Malaria, malnutrition, and birthweight: A meta-analysis using individual participant data

RESEARCH ARTICLE Malaria, malnutrition, and birthweight: A meta-analysis using individual participant data Jordan E. Cates 1 *, Holger W. Unger 2,3 , Valerie Briand 4 , Nadine Fievet 4 , Innocent Valea 5,6 , Halidou Tinto 5,6 , Umberto D’Alessandro 7 , Sarah H. Landis 8 , Seth Adu-Afarwuah 9 , Kathryn G. Dewey 10 , Feiko O. ter Kuile 11 , Meghna Desai 12 , Stephanie Dellicour 11 , Peter Ouma 13 , Julie Gutman 12 , Martina Oneko 13 , Laurence Slutsker 14 , Dianne J. Terlouw 11,15 , Simon Kariuki 13 , John Ayisi 13 , Mwayiwawo Madanitsa 11,16 , Victor Mwapasa 16 , Per Ashorn 17 , Kenneth Maleta 16 , Ivo Mueller 18 , Danielle Stanisic 19 , Christentze Schmiegelow 20 , John P. A. Lusingu 20,21 , Anna Maria van Eijk 11 , Melissa Bauserman 22,23 , Linda Adair 23 , Stephen R. Cole 1 , Daniel Westreich 1‡ , Steven Meshnick 1‡ , Stephen Rogerson 3‡ 1Department of Epidemiology, UNC-Chapel Hill, Chapel Hill, North Carolina, United States of America, 2Department of Obstetrics and Gynaecology, Edinburgh Royal Infirmary, Edinburgh, United Kingdom, 3Department of Medicine at the Doherty Institute, The University of Melbourne, Parkville, Victoria, Australia, 4UMR216-MERIT, French National Research Institute for Sustainable Development (IRD), Paris Descartes University, Paris, France, 5Unite de Recherche Clinique de Nanoro, Institut de Recherche en Sciences de la Sante ´-DRO, Bobo-Dioulasso, Burkina Faso, 6Departement de Recherche Clinique, Centre Muraz, BoboDioulasso, Burkina Faso, 7Medical Research Council Unit, The Gambia; London School of Hygiene and Tropical Medicine, London, United Kingdom, 8Worldwide Epidemiology, GlaxoSmithKline, Uxbridge, United Kingdom, 9Department of Nutrition and Food Science, University of Ghana, Legon, Accra, Ghana, 10 Department of Nutrition, University of California, Davis, California, United States of America, 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 Control and Prevention, Atlanta, Georgia, United States of America, 13 Kenya Medical Research Institute (KEMRI)/ Centre for Global Health Research, Kisumu, Kenya, 14 Malaria and Neglected Tropical Diseases, Center for Malaria Control and Elimination, PATH, Seattle, Washington, United States of America, 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 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 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 Institute for Medical Research, Tanga Centre, Tanga, Tanzania, 22 Department of Pediatrics, Division of Neonatal-Perinatal Medicine, School of Medicine, UNC-Chapel Hill, Chapel Hill, North Carolina, United States of America, 23 Department of Nutrition, UNC-Chapel Hill, Chapel Hill, North Carolina, United States of America ‡These authors are joint senior authors on this work. *[email protected]om Abstract Background Four studies previously indicated that the effect of malaria infection during pregnancy on the risk of low birthweight (LBW; <2,500 g) may depend upon maternal nutritional status. We investigated this dependence further using a large, diverse study population. PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002373 August 8, 2017 1 / 20 a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Cates JE, Unger HW, Briand V, Fievet N, Valea I, Tinto H, et al. (2017) Malaria, malnutrition, and birthweight: A meta-analysis using individual participant data. PLoS Med 14(8): e1002373. https://doi.org/10.1371/journal.pmed.1002373 Academic Editor: Lorenz von Seidlein, MahidolOxford Tropical Medicine Research Unit, THAILAND Received: March 6, 2017 Accepted: July 11, 2017 Published: August 8, 2017 Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. Data Availability Statement: Data are available from the WWARN data repository (http://www. wwarn.org/working-together/sharing-data/ accessing-data) for researchers who meet the criteria for access to confidential data. Funding: JC was funded by the National Institute of Allergy and Infectious Diseases at the National Institutes of Health (Pre-doctoral Training in Infectious Disease Epidemiology grant #5 T32 AI070114). The STOPPAM project, "Strategies To Prevent Pregnancy-Associated Malaria," was Methods and findings We evaluated the interaction between maternal malaria infection and maternal anthropometric status on the risk of LBW using pooled data from 14,633 pregnancies from 13 studies (6 cohort studies and 7 randomized controlled trials) conducted in Africa and the Western Pacific from 1996–2015. Studies were identified by the Maternal Malaria and Malnutrition (M3) initiative using a convenience sampling approach and were eligible for pooling given adequate ethical approval and availability of essential variables. Study-specific adjusted effect estimates were calculated using inverse probability of treatment-weighted linear and log-binomial regression models and