
Pregnancy (MiP) Consortium, which is funded
pared to women with only 1 risk factor or none, but malaria and malnutrition do not act
through a grant from the Bill & Melinda Gates
Foundation to the Liverpool School of Tropical synergistically.
Medicine, UK. The ISTp study (Malawi) was partly
supported by the Malaria in Pregnancy (MiP)
Consortium, which is funded through a grant from
the Bill & Melinda Gates Foundation to the
Liverpool School of Tropical Medicine, UK and Author summary
partly funded by the European and Developing
Countries Clinical Trials Partnership (EDCTP). The
LAIS study was supported by grants from the Why was this study done?
Academy of Finland (grants 79787 and 207010),
the Foundation for Pediatric Research in Finland, • More than 125 million pregnant women are at risk of malaria in pregnancy annually,
and the Medical Research Fund of Tampere producing detrimental effects on maternal, newborn, and infant health.
University Hospital. Azithromycin and its placebo
• Maternal undernutrition is estimated to be responsible for 800,000 newborn deaths
were provided free of charge by Pfizer Inc (New
York, New York), which also provided funding for annually.
PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002373 August 8, 2017 2 / 20
Malaria, malnutrition, and birthweight
the polymerase chain reaction testing of the
sexually transmitted infections. The IPTp study • Prior evidence from 4 small studies indicated that the harmful impact of malaria on
(Papua New Guinea [PNG]) was funded by the MiP fetal growth and birthweight (BW) may depend upon the macronutrient nutritional sta-
Consortium, through a grant from the Bill &
tus of the mother.
Melinda Gates Foundation (46099); the Pregvax
Consortium, through a grant from the EU FP7- • If malaria and maternal undernutrition have synergistic negative impacts on pregnancy
2007-HEALTH (PREGVAX 201588) and the outcomes, interventions targeted to high-risk women might provide substantial public
Spanish Government (EUROSALUD 2008
benefit.
Programme); and Pfizer Inc., through an
investigator-initiated research grant (WS394663). • The present study provides a robust assessment of potential malaria–nutrition interac-
The Sek study (PNG) was supported by AusAID
tions in pregnancy and overcomes size and methodological limitations of earlier explor-
(grant to PNG Institute of Medical Research
atory studies.
[IMR]), the National Health and Medical Research
Council of Australia; Australian Research Council;
Wellcome Trust; and Veterans Affairs Research
Service. The Walter and Eliza Hall Institute is
supported by the NHMRC Infrastructure for What did the researchers do and find?
Research Institutes Support Scheme and Victorian
State Government Operational Infrastructure • We present a large, pooled analysis of individual participant data from 13 studies con-
Support. The funders had no role in study design, ducted in sub-Saharan Africa and the Western Pacific investigating the interaction
data collection and analysis, decision to publish, or between maternal malaria infection and malnutrition on the risk of low birthweight
preparation of the manuscript. (LBW) and reduced mean BW.
Competing interests: The authors of this
• The findings suggest that women who are both infected with malaria and malnourished
manuscript have the following competing interests:
are at greater risk of LBW than their uninfected, well-nourished counterparts.
SHL is is a full-time employee of GlaxoSmithKline
and holds shares in GlaxoSmithKline. SR is a • However, the study found no conclusive evidence of interaction between the 2, i.e., the
member of the Editorial Board of PLOS Medicine.
impact of malaria on BW was independent of the macronutrient nutritional status of
Abbreviations: aRR, adjusted risk ratio; BMI, body the mother.
mass index; BW, birthweight; CI, confidence
interval; DRC, Democratic Republic of the Congo; • Subgroup analyses did find that studies conducted just in Africa had slight evidence of
EMM, effect measure modification; FGR, fetal interaction, but this was not consistent throughout all analyses.
growth restriction; HIV, human immunodeficiency
virus; IPTp, intermittent preventive treatment in
pregnancy; IPTW, inverse probability of treatment
weights; LBW, low birthweight; LM, light What do these findings mean?
microscopy; LMIC, low- and middle-income
countries; M3, Maternal Malaria and Malnutrition; • Although there was no overall evidence of malaria–nutrition interactions, more than 1
MiPc, Malaria in Pregnancy Consortium; MUAC, in 3 pregnant women suffered from malaria and/or undernutrition, emphasizing the
mid-upper arm circumference; PCR, polymerase
importance of joint approaches to decrease maternal malaria and improve nutrition to
chain reaction; PEI, population effects interval;
PNG, Papua New Guinea; RDT, rapid diagnostic minimize adverse pregnancy outcomes.
test; RR, risk ratio; SGA, small for gestational age;
SP, sulfadoxine-pyrimethamine; WHO, World
Health Organization.
Introduction
Annually, over 20 million infants are born low birthweight (LBW; <2,500 g), predominantly
in low- and middle-income countries (LMICs) [1]. LBW can have negative impacts on neona-
tal mortality and childhood neurological, metabolic, and physical development [2]. The World
Health Organization (WHO) has set a Global Nutrition Target of 30% reduction in LBW by
2025 [1].
One preventable cause of LBW in LMICs is maternal malaria infection [2,3]. Its prevalence
remains high, despite targeted malaria prevention programs [2]. Annually, 125 million preg-
nant women are at risk for malaria [4]. The predominant species, Plasmodium falciparum,
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Malaria, malnutrition, and birthweight
sequesters in the placenta, causing LBW through fetal growth restriction (FGR) and preterm
delivery [2]. Prior estimates from Africa suggest that malaria infection doubles the risk of
LBW [2,4]. The prevention of malaria infection during pregnancy remains a public health
priority.
Another modifiable risk factor for impaired fetal growth is maternal malnutrition, specifi-
cally undernutrition [5]. Up to 20% of African women of reproductive age are undernourished
[5–7]. Maternal protein-energy-fat (macronutrient) and micronutrient reserves and dietary
consumption influence fetal growth. Micronutrient deficiencies are difficult and costly to
assess; therefore, anthropometrics are commonly used as sensitive but nonspecific indicators
of protein reserves, fat stores, and malnutrition more broadly [7].
