Discrete glucose profiles identified using continuous glucose monitoring data and their association with adverse pregnancy outcomes.

BACKGROUND: Continuous glucose monitoring has facilitated the evaluation of dynamic changes in glucose throughout the day and their effect on fetal growth abnormalities in pregnancy. However, studies of multiple continuous glucose monitoring metrics combined and their association with other adverse pregnancy outcomes are limited. OBJECTIVE: This study aimed to (1) use machine learning techniques to identify discrete glucose profiles based on weekly continuous glucose monitoring metrics in pregnant individuals with pregestational diabetes mellitus and (2) investigate their association with adverse pregnancy outcomes. STUDY DESIGN: This study analyzed data from a retrospective cohort study of pregnant patients with type 1 or 2 diabetes mellitus who used Dexcom G6 continuous glucose monitoring and delivered a nonanomalous, singleton pregnancy at a tertiary center between 2019 and 2023. Continuous glucose monitoring data were collapsed into 39 weekly glycemic measures related to centrality, spread, excursions, and circadian cycle patterns. Principal component analysis and k-means clustering were used to identify 4 discrete groups, and patients were assigned to the group that best represented their continuous glucose monitoring patterns during pregnancy. Finally, the association between glucose profile groups and outcomes (preterm birth, cesarean delivery, preeclampsia, large-for-gestational-age neonate, neonatal hypoglycemia, and neonatal intensive care unit admission) was estimated using multivariate logistic regression adjusted for diabetes mellitus type, maternal age, insurance, continuous glucose monitoring use before pregnancy, and parity. RESULTS: Of 177 included patients, 90 (50.8%) had type 1 diabetes mellitus, and 85 (48.3%) had type 2 diabetes mellitus. This study identified 4 glucose profiles: (1) well controlled; (2) suboptimally controlled with high variability, fasting hypoglycemia, and daytime hyperglycemia; (3) suboptimally controlled with minimal circadian variation; and (4) poorly controlled with peak hyperglycemia overnight. Compared with the well-controlled profile, the suboptimally controlled profile with high variability had higher odds of a large-for-gestational-age neonate (adjusted odds ratio, 3.34; 95% confidence interval, 1.15-9.89). The suboptimally controlled with minimal circadian variation profile had higher odds of preterm birth (adjusted odds ratio, 2.59; 95% confidence interval, 1.10-6.24), cesarean delivery (adjusted odds ratio, 2.76; 95% confidence interval, 1.09-7.46), and neonatal intensive care unit admission (adjusted odds ratio, 4.08; 95% confidence interval, 1.58-11.40). The poorly controlled profile with peak hyperglycemia overnight had higher odds of preeclampsia (adjusted odds ratio, 2.54; 95% confidence interval, 1.02-6.52), large-for-gestational-age neonate (adjusted odds ratio, 3.72; 95% confidence interval, 1.37-10.4), neonatal hypoglycemia (adjusted odds ratio, 3.53; 95% confidence interval, 1.37-9.71), and neonatal intensive care unit admission (adjusted odds ratio, 3.15; 95% confidence interval, 1.20-9.09). CONCLUSION: Discrete glucose profiles of pregnant individuals with pregestational diabetes mellitus were identified through joint consideration of multiple continuous glucose monitoring metrics. Prolonged exposure to maternal hyperglycemia may be associated with a higher risk of adverse pregnancy outcomes than suboptimal glycemic control characterized by high glucose variability and intermittent hyperglycemia.

Missing data and missed infections: investigating racial and ethnic disparities in SARS-CoV-2 testing and infection rates in Holyoke, Massachusetts.

Routinely collected testing data have been a vital resource for public health response during the COVID-19 pandemic and have revealed the extent to which Black and Hispanic persons have borne a disproportionate burden of SARS-CoV-2 infections and hospitalizations in the United States. However, missing race and ethnicity data and missed infections due to testing disparities limit the interpretation of testing data and obscure the true toll of the pandemic. We investigated potential bias arising from these 2 types of missing data through a case study carried out in Holyoke, Massachusetts, during the prevaccination phase of the pandemic. First, we estimated SARS-CoV-2 testing and case rates by race and ethnicity, imputing missing data using a joint modeling approach. We then investigated disparities in SARS-CoV-2 reported case rates and missed infections by comparing case rate estimates with estimates derived from a COVID-19 seroprevalence survey. Compared with the non-Hispanic White population, we found that the Hispanic population had similar testing rates (476 tested per 1000 vs 480 per 1000) but twice the case rate (8.1% vs 3.7%). We found evidence of inequitable testing, with a higher rate of missed infections in the Hispanic population than in the non-Hispanic White population (79 infections missed per 1000 vs 60 missed per 1000).

