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Aims: To estimate the effectiveness of metformin on glycaemic parameters among participants with incident prediabetes attending Australian general practices. Methods: This retrospective cohort study used electronic health records of regular participants (3+ visits in two consecutive years) attending 383 Australian general practices (MedicineInsight). Participants with 'incident' prediabetes (newly recorded diagnosis between 2012 and 2017) and their glycaemic parameters (haemoglobin A1c [HbA1c] or fasting blood glucose [FBG]) at 6‐, 12‐, and 18–24 months post diagnosis (unexposed) or post‐management with metformin (treatment) were identified from the database. We estimated the average treatment effect (ATE) of metformin management on glycaemic parameters using both linear regression and augmented inverse probability weighting. Results: Of the 4770 investigated participants with 'incident' prediabetes, 10.2% were managed with metformin. Participants on metformin had higher HbA1c levels at the baseline than those unexposed (mean 45 mmol/mol [6.2%] and 41 mmol/mol [5.9%], respectively), but no differences were observed at 6–12 months (mmol/mol ATE 0.0, 95% CI −0.4; 0.7) or 12–18 months (ATE −0.3, 95% CI −1.2; 0.3). However, participants on metformin had lower mean HbA1c mmol/mol at 18–24 months (ATE −1.1, 95% CI −2.0; 0.1) than those unexposed. Consistent results were observed for FBG (ATE at 6–12 months −0.14 [95% CI −0.25; −0.04], 12–18 months 0.02 [95% CI −0.08; 0.13] and 18–24 months −0.07 [95% CI −0.25; 0.12]). Conclusion: The higher HbA1c and FBG baseline levels among participants with 'incident' prediabetes managed with metformin improved after 6–12 months of starting pharmacological management, and the effect persisted for up to 24 months. Management with metformin could prevent further deterioration of glycaemic levels. [ABSTRACT FROM AUTHOR]

Objectives: Persons with Parkinson's disease (PD) have a higher prevalence of oral diseases and orofacial dysfunction, but knowledge about the use of dental care and whether their dental care needs are met is sparse. This study aimed to investigate the dental attendance and usage of dental care services of the total PD population in Denmark and compare it with a control group. Methods: National registers were used to identify the total PD population in Denmark (n = 6874) and to obtain data on their dental care from 2015 to 2019. These data were compared with a five‐fold age‐, gender‐ and geographically matched control group without PD (n = 34 285). Register data on age, gender, civil status, educational level, income, nursing homes status and mortality were also collected and adjusted for in the analyses. The dental attendance was analysed using χ2‐test with Bonferroni correction, and the type of dental care services was analysed using negative binomial regression analysis. Results: A significantly higher proportion of persons with PD were irregular attenders of the dental care system (21.0%), compared with the control group (16.9%). Persons with PD had a significantly higher overall usage of dental cares services. Most prominent was the high usage of treatment services, where persons with PD had a 1.50 times higher incidence rate of tooth extractions and a 1.71 times higher incidence rate of tooth fillings in the five years compared with the control group. Conclusion: Persons with PD are more often irregular users of dental care and receive more treatment services than the control group. This indicates a need for high‐quality prophylactic initiatives to prevent high filling and tooth extraction rates. Furthermore, this knowledge can be used by clinicians and decision makers to ensure optimal dental care for persons with PD. [ABSTRACT FROM AUTHOR]

Background: Measuring multiple and higher‐order interaction effects between multiple categorical variables proves challenging. Objectives: To illustrate a multilevel modelling approach to studying complex interactions. Methods: We apply a two‐level random‐intercept linear regression to a binary outcome for individuals (level‐1) nested within strata (level‐2) defined by all observed combinations of multiple categorical exposure variables. As a pedagogic application, we analyse 36 strata defined by five risk factors of preeclampsia (parity, previous preeclampsia, chronic hypertension, multiple pregnancies, body mass index category) among 652,603 women in the Swedish Medical Birth Registry between 2002 and 2010. Results: The absolute risk of preeclampsia was 4% but was predicted to vary from 1% to 44% across strata. The stratum discriminatory accuracy was 30% according to the variance partition coefficient (VPC) and 0.73 according to the area under the receiver operating characteristic curve (AUC). While the risk heterogeneity across strata was primarily due to the main effects of the categories defining the strata, 5% of the variation was attributable to their two‐ and higher‐way interaction effects. One stratum presented a positive interaction, and two strata presented negative interaction. Conclusions: Multilevel modelling is an innovative tool for identifying and analysing higher‐order interaction effects. Further work is needed to explore how this approach can best be applied to making causal inferences. [ABSTRACT FROM AUTHOR]

