Dataset Information

Disentangling monitoring programs: design, analysis, and application considerations.

ABSTRACT: Monitoring programs are an essential tool for assessing and informing conservation efforts but the methods used to gather monitoring data directly influence results. This presents a challenge to conservation professionals when deciding on existing data to inform a given question. We illustrate the challenges of using monitoring data by comparing population trends from two large-scale avian monitoring programs in the western United States: the Breeding Bird Survey and Integrated Monitoring in Bird Conservation Regions programs. We used publicly available data to compare trend trajectory between 2008 and 2015 for 148 species across Colorado, Montana, and Wyoming. Trends were inconsistent for 62% of the comparisons, with species having opposite trends in 21 cases. The inconsistencies found within our species comparisons reflect the inherent differences between program sampling design and analytical approach. Periodically revisiting how and why we monitor natural resources is necessary to advance conservation and management as the lessons learned from long-standing programs guide the development of more recent efforts. Our results emphasize that prior to management actions and policy decisions, managers must be aware of both the sampling design and appropriate ecological inference of any monitoring program.

SUBMITTER: Janousek WM

PROVIDER: S-EPMC9286664 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Disentangling monitoring programs: design, analysis, and application considerations.

Janousek William M WM Hahn Beth A BA Dreitz Victoria J VJ

Ecological applications : a publication of the Ecological Society of America 20190529 5

Monitoring programs are an essential tool for assessing and informing conservation efforts but the methods used to gather monitoring data directly influence results. This presents a challenge to conservation professionals when deciding on existing data to inform a given question. We illustrate the challenges of using monitoring data by comparing population trends from two large-scale avian monitoring programs in the western United States: the Breeding Bird Survey and Integrated Monitoring in Bir ...[more]

PMID: 31066957

Similar Datasets

Project description:BackgroundPromoting safe and efficient transitions of care is critical to reducing readmission rates and associated costs and improving the quality of patient care. A growing body of literature suggests that transitional care (TC) programs are effective in improving quality of life and reducing unplanned readmissions for several patient groups. TC programs are highly complex and multidimensional, requiring evidence on how specific practices and system characteristics influence their effectiveness in patient care, readmission reduction and costs.MethodsUsing a systematic review and a configurational approach, the study examines the role played by system characteristics (size, ownership, professional skills, technology used), the organizational components implemented, analyzing their combinations, and the potential economic impact of TC programs.ResultsThe more organizational components are implemented, the greater the likelihood that a TC program will be successful in reducing readmission rates. Not all components have the same effect. The results show that certain components, 'post-discharge symptom monitoring and management' and 'discharge planning', are necessary but not sufficient to achieve the outcome. The results indicate the existence of two different combinations of components that can be considered sufficient for the reduction of readmissions. Furthermore, while system characteristics are underexplored, the study shows different ways of incorporating the skill mix of professionals and their mode of coordination in TC programs. Four organizational models emerge: the health-based monocentric, the social-based monocentric, the multidisciplinary team and the mono-specialist team. The economic impact of the programs is generally positive. Despite an increase in patient management costs, there is an overall reduction in all post-intervention costs, particularly those related to readmissions.ConclusionsThe results underline the importance of examining in depth the role of system characteristics and organizational factors in facilitating the creation of a successful TC program. The work gives preliminary insights into how to systematize organizational practices and different coordination modes for facilitating decision-makers' choices in TC implementation. While there is evidence that TC programs also have economic benefits, the quality of economic evaluations is relatively low and needs further study.

