The Effects of Virtual Reality Training on Cognition in Older Adults: A Systematic Review, Meta-Analysis, and Meta-Regression of Randomized Controlled Trials.

The aim of this systematic review, meta-analysis, and meta-regression was to examine the effects of virtual reality-based training on global cognition and executive function compared with conventional training or information-based treatment in older adults, regardless of cognitive level. A systematic literature search was conducted using four databases. A total of 31 randomized controlled trials were identified. Pooled effect sizes were calculated, the risk of bias was assessed, and evidence was graded. The primary analyses showed a small but statistically significant effect of virtual reality-based training compared with control on global cognition (Hedges' g 0.42, 95% confidence interval [0.17, 0.68], I2 = 70.1%, n = 876, 20 randomized controlled trials, low evidence) and executive function (Hedges' g 0.35, 95% confidence interval [0.06, 0.65], I2 = 68.4%, n = 810, 16 randomized controlled trials, very low evidence). Meta-regression yielded inconclusive results. Virtual reality-based training may be more effective than control in improving cognition in older adults; however, more high-quality studies are needed.

Underweight and obesity are related to higher mortality in patients undergoing coronary angiography: The KARDIO invasive cardiology register study.

BACKGROUND: In patients with some cardiovascular disease conditions, slightly elevated body mass index (BMI) is associated with a lower mortality risk (termed "obesity paradox"). It is uncertain, however, if this obesity paradox exists in patients who have had invasive cardiology procedures. We evaluated the association between BMI and mortality in patients who underwent coronary angiography. METHODS: We utilised the KARDIO registry, which comprised data on demographics, prevalent diseases, risk factors, coronary angiographies, and interventions on 42,636 patients. BMI was categorised based on WHO cut-offs or transformed using P-splines. Hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated for all-cause mortality. RESULTS: During a median follow-up of 4.9 years, 4688 all-cause deaths occurred. BMI was nonlinearly associated with mortality risk: compared to normal weight category (18.5-25 kg/m2 ), the age-adjusted HRs (95% CIs) for all-cause mortality were 1.90 (1.49, 2.43), 0.96 (0.92, 1.01), 1.04 (0.99, 1.09), 1.08 (0.96, 1.20), and 1.45 (1.22, 1.72) for underweight (<18.5 kg/m2 ), preobesity (25 to <30 kg/m2 ), obesity class I (30 to <35 kg/m2 ), obesity class II (35 to <40 kg/m2 ), and obesity class III (>40 kg/m2 ), respectively. The corresponding multivariable adjusted HRs (95% CIs) were 2.00 (1.55, 2.58), 0.92 (0.88, 0.97) 1.01 (0.95, 1.06), 1.10 (0.98, 1.23), and 1.49 (1.26, 1,78), respectively. CONCLUSIONS: In patients undergoing coronary angiography, underweight and obesity class III are associated with increased mortality risk, and the lowest mortality was observed in the preobesity class. It appears the obesity paradox may be present in patients who undergo invasive coronary procedures.

Do-search: A Tool for Causal Inference and Study Design with Multiple Data Sources.

Epidemiologic evidence is based on multiple data sources including clinical trials, cohort studies, surveys, registries, and expert opinions. Merging information from different sources opens up new possibilities for the estimation of causal effects. We show how causal effects can be identified and estimated by combining experiments and observations in real and realistic scenarios. As a new tool, we present do-search, a recently developed algorithmic approach that can determine the identifiability of a causal effect. The approach is based on do-calculus, and it can utilize data with nontrivial missing data and selection bias mechanisms. When the effect is identifiable, do-search outputs an identifying formula on which numerical estimation can be based. When the effect is not identifiable, we can use do-search to recognize additional data sources and assumptions that would make the effect identifiable. Throughout the article, we consider the effect of salt-adding behavior on blood pressure mediated by the salt intake as an example. The identifiability of this effect is resolved in various scenarios with different assumptions on confounding. There are scenarios where the causal effect is identifiable from a chain of experiments but not from survey data, as well as scenarios where the opposite is true. As an illustration, we use survey data from the National Health and Nutrition Examination Survey 2013-2016 and the results from a meta-analysis of randomized controlled trials and estimate the reduction in average systolic blood pressure under an intervention where the use of table salt is discontinued.

