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Nephron. Clinical Practice 2010To describe how often a disease or another health event occurs in a population, different measures of disease frequency can be used. The prevalence reflects the number...
To describe how often a disease or another health event occurs in a population, different measures of disease frequency can be used. The prevalence reflects the number of existing cases of a disease. In contrast to the prevalence, the incidence reflects the number of new cases of disease and can be reported as a risk or as an incidence rate. Prevalence and incidence are used for different purposes and to answer different research questions. In this article, we discuss the different measures of disease frequency and we explain when to apply which measure.
Topics: Data Interpretation, Statistical; Epidemiologic Methods; Humans; Incidence; Prevalence; Terminology as Topic
PubMed: 20173345
DOI: 10.1159/000286345 -
International Journal of Nursing Studies Jan 2022The aim of this systematic review and meta-analysis was to investigate the pooled prevalence of cognitive frailty among community-dwelling older adults and provide... (Meta-Analysis)
Meta-Analysis Review
OBJECTIVE
The aim of this systematic review and meta-analysis was to investigate the pooled prevalence of cognitive frailty among community-dwelling older adults and provide evidence-based support for policy-makers planning health and social care policies.
DESIGN
A systematic review and meta-analysis.
METHODS
PubMed, Web of Science, Embase and the Cochrane Library were systematically searched from their inception to December 10, 2020. Descriptive studies (cross-sectional studies or population-based longitudinal studies) and cohort studies were available. Participants were community-dwelling older adults aged 60 years and above. Two researchers independently screened the literature, extracted the data and evaluated the quality of the included studies. All statistical analyses were conducted using Stata 15.0.
RESULTS
We screened 2815 records, among which 24 studies met the inclusion criteria and were included in the review. The pooled prevalence of cognitive frailty was 9% (95% CI: 8%-11%, I = 99.3%). The results of the subgroup analysis showed that the pooled prevalence of cognitive frailty was 11% (95% CI: 9%-14%) in men and 15% (95% CI: 11%-19%) in women. The pooled prevalence of cognitive frailty based on the descriptive studies and cohort studies was 7% (95% CI: 5%-9%) and 17% (95% CI: 11%-22%), respectively. The pooled estimates of cognitive frailty prevalence were 6% (95% CI: 4%-8%) from 2012 to 2017 and 11% (95% CI: 9%-14%) from 2018 to 2020.
CONCLUSIONS
This systematic review analyzed the available literature and revealed that the pooled prevalence of cognitive frailty among community-dwelling older adults was 9%. The stratified analysis showed that the prevalence of cognitive frailty was higher in older women. In addition, the prevalence has increased in recent years, which has important implications for adapting health and social care systems.
Topics: Aged; Cognition; Cross-Sectional Studies; Female; Frail Elderly; Frailty; Humans; Independent Living; Male; Prevalence
PubMed: 34758429
DOI: 10.1016/j.ijnurstu.2021.104112 -
BMJ Open Mar 2022To assess the prevalence, risk factors and psychological impact of infertility among females. This review summarises the available evidence, effect estimates and... (Meta-Analysis)
Meta-Analysis
OBJECTIVES
To assess the prevalence, risk factors and psychological impact of infertility among females. This review summarises the available evidence, effect estimates and strength of statistical associations between infertility and its risk factors.
STUDY DESIGN
Systematic review and meta-analysis.
DATA SOURCES
MEDLINE, CINAHL and ScienceDirect were searched through 23 January 2022.
ELIGIBILITY CRITERIA
The inclusion criteria involved studies that reported the psychological impact of infertility among women. We included cross-sectional, case-control and cohort designs, published in the English language, conducted in the community, and performed at health institution levels on prevalence, risk factors and psychological impact of infertility in women.
DATA EXTRACTION AND SYNTHESIS
Two reviewers independently extracted and assess the quality of data using the Joanna Briggs Institute Meta-Analysis. The outcomes were assessed with random-effects model and reported as the OR with 95% CI using the Review Manager software.
RESULTS
Thirty-two studies with low risk of bias involving 124 556 women were included. The findings indicated the overall pooled prevalence to be 46.25% and 51.5% for infertility and primary infertility, respectively. Smoking was significantly related to infertility, with the OR of 1.85 (95% CI 1.08 to 3.14) times higher than females who do not smoke. There was a statistical significance between infertility and psychological distress among females, with the OR of 1.63 (95% CI 1.24 to 2.13). A statistical significance was noted between depression and infertility among females, with the OR of 1.40 (95% CI 1.11 to 1.75) compared with those fertile.
CONCLUSIONS
The study results highlight an essential and increasing mental disorder among females associated with infertility and may be overlooked. Acknowledging the problem and providing positive, supportive measures to females with infertility ensure more positive outcomes during the therapeutic process. This review is limited by the differences in definitions, diagnostic cut points, study designs and source populations.
