Deviation in the Age Structure of Mortality as an Indicator of COVID-19 Pandemic Severity

2022 ◽  
Vol 112 (1) ◽  
pp. 165-168
Author(s):  
Siddharth Chandra ◽  
Madhur Chandra
Keyword(s):  
Age Structure ◽  
Goodness Of Fit ◽  
Age Distribution ◽  
The United States ◽  
Age At Death ◽  
Fit Statistics ◽  
Kolmogorov Smirnov ◽  
Highly Correlated ◽  
Pandemic Severity ◽  
Death Data

Objectives. To test whether distortions in the age distribution of deaths can track pandemic activity. Methods. We compared weekly distributions of all-cause deaths by age during the COVID-19 pandemic in the United States from March to December 2020 with corresponding prepandemic weekly baseline distributions derived from data for 2015 to 2019. We measured distortions via Kolmogorov–Smirnov (K-S) and χ2 goodness-of-fit statistics as well as deaths among individuals aged 65 years or older as a percentage of total deaths (PERC65+). We computed bivariate correlations between these measures and the number of recorded COVID-19 deaths for the corresponding weeks. Results. Elevated COVID-19-associated fatalities were accompanied by greater distortions in the age structure of mortality. Distortions in the age distribution of weekly US COVID-19 deaths in 2020 relative to earlier years were highly correlated with COVID fatalities (K-S: r = 0.71, P < .001; χ2: r = 0.90, P < .001; PERC65+: r = 0.85, P < .001). Conclusions. A population-representative sample of age-at-death data can serve as a useful means of pandemic activity surveillance when precise cause-of-death data are incomplete, inaccurate, or unavailable, as is often the case in low-resource environments. (Am J Public Health. 2022;112(1):165–168. https://doi.org/10.2105/AJPH.2021.306567 )

scholarly journals Tracking Pandemic Severity Using Data on the Age Structure of Mortality: Lessons From the 1918 Influenza Pandemic in Michigan

2021 ◽  
Vol 111 (S2) ◽  
pp. S149-S155
Author(s):  
Siddharth Chandra ◽  
Julia Christensen
Keyword(s):  
Public Health ◽  
Age Structure ◽  
Goodness Of Fit ◽  
Influenza Pandemic ◽  
Age Distribution ◽  
Test Statistic ◽  
Goodness Of Fit Test ◽  
1918 Influenza ◽  
Death Data ◽  
Excess Deaths

Objectives. To test whether distortions in the age structure of mortality during the 1918 influenza pandemic in Michigan tracked the severity of the pandemic. Methods. We calculated monthly excess deaths during the period of 1918 to 1920 by using monthly data on all-cause deaths for the period of 1912 to 1920 in Michigan. Next, we measured distortions in the age distribution of deaths by using the Kuiper goodness-of-fit test statistic comparing the monthly distribution of deaths by age in 1918 to 1920 with the baseline distribution for the corresponding month for 1912 to 1917. Results. Monthly distortions in the age distribution of deaths were correlated with excess deaths for the period of 1918 to 1920 in Michigan (r = 0.83; P < .001). Conclusions. Distortions in the age distribution of deaths tracked variations in the severity of the 1918 influenza pandemic. Public Health Implications. It may be possible to track the severity of pandemic activity with age-at-death data by identifying distortions in the age distribution of deaths. Public health authorities should explore the application of this approach to tracking the COVID-19 pandemic in the absence of complete data coverage or accurate cause-of-death data.

scholarly journals Age, Sex, and Race Selection at Death from Leukemia and the Lymphomas

Blood ◽  
1953 ◽  
Vol 8 (8) ◽  
pp. 693-702 ◽  
Author(s):  
ALEXANDER G. GILLIAM
Keyword(s):  
United States ◽  
Causes Of Death ◽  
Age Distribution ◽  
Large City ◽  
The United States ◽  
Cooperative Study ◽  
State Data ◽  
Hospital Experience ◽  
Death Data

Abstract Attention has been called to the distinction between "age incidence", which is a measure of risk, and "age distribution" which is not such a measure except under certain unusual circumstances which probably do not exist for any hospital experience in the United States. Examples to illustrate this distinction were drawn from death data for deaths attributed to leukemia and the lymphomas in the United States in 1949. The sex and race selection have been recorded for the types of leukemia and lymphoma separable in the sixth revision of The International List of Causes of Death. The age selection at death attributed to the numerically important of these causes has also been presented. To determine the age, sex, and race selection (incidence) of these diseases, with full confidence in adequacy of their classification, will require a cooperative study designed to apply uniform diagnostic technics to all cases occurring in some definable population such as a large city or a state. Data derived from individual hospitals or from literature summations are generally inadequate for this purpose.

scholarly journals Polycythemia Vera: Age Relationships and Survival

Blood ◽  
1965 ◽  
Vol 26 (3) ◽  
pp. 243-256 ◽  
Author(s):  
EDWIN E. OSGOOD
Keyword(s):  
Normal Distribution ◽  
Polycythemia Vera ◽  
Age At Onset ◽  
Age Distribution ◽  
Previous Treatment ◽  
Age At Death ◽  
Survival Times ◽  
Relative Age ◽  
Highly Correlated ◽  
Log Normal

Abstract Study of our first 201 cases of polycythemia vera and of cases reported in the literature shows a remarkable age distribution for this disease. The age at onset, age at diagnosis, age at first treatment, and age at death, as well as survival times, each fits a normal distribution. In our cases, the median age at onset is 57 years with a standard deviation of 13 years, an entirely different distribution from that for the population of Oregon. This requires a logarithmic increase in relative age specific incidence from age 20 to 40, when there is little difference in the number of persons alive at each age, with a doubling of the proportion occurring in each 5-year interval every 7.5 years. After age 55, the proportion of new cases developing follows the slope of the number alive in the population. This means that the incidence remains almost constant after the peak age incidence is reached. Unfortunately, no data exist to transform these figures to absolute values, but if enough of the population could be studied to give absolute values at any one point, all other points could be determined. The implication is that polycythemia vera is due to a single cause which is highly correlated with age. The normal distribution of survival times means that polycythemia vera is not a malignant process since survival times in all malignancies studied fit a log normal distribution. The constant value of age at death means that age at first treatment is a most important prognostic factor. The mean age at death of patients with polycythemia vera, treated with P32, is 69 years ± 1, and treated without radiation therapy is 65 years ± 1. Apparently, if a patient lives to be treated with P32 previous treatment by other modalities or no treatment prior to that, will not affect the years to be gained by P32 treatment. However, the total survival time is highly correlated with age and the sooner after onset that the patient can be treated the more likely it is he will benefit from P32 therapy.

Predictive Model of Listeria monocytogenes Growth in Queso Fresco

2019 ◽  
Vol 82 (12) ◽  
pp. 2071-2079 ◽  
Author(s):  
MERLYN THOMAS ◽  
RATNESH TIWARI ◽  
ABHINAV MISHRA
Keyword(s):  
Listeria Monocytogenes ◽  
Goodness Of Fit ◽  
Storage Temperature ◽  
The United States ◽  
Temperature Profiles ◽  
Growth Data ◽  
Fit Statistics ◽  
Primary Model ◽  
Fresh Cheese ◽  
Specific Growth Rates

ABSTRACT Listeria monocytogenes is a hardy psychrotrophic pathogen that has been linked to several cheese-related outbreaks in the United States, including a recent outbreak in which a fresh cheese (queso fresco) was implicated. The purpose of this study was to develop primary, secondary, and tertiary predictive models for the growth of L. monocytogenes in queso fresco and to validate these models using nonisothermal time and temperature profiles. A mixture of five strains of L. monocytogenes was used to inoculate pasteurized whole milk to prepare queso fresco. Ten grams of each fresh cheese sample was vacuum packaged and stored at 4, 10, 15, 20, 25, and 30°C. From samples at each storage temperature, subsamples were removed at various times and diluted in 0.1% peptone water, and bacteria were enumerated on Listeria selective agar. Growth data from each temperature were fitted using the Baranyi model as the primary model and the Ratkowsky model as the secondary model. Models were then validated using nonisothermal conditions. The Baranyi model was fitted to the isothermal growth data with acceptable goodness of fit statistics (R2 = 0.928; root mean square error = 0.317). The Ratkowsky square root model was fitted to the specific growth rates at different temperatures (R2 = 0.975). The tertiary model developed from these models was validated using the growth data with two nonisothermal time and temperature profiles (4 to 20°C for 19 days and 15 to 30°C for 11 days). Data for these two profiles were compared with the model prediction using an acceptable prediction zone analysis; &gt;70% of the growth observations were within the acceptable prediction zone (between −1.0 and 0.5 log CFU/g). The model developed in this study will be useful for estimating the growth of L. monocytogenes in queso fresco. These predictions will help in estimation of the risk of listeriosis from queso fresco under extended storage and temperature abuse conditions. HIGHLIGHTS

scholarly journals Mathematical pattern of Kessler psychological distress distribution in the general population of the U.S. and Japan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shinichiro Tomitaka ◽  
Toshiaki A. Furukawa
Keyword(s):  
United States ◽  
General Population ◽  
National Survey ◽  
Goodness Of Fit ◽  
Quadratic Function ◽  
The United States ◽  
Depression Screening ◽  
Distribution Model ◽  
Epidemiological Surveys ◽  
Screening Scale