Body weight and premature retirement: population-based evidence from Finland.

BACKGROUND: Health status is a principal determinant of labour market participation. In this study, we examined whether excess weight is associated with withdrawal from the labour market owing to premature retirement. METHODS: The analyses were based on nationally representative data from Finland over the period 2001-15 (N ∼ 2500). The longitudinal data included objective measures of body weight (i.e. body mass index and waist circumference) linked to register-based information on actual retirement age. The association between the body weight measures and premature retirement was modelled using cubic b-splines via logistic regression. The models accounted for other possible risk factors and potential confounders, such as smoking and education. RESULTS: Excess weight was associated with an increased risk of premature retirement for both men and women. A closer examination revealed that the probability of retirement varied across the weight distribution and the results differed between sexes and weight measures. CONCLUSION: Body weight outside a recommended range elevates the risk of premature retirement.

Effectiveness of Technology-Based Distance Physical Rehabilitation Interventions for Improving Physical Functioning in Stroke: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

OBJECTIVE: To study the effectiveness of technology-based distance physical rehabilitation interventions on physical functioning in stroke. DATA SOURCES: A systematic literature search was conducted in 6 databases from January 2000 to May 2018. STUDY SELECTION: Inclusion criteria applied the patient, intervention, comparison, outcome, study design framework as follows: (P) stroke; (I) technology-based distance physical rehabilitation interventions; (C) any comparison without the use of technology; (O) physical functioning; (S) randomized controlled trials (RCTs). The search identified in total 693 studies, and the screening of 162 full-text studies revealed 13 eligible studies. DATA EXTRACTION: The studies were screened using the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines and assessed for methodological quality and quality of evidence. Meta-analysis was performed if applicable. DATA SYNTHESIS: A total of 13 studies were included, and online video monitoring was the most used technology. Seven outcomes of physical functioning were identified-activities of daily living (ADL), upper extremity functioning, lower extremity functioning, balance, walking, physical activity, and participation. A meta-analysis of 6 RCTs indicated that technology-based distance physical rehabilitation had a similar effect on ADL (standard mean difference 0.06; 95% confidence interval: -0.22 to 0.35, P=.67) compared to the combination of traditional treatments (usual care, similar and other treatment). Similar results were obtained for other outcomes, except inconsistent findings were noted for walking. Methodological quality of the studies and quality of evidence were considered low. CONCLUSIONS: The findings suggest that the effectiveness of technology-based distance physical rehabilitation interventions on physical functioning might be similar compared to traditional treatments in stroke. Further research should be performed to confirm the effectiveness of technology-based distance physical rehabilitation interventions for improving physical functioning of persons with stroke.

Recommendations for design and analysis of health examination surveys under selective non-participation.

Background: The decreasing participation rates and selective non-participation peril the representativeness of health examination surveys (HESs). Methods: Finnish HESs conducted in 1972-2012 are used to demonstrate that survey participation rates can be enhanced with well-planned recruitment procedures and auxiliary information about survey non-participants can be used to reduce selection bias. Results: Experiments incorporated to pilot surveys and experience from previously conducted surveys lead to practical improvements. For example, SMS reminders were taken as a routine procedure to the Finnish HESs after testing their effect on a pilot study and finding them as a cost-effective way to increase participation rate especially among younger age groups. Auxiliary information about survey non-participants can be obtained from many sources: sampling frames, previous measurements in longitudinal setting, re-contacts and non-response questionnaires, and record linkage to administrative data sources. These data can be used in statistical modelling to adjust the population level estimates for the selection bias. Information on the characteristics of non-participants also helps to improve targeting the recruitment in the future. Conclusion: All methods discussed and recommended are relatively easy to incorporate to any national HES in Europe except the record linkage of survey data from administrative data sources. This is not feasible in all European countries because of non-existence of registries, lack of an identifier needed for record linkage, or national data protection legislation which restricts the data use.