Oral conditions represent a critical public health challenge, and together with descriptive and predictive epidemiology, causal inference has a crucial role in developing and testing preventive oral health interventions. By identifying not just correlations but actual causes of disease, causal inference may quantify the average effect of interventions and guide policies. Although authors are not usually explicit about it, most oral health studies are guided by causal questions. However, methodological deficiencies limit their interpretability and the implementation of their findings. This manuscript is a call to action on the use of causal inference in oral research. Its application starts with asking theoretically sound questions and being explicit about causal relationships, defining the estimates to evaluate, and measuring them properly. Beyond promoting causal analytical approaches, we emphasize the need for more causal thinking to promote thoughtful research questions and the use of appropriate methods to answer them. Causal inference relies on the plausibility of assumptions underlying the data analysis and the quality of the data, and we argue that high‐quality observational studies can be used to estimate average causal effects. Although individual efforts to embrace causal inference in dentistry are essential, they will not yield substantial results if not led by a systematic and structural change in the field. We urge scientific societies, funding bodies, dental schools, and journals to promote transparency in research, causal thinking, and causal inference projects to move the field toward more meaningful studies. It is also time for researchers to move forward and connect with the community, co‐produce investigations and translate their findings, and engage in interventions that impact public health. We conclude by highlighting the importance of triangulating results from different data sources and methods to support causal inference and inform decision‐making on interventions to effectively improve population oral health. [ABSTRACT FROM AUTHOR]

Conceptual or theoretical models are crucial in developing causal hypotheses and interpreting study findings, but they have been underused and misused in aetiological research, particularly in dentistry and oral epidemiology. Good models should incorporate updated evidence and clarify knowledge gaps to derive logical hypotheses. Developing models and deriving testable hypotheses in operational models can be challenging, as seen in the four examples referred to in this commentary. One challenge concerns the theoretical validity of the model, while another relates to difficulties in operationalizing abstract concepts. A third challenge refers to the lack of sufficient information in the dataset to test partially or even the whole model. Finally, a common challenge is the application of a conceptual model to different contexts. Among the existing methodological approaches to operationalize conceptual models, causal graphs may be helpful, especially when combined with approaches from diverse disciplinary fields via triangulation. [ABSTRACT FROM AUTHOR]

Objective: To evaluate associations between spousal caregiving and mental and physical health among older adults in Mexico. Methods: Data come from the Mexican Health & Aging Study, a national population‐based study of adults ≥50 years and their spouses (2001–2015). We compared outcomes for spousal caregivers to outcomes for those whose spouses had difficulty with at least one basic or instrumental activity of daily living (I/ADL) but were not providing care; the control group conventionally includes all married respondents regardless of spouse's need for care. We used targeted maximum likelihood estimation to evaluate the associations with past‐week depressive symptoms, lower‐body functional limitations, and chronic health conditions. Results: At baseline, 846 women and 629 men had a spouse with ≥1 I/ADL. Of these, 60.9% of women and 52.6% of men were spousal caregivers. Spousal caregiving was associated with more past‐week depressive symptoms for men (Marginal Risk Difference (RD): 0.27, 95% confidence internal [CI]: 0.03, 0.51) and women (RD: 0.15, 95% CI: 0.07, 0.23). We could not draw conclusions about associations with lower‐body functional limitations and chronic health conditions. On average, all respondents whose spouses had caregiving needs had poorer health than the overall sample. Conclusion: We found evidence of an association between spousal caregiving and mental health among older Mexican adults with spouses who had need for care. However, our findings suggest that older adults who are both currently providing or at risk of providing spousal care may need targeted programs and policies to support health and long‐term care needs. [ABSTRACT FROM AUTHOR]

