Eric J. Daza, DrPH, MPS

Provided herein are systems, methods, computer-readable media, and techniques for generating a personalized recommended intervention for a subject based on causal inference, including: obtaining a first set of time series data and a second set of time series data, the first set of time series data relating to a first variable indicative of a health behavior of the subject, and the second set of time series data relating to a second variable indicative of a health condition of the subject;… Show more

Provided herein are systems, methods, computer-readable media, and techniques for generating a personalized recommended intervention for a subject based on causal inference, including: obtaining a first set of time series data and a second set of time series data, the first set of time series data relating to a first variable indicative of a health behavior of the subject, and the second set of time series data relating to a second variable indicative of a health condition of the subject; determining a causal effect of the first variable on the second variable by estimating an average treatment effect, wherein the average treatment effect is estimated by processing the first set of time series data and the second set of time series data using a model-twin randomization method; and generating a personalized treatment or intervention recommendation for the subject to change the health condition based on the causal effect. Show less

Collaboration between people with type 2 diabetes (T2DM) and their health care teams is important for optimal control of the disease and outcomes. Digital technologies could potentially tie together several health care-related devices and platforms into connected ecosystems (CES), but attitudes about CES are unknown.

We surveyed convenience samples of patients and physicians to better understand which patient characteristics are associated with higher likelihoods of (1) participating in… Show more

Collaboration between people with type 2 diabetes (T2DM) and their health care teams is important for optimal control of the disease and outcomes. Digital technologies could potentially tie together several health care-related devices and platforms into connected ecosystems (CES), but attitudes about CES are unknown.

We surveyed convenience samples of patients and physicians to better understand which patient characteristics are associated with higher likelihoods of (1) participating in a potential CES program, as self-reported by patients with T2DM and (2) clinical benefit from participation in a potential CES program, as reported by physicians. Show less

Results

We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate.

Conclusions

This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep… Show more

Results

We found that physical activity can increase the nighttime heart rate amplitude, whereas there were no strong conclusions about its suggested effect on total sleep time. Self-reported states such as exercise, yoga, and stress were associated with increased (for the first two) and decreased (last one) average nighttime heart rate.

Conclusions

This study implemented the MoTR method evaluating the suggested effects of daily stressors on nighttime heart rate, sleep time, and physical activity in an individualized way: via the N-of-1 approach. A Python implementation of MoTR is freely available. Show less

Temporally dense single-person "small data" have become widely available thanks to mobile apps and wearable sensors. Many caregivers and self-trackers want to use these data to help a specific person change their behavior to achieve desired health outcomes. Ideally, this involves discerning possible causes from correlations using that person's own observational time series data. In this paper, we estimate within-individual average treatment effects of physical activity on sleep duration, and… Show more

Temporally dense single-person "small data" have become widely available thanks to mobile apps and wearable sensors. Many caregivers and self-trackers want to use these data to help a specific person change their behavior to achieve desired health outcomes. Ideally, this involves discerning possible causes from correlations using that person's own observational time series data. In this paper, we estimate within-individual average treatment effects of physical activity on sleep duration, and vice-versa. We introduce the model twin randomization (MoTR; "motor") method for analyzing an individual's intensive longitudinal data. Formally, MoTR is an application of the g-formula (i.e., standardization, back-door adjustment) under serial interference. It estimates stable recurring effects, as is done in n-of-1 trials and single case experimental designs. We compare our approach to standard methods (with possible confounding) to show how to use causal inference to make better personalized recommendations for health behavior change, and analyze 222 days of Fitbit sleep and steps data for one of the authors. Show less

Question How can the true burden of influenza-like illnesses (ILIs) be estimated given that most cases of ILIs are mild and go undocumented ?

Findings This cohort study of 15 122 adults who reported ILI symptoms and had data from wearable sensors at symptom onset found an overall reduction in mobility equivalent to 15% of the active US population becoming completely immobilized for 1 day. More than 60% of this reduction occurred among persons who had sought no medical… Show more

Question How can the true burden of influenza-like illnesses (ILIs) be estimated given that most cases of ILIs are mild and go undocumented ?

Findings This cohort study of 15 122 adults who reported ILI symptoms and had data from wearable sensors at symptom onset found an overall reduction in mobility equivalent to 15% of the active US population becoming completely immobilized for 1 day. More than 60% of this reduction occurred among persons who had sought no medical care.

