The use, adherence, and evaluation of interactive text-messaging among women admitted to prevention of mother-to-child transmission of HIV care in Kenya (WelTel PMTCT).

BACKGROUND: To improve future mobile health (mHealth) interventions in resource-limited settings, knowledge of participants' adherence to interactive interventions is needed, but previous studies are limited. We aimed to investigate how women in prevention of mother-to-child transmission of HIV (PMTCT) care in Kenya used, adhered to, and evaluated an interactive text-messaging intervention. METHODS: We conducted a cohort study nested within the WelTel PMTCT trial among 299 pregnant women living with HIV aged ≥ 18 years. They received weekly text messages from their first antenatal care visit until 24 months postpartum asking "How are you?". They were instructed to text within 48 h stating that they were "okay" or had a "problem". Healthcare workers phoned non-responders and problem-responders to manage any issue. We used multivariable-adjusted logistic and negative binomial regression to estimate adjusted odds ratios (aORs), rate ratios (aRRs) and 95% confidence intervals (CIs) to assess associations between baseline characteristics and text responses. Perceptions of the intervention were evaluated through interviewer-administered follow-up questionnaires at 24 months postpartum. RESULTS: The 299 participants sent 15,183 (48%) okay-responses and 438 (1%) problem-responses. There were 16,017 (51%) instances of non-response. The proportion of non-responses increased with time and exceeded 50% around 14 months from enrolment. Most reported problems were health related (84%). Having secondary education was associated with reporting a problem (aOR:1.88; 95%CI: 1.08-3.27) compared to having primary education or less. Younger age (18-24 years) was associated with responding to < 50% of messages (aOR:2.20; 95%CI: 1.03-4.72), compared to being 35-44 years. Women with higher than secondary education were less likely (aOR:0.28; 95%CI: 0.13-0.64), to respond to < 50% of messages compared to women with primary education or less. Women who had disclosed their HIV status had a lower rate of non-response (aRR:0.77; 95%CI: 0.60-0.97). In interviews with 176 women, 167 (95%) agreed or strongly agreed that the intervention had been helpful, mainly by improving access to and communication with their healthcare providers (43%). CONCLUSION: In this observational study, women of younger age, lower education, and who had not disclosed their HIV status were less likely to adhere to interactive text-messaging. The majority of those still enrolled at the end of the intervention reported that text-messaging had been helpful, mainly by improving access to healthcare providers. Future mHealth interventions aiming to improve PMTCT care need to be targeted to attract the attention of women with lower education and younger age.

Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation.

There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the g $$ g $$ -formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains 'unbalanced' even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator.

The effect of an interactive weekly text-messaging intervention on retention in prevention of mother-to-child transmission of HIV care: a randomised controlled trial (WelTel PMTCT).

Retention in prevention of mother-to-child transmission (PMTCT) care is critical to prevent vertical HIV transmission and reduce morbidity and mortality of mother-infant pairs. We investigated whether weekly, interactive text-messaging improved 18-month postpartum retention in PMTCT care. This randomised, two-armed, parallel trial was conducted at six PMTCT clinics in western Kenya. Pregnant women with HIV at least 18 years of age with access to a mobile phone, able to text-message, or had somebody who could text on their behalf, were eligible. Participants were randomly assigned at a 1:1 ratio in block sizes of four to the intervention or control group. The intervention group received weekly text messages asking "How are you?" ("Mambo?" in Swahili) and were requested to respond within 48 h. Healthcare workers called women who indicated a problem or did not respond. The intervention was administered up to 24 months after delivery. Both groups received standard care. The primary outcome was retention in care at 18 months postpartum (i.e., clinic attendance 16-24 months after delivery based on data from patient files, patient registers and Kenya's National AIDS and STI Control Programme database), which was analysed by intention-to-treat. Researchers and data collectors were masked to group assignment, while healthcare workers were not. Between June 25th, 2015, and July 5th, 2016, we randomly assigned 299 women to the intervention and 301 to standard care only. Follow-up concluded on July 26th, 2019. The proportion of women retained in PMTCT care at 18 months postpartum was not significantly different between the intervention (n = 210/299) and control groups (n = 207/301) (risk ratio 1.02, 95% confidence interval 0.92-1.14, p = 0.697). No adverse events related to the mobile phone intervention were reported. Weekly, interactive text-messaging was not associated with improved retention in PMTCT care at 18 months postpartum or linkage to care up to 30 months postpartum in this setting. (ISRCTN No. 98818734).

