Scope of Knowledge About Zika Among Women in US–Mexico Border Communities

Maternal Zika virus infection (ZIKV) has serious health consequences for unborn offspring. Knowledge about prevention is critical to reducing risk, yet what women in the high-risk US–Mexico border region know about protecting themselves and their babies from ZIKV is mostly unknown. This study aimed to assess knowledge of ZIKV among pregnant and inter-conception women and to identify sources of information that might address knowledge gaps. Clients in five federally-funded, border region Healthy Start programs (N?=?326) were interviewed in late 2016 about their knowledge of ZIKV prevention methods and whether they believed themselves or their babies to be at risk. Sources of information about ZIKV and demographic characteristics were also measured. Chi square tests identified important associations between variables; adjusted odds ratios (AOR) and 95% confidence intervals for knowledge and beliefs were calculated. Among the 305 women aware of ZIKV, 69.5% could name two ways to prevent infection. Only 16.1% of women named using condoms or abstaining from sex as a prevention method. While 75.3% heard about ZIKV first from TV/radio, just 9.5% found the information helpful. Women who received helpful information from health care providers had greater odds of knowing two prevention methods (AOR?=?2.0; 1.1–3.7), when to test for ZIKV (AOR?=?5.2; 2.1–13.2), and how long to delay pregnancy after infection in a male partner (AOR?=?1.9; 1.1–3.2). Those who said web-based and social media sources were helpful had greater odds of knowing when to test for ZIKV (AOR?=?2.8; 1.3–6.3). Results can inform messaging for safe pregnancy and ZIKV prevention.     

Perception of Community on Attachment and Resilience According to Closing of Hospitals

This study includes a comparison analysis of how community resilience and its influence factor of community attachment were changed before and after the closing of the hospital in the rural community. In order to analyze the influence factors of healthcare and medical vulnerability on community resilience, as well as the mean difference from before and after the closing of the hospital, this study conducted matching sample t test, hierarchical regression analysis, difference in differences (DID) analysis. The results indicate that community attachment consists of such factors as closeness, preference, affective bond, and identity, and they showed significance in confidence. It showed that resilience and community attachment are lower after closing than before closing. Moreover, as a result of DID analysis where the variation of community attachment affects community resilience, closeness together with closeness had an influence on the community resilience. It needs to expand public projects to improve the settlement environment of the basic local government, and in particular, it should consider various support measures to overcome vulnerabilities in the healthcare and medical sector.     

Use of Evidence-Based Interventions and Implementation Strategies to Increase Colorectal Cancer Screening in Federally Qualified Health Centers

While colorectal cancer (CRC) screening rates have been increasing in the general population, rates are considerably lower in Federally Qualified Health Centers (FQHCs), which serve a large proportion of uninsured and medically vulnerable patients. Efforts to screen eligible patients must be accelerated if we are to reach the national screening goal of 80% by 2018 and beyond. To inform this work, we conducted a survey of key informants at FQHCs in eight states to determine which evidence-based interventions (EBIs) to promote CRC screening are currently being used, and which implementation strategies are being employed to ensure that the interventions are executed as intended. One hundred and forty-eight FQHCs were invited to participate in the study, and 56 completed surveys were received for a response rate of 38%. Results demonstrated that provider reminder and recall systems were the most commonly used EBIs (44.6%) while the most commonly used implementation strategy was the identification of barriers (84.0%). The mean number of EBIs that were fully implemented at the centers was 2.4 (range 0–7) out of seven. Almost one-quarter of respondents indicated that their FQHCs were not using any EBIs to increase CRC screening. Full implementation of EBIs was correlated with higher CRC screening rates. These findings identify gaps as well as the preferences and needs of FQHCs in selecting and implementing EBIs for CRC screening.     

Motives for Filing a False Allegation of Rape

The list of motives by Kanin (1994) is the most cited list of motives to file a false allegation of rape. Kanin posited that complainants file a false allegation out of revenge, to produce an alibi or to get sympathy. A new list of motives is proposed in which gain is the predominant factor. In the proposed list, complainants file a false allegation out of material gain, emotional gain, or a disturbed mental state. The list can be subdivided into eight different categories: material gain, alibi, revenge, sympathy, attention, a disturbed mental state, relabeling, or regret. To test the validity of the list, a sample of 57 proven false allegations were studied at and provided by the National Unit of the Dutch National Police (NU). The complete files were studied to ensure correct classification by the NU and to identify the motives of the complainants. The results support the overall validity of the list. Complainants were primarily motivated by emotional gain. Most false allegations were used to cover up other behavior such as adultery or skipping school. Some complainants, however, reported more than one motive. A large proportion, 20% of complainants, said that they did not know why they filed a false allegation. The results confirm the complexity of motivations for filing false allegations and the difficulties associated with archival studies. In conclusion, the list of Kanin is, based on the current results, valid but insufficient to explain all the different motives of complainants to file a false allegation.     