Meaning This study suggests that the burden of ILIs is much greater than had previously been understood. Show less

N-of-1 randomized controlled trials (RCTs) provide an opportunity to evaluate individual patient response to interventions, by randomly allocating different time periods within an individual to repeated intervention and control conditions and comparing responses. N-of-1 observational studies involve the repeated measurement of an outcome (e.g. pain) in a patient over time, but with no intervention implemented, in order to draw conclusions about naturally-occurring patterns and predictors of… Show more

N-of-1 randomized controlled trials (RCTs) provide an opportunity to evaluate individual patient response to interventions, by randomly allocating different time periods within an individual to repeated intervention and control conditions and comparing responses. N-of-1 observational studies involve the repeated measurement of an outcome (e.g. pain) in a patient over time, but with no intervention implemented, in order to draw conclusions about naturally-occurring patterns and predictors of outcomes over time. Both N-of-1 RCTs and observational studies can have a ‘self-study’ design, where an individual conducts the study on themselves, to answer research questions they have generated themselves. N-of-1 RCTs and observational studies provide individualized findings that can be aggregated to produce results equivalent to those found in traditional group-based RCTs and population-level epidemiological studies, respectively, but requiring fewer patients for the same power.

N-of-1 RCTs and observational studies are well-suited to complement, strengthen, and generate advances in precision medicine, patient-centred healthcare, and personalised health. Since 2015, the number of N-of-1 articles has doubled annually.
Similarly, digital health is an exploding field, with over 1,000 studies registered on clinicaltrials.gov.
Digital health, and digital therapeutics in particular, complement N-of-1 RCTs and observational studies by providing relevant individualized health data from, for example, worn sensors, implants, regular lab assays, or -omics sequencing. Such data can be compared to population-health databases to target a patient’s strongest possible treatment option (as in cancer-risk studies) and, in turn, inform the design of an N-of-1 RCT to evaluate it.
Digital health data can also be continuously monitored during the study itself and used to help tailor a treatment to the needs and preferences of patients in real time. Show less

Introduction
California's landmark 1994 Smokefree Workplace Act contained numerous exemptions, or loopholes, believed to contribute to inequities in smokefree air protections among low-income communities and communities of color (e.g., permitting smoking in warehouses, hotel common areas). Cities/counties were not prevented from adopting stronger laws. This study coded municipal laws and state law changes (in 2015–2016) for loophole closures and determined their effects in reducing… Show more

Introduction
California's landmark 1994 Smokefree Workplace Act contained numerous exemptions, or loopholes, believed to contribute to inequities in smokefree air protections among low-income communities and communities of color (e.g., permitting smoking in warehouses, hotel common areas). Cities/counties were not prevented from adopting stronger laws. This study coded municipal laws and state law changes (in 2015–2016) for loophole closures and determined their effects in reducing inequities in smokefree workplace protections.

Methods
Public health attorneys reviewed current laws for 536 of California's 539 cities and counties from January 2017 to May 2018 and coded for 19 loophole closures identified from legislative actions (inter-rater reliability, 87%). The local policy data were linked with population demographics from intercensal estimates (2012–2016) and adult smoking prevalence (2014). The analyses were cross-sectional and conducted in February–June 2019.

Results
Between 1994 and 2018, jurisdictions closed 6.09 loopholes on average (SD=5.28). Urban jurisdictions closed more loopholes than rural jurisdictions (mean=6.40 vs 3.94, p<0.001), and loophole closure scores correlated positively with population size, median household income, and percentage white, non-Hispanic residents (p<0.001 for all). Population demographics and the loophole closure score explained 43% of the variance in jurisdictions’ adult smoking prevalence. State law changes in 2015–2016 increased loophole closure scores and decreased jurisdiction variation (mean=9.74, SD=3.56); closed more loopholes in rural versus urban jurisdictions (meangain=4.44 vs 3.72, p=0.002); and in less populated, less affluent jurisdictions, with greater racial/ethnic diversity, and higher smoking prevalence (p<0.001 for all).