pooled using a random-effects model. The adjusted risk of delivering a baby with LBW was 8.8% among women with malaria infection at antenatal enrollment compared to 7.7% among uninfected women (adjusted risk ratio [aRR] 1.14 [95% confidence interval (CI): 0.91, 1.42]; N= 13,613), 10.5% among women with malaria infection at delivery compared to 7.9% among uninfected women (aRR 1.32 [95% CI: 1.08, 1.62]; N= 11,826), and 15.3% among women with low mid-upper arm circumference (MUAC <23 cm) at enrollment compared to 9.5% among women with MUAC 23 cm (aRR 1.60 [95% CI: 1.36, 1.87]; N= 9,008). The risk of delivering a baby with LBW was 17.8% among women with both malaria infection and low MUAC at enrollment compared to 8.4% among uninfected women with MUAC 23 cm (joint aRR 2.13 [95% CI: 1.21, 3.73]; N= 8,152). There was no evidence of synergism (i.e., excess risk due to interaction) between malaria infection and MUAC on the multiplicative (p= 0.5) or additive scale (p= 0.9). Results were similar using body mass index (BMI) as an anthropometric indicator of nutritional status. Meta-regression results indicated that there may be multiplicative interaction between malaria infection at enrollment and low MUAC within studies conducted in Africa; however, this finding was not consistent on the additive scale, when accounting for multiple comparisons, or when using other definitions of malaria and malnutrition. The major limitations of the study included availability of only 2 cross-sectional measurements of malaria and the limited availability of ultrasound-based pregnancy dating to assess impacts on preterm birth and fetal growth in all studies. Conclusions Pregnant women with malnutrition and malaria infection are at increased risk of LBW compared to women with only 1 risk factor or none, but malaria and malnutrition do not act synergistically. Author summary Why was this study done? • More than 125 million pregnant women are at risk of malaria in pregnancy annually, producing detrimental effects on maternal, newborn, and infant health. • Maternal undernutrition is estimated to be responsible for 800,000 newborn deaths annually. Malaria, malnutrition, and birthweight PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002373 August 8, 2017 2 / 20 supported by the European Union’s Seventh Framework Programme (EU FP7); STOPPAM contract number: 200889. STOPPAM I (Benin) and STOPPAM II (Tanzania). The FSP/MISAME study (Burkina Faso) was funded by Nutrition Third World, The Belgium Ministry of Development, Flemish Interuniversity Council, and French Ministry of Development. The ECHO study (Democratic Republic of the Congo) was funded by the Department of Epidemiology, University of North Carolina Chapel Hill, UNC Gillings School of Global Public Health. The iLiNS-DYAD (Ghana) trial was funded by a grant to the University of California, Davis from the Bill & Melinda Gates Foundation. EMEP 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 partly by the US Centers for Disease Control and Prevention (CDC), Division of Parasitic Diseases and Malaria through a cooperative agreement with Kenya Medical Research Institute (KEMRI), Center for Global Health Research (CGHR), Kisumu, Kenya. The IPTp-MON study (Kenya) was partly supported by the MiP Consortium, which is funded through a grant from the Bill & Melinda Gates Foundation to the Liverpool School of Tropical Medicine, UK and partly supported by the CDC. The ITN project (Kenya) was funded by the US Agency for International Development. The Special Health Support Fund from the Royal Netherlands Embassy (Nairobi, Kenya) provided additional support for the study of the impact of ITN in pregnancy. The Kisumu study (Kenya) was funded by US Agency for International Development (grants AOT0483-PH1-2171 and HRN-A-00-0400010-02) and the Netherlands Foundation for the Advancement of Tropical Research. The STOPMIP study (Kenya) was funded 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. 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 partly funded by the European and Developing Countries Clinical Trials Partnership (EDCTP). The LAIS study was supported by grants from the Academy of Finland (grants 79787 and 207010), the Foundation for Pediatric Research in Finland, and the Medical Research Fund of Tampere University Hospital. Azithromycin and its placebo were provided free of charge by Pfizer Inc (New York, New York), which also provided funding for • Prior evidence from 4 small studies indicated that the harmful impact of malaria on fetal growth and birthweight (BW) may depend upon the macronutrient nutritional status of the mother. • If malaria and maternal undernutrition have synergistic negative impacts on pregnancy outcomes, interventions targeted to high-risk women might provide substantial public benefit. • The present study provides a robust assessment of potential malaria–nutrition interactions