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At least 200,000-250,000 people died in the war in Bosnia. \"There are three million child soldiers in Africa.\" \"More than 650,000 civilians have been killed as a result of the U.S. occupation of Iraq.\" \"Between 600,000 and 800,000 women are trafficked across borders every year.\" \"Money laundering represents as much as 10 percent of global GDP.\" \"Internet child porn is a $20 billion-a-year industry.\" These are big, attention-grabbing numbers, frequently used in policy debates and media reporting. Peter Andreas and Kelly M. Greenhill see only one problem: these numbers are probably false. Their continued use and abuse reflect a much larger and troubling pattern: policymakers and the media naively or deliberately accept highly politicized and questionable statistical claims about activities that are extremely difficult to measure. As a result, we too often become trapped by these mythical numbers, with perverse and counterproductive consequences. This problem exists in myriad policy realms. But it is particularly pronounced in statistics related to the politically charged realms of global crime and conflict-numbers of people killed in massacres and during genocides, the size of refugee flows, the magnitude of the illicit global trade in drugs and human beings, and so on. In Sex, Drugs, and Body Counts , political scientists, anthropologists, sociologists, and policy analysts critically examine the murky origins of some of these statistics and trace their remarkable proliferation. They also assess the standard metrics used to evaluate policy effectiveness in combating problems such as terrorist financing, sex trafficking, and the drug trade. Big, attention-grabbing numbers are frequently used in policy debates and media reporting: \"At least 200,000-250,000 people died in the war in Bosnia.\" \"There are three million child soldiers in Africa.\" \"More than 650,000 civilians have been killed as a result of the U.S. occupation of Iraq.\" \"Between 600,000 and 800,000 women are trafficked across borders every year.\" \"Money laundering represents as much as 10 percent of global GDP.\" \"Internet child porn is a $20 billion-a-year industry.\" Peter Andreas and Kelly M. Greenhill see only one problem: these numbers are probably false. Their continued use and abuse reflect a much larger and troubling pattern: policymakers and the media naively or deliberately accept highly politicized and questionable statistical claims about activities that are extremely difficult to measure. As a result, we too often become trapped by these mythical numbers, with perverse and counterproductive consequences. This problem exists in myriad policy realms. But it is particularly pronounced in statistics related to the politically charged realms of global crime and conflict-numbers of people killed in massacres and during genocides, the size of refugee flows, the magnitude of the illicit global trade in drugs and human beings, and so on. In Sex, Drugs, and Body Counts , political scientists, anthropologists, sociologists, and policy analysts critically examine the murky origins of some of these statistics and trace their remarkable proliferation. They also assess the standard metrics used to evaluate policy effectiveness in combating problems such as terrorist financing, sex trafficking, and the drug trade. Contributors: Peter Andreas, Brown University; Thomas J. Biersteker, Graduate Institute of International and Development Studies-Geneva; Sue E. Eckert, Brown University; David A. Feingold, Ophidian Research Institute and UNESCO; H. Richard Friman, Marquette University; Kelly M. Greenhill, Tufts University and Harvard University; John Hagan, Northwestern University; Lara J. Nettelfield, Institut Barcelona D'Estudis Internacionals and Simon Fraser University; Wenona Rymond-Richmond, University of Massachusetts Amherst; Winifred Tate, Colby College; Kay B. Warren, Brown University

In recent years, there has been a strong drive to improve the inclusion of animals of both sexes in the design of in vivo research studies, driven by a need to increase sex representation in fundamental biology and drug development. This has resulted in inclusion mandates by funding bodies and journals, alongside numerous published manuscripts highlighting the issue and providing guidance to scientists. However, progress is slow and barriers to the routine use of both sexes remain. A frequent, major concern is the perceived need for a higher overall sample size to achieve an equivalent level of statistical power, which would result in an increased ethical and resource burden. This perception arises from either the belief that sex inclusion will increase variability in the data (either through a baseline difference or a treatment effect that depends on sex), thus reducing the sensitivity of statistical tests, or from misapprehensions about the correct way to analyse the data, including disaggregation or pooling by sex. Here, we conduct an in-depth examination of the consequences of including both sexes on statistical power. We performed simulations by constructing artificial datasets that encompass a range of outcomes that may occur in studies studying a treatment effect in the context of both sexes. This includes both baseline sex differences and situations in which the size of the treatment effect depends on sex in both the same and opposite directions. The data were then analysed using either a factorial analysis approach, which is appropriate for the design, or a t test approach following pooling or disaggregation of the data, which are common but erroneous strategies. The results demonstrate that there is no loss of power to detect treatment effects when splitting the sample size across sexes in most scenarios, providing that the data are analysed using an appropriate factorial analysis method (e.g., two-way ANOVA). In the rare situations where power is lost, the benefit of understanding the role of sex outweighs the power considerations. Additionally, use of the inappropriate analysis pipelines results in a loss of statistical power. Therefore, we recommend analysing data collected from both sexes using factorial analysis and splitting the sample size across male and female mice as a standard strategy.

