Factors influencing fever care-seeking for children under five years of age in The Gambia: a secondary analysis of 2019-20 DHS data.

BACKGROUND: Malaria contributes to excess child mortality in The Gambia. Children under five are at risk of severe malaria and death if not treated promptly and appropriately. It is crucial that a child with fever receive appropriate care from a trained provider. The aim was to identify influences on child fever care-seeking in The Gambia to inform malaria control strategies. METHODS: This cross-sectional analysis of The Gambia 2019-20 Demographic and Health Survey used logistic regression analysis to identify associations between source of care for a child with fever (public or private healthcare provider, other, or no treatment) and mother, child, and household characteristics. RESULTS: Only 52.0% of mothers sought care from a trained healthcare provider for a child with fever-45.1% from a public facility and 7.0% from the private sector. 35.2% of mothers did not seek treatment. Mothers in urban households were 2.67 times as likely (aOR, 95% CI 1.504-4.736) as mothers in rural households to seek care from an informal source (e.g., pharmacy) versus not seeking treatment, and 0.29 times as likely (aOR, 95% CI 0.165-0.515) as mothers in rural households to seek care from a public provider versus informal source. Mothers in wealthier households were 2.30 times as likely (aOR, 95% CI 1.274-4.164) as mothers in poorer households to seek care from an informal source versus no treatment and half as likely as mothers in poorer households to seek care from a public provider versus informal source (aOR 0.53, 95% CI 0.291-0.959). CONCLUSIONS: Maintaining The Gambia's malaria control achievements will require the active engagement and oversight of private pharmacies along with continued integrated community case management to reach mothers who do not seek care for a child with fever, and remove challenges to seeking appropriate care from trained providers. Whether influenced by convenience, costs, perceived urgency, or other factors, given the likelihood of urban mothers and mothers in wealthier households to seek care from private pharmacies, it will be necessary to incorporate private pharmacies into malaria control strategies while building public sector capacity and workforce, and initiating more effective attitude and behavioural change among mothers and households.

Representative Public Health Surveys Pose Several Challenges: Lessons Learned Across 9 Communities During the COVID-19 Pandemic.

Community surveillance surveys offer an opportunity to obtain important and timely public health information that may help local municipalities guide their response to public health threats. The objective of this paper is to present approaches, challenges, and solutions from SARS-CoV-2 surveillance surveys conducted in different settings by 2 research teams. For rapid assessment of a representative sample, a 2-stage cluster sampling design was developed by an interdisciplinary team of researchers at Oregon State University between April 2020 and June 2021 across 6 Oregon communities. In 2022, these methods were adapted for New York communities by a team of veterinary, medical, and public health practitioners. Partnerships were established with local medical facilities, health departments, COVID-19 testing sites, and health and public safety staff. Field staff were trained using online modules, field manuals describing survey methods and safety protocols, and in-person meetings with hands-on practice. Private and secure data integration systems and public awareness campaigns were implemented. Pilot surveys and field previews revealed challenges in survey processes that could be addressed before surveys proceeded. Strong leadership, robust trainings, and university-community partnerships proved critical to successful outcomes. Cultivating mutual trust and cooperation among stakeholders is essential to prepare for the next pandemic.

Trends and Factors Affecting Knowledge of and Stigma and Violence Towards Female Sex Workers in Morocco.

BACKGROUND: We investigate factors affecting HIV knowledge, stigma, and violence among female sex workers (FSW) in Agadir, Rabat, Fes, and Tangier, Morocco, over three rounds of HIV surveillance surveys (2012, 2016, and 2019) conducted using respondent-driven sampling (RDS). Multivariable analyses from RDS studies examining the relationships between variables are under-utilized, particularly analyses that combine multiple locations and years of data together in a principled manner. METHOD: We fit three weighted logistic regression models for HIV transmission knowledge, having been denied service (experienced stigma), and having been hit (experienced violence) in the last 12 months, and perform model selection using 41 possible explanatory variables. RESULTS: Variables significantly associated with higher risk included reasons for sex work, how FSW solicit clients, if female family members are also involved in sex work, ever being forced to have sex, and ever being arrested or jailed. There were also significant differences between cities and in trends over time, with HIV transmission knowledge increasing and having been denied health services and having been hit decreasing. CONCLUSION: We found associations indicating that some particularly vulnerable FSW may be subject to multiple intersecting risks related to HIV knowledge, stigma, and violence which may decrease their agency to receive proper HIV testing, care, and treatment. Although Morocco has made excellent progress in providing HIV services to vulnerable populations, this additional analysis will be useful as Morocco makes programmatic decisions related to ending HIV by 2030.