Recent evidence indicates that the relationship between malaria infection and LBW may
depend upon the mother’s nutritional status [8]. Studies in Papua New Guinea (PNG) and
Benin found inconsistent evidence of modification of the malaria infection–LBW relationship
by maternal anthropometric status, but studies from Kenya and the Democratic Republic of
the Congo (DRC) reported significant modification [9–12]. Notably, in the DRC, the risk of
FGR associated with malaria infection was 2 to 8 times higher among malnourished women
[11]. Malaria infection and malnutrition may act along similar physiological pathways by
affecting placental development and nutrient transfer [2,4,5].
To date, work on this potential interaction has been limited to 4 studies, with only 1,318
pregnant women from Africa and 1,369 pregnant women from PNG. Not only were these
studies somewhat inconsistent in their findings, but their interpretation is hindered by rela-
tively small sample sizes, and their findings may not be generalizable to other malaria-endemic
countries. The objective of this study was to investigate the putative interaction between
maternal malaria infection and malnutrition in relation to birthweight (BW) using a large,
pooled dataset of 14,633 live birth pregnancies from women participating in 13 studies con-
ducted in multiple LMICs. We hypothesized that there would be a synergistic interaction, such
that the observed joint effect of being both infected with malaria and malnourished would be
greater than expected if considering each exposure independently.
Methods
Study population
We used data from 14,633 singleton live birth pregnancies from women participating in 13
studies conducted from 1996 to 2015 in 8 African countries and the Western Pacific (PNG) as
part of the Maternal Malaria and Malnutrition (M3) initiative [9,11,13–24]. The M3 initiative
has been described in detail previously [25]. Briefly, the M3 initiative is a collaboration with
the Malaria in Pregnancy Consortium (MiPc) and affiliated malaria and nutrition researchers
who agreed to pool resources to improve the understanding of malaria–nutrition interactions.
A convenience sampling approach was taken to obtain eligible studies identified by researchers
within the MiPc, and inclusion of studies for the individual participant data meta-analysis
stopped 1 January 2016. Studies were eligible if they were an observational study or random-
ized controlled trial conducted between 1996 and 2015 enrolling pregnant women during
pregnancy with follow-up through delivery and they met the following criteria: ethical
approval allowed for secondary analyses and data sharing, malaria was endemic in the area
with medium to high transmission, assessment of malariometric indices (light microscopy
[LM] and/or rapid diagnostic tests [RDT]) at enrollment/first antenatal care visit (ANC),
assessment of anthropometric indicators at enrollment (mid-upper arm circumference
[MUAC] and/or body mass index [BMI]), and assessment of infant weight within 24 hours
postpartum or within 7 days of birth if timing of weight measurement data was available. Data
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Malaria, malnutrition, and birthweight
was shared by each individual study using a standardized data transfer file. Participating stud-
ies had been undertaken for a range of objectives, including investigation of the mechanisms
leading to LBW as a result of malaria, evaluation of antimalarial interventions during preg-
nancy such as intermittent preventive therapy during pregnancy (IPTp) or insecticide-treated
bed nets (ITN), or the assessment of the potential of nutritional supplementation during preg-
nancy to improve birth outcomes (S1 Table). All studies received approval by their local ethics
board and obtained informed consent from all participants. The prospective protocol for the
IPD analysis is included in the supplemental text (S2 Text).
Outcomes and exposures
The main outcome measure was BW, analyzed both continuously and dichotomized at 2,500
grams (LBW) [1]. Ten studies used digital scales to weigh newborns, 2 studies used spring or
digital scales, and 1 study used a hanging weighing scale (S2 Table). Weights measured after 24
hours (13% of weights) were adjusted using a cubic regression model to account for weight
changes in the first week of life [26]. Among 9 studies with ultrasound-dated gestational age,
we considered 2 secondary outcomes: small for gestational age (SGA; a BW less than the 10th
percentile of the INTERGROWTH-21st reference) and preterm birth (PTB; gestational age less
than 37 weeks) [27].
Diagnostics for malaria were collected at study enrollment and at delivery. For the interac-
tion analyses, we chose to focus on malaria infection at enrollment instead of at delivery for 2
reasons. First, from a public health perspective, if there was interaction at the time of study
enrollment, this might help inform future interventions that could be implemented during
antenatal care. Second, it has been hypothesized that malaria infection and malnutrition may
act along similar physiological pathways to alter fetal growth by decreasing maternal–fetal oxy-
gen transfer and reducing uteroplacental blood flow; 2 mechanisms that would be altered ear-
lier in pregnancy versus at delivery. At study enrollment, we defined malaria based on LM
examination of a Giemsa-stained peripheral blood smear or a RDT for malaria antigen [28].
At delivery, we defined malaria based on peripheral or placental LM or placental histology
(active or past infection). Given the uncertain impact of submicroscopic infections on LBW
and the variation in the availability of polymerase chain reaction (PCR) diagnostics across
studies, we excluded PCR results [29]. In sensitivity analyses, we explored alternative defini-
tions of malaria, including any PCR results and “any malaria,” defined as a positive LM, RDT,
or PCR at enrollment, delivery, or during pregnancy (in 5 studies with repeat malaria diagnos-
tics throughout pregnancy).
The primary measure of maternal malnutrition was low MUAC at enrollment, dichoto-
mized at 23 cm [7]. MUAC changes little over pregnancy, making it a useful measure of mal-
nutrition [7]. Since some studies did not measure MUAC, we used BMI as a secondary
measure of malnutrition. According to WHO, a prepregnancy BMI <18.5 kg/m2 is predictive
of adverse birth outcomes [30]. BMI at enrollment was used to estimate prepregnancy BMI by
adjusting maternal weight measured in the second/third trimesters using a cubic regression
model to account for gestational weight gain [30]. Low adjusted-BMI was defined as values
under 18.5 kg/m2. As the correlation between BMI and MUAC is not perfect, indicators were
analyzed separately [7]. The reason for dichotomizing MUAC and BMI was 2-fold. First, cut-
offs are endorsed by WHO, are clinically easier to use, and are commonly used in the current
literature to define undernutrition [7]. Second, while continuous exposures can be assessed in
interaction models, interpretation is difficult, as the interaction estimates vary according to the
levels of the exposures being compared and can vary in directionality as well [31].