Q fever is a zoonotic disease caused by the intracellular bacterium, Coxiella burnetii. Its primary mode of transmission is by inhalation of aerosols originating from infected animals and contaminated environments. The organism has a very low infective dose, can persist in the environment for long periods of time and large outbreaks fuelled by windborne spread have been previously reported. Detection of C. burnetii in the environment is therefore important during human and animal outbreak investigations and for the control and prevention of Q fever. This study aimed to systematically review and critically appraise the published literature on sampling methods used to detect C. burnetii from different environmental samples. A search of four electronic databases with subsequent hand searching identified 47 eligible articles published since 1935. These articles described sampling of dust, air, soil and liquids in attempts to detect C. burnetii during 19 Q fever outbreaks and in 28 endemic settings. Environmental positivity was most commonly associated with ruminant livestock populations. Evidence describing spatio‐temporal characteristics and associated geographical dispersion gradients was limited. The most commonly tested sample type was dust which also yielded the highest bacterial loads of >108 bacteria/cloth. The MD8 (Sartorius) air sampler was used widely for air sampling. Soil was the only sample type for which a validated laboratory protocol was established specifically for C. burnetii. Each environmental sample type has its advantages and limitations which are discussed in detail and a simplified framework to guide decisions around environmental sampling for C. burnetii is provided. In any type of environmental sampling, it is recommended to use standardized and validated methods and to match the most ideal sampling strategy and timing with the research context. These conditions are essential to be considered when designing future Q fever management plans that involve environmental sampling for C. burnetii. [ABSTRACT FROM AUTHOR]

Background: Large birth cohort studies are extremely valuable in assessing associations between early life exposures and long-term outcomes. Establishing new birth cohorts is challenging due to declining participation rates. Online methods of data collection may increase feasibility, but have not been evaluated thoroughly.Objective: The primary objective of the ongoing PRegnancy and Infant DEvelopment (PRIDE) Study is to identify exposures during pregnancy and in early life that may affect short-term or long-term health of mother and/or child. In this manuscript, we aimed to evaluate methods of recruitment and online data collection applied.Population: Dutch women aged ≥18 years in early pregnancy.Design: Prospective cohort study.Methods: Initially, only prenatal care providers recruited participants, but alternative recruitment methods were added as a result of disappointing participation rates, including collaboration with "Moeders voor Moeders" (organisation that visits women in early pregnancy) and Facebook advertisements. Data on demographic characteristics, obstetric history, maternal health, life style factors, occupational exposures, nutrition, pregnancy complications, and infant outcomes are primarily collected through Web-based questionnaires at multiple time points during and after pregnancy. Additional data collection components include paternal questionnaires, blood and saliva sampling, and linkage to medical records.Preliminary Results: By September 2019, 9573 women were included in the PRIDE Study, of which 1.3% completed paper-based questionnaires. Mean age of the women analysed was 30.6 years, 71.1% had a high level of education, 57.2% were primiparae, and mean gestational age at enrolment was 9.9 (range 3, 37) weeks, with slight differences between recruitment methods. Pregnancy outcome was known for 89.8%. Retention rate at 6 months after the estimated date of delivery was estimated at 70%. Multiple validation studies conducted within the PRIDE Study indicated high data quality.Conclusion(s): Although challenging and time-consuming, online methods for recruitment and data collection may enable the establishment of new birth cohort studies. [ABSTRACT FROM AUTHOR]

Study quality depends on a number of factors, one of them being internal validity. Such validity can be affected by random and systematic error, the latter also known as bias. Both make it more difficult to assess a correct frequency or the true relationship between exposure and outcome. Where random error can be addressed by increasing the sample size, a systematic error in the design, the conduct or the reporting of a study is more problematic. In this article, we will focus on bias, discuss different types of selection bias (sampling bias, confounding by indication, incidence‐prevalence bias, attrition bias, collider stratification bias and publication bias) and information bias (recall bias, interviewer bias, observer bias and lead‐time bias), indicate the type of studies where they most frequently occur and provide suggestions for their prevention. SUMMARY AT A GLANCE: This article discusses the different types of selection bias and information bias that are found in studies, indicate the type of studies where they most frequently occur and proposes suggestions for their prevention. [ABSTRACT FROM AUTHOR]

