BACKGROUND: Despite the increasing interest in transgender health research, to date little is known about the size of the transgender and gender diverse (TGD) population.
METHODS: A web-based questionnaire survey was developed, including a collection of socio-demographic characteristics and disseminated online through social media. Gender incongruence was evaluated by using a 2-item approach assessing gender recorded at birth and gender identity. The primary objective of the present population-based study was to estimate the proportion of TGD people across ages among a large sample of people who answered a web-based survey. The secondary endpoints were to identify gender-affirming needs and possible barriers to healthcare access.
RESULTS: A total of 19,572 individuals participated in the survey, of whom 7.7% reported a gender identity different from the sex recorded at birth. A significantly higher proportion of TGD people was observed in the youngest group of participants compared with older ones. Among TGD people who participated in the study, 58.4% were nonbinary, and 49.1% experienced discrimination in accessing health care services. Nonbinary TGD participants reported both the need for legal name and gender change, along with hormonal and surgical interventions less frequently compared to binary persons.
CONCLUSIONS: Being TGD is not a marginal condition In Italy. A large proportion of TGD persons may not need medical and surgical treatments. TGD people often experience barriers to healthcare access relating to gender identity.

To provide an evidence-based, comprehensive assessment of the current burden of infection-related cancers in Italy.
We calculated the proportion of cancers attributable to infectious agents (Helicobacter pylori [Hp]; hepatitis B virus [HBV] and hepatitis C virus [HCV]; human papillomavirus [HPV]; human herpesvirus-8 [HHV8]; Epstein-Barr virus [EBV]; and human immunodeficiency virus [HIV]) to estimate the burden of infection-related cancer incidence (2020) and mortality (2017). Data on the prevalence of infections were derived from cross-sectional surveys of the Italian population, and relative risks from meta-analyses and large-scale studies. Attributable fractions were calculated based on the counterfactual scenario of a lack of infection.
We estimated that 7.6% of total cancer deaths in 2017 were attributable to infections, with a higher proportion in men (8.1%) than in women (6.9%). The corresponding figures for incident cases were 6.5%, 6.9% and 6.1%. Hp was the first cause of infection-related cancer deaths (3.3% of the total), followed by HCV (1.8%), HIV (1.1%), HBV (0.9%), HPV, EBV and HHV8 (each ≤0.7%). Regarding incidence, 2.4% of the new cancer cases were due to Hp, 1.3% due to HCV, 1.2% due to HIV, 1.0% due to HPV, 0.6% due to HBV and <0.5% due to EBV and HHV8.
Our estimate of 7.6% of cancer deaths and 6.9% of incident cases that were attributable to infections in Italy is higher than those estimated in other developed countries. Hp is the major cause of infection-related cancer in Italy. Prevention, screening and treatment policies are needed to control these cancers, which are largely avoidable.

Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most authors do not attempt to describe what the treatment effect represents, and instead require readers to deduce this based on the reported statistical methods. However, this approach is challenging, because many methods provide counterintuitive results. For example, some methods include data from all patients, yet the resulting treatment effect applies only to a subset of patients, whereas other methods will exclude certain patients while results will apply to everyone. Additionally, some analyses provide estimates pertaining to hypothetical settings in which patients never die or discontinue treatment. Herein we introduce estimands as a solution to the aforementioned problem. An estimand is a clear description of what the treatment effect represents, thus saving readers the necessity of trying to infer this from study methods and potentially getting it wrong. We provide examples of how estimands can remove ambiguity from reported treatment effects and describe their current use in practice. The crux of our argument is that readers should not have to infer what investigators are estimating; they should be told explicitly.

Information on recent cancer statistics is important for planning, monitoring and evaluating cancer control activities. This article aims to provide an update on the cancer incidence estimates in India by sex, age groups and anatomical sites for the year 2022.
The National Cancer Registry Programme Report 2020, reported the cancer incidence from 28 Population-Based Cancer Registries (PBCRs) for the years 2012-2016. This was used as the basis to calculate cancer estimates in India. Information pertaining to the population at risk was extracted from the Census of India (2001 and 2011) for the estimation of age-sex stratified population. PBCRs were categorised into the respective State and regions of the country to understand the epidemiology of cancer. The age-specific incidence rate for each specific anatomical site of cancer was applied to the estimated population to derive the number of cancer cases in India for 2022.
The estimated number of incident cases of cancer in India for the year 2022 was found to be 14,61,427 (crude rate:100.4 per 100,000). In India, one in nine people are likely to develop cancer in his/her lifetime. Lung and breast cancers were the leading sites of cancer in males and females, respectively. Among the childhood (0-14 yr) cancers, lymphoid leukaemia (boys: 29.2% and girls: 24.2%) was the leading site. The incidence of cancer cases is estimated to increase by 12.8 per cent in 2025 as compared to 2020.
The cancer incidence is continuing to increase in India. The new estimates will be helpful in planning cancer prevention and control activities through the intervention of early detection, risk reduction and management.