Follow-Up Data Improve the Estimation of the Prevalence of Heavy Alcohol Consumption.

AIMS: We aim to adjust for potential non-participation bias in the prevalence of heavy alcohol consumption. METHODS: Population survey data from Finnish health examination surveys conducted in 1987-2007 were linked to the administrative registers for mortality and morbidity follow-up until end of 2014. Utilising these data, available for both participants and non-participants, we model the association between heavy alcohol consumption and alcohol-related disease diagnoses. RESULTS: Our results show that the estimated prevalence of heavy alcohol consumption is on average of 1.5 times higher for men and 1.8 times higher for women than what was obtained from participants only (complete case analysis). The magnitude of the difference in the mean estimates by year varies from 0 to 9 percentage points for men and from 0 to 2 percentage points for women. CONCLUSION: The proposed approach improves the prevalence estimation but requires follow-up data on non-participants and Bayesian modelling.

Adjusting for selective non-participation with re-contact data in the FINRISK 2012 survey.

AIMS: A common objective of epidemiological surveys is to provide population-level estimates of health indicators. Survey results tend to be biased under selective non-participation. One approach to bias reduction is to collect information about non-participants by contacting them again and asking them to fill in a questionnaire. This information is called re-contact data, and it allows to adjust the estimates for non-participation. METHODS: We analyse data from the FINRISK 2012 survey, where re-contact data were collected. We assume that the respondents of the re-contact survey are similar to the remaining non-participants with respect to the health given their available background information. Validity of this assumption is evaluated based on the hospitalisation data obtained through record linkage of survey data to the administrative registers. Using this assumption and multiple imputation, we estimate the prevalences of daily smoking and heavy alcohol consumption and compare them to estimates obtained with a commonly used assumption that the participants represent the entire target group. RESULTS: When adjusting for non-participation using re-contact data, higher prevalence estimates were observed compared to prevalence estimates based on participants only. Among men, the smoking prevalence estimate was 28.5% (23.2% for participants) and heavy alcohol consumption prevalence was 9.4% (6.8% for participants). Among women, smoking prevalence was 19% (16.5% for participants) and heavy alcohol consumption was 4.8% (3% for participants). CONCLUSIONS: The utilisation of re-contact data is a useful method to adjust for non-participation bias on population estimates in epidemiological surveys.

Non-participation modestly increased with distance to the examination clinic among adults in Finnish health examination surveys.

AIMS: Health examination surveys (HES) provide important information about population health and health-related factors, but declining participation rates threaten the representativeness of collected data. It is hard to conduct national HESs at examination clinics near to every sampled individual. Thus, it is interesting to look into the possible association between the distance from home to the examination clinic and non-participation, and whether there is a certain distance after which the participation activity decreases considerably. METHODS: Data from two national HESs conducted in Finland in 2011 and 2012 were used and a logistic regression model was fitted to investigate how distance was related to non-participation. RESULTS: We found out that non-participation modestly increased with distance to the examination clinic. An additional analysis indicated that the option of having an examination at home may decrease the effect of distance to participation. CONCLUSIONS: Long distances from home to the examination clinic are one reason for low participation activity. Possible bias caused by these differences in participation could be decreased by providing the option of a home examination.

Participation rates by educational levels have diverged during 25 years in Finnish health examination surveys.