Effectiveness of a bedaquiline, linezolid, clofazimine "core" for multidrug-resistant tuberculosis.

Rationale: Treatment outcomes may be compromised among patients with multidrug- or rifampicin-resistant tuberculosis with additional fluoroquinolone resistance. Evidence is needed to inform optimal treatment for these patients. Objectives: We compared the effectiveness of longer individualized regimens comprised of bedaquiline for 5 to 8 months, linezolid, and clofazimine to those reinforced with at least 1 third-tier drug and/or longer duration of bedaquiline. Methods: We emulated a target trial to compare the effectiveness of initiating and remaining on the core regimen to one of five regimens reinforced with (1) bedaquiline for ≥9 months, (2) bedaquiline for ≥9 months and delamanid, (3) imipenem, (4) a second-line injectable, or (5) delamanid and imipenem. We included patients in whom a fluoroquinolone was unlikely to be effective based on drug susceptibility testing and/or prior exposure. Our analysis consisted of cloning, censoring, and inverse-probability weighting to estimate the probability of successful treatment. Measurements and Main Results: Adjusted probabilities of successful treatment were high across regimens, ranging from 0.75 (95%CI:0.61, 0.89) to 0.84 (95%CI:0.76, 0.91). We found no substantial evidence that any of the reinforced regimens improved effectiveness of the core regimen, with ratios of treatment success ranging from 1.01 for regimens reinforced with bedaquiline ≥9 months (95%CI:0.79, 1.28) and bedaquiline ≥9 months plus delamanid (95%CI:0.81, 1.31) to 1.11 for regimens reinforced by a second-line injectable (95%CI:0.92, 1.39) and delamanid and imipenem (95%CI:0.90, 1.41). Conclusions: High treatment success underscores the effectiveness of regimens comprised of bedaquiline, linezolid, and clofazimine, highlighting the need for expanded access to these drugs.

Disparities in SARS-CoV-2 Infection by Race, Ethnicity, Language, and Social Vulnerability: Evidence from a Citywide Seroprevalence Study in Massachusetts, USA.

OBJECTIVES: Uncovering and addressing disparities in infectious disease outbreaks require a rapid, methodical understanding of local epidemiology. We conducted a seroprevalence study of SARS-CoV-2 infection in Holyoke, Massachusetts, a majority Hispanic city with high levels of socio-economic disadvantage to estimate seroprevalence and identify disparities in SARS-CoV-2 infection. METHODS: We invited 2000 randomly sampled households between 11/5/2020 and 12/31/2020 to complete questionnaires and provide dried blood spots for SARS-CoV-2 antibody testing. We calculated seroprevalence based on the presence of IgG antibodies using a weighted Bayesian procedure that incorporated uncertainty in antibody test sensitivity and specificity and accounted for household clustering. RESULTS: Two hundred eighty households including 472 individuals were enrolled. Three hundred twenty-eight individuals underwent antibody testing. Citywide seroprevalence of SARS-CoV-2 IgG was 13.1% (95% CI 6.9-22.3) compared to 9.8% of the population infected based on publicly reported cases. Seroprevalence was 16.1% (95% CI 6.2-31.8) among Hispanic individuals compared to 9.4% (95% CI 4.6-16.4) among non-Hispanic white individuals. Seroprevalence was higher among Spanish-speaking households (21.9%; 95% CI 8.3-43.9) compared to English-speaking households (10.2%; 95% CI 5.2-18.0) and among individuals in high social vulnerability index (SVI) areas based on the CDC SVI (14.4%; 95% CI 7.1-25.5) compared to low SVI areas (8.2%; 95% CI 3.1-16.9). CONCLUSIONS: The SARS-CoV-2 IgG seroprevalence in a city with high levels of social vulnerability was 13.1% during the pre-vaccination period of the COVID-19 pandemic. Hispanic individuals and individuals in communities characterized by high SVI were at the highest risk of infection. Public health interventions should be designed to ensure that individuals in high social vulnerability communities have access to the tools to combat COVID-19.

Estimating Post-treatment Recurrence After Multidrug-Resistant Tuberculosis Treatment Among Patients With and Without Human Immunodeficiency Virus: The Impact of Assumptions About Death and Missing Follow-up.

