Stem cell transplantation for type 1 diabetes mellitus

<p>Abstract</p> <p>Background</p> <p>The use of stem cells to treat type 1 diabetes mellitus has been proposed for many years, both to downregulate the immune system and to provide β cell regeneration.</p> <p>Conclusion</p> <p>High dose immunosuppression followed by autologous hematopoietic stem cell transplantation is able to induce complete remission (insulin independence) in most patients with early onset type 1 diabetes mellitus.</p

Oral Insulin

Oral insulin is an exciting area of research and development in the field of diabetology. This brief review covers the various approaches used in the development of oral insulin, and highlights some of the recent data related to novel oral insulin preparation

Glycaemic control in people with type 2 diabetes mellitus during and after cancer treatment: A systematic review and meta-analysis

Background Cancer and Diabetes Mellitus (DM) are leading causes of death worldwide and the prevalence of both is escalating. People with co-morbid cancer and DM have increased morbidity and premature mortality compared with cancer patients with no DM. The reasons for this are likely to be multifaceted but will include the impact of hypo/hyperglycaemia and diabetes therapies on cancer treatment and disease progression. A useful step toward addressing this disparity in treatment outcomes is to establish the impact of cancer treatment on diabetes control. Aim The aim of this review is to identify and analyse current evidence reporting glycaemic control (HbA1c) during and after cancer treatment. Methods Systematic searches of published quantitative research relating to comorbid cancer and type 2 diabetes mellitus were conducted using databases, including Medline, Embase, PsychINFO, CINAHL and Web of Science (February 2017). Full text publications were eligible for inclusion if they: were quantitative, published in English language, investigated the effects of cancer treatment on glycaemic control, reported HbA1c (%/mmols/mol) and included adult populations with diabetes. Means, standard deviations and sample sizes were extracted from each paper; missing standard deviations were imputed. The completed datasets were analysed using a random effects model. A mixed-effects analysis was undertaken to calculate mean HbA1c (%/mmols/mol) change over three time periods compared to baseline. Results The available literature exploring glycaemic control post-diagnosis was mixed. There was increased risk of poor glycaemic control during this time if studies of surgical treatment for gastric cancer are excluded, with significant differences between baseline and 12 months (p < 0.001) and baseline and 24 months (p = 0.002). Conclusion We found some evidence to support the contention that glycaemic control during and/or after non-surgical cancer treatment is worsened, and the reasons are not well defined in individual studies. Future studies should consider the reasons why this is the case

A novel framework for validating and applying standardized small area measurement strategies

<p>Abstract</p> <p>Background</p> <p>Local measurements of health behaviors, diseases, and use of health services are critical inputs into local, state, and national decision-making. Small area measurement methods can deliver more precise and accurate local-level information than direct estimates from surveys or administrative records, where sample sizes are often too small to yield acceptable standard errors. However, small area measurement requires careful validation using approaches other than conventional statistical methods such as in-sample or cross-validation methods because they do not solve the problem of validating estimates in data-sparse domains.</p> <p>Methods</p> <p>A new general framework for small area estimation and validation is developed and applied to estimate Type 2 diabetes prevalence in US counties using data from the Behavioral Risk Factor Surveillance System (BRFSS). The framework combines the three conventional approaches to small area measurement: (1) pooling data across time by combining multiple survey years; (2) exploiting spatial correlation by including a spatial component; and (3) utilizing structured relationships between the outcome variable and domain-specific covariates to define four increasingly complex model types - coined the Naive, Geospatial, Covariate, and Full models. The validation framework uses direct estimates of prevalence in large domains as the gold standard and compares model estimates against it using (i) all available observations for the large domains and (ii) systematically reduced sample sizes obtained through random sampling with replacement. At each sampling level, the model is rerun repeatedly, and the validity of the model estimates from the four model types is then determined by calculating the (average) concordance correlation coefficient (CCC) and (average) root mean squared error (RMSE) against the gold standard. The CCC is closely related to the intraclass correlation coefficient and can be used when the units are organized in groups and when it is of interest to measure the agreement between units in the same group (e.g., counties). The RMSE is often used to measure the differences between values predicted by a model or an estimator and the actually observed values. It is a useful measure to capture the precision of the model or estimator.</p> <p>Results</p> <p>All model types have substantially higher CCC and lower RMSE than the direct, single-year BRFSS estimates. In addition, the inclusion of relevant domain-specific covariates generally improves predictive validity, especially at small sample sizes, and their leverage can be equivalent to a five- to tenfold increase in sample size.</p> <p>Conclusions</p> <p>Small area estimation of important health outcomes and risk factors can be improved using a systematic modeling and validation framework, which consistently outperformed single-year direct survey estimates and demonstrated the potential leverage of including relevant domain-specific covariates compared to pure measurement models. The proposed validation strategy can be applied to other disease outcomes and risk factors in the US as well as to resource-scarce situations, including low-income countries. These estimates are needed by public health officials to identify at-risk groups, to design targeted prevention and intervention programs, and to monitor and evaluate results over time.</p

Ethnic differences in Internal Medicine referrals and diagnosis in the Netherlands

As in other Western countries, the number of immigrants in the Netherlands is growing rapidly. In 1980 non-western immigrants constituted about 3% of the population, in 1990 it was 6% and currently it is more than 10%. Nearly half of the migrant population lives in the four major cities. In the municipality of Rotterdam 34% of the inhabitants are migrants. Health policy is based on the ideal that all inhabitants should have equal access to health care and this requires an efficient planning of health care resources, like staff and required time per patient. The aim of this study is to examine ethnic differences in the use of internal medicine outpatient care, specifically to examine ethnic differences in the reason for referral and diagnosis. Methods We conducted a study with an open cohort design. We registered the ethnicity, sex, age, referral reasons, diagnosis and living area of all ne