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Malaria, malnutrition, and birthweight
Risk of bias assessment
We developed a checklist of study characteristics for each of the included individual studies to
assess the risk of bias for the main evaluation of the interaction between malaria infection and
maternal malnutrition on BW. Criteria were specific to the research question and were
informed by the Newcastle-Ottawa Scale, Downs and Black instrument, and the Meta-Analysis
of Observational Studies in Epidemiology checklist [32–34]. For each included study, we eval-
uated the individual study publications or contacted individual study collaborators to identify
the following items to categorize studies as being either at lower or higher risk of bias: partici-
pant retention rate (<75% versus75%), measurement of important confounders (maternal
age, gravidity, rural versus urban residence, HIV infection, and anemia at enrollment), clearly
described measurement of malaria parasitemia, measurement of MUAC and/or BMI,>80%
of BWs measured using electronic scale with known precision20 g, and>80% BWs mea-
sured within 24 hours. Studies were defined as at lower risk of bias if every item was deter-
mined to be at a lower risk of bias.
Statistical analysis
We analyzed maternal malaria infection and malnutrition as coprimary exposures and
assessed malnutrition as a modifier of the malaria–LBW relationship. While effect measure
modification (EMM) assesses how the effect of 1 exposure varies across strata of another vari-
able, interaction analyses assess the joint effects of 2 exposures [35]. We performed both inter-
action and EMM analyses; however, in the context of this work, interaction is preferable to
EMM because interventions for both malaria infection and malnutrition might prevent LBW.
There are 2 commonly employed approaches for handling individual pooled data, a 1-stage
and a 2-stage approach, although there is no consensus as to which approach is preferable
[36–38]. We employed a 2-stage approach, as it is generally considered more easily interpret-
able and allows the investigator to visually present forest plots and quantify statistical heteroge-
neity [36]. We examined the consistency of results with a 1-stage approach, fitting a
generalized mixed model with random intercepts and slopes. Study-specific risk ratios (RRs)
and mean BW differences were calculated using linear and log-binomial regression models
controlling for confounding using inverse probability of treatment weights (IPTW) truncated
at the 1st and 99th percentiles. A minimally sufficient set of confounders was identified using a
directed acyclic graph based upon background knowledge of covariate relationships [39]. We
identified confounders for both malaria infection and malnutrition relative to LBW since we
were analyzing them as coprimary exposures. Confounders for the relationship between
malaria infection at enrollment and LBW included maternal age, gravidity, rural versus urban
residence, malnutrition (MUAC when available, otherwise BMI), and HIV infection. Because
malaria infection is a cause of anemia, the latter was considered a mediator and not a con-
founder. We explored modification of the effect of malaria infection at enrollment on LBW by
maternal gravidity and doses of intermittent preventive therapy (IPTp) received. When assess-
ing malaria infection at delivery, anemia at enrollment and the number of IPTp doses were
considered additional confounders. Confounders for the malnutrition–LBW relationship
included maternal age, gravidity, rural versus urban residence, anemia at enrollment, and HIV
infection. Partially missing data were imputed using multivariate normal multiple imputation
(S1 Text) [40]. We calculated interaction estimates using a product term in the multiplicative
and additive model for LBW and the additive model for mean BW [35]. These estimates reflect
whether the effect of exposure to both malaria infection and malnutrition exceeds the product
(or sum) of the effects of each exposure considered separately, defined as synergy. A product
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Malaria, malnutrition, and birthweight
term greater than 1 on the multiplicative scale or greater than 0 on the additive scale is indica-
tive of synergistic interaction between malaria infection and malnutrition.
Study-specific estimates were pooled using DerSimonian and Laird restricted maximum
likelihood method random-effects models [41]. When τ2, the estimated variance of the ran-
dom-effects distribution, was greater than 0, we calculated 95% population effects intervals
(PEI), which incorporate the estimated variance between studies [41]. If τ2 equaled 0, the ran-
dom-effects model was interpreted as a fixed-effects model. We decided a priori to evaluate
the modification of the results by time period (before versus after 2008) due to changes in anti-
malarial recommendations, study type (trial/cohort), location (Africa/Western Pacific), and
the study-level prevalence of malaria infection at study enrollment and delivery based on the
individual study data, using meta-regression. We further decided post hoc to conduct a sensi-
tivity analysis for the interaction analyses restricted to adolescent women.
Results
Using a convenience sample approach, a total of 18 studies were considered for inclusion by
the time of our inclusion cutoff date (1 January 2016), of which 13 were included in the pooled
analysis (Fig 1). We excluded 5 studies: 2 studies did not assess malaria at antenatal enrollment
[42,43], 1 study had data that were not yet available for inclusion [44], 1 recruited women com-
paratively late in pregnancy [10], and 1 had not directly measured the number of sulfadoxine-
pyrimethamine (SP) doses given for IPTp [45]. Following the cutoff date, 5 further studies
were identified, of which 4 could be eligible with a collective sample size of 3,528 pregnant
women (S3 Table) [46–50].
Study population characteristics
Twenty-five percent of the pooled dataset comprised adolescent women aged 19 or younger.
The trimester at enrollment, anemia prevalence, gravidity distribution, area of residence, and
HIV prevalence varied across studies (Tables 1 and 2). The prevalence of malaria infection at
enrollment, malaria infection at delivery, low MUAC, and joint malaria infection at enroll-
ment and low MUAC also varied by study (Fig 2 and S5 Fig). Among 8,152 women with both
measurements, only 2% had both low MUAC and malaria infection at enrollment. The preva-
lence of malaria infection among women with low MUAC was 16%, compared to 12% among
well-nourished women (p = 0.0005). The prevalence of low BMI varied across studies and was
different from, although correlated with, the prevalence of low MUAC (χ2 p< 0.0001; S1 Fig).
The joint prevalence of malaria infection at enrollment and low BMI was also 2%. Of all 14,633
women, 35% were infected with malaria at either enrollment or delivery or had low MUAC or
BMI. The prevalence of LBW was 9% (range 5% to 15% among studies). Among 9 studies with
ultrasound-dated gestational age, the prevalence of SGA was 19% (range 13% to 25%), and the
prevalence of PTB was 11% (range 3% to 20%).
Five of the thirteen included studies were judged to be at a lower risk of bias for the assess-
ment of interaction between malaria infection and maternal malnutrition on BW (S4 Table).
Among the 8 other studies, 3 had a<75% retention rate for the primary outcome, 5 did not
measure at least 80% of BWs with an electronic scale with known precision20 g, and 3 did
not measure at least 80% of BWs within 24 hours.