in pregnancy and overcomes size and methodological limitations of earlier exploratory studies. What did the researchers do and find? • We present a large, pooled analysis of individual participant data from 13 studies conducted in sub-Saharan Africa and the Western Pacific investigating the interaction between maternal malaria infection and malnutrition on the risk of low birthweight (LBW) and reduced mean BW. • The findings suggest that women who are both infected with malaria and malnourished are at greater risk of LBW than their uninfected, well-nourished counterparts. • However, the study found no conclusive evidence of interaction between the 2, i.e., the impact of malaria on BW was independent of the macronutrient nutritional status of the mother. • Subgroup analyses did find that studies conducted just in Africa had slight evidence of interaction, but this was not consistent throughout all analyses. What do these findings mean? • Although there was no overall evidence of malaria–nutrition interactions, more than 1 in 3 pregnant women suffered from malaria and/or undernutrition, emphasizing the importance of joint approaches to decrease maternal malaria and improve nutrition to minimize adverse pregnancy outcomes. Introduction Annually, over 20 million infants are born low birthweight (LBW; <2,500 g), predominantly in lowand middle-income countries (LMICs) [1]. LBW can have negative impacts on neonatal 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 pregnant women are at risk for malaria [4]. The predominant species, Plasmodium falciparum, Malaria, malnutrition, and birthweight PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002373 August 8, 2017 3 / 20 the polymerase chain reaction testing of the sexually transmitted infections. The IPTp study (Papua New Guinea [PNG]) was funded by the MiP Consortium, through a grant from the Bill & Melinda Gates Foundation (46099); the Pregvax Consortium, through a grant from the EU FP72007-HEALTH (PREGVAX 201588) and the Spanish Government (EUROSALUD 2008 Programme); and Pfizer Inc., through an investigator-initiated research grant (WS394663). The Sek study (PNG) was supported by AusAID (grant to PNG Institute of Medical Research [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 Research Institutes Support Scheme and Victorian State Government Operational Infrastructure Support. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors of this manuscript have the following competing interests: SHL is is a full-time employee of GlaxoSmithKline and holds shares in GlaxoSmithKline. SR is a member of the Editorial Board of PLOS Medicine. Abbreviations: aRR, adjusted risk ratio; BMI, body mass index; BW, birthweight; CI, confidence interval; DRC, Democratic Republic of the Congo; EMM, effect measure modification; FGR, fetal growth restriction; HIV, human immunodeficiency virus; IPTp, intermittent preventive treatment in pregnancy; IPTW, inverse probability of treatment weights; LBW, low birthweight; LM, light microscopy; LMIC, lowand middle-income countries; M3, Maternal Malaria and Malnutrition; MiPc, Malaria in Pregnancy Consortium; MUAC, mid-upper arm circumference; PCR, polymerase chain reaction; PEI, population effects interval; PNG, Papua New Guinea; RDT, rapid diagnostic test; RR, risk ratio; SGA, small for gestational age; SP, sulfadoxine-pyrimethamine; WHO, World Health Organization. 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, specifically 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 relatively 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 conducted 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 randomized 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 Malaria, malnutrition, and birthweight PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002373 August 8, 2017 4 / 20 was shared by each individual study using a standardized data transfer file. Participating studies 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 pregnancy such as intermittent preventive therapy during pregnancy (IPTp) or insecticide-treated bed nets (ITN), or the assessment of the potential of nutritional supplementation during pregnancy 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 10 th percentile of the INTERGROWTH-21 st 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 interaction 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 oxygen transfer and reducing uteroplacental blood flow; 2 mechanisms that would be altered earlier 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 definitions 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 diagnostics throughout pregnancy). The primary measure of maternal malnutrition was low MUAC at enrollment, dichotomized at 23 cm [7]. MUAC changes little over pregnancy, making it a useful measure of malnutrition [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/m 2 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/m 2 . 