Background: Declining participation rates in health examination surveys may impair the representativeness of surveys and introduce bias into the comparison of results between population groups if participation rates differ between them. Changes in the characteristics of non-participants over time may also limit comparability with earlier surveys. Methods: We studied the association of socio-economic position with participation, and its changes over the past 25 years. Occupational class and educational level are used as indicators of socio-economic position. Data from six cross-sectional FINRISK surveys conducted between 1987 and 2012 in Finland were linked to national administrative registers, which allowed investigation of the differences between survey participants and non-participants. Results: Our results show that individuals with low occupational class or low level of education were less likely to participate than individuals with high occupational class or high level of education. Participation rates decreased in all subgroups of the population but the decline was fastest among those with low level of education. Conclusions: The differences in participation rates must be taken into account to avoid biased estimates because socio-economic position has also been shown to be strongly related to health, health behaviour and biological risk factors. Particular attention should be paid to the recruitment of the less-educated population groups.

Prioritizing covariates in the planning of future studies in the meta-analytic framework.

Science can be seen as a sequential process where each new study augments evidence to the existing knowledge. To have the best prospects to make an impact in this process, a new study should be designed optimally taking into account the previous studies and other prior information. We propose a formal approach for the covariate prioritization, that is the decision about the covariates to be measured in a new study. The decision criteria can be based on conditional power, change of the p-value, change in lower confidence limit, Kullback-Leibler divergence, Bayes factors, Bayesian false discovery rate or difference between prior and posterior expectation. The criteria can be also used for decisions on the sample size. As an illustration, we consider covariate prioritization based on genome-wide association studies for C-reactive protein levels and make suggestions on the genes to be studied further.

Circulating miRNA Signature Predicts Cancer Incidence in Lynch Syndrome-A Pilot Study.

Lynch syndrome (LS) is the most common autosomal dominant cancer syndrome and is characterized by high genetic cancer risk modified by lifestyle factors. This study explored whether a circulating miRNA (c-miR) signature predicts LS cancer incidence within a 4-year prospective surveillance period. To gain insight how lifestyle behavior could affect LS cancer risk, we investigated whether the cancer-predicting c-miR signature correlates with known risk-reducing factors such as physical activity, body mass index (BMI), dietary fiber, or NSAID usage. The study included 110 c-miR samples from LS carriers, 18 of whom were diagnosed with cancer during a 4-year prospective surveillance period. Lasso regression was utilized to find c-miRs associated with cancer risk. Individual risk sum derived from the chosen c-miRs was used to develop a model to predict LS cancer incidence. This model was validated using 5-fold cross-validation. Correlation and pathway analyses were applied to inspect biological functions of c-miRs. Pearson correlation was used to examine the associations of c-miR risk sum and lifestyle factors. hsa-miR-10b-5p, hsa-miR-125b-5p, hsa-miR-200a-3p, hsa-miR-3613-5p, and hsa-miR-3615 were identified as cancer predictors by Lasso, and their risk sum score associated with higher likelihood of cancer incidence (HR 2.72, 95% confidence interval: 1.64-4.52, C-index = 0.72). In cross-validation, the model indicated good concordance with the average C-index of 0.75 (0.6-1.0). Coregulated hsa-miR-10b-5p, hsa-miR-125b-5p, and hsa-miR-200a-3p targeted genes involved in cancer-associated biological pathways. The c-miR risk sum score correlated with BMI (r = 0.23, P < 0.01). In summary, BMI-associated c-miRs predict LS cancer incidence within 4 years, although further validation is required. PREVENTION RELEVANCE: The development of cancer risk prediction models is key to improving the survival of patients with LS. This pilot study describes a serum miRNA signature-based risk prediction model that predicts LS cancer incidence within 4 years, although further validation is required.

Menopausal symptoms and cardiometabolic risk factors in middle-aged women: A cross-sectional and longitudinal study with 4-year follow-up.

