scholarly journals Outcomes for high risk New Zealand newborn infants in 1998-1999: a population based, national study

2003 ◽  
Vol 88 (1) ◽  
pp. 15F-22 ◽  
Author(s):  
A E Cust
BMJ Open ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. e022984
Author(s):  
Olivia Currie ◽  
Jonathan Williman ◽  
Dee Mangin ◽  
Bianca McKinnon-Gee ◽  
Paul Bridgford

ObjectiveNewer antipsychotics are increasingly prescribed off-label for non-psychotic ailments both in primary and secondary care settings, despite the purported risk of weight gain and development of type 2 diabetes mellitus. This study aims to determine any relationship between the development of clinically significant new-onset type 2 diabetes mellitus and novel antipsychotic use in New Zealand using hypnotic drugs as control.DesignA population-based clustered multiple baseline time series design.SettingRoutinely collected data from a complete national pharmaceutical database in New Zealand between 2005 and 2011.ParticipantsPatients aged 40–60 years in the year 2006 who were ever dispensed antipsychotics (exposure groups—first-generation antipsychotics, second-generation antipsychotics and antipsychotics with low, medium and high risk for weight gain) or hypnotics (control group) between 2006 and 2011.Main outcome measureFirst ever metformin dispensed to patients in each study group between 2006 and 2011 as proxy for development of clinically significant type 2 diabetes mellitus, no longer amendable by lifestyle modifications.ResultsPatients dispensed a second-generation antipsychotic had 1.49 times increased risk (95% CI 1.10 to 2.03, p=0.011) of subsequently commencing metformin. Patients dispensed an antipsychotic with high risk of weight gain also had a 2.41 times increased risk of commencing on metformin (95% CI 1.42 to 4.09, p=0.001).ConclusionsPatients dispensed a second-generation antipsychotic and antipsychotics with high risk of weight gain appear to be at increased risk of being secondarily dispensed metformin. Caution should be taken with novel antipsychotic use for patients with increased baseline risk of type 2 diabetes mellitus.


2018 ◽  
Vol 60 (1) ◽  
pp. 38-44 ◽  
Author(s):  
J Mark Elwood ◽  
Stella J-H Kim ◽  
Ken H-K Ip ◽  
Amanda Oakley ◽  
Marius Rademaker

Author(s):  
N.V. Rudakov ◽  
N.A. Penyevskaya ◽  
D.A. Saveliev ◽  
S.A. Rudakova ◽  
C.V. Shtrek ◽  
...  

Research objective. Differentiation of natural focal areas of Western Siberia by integral incidence rates of tick-borne infectious diseases for determination of the strategy and tactics of their comprehensive prevention. Materials and methods. A retrospective analysis of official statistics for the period 2002-2018 for eight sub-federal units in the context of administrative territories was carried out. The criteria of differentiation were determined by means of three evaluation scales, including long-term mean rates of tick-borne encephalitis, tick-borne borreliosis, and Siberian tick-borne typhus. As a scale gradation tool, we used the number of sample elements between the confidence boundaries of the median. The integral assessment was carried out by the sum of points corresponding to the incidence rates for each of the analyzed infections. Results. The areas of low, medium, above average, high and very high risk of tick-borne infectious diseases were determined. Recommendations on the choice of prevention strategy and tactics were given. In areas of very high and high incidence rates, a combination of population-based and individual prevention strategies is preferable while in other areas a combination of high-risk and individual strategies is recommended. Discussion. Epidemiologic zoning should be the basis of a risk-based approach to determining optimal volumes and directions of preventive measures against natural focal infections. It is necessary to improve the means and methods of determining the individual risk of getting infected and developing tick-borne infectious diseases in case of bites, in view of mixed infection of vectors, as well as methods of post-exposure disease prevention (preventive therapy).


This handbook signals a paradigm shift in health research. Population-based disciplines have employed large national samples to examine how sociodemographic factors contour rates of morbidity and mortality. Behavioral and psychosocial disciplines have studied the factors that influence these domains using small, nonrepresentative samples in experimental or longitudinal contexts. Biomedical disciplines, drawing on diverse fields, have examined mechanistic processes implicated in disease outcomes. The collection of chapters in this handbook embraces all such prior approaches and, via targeted questions, illustrates how they can be woven together. Diverse contributions showcase how social structural influences work together with psychosocial influences or experiential factors to impact differing health outcomes, including profiles of biological risk across distinct physiological systems. These varied biopsychosocial advances have grown up around the Midlife in the United States (MIDUS) national study of health, begun over 20 years ago and now encompassing over 12,000 Americans followed through time. The overarching principle behind the MIDUS enterprise is that deeper understanding of why some individuals remain healthy and well as they move across the decades of adult life, while others succumb to differing varieties of disease, dysfunction, or disability, requires a commitment to comprehensiveness that attends to the interplay of multiple interacting influences. Put another way, all of the disciplines mentioned have reliably documented influences on health, but in and of themselves, each is inherently limited because it neglects factors known to matter for health outside the discipline’s purview. Integrative health science is the alternative seeking to overcome these limitations.


2021 ◽  
Vol 28 (Supplement_1) ◽  
Author(s):  
M Chlabicz ◽  
J Jamolkowski ◽  
W Laguna ◽  
P Sowa ◽  
M Paniczko ◽  
...  

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): Medical University of Bialystok, Poland Background Cardiovascular disease (CVD) is a major, worldwide problem and remain the dominant cause of premature mortality in the word. Simultaneously the metabolic syndrome is a growing problem. The aim of this study was to investigate the cardiometabolic profile among cardiovascular risk classes, and to estimate CV risk using various calculators. Methods The longitudinal, population-based study, was conducted in 2017-2020. A total of 931 individuals aged 20-79 were included. Anthropometric and biochemical profiles were measured according to a standardized protocols. The study population was divided into CV risk classes according to the latest recommendation. Comparisons variables between subgroups were conducted using Dwass-Steele-Critchlow-Fligner test. To estimate CV risk were used: the  Systematic Coronary Risk Estimation system, Framingham Risk Score and LIFEtime-perspective model for individualizing CardioVascular Disease prevention strategies in apparently healthy people (LIFE-CVD). Results The mean age was 49.1± 15.5 years, 43.2% were male. Percentages of low-risk, moderate-risk, high-risk and very-high CV risk were 46.1%, 22.8%, 13.5%, 17.6%, respectively. Most of the analyzed anthropometric, body composition and laboratory parameters did not differ between the moderate and high CV risk participants, whereas the low risk group differed significantly. In the moderate and high-risk groups, abdominal distribution of adipose tissue dominated with significantly elevated parameters of insulin resistance. Interestingly, estimating lifetime risk of myocardial infarction, stroke or CV death using LIFE-CVD calculator yielded similar results in moderate and high CV risk classes. Conclusion The participants belonging to moderate and high CV risk classes have a very similar unfavorable cardiometabolic profile which may result in the similar lifetime CV risk. This may imply the need for more aggressive pharmacological and non-pharmacological management of CV risk factors in the moderate CV risk population. It would be advisable to consider combining the moderate and high risk classes into one high CV risk class, or it may be worth adding one of the parameters of abdominal fat distribution to the CV risk calculators as an expression of increased insulin resistance. Abstract Figure 1.


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