Target parameters and bias in non-causal change-score analyses with measurement errors.

In studies where the outcome is a change-score, it is often debated whether or not the analysis should adjust for the baseline score. When the aim is to make causal inference, it has been argued that the two analyses (adjusted vs. unadjusted) target different causal parameters, which may both be relevant. However, these arguments are not applicable when the aim is to make predictions rather than to estimate causal effects. When the scores are measured with error, there have been attempts to quantify the bias resulting from adjustment for the (mis-)measured baseline score or lack thereof. However, these bias results have been derived under an unrealistically simple model, and assuming that the target parameter is the unadjusted (for the true baseline score) association, thus dismissing the adjusted association as a possibly relevant target parameter. In this paper we address these limitations. We argue that, even if the aim is to make predictions, there are two possibly relevant target parameters; one adjusted for the baseline score and one unadjusted. We consider both the simple case when there are no measurement errors, and the more complex case when the scores are measured with error. For the latter case, we consider a more realistic model than previous authors. Under this model we derive analytic expressions for the biases that arise when adjusting or not adjusting for the (mis-)measured baseline score, with respect to the two possible target parameters. Finally, we use these expressions to discuss when adjustment is warranted in change-score analyses.

Causal inference in survival analysis under deterministic missingness of confounders in register data.

Long-term register data offer unique opportunities to explore causal effects of treatments on time-to-event outcomes, in well-characterized populations with minimum loss of follow-up. However, the structure of the data may pose methodological challenges. Motivated by the Swedish Renal Registry and estimation of survival differences for renal replacement therapies, we focus on the particular case when an important confounder is not recorded in the early period of the register, so that the entry date to the register deterministically predicts confounder missingness. In addition, an evolving composition of the treatment arms populations, and suspected improved survival outcomes in later periods lead to informative administrative censoring, unless the entry date is appropriately accounted for. We investigate different consequences of these issues on causal effect estimation following multiple imputation of the missing covariate data. We analyse the performance of different combinations of imputation models and estimation methods for the population average survival. We further evaluate the sensitivity of our results to the nature of censoring and misspecification of fitted models. We find that an imputation model including the cumulative baseline hazard, event indicator, covariates and interactions between the cumulative baseline hazard and covariates, followed by regression standardization, leads to the best estimation results overall, in simulations. Standardization has two advantages over inverse probability of treatment weighting here: it can directly account for the informative censoring by including the entry date as a covariate in the outcome model, and allows for straightforward variance computation using readily available software.

Flexible evaluation of surrogacy in platform studies.

Trial-level surrogates are useful tools for improving the speed and cost effectiveness of trials but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. There have been many proposed methods for trial-level surrogate evaluation, but none, to our knowledge, for the specific setting of platform studies. As platform studies are becoming more popular, methods for surrogate evaluation using them are needed. These studies also offer a rich data resource for surrogate evaluation that would not normally be possible. However, they also offer a set of statistical issues including heterogeneity of the study population, treatments, implementation, and even potentially the quality of the surrogate. We propose the use of a hierarchical Bayesian semiparametric model for the evaluation of potential surrogates using nonparametric priors for the distribution of true effects based on Dirichlet process mixtures. The motivation for this approach is to flexibly model relationships between the treatment effect on the surrogate and the treatment effect on the outcome and also to identify potential clusters with differential surrogate value in a data-driven manner so that treatment effects on the surrogate can be used to reliably predict treatment effects on the clinical outcome. In simulations, we find that our proposed method is superior to a simple, but fairly standard, hierarchical Bayesian method. We demonstrate how our method can be used in a simulated illustrative example (based on the ProBio trial), in which we are able to identify clusters where the surrogate is, and is not useful. We plan to apply our method to the ProBio trial, once it is completed.

Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment.

