Decreasing trends in total suspended solids and cumulative effects of nonpoint source projects in the Cuyahoga River Watershed, OH.

Using the US EPA's Grants Reporting and Tracking System (GRTS), we test if completion of best management practices (BMPs) through the Clean Water Act Section (§)319 National Nonpoint Source Program was associated with a decreasing trend in total suspended solids (TSS) load (metric tons/year). The study area chosen had 21 completed projects in the Cuyahoga River watershed in northeastern Ohio from 2000 to 2018. The §319 projects ranged from dam removal, floodplain/wetland restoration to stormwater projects. There was an overall decreasing trend in TSS loads. We identified three phases of project implementation and completion, where phase 1 had ongoing projects, but none completed (2000-2004). The steepest decrease in loads, identified as phase 2 (2005-2011), was associated with completion of low-head dam modification and removal projects on the mainstem of the Cuyahoga River. A likely decreasing trend was associated with projects completed in the tributaries, such as natural channel design restoration and stormwater green infrastructure (phase 3). Pairing sediment reduction estimates from projects with the river's flow normalized TSS loading trend, we estimated that the §319 effort may account for a small fraction of the TSS load reduction. Other stream restoration projects (non-§319) have also been done in the Cuyahoga watershed by other organizations. However, trying to compile these other projects is challenging in larger watersheds having multiple municipalities, agencies, and nonprofits doing restoration without better coordinated record keeping and monitoring. While a decreasing trend in a pollutant load is a desirable water quality outcome, determining what contributed to that trend remains difficult.

Spatially balanced sampling designs for environmental surveys.

Some environmental studies use non-probabilistic sampling designs to draw samples from spatially distributed populations. Unfortunately, these samples can be difficult to analyse statistically and can give biased estimates of population characteristics. Spatially balanced sampling designs are probabilistic designs that spread the sampling effort evenly over the resource. These designs are particularly useful for environmental sampling because they produce good-sample coverage over the resource, they have precise design-based estimators and they can potentially reduce the sampling cost. The most popular spatially balanced design is Generalized Random Tessellation Stratified (GRTS), which has many desirable features including a spatially balanced sample, design-based estimators and the ability to select spatially balanced oversamples. This article considers the popularity of spatially balanced sampling, reviews several spatially balanced sampling designs and shows how these designs can be implemented in the statistical programming language R. We hope to increase the visibility of spatially balanced sampling and encourage environmental scientists to use these designs.

A spatially balanced design with probability function proportional to the within sample distance.

The units observed in a biological, agricultural, and environmental survey are often randomly selected from a finite population whose main feature is to be geo-referenced thus its spatial distribution should be used as essential information in designing the sample. In particular our interest is focused on probability samples that are well spread over the population in every dimension which in recent literature are defined as spatially balanced samples. To approach the problem we used the within sample distance as the summary index of the spatial distribution of a random selection criterion. Moreover numerical comparisons are made between the relative efficiency, measured with respect to the simple random sampling, of the suggested design and some other classical solutions as the Generalized Random Tessellation Stratified (GRTS) design used by the US Environmental Protection Agency (EPA) and other balanced or spatially balanced selection procedures as the Spatially Correlated Poisson Sampling (SCPS), the balanced sampling (CUBE), and the Local Pivotal method (LPM). These experiments on real and simulated data show that the design based on the within sample distance selects samples with a better spatial balance thus gives estimates with a lower sampling error than those obtained by using the other methods. The suggested method is very flexible to the introduction of stratification and coordination of samples and, even if in its nature it is computationally intensive, it is shown to be a suitable solution even when dealing with high sampling rates and large population frames where the main problem arises from the size of the distance matrix.

Amortization of gene replacement therapies: A health policy analysis exploring a mechanism for mitigating budget impact of high-cost treatments.

With gene replacement therapies (GRTs) increasingly and rapidly reaching the healthcare marketplace, the vast potential for improving patient health is matched by the potential budgetary impact for healthcare payers. GRTs are highly valuable given their potential life-extending or even curative benefits and may provide significant cost-offsets compared with standard of care. Current healthcare systems are, however, struggling to fund such valuable but costly therapies. Some payers have already implemented specific financing models to account for the new treatment paradigms, but these do not address the budget impact in the year of acquisition or administration of these costly technologies. This health policy analysis aimed to assess the rationale and feasibility of amortization, within the context of financing healthcare technologies, and specifically GRTs. Amortization is an accounting concept applied to intangible assets that allows for spreading the cost an intangible asset over time, allowing for repayment to occur via interest and principal payments sufficient to repay the intangible asset in full by its maturity. Our systematic scoping review on the amortization of healthcare technologies found a very small literature base with even that being unclear and inconsistent in its understanding of the issues. Where amortization was proposed as a solution for funding costly, but highly valuable GRTs, the concept was not fully investigated in detail, nor was the feasibility of the approach fully challenged. However, by providing clear definitions of relevant concepts along with an example of amortization models applied to some example GRTs, we propose that amortization can offer a promising method for funding of extraordinarily high-value healthcare technologies, thereby increasing market and patient access for these technologies. Nonetheless, healthcare accounting principles and financing guidelines must evolve to apply amortization to the rapidly developing GRTs.

A comparison of design-based and model-based approaches for finite population spatial sampling and inference.

The design-based and model-based approaches to frequentist statistical inference rest on fundamentally different foundations. In the design-based approach, inference relies on random sampling. In the model-based approach, inference relies on distributional assumptions. We compare the approaches in a finite population spatial context.We provide relevant background for the design-based and model-based approaches and then study their performance using simulated data and real data. The real data is from the United States Environmental Protection Agency's 2012 National Lakes Assessment. A variety of sample sizes, location layouts, dependence structures, and response types are considered. The population mean is the parameter of interest, and performance is measured using statistics like bias, squared error, and interval coverage.When studying the simulated and real data, we found that regardless of the strength of spatial dependence in the data, the generalized random tessellation stratified (GRTS) algorithm, which explicitly incorporates spatial locations into sampling, tends to outperform the simple random sampling (SRS) algorithm, which does not explicitly incorporate spatial locations into sampling. We also found that model-based inference tends to outperform design-based inference, even for skewed data where the model-based distributional assumptions are violated. The performance gap between design-based inference and model-based inference is small when GRTS samples are used but large when SRS samples are used, suggesting that the sampling choice (whether to use GRTS or SRS) is most important when performing design-based inference.There are many benefits and drawbacks to the design-based and model-based approaches for finite population spatial sampling and inference that practitioners must consider when choosing between them. We provide relevant background contextualizing each approach and study their properties in a variety of scenarios, making recommendations for use based on the practitioner's goals.