Background: Dairy product and calcium intakes have been associated with increased prostate cancer risk, but whether specific dairy products or calcium sources are associated with risk is ...unclear.Objective: In the Continuous Update Project, we conducted a meta-analysis of prospective studies on intakes of dairy products and calcium and prostate cancer risk.Design: PubMed and several other databases were searched up to April 2013. Summary RRs were estimated by using a random-effects model.Results: Thirty-two studies were included. Intakes of total dairy products summary RR: 1.07 (95% CI: 1.02, 1.12; n = 15) per 400 g/d, total milk summary RR: 1.03 (95% CI: 1.00, 1.07; n = 14) per 200 g/d, low-fat milk summary RR: 1.06 (95% CI: 1.01, 1.11; n = 6) per 200 g/d, cheese summary RR: 1.09 (95% CI: 1.02, 1.18; n = 11) per 50 g/d, and dietary calcium summary RR: 1.05 (95% CI: 1.02, 1.09; n = 15) per 400 mg/d were associated with increased total prostate cancer risk. Total calcium and dairy calcium intakes, but not nondairy calcium or supplemental calcium intakes, were also positively associated with total prostate cancer risk. Supplemental calcium was associated with increased risk of fatal prostate cancer.Conclusions: High intakes of dairy products, milk, low-fat milk, cheese, and total, dietary, and dairy calcium, but not supplemental or nondairy calcium, may increase total prostate cancer risk. The diverging results for types of dairy products and sources of calcium suggest that other components of dairy rather than fat and calcium may increase prostate cancer risk. Any additional studies should report detailed results for subtypes of prostate cancer.
Background:
Since the introduction of mobile phones, technology has been increasingly used to enable diabetes self-management education and support. This timely systematic review summarizes how ...currently available technology impacts outcomes for people living with diabetes.
Methods:
A systematic review of high quality review articles and meta analyses focused on utilizing technology in diabetes self-management education and support services was conducted. Articles were included if published between January 2013 and January 2017.
Results:
Twenty-five studies were included for analysis. The majority evaluated the use of mobile phones and secure messaging. Most studies described healthy eating, being active and metabolic monitoring as the predominant self-care behaviors evaluated. Eighteen of 25 reviews reported significant reduction in A1c as an outcome measure. Four key elements emerged as essential for improved A1c: (1) communication, (2) patient-generated health data, (3) education, and (4) feedback.
Conclusion:
Technology-enabled diabetes self-management solutions significantly improve A1c. The most effective interventions incorporated all the components of a technology-enabled self-management feedback loop that connected people with diabetes and their health care team using 2-way communication, analyzed patient-generated health data, tailored education, and individualized feedback. The evidence from this systematic review indicates that organizations, policy makers and payers should consider integrating these solutions in the design of diabetes self-management education and support services for population health and value-based care models. With the widespread adoption of mobile phones, digital health solutions that incorporate evidence-based, behaviorally designed interventions can improve the reach and access to diabetes self-management education and ongoing support.
Background: Current evidence indicates that red and processed meat intake increases the risk of colorectal cancer; however, the association with colorectal adenomas is unclear. Objective: To conduct ...a systematic review and meta-analysis of epidemiological studies of red and processed meat intake and risk of colorectal adenomas as part of the Continuous Update Project of the World Cancer Research Fund. Design: PubMed and several other databases were searched for relevant studies from their inception up to 31 December 2011. Summary relative risks (RRs) were estimated using a random effects model. Results: Nineteen case–control studies and seven prospective studies were included in the analyses. The summary RR per 100 g/day of red meat was 1.27 (95 % CI 1.16–1.40, I² = 5 %, n = 16) for all studies combined, 1.20 (95 % CI 1.06–1.36, I² = 0 %, n = 6) for prospective studies, and 1.34 (95 % CI 1.12–1.59, I² = 31 %, n = 10) for case–control studies. The summary RR per 50 g/day of processed meat intake was 1.29 (95 % CI 1.10–1.53, I² = 27 %, n = 10) for all studies combined, 1.45 (95 % CI 1.10–1.90, I² = 0 %, n = 2) for prospective studies, and 1.23 (95 % CI 0.99–1.52, I² = 37 %, n = 8) for case–control studies. There was evidence of a nonlinear association between red meat (pnonlinearity < 0.001) and processed meat (pnonlinearity = 0.01) intake and colorectal adenoma risk. Conclusion: These results indicate an elevated risk of colorectal adenomas with intake of red and processed meat, but further prospective studies are warranted.