BACKGROUND: Medical advances in the treatment of cancer have allowed the development of multiple approved treatments and prognostic and predictive biomarkers for many types of cancer. Identifying improved treatment strategies among approved treatment options, the study of which is termed comparative effectiveness, using predictive biomarkers is becoming more common. RCTs that incorporate predictive biomarkers into the study design, called prediction-driven RCTs, are needed to rigorously evaluate these treatment strategies. Although researched extensively in the experimental treatment setting, literature is lacking in providing guidance about prediction-driven RCTs in the comparative effectiveness setting. METHODS: Realistic simulations with time-to-event endpoints are used to compare contrasts of clinical utility and provide examples of simulated prediction-driven RCTs in the comparative effectiveness setting. RESULTS: Our proposed contrast for clinical utility accurately estimates the true clinical utility in the comparative effectiveness setting while in some scenarios, the contrast used in current literature does not. DISCUSSION: It is important to properly define contrasts of interest according to the treatment setting. Realistic simulations should be used to choose and evaluate the RCT design(s) able to directly estimate that contrast. In the comparative effectiveness setting, our proposed contrast for clinical utility should be used.

Cross-direct effects in settings with two mediators.

When multiple mediators are present, there are additional effects that may be of interest beyond the well-known natural (NDE) and controlled direct effects (CDE). These effects cross the type of control on the mediators, setting one to a constant level and one to its natural level, which differs across subjects. We introduce five such estimands for the cross-CDE and -NDE when two mediators are measured. We consider both the scenario where one mediator is influenced by the other, referred to as sequential mediators, and the scenario where the mediators do not influence each other. Such estimands may be of interest in immunology, as we discuss in relation to measured immunological responses to SARS-CoV-2 vaccination. We provide identifying expressions for the estimands in observational settings where there is no residual confounding, and where intervention, outcome, and mediators are of arbitrary type. We further provide tight symbolic bounds for the estimands in randomized settings where there may be residual confounding of the outcome and mediator relationship and all measured variables are binary.

Confidence bands in survival analysis.

BACKGROUND: Providing estimates of uncertainty for statistical quantities is important for statistical inference. When the statistical quantity of interest is a survival curve, which is a function over time, the appropriate type of uncertainty estimate is a confidence band constructed to account for the correlation between points on the curve, we will call this a simultaneous confidence band. This, however, is not the type of confidence band provided in standard software, which is constructed by joining the confidence intervals at given time points. METHODS: We show that this type of band does not have desirable joint/simultaneous coverage properties in comparison to simultaneous bands. RESULTS: There are different ways of constructing simultaneous confidence bands, and we find that bands based on the likelihood ratio appear to have the most desirable properties. Although there is no standard software available in the three major statistical packages to compute likelihood-based simultaneous bands, we summarise and give code to use available statistical software to construct other simultaneous forms of bands, which we illustrate using a study of colon cancer. CONCLUSIONS: There is a need for more user-friendly statistical software to compute simultaneous confidence bands using the available methods.

Survival stacking with multiple data types using pseudo-observation-based-AUC loss.

There have been many strategies to adapt machine learning algorithms to account for right censored observations in survival data in order to build more accurate risk prediction models. These adaptions have included pre-processing steps such as pseudo-observation transformation of the survival outcome or inverse probability of censoring weighted (IPCW) bootstrapping of the observed binary indicator of an event prior to a time point of interest. These pre-processing steps allow existing or newly developed machine learning methods, which were not specifically developed with time-to-event data in mind, to be applied to right censored survival data for predicting the risk of experiencing an event. Stacking or ensemble methods can improve on risk predictions, but in general, the combination of pseudo-observation-based algorithms, IPCW bootstrapping, IPC weighting of the methods directly, and methods developed specifically for survival has not been considered in the same ensemble. In this paper, we propose an ensemble procedure based on the area under the pseudo-observation-based-time-dependent ROC curve to optimally stack predictions from any survival or survival adapted algorithm. The real application results show that our proposed method can improve on single survival based methods such as survival random forest or on other strategies that use a pre-processing step such as inverse probability of censoring weighted bagging or pseudo-observations alone.

Direct modeling of relative and absolute risks in register data: Mortality risk in sarcoidosis.

PURPOSE: This paper aims to illustrate the use and interpretation of regression based on pseudo-observations for estimating risks of time-to-event outcomes in epidemiological studies. METHODS: We use pseudo-observation based regression for estimation of contrasts in the relative and absolute risks at specific times. This relaxes the proportional hazards assumption and directly estimates relative and absolute risks without the need for secondary calculations or standardization. Statistical software is available to use this method, and we demonstrate its use in a reanalysis of the mortality risk in sarcoidosis patients in Sweden. RESULTS: We report estimated adjusted mortality risk differences and risk ratios by age, and at different years of follow up. Compared to the hazard ratio of 1.62, which is assumed to be time constant, we find risk ratios ranging from 1.7 at 2 years of follow-up to 1.3 at 10 years. CONCLUSIONS: Pseudo-observation regression is a flexible and powerful tool for censored time-to-event data. The models are easy to run and interpret so they should be considered a standard tool alongside Cox regression and standardization. As with any statistical model, there are assumptions needed for valid inference, which should be assessed on a case-by-case basis.

