Background Psychiatric disorders constitute a major public health concern that are associated with substantial health and socioeconomic burden. Psychiatric patients may be more vulnerable to high ...temperatures, which under current climate change projections will most likely increase the burden of this public health concern. Objective This study investigated the short-term association between ambient temperature and mental health hospitalizations in Bern, Switzerland. Methods Daily hospitalizations for mental disorders between 1973 and 2017 were collected from the University Hospital of Psychiatry and Psychotherapy in Bern. Population-weighted daily mean ambient temperatures were derived for the catchment area of the hospital from 2.3-km gridded weather maps. Conditional quasi-Poisson regression with distributed lag linear models were applied to assess the association up to three days after the exposure. Stratified analyses were conducted by age, sex, and subdiagnosis, and by subperiods (1973-1989 and 1990-2017). Additional subanalyses were performed to assess whether larger risks were found during the warm season or were due to heatwaves. Results The study included a total number of 88,996 hospitalizations. Overall, the hospitalization risk increased linearly by 4.0% (95% CI 2.0%, 7.0%) for every 10°C increase in mean daily temperature. No evidence of a nonlinear association or larger risks during the warm season or heatwaves was found. Similar estimates were found across for all sex and age categories, and larger risks were found for hospitalizations related to developmental disorders (29.0%; 95% CI 9.0%, 54.0%), schizophrenia (10.0%; 95% CI 4.0%, 15.0%), and for the later rather than the earlier period (5.0%; 95% CI 2.0%, 8.0% vs. 2.0%; 95% CI -3.0%, 8.0%). Conclusions Our findings suggest that increasing temperatures could negatively affect mental status in psychiatric patients. Specific public health policies are urgently needed to protect this vulnerable population from the effects of climate change.
Previous literature suggests that ambient temperature may play a role in increasing the risk of suicide. Although in Switzerland suicides are an important cause of death, limited research exists on ...risk factors for suicidal behaviour, including ambient temperature. We aimed to assess the short-term association between ambient temperature and suicide risk in Switzerland between 1995 and 2016, and the differences by region, individual characteristics and method of suicide.
We collected daily data on suicides and mean temperatures in each canton of Switzerland. We used a two-stage approach, consisting of a case time series analysis using conditional quasi-Poisson and distributed lag non-linear models followed by a multivariate meta-regression analysis. We conducted subgroup analyses by sex, age (<35, 35-65 and >65 years) and method of suicide (violent or nonviolent).
Between 1995 and 2016, there were a total of 24,067 suicides in Switzerland. Overall, we found a positive and non-linear temperature-suicide association in all regions. On average, the risk of suicide increased by 34% (1.34 relative risk 95% confidence interval: 1.19-1.52) from the 10th to the 99th temperature percentile in Switzerland (lag period of 0-2 days). Indications of larger risks were mostly found in females, younger individuals (<35 years) and with nonviolent methods. Regional risks ranged from 24% (East region) to 55% (North-West region).
Our findings suggest that increasing temperatures could be considered a risk factor for suicidal behaviour in Switzerland. Knowledge of the profile of people committing suicide could help us to understand the mechanisms behind this association and thus support policymakers in suicide prevention.
Recent research has suggested that an increase in temperature can negatively affect mental health and increase hospitalization for mental illness. It is not clear, however, what factors or mechanisms ...mediate this association. We aimed to (1) investigate the associations between ambient temperatures and bad daily mood, and (2) identify variables affecting the strength of these associations (modifiers) including the time, the day of the week and the year of the mood rating, socio-demographic characteristics, sleep quality, psychiatric disorders and the personality trait neuroticism in the community.