BACKGROUND: Quantification of recurrence risk following successful treatment is crucial to evaluating regimens for multidrug- or rifampicin-resistant (MDR/RR) tuberculosis (TB). However, such analyses are complicated when some patients die or become lost during post-treatment follow-up. METHODS: We analyzed data on 1991 patients who successfully completed a longer MDR/RR-TB regimen containing bedaquiline and/or delamanid between 2015 and 2018 in 16 countries. Using 5 approaches for handling post-treatment deaths, we estimated 6-month post-treatment TB recurrence risk overall and by HIV status. We used inverse-probability weighting to account for patients with missing follow-up and investigated the impact of potential bias from excluding these patients without applying inverse-probability weights. RESULTS: The estimated TB recurrence risk was 7.4/1000 (95% credible interval: 3.3-12.8) when deaths were handled as non-recurrences and 7.6/1000 (3.3-13.0) when deaths were censored and inverse-probability weights were applied to account for the excluded deaths. The estimated risks of composite recurrence outcomes were 25.5 (15.3-38.1), 11.7 (6.4-18.2), and 8.6 (4.1-14.4) per 1000 for recurrence or (1) any death, (2) death with unknown or TB-related cause, or (3) TB-related death, respectively. Corresponding relative risks for HIV status varied in direction and magnitude. Exclusion of patients with missing follow-up without inverse-probability weighting had a small impact on estimates. CONCLUSIONS: The estimated 6-month TB recurrence risk was low, and the association with HIV status was inconclusive due to few recurrence events. Estimation of post-treatment recurrence will be enhanced by explicit assumptions about deaths and appropriate adjustment for missing follow-up data.

Estimating post-treatment recurrence after multidrug-resistant tuberculosis treatment among patients with and without HIV: the impact of assumptions about death and missing follow-up.

Background: Quantification of recurrence risk following successful treatment is crucial to evaluating regimens for multidrug- or rifampicin-resistant (MDR/RR) tuberculosis (TB). However, such analyses are complicated when some patients die or become lost during post-treatment-follow-up. Methods: We analyzed data on 1,991 patients who successfully completed a longer MDR/RR-TB regimen containing bedaquiline and/or delamanid between 2015 and 2018 in 16 countries. Using five approaches for handling post-treatment deaths, we estimated the six-month post-treatment TB recurrence risk overall, and by HIV status. We used inverse-probability-weighting to account for patients with missing follow-up and investigated the impact of potential bias from excluding these patients without applying inverse-probability weights. Results: The estimated TB recurrence risk was 6.6 per 1000 (95% confidence interval (CI):3.2,11.2) when deaths were handled as non-recurrences, and 6.7 per 1000 (95% CI:2.8,12.2) when deaths were censored and inverse-probability weights were applied to account for the excluded deaths. The estimated risk of composite recurrence outcomes were 24.2 (95% CI:14.1,37.0), 10.5 (95% CI:5.6,16.6), and 7.8 (95% CI:3.9,13.2) per 1000 for recurrence or 1) any death, 2) death with unknown or TB-related cause, 3) TB-related death, respectively. Corresponding relative risks for HIV status varied in direction and magnitude. Exclusion of patients with missing follow-up without inverse-probability-weighting had a small but apparent impact on estimates. Conclusion: The estimated six-month TB recurrence risk was low, and the association with HIV status was inconclusive due to few recurrence events. Estimation of post-treatment recurrence will be enhanced by explicit assumptions about deaths and appropriate adjustment for missing follow-up data.

Practical strategies for operationalizing optimal allocation in stratified cluster-based outcome-dependent sampling designs.

Cluster-based outcome-dependent sampling (ODS) has the potential to yield efficiency gains when the outcome of interest is relatively rare, and resource constraints allow only a certain number of clusters to be visited for data collection. Previous research has shown that when the intended analysis is inverse-probability weighted generalized estimating equations, and the number of clusters that can be sampled is fixed, optimal allocation of the (cluster-level) sample size across strata defined by auxiliary variables readily available at the design stage has the potential to increase efficiency in the estimation of the parameter(s) of interest. In such a setting, the optimal allocation formulae depend on quantities that are unknown in practice, currently making such designs difficult to implement. In this paper, we consider a two-wave adaptive sampling approach, in which data is collected from a first wave sample, and subsequently used to compute the optimal second wave stratum-specific sample sizes. We consider two strategies for estimating the necessary components using the first wave data: an inverse-probability weighting (IPW) approach and a multiple imputation (MI) approach. In a comprehensive simulation study, we show that the adaptive sampling approach performs well, and that the MI approach yields designs that are very near-optimal, regardless of the covariate type. The IPW approach, on the other hand, has mixed results. Finally, we illustrate the proposed adaptive sampling procedures with data on maternal characteristics and birth outcomes among women enrolled in the Safer Deliveries program in Zanzibar, Tanzania.