Background: Clinical conditions leading to delivery are heterogeneous. However, most studies examining the short- and long-term consequences of birth on child health only consider gestational age at delivery, not the underlying cause.Objective: To examine the effect of both gestational age at delivery and underlying cause of delivery on child health outcomes.Methods: This population-based retrospective cohort study of singleton infants born in Alberta (April 2004-March 2005) used linked administrative and perinatal data to identify birth subtypes by underlying cause (infection/inflammation (I/I), placental dysfunction (PD), both, or neither), gestational age at delivery, and child health outcomes (neonatal morbidity and mortality, paediatric complex chronic conditions, and neurodevelopmental disorders and disabilities). Poisson regression with robust variance was used to assess differences in the (adjusted) risk ratio (RR) of each outcome by gestational age, and by cause of delivery. The roles of gestational age and cause of delivery were examined using mediation analysis methods.Results: A total of 38,192 children were included, with 66.7% experiencing neither I/I nor PD (I/I: 4.0%, PD: 27.5%, both: 1.8%). Infants born preterm had higher risk of all outcomes compared to those born at term and late-term. Infants with exposure to both causes had higher risk of all outcomes (neonatal morbidity, RR 8.96, 95% confidence interval [CI] 7.55, 10.63; paediatric complex chronic conditions, RR 3.94, 95% CI 3.08, 5.05; and neurodevelopmental disorders, RR 1.58, 95% CI 1.37, 1.84). The effect of underlying cause of delivery on child health outcomes was partially explained by gestational age, more in cases involving I/I than in those involving PD alone.Conclusions: Short- and long-term child health outcomes differ by the underlying cause leading to delivery, as well as the gestational age at delivery. Having a clearer prognosis for infants may promote the use of clinical interventions earlier for children at increased risk. [ABSTRACT FROM AUTHOR]

Objective: Little is known about how the physical environment impacts physical activity behaviour among rural populations, who are typically less active and at higher risk of chronic disease than urban dwellers. The lack of individual-level instruments to assess the physical environment in rural areas limits advancement of this field. Among rural adults, this study aimed to evaluate (a) the test-retest reliability of a self-reported questionnaire of individual-level perceptions of the physical activity environment, and (b) the stability of a self-reported physical activity questionnaire. Design: Cross-sectional questionnaire repeated twice, 2 weeks apart. The questionnaire included 94 items relating to the perceived physical environment (representing nine summary scores), demographic characteristics and physical activity. Setting: Rural Australia. Participants: Rurally residing adults (>18 years) across three Australian states. Main outcome measures: Test-retest reliability evaluated by weighted Kappa statistics (individual items) and intra-class correlations (summary scores). Results: A total of 292 participants (20% men) completed both questionnaires, on average 22 days apart. Test-retest reliability of individual items ranged from weighted Kappa 0.37-0.85 (median: 0.59). Internal reliability for five summary scores was good to excellent (Cronbach's alpha: 0.81-0.97). Test-retest reliability was good to excellent for six summary scores (intra-class correlations: 0.67-0.77). Conclusions: The findings indicated good to excellent test-retest reliability for most items, particularly "fixed" constructs for this new questionnaire measuring the perceived physical environment in rural populations. This study represents an important step towards improving measurement of physical activity environments in rural populations, potentially leading to better tailored interventions to promote active and healthy living in rural areas. [ABSTRACT FROM AUTHOR]