BACKGROUND: Surveillance data are essential public health resources for guiding policy and allocation of human and capital resources. These data often consist of large collections of information based on nonrandom sample designs. Population estimates based on such data may be impacted by the underlying sample distribution compared to the true population of interest. In this study, we simulate a population of interest and allow response rates to vary in nonrandom ways to illustrate and measure the effect this has on population-based estimates of an important public health policy outcome.
OBJECTIVE: The aim of this study was to illustrate the effect of nonrandom missingness on population-based survey sample estimation.
METHODS: We simulated a population of respondents answering a survey question about their satisfaction with their community\'s policy regarding vaccination mandates for government personnel. We allowed response rates to differ between the generally satisfied and dissatisfied and considered the effect of common efforts to control for potential bias such as sampling weights, sample size inflation, and hypothesis tests for determining missingness at random. We compared these conditions via mean squared errors and sampling variability to characterize the bias in estimation arising under these different approaches.
RESULTS: Sample estimates present clear and quantifiable bias, even in the most favorable response profile. On a 5-point Likert scale, nonrandom missingness resulted in errors averaging to almost a full point away from the truth. Efforts to mitigate bias through sample size inflation and sampling weights have negligible effects on the overall results. Additionally, hypothesis testing for departures from random missingness rarely detect the nonrandom missingness across the widest range of response profiles considered.
CONCLUSIONS: Our results suggest that assuming surveillance data are missing at random during analysis could provide estimates that are widely different from what we might see in the whole population. Policy decisions based on such potentially biased estimates could be devastating in terms of community disengagement and health disparities. Alternative approaches to analysis that move away from broad generalization of a mismeasured population at risk are necessary to identify the marginalized groups, where overall response may be very different from those observed in measured respondents.

The US National Council on Radiation Protection and Measurements (NCRP) convened Scientific Committee 6-12 (SC 6-12) to examine methods for improving dose estimates for brain tissue for internally deposited radionuclides, with emphasis on alpha emitters. This Memorandum summarises the main findings of SC 6-12 described in the recently published NCRP Commentary No. 31, \'Development of Kinetic and Anatomical Models for Brain Dosimetry for Internally Deposited Radionuclides\'. The Commentary examines the extent to which dose estimates for the brain could be improved through increased realism in the biokinetic and dosimetric models currently used in radiation protection and epidemiology. A limitation of most of the current element-specific systemic biokinetic models is the absence of brain as an explicitly identified source region with its unique rate(s) of exchange of the element with blood. The brain is usually included in a large source region calledOtherthat contains all tissues not considered major repositories for the element. In effect, all tissues inOtherare assigned a common set of exchange rates with blood. A limitation of current dosimetric models for internal emitters is that activity in the brain is treated as a well-mixed pool, although more sophisticated models allowing consideration of different activity concentrations in different regions of the brain have been proposed. Case studies for 18 internal emitters indicate that brain dose estimates using current dosimetric models may change substantially (by a factor of 5 or more), or may change only modestly, by addition of a sub-model of the brain in the biokinetic model, with transfer rates based on results of published biokinetic studies and autopsy data for the element of interest. As a starting place for improving brain dose estimates, development of biokinetic models with explicit sub-models of the brain (when sufficient biokinetic data are available) is underway for radionuclides frequently encountered in radiation epidemiology. A longer-term goal is development of coordinated biokinetic and dosimetric models that address the distribution of major radioelements among radiosensitive brain tissues.