Improving access to specialty care has been identified as a critical issue in the delivery of health services, especially given an increasing burden of chronic disease. Identifying and addressing ...problems that impact access to specialty care for patients referred to speciality care for non-emergent procedures and how these deficiencies can be managed via health system delivery interventions is important to improve care for patients with chronic conditions. However, the primary-specialty care interface is complex and may be impacted by a variety of potential health services delivery deficiencies; with an equal range of interventions developed to correct them. Consequently, the literature is also diverse and difficult to navigate. We present a narrative review to identify existing literature, and provide a conceptual map that categorizes problems at the primary-specialty care interface with linkages to corresponding interventions aimed at ensuring that patient transitions across the primary-specialty care interface are necessary, appropriate, timely and well communicated.
We searched MEDLINE and EMBASE databases from January 1, 2005 until Dec 31, 2014, grey literature and reference lists to identify articles that report on interventions implemented to improve the primary-specialty care interface. Selected articles were categorized to describe: 1) the intervention context, including the deficiency addressed, and the objective of the intervention 2) intervention activities, and 3) intervention outcomes.
We identified 106 articles, producing four categories of health services delivery deficiencies based in: 1) clinical decision making; 2) information management; 3) the system level management of patient flows between primary and secondary care; and 4) quality-of-care monitoring. Interventions were divided into seven categories and fourteen sub-categories based on the deficiencies addressed and the intervention strategies used. Potential synergies and trade-offs among interventions are discussed. Little evidence exists regarding the synergistic and antagonistic interactions of alternative intervention strategies.
The categorization acts as an aid in identifying why the primary-specialty care interface may be failing and which interventions may produce improvements. Overlap and interconnectedness between interventions creates potential synergies and conflicts among co-implemented interventions.
Measurement errors in the dietary assessment of fruit and vegetable intake may attenuate associations with breast cancer risk and might explain the weak associations observed in epidemiologic ...studies. Carotenoid concentrations in blood are biomarkers of fruit and vegetable intake; however, no systematic assessment has compared dietary intake with blood concentrations of carotenoids and breast cancer risk.
We conducted a systematic review and meta-analysis of prospective studies of dietary intake and blood concentrations of carotenoids and breast cancer risk.
We searched PubMed and several other databases for relevant studies up to 31 August 2011. Random-effects models were used to estimate summary estimates.
Of the 6 dietary carotenoids assessed, only intake of β-carotene was significantly associated with a reduced breast cancer risk (summary RR: 0.95; 95% CI: 0.91, 0.99; I(2): 0%) per 5000 μg/d (n = 10). In contrast, the summary RR for blood concentrations of carotenoids was 0.78 (95% CI: 0.61, 0.99; I(2): 53%) per 100 μg total carotenoids/dL (n = 7), 0.74 (95% CI: 0.57, 0.97; I(2): 43%) per 50 μg β-carotene/dL (n = 13), 0.82 (95% CI: 0.73, 0.92, I(2): 3%) per 10 μg α-carotene/dL (n = 12), and 0.68 (95% CI: 0.52, 0.89; I(2): 0%) per 25 μg lutein/dL (n = 6).
Blood concentrations of carotenoids are more strongly associated with reduced breast cancer risk than are carotenoids assessed by dietary questionnaires. Our results suggest that the use of certain biomarkers may clarify inconsistent and weak results between dietary intake and breast cancer risk.