The effect of weekly interactive text-messaging on early infant HIV testing in Kenya: a randomised controlled trial (WelTel PMTCT).

Mother-to-child transmission of HIV remains a significant concern in Africa despite earlier progress. Early infant diagnosis (EID) of HIV is crucial to reduce mortality among infected infants through early treatment initiation. However, a large proportion of HIV-exposed infants are still not tested in Kenya. Our objective was to investigate whether weekly interactive text-messages improved prevention of mother-to-child transmission (PMTCT) of HIV care outcomes including EID HIV testing. This multicentre, parallel-group, randomised, open-label trial included six antenatal care clinics across western Kenya. Pregnant women living with HIV, aged 18 years or older, with mobile phone access, were randomised in a 1:1 ratio to weekly text messages that continued until 24 months postpartum, asking "How are you?" ("Mambo?") to which they were asked to respond within 48 h, or a control group. Healthcare workers contacted participants reporting problems and non-responders by phone. Participants in both groups received routine PMTCT care. The prespecified secondary outcome reported in this paper is EID HIV testing by eight weeks of age (blinded outcome assessment). Final 24-months trial results will be published separately. We estimated risk ratios using Poisson regression with robust standard errors. Between June 2015-July 2016, we screened 735 pregnant women, of whom 600 were enrolled: 299 were allocated to the intervention and 301 to the control group. By eight weeks of age, the uptake of EID HIV testing out of recorded live births was 85.5% in the intervention and 84.7% in the control group (71.2% vs. 71.8% of participants randomised, including miscarriages, stillbirths, etc.). The intention-to-treat risk ratio was 0.99; 95% CI: 0.90-1.10; p = 0.89. The proportion of infants diagnosed with HIV was 0.8% in the intervention and 1.2% in the control group. No adverse events were reported. We found no evidence to support that the WelTel intervention improved EID HIV testing. A higher uptake of EID testing than expected in both groups may be a result of lower barriers to EID testing and improved PMTCT care in western Kenya, including the broader standard use of mobile phone communication between healthcare workers and patients. (ISRCTN No. 98818734. Funded by the European-Developing Countries Clinical Trial Partnership and others).

Distinct Metabolic Profile Associated with a Fatal Outcome in COVID-19 Patients during the Early Epidemic in Italy.

In one year of the coronavirus disease 2019 (COVID-19) pandemic, many studies have described the different metabolic changes occurring in COVID-19 patients, linking these alterations to the disease severity. However, a complete metabolic signature of the most severe cases, especially those with a fatal outcome, is still missing. Our study retrospectively analyzes the metabolome profiles of 75 COVID-19 patients with moderate and severe symptoms admitted to Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (Lombardy Region, Italy) following SARS-CoV-2 infection between March and April 2020. Italy was the first Western country to experience COVID-19, and the Lombardy Region was the epicenter of the Italian COVID-19 pandemic. This cohort shows a higher mortality rate compared to others; therefore, it represents a unique opportunity to investigate the underlying metabolic profiles of the first COVID-19 patients in Italy and to identify the potential biomarkers related to the disease prognosis and fatal outcome. IMPORTANCE Understanding the metabolic alterations occurring during an infection is a key element for identifying potential indicators of the disease prognosis, which are fundamental for developing efficient diagnostic tools and offering the best therapeutic treatment to the patient. Here, exploiting high-throughput metabolomics data, we identified the first metabolic profile associated with a fatal outcome, not correlated with preexisting clinical conditions or the oxygen demand at the moment of diagnosis. Overall, our results contribute to a better understanding of COVID-19-related metabolic disruption and may represent a useful starting point for the identification of independent prognostic factors to be employed in therapeutic practice.

Prediction-driven pooled testing methods: Application to HIV treatment monitoring in Rakai, Uganda.