The contribution of greenhouse gas (GHG) emissions from ruminant production systems varies between countries and between regions within individual countries. The appropriate quantification of GHG emissions, specifically methane (CH4), has raised questions about the correct reporting of GHG inventories and, perhaps more importantly, how best to mitigate CH4 emissions. This review documents existing methods and methodologies to measure and estimate CH4 emissions from ruminant animals and the manure produced therein over various scales and conditions. Measurements of CH4 have frequently been conducted in research settings using classical methodologies developed for bioenergetic purposes, such as gas exchange techniques (respiration chambers, headboxes). While very precise, these techniques are limited to research settings as they are expensive, labor-intensive, and applicable only to a few animals. Head-stalls, such as the GreenFeed system, have been used to measure expired CH4 for individual animals housed alone or in groups in confinement or grazing. This technique requires frequent animal visitation over the diurnal measurement period and an adequate number of collection days. The tracer gas technique can be used to measure CH4 from individual animals housed outdoors, as there is a need to ensure low background concentrations. Micrometeorological techniques (e.g., open-path lasers) can measure CH4 emissions over larger areas and many animals, but limitations exist, including the need to measure over more extended periods. Measurement of CH4 emissions from manure depends on the type of storage, animal housing, CH4 concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the variable that contributes the greatest to measurement uncertainty. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH4 emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources (enteric and manure). In contrast, top-down approaches estimate the amount of CH4 in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point. While these two estimation approaches rarely agree, they help identify knowledge gaps and research requirements in practice.
There is a need to accurately and precisely quantify greenhouse gas (GHG) emissions, specifically methane (CH4), to ensure correct reporting of GHG inventories and, perhaps more importantly, determine how to best mitigate CH4 emissions. The objective of this study was to review existing methods and methodologies to quantify and estimate CH4 emissions from ruminants. Historically, most techniques were developed for specific purposes that may limit their widespread use on commercial farms and for inventory purposes and typically required frequent calibration and equipment maintenance. Whole animal and head respiration chambers, spot sampling techniques, and tracer gas methods can be used to measure enteric CH4 from individual animals, but each technique has its own inherent limitations. The measurement of CH4 emissions from manure depends on the type of storage, animal housing, CH4 concentration inside and outside the boundaries of the area of interest, and ventilation rate, which is likely the most complex variable creating many uncertainties. For large-scale areas, aircraft, drones, and satellites have been used in association with the tracer flux method, inverse modeling, imagery, and LiDAR (Light Detection and Ranging), but research is lagging in validating these methods. Bottom-up approaches to estimating CH4 emissions rely on empirical or mechanistic modeling to quantify the contribution of individual sources. Top-down approaches estimate the amount of CH4 in the atmosphere using spatial and temporal models to account for transportation from an emitter to an observation point.

The plan for Ending the HIV (human immunodeficiency virus) Epidemic (EHE) in the United States aims to reduce new infections by 75% by 2025 and by 90% by 2030. For EHE to be successful, it is important to accurately measure changes in numbers of new HIV infections after 5 and 10 years (to determine whether the EHE goals have been achieved) but also over shorter timescales (to monitor progress and intensify prevention efforts if required). In this viewpoint, we aim to demonstrate why the method used to monitor progress toward the EHE goals must be carefully considered. We briefly describe and discuss different methods to estimate numbers of new HIV infections based on longitudinal cohort studies, cross-sectional incidence surveys, and routine surveillance data. We particularly focus on identifying conditions under which unadjusted and adjusted estimates based on routine surveillance data can be used to estimate changes in new HIV infections.

Over the years, questions have been raised over R&D costs of new medicines. The aim of this study is to conduct a landscape review of the (drivers of) R&D costs of a new medicine derived from the peer-reviewed and grey literature. Included studies have drawn data either from confidential company surveys or from publicly available company financial statements, in addition to accessing the literature and medicine information databases. Although there were differences in methodology, parameter values, samples and time periods between studies, estimates of R&D costs per new medicine (accounting for the cost of failures) ranged from US$944m to US$2,826m (adjusted to 2019 prices). The evidence also suggested that R&D costs per new medicine have increased over time. A few studies have broken down total costs and showed that clinical development accounts for 50-58% of R&D costs per new medicine. R&D costs were influenced by costs of discovery and pre-clinical development, costs of clinical development, cost of capital, company and product profile. Finally, cost estimates are likely to be dynamic as the biopharmaceutical industry and the broader environment continue to evolve.

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