Optimal sampling allocation for outcome-dependent designs in cluster-correlated data settings.

In clinical and public health studies, it is often the case that some variables relevant to the analysis are too difficult or costly to measure for all individuals in the population of interest. Rather, a subsample of these individuals must be identified for additional data collection. A sampling scheme that incorporates readily-available information for the entire target population at the design stage can increase the statistical efficiency of the intended analysis. While there is no universally optimal sampling design, under certain principles and restrictions, a well-designed and efficient sampling strategy can be implemented. In two-phase designs, efficiency can be gained by stratifying on the outcome and/or auxiliary information that is known at phase I. Additional gains in efficiency can be obtained by determining the optimal allocation of the sample sizes across the strata, which depends on the quantity that is being estimated. In this paper, the inference is concerned with one or multiple regression parameter(s) where the study units are naturally clustered and, thus, exhibit correlation in outcomes. We propose several allocation strategies within the framework of two-phase designs for the estimation of the regression parameter(s) obtained from weighted generalized estimating equations. The proposed methods extend existing theory to address the objective of the estimating regression parameters in cluster-correlated data settings by minimizing the asymptotic variance of the estimator subject to a fixed sample size. Through a comprehensive simulation study, we show that the proposed allocation schemes have the potential to yield substantial efficiency gains over alternative strategies.

Gender equity in health research publishing in Africa.

INTRODUCTION: Women researchers find it more difficult to publish in academic journals than men, an inequity that affects women's careers and was exacerbated during the pandemic, particularly for women in low-income and middle-income countries. We measured publishing by sub-Saharan African (SSA) women in prestigious authorship positions (first or last author, or single author) during the time frame 2014-2016. We also examined policies and practices at journals publishing high rates of women scientists from sub-Saharan Africa, to identify potential structural enablers affecting these women in publishing. METHODS: The study used Namsor V.2, an application programming interface, to conduct a secondary analysis of a bibliometric database. We also analysed policies and practices of ten journals with the highest number of SSA women publishing in first authorship positions. RESULTS: Based on regional analyses, the greatest magnitude of authorship inequity is in papers from sub-Saharan Africa, where men comprised 61% of first authors, 65% of last authors and 66% of single authors. Women from South Africa and Nigeria had greater success in publishing than those from other SSA countries, though women represented at least 20% of last authors in 25 SSA countries. The journals that published the most SSA women as prominent authors are journals based in SSA. Journals with overwhelmingly male leadership are also among those publishing the highest number of SSA women. CONCLUSION: Women scholars in SSA face substantial gender inequities in publishing in prestigious authorship positions in academic journals, though there is a cadre of women research leaders across the region. Journals in SSA are important for local women scholars and the inequities SSA women researchers face are not necessarily attributable to gender discrepancy in journals' editorial leadership.

Small-sample inference for cluster-based outcome-dependent sampling schemes in resource-limited settings: Investigating low birthweight in Rwanda.

The neonatal mortality rate in Rwanda remains above the United Nations Sustainable Development Goal 3 target of 12 deaths per 1000 live births. As part of a larger effort to reduce preventable neonatal deaths in the country, we conducted a study to examine risk factors for low birthweight. The data were collected via a cost-efficient cluster-based outcome-dependent sampling (ODS) scheme wherein clusters of individuals (health centers) were selected on the basis of, in part, the outcome rate of the individuals. For a given data set collected via a cluster-based ODS scheme, estimation for a marginal model may proceed via inverse-probability-weighted generalized estimating equations, where the cluster-specific weights are the inverse probability of the health center's inclusion in the sample. In this paper, we provide a detailed treatment of the asymptotic properties of this estimator, together with an explicit expression for the asymptotic variance and a corresponding estimator. Furthermore, motivated by the study we conducted in Rwanda, we propose a number of small-sample bias corrections to both the point estimates and the standard error estimates. Through simulation, we show that applying these corrections when the number of clusters is small generally reduces the bias in the point estimates, and results in closer to nominal coverage. The proposed methods are applied to data from 18 health centers and 1 district hospital in Rwanda.

