Malaria infects around 230 million people each year, mostly in sub-Saharan Africa, and causes more than 400,000 deaths. Pregnant women are particularly susceptible to malaria. The parasite that causes malaria can sap the mother’s iron stores and may starve the baby of nutrients. Babies born to infected mothers often have low birth weights, which can have lasting effects on their health and brain development.

Previous studies suggest that preventing malaria in pregnant women using insecticide-treated bed nets or medications may improve birth outcomes. Successful efforts to prevent malaria have led to substantially fewer infections in sub-Saharan Africa. But success has been uneven with some communities continuing to have high rates of infection. These differences may allow scientists to better understand the community-level impact of falling rates of malaria on pregnancy outcomes in the real world.

Heng et al. estimated that reducing malaria transmission minimises the number of infants born with low birth weights in communities in sub-Saharan Africa. In an observational study, they used data on more than 18,000 births in 19 countries in this region between 2000 and 2015 to assess the effects of declining malaria rates on birth weights. They found that a decrease of malaria prevalence is estimated to reduce the rate of low birth weight by 1.48%, which is a 17% reduction in the number of observed newborns with low birth weight in the study population. First-born infants appeared to benefit the most.

This highlights that malaria interventions are beneficial for pregnant women and their newborns. Most analyses of the impact and cost-benefit of malaria control have ignored the potential advantages of malaria control on birth weight, and may thus undermine the benefits of malaria control. The approach used by Heng et al. may further be useful for other epidemiologists studying global health.

In 2018, according to the WHO, 2019 published by the WHO, an estimated 228 million malaria cases occurred worldwide, with an estimated 405,000 deaths from malaria globally (WHO, 2019). Dellicour et al., 2010 estimated that around 85 million pregnancies occurred in 2007 in areas with stable Plasmodium falciparum (one of the most prevalent malaria parasites) transmission and therefore were at risk of malaria. Pregnant women are particularly susceptible to malaria, even if they have developed immunity from childhood infections, in part because parasitized cells in the placenta express unique variant surface antigens (Rogerson et al., 2007). Women who are infected during pregnancy may or may not experience symptoms, but the presence of parasites has grave consequences for both mother and unborn baby. Parasites exacerbate maternal anemia and they also sequester in the placenta, leading to intrauterine growth restriction, low birth weight (i.e. birth weight <2500 g), preterm delivery and even stillbirth and neonatal death. Preventing malaria during pregnancy with drugs or insecticide treated nets has a significant impact on pregnancy outcomes (Eisele et al., 2012; Kayentao et al., 2013; Radeva-Petrova et al., 2014).

Observational and interventional studies of malaria in pregnant women are complicated by the difficulty of enrolling women early in their pregnancy. However, in one study, early exposure to Plasmodium falciparum (before 120 days gestation), prior to initiating malaria prevention measures, was associated with a reduction in birth weight of more than 200 g and reduced average gestational age of nearly 1 week (Schmiegelow et al., 2017). For other representative studies on the negative influence of malaria infection during early pregnancy on birth outcomes, see Menendez et al., 2000, Ross and Smith, 2006, Huynh et al., 2011, Valea et al., 2012, Walker et al., 2014, and Huynh et al., 2015. These results suggest the impact of malaria infection on stillbirths, perinatal, and neonatal mortality may be substantial and needs more careful examination (Fowkes et al., 2020; Gething et al., 2020).

In the last few decades, malaria burden has declined in many parts of the world. Although the magnitude of the decline is difficult to estimate precisely, some reports suggest that the global cases of malaria declined by an estimated 41% between 2000 and 2015 (WHO, 2016) and the clinical cases of Plasmodium falciparum malaria declined by 40% in Africa between 2000 and 2015 (Bhatt et al., 2015). However, estimates of changing morbidity and mortality do not account for the effects of malaria in pregnancy. In the context of global reductions in malaria transmission, we expect fewer pregnancies are being exposed to infection and/or exposed less frequently. This should result in a significant reduction in preterm delivery, low birth weight and stillbirths. However, declining transmission will also lead to reductions in maternal immunity to malaria. Maternal immunity is important in mitigating the effects of malaria infection during pregnancy as is evidenced by the reduced impact of malaria exposure on the second, third and subsequent pregnancies. Thus, we anticipate a complex relationship between declining exposure and pregnancy outcomes that depends on both current transmission and historical transmission and community-level immunity (Mayor et al., 2015).