Will biomarker-based diagnosis of Alzheimer’s disease maximize scientific progress? Evaluating proposed diagnostic criteria

A recently published framework for the diagnosis of Alzheimer’s disease (AD) in research studies would allow diagnosis on the sole basis of two biomarkers (β-amyloid and pathologic tau), even in people with no objective or subjective memory or cognitive changes. This revision will have substantial implications for future Alzheimer’s research, and the changes should be rigorously evaluated before widespread adoption. We propose three principles for evaluating any revision to diagnostic frameworks for AD: (1) does the revision improve the validity of the diagnosis; (2) does the revision improve the reliability or reduce the expense of the diagnosis; and (3) will the revision foster innovative and rigorous research across populations. The new diagnostic framework is unlikely to achieve any of these goals. Instead, it has the potential to handicap future researchers, and slow progress towards identifying effective strategies to prevent or treat AD.     

The combination of cardiorespiratory fitness and muscle strength,and mortality risk

Little is known about the combined associations of cardiorespiratory fitness (CRF) and hand grip strength (GS) with mortality in general adult populations. The purpose of this study was to compare the relative risk of mortality for CRF, GS, and their combination. In UK Biobank, a prospective cohort of >?0.5 million adults aged 40–69 years, CRF was measured through submaximal bike tests; GS was measured using a hand-dynamometer. This analysis is based on data from 70,913 men and women (832 all-cause, 177 cardiovascular and 503 cancer deaths over 5.7-year follow-up) who provided valid CRF and GS data, and with no history of heart attack/stroke/cancer at baseline. Compared with the lowest CRF category, the hazard ratio (HR) for all-cause mortality was 0.76 [95% confidence interval (CI) 0.64–0.89] and 0.65 (95% CI 0.55–0.78) for the middle and highest CRF categories, respectively, after adjustment for confounders and GS. The highest GS category had an HR of 0.79 (95% CI 0.66–0.95) for all-cause mortality compared with the lowest, after adjustment for confounders and CRF. Similar results were found for cardiovascular and cancer mortality. The HRs for the combination of highest CRF and GS were 0.53 (95% CI 0.39–0.72) for all-cause mortality and 0.31 (95% CI 0.14–0.67) for cardiovascular mortality, compared with the reference category of lowest CRF and GS: no significant association for cancer mortality (HR 0.70; 95% CI 0.48–1.02). CRF and GS are both independent predictors of mortality. Improving both CRF and muscle strength, as opposed to either of the two alone, may be the most effective behavioral strategy to reduce all-cause and cardiovascular mortality risk.     

Individually-matched etiologic studies: classical estimators made new again

With greater access to regression-based methods for confounder control, the etiologic study with individual matching, analyzed by classical (calculator) methods, lost favor in recent decades. This design was costly, and the data sometimes mis-analyzed. Now, with Big Data, individual matching becomes an economical option. To many, however, conditional logistic regression, commonly used to estimate the incidence density ratio parameter, is somewhat of a black box whose output is not easily checked. An epidemiologist-statistician pair recently proposed a new estimator that is easily applied to data from individually-matched series with a 2:1 ratio (and no other confounding variables) using just a hand calculator or spreadsheet. Surprisingly—or possibly not—they overlooked classical estimators developed in earlier decades. This prompts me to re-introduce some of these, to highlight their considerable flexibility and ease of use, and to update them. Nowadays, for any matching ratio (M:1), the Maximum Likelihood result can be easily computed from data gathered under the matched design in two different ways, each using just the summary data. One is via any binomial regression program that allows offsets, applied to just M ‘rows’ of data. The other is by hand! The aim of this note is not to save on computation; instead, it is to make connections between classical and regression-based methods, to promote terminology that reflects the concepts and structure of the etiologic study, and to focus attention on what parameter is being estimated.