Project description:BackgroundA large multi-center survey was conducted to understand patients' perspectives on biobank study participation with particular focus on racial and ethnic minorities. In order to enrich the study sample with racial and ethnic minorities, disproportionate stratified sampling was implemented with strata defined by electronic health records (EHR) that are known to be inaccurate. We investigate the effect of sampling strata misclassification in complex survey design.MethodsUnder non-differential and differential misclassification in the sampling strata, we compare the validity and precision of three simple and common analysis approaches for settings in which the primary exposure is used to define the sampling strata. We also compare the precision gains/losses observed from using a disproportionate stratified sampling scheme compared to using a simple random sample under varying degrees of strata misclassification.ResultsDisproportionate stratified sampling can result in more efficient parameter estimates of the rare subgroups (race/ethnic minorities) in the sampling strata compared to simple random sampling. When sampling strata misclassification is non-differential with respect to the outcome, a design-agnostic analysis was preferred over model-based and design-based analyses. All methods yielded unbiased parameter estimates but standard error estimates were lowest from the design-agnostic analysis. However, when misclassification is differential, only the design-based method produced valid parameter estimates of the variables included in the sampling strata.ConclusionsIn complex survey design, when the interest is in making inference on rare subgroups, we recommend implementing disproportionate stratified sampling over simple random sampling even if the sampling strata are misclassified. If the misclassification is non-differential, we recommend a design-agnostic analysis. However, if the misclassification is differential, we recommend using design-based analyses.

Project description:Introduction: Many studies have reported the association between air pollution and human health based on regulatory air pollution monitoring data. However, because regulatory monitoring networks were not designed for epidemiological studies, the collected data may not provide sufficient spatial contrasts for assessing such associations. Our goal was to develop a monitoring design supplementary to the regulatory monitoring network in Seoul, Korea. This design focused on the selection of 20 new monitoring sites to represent the variability in PM2.5 across people's residences for cohort studies. Methods: We obtained hourly measurements of PM2.5 at 37 regulatory monitoring sites in 2010 in Seoul, and computed the annual average at each site. We also computed 313 geographic variables representing various pollution sources at the regulatory monitoring sites, 31,097 children's homes from the Atopy Free School survey, and 412 community service centers in Seoul. These three types of locations represented current, subject, and candidate locations. Using the regulatory monitoring data, we performed forward variable selection and chose five variables most related to PM2.5. Then, k-means clustering was applied to categorize all locations into several groups representing a diversity in the spatial variability of the five selected variables. Finally, we computed the proportion of current to subject location in each cluster, and randomly selected new monitoring sites from candidate sites in the cluster with the minimum proportion until 20 sites were selected. Results: The five selected geographic variables were related to traffic or urbanicity with a cross-validated R² value of 0.69. Clustering analysis categorized all locations into nine clusters. Finally, one to eight new monitoring sites were selected from five clusters. Discussion: The proposed monitoring design will help future studies determine the locations of new monitoring sites representing spatial variability across residences for epidemiological analyses.

Project description:BackgroundThere are many methodological challenges in the conduct and analysis of cluster randomised controlled trials, but one that has received little attention is that of post-randomisation changes to cluster composition. To illustrate this, we focus on the issue of cluster merging, considering the impact on the design, analysis and interpretation of trial outcomes.MethodsWe explored the effects of merging clusters on study power using standard methods of power calculation. We assessed the potential impacts on study findings of both homogeneous cluster merges (involving clusters randomised to the same arm of a trial) and heterogeneous merges (involving clusters randomised to different arms of a trial) by simulation. To determine the impact on bias and precision of treatment effect estimates, we applied standard methods of analysis to different populations under analysis.ResultsCluster merging produced a systematic reduction in study power. This effect depended on the number of merges and was most pronounced when variability in cluster size was at its greatest. Simulations demonstrate that the impact on analysis was minimal when cluster merges were homogeneous, with impact on study power being balanced by a change in observed intracluster correlation coefficient (ICC). We found a decrease in study power when cluster merges were heterogeneous, and the estimate of treatment effect was attenuated.ConclusionsExamples of cluster merges found in previously published reports of cluster randomised trials were typically homogeneous rather than heterogeneous. Simulations demonstrated that trial findings in such cases would be unbiased. However, simulations also showed that any heterogeneous cluster merges would introduce bias that would be hard to quantify, as well as having negative impacts on the precision of estimates obtained. Further methodological development is warranted to better determine how to analyse such trials appropriately. Interim recommendations include avoidance of cluster merges where possible, discontinuation of clusters following heterogeneous merges, allowance for potential loss of clusters and additional variability in cluster size in the original sample size calculation, and use of appropriate ICC estimates that reflect cluster size.

Dataset Information

Disentangling monitoring programs: design, analysis, and application considerations.

Publications

Disentangling monitoring programs: design, analysis, and application considerations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets