Comparing rates between and within populations

2018 ◽  
pp. 19-27
Author(s):  
Alan J. Silman ◽  
Gary J. Macfarlane ◽  
Tatiana Macfarlane
Keyword(s):  
Population Change ◽  
Mortality Rates ◽  
Change Over Time ◽  
Time Points ◽  
Disease Rates ◽  
Over Time

In comparing rates between populations, it is important that one is comparing ‘like with like’. One population may be considerably older than a population to which it is compared and therefore it would not be surprising that mortality rates were higher. Instead it is more useful to make comparisons taking account of differences in characteristics such as age or gender. The same considerations apply to examining disease rates over time in a given population. If the characteristics of the population change over time (e.g. the population gets older), this needs to be considered. To formulate hypotheses, the rate of a disease under study in a population may be compared with the rate in other populations, or in the same population at difierent time points. If the rates vary significantly between populations or are changing within a population, then this provides impetus for investigating the reasons underlying these differences or changes.

World Population Change over Time

2012 ◽  
pp. 265-285
Author(s):  
Dudley L. Jr. Poston ◽  
Leon F. Bouvier
Keyword(s):  
Population Change ◽  
World Population ◽  
Change Over Time ◽  
Over Time

scholarly journals COVID-19 mortality rates for persons with intellectual and developmental disabilities

2021 ◽  
Vol 2 (1) ◽  
pp. 179-183
Author(s):  
Mark D. Davis ◽  
Scott Spreat ◽  
Ryan Cox ◽  
Matthew Holder ◽  
Kathryn M. Burke ◽  
...  
Keyword(s):  
United States ◽  
Developmental Disabilities ◽  
General Population ◽  
General Public ◽  
The United States ◽  
Mortality Rates ◽  
Intellectual And Developmental Disabilities ◽  
Time Points ◽  
Probability Of Death ◽  
Over Time

People with intellectual and developmental disabilities (IDD) appear to have an increased probability of death from COVID-19 once infected. We report infection and mortality rates for people with IDD compared to the general population of eight states at two time points during the COVID-19 pandemic. Note that these eight states contain approximately 1/3 of the population of the United States. These data suggest individuals with IDD are less likely to be infected with the COVID-19 virus (5.62%) than the general public (7.57%). However, while mortality rates for both groups have declined over time, people with IDD are over twice as likely (2.29) to die from the infection as members of the general public.

scholarly journals Analgesic use in a Norwegian general population: change over time and high-risk use - The Tromsø Study

2015 ◽  
Vol 16 (1) ◽  
Author(s):  
Per-Jostein Samuelsen ◽  
Lars Slørdal ◽  
Ulla Dorte Mathisen ◽  
Anne Elise Eggen
Keyword(s):  
High Risk ◽  
General Population ◽  
Population Change ◽  
Change Over Time ◽  
Analgesic Use ◽  
Over Time

Pancake Evolution: A Novel & Engaging Illustration of Natural Selection

2019 ◽  
Vol 81 (2) ◽  
pp. 110-114 ◽  
Author(s):  
D. Andrew Crain ◽  
Matthew Hale
Keyword(s):  
Genetic Diversity ◽  
Natural Selection ◽  
Second Generation ◽  
Population Change ◽  
Allelic Variation ◽  
Postsecondary Students ◽  
Change Over Time ◽  
Changing Environments ◽  
Over Time ◽  
Made In

Understanding the theory of natural selection is crucial for any student of biology, but many secondary and postsecondary students struggle with the concepts. We present a novel, engaging exercise to illustrate natural selection through making pancakes. After students make pancakes (representing offspring) with various ingredients (illustrating genetic diversity and allelic variation), other students (representing the environment) judge the pancakes on the basis of taste. Only the highest-ranking pancakes are made in a second generation (illustrating population change over time), and new ingredients are added. After several generations of pancakes, with each generation exposed to ever-changing “environments,” students understand the fundamental concepts associated with the theory of natural selection.

scholarly journals Ignoring individual differences in times of assessment in growth curve modeling

2015 ◽  
Vol 40 (1) ◽  
pp. 76-86 ◽  
Author(s):  
Patrick Coulombe ◽  
James P. Selig ◽  
Harold D. Delaney
Keyword(s):  
Individual Differences ◽  
Growth Curve ◽  
Type I Error ◽  
Growth Curve Modeling ◽  
Type I ◽  
Residual Variance ◽  
Change Over Time ◽  
Time Points ◽  
Curve Modeling ◽  
Over Time