Independent effects of malaria infection and malnutrition
The pooled IPTW-adjusted risk ratio (aRR) for the effect of malaria infection at enrollment on
LBW was 1.14 (95% CI: 0.91, 1.42; 95% τ2 = 0.05 [95% CI: 0.00, 0.25]; PEI: 0.72, 1.80), and the
mean BW difference was −55 g (95% CI: −79, −30; τ2 = 0 [95% CI: 0.00, 1,610]) (Fig 3a). The
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Malaria, malnutrition, and birthweight
Fig 1. Flow diagram of studies included in the individual participant meta-analysis of the interaction between malaria infection and maternal
malnutrition on birthweight. M3, Maternal Malaria and Malnutrition Initiative.
https://doi.org/10.1371/journal.pmed.1002373.g001
effect of malaria infection at delivery was more pronounced: aRR, 1.32 (95% CI: 1.08, 1.62;
τ2 = 0.04 [95% CI: 0.00, 0.39]; 95% PEI: 0.91, 1.91) (Fig 3b). When considering SGA and PTB
as secondary outcomes, results were similar for malaria infection at enrollment and attenuated
for malaria infection at delivery (S5 Table). The effect of malaria infection at enrollment was
attenuated among those with more than 1 IPTp dose versus 1 or 0 doses (aRR 0.98 versus 1.22)
and was slightly stronger among primi/secundigravid versus multigravida women (aRR 1.19
versus 1.14). A slightly stronger effect of malaria infection was seen among women enrolled in
studies conducted prior to 2008, in Africa, or with malaria infection prevalence at or above the
median (S2 Fig).
The aRR for the effect of low MUAC on LBW was 1.60 (95% CI: 1.36, 1.87; τ2 = 0 [95%
CI: 0.00, 0.05]); the mean BW difference was −142 g (95% CI: −171, −113; τ2 = 0 [95% CI: 0,
100] (Fig 4a). Results were similar for low BMI: aRR, 1.49 (95% CI: 1.26, 1.76; τ2 = 0 [95% CI:
0.00, 0.16]); mean BW difference −133 g (95% CI: −158, −108; τ2 = 0 [95% CI: 0.00, 0.00]) (Fig
4b). There was no modification by study characteristics on the malnutrition–LBW relationship
(S3 Fig). Similar but weaker trends were observed when SGA was used as the outcome among
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Malaria, malnutrition, and birthweight
Table 1. The characteristics of women included in the Maternal Malaria and Malnutrition (M3) initiative from the following 6 out of 13 M3 studies:
Kisumu-Kenya, IPTp-PNG, ISTp-Malawi, STOPMIP-Kenya, LAIS-Malawi, and iLiNS-Ghana.
Kisumu-Kenya IPTp-PNG ISTp-Malawi STOPMIP-Kenya LAIS-Malawi iLiNS-Ghana
(N = 3,388) (N = 1,943) (N = 1,602) (N = 1,203) (N = 1,190) (N = 1,068)
Study enrollment (years) 1996–2001 2009–2013 2011–2013 2012–2015 2003–2006 2009–2012
Maternal age 20 (18–24) 24 (20–28) 21 (18–26) 22 (19–27) 24 (20–29) 26 (22–30)
Gravidity
1 (Primi-) 1,656 (49) 966 (50) 542 (34) 403 (34) 267 (22) 349 (33)
2 (Secundi-) 748 (22) 494 (21) 448 (28) 237 (20) 213 (18) 351 (33)
3+ (Multi-) 984 (29) 573 (29) 612 (38) 563 (47) 710 (60) 368 (34)
Trimester*
1 0 (0) 72 (4) 0 (0) 21 (2) 0 (0) 103 (10)
2 0 (0) 1,780 (92) 1,585 (99) 991 (82) 1,190 (100) 881 (82)
3 3,388 (100) 91 (5) 17 (1) 191 (16) 0 (0) 81 (8)
Missing GA 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 3 (0)
Anemic†
Yes 2,548 (75) 1,348 (69) 533 (33) 591 (49) 459 (39) 305 (29)
No 808 (24) 512 (26) 1,069 (67) 612 (51) 731 (61) 763 (71)
Missing 32 (1) 83 (4) 0 (0) 0 (0) 0 (0) 0 (0)
HIV
Yes 810 (24) – 0 (0) 0 (0) 144 (12) 0 (0)
No 2,560 (76) – 1,602 (100) 1,203 (100) 931 (78) 1,059 (99)
Missing 18 (1) 1,943 (100) 0 (0) 0 (0) 115 (10) 9 (1)
Area of Residence
Rural 722 (21) 1,185 (61) 1,590 (99) 1027 (85) 1,190 (100) 0 (0)
Urban 2,666 (77) 758 (39) 10 (1) 169 (14) 0 (0) 1,068 (100)
Missing 0 (0) 0 (0) 2 (0) 7 (1) 0 (0) 0 (0)
IPTp doses 0 (0–0) 1 (1–3) 4 (3–4)‡ 2 (1–3)‡ 4 (2–4) –
Bed net ownership
Yes – 1,798 (93) 327 (20) 681 (57) 877 (74) –
No – 145 (7) 1,275 (80) 522 (43) 313 (26) –
Missing 3,388 (100) 0 (0) 0 (0) 0 (0) 0 (0) 1,068 (100)
Categorical variables are expressed as N (%) and continuous variables are expressed as median (IQR). A dash indicates information on particular factor
was not assessed in parent study.
* Based on ultrasound if measured, otherwise based on Ballard’s score or symphysis-pubis fundal height (SFH). When using SFH, to adjust for
misclassification in the first trimester, a fundal height <7 cm was defined as first trimester, while SFH < 28 cm was defined as second trimester, and
SFH 28 cm was defined as third trimester.
† Anemic = hemoglobin <11 g/dL of venous blood, if available, or hematocrit <33% in the first and third trimesters and less than 10.5 g/dL and 32%,
respectively, for the second trimester.
‡ Excluding women randomized to the intermittent screening and treatment group.
GA, gestational age; iLiNS, International Lipid-Based Nutrient Supplements; IPTp, intermittent preventive treatment in pregnancy; ISTp, intermittent
screening for malaria infection during pregnancy; LAIS, Lungwena Antenatal Intervention Study; M3, Maternal Malaria and Malnutrition; PNG, Papua New
Guinea; STOPMIP, strategies to prevent malaria infection during pregnancy.
https://doi.org/10.1371/journal.pmed.1002373.t001
the studies with ultrasound data, but low MUAC or low BMI were significantly associated
with an increased risk of PTB (S5 Table).
Interaction and EMM
The joint aRR for both malaria infection at enrollment and low MUAC was 2.13 (95% CI:
1.21, 3.73; τ2 = 0.25 [95% CI: 0.00, 1.82]; 95% PEI: 0.80, 5.67), and the mean BW difference was
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Malaria, malnutrition, and birthweight
Table 2. The characteristics of women included in the Maternal Malaria and Malnutrition (M3) initiative from the following 7 out of 13 M3 studies:
FSP/MISAME-BF, STOPPAM-Benin, STOPPAM-Tanzania, ITN-Kenya, EMEP/IPTp-MON-Kenya, Sek-PNG, ECHO-DRC.
FSP/ MISAME- STOPPAM- STOPPAM-Tanzania ITN- Kenya EMEP/IPTp-MON- Sek-PNG ECHO- DRC
BF Benin (N = 789) (N = 711) Kenya (N = 293) (N = 164)
(N = 1020) (N = 791) (N = 471)
Study enrollment 2006–2008 2008–2010 2008–2010 1996–1999 2011–2013 2005–2007 2005–2006
(years)
Maternal age 23 (19.5–28) 25 (22–30) 26 (22–31) 24 (20–30) 24 (20–30) 24 (21–28) 27 (23.5–31)
Gravidity
1 (Primi-) 205 (20) 147 (19) 162 (21) 127 (18) 94 (20) 115 (39) 43 (26)
2 (Secundi-) 216 (21) 173 (22) 201 (25) 118 (17) 77 (16) 54 (18) 22 (13)
3+ (Multi-) 599 (59) 471 (59) 426 (54) 466 (66) 300 (64) 124 (42) 99 (60)
Trimester*
1 385 (38) 174 (22) 88 (11) 3 (0) 67 (14) 0 (0) 6 (4)
2 595 (58) 616 (78) 701 (89) 376 (53) 247 (52) 214 (73) 158 (96)
3 40 (4) 1 (0) 0 (0) 292 (41) 140 (30) 75 (26) 0 (0)
Missing GA 0 (0) 0 (0) 0 (0) 40 (6) 17 (4) 4 (1) 0 (0)
Anemic†
Yes 372 (36) 354 (45) 289 (37) 416 (59) 169 (36) 272 (93) 43 (26)
No 630 (62) 433 (55) 497 (63) 293 (41) 297 (63) 21 (7) 107 (65)
Missing 18 (2) 4 (1) 3 (0) 2 (0) 5 (1) 0 (0) 14 (9)
HIV
Yes – 13 (2) 39 (4) 51 (7) 0 (0) – 4 (2)
No – 699 (88) 693 (88) 234 (33) 468 (99) – 160 (98)
Missing 1,020 (100) 79 (10) 57 (7) 426 (60) 3 (1) 293 (100) 0 (0)
Area of Residence
Rural 1,020 (100) 791 (100) 430 (55) 711 (100) 471 (100) 282 (96) 0 (0)
Urban 0 (0) 0 (0) 354 (45) 0 (0) 0 (0) 8 (3) 164 (100)
Missing 0 (0) 0 (0) 5 (1) 0 (0) 0 (0) 3 (1) 0 (0)
IPTp dosage 2 (1–2) 2 (2–2) 2 (2–2) 0 (0–0) 3 (2–3) – 2 (2–2)
Bed net ownership
Yes – 254 (32) 571 (72) 348 (49) – 240 (82) 164 (100)
No – 537 (68) 52 (7) 363 (51) – 49 (17) 0 (0)
Missing 1,020 (100) 0 (0) 166 (21) 0 (0) 471 (100) 4 (1) 0 (0)
Categorical variables are expressed as N (%) and continuous variables are expressed as median (IQR). A dash indicates information on particular factor
was not assessed in parent study.
* Based on ultrasound if measured, otherwise based on Ballard’s score or symphysis-pubis fundal height (SFH). When using SFH, to adjust for
misclassification in the first trimester, a fundal height <7 cm was defined as first trimester, while SFH < 28 cm was defined as second trimester, and
SFH 28 cm was defined as third trimester.
† Anemic = hemoglobin <11 g/dL of venous blood, if available, or hematocrit <33% in the first and third trimesters and less than 10.5 g/dL and 32%,
respectively, for the second trimester.
BF, Burkina Faso; DRC, Democratic Republic of the Congo; EMEP, Evaluation of Medications used in Early Pregnancy; GA, gestational age; IPTp,
intermittent preventive treatment in pregnancy; ITN, insecticide-treated bed nets; M3, Maternal Malaria and Malnutrition; PNG, Papua New Guinea;
STOPPAM, Strategies to Prevent Pregnancy-Associated Malaria.
https://doi.org/10.1371/journal.pmed.1002373.t002
−163 g (95% CI: −253, −75; τ2 = 6,995 [95% CI: 0, 58,414]; 95% PEI: −328, 0). The multiplica-
tive interaction term for LBW was 1.30 (95% CI: 0.62, 2.72; τ2 = 0.37 [95% CI: 0.00, 3.97];
95% PEI: 0.39, 4.31), the additive interaction term for LBW was −0.01 (95% CI: −0.09, 0.08;
τ2 = 0.003 [95% CI: 0.00, 0.04]; 95% PEI: −0.11, 0.09), and the additive interaction term for
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Malaria, malnutrition, and birthweight
Fig 2. Prevalence of malaria infection at enrollment, malnutrition (mid-upper arm circumference [MUAC] < 23 cm or body mass
index [BMI] < 18.5 kg/m2), and joint malaria infection and malnutrition among 14,633 live birth pregnancies from women
participating in studies (years 1996–2015) included in the Maternal Malaria and Malnutrition (M3) initiative.