Aim: Duration of untreated psychosis (DUP) is considered as a key prognostic variable in psychosis. Yet, it is unclear whether a longer DUP causes worse outcomes or whether reported associations have alternative explanations. Methods: Data from 2 cohorts of patients with first episode psychosis were used (n = 2134). Measures of DUP were assessed at baseline and outcomes at 12 months. Regression models were used to investigate the associations between DUP and outcomes. We also investigated whether any associations were replicated using instrumental variables (IV) analysis to reduce the effect of residual confounding and measurement bias. Results: There were associations between DUP per 1‐year increase and positive psychotic symptoms (7.0% in symptom score increase 95% confidence interval (CI) 4.0%, 10.0%, P < .001), worse recovery (risk difference [RD] 0.78, 95%, CI 0.68, 0.83, P < .001) and worse global functioning (0.62 decrease in functioning score 95% CI −1.19, −0.04, P = .035). There was no evidence of an association with negative psychotic symptoms (1.0%, 95%, CI −2.0%, 5.0%, P = .455). The IV analysis showed weaker evidence of associations in the same direction between DUP per 1‐year increase and positive psychotic symptoms, recovery and global functioning. However, there was evidence of an inverse association with negative psychotic symptoms (decrease of 15.0% in symptom score 95% CI −26.0%, −3.0%, P = .016). Conclusions: We have confirmed previous findings of a positive association between positive psychotic symptoms, global functioning and recovery and DUP using regression analysis. IV analysis shows some support for these findings. Future investigation using IV analysis should be repeated in large data sets. [ABSTRACT FROM AUTHOR]

Introduction and Aims: There is a paucity of data on volume of alcohol use from Muslim majority countries. We aimed to present estimation methods for alcohol consumption with the use of survey data for these societies and provide an estimation for age 15+ per capita consumption of pure alcohol for Iran.Design and Methods: The Iranian Mental Health Survey was a nationally representative household survey on individuals aged 15-64 years, with a multistage, cluster sampling design. We used the 'Last Week' method and 'Quantity-Frequency' methods for gathering data on alcohol consumption and combined these to provide more complete estimates.Results: The response rate was 85.7%. From the total of 7840 respondents, 5.7% and 1% reported past 12 months and past week alcohol use, respectively. The highest estimation for age 15+ per capita consumption of pure alcohol was yielded by the 'combination method' (0.108 L ethanol/person/year) followed by the Quantity-Frequency method (0.079 L). The 'Last Week' method provided the lowest estimate (0.059 L).Discussion and Conclusions: Unlike in surveys of non-Muslim countries, frequency of drinking from recent recall (last week) was much lower than from recall of usual drinking in the last year. We conclude that 0.108 L (SE = 0.03) is the best survey-based estimate of age 15+ per capita consumption, which translates to about 5 750 000 L of national consumption per year in Iran. However, this method is still likely to under-estimate per capita consumption due to evidence of under-reporting in the survey. [ABSTRACT FROM AUTHOR]

Longitudinal cohort studies can provide important evidence about preventable causes of disease, but the success relies heavily on the commitment of their participants, both at recruitment and during follow up. Initial participation rates have decreased in recent decades as have willingness to participate in subsequent follow ups. It is important to examine how such selection affects the validity of the results. In this article, we describe the conceptual framework for selection bias due to nonparticipation and loss to follow up in cohort studies, using both a traditional epidemiological approach and directed acyclic graphs. Methods to quantify selection bias are introduced together with analytical strategies to adjust for the bias including controlling for covariates associated with selection, inverse probability weighting and bias analysis. We use several studies conducted in the Danish National Birth Cohort as examples of how to quantify selection bias and also understand the underlying selection mechanisms. Although women who chose to participate in this cohort were typically of higher social status, healthier and with less disease than all those eligible for study, differential selection was modest and the influence of selection bias on several selected exposure-outcome associations was limited. These findings are reassuring and support enrolling a subset of motivated participants who would engage in long-term follow up rather than prioritize representativeness. Some of the presented methods are applicable even with limited data on nonparticipants and those lost to follow up, and can also be applied to other study designs such as case-control studies and surveys. [ABSTRACT FROM AUTHOR]

Confounding is an important source of bias, but it is often misunderstood. We consider how confounding occurs and how to address confounding using examples. Study results are confounded when the effect of the exposure on the outcome, mixes with the effects of other risk and protective factors for the outcome. This problem arises when these factors are present to different degrees among the exposed and unexposed study participants, but not all differences between the groups result in confounding. Thinking about an ideal study where all of the population of interest is exposed in one universe and is unexposed in a parallel universe helps to distinguish confounders from other differences. In an actual study, an observed unexposed population is chosen to stand in for the unobserved parallel universe. Differences between this substitute population and the parallel universe result in confounding. Confounding by identified factors can be addressed analytically and through study design, but only randomization has the potential to address confounding by unmeasured factors. Nevertheless, a given randomized study may still be confounded. Confounded study results can lead to incorrect conclusions about the effect of the exposure of interest on the outcome. [ABSTRACT FROM AUTHOR]