Hidden population size estimation and diagnostics using two respondent-driven samples with applications in Armenia.

Estimating the size of hidden populations is essential to understand the magnitude of social and healthcare needs, risk behaviors, and disease burden. However, due to the hidden nature of these populations, they are difficult to survey, and there are no gold standard size estimation methods. Many different methods and variations exist, and diagnostic tools are needed to help researchers assess method-specific assumptions as well as compare between methods. Further, because many necessary mathematical assumptions are unrealistic for real survey implementation, assessment of how robust methods are to deviations from the stated assumptions is essential. We describe diagnostics and assess the performance of a new population size estimation method, capture-recapture with successive sampling population size estimation (CR-SS-PSE), which we apply to data from 3 years of studies from three cities and three hidden populations in Armenia. CR-SS-PSE relies on data from two sequential respondent-driven sampling surveys and extends the successive sampling population size estimation (SS-PSE) framework by using the number of individuals in the overlap between the two surveys and a model for the successive sampling process to estimate population size. We demonstrate that CR-SS-PSE is more robust to violations of successive sampling assumptions than SS-PSE. Further, we compare the CR-SS-PSE estimates to population size estimations using other common methods, including unique object and service multipliers, wisdom of the crowd, and two-source capture-recapture to illustrate volatility across estimation methods.

Evaluation of a Wastewater-Based Epidemiological Approach to Estimate the Prevalence of SARS-CoV-2 Infections and the Detection of Viral Variants in Disparate Oregon Communities at City and Neighborhood Scales.

BACKGROUND: Positive correlations have been reported between wastewater SARS-CoV-2 concentrations and a community's burden of infection, disease or both. However, previous studies mostly compared wastewater to clinical case counts or nonrepresentative convenience samples, limiting their quantitative potential. OBJECTIVES: This study examined whether wastewater SARS-CoV-2 concentrations could provide better estimations for SARS-CoV-2 community prevalence than reported cases of COVID-19. In addition, this study tested whether wastewater-based epidemiology methods could identify neighborhood-level COVID-19 hotspots and SARS-CoV-2 variants. METHODS: Community SARS-CoV-2 prevalence was estimated from eight randomized door-to-door nasal swab sampling events in six Oregon communities of disparate size, location, and demography over a 10-month period. Simultaneously, wastewater SARS-CoV-2 concentrations were quantified at each community's wastewater treatment plant and from 22 Newport, Oregon, neighborhoods. SARS-CoV-2 RNA was sequenced from all positive wastewater and nasal swab samples. Clinically reported case counts were obtained from the Oregon Health Authority. RESULTS: Estimated community SARS-CoV-2 prevalence ranged from 8 to 1,687/10,000 persons. Community wastewater SARS-CoV-2 concentrations ranged from 2.9 to 5.1 log10 gene copies per liter. Wastewater SARS-CoV-2 concentrations were more highly correlated (Pearson's r=0.96; R2=0.91) with community prevalence than were clinically reported cases of COVID-19 (Pearson's r=0.85; R2=0.73). Monte Carlo simulations indicated that wastewater SARS-CoV-2 concentrations were significantly better than clinically reported cases at estimating prevalence (p<0.05). In addition, wastewater analyses determined neighborhood-level COVID-19 hot spots and identified SARS-CoV-2 variants (B.1 and B.1.399) at the neighborhood and city scales. DISCUSSION: The greater reliability of wastewater SARS-CoV-2 concentrations over clinically reported case counts was likely due to systematic biases that affect reported case counts, including variations in access to testing and underreporting of asymptomatic cases. With these advantages, combined with scalability and low costs, wastewater-based epidemiology can be a key component in public health surveillance of COVID-19 and other communicable infections. https://doi.org/10.1289/EHP10289.

Populations size estimations using SS-PSE among MSM in four European cities: how many MSM are living with HIV?

