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Prevalence, Mortality, and Risk Factors
Published in Jahangir Moini, Matthew Adams, Anthony LoGalbo, Complications of Diabetes Mellitus, 2022
Jahangir Moini, Matthew Adams, Anthony LoGalbo
Cumulative incidence is the number of people in a candidate population that develops a disease over a specific amount of time. This is expressed as the number of new cases, divided by the amount in the candidate population, over a certain time period. The possible value of cumulative incidence ranges between 0 and 1. If it is thought of as a percentage, the cumulative incidence ranges between 0% and 100%. For example, if there were 3,055 new cases of diabetes in a candidate population of 28,000 people over a study period of 1 year, the cumulative incidence would be 10.9% for that time period. Another way to understand this is that cumulative incidence illustrates the average risk of developing a disease over a certain period of time. A risk is the likelihood of the disease being developed. The lifetime risk of diabetes mellitus is about 40% of the global population. Generally, cumulative incidence is higher over a lifetime or many years than for only a few years.
Criticism of Incidence Rates
Published in Peter Cummings, Analysis of Incidence Rates, 2019
Kraemer noted that the risk ratio and the odds ratio are equal to 1 at time 15.0 when the two risk curves cross each other. At that point the cumulative incidence of death is the same in both groups. But since the incidence rate ratio is 1 at time 17.8, she declared that the incidence rate ratio “is uninterpretable.” But this ratio has a ready interpretation; the rate ratio reflects the fact that events come more quickly in Group 1 and at 17.8 time units the ratio of cumulated events to cumulated event-free person-time is equal in both groups. The speed with which events occurred is used in the calculation of the rate ratio. The risk and odds ratios, however, compare the proportion that survive at each time, without any concern that the events came more quickly in one group compared with the other. The reasons for differences in cumulative incidence and incidence rate were discussed in Chapter 2 in relation to Figures 2.4 and 2.5.
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Published in Filomena Pereira-Maxwell, Medical Statistics, 2018
An alternative term for risk or incidence risk. The cumulative incidence is the proportion of individuals who have developed the outcome of interest by the end of the follow-up period for a research study, in reference to the number at risk at the start of the study. This is in contrast with the incidence rate, which also measures incidence, but in reference to the total person-time at risk. The cumulative incidence tends to increase in magnitude the longer the follow-up period, as more cases are likely to occur in reference to the same initial number at risk. On the other hand, the higher the rate of occurrence of the outcome in question (assuming it remains constant over time), the fewer cases are produced as time goes on, as the pool of individuals at risk will decrease concomitantly. See also attack rate.
Glaucoma is probably not useful as a red flag for amyloidosis
Published in Scandinavian Cardiovascular Journal, 2023
Oscar Westin, Jawad H. Butt, Steffen Heegaard, Emil L. Fosbøl, Finn Gustafsson
Baseline characteristics for study subjects with and without glaucoma were reported as medians and interquartile ranges (IQR) for continuous variables and as percentages for categorical variables. Chi-squared test and Wilcoxon-test were used to assess differences, as appropriate. Cumulative incidence functions were used to compare incidences of outcomes, incorporating competing risk of death. Hazard ratios were calculated using crude and adjusted Cox proportional hazard models. The Cox models were adjusted for sex, age group, hypertension, ischemic heart disease, heart failure, atrial fibrillation, diabetes mellitus, chronic obstructive lung disease (COPD) and chronic renal failure. The proportional hazards assumption was examined graphically using log(-log(survival function)) vs. time plots for the exposure variable, and found valid. Age did not meet linearity assumptions and was categorized. Statistical analyses were made using SAS statistical software (version 9.4, Cary, NC, USA). A two-sided p-value below .05 was considered statistically significant.
Prevalence and incidence of hearing impairment among adults: a 13-year follow-up study
Published in International Journal of Audiology, 2021
Venla Lohi, Pasi Ohtonen, Martti Sorri, Elina Mäki-Torkko, Samuli Hannula
The average air conduction thresholds (pure tone average, PTA0.5-4 kHz) at frequencies of 0.5, 1, 2 and 4 kHz for right and left ears were used to define HI. Better ear hearing level (BEHL) was applied for bilateral HI and worse ear hearing level (WEHL, dB) to include unilateral HI. As recommended by the European expert group (Stephens 1996; Stevens et al. 2013) and recently by the Global burden of disease expert group on hearing loss (Humes 2019; Olusanya, Davis, and Hoffman 2019), HI is defined as PTA0.5-4 kHz ≥ 20 dB in the better ear for bilateral HI (BEHL0.5-4 kHz ≥ 20, hereafter “the EU definition”). Another definition is that of WHO, PTA0.5-4 kHz ≥ 26 in the better ear (WHO 1991), which has been used in many previous studies (BEHL0.5-4 kHz ≥ 26, hereafter “the WHO definition”). To enable comparison with previous studies, we calculated the prevalence and incidence of HI using both the EU and the WHO definitions. Furthermore, PTA0.5-4 kHz ≥ 35 dB was applied as a definition for moderate or worse hearing impairment (Olusanya et al. 2019; Stevens et al. 2013). Cumulative incidence was calculated as the number of new cases of HI during the 13-year follow-up time divided by the total number of individuals in the study population at risk. To estimate the annual incidence, we calculated the sum of the follow-up years for subjects who did not have HI at the baseline examination, and the incidence rate figures are reported as cases per 1000 person years.
Sex differences in incidence rate, and temporal changes in surgical management and adverse events after hip fracture surgery in Denmark 1997–2017: a register-based study of 153,058 hip fracture patients
Published in Acta Orthopaedica, 2021
Liv R Wahlsten, Henrik Palm, Gunnar H Gislason, Stig Brorson
Differences in the distribution of comorbidity and living setting at baseline, between males and females, were calculated using Student’s t-test for continuous variables and a chi-square test for categorical variables. Age and sex stratified incidence rates were calculated in a time-updated model as number of first ever hip fractures per 1,000 person-years in that age and sex category, where the number of person years and new events, in each category, were updated each month. Cumulative incidence functions were applied when time to event and absolute crude risks were of interest, i.e., mortality and readmissions. For readmissions, the Aalen–Johansen estimator was used in amendment of the cumulative incidence function to calculate competing risk of death. 95% confidence intervals (CI) were calculated for all estimated parameters. All analyses were performed with SAS statistical software version 9.4 (SAS Institute Inc, Cary, NC, USA) and R-studio version 3.2.2 (2016-10-31) (R Foundation for Statistical Computing, Vienna, Austria).