OBJECTIVE: To study associations of menopausal symptoms with cardiometabolic risk factors. STUDY DESIGN: A cross-sectional and longitudinal study of a representative population sample of 1393 women aged 47-55 years with a sub-sample of 298 followed for four years. The numbers of vasomotor, psychological, somatic or pain, and urogenital menopausal symptoms were ascertained at baseline through self-report. Their associations with cardiometabolic risk factors were studied using linear regression and linear mixed-effect models. Models were adjusted for age, menopausal status, body mass index, the use of hormonal preparations, education, smoking, and alcohol consumption. MAIN OUTCOME MEASURES: Cardiometabolic risk factors included total cholesterol, low-density and high-density lipoprotein cholesterol, blood pressure, glucose, triglycerides, total and android fat mass, and physical activity. RESULTS: All cholesterol and fat mass measures had modest positive associations with menopausal symptoms. The number of vasomotor symptoms, in particular, was associated with total cholesterol (B = 0.13 mmol/l, 95 % CI [0.07, 0.20]; 0.15 mmol/l [0.02, 0.28]) and low-density lipoprotein cholesterol (0.08 mmol/l [0.03, 0.14]; 0.12 mmol/l [0.01, 0.09]) in cross-sectional and longitudinal analyses, respectively. However, these associations disappeared after adjusting for confounders. The number of symptoms was not associated with blood pressure, glucose, triglycerides, and physical activity. Menopausal symptoms at baseline did not predict the changes in the risk factors during the follow-up. CONCLUSIONS: Menopausal symptoms may not be independently associated with cardiometabolic risk, and they do not seem to predict the changes in risk factors during the menopausal transition.

Impact or No Impact for Women With Mild Knee Osteoarthritis: A Bayesian Meta-Analysis of Two Randomized Controlled Trials With Contrasting Interventions.

OBJECTIVE: To predict the probability of a benefit from 2 contrasting exercise programs for women with a new diagnosis of mild knee osteoarthritis, and to estimate the short- and long-term effects of aquatic resistance training (ART) and high-impact aerobic land training (HLT) compared with a control. METHODS: Original data sets from 2 previously conducted randomized controlled trials were combined and used in a Bayesian meta-analysis. Group differences in multiple response variables were estimated. Variables included cardiorespiratory fitness, dynamic maximum leg muscle power, maximal isometric knee extension and flexion force, pain, other symptoms, and quality of life. The statistical model included a latent commitment variable for each female participant. RESULTS: ART had a 55-71% probability of benefits in the outcome variables, and as the main effect, the intervention outperformed the control in cardiorespiratory fitness, with a probability of 71% immediately after the intervention period. HLT had a 46-63% probability of benefits after intervention with the outcome variables, but differently from ART; the positive effects of physical performance fade away during the follow-up period. Overall, the differences between groups were small, and the variation in the predictions between individuals was high. CONCLUSION: Both interventions had benefits, but ART has a slightly higher probability of long-term benefits on physical performance. Because of high individual variation and no clear advantage of one training method over the other, personal preferences should be considered in the selection of the exercise program to ensure highest commitment to training.

Physical activity and aerobic fitness in relation to local and interhemispheric functional connectivity in adolescents' brains.

INTRODUCTION: Adolescents have experienced decreased aerobic fitness levels and insufficient physical activity levels over the past decades. While both physical activity and aerobic fitness are related to physical and mental health, little is known concerning how they manifest in the brain during this stage of development, characterized by significant physical and psychosocial changes. The aim of the study is to examine the associations between both physical activity and aerobic fitness with brains' functional connectivity. METHODS: Here, we examined how physical activity and aerobic fitness are associated with local and interhemispheric functional connectivity of the adolescent brain (n = 59), as measured with resting-state functional magnetic resonance imaging. Physical activity was measured by hip-worn accelerometers, and aerobic fitness by a maximal 20-m shuttle run test. RESULTS: We found that higher levels of moderate-to-vigorous intensity physical activity, but not aerobic fitness, were linked to increased local functional connectivity as measured by regional homogeneity in 13-16-year-old participants. However, we did not find evidence for significant associations between adolescents' physical activity or aerobic fitness and interhemispheric connectivity, as indicated by homotopic connectivity. CONCLUSIONS: These results suggest that physical activity, but not aerobic fitness, is related to local functional connectivity in adolescents. Moreover, physical activity shows an association with a specific brain area involved in motor functions but did not display any widespread associations with other brain regions. These results can advance our understanding of the behavior-brain associations in adolescents.