Understanding the potential causal effect of a reduction in malaria burden on the low birth weight rate is crucial as low birth weight is strongly associated with poor cognitive and physical development of children (McCormick et al., 1992; Avchen et al., 2001; Guyatt and Snow, 2004). Although we know from previous interventional studies that preventing malaria in pregnancy is associated with higher birth weight (Eisele et al., 2012; Radeva-Petrova et al., 2014), we do not know whether natural changes in malaria transmission intensity are similarly associated with improved birth outcomes. To address this question, we make use of the fact that while the overall prevalence of malaria has declined in sub-Saharan Africa, the decline has been uneven, with some malaria-endemic areas experiencing sharp drops and others experiencing little change. We use this heterogeneity to assess whether reductions in malaria prevalence reduce the proportion of infants born with low birth weight in sub-Saharan African countries. Our approach conducts a difference-in-differences study (Card and Krueger, 2000; Angrist and Pischke, 2008; St. Clair and Cook, 2015) by leveraging recent developments in matching, a nonparametric statistical analysis approach that can make studies more robust to bias that can arise from statistical model misspecification (Rubin, 1973; Rubin, 1979; Hansen, 2004; Ho et al., 2007).

In this section, we report and interpret the results of matching, primary analysis, secondary analyses, and sensitivity analyses relating changes in malaria burden to changes in the birth weight rate between 2000–2015 in sub-Saharan Africa. The R (R Development Core Team, 2020) code for producing all the main results and tables of this article is posted on GitHub (https://github.com/siyuheng/Malaria-and-Low-Birth-Weight, Heng, 2021a, copy archived at swh:1:rev:faf6455f95bca6bab364ab95699ea7cd81af1061 Heng, 2021b).

Effect of reduced malaria burden on the low birth weight rate

Appendix 1—table 3 summarizes the low malaria prevalence indicators, the time indicators, the group indicators, the covariates, and the birth weights of the 18,112 births in the matched clusters. Table 6 reports the estimated causal effect of reduced malaria burden (low vs. high malaria prevalence) on the rate of births with low birth weight, which is represented as the coefficient on the malaria prevalence indicator (diagnostics for the multiple imputation that was used in generating the estimates in Table 6 are shown in Appendix 2—table 1). We estimate that a decline in malaria prevalence from ${\text{𝑃𝑓PR}}_{2-10}$ > 0.40 to less than 0.20 reduces the rate of low birth weight by 1.48 percentage points (95% confidence interval: 3.70 percentage points reduction, 0.74 percentage points increase). A reduction in the low birth weight rate of 1.48 percentage points is substantial; recall that among the study individuals with nonmissing birth weight, the low birth weight rate was 8.6%, so a 1.48 percentage points reduction corresponds to a 17% reduction in the low birth weight rate. The results in Table 6 also show that there is strong evidence that mother’s age, child’s birth order, mother’s education level and child’s sex are also associated with the low birth weight rate. For example, mothers with higher education level are less likely to deliver a child with low birth weight, and boys are less likely to have low birth weight than girls, which agrees with the previous literature (e.g. Brooke et al., 1989; Valero De Bernabé et al., 2004; Zeka et al., 2008). Our estimated reduction in the low birth weight rate of 1.48 percentage points from reducing malaria prevalence from high to low is similar to that from a naive difference-in-differences estimator that ignores covariates and missingness of birth weight records. The observed low birth weight rates among the records with observed birth weight within the early year clusters in high-low pairs is 9.33%, in the late year clusters in high-low pairs is 7.52%, in the early year clusters in high-high pairs is 9.18%, and in the late year clusters in high-high pairs is 9.06%. Therefore, the naive difference-in-differences estimator for the effect of reduced malaria burden without adjusting for covariates and missingness of birth weight records is (7.52% ${\displaystyle -}$ 9.33%) ${\displaystyle -}$ (9.06% ${\displaystyle -}$ 9.18%) = ${\displaystyle -}$ 1.69% (i.e. 1.69 percentage points reduction on the low birth weight rate).

