Differences between main-channel and off-channel food webs in the upper Mississippi River revealed by fatty acid profiles of consumers

Large river systems are often thought to contain a mosaic of patches with different habitat characteristics driven by differences in flow and mixing environments. Off-channel habitats (e.g., backwater areas, secondary channels) can become semi-isolated from main-channel water inputs, leading to the development of distinct biogeochemical environments. Observations of adult bluegill (Lepomis macrochirus) in the main channel of the Mississippi River led to speculation that the main channel offered superior food resources relative to off-channel areas. One important aspect of food quality is the quantity and composition of polyunsaturated fatty acids (PUFA). We sampled consumers from main-channel and backwater habitats to determine whether they differed in PUFA content. Main-channel individuals for relatively immobile species (young-of-year bluegill, zebra mussels [Dreissena polymorpha], and plain pocketbook mussels [Lampsilis cardium]) had significantly greater PUFA content than off-channel individuals. No difference in PUFA was observed for the more mobile gizzard shad (Dorsoma cepedianum), which may move between main-channel and off-channel habitats even at early life-history stages. As off-channel habitats become isolated from main-channel waters, flow and water column nitrogen decrease, potentially improving conditions for nitrogen-fixing cyanobacteria and vascular plants that, in turn, have low PUFA content. We conclude that main-channel food webs of the upper Mississippi River provide higher quality food resources for some riverine consumers as compared to food webs in off-channel habitats

Naomi: a new modelling tool for estimating HIV epidemic indicators at the district level in sub-Saharan Africa.

INTRODUCTION: HIV planning requires granular estimates for the number of people living with HIV (PLHIV), antiretroviral treatment (ART) coverage and unmet need, and new HIV infections by district, or equivalent subnational administrative level. We developed a Bayesian small-area estimation model, called Naomi, to estimate these quantities stratified by subnational administrative units, sex, and five-year age groups. METHODS: Small-area regressions for HIV prevalence, ART coverage and HIV incidence were jointly calibrated using subnational household survey data on all three indicators, routine antenatal service delivery data on HIV prevalence and ART coverage among pregnant women, and service delivery data on the number of PLHIV receiving ART. Incidence was modelled by district-level HIV prevalence and ART coverage. Model outputs of counts and rates for each indicator were aggregated to multiple geographic and demographic stratifications of interest. The model was estimated in an empirical Bayes framework, furnishing probabilistic uncertainty ranges for all output indicators. Example results were presented using data from Malawi during 2016-2018. RESULTS: Adult HIV prevalence in September 2018 ranged from 3.2% to 17.1% across Malawi's districts and was higher in southern districts and in metropolitan areas. ART coverage was more homogenous, ranging from 75% to 82%. The largest number of PLHIV was among ages 35 to 39 for both women and men, while the most untreated PLHIV were among ages 25 to 29 for women and 30 to 34 for men. Relative uncertainty was larger for the untreated PLHIV than the number on ART or total PLHIV. Among clients receiving ART at facilities in Lilongwe city, an estimated 71% (95% CI, 61% to 79%) resided in Lilongwe city, 20% (14% to 27%) in Lilongwe district outside the metropolis, and 9% (6% to 12%) in neighbouring Dowa district. Thirty-eight percent (26% to 50%) of Lilongwe rural residents and 39% (27% to 50%) of Dowa residents received treatment at facilities in Lilongwe city. CONCLUSIONS: The Naomi model synthesizes multiple subnational data sources to furnish estimates of key indicators for HIV programme planning, resource allocation, and target setting. Further model development to meet evolving HIV policy priorities and programme need should be accompanied by continued strengthening and understanding of routine health system data

Comparative performance of multiple-list estimators of key population size.

