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Estimation of policy-relevant reference conditions throughout national river networks

ABSTRACT: A method for objectively estimating reference states for suspended fine sediment (turbidity) is presented. To be fit for water policy development and implementation the method had to satisfy four requirements: (1) the method must not be dependent on data from minimally-disturbed reference sites; (2) the method must facilitate characterization of reference states throughout heterogeneous river networks, given patchy data; (3) the classification of reference states must be relevant and legitimate to end-users; (4) the method should provide several classifications of reference states at different spatial resolutions allowing selection of the resolution yielding the most parsimonious classification of reference states throughout the network. Implementing the method involves two stages: (1) Development of a river classification based on sediment supply and retention regimes (defining ‘turbidity classes’) at multiple spatial resolutions. (2) At each resolution, for each turbidity class, estimation of a reference state based on relationships between turbidity and anthropogenic stressors, then objective selection of the resolution yielding the most parsimonious classification of reference states throughout the network. Implementing the method requires a river network GIS and turbidity data within classes, preferably from monitoring sites spanning the domains of the anthropogenic stressor variables used for model-based estimation of reference states.• A method is presented for estimating reference states for suspended fine sediment (turbidity) throughout spatially heterogeneous river networks.• Development of the method was guided by the requirements of policy analysts during reform of water policy in New Zealand.• The method presented was used to develop fine sediment regulatory thresholds of national water policy. Graphical abstract Image, graphical abstract

SUBMITTER: Stoffels R

PROVIDER: S-EPMC8563678 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundReference intervals represent the expected range of physiological test results in a healthy population and are essential to support medical decision making. Particularly in the context of pediatric reference intervals, where recruitment regulations make prospective studies challenging to conduct, indirect estimation strategies are becoming increasingly important. Established indirect methods enable robust identification of the distribution of "healthy" samples from laboratory databases, which include unlabeled pathologic cases, but are currently severely limited when adjusting for essential patient characteristics such as age. Here, we propose the use of mixture density networks (MDN) to overcome this problem and model all parameters of the mixture distribution in a single step.ResultsEstimated reference intervals from varying settings with simulated data demonstrate the ability to accurately estimate latent distributions from unlabeled data using different implementations of MDNs. Comparing the performance with alternative estimation approaches further highlights the importance of modeling the mixture component weights as a function of the input in order to avoid biased estimates for all other parameters and the resulting reference intervals. We also provide a strategy to generate partially customized starting weights to improve proper identification of the latent components. Finally, the application on real-world hemoglobin samples provides results in line with current gold standard approaches, but also suggests further investigations with respect to adequate regularization strategies in order to prevent overfitting the data.ConclusionsMixture density networks provide a promising approach capable of extracting the distribution of healthy samples from unlabeled laboratory databases while simultaneously and explicitly estimating all parameters and component weights as non-linear functions of the covariate(s), thereby allowing the estimation of age-dependent reference intervals in a single step. Further studies on model regularization and asymmetric component distributions are warranted to consolidate our findings and expand the scope of applications.

Project description:Estimating population sizes has long been of interest, from the estimation of the human or ecological population size within regions or countries to the hidden number of civilian casualties in a war. Total enumeration of the population, for example, via a census, is often infeasible or simply impractical. However, a series of partial enumerations or observations of the population is often possible. This has led to the ideas of capture-recapture methods, which have been extensively used within ecology to estimate the size of wildlife populations, with an associated measure of uncertainty, and are most effectively applied when there are multiple capture occasions. Capture-recapture ideology can be more widely applied to multiple data-sources, by the linkage of individuals across the multiple lists. This is often referred to as Multiple Systems Estimation (MSE). The MSE approach has been preferred when estimating "capture-shy" or hard-to-reach populations, including those caught up in the criminal justice system; or homeless; or trafficked; or civilian casualties of war. Motivated by a range of public policy applications of MSE, each briefly introduced, we discuss practical problems with potentially substantial methodological implications. They include: "period" definition; "case" definition; when an observed count is not a true count of the population of interest but an upper bound due to mismatched definitions; exact or probabilistic matching of "cases" across different lists; demographic or other information about the "case" which may influence capture-propensities; required permissions to access extant-lists; list-creation by research-teams or interested parties; referrals (if presence on list A results - almost surely - in presence on list B); different mathematical models leading to widely different estimated population sizes; uncertainty in estimation; computational efficiency; external validation; hypothesis-generation; and additional independent external information. Returning to our motivational applications, we focus on whether the uncertainty which qualified their estimates was sufficiently narrow to orient public policy; and, if not, what options were available and/or taken to reduce the uncertainty or to seek external validation. We also consider whether MSE was hypothesis-generating: in the sense of having spawned new lines of inquiry.

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Estimation of policy-relevant reference conditions throughout national river networks

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