Table 6

Regressor	Estimate	95% CI	p-value
0 - high prevalence; 1 - low prevalence	−1.48	[−3.70, 0.74]	0.191
0 - early year; 1 - late year	−0.06	[−1.82, 1.69]	0.943
0 - high-high pairs; 1 - high-low pairs	0.21	[−1.40, 1.82]	0.797
Mother’s age (linear term)	−1.86	[−2.48, −1.23]	<0.001***
Mother’s age (quadratic term)	0.03	[0.02, 0.04]	<0.001***
Child’s birth order (linear term)	−13.91	[−18.49, −9.32]	<0.001***
Child’s birth order (quadratic term)	2.91	[1.82, 4.00]	<0.001***
Wealth index	0.09	[−0.38, 0.56]	0.709
0 - rural; 1 - urban	0.82	[−0.63, 2.27]	0.269
Mother’s education level	−2.02	[−2.82, −1.22]	<0.001***
Child is boy	−1.75	[−2.75, −0.74]	<0.001***
Mother is married or living together	−1.43	[−3.04, 0.19]	0.083
Antenatal care indicator	−0.96	[−2.06, 0.13]	0.085

Among all the high-low pairs of clusters in our sample, there has been a decrease in the low birth weight rate from the early years to the late years of 1.81 percentage points (from 9.33% to 7.52%) for records with observed birth weight and an estimated decrease of 2.04 percentage points (from 10.48% to 8.44%) when multiple imputation is used to impute missing birth weight records. We now explore how much of this decrease can be attributed to reduced malaria burden over time. The estimated effect in Table 6 of the time indicator (late year vs. early year) is a 0.06 percentage points reduction, which is much less than that of the low malaria prevalence indicator. Moreover, the estimated change in the low birth weight rate over time among high-low pairs that comes from changes in the covariates over time is a 0.52 percentage points reduction. This is calculated by looking at the difference between ${\widehat{\beta }}^T{\overline{\mathbf{𝐱}}}_{\text{early}}$ and ${\widehat{\beta }}^T{\overline{\mathbf{𝐱}}}_{\text{late}}$, where ${\widehat{\beta }}^T$ is the estimated coefficients of the covariate regressors listed in Table 6, and ${\overline{\mathbf{𝐱}}}_{\text{early}}$ and ${\overline{\mathbf{𝐱}}}_{\text{late}}$ are the average values in high-low pairs of the covariate regressors of the individuals within the early year clusters and those within the late year clusters respectively. These results suggest that after adjusting for the observed covariates listed in Table 6 and missingness of birth weight records, the observed decrease in the low birth weight rate over time in high-low pairs comes mainly from reduced malaria burden over time instead of changes over time in the low birth weight rate that affect both high-low and high-high pairs of clusters. To illustrate this point and further verify the potentially substantial effect of reduced malaria burden on the low birth weight rate, we also plot the estimated low birth weight rate of each cluster among the high-high pairs and high-low pairs in our study sample in Figure 3. From Figure 3, we can see that although in general, for both high-high pairs and high-low pairs, the birth weight rates of the late year clusters are lower than those of the early year clusters, it is clear that the reductions in low birth weight rate from early year to late year among the high-low pairs are considerably greater than those among high-high pairs, suggesting that reducing community-level malaria burden can potentially substantially reduce the low birth weight rate.

Figure 3

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We have developed a pair-of-pairs matching approach to conduct a difference-in-differences study to examine the causal effect of a reduction in malaria prevalence on the low birth weight rate in sub-Saharan Africa during the years 2000–2015. Although we cannot rule out no effect at a 95% confidence level, the magnitude of the estimated effect of a reduction from high malaria prevalence to low malaria prevalence on the low birth weight rate (1.48 percentage points) is even greater than the estimated effect of a factor thought to be important, antenatal care during pregnancy (0.96 percentage points). In a secondary analysis, we find that reduction in malaria burden from high to low is estimated to be especially crucial for reducing the low birth weight rate of first born children, reducing it by 3.73 percentage points (95% CI: 9.11 percentage points reduction, 1.64 percentage points increase). This agrees with previous studies which demonstrate that the effects of malaria on birth outcomes are most pronounced in the first pregnancy (e.g. McGregor et al., 1983).