In the World Cancer Research Fund/American Institute for Cancer Research report from 2007 the evidence relating body fatness to ovarian cancer risk was considered inconclusive, while the evidence ...supported a probably causal relationship between adult attained height and increased risk. Several additional cohort studies have since been published, and therefore we conducted an updated meta‐analysis of the evidence as part of the Continuous Update Project. We searched PubMed and several other databases up to 20th of August 2014. Summary relative risks (RRs) were calculated using a random effects model. The summary relative risk for a 5‐U increment in BMI was 1.07 (95% CI: 1.03–1.11, I2 = 54%, n = 28 studies). There was evidence of a nonlinear association, pnonlinearity < 0.0001, with risk increasing significantly from BMI∼28 and above. The summary RR per 5 U increase in BMI in early adulthood was 1.12 (95% CI: 1.05–1.20, I2 = 0%, pheterogeneity= 0.54, n = 6), per 5 kg increase in body weight was 1.03 (95% CI: 1.02–1.05, I2 = 0%, n = 4) and per 10 cm increase in waist circumference was 1.06 (95% CI: 1.00–1.12, I2 = 0%, n = 6). No association was found for weight gain, hip circumference or waist‐to‐hip ratio. The summary RR per 10 cm increase in height was 1.16 (95% CI: 1.11–1.21, I2 = 32%, n = 16). In conclusion, greater body fatness as measured by body mass index and weight are positively associated risk of ovarian cancer, and in addition, greater height is associated with increased risk. Further studies are needed to clarify whether abdominal fatness and weight gain is associated with risk.
What's new?
While past investigations of relationships between obesity and ovarian cancer yielded inconclusive results, recent cohort studies have offered new insight, warranting reassessment of potential associations. In this meta‐analysis, the authors found evidence of a nonlinear positive association between ovarian cancer risk and body mass index (BMI) and weight, with a significant association for a BMI of 28 or higher. Risk also rose significantly with increasing height. Thus, strong evidence that greater body fatness increases ovarian cancer risk has amassed from cohort studies, which has important public health implications, particularly in light of rising obesity rates.
Background:
Blood glucose meters remain an effective tool for blood glucose monitoring (BGM) but not all meters provide the same level of insight beyond the numerical glucose result.
Objective:
To ...investigate healthcare professional (HCP) perceptions of four meters and how these meters support the achievement of self-management goals recommended by diabetes clinical practice guidelines.
Methods:
Three hundred and fifty-three HCPs from five countries reviewed the features and benefits of four meters using interactive webpages and then responded to statements about the utility of each meter and ranked each meter in terms of clinical value.
Results:
Meter D ranked significantly higher in terms of clinical utility for all 13 guideline questions (70%-84%, P < .05) compared to other meters. Endocrinologists (69%-85%), primary care physicians (PCP; 63%-80%), and diabetes nurses (DN; 80%-89%) consistently ranked meter D highest for all guideline questions. DNs ranked selected questions significantly higher compared to PCPs (8 of 13) or endocrinologists (3 of 13; P < .05). Meter D achieved strong endorsement from HCPs in France and Germany, followed by the United States and Canada, with comparatively lower responses from Italian HCPs (P < 0.05). With respect to self-management, 80% of HCPs selected meter D as their first choice for patients with type 1 diabetes to help patients improve diabetes management or understand their numbers to help them stay in range.
Conclusions:
HCPs had strong preference for a meter providing additional insights, messages, and guidance direct to the patient to support achievement of self-management goals recommended by diabetes clinical practice guidelines.
Chronic illnesses are significant to individuals and costly to society. When systematically implemented, the well-established and tested Chronic Care Model (CCM) is shown to improve health outcomes ...for people with chronic conditions. Since the development of the original CCM, tremendous information management, communication, and technology advancements have been established. An opportunity exists to improve the time-honored CCM with clinically efficacious eHealth tools.
The first goal of this paper was to review research on eHealth tools that support self-management of chronic disease using the CCM. The second goal was to present a revised model, the eHealth Enhanced Chronic Care Model (eCCM), to show how eHealth tools can be used to increase efficiency of how patients manage their own chronic illnesses.
Using Theory Derivation processes, we identified a "parent theory", the Chronic Care Model, and conducted a thorough review of the literature using CINAHL, Medline, OVID, EMBASE PsychINFO, Science Direct, as well as government reports, industry reports, legislation using search terms "CCM or Chronic Care Model" AND "eHealth" or the specific identified components of eHealth. Additionally, "Chronic Illness Self-management support" AND "Technology" AND several identified eHealth tools were also used as search terms. We then used a review of the literature and specific components of the CCM to create the eCCM.
