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The utility of joinpoint regression for estimating population parameters given changes in population structure.

ABSTRACT: The method of joinpoint regression has been used in numerous domains to assess changes in time series data, including such things as cancer mortality rates, motor vehicle collision mortalities, and disease risk. To help improve estimation of population parameters for use in ecological risk assessment and management, we present a simulation and analysis to describe the utility of this method for the ecological domain. We demonstrate how joinpoint regression can accurately identify if the population structure changes based on time series of abundance, as well as identify when this change occurs. In addition, we compare and contrast population parameter estimates derived through joinpoint and surplus production methods to those derived from standard surplus production methods alone. When considering a change point at 32 years (out of a 64 year simulation), the joinpoint regression model was able, on average, to estimate a joinpoint time of 32.31 years with a variance of 6.82 and 95% confidence interval for the mean relative bias of (0.0085, 0.0112). The model was able to consistently estimate population parameters, with variance of these estimations decreasing as the change in these population parameters increased. We conclude that joinpoint regression be added to the list of methods employed by those who assess ecological risk to allow for a more accurate and complete understanding of population dynamics.

SUBMITTER: Gillis D

PROVIDER: S-EPMC6872810 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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The utility of joinpoint regression for estimating population parameters given changes in population structure.

Gillis Daniel D Edwards Brandon P M BPM

Heliyon 20191119 11

The method of joinpoint regression has been used in numerous domains to assess changes in time series data, including such things as cancer mortality rates, motor vehicle collision mortalities, and disease risk. To help improve estimation of population parameters for use in ecological risk assessment and management, we present a simulation and analysis to describe the utility of this method for the ecological domain. We demonstrate how joinpoint regression can accurately identify if the populati ...[more]

PMID: 31768426

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Project description:BackgroundInjury is a social problem that causes health and property losses, and it is important to identify the size and trend of injury for efficient prevention and management. Therefore, this study analyzed the trends in injury mortality and hospitalization rates from 2005 to 2019 in Korea.MethodsUsing mortality data by Statistics Korea and Korea National Hospital Discharge In-depth injury survey by the Korea Disease Control and Prevention Agency (KDCA), age standardized rates were calculated for death and hospitalization to analyze trends and annual changes with the joinpoint regression model. In addition, annual changes in the hospitalization rate of the transport accident and fall injuries by age group were analyzed, which are the major causes of injuries.ResultsFrom 2005 to 2019, the injury mortality rate has been on the decline, but the injury hospitalization rate has been on the rise. The annual rate of change varied depending on the injury mechanism, but the mortality rate tended to decrease or remain similar level, while the rate of hospitalization has steadily increased. In addition, by age group, injury mortality and hospitalization rates were high in the elderly. In particular, the hospitalization rate of the elderly was higher when comparing the hospitalization rate of the children in transport accidents and falls. Pedestrian transport accidents tended to decrease under the age of 15, but remained similar for those aged 65 and older, and bicycle accidents tended to increase in both groups. In addition, hospitalization rates were higher in the fall, with both groups showing a statistically significant increase in hospitalization rates caused by falls.ConclusionThis study analyzed the trend of injury mortality and hospitalization and found that transport accidents and falls may vary depending on the means or age of the accident. Since injury is a big social problem that is a burden of disease, safety education and legal sanctions for injury prevention should be further improved in the future, especially by prioritizing vulnerable groups by age and detailed mechanisms of injury.

Project description:Cancer is one of the leading causes of death worldwide and its incidence is expected to increase in the future. In Panama, cancer is also one of the leading causes of death. In 1964, a nationwide cancer registry was started and it was restructured and improved in 2012. The aim of this study is to utilize Joinpoint regression analysis to study the trends of the incidence and mortality of cancer in Panama in the last decade. Cancer mortality was estimated from the Panamanian National Institute of Census and Statistics Registry for the period 2001 to 2011. Cancer incidence was estimated from the Panamanian National Cancer Registry for the period 2000 to 2009. The Joinpoint Regression Analysis program, version 4.0.4, was used to calculate trends by age-adjusted incidence and mortality rates for selected cancers. Overall, the trend of age-adjusted cancer mortality in Panama has declined over the last 10 years (-1.12% per year). The cancers for which there was a significant increase in the trend of mortality were female breast cancer and ovarian cancer; while the highest increases in incidence were shown for breast cancer, liver cancer, and prostate cancer. Significant decrease in the trend of mortality was evidenced for the following: prostate cancer, lung and bronchus cancer, and cervical cancer; with respect to incidence, only oral and pharynx cancer in both sexes had a significant decrease. Some cancers showed no significant trends in incidence or mortality. This study reveals contrasting trends in cancer incidence and mortality in Panama in the last decade. Although Panama is considered an upper middle income nation, this study demonstrates that some cancer mortality trends, like the ones seen in cervical and lung cancer, behave similarly to the ones seen in high income countries. In contrast, other types, like breast cancer, follow a pattern seen in countries undergoing a transition to a developed economy with its associated lifestyle, nutrition, and body weight changes.

Project description:BackgroundThe Approximate Bayesian Computation (ABC) approach has been used to infer demographic parameters for numerous species, including humans. However, most applications of ABC still use limited amounts of data, from a small number of loci, compared to the large amount of genome-wide population-genetic data which have become available in the last few years.ResultsWe evaluated the performance of the ABC approach for three 'population divergence' models - similar to the 'isolation with migration' model - when the data consists of several hundred thousand SNPs typed for multiple individuals by simulating data from known demographic models. The ABC approach was used to infer demographic parameters of interest and we compared the inferred values to the true parameter values that was used to generate hypothetical "observed" data. For all three case models, the ABC approach inferred most demographic parameters quite well with narrow credible intervals, for example, population divergence times and past population sizes, but some parameters were more difficult to infer, such as population sizes at present and migration rates. We compared the ability of different summary statistics to infer demographic parameters, including haplotype and LD based statistics, and found that the accuracy of the parameter estimates can be improved by combining summary statistics that capture different parts of information in the data. Furthermore, our results suggest that poor choices of prior distributions can in some circumstances be detected using ABC. Finally, increasing the amount of data beyond some hundred loci will substantially improve the accuracy of many parameter estimates using ABC.ConclusionsWe conclude that the ABC approach can accommodate realistic genome-wide population genetic data, which may be difficult to analyze with full likelihood approaches, and that the ABC can provide accurate and precise inference of demographic parameters from these data, suggesting that the ABC approach will be a useful tool for analyzing large genome-wide datasets.

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The utility of joinpoint regression for estimating population parameters given changes in population structure.

Publications

The utility of joinpoint regression for estimating population parameters given changes in population structure.

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