https://doi.org/10.1371/journal.pmed.1002373.g002
mean BW difference was 38 g (95% CI: −90, 166; τ2 = 17,198 [95% CI: 0, 120,165]; 95%
PEI: −219, 295). Sensitivity analyses that varied the definitions of malaria, malnutrition, out-
come, and analytic approach largely did not qualitatively alter the results; however, restriction
to adolescent women did suggest potential multiplicative and additive interaction between low
MUAC and malaria infection at enrollment among this subgroup (product term 2.49 [95% CI:
0.88, 7.02]; additive interaction term 0.08 [95% CI: −0.07, 0.22]) (S6 Table). Additionally,
meta-regression indicated apparent multiplicative interaction and slight additive interaction
between MUAC and malaria infection at enrollment among studies conducted in Africa (mul-
tiplicative interaction term, 2.47 [95% CI: 1.12, 5.42]; additive interaction contrast, 0.06 [95%
CI: −0.05, 0.17] S4 Fig), but this interaction was not seen when assessing malaria infection at
delivery or BMI or when accounting for multiple comparisons with a Bonferroni correction
(99% CI: 0.88, 6.95). In EMM analyses, the aRR for the effect of malaria infection at enrollment
on LBW among low MUAC women was 1.32 (95% CI: 0.66, 2.63; τ2 = 0.43 [95% CI: 0.00,
3.40]; 95% PEI: 0.36, 4.79), compared to 0.98 (95% CI: 0.74, 1.29; τ2 = 0 [95% CI: 0.00, 0.32])
among well-nourished women.
The joint aRR for both malaria infection at delivery and low MUAC was 2.16 (95% CI: 1.25,
3.74; τ2 = 0.23 [95% CI: 0.00, 1.61]; 95% PEI: 0.84, 5.55), and the mean BW difference was
−196 g (95% CI: −301, −92; τ2 = 10,904 [95% CI: 0, 86,721]; 95% PEI: −401, 8). The multiplica-
tive interaction term for LBW was 0.82 (95% CI: 0.50, 1.33; τ2 = 0 [95% CI: 0.00, 3.79]), the
additive interaction term for LBW was −0.01 (95% CI: −0.10, 0.07; τ2 = 0 [95% CI: 0.00, 0.06]),
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Malaria, malnutrition, and birthweight
Fig 3. The independent effects of (a) malaria infection at enrollment and (b) malaria infection at delivery on risk of low birthweight and mean
birthweight among women enrolled in 1 of 13 studies from the Maternal Malaria and Malnutrition (M3) initiative. The inverse probability of
treatment weighted (IPTW) estimates controlled for confounding between malaria at enrollment and low birthweight (LBW) by maternal age, gravidity,
area of residence, mid-upper arm circumference at enrollment, and HIV infection. IPTW estimates controlled for confounding between malaria at delivery
and LBW and additionally controlled for anemia and number of doses of antimalarial intermittent preventive therapy received during pregnancy. aRR,
adjusted risk ratio; BF, Burkina Faso; BW, Birthweight; CI, Confidence interval; DRC, Democratic Republic of the Congo; LBW, Low birthweight; N/A, not
available; N/C, no model convergence; PNG, Papua New Guinea.
https://doi.org/10.1371/journal.pmed.1002373.g003
and the additive interaction term for mean BW difference was −49 g (95% CI: −190, 93; τ2 =
20,087 [95% CI: 0, 154,675]; 95% PEI: −326, 229).
Discussion
Using the large M3 initiative dataset, we found that pregnant women who were both infected
with malaria and malnourished were at greater risk of LBW and reduced mean BW compared
to their uninfected, well-nourished counterparts, but there was overall no convincing evidence
of synergism, i.e., excess risk due to interaction. This finding was consistent for both time
points of malaria diagnosis (at enrollment and delivery) and both definitions of malnutrition
(MUAC and BMI). This suggests that malaria infection and malnutrition largely act indepen-
dently to influence fetal growth and gestational length.
A 2004 review estimated that women infected with placental malaria were twice as likely to
have a LBW infant [51]. Our findings are broadly consistent with this review, although with
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Malaria, malnutrition, and birthweight
Fig 4. The independent effect of malnutrition at enrollment, (a) mid-upper arm circumference (MUAC) < 23 cm and (b) Body Mass Index (BMI)*
< 18.5 kg/m2, on risk of low birthweight (LBW) and mean birthweight among 14,633 women enrolled in 1 of 13 studies from the Maternal Malaria
and Malnutrition (M3) initiative from 1996 to 2015. BMI is adjusted for gestational age to reflect the estimated first trimester weight. Inverse probability
of treatment weighted (IPTW) estimates controlling for confounding between malnutrition (MUAC or BMI) at enrollment and LBW by maternal age,
gravidity, area of residence, anemia, and HIV infection (where available). BMI, Body mass index; BW, Birthweight. CI, Confidence interval; DRC,
Democratic Republic of the Congo; MUAC, mid-upper arm circumference; N/A, not available; N/C, no model convergence; PNG, Papua New Guinea.
https://doi.org/10.1371/journal.pmed.1002373.g004
weaker effects on LBW (overall aRR for malaria infection at delivery: 1.32 [95% CI: 1.08, 1.62],
aRR restricted to African studies: 1.55 [95% CI: 1.29, 1.85]), possibly reflecting increased access
to preventive strategies and fewer chronic infections [3,4]. In support of this hypothesis, the
effect of malaria infection on LBW appears lower in women who received more doses of IPTp.
The effects of malaria infection at enrollment on LBW were weaker than at delivery, contra-
dicting the theory that malaria infection earlier in pregnancy is more disruptive to placental
function [2]. This weaker effect at enrollment could either suggest that antimalarial treatment,
provided in most studies, cleared infection and allowed catch-up growth or that infection at
delivery represents more severe infections that were not cleared despite medications. Both
malaria infection at enrollment and delivery were associated with a reduction in BW of around
55 grams, which has been found in other studies [52].
Our data are consistent with a 2011 meta-analysis, which estimated that underweight
women had increased risk of LBW (aRR: 1.64 [95% CI: 1.38, 1.94]), although studies included
in that meta-analysis used different definitions for underweight [53]. In our study, using con-
sistent cutoffs of malnutrition across studies, both low MUAC (aRR 1.60 [95% CI: 1.36, 1.87])
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Malaria, malnutrition, and birthweight
and low BMI (aRR 1.49 [95% CI: 1.26, 1.76]) increased the risk of LBW. This information is
consistent with other evidence that adequate maternal nutrition is integral for fetal growth [5].