Researchers often collect longitudinal data to model change over time in a phenomenon of interest. Inevitably, there will be some variation across individuals in specific time intervals between assessments. In this simulation study of growth curve modeling, we investigate how ignoring individual differences in time points when modeling change over time relates to convergence and admissibility of solutions, bias in estimates of parameters, efficiency, power to detect change over time, and Type I error rate. We manipulated magnitude of the individual differences in assessment times, distribution of assessment times, magnitude of change over time, number of time points, and sample size. In contrast to the correct analysis, ignoring individual differences in time points frequently led to inadmissible solutions, especially with few time points and small samples, regardless of the specific magnitude of individual differences that were ignored. Mean intercept and slope were generally estimated without bias. Ignoring individual differences in time points sometimes yielded overestimated intercept and slope variances and underestimated intercept–slope covariance and residual variance. Parameter efficiency as well as power and Type I error rates for the linear slope were unaffected by the type of analysis.

scholarly journals Erratum to: Analgesic use in a Norwegian general population: change over time and high-risk use - The Tromsø Study

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Per-Jostein Samuelsen ◽  
Lars Slørdal ◽  
Ulla Dorte Mathisen ◽  
Anne Elise Eggen
Keyword(s):  
High Risk ◽  
General Population ◽  
Population Change ◽  
Change Over Time ◽  
Analgesic Use ◽  
Over Time

scholarly journals Independent Prognostic Value of MRproANP (Midregional Proatrial Natriuretic Peptide) Levels in Patients With Stroke Is Unaltered Over Time

Stroke ◽  
2020 ◽  
Vol 51 (6) ◽  
pp. 1873-1875
Author(s):  
Markus Arnold ◽  
Christos Nakas ◽  
Andreas Luft ◽  
Mirjam Christ-Crain ◽  
Alexander Leichtle ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Natriuretic Peptide ◽  
Odds Ratio ◽  
Prognostic Value ◽  
Cardioembolic Stroke ◽  
Multiple Time ◽  
Change Over Time ◽  
Time Points ◽  
Over Time

Background and Purpose— MRproANP (midregional proatrial natriuretic peptide) is known to be independently associated with cardioembolic stroke cause and to improve risk stratification for 90-day mortality when measured within 24 to 72 hours after symptom onset in patients with acute ischemic stroke. However, the optimal time point for assessment remains unclear. This study aimed to evaluate prognostic utility of MRproANP at different time points during the first 5 days of hospitalization in patients with acute ischemic stroke. Methods— Samples of MRproANP were collected on admission (<72 hours after onset) and at multiple time points during the first 5 days of hospitalization in 348 consecutively enrolled patients with acute ischemic stroke. The prognostic value for 90-day mortality, 90-day functional outcome, and the association with cardioembolic stroke cause was assessed regarding the time of measurement, and change over time was modeled using generalized estimating equations. Results— MRproANP levels modestly decease over the initial 5 days but remain highly predictive for cardioembolic stroke cause (odds ratio, 9.75 [95% CI, 3.2–29]; 10.62 [95% CI, 3.4–33.3]; 10.8 [95% CI, 3.1–37.1]; 19.4 [95% CI, 5.49–68.7] on admission, day 1, 3 and 5) and 90-day mortality (odds ratio, 59.4 [95% CI, 7.4–480.7]; 78.3 [95% CI, 7.9–772.6]; 14.5 [95% CI, 1.4–145]; 19.81 [95% CI, 2.7–143.4] on admission, day 1, 3, and 5). Change over time does not significantly modify the prognostic value of MRproANP ( P =0.65 and P =0.56 for the interaction term in the multivariate model). Conclusions— Independent prognostic value of MRproANP remains unaltered in the acute phase of stroke at least up to 5 days; repeated measurements do not improve the prognostic value.

How is the COVID-19 pandemic affecting cooperation?

2020 ◽  
Author(s):  
Jessica Daphne Ayers ◽  
Diego Guevara Beltran ◽  
Joe Alcock ◽  
Cristina Baciu ◽  
Scott Claessens ◽  
...  
Keyword(s):  
Medical Condition ◽  
Likert Scale ◽  
Cooperation Agreement ◽  
Change Over Time ◽  
Time Points ◽  
Multiple Measures ◽  
Time Period ◽  
Willingness To Help ◽  
The Right ◽  
Over Time

Do crises bring people together or pull them apart? Here we examine how people’s willingness to help others and their perceived interdependence with others changed during the COVID-19 pandemic, and assess what factors are associated with any change. We collected data at 4 time points from the same cohort of 497 paid participants, starting on March 6th, before the pandemic was declared, through April 2. We found that perceived interdependence with neighbors and with humanity increased over time on multiple measures. However, regarding cooperation, agreement with the statement that helping someone in need “is the right thing to do” decreased over time (towards both a neighbor and a citizen of another country). Although the changes per time period were small for some of these effects, cumulatively they were non-trivial (ranging from a .33 to a .75 change on a 7 point likert scale). There was no change over time in participants' reported willingness to help somebody in their neighborhood (cooperation) or their feelings that when “All of humanity succeeds” they feel good (interdependence). We found reliable associations of change in cooperation and interdependence with sex, age, and pre-existing medical condition. We are collecting data on an ongoing basis which will allow us to investigate how these variables continue to change or not as the pandemic unfolds.

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