Mobile health (mHealth) and related digital health interventions in the past decade have not always scaled globally as anticipated earlier despite large investments by governments and philanthropic foundations. The implementation of digital health tools has suffered from 2 limitations: (1) the interventions commonly ignore the “law of amplification” that states that technology is most likely to succeed when it seeks to augment and not alter human behavior; and (2) end-user needs and clinical gaps are often poorly understood while designing solutions, contributing to a substantial decrease in usage, referred to as the “law of attrition” in eHealth. The COVID-19 pandemic has addressed the first of the 2 problems—technology solutions, such as telemedicine, that were struggling to find traction are now closely aligned with health-seeking behavior. The second problem (poorly designed solutions) persists, as demonstrated by a plethora of poorly designed epidemic prediction tools and digital contact-tracing apps, which were deployed at scale, around the world, with little validation. The pandemic has accelerated the Indian state’s desire to build the nation’s digital health ecosystem. We call for the inclusion of regulatory sandboxes, as successfully done in the fintech sector, to provide a real-world testing environment for mHealth solutions before deploying them at scale.

The Rohingya people of Myanmar have been subject to human rights violations through government-sponsored discrimination and violence. Since August 2017, an intensified assault by Myanmar authorities has resulted in a rapid increase of Rohingya pouring into Bangladesh, and the expansion of refugee settlements in the district of Cox's Bazar has strained humanitarian and government relief efforts. Assessing Rohingya and host community needs is critical for prioritizing resource allocations and for documenting the rights violations suffered by Rohingya refugees. From March 15 to 18, 2018, we conducted a rapid needs assessment of recently arrived Rohingya and host community households. We collected data on demographics, mortality, education, livelihoods, access to food and water, vaccination, and health care. Among other things, our survey found high levels of mortality among young Rohingya men, alarmingly low levels of vaccination among children, poor literacy, and rising poverty. Denied formal refugee status, the Rohingya cannot access due protections and find themselves in a state of insecurity in which they are unsure of their future and unable to formally seek work or send their children to school. While the government of Bangladesh explores the options of repatriation, relocation, and third-country resettlement for these refugees, it is important to ensure that they are not denied a life of dignity.

Background: Nirmatrelvir with ritonavir (Paxlovid) is indicated for patients with Coronavirus Disease 2019 (COVID-19) who are at risk for progression to severe disease due to the presence of one or more risk factors. Millions of treatment courses have been prescribed in the United States alone. Paxlovid was highly effective at preventing hospitalization and death in clinical trials. Several studies have found a protective association in real-world data, but they variously used less recent study periods, correlational methods, and small, local cohorts. Their estimates also varied widely. The real-world effectiveness of Paxlovid remains uncertain, and it is unknown whether its effect is homogeneous across demographic strata. This study leverages electronic health record data in the National COVID Cohort Collaborative’s (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing severe COVID-19 outcomes. Methods and findings: This target trial emulation used a cohort of 703,647 patients with COVID-19 seen at 34 clinical sites across the United States between April 1, 2022 and August 28, 2023. Treatment was defined as receipt of a Paxlovid prescription within 5 days of the patient’s COVID-19 index date (positive test or diagnosis). To emulate randomization, we used the clone-censor-weight technique with inverse probability of censoring weights to balance a set of covariates including sex, age, race and ethnicity, comorbidities, community well-being index (CWBI), prior healthcare utilization, month of COVID-19 index, and site of care provision. The primary outcome was hospitalization; death was a secondary outcome. We estimated that Paxlovid reduced the risk of hospitalization by 39% (95% confidence interval (CI) [36%, 41%]; p < 0.001), with an absolute risk reduction of 0.9 percentage points (95% CI [0.9, 1.0]; p < 0.001), and reduced the risk of death by 61% (95% CI [55%, 67%]; p < 0.001), with an absolute risk reduction of 0.2 percentage points (95% CI [0.1, 0.2]; p < 0.001). We also conducted stratified analyses by vaccination status and age group. Absolute risk reduction for hospitalization was similar among patients that were vaccinated and unvaccinate, but was much greater among patients aged 65+ years than among younger patients. We observed disparities in Paxlovid treatment, with lower rates among black and Hispanic or Latino patients, and within socially vulnerable communities. This study’s main limitation is that it estimates causal effects using observational data and could be biased by unmeasured confounding. Conclusions: In this study of Paxlovid’s real-world effectiveness, we observed that Paxlovid is effective at preventing hospitalization and death, including among vaccinated patients, and particularly among older patients. This remains true in the era of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron subvariants. However, disparities in Paxlovid treatment rates imply that the benefit of Paxlovid’s effectiveness is not equitably distributed.