Data stemmed from the second follow-up evaluation of CoLaus|PsyCoLaus, a prospective cohort study conducted in the general population of Lausanne (Switzerland). The 906 participants rated their mood level four times a day during seven days using a cell phone app. Mixed-effects logistic regression was used to determine the association between daily maximum temperature and mood level. Participant ID was inserted as a random effect in the model, whereas the time of the day, the day of the week and the year were inserted as fixed effects. Models were controlled for several confounders (socio-demographic characteristics, sleep quality, weather parameters and air pollutants). Stratified analyses were conducted based on socio-demographic characteristics, sleep quality, presence of psychiatric disorders or a high neuroticism.
Overall, the probability of having a bad mood for the entire day decreased by 7.0% (OR: 0.93: 95% CI 0.88, 0.99) for each 5 °C increase in maximum temperature. A smaller and less precise effect (-3%; OR: 0.97: 95% CI 0.91, 1.03) was found when controlling for sunshine duration. A higher association was found in participants with bipolar disorder (-23%; OR: 0.77: 95% CI 0.51, 1.17) and in participants with a high neuroticism (-13%; OR: 0.87 95% CI 0.80, 0.95), whereas the association was reversed for participants with anxiety (20%; OR: 1.20: 95% CI 0.90, 1.59), depression (18%; OR: 1.18 95% CI 0.94, 1.48) and schizophrenia (193%; OR: 2.93 95% CI 1.17, 7.73).
According to our findings, rising temperatures may positively affect mood in the general population. However, individuals with certain psychiatric disorders, such as anxiety, depression, and schizophrenia, may exhibit altered responses to heat, which may explain their increased morbidity when exposed to high temperatures. This suggests that tailored public health policies are required to protect this vulnerable population.
Both genetic background and diet are important determinants of cardiovascular diseases (CVD). Understanding gene-diet interactions could help improve CVD prevention and prognosis. We aimed to ...summarise the evidence on gene-diet interactions and CVD outcomes systematically.
We searched MEDLINE
via Ovid, Embase, PubMed
, and The Cochrane Library for relevant studies published until June 6th 2022. We considered for inclusion cross-sectional, case-control, prospective cohort, nested case-control, and case-cohort studies as well as randomised controlled trials that evaluated the interaction between genetic variants and/or genetic risk scores and food or diet intake on the risk of related outcomes, including myocardial infarction, coronary heart disease (CHD), stroke and CVD as a composite outcome. The PROSPERO protocol registration code is CRD42019147031.
We included 59 articles based on data from 29 studies; six articles involved multiple studies, and seven did not report details of their source population. The median sample size of the articles was 2562 participants. Of the 59 articles, 21 (35.6%) were qualified as high quality, while the rest were intermediate or poor. Eleven (18.6%) articles adjusted for multiple comparisons, four (7.0%) attempted to replicate the findings, 18 (30.5%) were based on Han-Chinese ethnicity, and 29 (49.2%) did not present Minor Allele Frequency. Fifty different dietary exposures and 52 different genetic factors were investigated, with alcohol intake and ADH1C variants being the most examined. Of 266 investigated diet-gene interaction tests, 50 (18.8%) were statistically significant, including CETP-TaqIB and ADH1C variants, which interacted with alcohol intake on CHD risk. However, interactions effects were significant only in some articles and did not agree on the direction of effects. Moreover, most of the studies that reported significant interactions lacked replication. Overall, the evidence on gene-diet interactions on CVD is limited, and lack correction for multiple testing, replication and sample size consideration.
Clear evidence on the benefit-harm balance and cost effectiveness of population-based screening for colorectal cancer (CRC) is missing. We aim to systematically evaluate the long-term effectiveness, ...harms and cost effectiveness of different organized CRC screening strategies in Austria.