Toxoplasma gondii (T. gondii) is an intracellular pathogen that can lead to abortion in pregnant women infected with this parasite. Therefore, the present study aimed to estimate the global seroprevalence of anti-T. gondii antibodies in women who had spontaneous abortion based on the results of published articles and evaluate the relationship between seroprevalence of anti-T. gondii antibodies and abortion via a systematical review and meta-analysis. Different databases were searched in order to gain access to all studies on the seroprevalence of anti- T. gondii antibodies in women who had spontaneous abortion and association between seroprevalence of anti-T. gondii antibodies and abortion published up to April 25th, 2019. Odds ratio (OR) and the pooled rate seroprevalence of T. gondii with a 95% confidence interval (CI) were calculated using the random effects model. In total, 8 cross-sectional studies conducted on 1275 women who had abortion in present pregnancy, 40 cross-sectional studies performed on 9122 women who had a history of abortion, and 60 articles (involving 35 cross-sectional studies including 4436 women who had spontaneous abortion as case and 10398 as control and 25 case-control studies entailing 4656 cases and 3178 controls) were included for the final analyses. The random-effects estimates of the prevalence of anti-T. gondii IgG antibody in women who had abortion in present pregnancy and women who had a history of abortion were 33% (95% CI: 17%-49%) and 43% (95% CI: 27%-60%), respectively. In addition, the pooled OR for anti-T. gondii IgG antibody in cross-sectional and case-control studies among women who had spontaneous abortion were 1.65 (95% CI: 1.31-2.09) and 2.26 (95% CI: 1.56-3.28), respectively. Also, statistical analysis showed that the pooled OR of the risk of anti-T. gondii IgM antibody 1.39 (95% CI: 0.61-3.15) in cross-sectional and 4.33 (95% CI: 2.42-7.76) in case-control studies. Based on the results of the current study, T. gondii infection could be considered a potential risk factor for abortion. It is recommended to carry out further and more comprehensive investigations to determine the effect of T. gondii infection on abortion to prevent and control toxoplasmosis among pregnant women around the world.

In health care and child welfare, clinical records and case notes serve multiple functions. When records are aggregated and processed to create administrative data, they can be analyzed and used to inform policy development and decision-making. To be useful, such data should be complete, accurate, and recorded in a standardized way. However, sources of bias and error can impact the quality of administrative data. During the development of national child welfare data in Canada, child welfare sector partners expressed concerns about the accuracy and completeness of data about children and families. This protocol describes a study that seeks to answer two questions: 1) What individual and institutional factors influence how client data is recorded by child welfare workers in Canada? 2) What data quality issues are created through documentation and case recording practices that may impact the use of clinical case management system data for public health statistics? In this protocol, we describe an exploratory mixed methods study that involves an online survey, interviews with a purposive sample of child welfare workers, and a document review of case recording guidelines. To be eligible for the study, participants must have worked at a child welfare agency or department with clinical documentation responsibilities as a part of their job. We will use descriptive statistics to analyze the survey data and thematic analysis to analyze the qualitative data. This study will help uncover strengths, limitations, and possible sources of bias created through case recording and documentation practices in child welfare. Study results will be shared through presentations to interest holders and will inform the further development of national child welfare data in Canada.

Transit-oriented development (TOD) is a tool that aids in achieving sustainable urban development. It promotes economic, environmental, and social sustainability by integrating land use and transportation planning. Many researchers have investigated mass rapid transit (MRT) station regions for TOD in developed cities. However, in a developing city such as Dhaka, measuring node-based TOD (TOD index) during MRT construction has been disregarded in planning future land use. Furthermore, no prior research on quantitative TOD measurement in Dhaka exists. As a result, we developed a framework for both quantitative and spatial node-based TOD measurement based on the four Ds (density, diversity, destination accessibility, and design) of the TOD concept. With 17 stations under construction, MRT 6 was selected as our study area. The TOD index was measured by nine indicators based on the four criteria (4Ds), spatially in the geographic information system (GIS). After calculating the indicators, the TOD index for each station’s 800m buffer was estimated using the spatial multi-criteria analysis (SMCA). A sensitivity analysis of four TOD scenarios was performed to check the model’s robustness. Additionally, a heatmap of the TOD index for MRT 6 was created for informed planning and policymaking. Furthermore, statistically significant hotspots (both Getis Org Gi* and Anselen Local Moran Statistics) and hotspot clusters were identified. Finally, we illustrate the station-based ranking based on the maximum TOD score. In addition, a detailed spider-web of nine indicators for 17 stations depicts sustainable TOD planning. However, regarding density and diversity, sustainable development and (re)development policies should be implemented not only for MRT 6 but for all Dhaka’s TOD regions.

Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addressing the tradeoff between biological fidelity and model simplicity, and the reality of misspecification for models at all levels of complexity. We assess current methodological approaches to these issues via a case study of the 2010-2019 cholera epidemic in Haiti. We consider three dynamic models developed by expert teams to advise on vaccination policies. We evaluate previous methods used for fitting these models, and we demonstrate modified data analysis strategies leading to improved statistical fit. Specifically, we present approaches for diagnosing model misspecification and the consequent development of improved models. Additionally, we demonstrate the utility of recent advances in likelihood maximization for high-dimensional nonlinear dynamic models, enabling likelihood-based inference for spatiotemporal incidence data using this class of models. Our workflow is reproducible and extendable, facilitating future investigations of this disease system.

Cohort studies in Europe, but not in North-America, showed an association between exposure to outdoor particulate matter with aerodynamic diameter ≤10 μm (PM10) and lung cancer risk. Only a case-control study on lung cancer and PM10 in South Korea has so far been performed. For the first time in Europe we analyzed quantitatively this association using a case-control study design in highly polluted areas in Italy. The Environment And Genetics in Lung cancer Etiology (EAGLE) study, a population-based case-control study performed in the period 2002-2005 in the Lombardy Region, north-west Italy, enrolled 2099 cases and 2120 controls frequency-matched for area of residence, gender, and age. For this study we selected subjects with complete active and passive smoking history living in the same municipality since 1980 until study enrollment. Fine resolution annual PM10 estimates obtained by applying land use regression modeling to satellite data calibrated with fixed site monitor measurements were used. We assigned each subject the PM10 average estimates for year 2000 based on enrollment address. We used logistic regression models to calculate odds ratios (OR) and 95% confidence intervals (CI) adjusted for matching variables, education, smoking, and dietary and occupational variables. We included 3473 subjects, 1665 cases (1318 men, 347 women) and 1808 controls (1368 men, 440 women), with PM10 individual levels ranging from 2.3 to 53.8 μg/m3 (mean: 46.3). We found increasing lung cancer risk with increasing PM10 category (P-value for trend: 0.04). The OR per 10 μg/m3 was 1.28 (95% CI: 0.95-1.72). The association appeared stronger for squamous cell carcinoma (OR 1.44, 95% CI: 0.90-2.29). In a population living in highly polluted areas in Italy, our study added suggestive evidence of a positive association between PM10 exposure and lung cancer risk. This study emphasizes the need to strengthen policies to reduce airborne pollution.

As an important production base for livestock and a unique ecological zone in China, the northeast Tibetan Plateau has experienced dramatic land use/land cover (LULC) changes with increasing human activities and continuous climate change. However, extensive cloud cover limits the ability of optical remote sensing satellites to monitor accurately LULC changes in this area. To overcome this problem in LULC mapping in the Ganan Prefecture, 2000–2018, we used the dense time stacking of multi-temporal Landsat images and random forest algorithm based on the Google Earth Engine (GEE) platform. The dynamic trends of LULC changes were analyzed, and geographical detectors quantitatively evaluated the key driving factors of these changes. The results showed that (1) the overall classification accuracy varied between 89.14% and 91.41%, and the kappa values were greater than 86.55%, indicating that the classification results were reliably accurate. (2) The major LULC types in the study area were grassland and forest, and their area accounted for 50% and 25%, respectively. During the study period, the grassland area decreased, while the area of forest land and construction land increased to varying degrees. The land-use intensity presents multi-level intensity, and it was higher in the northeast than that in the southwest. (3) Elevation and population density were the major driving factors of LULC changes, and economic development has also significantly affected LULC. These findings revealed the main factors driving LULC changes in Gannan Prefecture and provided a reference for assisting in the development of sustainable land management and ecological protection policy decisions.