We identified 260 papers at the intersection of technology, chronic disease self-management support, the CCM, and eHealth and organized a high-quality subset (n=95) using the components of CCM, self-management support, delivery system design, clinical decision support, and clinical information systems. In general, results showed that eHealth tools make important contributions to chronic care and the CCM but that the model requires modification in several key areas. Specifically, (1) eHealth education is critical for self-care, (2) eHealth support needs to be placed within the context of community and enhanced with the benefits of the eCommunity or virtual communities, and (3) a complete feedback loop is needed to assure productive technology-based interactions between the patient and provider.
The revised model, eCCM, offers insight into the role of eHealth tools in self-management support for people with chronic conditions. Additional research and testing of the eCCM are the logical next steps.
Diabetes self-management education and support (DSMES) is a critical element of care for all people with diabetes. DSMES is the ongoing process of facilitating the knowledge, skills, and ability ...necessary for diabetes self-care, as well as activities that assist a person in implementing and sustaining the behaviors needed to manage his or her condition on an ongoing basis, beyond or outside of formal self-management training. In previous National Standards for Diabetes Self-Management Education and Support (Standards), DSMS and DSME were defined separately, but these Standards aim to reflect the value of ongoing support and multiple services. Here, Beck et al discuss the current National Standards for DSMES.
Type 2 diabetes mellitus is a worldwide challenge. Practice guidelines promote structured self-monitoring of blood glucose (SMBG) for informing health care providers about glycemic control and ...providing patient feedback to increase knowledge, self-efficacy, and behavior change. Paired glucose testing—pairs of glucose results obtained before and after a meal or physical activity—is a method of structured SMBG. However, frequent access to glucose data to interpret values and recommend actions is challenging. A complete feedback loop—data collection and interpretation combined with feedback to modify treatment—has been associated with improved outcomes, yet there remains limited integration of SMBG feedback in diabetes management. Incorporating telehealth remote monitoring and asynchronous electronic health record (EHR) feedback from certified diabetes educators (CDEs)—specialists in glucose pattern management—employ the complete feedback loop to improve outcomes.
The purpose of this study was to evaluate a telehealth remote monitoring intervention using paired glucose testing and asynchronous data analysis in adults with type 2 diabetes. The primary aim was change in glycated hemoglobin (A(1c))—a measure of overall glucose management—between groups after 6 months. The secondary aims were change in self-reported Summary of Diabetes Self-Care Activities (SDSCA), Diabetes Empowerment Scale, and Diabetes Knowledge Test.
A 2-group randomized clinical trial was conducted comparing usual care to telehealth remote monitoring with paired glucose testing and asynchronous virtual visits. Participants were aged 30-70 years, not using insulin with A1c levels between 7.5% and 10.9% (58-96 mmol/mol). The telehealth remote monitoring tablet computer transmitted glucose data and facilitated a complete feedback loop to educate participants, analyze actionable glucose data, and provide feedback. Data from paired glucose testing were analyzed asynchronously using computer-assisted pattern analysis and were shared with patients via the EHR weekly. CDEs called participants monthly to discuss paired glucose testing trends and treatment changes. Separate mixed-effects models were used to analyze data.
Participants (N=90) were primarily white (64%, 56/87), mean age 58 (SD 11) years, mean body mass index 34.1 (SD 6.7) kg/m2, with diabetes for mean 8.2 (SD 5.4) years, and a mean A(1c) of 8.3% (SD 1.1; 67 mmol/mol). Both groups lowered A(1c) with an estimated average decrease of 0.70 percentage points in usual care group and 1.11 percentage points in the treatment group with a significant difference of 0.41 percentage points at 6 months (SE 0.08, t159=-2.87, P=.005). Change in medication (SE 0.21, t157=-3.37, P=.009) was significantly associated with lower A(1c) level. The treatment group significantly improved on the SDSCA subscales carbohydrate spacing (P=.04), monitoring glucose (P=.001), and foot care (P=.02).
An eHealth model incorporating a complete feedback loop with telehealth remote monitoring and paired glucose testing with asynchronous data analysis significantly improved A(1c) levels compared to usual care.
Clinicaltrials.gov NCT01715649; https://www.clinicaltrials.gov/ct2/show/NCT01715649 (Archived by WebCite at http://www.webcitation.org/6ZinLl8D0).
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