Previous studies have shown that individual malaria prevention during pregnancy reduces the chances of the woman’s baby having low birth weight (Kayentao et al., 2013). In this paper, we examine the community-level effect of reductions in malaria on pregnancy outcomes as opposed to the individual-level effect of malaria prevention interventions during pregnancy. Our results support extrapolation of studies of antenatal malaria interventions on birth weight to populations experiencing declining malaria burden. Furthermore, we conclude that reports of declining malaria mortality underestimate the contribution of reduced malaria exposure during pregnancy on pregnancy outcomes and neonatal survival. Although some studies have documented higher rates of adverse pregnancy outcomes in malaria-infected women with declining antimalarial immunity, such as may be seen in communities with declining malaria exposure (Mayor et al., 2015), our study demonstrates that overall reduction in exposure to infection, including during pregnancy, outweighs these individual changes in risk once infected.

Strengths of our study include that we use state-of-the-art causal inference methods on a large representative data set. We develop a novel pair-of-pairs matching approach to conduct a difference-in-differences study to estimate the real world effectiveness of public health interventions by combining DHS data with other data sources. There are two major difficulties when using the DHS data to conduct a difference-in-differences study. The first major difficulty is that within each country the DHS samples different locations (clusters) over different survey years. Our first-step matching handles this difficulty through using optimal matching to pair the early year DHS clusters and the late year DHS clusters within the same country based on the geographic proximity of their locations. Then each formed pair of clusters can mimic a single cluster measured twice in two different survey years, which serves as the foundation of a difference-in-differences study. The second major difficulty is that although an advantage of the DHS data is that they contain many potentially important cluster-level and individual-level covariates, it may be difficult to come up with a statistical model that is both efficient and robust to adjust for these covariates. A traditional approach to estimating the real world effectiveness of an intervention in such settings is to run a regression of an outcome of interest on a measure of adherence to the treatment (zero if in the period before the intervention was available and ranging from 0 to 1 after the intervention was available), covariates (individual-level and cluster-level covariates) and a random effect for the cluster (Goetgeluk and Vansteelandt, 2008). This regression approach relies heavily on correct specification of the model by which the covariates affect the outcome (e.g. linear, quadratic, cubic), therefore the result can be severely biased by model misspecification (Rubin, 1973; Hansen, 2004; Ho et al., 2007). We instead use a second-step matching to first optimally select and match the treated units (i.e. high-low pairs of clusters) and control units (i.e. high-high pairs of clusters) to ensure that they have balanced distributions of covariates across time and then run the regression with the dummy variables for the matched sets. Such a nonparametric data preprocessing step before running a regression can potentially reduce bias due to model misspecification (Rubin, 1973; Hansen, 2004; Ho et al., 2007).

Our merged study data set makes use of two aspects of the richness of the relevant data resources. First, from the perspective of sample size and length of time span, the data set includes over 18,000 births in 19 countries in sub-Saharan Africa and describes changes in the low birth weight rate over a 15-year period. Some of the studied regions had substantial changes in malaria parasite prevalence during this time period, whereas others did not, which provides us ample heterogeneity necessary for conducting a difference-in-differences study. Second, from the perspective of the comprehensiveness of information, our merged data set includes various types of information: from cluster-level to individual-level records; from geographic to sociodemographic characteristics; from surveyed data to predicted data.