Predicting the age at natural menopause in middle-aged women.

OBJECTIVE: To predict the age at natural menopause (ANM). METHODS: Cox models with time-dependent covariates were utilized for ANM prediction using longitudinal data from 47 to 55-year-old women (n = 279) participating in the Estrogenic Regulation of Muscle Apoptosis study. The ANM was assessed retrospectively for 105 women using bleeding diaries. The predictors were chosen from the set of 32 covariates by using the lasso regression (model 1). Another easy-to-access model (model 2) was created by using a subset of 16 self-reported covariates. The predictive performance was quantified with c-indices and by studying the means and standard deviations of absolute errors (MAE ±â€ŠSD) between the predicted and observed ANM. RESULTS: Both models included alcohol consumption, vasomotor symptoms, self-reported physical activity, and relationship status as predictors. Model 1 also included estradiol and follicle-stimulating hormone levels as well as SD of menstrual cycle length, while model 2 included smoking, education, and the use of hormonal contraception as additional predictors. The mean c-indices of 0.76 (95% CI 0.71-0.81) for model 1 and 0.70 (95% CI 0.65-0.75) for model 2 indicated good concordance between the predicted and observed values. MAEs of 0.56 ±â€Š0.49 and 0.62 ±â€Š0.54 years respectively for model 1 and 2 were clearly smaller than the MAE for predicted sample mean (1.58 ±â€Š1.02). CONCLUSIONS: In addition to sex hormone levels, irregularity of menstrual cycle, and menopausal symptoms, also life habits and socioeconomic factors may provide useful information for ANM prediction. The suggested approach could add value for clinicians' decision making related to the use of contraception and treatments for menopausal symptoms in perimenopausal women.

Video Summary:http://links.lww.com/MENO/A743 .

Effectiveness of Exergame Intervention on Walking in Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

OBJECTIVE: The objective of this review was to systematically evaluate the effectiveness of exergaming on walking in older adults. In addition, the aim was to investigate the relationship between the exergaming effect and age, baseline walking performance, exercise traits, technology used, and the risk of bias. METHODS: A literature search was carried out in the databases MEDLINE, CINAHL, CENTRAL, EMBASE, WoS, PsycInfo, and PEDro up to January 10, 2020. Studies with a randomized controlled trial design, people ≥60 years of age without neurological disorders, comparison group with other exercise or no exercise, and walking-related outcomes were included. Cochrane RoB2, meta-analysis, meta-regression, and Grading of Recommendations, Assessment, Development and Evaluation were used to estimate quality, treatment effect, covariates' effect, and the certainty of evidence, respectively. RESULTS: In the studies included (n = 66), the overall risk of bias was low (n = 2), unclear (n = 48), or high (n = 16). Compared with comparison groups, exergaming interventions were more effective for walking improvements (standardized mean difference = -0.21; 95% CI = -0.36 to -0.06; 3102 participants, 58 studies; moderate-quality evidence) and more or equally effective (standardized mean difference = -0.32; 95% CI = -0.64 to 0.00; 1028 participants, 13 studies; low-quality evidence) after nonexergaming follow-up. The strongest effect for covariates was observed with the type of comparison group, explaining 18.6% of the variance. CONCLUSION: For older adults without neurological disorders, exergame-based training improved walking, and improvements were maintained at follow-up. Greater benefits were observed when exergaming groups were compared with inactive comparison groups. To strengthen the evidence, further randomized controlled trials on the effectiveness of gamified exercise intervention are needed. IMPACT: Exergaming has an effect equivalent to other types of exercising on improving walking in older adults. Physical therapists and other rehabilitation professionals may consider exergaming as a promising form of exercise in this age group.