Prior literature on the interaction between malaria infection and malnutrition is sparse.
Two studies in the DRC and Kenya showed that the association between malaria infection and
reduced fetal growth was greatest among malnourished women [10,11]. In a third study in
Benin, the effect of malaria infection on fetal growth velocity was greatest among women with
low anthropometric status, but there was no modification by maternal nutrition on the effect
of malaria infection on BW z-scores. A fourth study in PNG found that the effect of histology-
defined placental malaria infection on LBW was higher among women with a low BMI, but
that study found that malnutrition did not modify the association between peripheral blood
malaria infection parasitemia and SGA [9]. The Benin, Congo, and PNG studies were included
in the present analysis, but our analytic approach differed from the original publications in the
assessment of both interaction and modification. Unlike these prior studies, our pooled results
suggest that there is a negligible impact of maternal anthropometry on the relationship
between malaria infection and LBW and further indicate that there is no evidence of excess
risk of LBW due to interaction (i.e., synergism). There was some indication of multiplicative
and additive interaction between low MUAC and malaria infection at enrollment among ado-
lescent women; however, these estimates were very imprecise and were only pooled across 4
studies that enrolled enough adolescent women to assess this subgroup. Adolescent women
are recognized to be at high risk of adverse pregnancy outcomes [54], and tailored antenatal
care programs addressing malaria, nutrition, and other health issues should be considered for
this group. In an a priori sensitivity analysis restricted to African studies, there was apparent
interaction between malaria infection at enrollment and MUAC, which is consistent with the
prior publications. Regional differences could be due to genetics, low MUAC, or anemia prev-
alence; however, these subregion effects were not statistically significant when properly
accounting for multiple comparisons and were absent when using other definitions of malaria
(i.e., at delivery) or malnutrition (i.e., BMI). Additionally, the additive interaction, which has
been argued to be the more relevant measure for public health impact [55], was only slightly
elevated among the African studies. Notably, only 183 women (2%) were jointly infected and
malnourished (low MUAC). Thus, even if there is a multiplicative interaction between malaria
infection and MUAC among African women or among adolescent women, the proportion of
women implicated is small, and does not indicate a large public health burden. However, even
in the absence of strong interaction between malaria infection and malnutrition on LBW, we
emphasize that interventions on both malaria infection and malnutrition are warranted given
their independent effects.
This work had several strengths and limitations. We substantially increased the number of
women in whom the hypothesized interaction between malaria infection, malnutrition, and
LBW was investigated; notably, the number of pregnant women from Africa was almost 10
times more than all prior studies. Analyzed studies were performed in a variety of settings,
increasing the generalizability of these results. Furthermore, availability of individual-level
data enabled us to harmonize definitions and minimize heterogeneity. Our work is strength-
ened by providing results for SGA and PTB as secondary outcomes, which showed findings
consistent with LBW. However, we were only able to assess SGA and PTB among a subset of
nine of the 13 studies with available ultrasound-dated gestational age. There is no alternative
satisfactory dating tool to ultrasound in later pregnancy, thus we used all ultrasound data pro-
vided regardless of gestation. Some women were enrolled after 24 weeks gestation (S2 Table),
reducing the accuracy of ultrasound among these pregnancies and potentially underestimating
gestational age in some SGA babies. Missing data were imputed using multivariate normal
multiple imputation, and while not all variables followed a normal distribution (e.g., the binary
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Malaria, malnutrition, and birthweight
variable LBW), simulation studies have shown that multivariate normal multiple imputation
provides less biased estimates than complete-case analysis even when imputing binary or ordi-
nal variables [56]. We were obliged to pool malaria diagnostics of varying sensitivity and speci-
ficity, and we were limited to 2 cross-sectional assessments of malaria infection. Nevertheless,
sensitivity analyses that evaluated alternative definitions of malaria, or incorporated repeat
diagnostics during pregnancy, were consistent with the main results. Additionally, there may
be selection bias due to excluding pregnancy losses. There were only 116 (3%) pregnancy losses
in 4 studies (N = 4,571) in the M3 initiative that collected these data, but this is almost certainly
an underestimate, since many studies enrolled women after the first trimester. We were
obliged to extrapolate prepregnancy BMI using gestational age and BMI at enrollment. Addi-
tionally, the M3 initiative represents a convenience sample of available and eligible studies
identified through the MiPc and not an exhaustive aggregation of all existing studies available
to assess interactions between malaria and malnutrition on LBW. This could potentially lead
to selection bias if selection of studies were associated with the effect estimates in that study;
however, we did not observe any qualitative differences between studies providing individual
participant data and those studies not included in the meta-analysis (S3 Table). Furthermore,
women enrolled in studies were likely healthier and received better antenatal care than the
general population; the effects of malaria and malnutrition in reality might well be greater
than were observed within these research settings. The risk of bias assessment identified 10
studies as being at a higher risk of bias, primarily due to BW not being measured with an elec-
tronic scale within 24 hours of delivery. Finally, we cannot discount possible unmeasured con-
founding, particularly by helminth infections, sexually transmitted infections, environmental
pollutants, or micronutrient deficiencies; however, it is important to note that because neither
malnutrition nor malaria could be randomized, large-scale, multisite cohort analyses such as
this one are necessarily the gold standard for addressing these scientific questions. Future stud-
ies may wish to assess joint effects of malaria with other nutritional indicators (e.g., height,
obesity, anemia, other micronutrients). Additionally, future studies may wish to further inves-
tigate possible interactions between malaria infection and malnutrition on risk of LBW in ado-
lescent mothers.
In summary, our findings suggest that women who are both infected with malaria and mal-
nourished are at greater risk of LBW than their uninfected, well-nourished counterparts but
that there is no conclusive evidence of synergistic interaction between the 2. Rather, we pro-
pose that malaria infection and malnutrition act independently to disrupt fetal growth and
that malnutrition in particular has a strong effect on LBW. Of all 14,633 pregnancies, 35%
were affected by malaria infection and/or malnutrition, illustrating the high burden of at-
risk pregnancies in LMICs. Malaria infection and malnutrition represent 2 established and
modifiable causes of LBW that should both be addressed to optimize pregnancy outcomes in
LMIC.