Some potential limitations of our study should be considered. First, we discretized the mean malaria prevalence (i.e. ${\text{𝑃𝑓PR}}_{2-10}$ from 0 to 1) into high (${\text{𝑃𝑓PR}}_{2-10}$ > 0.4), medium (${\text{𝑃𝑓PR}}_{2-10}$ lies in [0.2, 0.4]), and low (${\text{𝑃𝑓PR}}_{2-10}$ < 0.2), which means that the magnitude of the estimated causal effect depends on how we define these cut-offs. Our primary analysis suggests that reducing the malaria burden from high to low may substantially help control the low birth weight rate, and our secondary analyses suggest that a more dramatic reduction in malaria prevalence can lead to a more dramatic drop in the low birth weight rate. More research needs to be done on the minimum magnitude of the reduction in malaria prevalence that is needed to cause a substantial drop in the low birth weight rate. Second, we assigned the malaria prevalence (i.e. ${\text{𝑃𝑓PR}}_{2-10}$) data to children’s records based on the DHS survey years which may not be exactly the same years as children’s actual birth years. For example, a child whose age is three years at the corresponding DHS survey year should have been born three years earlier before that DHS survey year, in which case we might have assigned the wrong ${\text{𝑃𝑓PR}}_{2-10}$ to that child’s gestational period. We examined this issue via SA1 and the result suggested that this did not induce much bias to the results of our primary analysis.

The novel design-based causal inference approach developed in this work, a pair-of-pairs matching approach to conduct a difference-in-differences study (i.e. the two-step matching procedure to form matched pairs of pairs as a nonparametric data preprocessing step in a difference-in-differences study), is potentially useful for researchers who would like to reduce the estimation bias due to potential model misspecification in the traditional difference-in-differences approach. Moreover, the general statistical methodology developed in this work can be applied beyond the malaria settings to handle the heterogeneity of survey time points and locations in data sets such as the Demographic and Health Surveys (DHS).

In summary, the contribution of malaria to stillbirth and neonatal mortality, for which low birth weight is a proxy, are currently not accounted for in global estimates of malaria mortality. Using a large representative data set and innovative statistical evidence, we found point estimates that suggested that reductions in malaria burden at the community level substantially reduce the low birth weight rate. To our knowledge, this is the first study of its kind to evaluate the causal effects of malaria control on birth outcomes using a causal inference framework. Although our confidence intervals do include a possibility of no effect, the evidence from our primary analysis and secondary analyses is strong enough to merit further study and motivate further investments in mitigating the intolerable burden of malaria.

Our study is an observational study that uses the following three publicly available data sets: (1) Annual Mean of Plasmodium falciparum Parasite Rate, created by the Malaria Atlas Project, available at https://malariaatlas.org/malaria-burden-data-download/; (2) Integrated Public Use Microdata Series' recoding of the DHS variables (IPUMS-DHS), created by the IPUMS-DHS Team, available at https://www.idhsdata.org/idhs/index.shtml; (3) DHS-GPS data, created by the Demographic and Health Surveys (DHS) Program, available at https://dhsprogram.com/data/available-datasets.cfm. The data resources are also described in the "Data resources" section of the manuscript. The source code for producing the results in Figure 1, the results in Figure 3, the results in Tables 2, 3, 5, and 6, the results in Table 4, the results in Appendix 1—table 2, the results in Appendix 1—table 3, and the results in Appendix 3 —table 1 can be found in Source code 1 and on GitHub at https://github.com/siyuheng/Malaria-and-Low-Birth-Weight (copy archived at https://archive.softwareheritage.org/swh:1:rev:faf6455f95bca6bab364ab95699ea7cd81af1061).

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Author details

1. Siyu Heng

   1. Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, United States
   2. Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, United States

   Contribution

   Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9313-3667

2. Wendy P O'Meara

   Global Health Institute, School of Medicine, Duke University, Durham, United States

   Contribution

   Conceptualization, Resources, Data curation, Supervision, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

3. Ryan A Simmons

   1. Global Health Institute, School of Medicine, Duke University, Durham, United States
   2. Department of Biostatistics and Bioinformatics, School of Medicine, Duke University, Durham, United States

   Contribution

   Resources, Data curation, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

4. Dylan S Small

   Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, United States

   Contribution

   Conceptualization, Resources, Supervision, Investigation, Methodology, Writing - review and editing

   For correspondence

   dsmall@wharton.upenn.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4928-2646

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