Optimal allocation in stratified cluster-based outcome-dependent sampling designs.

In public health research, finite resources often require that decisions be made at the study design stage regarding which individuals to sample for detailed data collection. At the same time, when study units are naturally clustered, as patients are in clinics, it may be preferable to sample clusters rather than the study units, especially when the costs associated with travel between clusters are high. In this setting, aggregated data on the outcome and select covariates are sometimes routinely available through, for example, a country's Health Management Information System. If used wisely, this information can be used to guide decisions regarding which clusters to sample, and potentially obtain gains in efficiency over simple random sampling. In this article, we derive a series of formulas for optimal allocation of resources when a single-stage stratified cluster-based outcome-dependent sampling design is to be used and a marginal mean model is specified to answer the question of interest. Specifically, we consider two settings: (i) when a particular parameter in the mean model is of primary interest; and, (ii) when multiple parameters are of interest. We investigate the finite population performance of the optimal allocation framework through a comprehensive simulation study. Our results show that there are trade-offs that must be considered at the design stage: optimizing for one parameter yields efficiency gains over balanced and simple random sampling, while resulting in losses for the other parameters in the model. Optimizing for all parameters simultaneously yields smaller gains in efficiency, but mitigates the losses for the other parameters in the model.

The Global Cohort of Doctoral Students: Building Shared Global Health Research Capacity in High-Income and Low- and Middle-Income Countries.

Doctoral students in high- and low-income countries pursuing careers in global health face gaps in their training that could be readily filled through structured peer-learning activities with students based at partnering institutions in complimentary settings. We share lessons learned from the Global Cohort of Doctoral Students, a community of doctoral students based at the Harvard T. H. Chan School of Public Health, Haramaya University, University of Gondar, University of Botswana, and University of Rwanda College of Medicine and Health Sciences. Students in the Global Cohort program engage in collaborative research, forums for constructive feedback, and professional development activities. We describe the motivation for the program, core activities, and early successes.

Health management information system (HMIS) data verification: A case study in four districts in Rwanda.

INTRODUCTION: Reliable Health Management and Information System (HMIS) data can be used with minimal cost to identify areas for improvement and to measure impact of healthcare delivery. However, variable HMIS data quality in low- and middle-income countries limits its value in monitoring, evaluation and research. We aimed to review the quality of Rwandan HMIS data for maternal and newborn health (MNH) based on consistency of HMIS reports with facility source documents. METHODS: We conducted a cross-sectional study in 76 health facilities (HFs) in four Rwandan districts. For 14 MNH data elements, we compared HMIS data to facility register data recounted by study staff for a three-month period in 2017. A HF was excluded from a specific comparison if the service was not offered, source documents were unavailable or at least one HMIS report was missing for the study period. World Health Organization guidelines on HMIS data verification were used: a verification factor (VF) was defined as the ratio of register over HMIS data. A VF<0.90 or VF>1.10 indicated over- and under-reporting in HMIS, respectively. RESULTS: High proportions of HFs achieved acceptable VFs for data on the number of deliveries (98.7%;75/76), antenatal care (ANC1) new registrants (95.7%;66/69), live births (94.7%;72/76), and newborns who received first postnatal care within 24 hours (81.5%;53/65). This was slightly lower for the number of women who received iron/folic acid (78.3%;47/60) and tested for syphilis in ANC1 (67.6%;45/68) and was the lowest for the number of women with ANC1 standard visit (25.0%;17/68) and fourth standard visit (ANC4) (17.4%;12/69). The majority of HFs over-reported on ANC4 (76.8%;53/69) and ANC1 (64.7%;44/68) standard visits. CONCLUSION: There was variable HMIS data quality by data element, with some indicators with high quality and also consistency in reporting trends across districts. Over-reporting was observed for ANC-related data requiring more complex calculations, i.e., knowledge of gestational age, scheduling to determine ANC standard visits, as well as quality indicators in ANC. Ongoing data quality assessments and training to address gaps could help improve HMIS data quality.

Variance estimation for effective coverage measures: A simulation study.