BACKGROUND: Although men who have sex with men (MSM) are considered at high risk for transmission of sexually transmitted infections, including HIV, there are few studies estimating the population size of MSM in Europe. We used network data from a survey of MSM in four cities to perform successive sampling-population size estimations (SS-PSE) to estimate MSM population sizes. METHODS: Data were collected in 2013-14 in Bratislava, Bucharest, Verona and Vilnius using respondent-driven sampling (RDS). SS-PSE uses a Bayesian framework to approximate the RDS sampling structure via a successive sampling model and uses the selection order of the sample to provide information about the distribution of network sizes over the population members of MSM. RESULTS: We estimate roughly 4600 MSM in Bratislava, 25 300 MSM in Bucharest, 7200 in Verona and 2900 in Vilnius. This represents 2.9% of the estimated adult male population in Bratislava, 2.3% in Bucharest, 2.7% in Verona and 1.5% in Vilnius. The number of MSM living with HIV would roughly be 200 in Bratislava, 4554 in Bucharest, 690 in Verona and 100 in Vilnius. CONCLUSIONS: Benefits of this method are that no additional information from an RDS survey needs to be collected, that the sizes can be calculated ex post facto a survey and that there is a software programme that can run the SS-PSE models. However, this method relies on having reliable priors. Although many countries are estimating the sizes of their vulnerable populations, European countries have yet to incorporate similar and novel methods.

Linking subsistence harvest diversity and productivity to adaptive capacity in an Alaskan food sharing network.

BACKGROUND: Although anthropogenic climate change poses existential challenges for Indigenous communities in the Arctic, these challenges are not entirely unprecedented. Over many generations, Arctic peoples have developed a wide range of behavioral strategies to navigate environmental change and uncertainty, and these strategies provide a foundation for contemporary adaptation. AIMS: In this article, we focus on mixed cash-subsistence economies and the social networks that underlie them in Alaska. The patterns of food production, labor exchange, and food sharing in subsistence-oriented communities throughout Alaska are driven by the productivity of keystone households who regularly harvest and share resources within and between communities. MATERIALS & METHODS: Building on previous research suggesting the critical importance of these networks to community resilience, we use network analysis to investigate whether patterns in resource transfers between households are associated with subsistence harvest diversity-the diversity of species harvested by a household unit. We use exponential random graph models to describe the structure of a sharing network from Aniak, Alaska, and model the links between harvest productivity, harvest diversity, and household position in this network. RESULTS: Our results indicate that both productivity and diversity are positively associated with network connections, and that productivity alone provides an incomplete model of network structure. DISCUSSION: We suggest that subsistence harvest diversity may play a unique role in supporting adaptive capacity and resilience by maintaining the productivity of keystone households despite changing environments and sustaining social network structures that circulate resources throughout the community. Harvest diversity may also serve as a broad indicator of Indigenous ecological knowledge and a tangible representation of cultural practices, values, and worldviews that underlie subsistence in Alaska. CONCLUSION: Greater attention to harvest diversity is important for understanding how subsistence networks adapt to environmental change and uncertainty linked to social and ecological dynamics of anthropogenic climate change.

Population Size Estimations Among Hidden Populations Using Respondent-Driven Sampling Surveys: Case Studies From Armenia.

BACKGROUND: Estimates of the sizes of hidden populations, including female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID), are essential for understanding the magnitude of vulnerabilities, health care needs, risk behaviors, and HIV and other infections. OBJECTIVE: This article advances the successive sampling-population size estimation (SS-PSE) method by examining the performance of a modification allowing visibility to be jointly modeled with population size in the context of 15 datasets. Datasets are from respondent-driven sampling (RDS) surveys of FSW, MSM, and PWID from three cities in Armenia. We compare and evaluate the accuracy of our imputed visibility population size estimates to those found for the same populations through other unpublished methods. We then suggest questions that are useful for eliciting information needed to compute SS-PSE and provide guidelines and caveats to improve the implementation of SS-PSE for real data. METHODS: SS-PSE approximates the RDS sampling mechanism via the successive sampling model and uses the order of selection of the sample to provide information on the distribution of network sizes over the population members. We incorporate visibility imputation, a measure of a person's propensity to participate in the study, given that inclusion probabilities for RDS are unknown and social network sizes, often used as a proxy for inclusion probability, are subject to measurement errors from self-reported study data. RESULTS: FSW in Yerevan (2012, 2016) and Vanadzor (2016) as well as PWID in Yerevan (2014), Gyumri (2016), and Vanadzor (2016) had great fits with prior estimations. The MSM populations in all three cities had inconsistencies with expert prior values. The maximum low prior value was larger than the minimum high prior value, making a great fit impossible. One possible explanation is the inclusion of transgender individuals in the MSM populations during these studies. There could be differences between what experts perceive as the size of the population, based on who is an eligible member of that population, and what members of the population perceive. There could also be inconsistencies among different study participants, as some may include transgender individuals in their accounting of personal network size, while others may not. Because of these difficulties, the transgender population was split apart from the MSM population for the 2018 study. CONCLUSIONS: Prior estimations from expert opinions may not always be accurate. RDS surveys should be assessed to ensure that they have met all of the assumptions, that variables have reached convergence, and that the network structure of the population does not have bottlenecks. We recommend that SS-PSE be used in conjunction with other population size estimations commonly used in RDS, as well as results of other years of SS-PSE, to ensure generation of the most accurate size estimation.