A decision-analytic cohort simulation model for colorectal adenoma and cancer with a lifelong time horizon was developed, calibrated to the Austrian epidemiological setting and validated against observed data. We compared four strategies: 1) No Screening, 2) FIT: annual immunochemical fecal occult blood test age 40-75 years, 3) gFOBT: annual guaiac-based fecal occult blood test age 40-75 years, and 4) COL: 10-yearly colonoscopy age 50-70 years. Predicted outcomes included: benefits expressed as life-years gained LYG, CRC-related deaths avoided and CRC cases avoided; harms as additional complications due to colonoscopy (physical harm) and positive test results (psychological harm); and lifetime costs. Tradeoffs were expressed as incremental harm-benefit ratios (IHBR, incremental positive test results per LYG) and incremental cost-effectiveness ratios ICER. The perspective of the Austrian public health care system was adopted. Comprehensive sensitivity analyses were performed to assess uncertainty.
The most effective strategies were FIT and COL. gFOBT was less effective and more costly than FIT. Moving from COL to FIT results in an incremental unintended psychological harm of 16 additional positive test results to gain one life-year. COL was cost saving compared to No Screening. Moving from COL to FIT has an ICER of 15,000 EUR per LYG.
Organized CRC-screening with annual FIT or 10-yearly colonoscopy is most effective. The choice between these two options depends on the individual preferences and benefit-harm tradeoffs of screening candidates.
Due to high survival rates and the relatively small benefit of adjuvant therapy, the application of personalized medicine (PM) through risk stratification is particularly beneficial in early breast ...cancer (BC) to avoid unnecessary harms from treatment. The new 21-gene assay (OncotypeDX, ODX) is a promising prognostic score for risk stratification that can be applied in conjunction with Adjuvant!Online (AO) to guide personalized chemotherapy decisions for early BC patients. Our goal was to evaluate risk-group specific cost effectiveness of adjuvant chemotherapy for women with early stage BC in Austria based on AO and ODX risk stratification.
A previously validated discrete event simulation model was applied to a hypothetical cohort of 50-year-old women over a lifetime horizon. We simulated twelve risk groups derived from the joint application of ODX and AO and included respective additional costs. The primary outcomes of interest were life-years gained, quality-adjusted life-years (QALYs), costs and incremental cost-effectiveness (ICER). The robustness of results and decisions derived were tested in sensitivity analyses. A cross-country comparison of results was performed.
Chemotherapy is dominated (i.e., less effective and more costly) for patients with 1) low ODX risk independent of AO classification; and 2) low AO risk and intermediate ODX risk. For patients with an intermediate or high AO risk and an intermediate or high ODX risk, the ICER is below 15,000 EUR/QALY (potentially cost effective depending on the willingness-to-pay). Applying the AO risk classification alone would miss risk groups where chemotherapy is dominated and thus should not be considered. These results are sensitive to changes in the probabilities of distant recurrence but not to changes in the costs of chemotherapy or the ODX test.
Based on our modeling study, chemotherapy is effective and cost effective for Austrian patients with an intermediate or high AO risk and an intermediate or high ODX risk. In other words, low ODX risk suggests chemotherapy should not be considered but low AO risk may benefit from chemotherapy if ODX risk is high. Our analysis suggests that risk-group specific cost-effectiveness analysis, which includes companion prognostic tests are essential in PM.
Because older adults are particularly vulnerable to nonoptimal temperatures, it is expected that the progressive population aging will amplify the health burden attributable to heat and cold due to ...climate change in future decades. However, limited evidence exists on the contribution of population aging on historical temperature-mortality trends.
We aimed to
) assess trends in heat- and cold-related mortality in Switzerland between 1969 and 2017 and
) to quantify the contribution of population aging to the observed patterns.
We collected daily time series of all-cause mortality by age group (
, 65-79, and 80 y and older) and mean temperature for each Swiss municipality (1969-2017). We performed a two-stage time-series analysis with distributed lag nonlinear models and multivariate longitudinal meta-regression to obtain temperature-mortality associations by canton, decade, and age group. We then calculated the corresponding excess mortality attributable to nonoptimal temperatures and compared it to the estimates obtained in a hypothetical scenario of no population aging.