BACKGROUND: Effective coverage research is increasing rapidly in global health and development, as researchers use a range of measures and combine data sources to adjust coverage for the quality of services received. However, most estimates of effective coverage that combine data sources are reported only as point estimates, which may be due to the challenge of calculating the variance for a composite measure. In this paper, we evaluate three methods to quantify the uncertainty in the estimation of effective coverage. METHODS: We conducted a simulation study to evaluate the performance of the exact, delta, and parametric bootstrap methods for constructing confidence intervals around point estimates that are calculated from combined data on coverage and quality. We assessed performance by computing the number of nominally 95% confidence intervals that contain the truth for a range of coverage and quality values and data source sample sizes. To illustrate these approaches, we applied the delta and exact methods to estimates of adjusted coverage of antenatal care (ANC) in Senegal. We used household survey data for coverage and health facility assessments for readiness to provide services. RESULTS: With small sample sizes, when the true effective coverage value was close to the boundaries 0 or 1, the exact and parametric bootstrap methods resulted in substantial over or undercoverage and, for the exact method, a high proportion of invalid confidence intervals, while the delta method yielded modest overcoverage. The proportion of confidence intervals containing the truth in all three methods approached the intended 95% with larger sample sizes and as the true effective coverage value moved away from the 0 or 1 boundary. Confidence intervals for adjusted ANC in Senegal were largely overlapping across the delta and exact methods, although at the sub-national level, the exact method produced invalid confidence intervals for estimates near 0 or 1. We provide the code to implement these methods. CONCLUSIONS: The uncertainty around an effective coverage estimate can be characterized; this should become standard practice if effective coverage estimates are to become part of national and global health monitoring. The delta method approach outperformed the other methods in this study; we recommend its use for appropriate inference from effective coverage estimates that combine data sources, particularly when either sample size is small. When used for estimates created from facility type or regional strata, these methods require assumptions of independence that must be considered in each example.

Stuck in the middle: a systematic review of authorship in collaborative health research in Africa, 2014-2016.

BACKGROUND: Collaborations are often a cornerstone of global health research. Power dynamics can shape if and how local researchers are included in manuscripts. This article investigates how international collaborations affect the representation of local authors, overall and in first and last author positions, in African health research. METHODS: We extracted papers on 'health' in sub-Saharan Africa indexed in PubMed and published between 2014 and 2016. The author's affiliation was used to classify the individual as from the country of the paper's focus, from another African country, from Europe, from the USA/Canada or from another locale. Authors classified as from the USA/Canada were further subclassified if the author was from a top US university. In primary analyses, individuals with multiple affiliations were presumed to be from a high-income country if they contained any affiliation from a high-income country. In sensitivity analyses, these individuals were presumed to be from an African country if they contained any affiliation an African country. Differences in paper characteristics and representation of local coauthors are compared by collaborative type using χ² tests. RESULTS: Of the 7100 articles identified, 68.3% included collaborators from the USA, Canada, Europe and/or another African country. 54.0% of all 43 429 authors and 52.9% of 7100 first authors were from the country of the paper's focus. Representation dropped if any collaborators were from USA, Canada or Europe with the lowest representation for collaborators from top US universities-for these papers, 41.3% of all authors and 23.0% of first authors were from country of paper's focus. Local representation was highest with collaborators from another African country. 13.5% of all papers had no local coauthors. DISCUSSION: Individuals, institutions and funders from high-income countries should challenge persistent power differentials in global health research. South-South collaborations can help African researchers expand technical expertise while maintaining presence on the resulting research.

Seasonal and Diurnal Air Pollution from Residential Cooking and Space Heating in the Eastern Tibetan Plateau.

Residential combustion of solid fuel is a major source of air pollution. In regions where space heating and cooking occur at the same time and using the same stoves and fuels, evaluating air-pollution patterns for household-energy-use scenarios with and without heating is essential to energy intervention design and estimation of its population health impacts as well as the development of residential emission inventories and air-quality models. We measured continuous and 48 h integrated indoor PM2.5 concentrations over 221 and 203 household-days and outdoor PM2.5 concentrations on a subset of those days (in summer and winter, respectively) in 204 households in the eastern Tibetan Plateau that burned biomass in traditional stoves and open fires. Using continuous indoor PM2.5 concentrations, we estimated mean daily hours of combustion activity, which increased from 5.4 h per day (95% CI: 5.0, 5.8) in summer to 8.9 h per day (95% CI: 8.1, 9.7) in winter, and effective air-exchange rates, which decreased from 18 ± 9 h(-1) in summer to 15 ± 7 h(-1) in winter. Indoor geometric-mean 48 h PM2.5 concentrations were over two times higher in winter (252 µg/m(3); 95% CI: 215, 295) than in summer (101 µg/m(3); 95%: 91, 112), whereas outdoor PM2.5 levels had little seasonal variability.