Conclusions
Although jurisdictions made important progress in closing loopholes in smokefree air law, state law changes achieved greater reductions in inequities in policy coverage. Show less

Abstract: Sleep deprivation is a prevalent and rising health concern, one with known effects on blood glucose (BG) levels, mood, and calorie consumption. However, the mechanisms by which sleep deprivation affects calorie consumption (e.g., measured via self-reported types craved food) are unclear, and may be highly idiographic (i.e., individual specific). Single-case or “n-of-1” randomized trials (N1RT) are useful in exploring such effects by exposing each subject to both sleep deprivation and… Show more

Abstract: Sleep deprivation is a prevalent and rising health concern, one with known effects on blood glucose (BG) levels, mood, and calorie consumption. However, the mechanisms by which sleep deprivation affects calorie consumption (e.g., measured via self-reported types craved food) are unclear, and may be highly idiographic (i.e., individual specific). Single-case or “n-of-1” randomized trials (N1RT) are useful in exploring such effects by exposing each subject to both sleep deprivation and baseline conditions, thereby characterizing effects specific to that individual. We had two objectives: (1) To test and generate individual-specific N1RT hypotheses of the effects of sleep deprivation on next-day BG level, mood, and food cravings in two non-diabetic individuals; (2) To refine and guide a future n-of-1 study design for testing and generating such idiographic hypotheses for personalized management of sleep behavior in particular, and for chronic health conditions more broadly. We initially did not find evidence for an idiographic effect of sleep deprivation, but better-refined post hoc findings indicate that sleep deprivation may have increased BG fluctuations, cravings, and negative emotions. We also introduce an application of mixed-effects models and pancit plots to assess idiographic effects over time. Show less

Single-subject health data are becoming increasingly available thanks to advances in self-tracking technology (e.g., mobile devices, apps, sensors, implants). Many users and health caregivers seek to use such observational time series data to recommend changing health practices in order to achieve desired health outcomes. However, there are few available causal inference approaches that are flexible enough to analyze such idiographic data. We develop a recently introduced framework, and… Show more

Single-subject health data are becoming increasingly available thanks to advances in self-tracking technology (e.g., mobile devices, apps, sensors, implants). Many users and health caregivers seek to use such observational time series data to recommend changing health practices in order to achieve desired health outcomes. However, there are few available causal inference approaches that are flexible enough to analyze such idiographic data. We develop a recently introduced framework, and implement a flexible random-forests g-formula approach to estimating a recurring individualized effect called the "average period treatment effect". In the process, we argue that our approach essentially resembles that of a longitudinal study by partitioning a single time series into periods taking on binary treatment levels. We analyze six years of the author's own self-tracked physical activity and weight data to demonstrate our approach, and compare the results of our analysis to one that does not properly account for confounding. Show less

Many of an individual's historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, clinical/chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-effect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an… Show more

Many of an individual's historically recorded personal measurements vary over time, thereby forming a time series (e.g., wearable-device data, self-tracked fitness or nutrition measurements, clinical/chronic conditions). Statistical analyses of such n-of-1 (i.e., single-subject) observational studies (N1OSs) can be used to discover possible cause-effect relationships to then self-test in an n-of-1 randomized trial (N1RT). However, a principled way of determining how and when to interpret an N1OS association as a causal effect (e.g., as if randomization had occurred) is needed. Our goal in this paper is to help bridge the methodological gap between risk-factor discovery and N1RT testing by introducing a basic counterfactual framework for N1OS design and personalized causal analysis. We introduce and characterize what we call the average period treatment effect (APTE), i.e., the estimand of interest in an N1RT, and build an analytical framework around it that can accommodate autocorrelation and time trends in the outcome, effect carryover from previous treatment periods, and slow onset or decay of the effect. The APTE is loosely defined as a contrast (e.g., difference, ratio) of average outcomes the individual can experience under different treatment levels at a given treatment period. To illustrate the utility of our framework for APTE discovery and estimation, two common causal inference methods are specified within the N1OS context. We then apply the framework and methods to search for estimable and interpretable APTEs using six years of the author's self-tracked weight and exercise data, and report both the preliminary findings and the challenges we faced in conducting N1OS causal discovery. Causal analysis of an individual's time series data can be facilitated by an N1RT counterfactual framework. For inference to be valid, the veracity of certain key assumptions must be assessed critically, and the hypothesized causal models must be interpretable and meaningful. Show less

Background: Well-differentiated thyroid carcinoma has a favorable prognosis, but patients with multiple recurrences have drastically lower survival. Filipinos in the US are known to have high thyroid cancer incidence and recurrence rates. It is unknown whether Filipinos also have higher thyroid cancer mortality rates.
Methods: We studied thyroid cancer mortality in Filipino, non-Filipino Asian (NFA), and non-Hispanic White (NHW) adults using US death records (2003-2012) and US Census data… Show more