The impact of newly identified loci on coronary heart disease, stroke and total mortality in the MORGAM prospective cohorts.

Recently, genome wide association studies (GWAS) have identified a number of single nucleotide polymorphisms (SNPs) as being associated with coronary heart disease (CHD). We estimated the effect of these SNPs on incident CHD, stroke and total mortality in the prospective cohorts of the MORGAM Project. We studied cohorts from Finland, Sweden, France and Northern Ireland (total N=33,282, including 1,436 incident CHD events and 571 incident stroke events). The lead SNPs at seven loci identified thus far and additional SNPs (in total 42) were genotyped using a case-cohort design. We estimated the effect of the SNPs on disease history at baseline, disease events during follow-up and classic risk factors. Multiple testing was taken into account using false discovery rate (FDR) analysis. SNP rs1333049 on chromosome 9p21.3 was associated with both CHD and stroke (HR=1.20, 95% CI 1.08-1.34 for incident CHD events and 1.15, 0.99-1.34 for incident stroke). SNP rs11670734 (19q12) was associated with total mortality and stroke. SNP rs2146807 (10q11.21) showed some association with the fatality of acute coronary event. SNP rs2943634 (2q36.3) was associated with high density lipoprotein (HDL) cholesterol and SNPs rs599839, rs4970834 (1p13.3) and rs17228212 (15q22.23) were associated with non-HDL cholesterol. SNPs rs2943634 (2q36.3) and rs12525353 (6q25.1) were associated with blood pressure. These findings underline the need for replication studies in prospective settings and confirm the candidacy of several SNPs that may play a role in the etiology of cardiovascular disease.

Joint analysis of prevalence and incidence data using conditional likelihood.

Disease prevalence is the combined result of duration, disease incidence, case fatality, and other mortality. If information is available on all these factors, and on fixed covariates such as genotypes, prevalence information can be utilized in the estimation of the effects of the covariates on disease incidence. Study cohorts that are recruited as cross-sectional samples and subsequently followed up for disease events of interest produce both prevalence and incidence information. In this paper, we make use of both types of information using a likelihood, which is conditioned on survival until the cross section. In a simulation study making use of real cohort data, we compare the proposed conditional likelihood method to a standard analysis where prevalent cases are omitted and the likelihood expression is conditioned on healthy status at the cross section.

The value of perfect and imperfect information in lake monitoring and management.

Uncertainty in the information obtained through monitoring complicates decision making about aquatic ecosystems management actions. We suggest the value of information (VOI) to assess the profitability of paying for additional monitoring information, when taking into account the costs and benefits of monitoring and management actions, as well as associated uncertainty. Estimating the monetary value of the ecosystem needed for deriving VOI is challenging. Therefore, instead of considering a single value, we evaluate the sensitivity of VOI to varying monetary value. We also extend the VOI analysis to the more realistic context where additional information does not result in perfect, but rather in imperfect information on the true state of the environment. Therefore, we analytically derive the value of perfect information in the case of two alternative decisions and two states of uncertainty. Second, we describe a Monte Carlo type of approach to evaluate the value of imperfect information about a continuous classification variable. Third, we determine confidence intervals for the VOI with a percentile bootstrap method. Results for our case study on 144 Finnish lakes suggest that generally, the value of monitoring exceeds the cost. It is particularly profitable to monitor lakes that meet the quality standards a priori, to ascertain that expensive and unnecessary management can be avoided. The VOI analysis provides a novel tool for lake and other environmental managers to estimate the value of additional monitoring data for a particular, single case, e.g. a lake, when an additional benefit is attainable through remedial management actions.