A general method for estimating the prevalence of influenza-like-symptoms with Wikipedia data.
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ABSTRACT:
SUBMITTER: De Toni G
PROVIDER: S-EPMC8407583 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesTo estimate the prevalence of common chronic conditions and multimorbidity among patients at GP encounters and among people in the Australian population. To assess the extent to which use of each individual patient's GP attendance over the previous year, instead of the average for their age-sex group, affects the precision of national population prevalence estimates of diagnosed chronic conditions.Design, setting and participantsA sub-study (between November 2012 and March 2016) of the Bettering the Evaluation and Care of Health program, a continuous national study of GP activity. Each of 1,449 GPs provided data for about 30 consecutive patients (total 43,501) indicating for each, number of GP attendances in previous year and all diagnosed chronic conditions, using their knowledge of the patient, patient self-report, and patient's health record.ResultsHypertension (26.5%) was the most prevalent diagnosed chronic condition among patients surveyed, followed by osteoarthritis (22.7%), hyperlipidaemia (16.6%), depression (16.3%), anxiety (11.9%), gastroesophageal reflux disease (GORD) (11.3%), chronic back pain (9.7%) and Type 2 diabetes (9.6%). After adjustment, we estimated population prevalence of hypertension as 12.4%, 9.5% osteoarthritis, 8.2% hyperlipidaemia, 8.0% depression, 5.8% anxiety and 5.2% asthma. Estimates were significantly lower than those derived using the previous method. About half (51.6%) the patients at GP encounters had two or more diagnosed chronic conditions and over one third (37.4%) had three or more. Population estimates were: 25.7% had two or more diagnosed chronic conditions and 15.8% had three or more.ConclusionsOf the three approaches we have tested to date, this study provides the most accurate method for estimation of population prevalence of chronic conditions using the GP as an expert interviewer, by adjusting for each patient's reported attendance.
| S-EPMC5344344 | biostudies-literature
Project description:ObjectivesTo determine to what extent underlying data published as part of Quality and Outcomes Framework (QOF) can be used to estimate smoking prevalence within practice populations and local areas and to explore the usefulness of these estimates.DesignCross-sectional, observational study of QOF smoking data. Smoking prevalence in general practice populations and among patients with chronic conditions was estimated by simple manipulation of QOF indicator data. Agreement between estimates from the integrated household survey (IHS) and aggregated QOF-based estimates was calculated. The impact of including smoking estimates in negative binomial regression models of counts of premature coronary heart disease (CHD) deaths was assessed.SettingPrimary care in the East Midlands.ParticipantsAll general practices in the area of study were eligible for inclusion (230). 14 practices were excluded due to incomplete QOF data for the period of study (2006/2007-2012/2013). One practice was excluded as it served a restricted practice list.MeasurementsEstimates of smoking prevalence in general practice populations and among patients with chronic conditions.ResultsMedian smoking prevalence in the practice populations for 2012/2013 was 19.2% (range 5.8-43.0%). There was good agreement (mean difference: 0.39%; 95% limits of agreement (-3.77, 4.55)) between IHS estimates for local authority districts and aggregated QOF register estimates. Smoking prevalence estimates in those with chronic conditions were lower than for the general population (mean difference -3.05%), but strongly correlated (Rp=0.74, p<0.0001). An important positive association between premature CHD mortality and smoking prevalence was shown when smoking prevalence was added to other population and service characteristics.ConclusionsPublished QOF data allow useful estimation of smoking prevalence within practice populations and in those with chronic conditions; the latter estimates may sometimes be useful in place of the former. It may also provide useful estimates of smoking prevalence in local areas by aggregating practice based data.
| S-EPMC4120299 | biostudies-literature
Project description:Anal disorders are largely underestimated in general practice. Studies have shown patients conceal anal symptoms leading to late diagnosis and treatment. Management by general practitioners is poorly described. The aim of this study is to assess the prevalence of anal symptoms and their management in general practice.In this prospective, observational, national study set in France, all adult patients consulting their general practitioner during 2 days of consultation were included. Anal symptoms, whether spontaneously revealed or not, were systematically collected and assessed. For symptomatic patients, the obstacles to anal examination were evaluated. The general practitioner's diagnosis was collected and a proctologist visit was systematically proposed in case of anal symptoms. If the proctologist was consulted, his or her diagnosis was collected.From October 2014 to April 2015, 1061 patients were included by 57 general practitioners. The prevalence of anal symptoms was 15.6% (95% CI: 14-18). However, 85% of these patients did not spontaneously share their symptoms with their doctors, despite a discomfort rating of 3 out of 10 (range 1-5). Although 65% of patients agreed to an anal examination, it was not proposed in 45% of cases with anal symptoms. Performing the examination was associated with a significantly higher diagnosis rate of 76% versus 20% (p < 0.001). Proctologist and general practitioner diagnoses were consistent in 14 out of 17 cases.Patients' concealed anal symptoms are significant in general practice despite the impact on quality of life. Anal examination is seldom done. Improved training of general practitioners is required to break the taboo.