Chronic medical conditions often necessitate regular testing for proper treatment. Regular testing of all afflicted individuals may not be feasible due to limited resources, as is true with HIV monitoring in resource-limited settings. Pooled testing methods have been developed in order to allow regular testing for all while reducing resource burden. However, the most commonly used methods do not make use of covariate information predictive of treatment failure, which could improve performance. We propose and evaluate four prediction-driven pooled testing methods that incorporate covariate information to improve pooled testing performance. We then compare these methods in the HIV treatment management setting to current methods with respect to testing efficiency, sensitivity, and number of testing rounds using simulated data and data collected in Rakai, Uganda. Results show that the prediction-driven methods increase efficiency by up to 20% compared with current methods while maintaining equivalent sensitivity and reducing number of testing rounds by up to 70%. When predictions were incorrect, the performance of prediction-based matrix methods remained robust. The best performing method using our motivating data from Rakai was a prediction-driven hybrid method, maintaining sensitivity over 96% and efficiency over 75% in likely scenarios. If these methods perform similarly in the field, they may contribute to improving mortality and reducing transmission in resource-limited settings. Although we evaluate our proposed pooling methods in the HIV treatment setting, they can be applied to any setting that necessitates testing of a quantitative biomarker for a threshold-based decision.

Alcohol and illicit and non-medical prescription drug use before and during pregnancy in Stockholm, Sweden: A cross-sectional study.

OBJECTIVES: To provide current estimates of alcohol and drug use among pregnant women attending antenatal care lectures in preparation for childbirth in Stockholm, Sweden. STUDY DESIGN: A cross-sectional study. Data was collected anonymously among women attending lectures in preparation for childbirth. MAIN OUTCOME MEASURES: The prevalence of alcohol and illicit and non-medical prescription drug use among pregnant women attending antenatal care lectures in preparation for childbirth. RESULTS: Nine hundred and thirty-six pregnant women attending lectures in preparation for childbirth participated. Among those answering all questions about alcohol use during pregnancy, 4.2 percent reported use (95% confidence interval (CI), 3.0-5.7%) and among those answering all questions about illicit or non-medical prescription drug use during pregnancy, 0.5 percent reported such use (95% CI, 0.1-1.3%). The prevalences of binge drinking during pregnancy and alcohol and drug use before pregnancy are presented. Comparisons of anonymously and non-anonymously collected data are included. CONCLUSIONS: Approximately one in 25 women reported using alcohol and approximately one in 200 reported using illicit or non-medical prescription drugs while pregnant. Alcohol use during pregnancy may have decreased in Stockholm, Sweden.

A Deferred-Vaccination Design to Assess Durability of COVID-19 Vaccine Effect After the Placebo Group Is Vaccinated.

Multiple candidate vaccines to prevent COVID-19 have entered large-scale phase 3 placebo-controlled randomized clinical trials, and several have demonstrated substantial short-term efficacy. At some point after demonstration of substantial efficacy, placebo recipients should be offered the efficacious vaccine from their trial, which will occur before longer-term efficacy and safety are known. The absence of a placebo group could compromise assessment of longer-term vaccine effects. However, by continuing follow-up after vaccination of the placebo group, this study shows that placebo-controlled vaccine efficacy can be mathematically derived by assuming that the benefit of vaccination over time has the same profile for the original vaccine recipients and the original placebo recipients after their vaccination. Although this derivation provides less precise estimates than would be obtained by a standard trial where the placebo group remains unvaccinated, this proposed approach allows estimation of longer-term effect, including durability of vaccine efficacy and whether the vaccine eventually becomes harmful for some. Deferred vaccination, if done open-label, may lead to riskier behavior in the unblinded original vaccine group, confounding estimates of long-term vaccine efficacy. Hence, deferred vaccination via blinded crossover, where the vaccine group receives placebo and vice versa, would be the preferred way to assess vaccine durability and potential delayed harm. Deferred vaccination allows placebo recipients timely access to the vaccine when it would no longer be proper to maintain them on placebo, yet still allows important insights about immunologic and clinical effectiveness over time.

Adjustment for Disease Severity in the Test-Negative Study Design.