Background: Well-differentiated thyroid carcinoma has a favorable prognosis, but patients with multiple recurrences have drastically lower survival. Filipinos in the US are known to have high thyroid cancer incidence and recurrence rates. It is unknown whether Filipinos also have higher thyroid cancer mortality rates.
Methods: We studied thyroid cancer mortality in Filipino, non-Filipino Asian (NFA), and non-Hispanic White (NHW) adults using US death records (2003-2012) and US Census data. Age-adjusted mortality rates (AMRs) and proportional mortality ratios (PMRs) were calculated. Gender, nativity status, age at death, and educational attainment were also examined.
Results: We examined 19,940,952 deaths. AMR due to thyroid cancer was highest in Filipinos (1.72 deaths per 100,000, 95% CI 1.51-1.95) compared to NFAs (1.03 per 100,000, 95% CI 0.95-1.12) and NHWs (1.17 per 100,000, 95% CI 1.16-1.18). Compared to NHWs, higher proportionate mortality was observed in Filipino women (3-5 times higher) across all age groups, and Filipino men had 2-3 times higher PMR in the subgroup over the age of 55. Filipinos that completed higher education had notably higher PMR (5.0) than their counterparts who had not (3.5).
Conclusions: Negative prognostic factors for thyroid cancer traditionally include “age greater than 45 years” and “male gender.” We demonstrate that Filipinos die of thyroid cancer at higher rates than NFAs and NHWs of similar ages. Highly-educated Filipinos and Filipino women may be especially at risk for poor thyroid cancer outcomes. Filipino ethnicity should be factored into clinical decision-making in the management of thyroid cancer. Show less

Abstract. Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241–258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for… Show more

Abstract. Individuals may drop out of a longitudinal study, rendering their outcomes unobserved but still well defined. However, they may also undergo truncation (for example, death), beyond which their outcomes are no longer meaningful. Kurland and Heagerty (2005, Biostatistics 6: 241–258) developed a method to conduct regression conditioning on nontruncation, that is, regression conditioning on continuation (RCC), for longitudinal outcomes that are monotonically missing at random (for example, because of dropout). This method first estimates the probability of dropout among continuing individuals to construct inverse-probability weights (IPWs), then fits generalized estimating equations (GEE) with these IPWs. In this article, we present the xtrccipw command, which can both estimate the IPWs required by RCC and then use these IPWs in a GEE estimator by calling the glm command from within xtrccipw. In the absence of truncation, the xtrccipw command can also be used to run a weighted GEE analysis. We demonstrate the xtrccipw command by analyzing an example dataset and the original Kurland and Heagerty (2005) data. We also use xtrccipw to illustrate some empirical properties of RCC through a simulation study. Show less

Introduction: Despite being considerably under reported as the underlying cause of death on death certificates, and consequently on mortality figures, diabetes is among the ten leading causes of death in the U.S. A multiple cause-of-death analysis shows the extent to which diabetes is associated with other leading causes of death.

Hypothesis: Analysis of multiple-cause-of-death will confirm prevalence rates of diabetes among racial/ethnic minority populations, demonstrate the impact of… Show more

Introduction: Despite being considerably under reported as the underlying cause of death on death certificates, and consequently on mortality figures, diabetes is among the ten leading causes of death in the U.S. A multiple cause-of-death analysis shows the extent to which diabetes is associated with other leading causes of death.

Hypothesis: Analysis of multiple-cause-of-death will confirm prevalence rates of diabetes among racial/ethnic minority populations, demonstrate the impact of diabetes in association with other causes of death, and highlight variations of burden of disease among different racial/ethnic groups.

Methods: Causes of death were identified using the Multiple Cause Mortality Files of the National Center for Health Statistics from 2003 to 2012. Age-adjusted mortality rates were calculated for diabetes both as the underlying cause of death (UCD) and as multiple causes of death (MCD) by racial/ethnic groups (NHWs, Blacks, Asians, and Hispanic/Latinos). Frequencies and proportions were calculated by race/ethnicity groups. Linear regression model was used for number of causes per death.

Results: A total of 2,335,198 decedents had diabetes listed as MCD in the U.S. national death records from 2003-2012. Mortality rates of diabetes as MCD were 3.4 times than UCD for Asians, 2.9 times for Blacks, 2.9 times for Hispanics and 3.7 times for NHWs (Figure). Minority populations had higher proportion of deaths with diabetes reported as MCD than NHWs (1.7 times higher for Hispanics, 1.5 times higher for Blacks and Asians). Adjusting for age, gender, and race/ethnicity, there were 1.7 more causes per death co-occurred for diabetes decedents compared to decedents who died due to all other causes (95% CI: 1.714, 1.718).