Abstract Background Although the 6-item Kessler psychological scale (K6) is a useful depression screening scale in clinical settings and epidemiological surveys, little is known about the distribution model of the K6 score in the general population. Using four major national survey datasets from the United States and Japan, we explored the mathematical pattern of the K6 distributions in the general population. Methods We analyzed four datasets from the National Health Interview Survey, the National Survey on Drug Use and Health, and the Behavioral Risk Factor Surveillance System in the United States, and the Comprehensive Survey of Living Conditions in Japan. We compared the goodness of fit between three models: exponential, power law, and quadratic function models. Graphical and regression analyses were employed to investigate the mathematical patterns of the K6 distributions. Results The exponential function had the best fit among the three models. The K6 distributions exhibited an exponential pattern, except for the lower end of the distribution across the four surveys. The rate parameter of the K6 distributions was similar across all surveys. Conclusions Our results suggest that, regardless of different sample populations and methodologies, the K6 scores exhibit a common mathematical distribution in the general population. Our findings will contribute to the development of the distribution model for such a depression screening scale.

scholarly journals Using Ultrasound and Inflammation to Improve Prediction of Ischemic Stroke: A Secondary Analysis of the Multi-Ethnic Study of Atherosclerosis

2021 ◽  
pp. 37-43
Author(s):  
Hediyeh Baradaran ◽  
Alen Delic ◽  
Ka-Ho Wong ◽  
Nazanin Sheibani ◽  
Matthew Alexander ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Goodness Of Fit ◽  
Primary Outcome ◽  
Stroke Risk ◽  
Secondary Analysis ◽  
Predictor Variables ◽  
Baseline Model ◽  
Fit Statistics ◽  
Ischemic Stroke Risk

Introduction: Current ischemic stroke risk prediction is primarily based on clinical factors, rather than imaging or laboratory markers. We examined the relationship between baseline ultrasound and inflammation measurements and subsequent primary ischemic stroke risk. Methods: In this secondary analysis of the Multi-Ethnic Study of Atherosclerosis (MESA), the primary outcome is the incident ischemic stroke during follow-up. The predictor variables are 9 carotid ultrasound-derived measurements and 6 serum inflammation measurements from the baseline study visit. We fit Cox regression models to the outcome of ischemic stroke. The baseline model included patient age, hypertension, diabetes, total cholesterol, smoking, and systolic blood pressure. Goodness-of-fit statistics were assessed to compare the baseline model to a model with ultrasound and inflammation predictor variables that remained significant when added to the baseline model. Results: We included 5,918 participants. The primary outcome of ischemic stroke was seen in 105 patients with a mean follow-up time of 7.7 years. In the Cox models, we found that carotid distensibility (CD), carotid stenosis (CS), and serum interleukin-6 (IL-6) were associated with incident stroke. Adding tertiles of CD, IL-6, and categories of CS to a baseline model that included traditional clinical vascular risk factors resulted in a better model fit than traditional risk factors alone as indicated by goodness-of-fit statistics. Conclusions: In a multiethnic cohort of patients without cerebrovascular disease at baseline, we found that CD, CS, and IL-6 helped predict the occurrence of primary ischemic stroke. Future research could evaluate if these basic ultrasound and serum measurements have implications for primary prevention efforts or clinical trial inclusion criteria.

scholarly journals A Hypothesis Test for the Goodness-of-Fit of the Marginal Distribution of a Time Series with Application to Stablecoin Data

2021 ◽  
Vol 5 (1) ◽  
pp. 10
Author(s):  
Mark Levene
Keyword(s):  
Time Series ◽  
Goodness Of Fit ◽  
Marginal Distribution ◽  
Hypothesis Test ◽  
Data Sets ◽  
Test Statistic ◽  
Sample Test ◽  
Kolmogorov Smirnov ◽  
Heavy Tailed ◽  
Jensen Shannon Divergence