Supporting information
S1 Table. Characteristics of the 13 individual studies included in the Maternal Malnutri-
tion and Malaria (M3) initiative.
(DOCX)
S2 Table. Descriptions of the scales used to measure birthweight, how gestational age was
assessed, and the median gestational age for each of the 13 studies in the Maternal Malaria
and Malnutrition (M3) initiative.
(DOCX)
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Malaria, malnutrition, and birthweight
S3 Table. Characteristics of studies not included in the Maternal Malaria and Malnutrition
(M3) initiative cohort.
(DOCX)
S4 Table. Assessment of risk of bias for the 13 studies included in the individual partici-
pant data meta-analysis.
(DOCX)
S5 Table. The independent and joint effects of malaria infection at enrollment, malaria
infection at delivery, low mid-upper arm circumference (MUAC), and low body mass
index (BMI) on the risk of small for gestational age (SGA) and risk of preterm birth
among a subset of 9 studies from the Maternal Malaria and Malnutrition (M3) initiative.
(DOCX)
S6 Table. Select sensitivity analysis results for the multiplicative interaction effects for
malaria and malnutrition on risk of adverse birth outcomes among the 13 studies in the
Maternal Malaria and Malnutrition (M3) initiative. Sensitivity analyses varied the defini-
tions of malaria, malnutrition, the outcome of interest, and the approach taken in pooling
study results.
(DOCX)
S7 Table. PRISMA 2009 checklist.
(DOC)
S8 Table. Individual participant data checklist.
(DOCX)
S1 Text. Multiple imputation.
(DOCX)
S2 Text. Protocol for the individual participant data project. Written 17 November 2014.
(DOCX)
S1 Fig. Prevalence of low mid-upper arm circumference (MUAC < 23cm) compared to
prevalence of low body mass index (BMI < 18.5 kg/m2) among the 13 studies in the Mater-
nal Malaria and Malnutrition (M3) initiative.
(DOCX)
S2 Fig. Meta-regression results for the effects of malaria infection at enrollment and deliv-
ery on risk of low birthweight (LBW) and mean birthweight (BW) by time period, study
type, location, and malaria prevalence. Median malaria prevalence across studies was 17% at
enrollment and 15% at delivery. RCT = randomized control trial.
(DOCX)
S3 Fig. Meta-regression results for the effects of malnutrition at enrollment, (a) low mid-
upper arm circumference (MUAC < 23 cm) and (b) low BMI (BMI < 18.5 kg/m2), on risk
of low birthweight (LBW) and mean birthweight (BW) by time period, study type, loca-
tion, and malaria prevalence. Median malaria prevalence across studies was 17% at enroll-
ment and 15% at delivery. RCT = randomized control trial.
(DOCX)
S4 Fig. Meta-regression results for the multiplicative and additive interaction effects for
malaria at enrollment or delivery and low mid-upper arm circumference (MUAC < 23
cm) on risk of low birthweight (LBW) and mean birthweight (BW) by time period, study
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Malaria, malnutrition, and birthweight
type, location, and malaria prevalence. Median malaria prevalence across studies was 17% at
enrollment and 15% at delivery.
(DOCX)
S5 Fig. Prevalence of malaria infection at delivery among the 13 studies in the Maternal
Malaria and Malnutrition (M3) initiative.
(DOCX)
Acknowledgments
The findings and conclusions presented in this manuscript are those of the authors and do not
necessarily reflect the official position of the U.S. Centers for Disease Control and Prevention
or the National Institutes of Health.
Author Contributions
Conceptualization: Jordan E. Cates, Holger W. Unger, Steven Meshnick, Stephen Rogerson.
Data curation: Holger W. Unger, Valerie Briand, Nadine Fievet, Innocent Valea, Halidou
Tinto, Umberto D’Alessandro, Sarah H. Landis, Seth Adu-Afarwuah, Kathryn G. Dewey,
Feiko O. ter Kuile, Meghna Desai, Stephanie Dellicour, Peter Ouma, Julie Gutman, Martina
Oneko, Laurence Slutsker, Dianne J. Terlouw, Simon Kariuki, John Ayisi, Mwayiwawo
Madanitsa, Victor Mwapasa, Per Ashorn, Kenneth Maleta, Ivo Mueller, Danielle Stanisic,
Christentze Schmiegelow, John P. A. Lusingu, Anna Maria van Eijk, Steven Meshnick, Ste-
phen Rogerson.
Formal analysis: Jordan E. Cates.
Investigation: Jordan E. Cates.
Methodology: Jordan E. Cates, Holger W. Unger, Melissa Bauserman, Linda Adair, Stephen
R. Cole, Daniel Westreich, Steven Meshnick, Stephen Rogerson.
Project administration: Holger W. Unger, Valerie Briand, Nadine Fievet, Innocent Valea,
Halidou Tinto, Umberto D’Alessandro, Sarah H. Landis, Seth Adu-Afarwuah, Kathryn G.
Dewey, Feiko O. ter Kuile, Meghna Desai, Stephanie Dellicour, Peter Ouma, Julie Gutman,
Martina Oneko, Laurence Slutsker, Dianne J. Terlouw, Simon Kariuki, John Ayisi, Mwayi-
wawo Madanitsa, Victor Mwapasa, Per Ashorn, Kenneth Maleta, Ivo Mueller, Danielle Sta-
nisic, Christentze Schmiegelow, John P. A. Lusingu, Anna Maria van Eijk, Steven
Meshnick, Stephen Rogerson.
Supervision: Holger W. Unger, Daniel Westreich, Steven Meshnick.
Visualization: Jordan E. Cates.
Writing – original draft: Jordan E. Cates.
Writing – review & editing: Jordan E. Cates, Holger W. Unger, Valerie Briand, Nadine Fievet,
Innocent Valea, Halidou Tinto, Umberto D’Alessandro, Sarah H. Landis, Seth Adu-Afar-
wuah, Kathryn G. Dewey, Feiko O. ter Kuile, Meghna Desai, Stephanie Dellicour, Peter
Ouma, Julie Gutman, Martina Oneko, Laurence Slutsker, Dianne J. Terlouw, Simon Kar-
iuki, John Ayisi, Mwayiwawo Madanitsa, Victor Mwapasa, Per Ashorn, Kenneth Maleta,
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