Estimates of the sizes of key populations (KPs) affected by HIV, including men who have sex with men, female sex workers and people who inject drugs, are required for targeting epidemic control efforts where they are most needed. Unfortunately, different estimators often produce discrepant results, and an objective basis for choice is lacking. This simulation study provides the first comparison of information-theoretic selection of loglinear models (LLM-AIC), Bayesian model averaging of loglinear models (LLM-BMA) and Bayesian nonparametric latent-class modeling (BLCM) for estimation of population size from multiple lists. Four hundred random samples from populations of size 1,000, 10,000 and 20,000, each including five encounter opportunities, were independently simulated using each of 30 data-generating models obtained from combinations of six patterns of variation in encounter probabilities and five expected per-list encounter probabilities, producing a total of 36,000 samples. Population size was estimated for each combination of sample and sequentially cumulative sets of 2-5 lists using LLM-AIC, LLM-BMA and BLCM. LLM-BMA and BLCM were quite robust and performed comparably in terms of root mean-squared error and bias, and outperformed LLM-AIC. All estimation methods produced uncertainty intervals which failed to achieve the nominal coverage, but LLM-BMA, as implemented in the dga R package produced the best balance of accuracy and interval coverage. The results also indicate that two-list estimation is unnecessarily vulnerable, and it is better to estimate the sizes of KPs based on at least three lists

Improving estimates of district HIV prevalence and burden in South Africa using small area estimation techniques.

Many countries, including South Africa, have implemented population-based household surveys to estimate HIV prevalence and the burden of HIV infection. Most household HIV surveys are designed to provide reliable estimates down to only the first subnational geopolitical level which, in South Africa, is composed of nine provinces. However HIV prevalence estimates are needed down to at least the second subnational level in order to better target the delivery of HIV care, treatment and prevention services. The second subnational level in South Africa is composed of 52 districts. Achieving adequate precision at the second subnational level therefore requires either a substantial increase in survey sample size or use of model-based estimation capable of incorporating other pre-existing data. Our purpose is demonstration of the efficacy of relatively simple small-area estimation of HIV prevalence in the 52 districts of South Africa using data from the South African National HIV Prevalence, Incidence and Behavior Survey, 2012, district-level HIV prevalence estimates obtained from testing of pregnant women who attended antenatal care (ANC) clinics in 2012, and 2012 demographic data. The best-fitting model included only ANC prevalence and dependency ratio as out-of-survey predictors. Our key finding is that ANC prevalence was the superior auxiliary covariate, and provided substantially improved precision in many district-level estimates of HIV prevalence in the general population. Inclusion of a district-level spatial simultaneously autoregressive covariance structure did not result in improved estimation

Population Size Estimation From Capture-Recapture Studies Using shinyrecap: Design and Implementation of a Web-Based Graphical User Interface

BackgroundPopulation size estimates (PSE) provide critical information in determining resource allocation for HIV services geared toward those at high risk of HIV, including female sex workers, men who have sex with men, and people who inject drugs. Capture-recapture (CRC) is often used to estimate the size of these often-hidden populations. Compared with the commonly used 2-source CRC, CRC relying on 3 (or more) samples (3S-CRC) can provide more robust PSE but involve far more complex statistical analysis. ObjectiveThis study aims to design and describe the Shiny application (shinyrecap), a user-friendly interface that can be used by field epidemiologists to produce PSE. Methodsshinyrecap is built on the Shiny web application framework for R. This allows it to seamlessly integrate with the sophisticated CRC statistical packages (eg, Rcapture, dga, LCMCR). Additionally, the application may be accessed online or run locally on the user’s machine. ResultsThe application enables users to engage in sample size calculation based on a simulation framework. It assists in the proper formatting of collected data by providing a tool to convert commonly used formats to that used by the analysis software. A wide variety of methodologies are supported by the analysis tool, including log-linear, Bayesian model averaging, and Bayesian latent class models. For each methodology, diagnostics and model checking interfaces are provided. ConclusionsThrough a use case, we demonstrated the broad utility of this powerful tool with 3S-CRC data to produce PSE for female sex workers in a subnational unit of a country in sub-Saharan Africa

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