A bootstrap-based hypothesis test of the goodness-of-fit for the marginal distribution of a time series is presented. Two metrics, the empirical survival Jensen–Shannon divergence (ESJS) and the Kolmogorov–Smirnov two-sample test statistic (KS2), are compared on four data sets—three stablecoin time series and a Bitcoin time series. We demonstrate that, after applying first-order differencing, all the data sets fit heavy-tailed α-stable distributions with 1<α<2 at the 95% confidence level. Moreover, ESJS is more powerful than KS2 on these data sets, since the widths of the derived confidence intervals for KS2 are, proportionately, much larger than those of ESJS.

scholarly journals 55 Malignant primary brain and other central nervous system tumours diagnosed in the Canadian population from 2009 to 2013

2018 ◽  
Vol 45 (S3) ◽  
pp. S16-S17
Author(s):  
EV Walker ◽  
F Davis ◽  
Keyword(s):  
United States ◽  
Central Nervous System ◽  
Nervous System ◽  
Brain Tumour ◽  
Age Distribution ◽  
Paediatric Population ◽  
The United States ◽  
Incidence Rates ◽  
Canadian Population ◽  
Cns Tumours

The Canadian Brain Tumour Registry (CBTR) project was established in 2016 with the aim of enhancing infrastructure for surveillance and clinical research to improve health outcomes for brain tumour patients in Canada. We present a national surveillance report on malignant primary brain and central nervous system (CNS) tumours diagnosed in the Canadian population from 2009-2013. Patients were identified through the Canadian Cancer Registry (CCR); an administrative dataset that includes cancer incidence data from all provinces/territories in Canada. Cancer diagnoses are coded using the ICD-O3 system. Tumour types were classified by site and histology using The Central Brain Tumour Registry of the United States definitions. Incidence rates (IR) and 95% confidence intervals (CI) were calculated per 100,000 person-years and standardized to the 2011 census population age-distribution. Overall, 12,115 malignant brain and CNS tumours were diagnosed in the Canadian population from 2009-2013 (IR:8.43;95%CI:8.28,8.58). Of these, 6,845 were diagnosed in males (IR:9.72;95%CI:9.49,9.95) and 5,270 in females (IR:7.20;95%CI:7.00,7.39). The most common histology overall was glioblastoma (IR:4.06;95%CI:3.95,4.16). Among those aged 0-19 years, 1,130 malignant brain and CNS tumours were diagnosed from 2009-2013 (IR:3.36;95%CI:3.16,3.56). Of these, 625 were diagnosed in males (IR:3.32;95%CI:3.34,3.92) and 505 in females (IR:3.08;95%CI:2.81,3.36). The most common histology among the paediatric population was pilocytic astrocytoma (IR:0.73;95%CI:0.64,0.83). The presentation will include: IRs for other histologies, the geographic distribution of cases and a comparison between Canada and the United States.

scholarly journals A SMOOTH TEST FOR THE EQUALITY OF DISTRIBUTIONS

2012 ◽  
Vol 29 (2) ◽  
pp. 419-446 ◽  
Author(s):  
Anil K. Bera ◽  
Aurobindo Ghosh ◽  
Zhijie Xiao
Keyword(s):  
New York ◽  
Goodness Of Fit ◽  
Age Distribution ◽  
Distribution Functions ◽  
Type I ◽  
Finite Sample ◽  
Community Rating ◽  
Smooth Test ◽  
Two Samples ◽  
Group Insurance

The two-sample version of the celebrated Pearson goodness-of-fit problem has been a topic of extensive research, and several tests like the Kolmogorov-Smirnov and Cramér-von Mises have been suggested. Although these tests perform fairly well as omnibus tests for comparing two probability density functions (PDFs), they may have poor power against specific departures such as in location, scale, skewness, and kurtosis. We propose a new test for the equality of two PDFs based on a modified version of the Neyman smooth test using empirical distribution functions minimizing size distortion in finite samples. The suggested test can detect the specific directions of departure from the null hypothesis. Specifically, it can identify deviations in the directions of mean, variance, skewness, or tail behavior. In a finite sample, the actual probability of type-I error depends on the relative sizes of the two samples. We propose two different approaches to deal with this problem and show that, under appropriate conditions, the proposed tests are asymptotically distributed as chi-squared. We also study the finite sample size and power properties of our proposed test. As an application of our procedure, we compare the age distributions of employees with small employers in New York and Pennsylvania with group insurance before and after the enactment of the “community rating” legislation in New York. It has been conventional wisdom that if community rating is enforced (where the group health insurance premium does not depend on age or any other physical characteristics of the insured), then the insurance market will collapse, since only older or less healthy patients would prefer group insurance. We find that there are significant changes in the age distribution in the population in New York owing mainly to a shift in location and scale.

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