Confounding biases study results when the effect of the exposure on the outcome mixes with the effects of other risk and protective factors for the outcome that are present differentially by exposure status. However, not all differences between the exposed and unexposed group cause confounding. Thus, sources of confounding must be identified before they can be addressed. Confounding is absent in an ideal study where all of the population of interest is exposed in one universe and is unexposed in a parallel universe. In an actual study, an observed unexposed population represents the unobserved parallel universe. Thinking about differences between this substitute population and the unexposed parallel universe helps identify sources of confounding. These differences can then be represented in a diagram that shows how risk and protective factors for the outcome are related to the exposure. Sources of confounding identified in the diagram should be addressed analytically and through study design. However, treating all factors that differ by exposure status as confounders without considering the structure of their relation to the exposure can introduce bias. For example, conditions affected by the exposure are not confounders. There are also special types of confounding, such as time-varying confounding and unfixable confounding. It is important to evaluate carefully whether factors of interest contribute to confounding because bias can be introduced both by ignoring potential confounders and by adjusting for factors that are not confounders. The resulting bias can result in misleading conclusions about the effect of the exposure of interest on the outcome. [ABSTRACT FROM AUTHOR]

Objectives To investigate whether height at the age of 31 is associated with the incidence of knee and hip osteoarthritis ( OA) in the following 15 years. Methods Participants in The Northern Finland Birth Cohort 1966 ( NFBC1966) diagnosed with knee or hip OA between the ages of 31 and 46 were used as OA cases. Study subjects without knee and hip OA were used as the controls. Height and weight were measured in a clinical examination at the age of 31 (baseline). Mean heights for the OA cases and the controls were compared by an independent samples t-test. Cox regression analysis was performed to calculate the risk for OA for different height quartiles. The results were adjusted for body mass index/weight, education, smoking and leisure-time physical activity at baseline. Additionally, a Kaplan-Meier analysis was performed. Results Men with knee OA were 2.6 cm taller ( P < 0.001) and women with knee OA 1.2 cm taller ( P = 0.048) than the controls. Hip OA cases were found to be slightly shorter than the controls, but no statistically significant differences were observed. The adjusted hazard ratios ( HRs) for knee OA and hip OA in the highest quartile were 2.5 (95% CI 1.4-4.5) and 1.0 (95% CI 0.3-3.4) for men and 1.8 (95% CI 1.0-3.1) and 0.7 (95% CI 0.2-2.3) for women. Conclusions Height at the age of 31 was associated with incidence of early knee OA, diagnosed prior to age 46. However, the low incidence of hip OA made our results for hip OA inconclusive. [ABSTRACT FROM AUTHOR]

Background A method applied in some large studies of weight and mortality is to begin with a well-defined analytic cohort and use successive restrictions to control for methodologic bias and arrive at final analytic results. Materials and methods Two observational studies of body mass index and mortality allow a comparative assessment of these restrictions in very large data sets. One was a meta-analysis of individual participant data with a sample size of 8 million. The second was a study of a South Korean cohort with a sample size of 12 million. Both presented results for participants without pre-existing disease before and after restricting the sample to never-smokers and deleting the first 5 years of follow-up. Results Initial results from both studies were generally similar, with hazard ratios ( HRs) below 1 for overweight and above 1 for underweight and obesity. The meta-analysis showed higher HRs for overweight and obesity after the restrictions, including a change in the direction of the HR for overweight from 0·99 (95% CI: 0·98-1·01) to 1·11 (95% CI: 1·10, 1·11). The South Korean data showed little effect of the restrictions and the HR for overweight changed from 0·85 (95% CI: 0·84-0·86) to 0·91 (95% CI: 0·90, 0·91). The summary effect size for overweight was 0·90 (95% CI: 0·89-0·91) before restrictions and 1·02 (95% CI: 1·02, 1·03) after restrictions. Conclusions The effect of the restrictions is not consistent across studies, weakening the argument that analyses without such restrictions lack validity. [ABSTRACT FROM AUTHOR]