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, cutoffs 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]. Malaria, malnutrition, and birthweight PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002373 August 8, 2017 5 / 20 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 evaluated 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: participant retention rate (<75% versus 75%), 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 precision 20 g, and >80% BWs measured within 24 hours. Studies were defined as at lower risk of bias if every item was determined 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 variable, interaction analyses assess the joint effects of 2 exposures [35]. We performed both interaction 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 interpretable and allows the investigator to visually present forest plots and quantify statistical heterogeneity [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 confounder. 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 assessing 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 Malaria, malnutrition, and birthweight PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002373 August 8, 2017 6 / 20 term greater than 1 on the multiplicative scale or greater than 0 on the additive scale is indicative 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 τ 2 , the estimated variance of the random-effects distribution, was greater than 0, we calculated 95% population effects intervals (PEI), which incorporate the estimated variance between studies [41]. If τ 2 equaled 0, the random-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 antimalarial 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 sensitivity 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 comparatively late in pregnancy [10], and 1 had not directly measured the number of sulfadoxinepyrimethamine (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 1and 2). The prevalence of malaria infection at enrollment, malaria infection at delivery, low MUAC, and joint malaria infection at enrollment 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 prevalence 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 (χ 2 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 assessment 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 precision 20 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% τ 2 = 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; τ 2 = 0 [95% CI: 0.00, 1,610]) (Fig 3a). The Malaria, malnutrition, and birthweight PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002373 August 8, 2017 7 / 20 effect of malaria infection at delivery was more pronounced: aRR, 1.32 (95% CI: 1.08, 1.62; τ 2 = 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; τ 2 = 0 [95% CI: 0.00, 0.05]); the mean BW difference was −142 g (95% CI: −171, −113; τ 2 = 0 [95% CI: 0, 100] (Fig 4a). Results were similar for low BMI: aRR, 1.49 (95% CI: 1.26, 1.76; τ 2 = 0 [95% CI: 0.00, 0.16]); mean BW difference −133 g (95% CI: −158, −108; τ 2 = 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 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 Malaria, malnutrition, and birthweight PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002373 August 8, 2017 8 / 20 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; τ 2 = 0.25 [95% CI: 0.00, 1.82]; 95% PEI: 0.80, 5.67), and the mean BW difference was 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 (N= 3,388) IPTp-PNG (N= 1,943) ISTp-Malawi (N= 1,602) STOPMIP-Kenya (N= 1,203) LAIS-Malawi (N= 1,190) iLiNS-Ghana (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 Malaria, malnutrition, and birthweight PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002373 August 8, 2017 9 / 20 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 participant 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 definitions 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/m 2 ) among the 13 studies in the Maternal Malaria and Malnutrition (M3) initiative. (DOCX) S2 Fig. Meta-regression results for the effects of malaria infection at enrollment and delivery 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 midupper arm circumference (MUAC <23 cm) and (b) low BMI (BMI <18.5 kg/m 2 ), 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) 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 Malaria, malnutrition, and birthweight PLOS Medicine | https://doi.org/10.1371/journal.pmed.1002373 August 8, 2017 16 / 20 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, Stephen 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, Mwayiwawo Madanitsa, Victor Mwapasa, Per Ashorn, Kenneth Maleta, Ivo Mueller, Danielle Stanisic, 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-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. 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