| S-EPMC5543599 | biostudies-other
Project description:Modern phylodynamic methods interpret an inferred phylogenetic tree as a partial transmission chain providing information about the dynamic process of transmission and removal (where removal may be due to recovery, death, or behavior change). Birth-death and coalescent processes have been introduced to model the stochastic dynamics of epidemic spread under common epidemiological models such as the SIS and SIR models and are successfully used to infer phylogenetic trees together with transmission (birth) and removal (death) rates. These methods either integrate analytically over past incidence and prevalence to infer rate parameters, and thus cannot explicitly infer past incidence or prevalence, or allow such inference only in the coalescent limit of large population size. Here, we introduce a particle filtering framework to explicitly infer prevalence and incidence trajectories along with phylogenies and epidemiological model parameters from genomic sequences and case count data in a manner consistent with the underlying birth-death model. After demonstrating the accuracy of this method on simulated data, we use it to assess the prevalence through time of the early 2014 Ebola outbreak in Sierra Leone.
| S-EPMC6681632 | biostudies-literature
Project description:BackgroundUnderstanding patterns of chlamydia prevalence is important for addressing inequalities and planning cost-effective control programs. Population-based surveys are costly; the best data for England come from the Natsal national surveys, which are only available once per decade, and are nationally representative but not powered to compare prevalence in different localities. Prevalence estimates at finer spatial and temporal scales are required.MethodsWe present a method for estimating local prevalence by modeling the infection, testing, and treatment processes. Prior probability distributions for parameters describing natural history and treatment-seeking behavior are informed by the literature or calibrated using national prevalence estimates. By combining them with surveillance data on numbers of chlamydia tests and diagnoses, we obtain estimates of local screening rates, incidence, and prevalence. We illustrate the method by application to data from England.ResultsOur estimates of national prevalence by age group agree with the Natsal-3 survey. They could be improved by additional information on the number of diagnosed cases that were asymptomatic. There is substantial local-level variation in prevalence, with more infection in deprived areas. Incidence in each sex is strongly correlated with prevalence in the other. Importantly, we find that positivity (the proportion of tests which were positive) does not provide a reliable proxy for prevalence.ConclusionThis approach provides local chlamydia prevalence estimates from surveillance data, which could inform analyses to identify and understand local prevalence patterns and assess local programs. Estimates could be more accurate if surveillance systems recorded additional information, including on symptoms. See video abstract at, http://links.lww.com/EDE/B211.
| S-EPMC5457828 | biostudies-literature
Project description:The availability of weekly Web-based participatory surveillance data on self-reported influenza-like illness (ILI), defined here as self-reported fever and cough/sore throat, over several influenza seasons allows for estimation of the incidence of influenza infection in population cohorts. We demonstrate this using syndromic data reported through the Influenzanet surveillance platform in the Netherlands. We used the 2011-2012 influenza season, a low-incidence season that began late, to assess the baseline rates of self-reported ILI during periods of low influenza circulation, and we used ILI rates above that baseline level from the 2012-1013 season, a major influenza season, to estimate influenza attack rates for that period. The latter conversion required estimates of age-specific probabilities of self-reported ILI given influenza (Flu) infection (P(ILI | Flu)), which were obtained from separate data (extracted from Hong Kong, China, household studies). For the 2012-2013 influenza season in the Netherlands, we estimated combined influenza A/B attack rates of 29.2% (95% credible interval (CI): 21.6, 37.9) among survey participants aged 20-49 years, 28.3% (95% CI: 20.7, 36.8) among participants aged 50-60 years, and 5.9% (95% CI: 0.4, 11.8) among participants aged ?61 years. Estimates of influenza attack rates can be obtained in other settings using analogous, multiseason surveillance data on self-reported ILI together with separate, context-specific estimates of P(ILI | Flu).
| S-EPMC4036214 | biostudies-literature
Project description:BackgroundPassive measures collected using smartphones have been suggested to represent efficient proxies for depression severity, but the performance of such measures across diagnoses has not been studied.MethodsWe enrolled a cohort of 45 individuals (11 with major depressive disorder, 11 with bipolar disorder, 11 with schizophrenia or schizoaffective disorder, and 12 individuals with no axis I psychiatric disorder). During the 8-week study period, participants were evaluated with a rater-administered Montgomery-Åsberg Depression Rating Scale (MADRS) biweekly, completed self-report PHQ-8 measures weekly on their smartphone, and consented to collection of smartphone-based GPS and accelerometer data in order to learn about their behaviors. We utilized linear mixed models to predict depression severity on the basis of phone-based PHQ-8 and passive measures.ResultsAmong the 45 individuals, 38 (84%) completed the 8-week study. The average root-mean-squared error (RMSE) in predicting the MADRS score (scale 0-60) was 4.72 using passive data alone, 4.27 using self-report measures alone, and 4.30 using both.ConclusionsWhile passive measures did not improve MADRS score prediction in our cross-disorder study, they may capture behavioral phenotypes that cannot be measured objectively, granularly, or over long-term via self-report.