Background: Preventing and treating post-acute sequelae of SARS-CoV-2 infection (PASC), commonly known as Long COVID, has become a public health priority. Researchers have begun to explore whether Paxlovid treatment in the acute phase of COVID-19 could help prevent the onset of PASC. Methods and Findings: We used electronic health records from the National Clinical Cohort Collaborative (N3C) to define a cohort of 410,026 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation framework to estimate the effect of Paxlovid treatment on PASC incidence. The treatment group was defined as outpatients prescribed Paxlovid within five days of COVID-19 index, and the control group was defined as all patients meeting eligibility criteria not in the treatment group. The follow-up period was 180 days. We estimated overall PASC incidence using a computable phenotype. We also measured incident cognitive, fatigue, and respiratory symptoms in the post-acute period. Paxlovid treatment had a small effect on overall PASC incidence (relative risk [RR] 0.94; 95% CI [0.90, 0.99]; p=0.011). It had a slightly stronger protective effect against cognitive (RR 0.86; 95% CI [0.77, 0.95]; p<0.001) and fatigue (RR 0.92; 95% CI [0.86, 0.97]; p=0.002) symptoms. Conclusions: In this study, Paxlovid had a weaker preventative effect on PASC than in prior observational studies, suggesting that Paxlovid is unlikely to become a definitive solution for preventing PASC. Differing effects by symptom cluster suggest that the etiology of cognitive and fatigue symptoms may be more closely related to viral load than that of respiratory symptoms. Future research should explore potential heterogeneous treatment effects across PASC subphenotypes. AUTHOR SUMMARY: Why was this study done?: Paxlovid is indicated to prevent severe COVID-19. Long COVID is more likely after more severe COVID-19, so there is a plausible mechanism for Paxlovid to reduce the risk of developing Long COVID by preventing severe COVID-19. Only a few studies have examined the relationship between Paxlovid and Long COVID, with mixed results. To our knowledge, no studies have used causal inference methods to estimate Paxlovid’s effect on PASC. If Paxlovid helps prevent Long COVID, it could be a powerful addition to the public health effort to reduce the burden of COVID-19. What did the researchers do and find?: We used a cohort of 410,026 patients from the National Covid Cohort Collaborative’s (N3C’s) electronic health record database to estimate the effect of Paxlovid treatment during acute COVID-19 on the likelihood of developing Long COVID. We used the target trial emulation technique to estimate the causal effect of Paxlovid treatment using observational data. We found that Paxlovid treatment had a small effect on overall Long COVID incidence. We found that Paxlovid treatment had a slightly stronger effect on certain symptoms (cognitive and fatigue) of Long COVID. What do these findings mean?: Paxlovid is unlikely to become a definitive solution for preventing Long COVID. However, Paxlovid may have a stronger effect on certain subtypes of Long COVID. This difference in effect by symptom also suggests that viral load may be a more common cause of cognitive and fatigue symptoms than of other Long COVID symptoms. This study’s main limitation is that it estimates causal effects, but it is not randomized like a clinical trial. Instead, we control for other variables which could bias the estimate, but if we missed some important variables, the estimates could be incorrect.

As communities worldwide shift from consuming traditional diets to more processed snacks and sugar-sweetened beverages (SSBs), increases in child obesity and tooth decay and persistence of undernutrition are particularly apparent in Latin American countries. Further evidence of shared risk factors between child undernutrition and poor oral health outcomes is needed to structure more effective health interventions for children’s nutrition. This study aims to identify dietary, oral health, and sociodemographic risk factors for child undernutrition and severe early childhood caries (sECC) among a convenience sample of 797 caregiver–child pairs from rural Salvadoran communities. Caregiver interviews on child dietary and oral health practices were conducted, and their children’s height, weight, and dental exam data were collected. Multivariable regression analyses were performed using RStudio (version 1.0.143). Caregiver use of SSBs in the baby bottle was identified as a common significant risk factor for child undernutrition (p = 0.011) and sECC (p = 0.047). Early childhood caries (p = 0.023) was also a risk factor for developing undernutrition. Future maternal–child health and nutrition programs should coordinate with oral health interventions to discourage feeding children SSBs in the baby bottle and to advocate for policies limiting SSB marketing to young children and their families.

In 2021, we used the National COVID Cohort Collaborative (N3C) as part of the National Institutes of Health RECOVER Initiative to develop a machine learning pipeline to identify patients with a high probability of having post-acute sequelae of SARS-CoV-2 infection or long COVID. However, the increased home testing, missing documentation, and reinfections that characterise the pandemic beyond 2022 necessitated the re-engineering of our original model to account for these changes in the COVID-19 research landscape. Trained on 72 745 patient records (36 238 with long COVID and 36 507 with no evidence of long COVID), our updated XGBoost model gathered data for each patient in overlapping 100-day periods that progressed through time and issued a probability of long COVID for each 100-day period. We ran the model on patients in N3C (n=5 875 065) who met at least one of the following criteria from Jan 1, 2020, to June 22, 2023: a U07·1 (COVID-19) diagnosis code; a positive SARS-CoV-2 test; a U09·9 (post-acute sequelae of SARS-CoV-2 infection) diagnosis code; a prescription for nirmatrelvir-ritonavir or remdesivir; or an M35·81 (multisystem inflammatory syndrome in children [MIS-C]) diagnosis code. Each patient was given a model score that predicted long COVID status for each 100-day window in which they were aged ≥18 years. If a patient had known acute COVID-19 during any 100-day window (including reinfections), we censored the data from 7 days before the diagnosis or positive test date to 28 days after. We ran the model on controls selected from pre-2020 data to assess the likelihood of false positives. The updated model had an area under the receiver operating characteristic curve of 0·90. Precision and recall could be adjusted according to a given use case, depending on whether greater sensitivity or specificity was warranted. Using our model, we estimate the overall prevalence of long COVID among the COVID-19 positive cohort within N3C repository to be 10.4%. By eschewing the COVID-19 index date as an anchor point for analysis, we can assess the probability of long COVID among patients who might have tested at home, or with suspected (but untested) cases of COVID-19, or multiple SARS-CoV-2 reinfections. We view this exercise as a model for maintaining and updating any machine learning pipeline used for clinical research and operations. National Institutes of Health RECOVER Initiative.