Measuring a hidden population: A novel technique to estimate the population size of women with sexual violence-related pregnancies in South Kivu Province, Democratic Republic of Congo.

Successive sampling (SS)-population size estimation (PSE) is a technique used to estimate the sizes of hidden populations using data collected in respondent-driven sampling (RDS) surveys. We assess past estimations and use new data from an RDS survey to calculate a new PSE. In 2012, 852 adult women in South Kivu Province, Democratic Republic of Congo, who self-identified as survivors of sexual violence, resulting in a pregnancy, since the start of the war (in 1996) were sampled using RDS. We used imputed visibility, enrollment order, and prior estimates for PSE using SS-PSE in RDS Analyst. Prior estimates varied between Congolese local experts and researchers. We calculated the PSE of women with a sexual violence-related pregnancy in South Kivu using researchers' priors to be approximately 17,400. SS-PSE is an effective method for estimating the population sizes of hidden populations, useful for providing evidence for services and resource allocation. SS-PSE is beneficial because population sizes can be calculated after conducting the survey and do not rely on separate studies or additional data (as in network scale-up, multiplier, and capture-recapture methods).

Estimating the Size of Hidden Populations Using Respondent-driven Sampling Data: Case Examples from Morocco.

BACKGROUND: Respondent-driven sampling is used worldwide to estimate the population prevalence of characteristics, such as HIV/AIDS and associated risk factors in hard-to-reach populations. Estimating the total size of these populations is of great interest to national and international organizations; however, reliable measures of population size often do not exist. METHODS: Successive sampling-population size estimation (SS-PSE) along with network size imputation allows population size estimates to be made without relying on separate studies or additional data (as in network scale-up, multiplier, and capture-recapture methods), which may be biased. RESULTS: Ten population size estimates were calculated for people who inject drugs, female sex workers, men who have sex with other men, and migrants from sub-Saharan Africa in six different cities in Morocco. SS-PSE estimates fell within or very close to the likely values provided by experts and the estimates from previous studies using other methods. CONCLUSIONS: SS-PSE is an effective method for estimating the size of hard-to-reach populations that leverages important information within respondent-driven sampling studies. The addition of a network size imputation method helps to smooth network sizes allowing for more accurate results. However, caution should be used particularly when there is reason to believe that clustered subgroups may exist within the population of interest or when the sample size is small in relation to the population.

Assaying organochlorines in archived serum for a large, long-term cohort: implications of combining assay results from multiple laboratories over time.

Conserving irreplaceable, archived serum samples may sometimes conflict with the objective of minimizing measurement error due to laboratory effects. We sought to determine whether we could successfully combine assay results for DDT-related compounds and polychlorinated biphenyls (PCBs) in serum from the same birth cohort obtained from different laboratories over time. Using the Child Health and Development Studies (CHDS) serum archive, we compared variability for assays of a quality control pool to variability for assays of subject serum. The quality control pool was created from native archived serum samples that were pooled, then aliquoted, blinded and inserted pair-wise into assay batches along with the subject serum for 5 studies using CHDS samples conducted over a 13year period by three different laboratories. We found that the variability between laboratory and over time within laboratory was small relative to inter-individual variability for p,p'-DDT (1,1,1-trichloro-2,2-bis(p-chlorophenyl)ethane), p,p'-DDE (1,1'-dichloro-2,2'-bis(p-chlorophenyl)ethylene) and o,p'-DDT (1,1,1-trichloro-2-(p-chlorophenyl)-2-(o-chlorophenyl)-ethane). Results were also consistent for most PCB congeners which were detectable in 85% or more of samples. Our results suggest that it is possible to combine assays for DDT and PCB congeners measured at positive levels as they are accumulated for cohort subjects without risking meaningful misclassification due to variation stemming from laboratory or time period. This has significant implications for future study costs, conservation of irreplaceable archived samples and for leveraging past investments for future research. For PCB congeners with very low levels, findings caution against pooling of assays without further exploration.