Conclusions: Our findings underscore the importance of a multiple-cause-of-death approach in the analyses for a more comprehensive understanding of the impact of diabetes. Show less

Epidemiologists often wish to estimate quantities that are easy to communicate and correspond to the results of realistic public health interventions. Methods from causal inference can answer these questions. We adopt the language of potential outcomes under Rubin’s original Bayesian framework and show that the parametric g-formula is easily amenable to a Bayesian approach. We show that the frequentist properties of the Bayesian g-formula suggest it improves the accuracy of estimates of causal… Show more

Epidemiologists often wish to estimate quantities that are easy to communicate and correspond to the results of realistic public health interventions. Methods from causal inference can answer these questions. We adopt the language of potential outcomes under Rubin’s original Bayesian framework and show that the parametric g-formula is easily amenable to a Bayesian approach. We show that the frequentist properties of the Bayesian g-formula suggest it improves the accuracy of estimates of causal effects in small samples or when data are sparse. We demonstrate an approach to estimate the effect of environmental tobacco smoke on body mass index among children aged 4–9 years who were enrolled in a longitudinal birth cohort in New York, USA. We provide an algorithm and supply SAS and Stan code that can be adopted to implement this computational approach more generally. Show less

Importance: Studies in the United States and Europe have found higher smoking prevalence among unemployed job seekers relative to employed workers. While consistent, the extant epidemiologic investigations of smoking and work status have been cross-sectional, leaving it underdetermined whether tobacco use is a cause or effect of unemployment.

Conclusions and Relevance: To our knowledge, this is the first study to prospectively track reemployment success by smoking status. Smokers had a… Show more

Importance: Studies in the United States and Europe have found higher smoking prevalence among unemployed job seekers relative to employed workers. While consistent, the extant epidemiologic investigations of smoking and work status have been cross-sectional, leaving it underdetermined whether tobacco use is a cause or effect of unemployment.

Conclusions and Relevance: To our knowledge, this is the first study to prospectively track reemployment success by smoking status. Smokers had a lower likelihood of reemployment at 1 year and were paid significantly less than nonsmokers when reemployed. Treatment of tobacco use in unemployment service settings is worth testing for increasing reemployment success and financial well-being. Show less

Individuals in a longitudinal study may have missing data for multiple reasons, including intermittent
missed visits or permanent study drop out. Additionally, individuals may experience
a truncating event, such as death, past which the outcomes of interest are no longer meaningful.
Kurland and Heagerty (2005) developed a method to conduct regression conditioning on being
alive (RCA), which constructs inverse-probability weights (IPWs) of the dropout probability
among continuing… Show more

Individuals in a longitudinal study may have missing data for multiple reasons, including intermittent
missed visits or permanent study drop out. Additionally, individuals may experience
a truncating event, such as death, past which the outcomes of interest are no longer meaningful.
Kurland and Heagerty (2005) developed a method to conduct regression conditioning on being
alive (RCA), which constructs inverse-probability weights (IPWs) of the dropout probability
among continuing individuals used to t generalized estimating equations (GEE). RCA has
since been extended to allow for intermittent missingness (IM) of outcomes (Shardell and Miller,
2008). We further extend these methods to simultaneously accommodate dierent mechanisms
for dropout and IM, and call our method regression conditioning on continuation (RCC). RCC is
illustrated using data from a recent study of mother-to-child transmission of HIV to draw inference
on mean infant weights subject to truncation from infant death or HIV infection.

Currently, there is no widely available software for conducting RCA. We present the xtrccipw
command in Stata, which can estimate the dropout IPWs required by RCC if there is no IM.
These IPWs estimated using xtrccipw are then used as weights in a GEE estimator using the
glm command, completing the RCC method. In the absence of truncation, the xtrccipw and
glm commands can also be used in a weighted GEE analysis. The xtrccipw command is demonstrated
by analyzing two example datasets and the original Kurland and Heagerty (2005) data. Show less

In northern Nigeria, interventions are urgently needed to narrow the large gap between international breastfeeding recommendations and actual breastfeeding practices. Studies of integrated microcredit and community health interventions documented success in modifying health behaviors but typically had uncontrolled designs. We conducted a cluster-randomized controlled trial in Bauchi State, Nigeria, with the aim of increasing early breastfeeding initiation and exclusive breastfeeding among… Show more

