BackgroundCollaborations 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.MethodsWe 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.ResultsOf 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.DiscussionIndividuals, 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.

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.

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.

The first part of this paper introduced various definitions of response and discussed their significance in the context of different study types. This second part addresses incentives as a method to increase response and evaluates the impact of non response or delayed response on the validity of the study results. Recruitment aims at minimising the proportion of refusal. To achieve this, incentives can be used and potential participants can be contacted in a sequence of increasing intensity. The effectiveness of different incentives was investigated within the pretest of the German survey on children and adolescents by the Robert Koch Institute. A low response is often interpreted in terms of non-response bias. This assumption, however, is as incorrect as would be opposite conclusion, that a high response guarantees valid results. Any study of the influence of nonresponse requires information on non-responders. The comparison between early and late responders as an indirect method to evaluate systematic differences between participants and non-participants by wave analysis is demonstrated within the Northern Germany Leukaemia and Lymphoma study (NLL). The German guidelines for Good Epidemiologic Practice recommend to solicit a minimum of information on the principal hypotheses of a study from non-participants. The example of a population-based health survey (Cooperative Health Research in the Region of Augsburg, KORA) illustrates how information on non-responders within a quantitative non-responder analysis can be achieved and used for the estimation of prevalences. Recommendations how to deal with the response in epidemiological studies in Germany are suggested.

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.

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.

Variance estimation for effective coverage measures: A simulation study Electronic supplementary material: The online version of this article contains supplementary material.

The neonatal mortality rate in Rwanda remains above the United Nations Sustainable Development Goal 3 target of 12 deaths per 1,000 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 was collected via a cost-efficient cluster-based outcome-dependent sampling scheme wherein clusters of individuals (health centers) were selected on the basis of, in part, the outcome rate of the individuals. For a given dataset collected via a cluster-based outcomedependent sampling 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.