Aim: To overcome the lack of a validated and robust Arabic instrument to measure breast cancer awareness. Background: Currently, there is no validated Arabic instrument for measuring breast cancer awareness levels. We adapted, translated and validated the Breast Cancer Awareness Measure developed by Cancer Research UK. Methods: The instrument was translated into Arabic and back ‐ translated for validation. Validation and reliability tests were conducted using purposively sampled 972 Arab women older than 20 years, living in Oman. The adapted content was validated by a panel of medical, linguistic and cultural experts, followed by cognitive interviews (n = 10), behavioural coding (n = 30) and criterion validation (n = 646). The instrument was tested for acceptability and its subscales for internal consistency. Inter ‐ rater reliability was estimated between two similar groups (n = 144 and n = 142) to test homogeneity. Results: The adapted and translated instrument had a high acceptability (98.7% completed). The validation process shaped the adaptation, and resulted in strong criterion validity (R = 0.58, P < 0.01). The instrument subscales for risk factors and warning signs had high internal consistency (Cronbach's alpha 0.856 and 0.890, respectively), with all floor and ceiling effects less than 15%. The correlation measure for inter ‐ rater reliability was 0.97 (P < 0.01). Discussion: Through the incorporation of contextual characteristics and prevalent beliefs among Arab populations, the adapted Best Cancer Awareness Measure is a robust Arabic instrument for the measurement of breast cancer awareness and early detection practices among Arab women. Limitations: The purposively selected sample may not be representative of the population. Conclusion and implications for health policy: Improvement of awareness and early detection of breast cancer can contribute towards reducing mortality from the disease. The adapted instrument has policy implications, since measurement of awareness levels is essential towards breast health promotion policies in Arab countries. [ABSTRACT FROM AUTHOR]

In psychiatry and psychology, relationship patterns connecting disorders and risk factors are always complex and intricate. Advanced statistical methods have been developed to overcome this issue, the most common being structural equation modelling (SEM). The main approach to SEM (CB-SEM for covariance-based SEM) has been widely used by psychiatry and psychology researchers to test whether a comprehensive theoretical model is compatible with observed data. While the validity of this approach method has been demonstrated, its application is limited in some situations, such as early-stage exploratory studies using small sample sizes. The partial least squares approach to SEM (PLS-SEM) has risen in many scientific fields as an alternative method that is especially useful when sample size restricts the use of CB-SEM. In this article, we aim to provide a comprehensive introduction to PLS-SEM intended to CB-SEM users in psychiatric and psychological fields, with an illustration using data on suicidality among prisoners. Researchers in these fields could benefit from PLS-SEM, a promising exploratory technique well adapted to studies on infrequent diseases or specific population subsets. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective: Despite the proliferation of databases with increasingly rich patient data, prediction of medication adherence remains poor. We proposed and evaluated approaches for improved adherence prediction.Data Sources: We identified Medicare beneficiaries who received prescription drug coverage through CVS Caremark and initiated a statin.Study Design: A total of 643 variables were identified at baseline from prior claims and linked Census data. In addition, we identified three postbaseline predictors, indicators of adherence to statins during each of the first 3 months of follow-up. We estimated 10 models predicting subsequent adherence, using logistic regression and boosted logistic regression, a nonparametric data-mining technique. Models were also estimated within strata defined by the index days supply.Results: In 77,703 statin initiators, prediction using baseline variables only was poor with maximum cross-validated C-statistics of 0.606 and 0.577 among patients with index supply ≤30 days and >30 days, respectively. Using only indicators of initial statin adherence improved prediction accuracy substantially among patients with shorter initial dispensings (C = 0.827/0.518), and, when combined with investigator-specified variables, prediction accuracy was further improved (C = 0.842/0.596).Conclusions: Observed adherence immediately after initiation predicted future adherence for patients whose initial dispensings were relatively short. [ABSTRACT FROM AUTHOR]