Between 1969 and 2017, heat- and cold-related mortality represented 0.28% 95% confidence interval (CI): 0.18, 0.37 and 8.91% (95% CI: 7.46, 10.21) of total mortality, which corresponded to 2.4 and 77 deaths per 100,000 people annually, respectively. Although mortality rates for heat slightly increased over time, annual number of deaths substantially raised up from 74 (12;125) to 181 (39;307) between 1969-78 and 2009-17, mostly driven by the
age group. Cold-related mortality rates decreased across all ages, but annual cold-related deaths still increased among the
, due to the increase in the population at risk. We estimated that heat- and cold-related deaths would have been 52.7% and 44.6% lower, respectively, in the most recent decade in the absence of population aging.
Our findings suggest that a substantial proportion of historical temperature-related impacts can be attributed to population aging. We found that population aging has attenuated the decrease in cold-related mortality and amplified heat-related mortality. https://doi.org/10.1289/EHP9835.
Background. In state-transition models (STMs), decision problems are conceptualized using health states and transitions among those health states after predefined time cycles. The naive, commonly ...applied method (C) for cycle length conversion transforms all transition probabilities separately. In STMs with more than 2 health states, this method is not accurate. Therefore, we aim to describe and compare the performance of method C with that of alternative matrix transformation methods. Design. We compare 2 alternative matrix transformation methods (Eigenvalue method E, Schure-Padé method SP) to method C applied in an STM of 3 different treatment strategies for women with breast cancer. We convert the given annual transition matrix into a monthly-cycle matrix and evaluate induced transformation errors for the transition matrices and the long-term outcomes: life years, quality-adjusted life-years, costs and incremental cost-effectiveness ratios, and the performance related to the decisions. In addition, we applied these transformation methods to randomly generated annual transition matrices with 4, 7, 10, and 20 health states. Results. In theory, there is no generally applicable correct transformation method. Based on our simulations, SP resulted in the smallest transformation-induced discrepancies for generated annual transition matrices for 2 treatment strategies. E showed slightly smaller discrepancies than SP in the strategy, where one of the direct transitions between health states was excluded. For long-term outcomes, the largest discrepancy occurred for estimated costs applying method C. For higher dimensional models, E performs best. Conclusions. In our modeling examples, matrix transformations (E, SP) perform better than transforming all transition probabilities separately (C). Transition probabilities based on alternative conversion methods should therefore be applied in sensitivity analyses.
Background
Budget impact analyses (BIAs) describe changes in intervention- and disease-related costs of new technologies. Evidence on the quality of BIAs for cancer screening is lacking.
Objectives
...We systematically reviewed the literature and methods to assess how closely BIA guidelines are followed when BIAs are performed for cancer-screening programs.
Data sources
Systematic searches were conducted in MEDLINE, EMBASE, EconLit, CRD (Centre for Reviews and Dissemination, University of York), and CEA registry of the Tufts Medical Center.
Study eligibility criteria
Eligible studies were BIAs evaluating cancer-screening programs published in English, 2010–2018.
Synthesis methods
Standardized evidence tables were generated to extract and compare study characteristics outlined by the ISPOR BIA Task Force.
Results
Nineteen studies were identified evaluating screening for breast (5), colorectal (6), cervical (3), lung (1), prostate (3), and skin (1) cancers. Model designs included decision-analytic models (13) and simple cost calculators (6). From all studies, only 53% reported costs for a minimum of 3 years, 58% compared to a mix of screening options, 42% reported model validation, and 37% reported uncertainty analysis for participation rates. The quality of studies appeared to be independent of cancer site.
Limitations
“Gray” literature was not searched, misinterpretation is possible due to limited information in publications, and focus was on international methodological guidelines rather than regional guidelines.
Conclusions
Our review highlights considerable variability in the extent to which BIAs evaluating cancer-screening programs followed recommended guidelines. The annual budget impact at least over the next 3–5 years should be estimated. Validation and uncertainty analysis should always be conducted. Continued dissemination efforts of existing best-practice guidelines are necessary to ensure high-quality analyses.
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