PROSPERO REGISTRATION NUMBER
CRD42021226414.
Topics: Cross-Sectional Studies; Female; Health Status; Humans; Infertility; Prevalence; Risk Factors
PubMed: 35354629
DOI: 10.1136/bmjopen-2021-057132 -
Genetic Epidemiology Mar 2021Estimating the prevalence of rare germline genetic mutations in the general population is of interest as it can inform genetic counseling and risk management. Most... (Meta-Analysis)
Meta-Analysis
Estimating the prevalence of rare germline genetic mutations in the general population is of interest as it can inform genetic counseling and risk management. Most studies that estimate the prevalence of mutations are performed in high-risk populations, and each study is designed with differing inclusion criteria, resulting in ascertained populations. Quantifying the effects of ascertainment is necessary to estimate the prevalence in the general population. This quantification is difficult as the inclusion criteria is often based on disease status and/or family history. Combining estimates from multiple studies through a meta-analysis is challenging due to the variety of study designs and ascertainment mechanisms as well as the complexity of quantifying the effect of these mechanisms. We provide guidelines on how to quantify the ascertainment mechanism for a wide range of settings and propose a general approach for conducting a meta-analysis in these complex settings by incorporating study-specific ascertainment mechanisms into a joint likelihood function. We implement the proposed likelihood-based approach using both frequentist and Bayesian methodologies. We evaluate these approaches in simulations and show that the methods are robust and produce unbiased estimates of the prevalence. An advantage of the Bayesian approach is that it can easily incorporate uncertainty in ascertainment probability values. We apply our methods to estimate the prevalence of PALB2 mutations in the United States by combining data from multiple studies and obtain a prevalence estimate of around 0.02%.
Topics: Bayes Theorem; Humans; Likelihood Functions; Models, Genetic; Mutation; Prevalence
PubMed: 33000511
DOI: 10.1002/gepi.22364 -
Medicina (Kaunas, Lithuania) May 2021Sensitivity, which denotes the proportion of subjects correctly given a positive assignment out of all subjects who are actually positive for the outcome, indicates how... (Review)
Review
Sensitivity, which denotes the proportion of subjects correctly given a positive assignment out of all subjects who are actually positive for the outcome, indicates how well a test can classify subjects who truly have the outcome of interest. Specificity, which denotes the proportion of subjects correctly given a negative assignment out of all subjects who are actually negative for the outcome, indicates how well a test can classify subjects who truly do not have the outcome of interest. Positive predictive value reflects the proportion of subjects with a positive test result who truly have the outcome of interest. Negative predictive value reflects the proportion of subjects with a negative test result who truly do not have the outcome of interest. Sensitivity and specificity are inversely related, wherein one increases as the other decreases, but are generally considered stable for a given test, whereas positive and negative predictive values do inherently vary with pre-test probability (e.g., changes in population disease prevalence). This article will further detail the concepts of sensitivity, specificity, and predictive values using a recent real-world example from the medical literature.
Topics: Biomedical Research; Humans; Predictive Value of Tests; Prevalence; Sensitivity and Specificity
PubMed: 34065637
DOI: 10.3390/medicina57050503 -
PloS One 2019Research in applied ecology provides scientific evidence to guide conservation policy and management. Applied ecology is becoming increasingly quantitative and model... (Review)
Review
Research in applied ecology provides scientific evidence to guide conservation policy and management. Applied ecology is becoming increasingly quantitative and model selection via information criteria has become a common statistical modeling approach. Unfortunately, parameters that contain little to no useful information are commonly presented and interpreted as important in applied ecology. I review the concept of an uninformative parameter in model selection using information criteria and perform a literature review to measure the prevalence of uninformative parameters in model selection studies applying Akaike's Information Criterion (AIC) in 2014 in four of the top journals in applied ecology (Biological Conservation, Conservation Biology, Ecological Applications, Journal of Applied Ecology). Twenty-one percent of studies I reviewed applied AIC metrics. Many (31.5%) of the studies applying AIC metrics in the four applied ecology journals I reviewed had or were very likely to have uninformative parameters in a model set. In addition, more than 40% of studies reviewed had insufficient information to assess the presence or absence of uninformative parameters in a model set. Given the prevalence of studies likely to have uninformative parameters or with insufficient information to assess parameter status (71.5%), I surmise that much of the policy recommendations based on applied ecology research may not be supported by the data analysis. I provide four warning signals and a decision tree to assist authors, reviewers, and editors to screen for uninformative parameters in studies applying model selection with information criteria. In the end, careful thinking at every step of the scientific process and greater reporting standards are required to detect uninformative parameters in studies adopting an information criteria approach.