In northern Nigeria, interventions are urgently needed to narrow the large gap between international breastfeeding recommendations and actual breastfeeding practices. Studies of integrated microcredit and community health interventions documented success in modifying health behaviors but typically had uncontrolled designs. We conducted a cluster-randomized controlled trial in Bauchi State, Nigeria, with the aim of increasing early breastfeeding initiation and exclusive breastfeeding among female microcredit clients. ... In conclusion, a breastfeeding promotion intervention integrated into microcredit increased the likelihood that women adopted recommended breastfeeding practices. This intervention could be scaled up in Nigeria, where local organizations provide microcredit to >500,000 clients. Furthermore, the intervention could be adopted more widely given that >150 million women, many of childbearing age, are involved in microfinance globally. This trial was registered at clinicaltrials.gov as NCT01352351. Show less

Background: Selenium is found in soils and is essential for human antioxidant defense and immune function. In Malawi, low soil selenium and dietary intakes coupled with low plasma selenium concentrations in HIV infection could have negative consequences for the health of HIV-infected mothers and their exclusively breastfed infants.

Objective: We tested the effects of lipid-based nutrient supplements (LNS) that contained 1.3 times the Recommended Dietary Allowance of sodium selenite and… Show more

Background: Selenium is found in soils and is essential for human antioxidant defense and immune function. In Malawi, low soil selenium and dietary intakes coupled with low plasma selenium concentrations in HIV infection could have negative consequences for the health of HIV-infected mothers and their exclusively breastfed infants.

Objective: We tested the effects of lipid-based nutrient supplements (LNS) that contained 1.3 times the Recommended Dietary Allowance of sodium selenite and antiretroviral drugs (ARV) on maternal plasma and breast-milk selenium concentrations.

...

Conclusions: Selenite supplementation of HIV-infected Malawian women was not associated with a change in their plasma or breast-milk selenium concentrations. Future research should examine effects of more readily incorporated forms of selenium (ie, selenomethionine) in HIV-infected breastfeeding women. This trial was registered at clinicaltrials.gov as NCT00164736. Show less

Infant iron status at birth is influenced by maternal iron status during pregnancy; however, there are limited data on the extent to which maternal iron status is associated with infant iron status during exclusive breastfeeding. We evaluated how maternal and infant hemoglobin and iron status [soluble transferrin receptors (TfR) and ferritin] were related during exclusive breastfeeding in HIV-infected women and their infants. The Breastfeeding, Antiretrovirals, and Nutrition Study was a… Show more

Infant iron status at birth is influenced by maternal iron status during pregnancy; however, there are limited data on the extent to which maternal iron status is associated with infant iron status during exclusive breastfeeding. We evaluated how maternal and infant hemoglobin and iron status [soluble transferrin receptors (TfR) and ferritin] were related during exclusive breastfeeding in HIV-infected women and their infants. The Breastfeeding, Antiretrovirals, and Nutrition Study was a randomized controlled trial in Lilongwe, Malawi, in which HIV-infected women were assigned with a 2 × 3 factorial design to a lipid-based nutrient supplement (LNS), or no LNS, and maternal, infant, or no antiretroviral drug, and followed for 24 wk. Longitudinal models were used to relate postpartum maternal hemoglobin (n = 1926) to concurrently measured infant hemoglobin, adjusting for initial infant hemoglobin values. In a subsample, change in infant iron status (hemoglobin, log ferritin, log TfR) between 2 (n = 352) or 6 wk (n = 167) and 24 wk (n = 519) was regressed on corresponding change in the maternal indicator, adjusting for 2 or 6 wk values. A 1 g/L higher maternal hemoglobin at 12, 18, and 24 wk was associated with a 0.06 g/L (P = 0.01), 0.10 g/L (P < 0.001), and 0.06 g/L (P = 0.01), respectively, higher infant hemoglobin. In the subsample, a reduction in maternal log TfR and an increase in hemoglobin from initial measurement to 24 wk were associated with the same pattern in infant values (log TfR β = −0.18 mg/L, P < 0.001; hemoglobin β = 0.13 g/L, P = 0.01). Given the observed influence of maternal and initial infant values, optimizing maternal iron status in pregnancy and postpartum is important to protect infant iron status. This trial was registered at clinicaltrials.gov as NCT00164736. Show less