| S-EPMC8865149 | biostudies-literature
Project description:BackgroundDetecting the onset of influenza epidemic is important for epidemiological surveillance and for investigating the factors driving spatiotemporal transmission patterns. Most approaches define the epidemic onset based on thresholds, which use subjective criteria and are specific to individual surveillance systems.MethodsWe applied the empirical threshold method (ETM), together with two non-thresholding methods, including the maximum curvature method (MCM) that we proposed and the segmented regression method (SRM), to determine onsets of influenza epidemics in each prefecture of Japan, using sentinel surveillance data of influenza-like illness (ILI) from 2012/2013 through 2017/2018. Performance of the MCM and SRM was evaluated, in terms of epidemic onset, end, and duration, with those derived from the ETM using the nationwide epidemic onset indicator of 1.0 ILI case per sentinel per week.ResultsThe MCM and SRM yielded complete estimates for each of Japan's 47 prefectures. In contrast, ETM estimates for Kagoshima during 2012/2013 and for Okinawa during all six influenza seasons, except 2013/2014, were invalid. The MCM showed better agreement in all estimates with the ETM than the SRM (R2 = 0.82, p < 0.001 vs. R2 = 0.34, p < 0.001 for epidemic onset; R2 = 0.18, p < 0.001 vs. R2 = 0.05, p < 0.001 for epidemic end; R2 = 0.28, p < 0.001 vs. R2 < 0.01, p = 0.35 for epidemic duration). Prefecture-specific thresholds for epidemic onset and end were established using the MCM.ConclusionsThe Japanese national epidemic onset threshold is not applicable to all prefectures, particularly Okinawa. The MCM could be used to establish prefecture-specific epidemic thresholds that faithfully characterize influenza activity, serving as useful complements to the influenza surveillance system in Japan.
| S-EPMC6383251 | biostudies-literature
Project description:Background and aims:The symptom-based diagnostic criteria for irritable bowel syndrome (IBS) have recently been revised in the Rome IV consensus. On the other hand, with rising public awareness of IBS, self-diagnosis and self-management is also increasing. We compared the prevalence and impact of Rome IV-based IBS vs self-diagnosed IBS in the general population. Methods:An internet panel filled out an online survey on bowel symptoms and their impact on health care utilization and daily activities. Results:A representative internet panel of 1012 individuals completed the online survey. Bowel symptoms were present in 68.6% of the population. Of these, 21% consulted a physician for these symptoms in the last year and 42% earlier. Rome IV IBS criteria were fulfilled by 5.5%, and these were younger and more likely to be female. In this subset, 37% had consulted a physician for IBS symptoms in the preceding year and 29% had done so earlier. A colonoscopy had been performed in 22%. Based on a brief description, 17.6% of the population self-identified as suffering from IBS (p < 0.001 compared to Rome IV IBS prevalence), and these were more likely to be female. Concordance with the Rome IV criteria was only 25%, but except for a lower reporting of pain, the symptom pattern, severity, impact on daily life, inability to work and health care utilization were similar to the Rome IV group. A total of 134 days of absence from work were attributed to bowel symptoms in those self-reporting with IBS. Conclusion:In the general population, bowel symptoms are highly prevalent, and the self-reported "IBS" is three times more prevalent than according to Rome IV criteria. Self-reported IBS is associated with a similar impact on health care utilization and quality of life but a higher impact on absence from work.
| S-EPMC6498809 | biostudies-other
Project description:The feasibility of using health system data to estimate prevalence of chronic kidney disease (CKD) stages 3-5 was explored.Cohort study.A 5% national random sample of patients from the Veterans Affairs (VA) health care system, enrollees in a managed care plan in Michigan (M-CARE), and participants from the 2005-2006 National Health and Nutrition Examination Survey (NHANES).Observed CKD prevalence estimates in the health system population were calculated as patients with an available outpatient serum creatinine measurement with estimated glomerular filtration rate <60 mL/min/1.73 m(2), among those with at least one outpatient visit during the year.A logistic regression model was fitted using data from the 2005-2006 NHANES to predict CKD prevalence in those untested for serum creatinine in the health system population, adjusted for demographics and comorbid conditions. Model results then were combined with the observed prevalence in tested patients to derive an overall predicted prevalence of CKD within the health systems.Patients in the VA system were older, had more comorbid conditions, and were more likely to be tested for serum creatinine than those in the M-CARE system. Observed prevalences of CKD stages 3-5 were 15.6% and 0.9% in the VA and M-CARE systems, respectively. Using data from NHANES, the overall predicted prevalences of CKD were 20.4% and 1.6% in the VA and M-CARE systems, respectively.Health system data quality was limited by missing data for laboratory results and race. A single estimated glomerular filtration rate value was used to define CKD, rather than persistence over 3 months.Estimation of CKD prevalence within health care systems is feasible, but discrepancies between observed and predicted prevalences suggest that this approach is dependent on data availability and quality of information for comorbid conditions, as well as the frequency of testing for CKD in the health care system.
| S-EPMC4605141 | biostudies-literature