Topics: Conservation of Natural Resources; Ecology; Models, Statistical; Prevalence
PubMed: 30730890
DOI: 10.1371/journal.pone.0206711 -
The Psychiatric Clinics of North America Dec 2018Several studies of the prevalence of borderline personality disorder in community and clinical settings have been carried out to date. Although results vary according to... (Review)
Review
Several studies of the prevalence of borderline personality disorder in community and clinical settings have been carried out to date. Although results vary according to sampling method and assessment method, median point prevalence is roughly 1%, with higher or lower rates in certain community subpopulations. In clinical settings, the prevalence is around 10% to 12% in outpatient psychiatric clinics and 20% to 22% among inpatient clinics. Further research is needed to identify the prevalence and correlates of borderline personality disorder in other clinical settings (eg, primary care) and to investigate the impact of demographic variables on borderline personality disorder prevalence.
Topics: Adult; Borderline Personality Disorder; Diagnostic and Statistical Manual of Mental Disorders; Global Health; Humans; Prevalence; Primary Health Care; United States
PubMed: 30447724
DOI: 10.1016/j.psc.2018.07.008 -
Clinical and Investigative Medicine.... Jun 2022Disease prevalence estimates from population-based administrative databases are often biased due to measurement (misclassification) errors. The purpose of this article... (Review)
Review
PURPOSE
Disease prevalence estimates from population-based administrative databases are often biased due to measurement (misclassification) errors. The purpose of this article is to review the methodology for estimating disease prevalence in administrative data, with a focus on bias correction.
SOURCE
Several approaches to bias correction in administrative data were reviewed and application of these methods was demonstrated using an example from the literature: physician claims and hospitalization data were employed to estimate diabetes prevalence in Ontario, Canada.
FINDINGS
Misclassification bias in prevalence estimates from administrative data can be reduced by developing and selecting an optimal algorithm for case identification, applying a bias correction formula, or using statistical modelling. An algorithm for which sensitivity equals positive predictive value provides an unbiased estimate of prevalence. Bias reduction methods generally require information about the measurement properties of the algorithm, such as sensitivity, specificity, or predictive value. These properties depend on disease type, prevalence, algorithm definition (including the observation window), and may vary by population and time. Prevalence estimates can be improved by applying multivariable disease prediction models.
CONCLUSION
Frequency of a positive case identification algorithm in administrative data is generally not equivalent to disease prevalence. Although prevalence estimates can be corrected for bias using known measurement properties of the algorithm, these properties may be difficult to estimate accurately; therefore, disease prevalence estimates based on administrative data must be treated with caution.
Topics: Databases, Factual; Diabetes Mellitus; Hospitalization; Humans; Ontario; Predictive Value of Tests; Prevalence
PubMed: 35752980
DOI: 10.25011/cim.v45i2.38100 -
Primary Care Mar 2023Adjustment disorder is a disorder characterized by an extreme emotional reaction to a stressor. It is defined diagnostically with either the Diagnostic and Statistical... (Review)
Review
Adjustment disorder is a disorder characterized by an extreme emotional reaction to a stressor. It is defined diagnostically with either the Diagnostic and Statistical Manual V or ICD-11 definitions. There is currently a diagnostic tool that is still being validated to assist with diagnosing adjustment disorder. The prevalence of this disorder ranges from 0.2% to 40%, depending on the stressful circumstances that the patient experiences. There are several treatments available for adjustment disorder, ranging from psychological interventions, natural therapies to pharmacotherapies.
Topics: Humans; Adjustment Disorders; Diagnostic and Statistical Manual of Mental Disorders; International Classification of Diseases; Prevalence; Primary Health Care
PubMed: 36822730
DOI: 10.1016/j.pop.2022.10.006 -
Diabetes Research and Clinical Practice Dec 2011Diabetes is an increasingly important condition globally and robust estimates of its prevalence are required for allocating resources.
INTRODUCTION
Diabetes is an increasingly important condition globally and robust estimates of its prevalence are required for allocating resources.
METHODS
Data sources from 1980 to April 2011 were sought and characterised. The Analytic Hierarchy Process (AHP) was used to select the most appropriate study or studies for each country, and estimates for countries without data were modelled. A logistic regression model was used to generate smoothed age-specific estimates which were applied to UN population estimates for 2011.
RESULTS
A total of 565 data sources were reviewed, of which 170 sources from 110 countries were selected. In 2011 there are 366 million people with diabetes, and this is expected to rise to 552 million by 2030. Most people with diabetes live in low- and middle-income countries, and these countries will also see the greatest increase over the next 19 years.
DISCUSSION
This paper builds on previous IDF estimates and shows that the global diabetes epidemic continues to grow. Recent studies show that previous estimates have been very conservative. The new IDF estimates use a simple and transparent approach and are consistent with recent estimates from the Global Burden of Disease study. IDF estimates will be updated annually.
Topics: Diabetes Mellitus; Global Health; Humans; Models, Statistical; Prevalence; Time Factors; World Health Organization
PubMed: 22079683
DOI: 10.1016/j.diabres.2011.10.029