The test-negative study design is often used to estimate vaccine effectiveness in influenza studies, but it has also been proposed in the context of other infectious diseases, such as cholera, dengue, or Ebola. It was introduced as a variation of the case-control design, in an attempt to reduce confounding bias due to health-care-seeking behavior, and has quickly gained popularity because of its logistic advantages. However, examination of the directed acyclic graphs that describe the test-negative design reveals that without strong assumptions, the estimated odds ratio derived under this sampling mechanism is not collapsible over the selection variable, such that the results obtained for the sampled individuals cannot be generalized to the whole population. In this paper, we show that adjustment for severity of disease can reduce this bias and, under certain assumptions, makes it possible to unbiasedly estimate a causal odds ratio. We support our findings with extensive simulations and discuss them in the context of recently published cholera test-negative studies of the effectiveness of cholera vaccines.

Pfs230 yields higher malaria transmission-blocking vaccine activity than Pfs25 in humans but not mice.

BACKGROUNDVaccines that block human-to-mosquito Plasmodium transmission are needed for malaria eradication, and clinical trials have targeted zygote antigen Pfs25 for decades. We reported that a Pfs25 protein-protein conjugate vaccine formulated in alum adjuvant induced serum functional activity in both US and Malian adults. However, antibody levels declined rapidly, and transmission-reducing activity required 4 vaccine doses. Functional immunogenicity and durability must be improved before advancing transmission-blocking vaccines further in clinical development. We hypothesized that the prefertilization protein Pfs230 alone or in combination with Pfs25 would improve functional activity.METHODSTransmission-blocking vaccine candidates based on gamete antigen Pfs230 or Pfs25 were conjugated with Exoprotein A, formulated in Alhydrogel, and administered to mice, rhesus macaques, and humans. Antibody levels were measured by ELISA and transmission-reducing activity was assessed by the standard membrane feeding assay.RESULTSPfs25-EPA/Alhydrogel and Pfs230D1-EPA/Alhydrogel induced similar serum functional activity in mice, but Pfs230D1-EPA induced significantly greater activity in rhesus monkeys that was enhanced by complement. In US adults, 2 vaccine doses induced complement-dependent activity in 4 of 5 Pfs230D1-EPA/Alhydrogel recipients but no significant activity in 5 Pfs25-EPA recipients, and combination with Pfs25-EPA did not increase activity over Pfs230D1-EPA alone.CONCLUSIONThe complement-dependent functional immunogenicity of Pfs230D1-EPA represents a significant improvement over Pfs25-EPA in this comparative study. The rhesus model is more predictive of the functional human immune response to Pfs230D1 than is the mouse model.TRIAL REGISTRATIONClinicalTrials.gov NCT02334462.FUNDINGIntramural Research Program of the National Institute of Allergy and Infectious Diseases, National Institutes of Health.

Frequent platelet donation is associated with lymphopenia and risk of infections: A nationwide cohort study.

BACKGROUND: Recently, plateletpheresis donations using a widely used leukoreduction system (LRS) chamber have been associated with T-cell lymphopenia. However, clinical health consequences of plateletpheresis-associated lymphopenia are still unknown. STUDY DESIGN AND METHODS: A nationwide cohort study using the SCANDAT3-S database was conducted with all platelet- and plasmapheresis donors in Sweden between 1996 and 2017. A Cox proportional hazards model, using donations as time-dependent exposures, was used to assess the risk of infections associated with plateletpheresis donations using an LRS chamber. RESULTS: A total of 74 408 apheresis donors were included. Among donors with the same donation frequency, plateletpheresis donors using an LRS chamber were at an increased risk of immunosuppression-related infections and common bacterial infections in a dose-dependent manner. While very frequent donors and infections were rare in absolute terms resulting in wide confidence intervals (CIs), the increased risk was significant starting at one-third or less of the allowed donation frequency in a 10-year exposure window, with hazard ratios reaching 10 or more. No plateletpheresis donors that used an LRS chamber experienced a Pneumocystis jirovecii, aspergillus, disseminated mycobacterial, or cryptococcal infection. In a subcohort (n = 42), donations with LRS were associated with low CD4+ T-cell counts (Pearson's R = -0.41; 95% CI, - 0.63 to -0.12). CONCLUSION: Frequent plateletpheresis donation using an LRS chamber was associated with CD4+ T-cell lymphopenia and an increased risk of infections. These findings suggest a need to monitor T-lymphocyte counts in frequent platelet donors and to conduct future investigations of long-term donor health and for regulators to consider steps to mitigate lymphodepletion in donors.