Purpose: The purpose of the paper is to determine the relationship between climate variability and the prevalence of malnutrition amongst children under five in Fiji Methods: The study used an ecological study design and used both quantitative and qualitative analysis. The quantitative and the qualitative parts of the paper will complement each other but otherwise remain distinct. Secondary data collection was done to extract malnutrition, climate and the other covariate variables. Trends of climate variables and malnutrition were examined and to determined how they behaved during the study period. A generalised additive model (GAM) using autoregressive integrated over moving averages (ARIMA) as Poisson process was used to determine the association between climate variables and malnutrition. Examination of existing works of literature was also carried out on other factors associated with malnutrition. Findings: The trend of average minimum, maximum temperature and average relative humidity did not vary from January 2006 to December 2016. There were variations in the rainfall trend with high rainfalls in 2008 and 2012. The indicators of malnutrition (underweight, growth faltering, severe malnutrition and anaemia) decreased in the first half of 2011 and increased in the second half of the year. In 2012 malnutrition increased in the half and decreased in the second half of the year. The Fiji Indians had a higher prevalence of malnutrition than the I- Taukei and the other minority race.
Ambient air pollution isa major environmental health risk factor and globally, it is estimated that at least seven million deaths are attributable to the effects of air pollution on an annual basis. In developing countries in the Western Pacific Region such as Fiji, major sources of air pollution are from the exhaust emissions from motor vehicles, burning of wastes and industrial activities. Countries in this region are also experiencing an intense process of urbanization resulting in the increasing number of vehicles on the road together with a vibrant agricultural practice that often leads to an increased burning of wastes. Despite these known sources of pollution, there is no evidence-based data delineating the relative contribution of these pollution sources in Fiji. Epidemiological studies have shown that increased emissions from motor vehicles and industrial activities are associated with an increased risk of developing cardiovascular diseases. Cardiovascular disease is quite common in Fiji and in 2016, ischaemic heart disease which includes heart diseases such as angina and myocardial infarction was ranked as the second most cause of all premature deaths in Fiji. To get a basic understanding of how air pollution may affect the cardiovascular health in Fiji, this study will attempt to investigate the association between air pollution and acute myocardial infarction hospitalisation in the city of Lautoka. Subsequently, a meta-analysis of the literature on the association between air pollution and acute cardiovascular infarction will be conducted and the risk estimates from the selected studies summarized. Values on the criteria pollutants from these selected studies will be obtained and based on the pooled risk estimates and the exposure assessment, a risk assessment of air pollution for heart diseases will then be conducted.
- Introduce Basic Principles and practice of grading of evidence - Grading of Evidence is Outcome Focused - Grade Outcomes from a Range of studies - Focus on Consistency and Transparency - GRADE is an approach - This Approach helps to frame questions - This is Outcomes Focused - Ask an explicit question, including - Specify all important outcomes. *Explicitly rate the quality of evidence - Recommend the Evidence Pertaining to the Outcome - Summarise evidence in succinct, transparent informative summary of findings - Quality of Evidence - Magnitude of Relative and Absolute Effects - Reason for the Quality Rating - Study design, - Risk of bias, - Levels of imprecision, - Levels of inconsistency, - Extent of indirectness - Magnitude of effect. - Recommendations Labelled as Strong or Weak - Quality of Evidence - Balance between Desirable and Undesirable Consequences - We have learned about risk of bias - We have learned about appraising individual studies - We have learned to assess studies individually across outcomes - This is different in the sense that we are focusing on - Different from Focus on Studies Across Outcomes [Schematic] - EP provides a record of the judgments - Judgments that were made by review or guideline authors - Intended for Review Authors and SoF table - Maintains Transparency - Broader Audience - End Users of Systematic Reviews and Guidelines - Concise Summary of Key Information - Use GRADEPro (Online) or SoF Table Generator [upordowngrade] - Specify Relevant Setting - Specify What Population You will be Working on - The Relevant Intervention - The specified outcomes - Population, Interventions and Outcomes should be as similar as possible - Specify importance of relative outcomes before starting the exercise - Respecify after completion of the exercise - If you work with surrogate outcomes … - When an outcome cannot be directly measured but measured with some other means - Test of a Diagnostic Test as an indicator for Survival - Measure of Bone Density for Risk of Bone Fracture in Osteoporosis in Post menopausal Women - How important are Patient specific outcomes? - In that case, Rate Down - If the Settings Do Not Translate Directly, Rate Down - These are Mapped to our Notions of Generalisability - Essentially qualitative: High, Moderate, Low, Very Low - Statt with an initial rating and then upgrade or downgrade - Apply to a Body of Evidence, NOT to individual studies - For SRs: are the effects of estimate CORRECT? - For Recommendations: Are the effect estimates ADEQUATE? - RCT: High - Observational Studies: Moderate to Low - Quality is more than Risk of Bias - Design - Risk of Bias - Imprecision - Inconsistency - Indirectness - Publication Bias - This is also Evidence - Why? - Include Expert Opinion - Experience with patients and colleagues - Understanding of biology - Understanding of preclinical research - Use Expert Opinion to Understand - Rate the Quality of that Evidence (see above) - Do not Rate Interpretation - Grading is about a BODY of Evidence - SR is about individual studies pooled together - Grading is about an outcome across studies - SR is about across outcomes across studies - Risk of Bias: Internal Validity - Chance (under powered? Correctly Powered?) - Bias (Selection? Response? Randomisation? Blinding?) - Confounding (?Multivariate Analysis, Matching?) - Say an SR on body pain treatment with Salicylates showed pooled RR = 6 (4 - 13) - Start with RR and 95% CI, and population selection, and risk of bias, - How many studies were included? How many patients included? - What was the level of publication bias? - Was the duration too short? - Which body part was studied? - These are all relevant questions for GRADE [Study Limitations of RCTs] - Grading of Studies is Distinct and Outcome Focused - Grading is integral part of both SRs and Guideline Development - Judge Studies across designs and for each outcome - Provisions for up or downgrading studies - Consider design, risk of bias, precision, consistency, directness, publication bias - More than just notions of quality in SRs and single studies
INTRODUCTION This set of analyses contains the R codes for the breast cancer study. The data were combined with the tables containing HRT usage data and the cancer incidence data were abstracted from the IARC ddatabases corresponding to the decline in the incidence rates three years after the reduction in the usage of HRT in the countries. In the following set of analyses, we present both the R codes that were used to generate the plots and analyses and the graphs and tables.
SUMMARY OR ABSTRACT OF THE THESIS The summary or abstract of the thesis needs more work! Smoking is a leading cause of preventable diseases and death world wide and within New Zealand.To date, a lot of previous research has been conducted and this suggests that Internet and Cell phone based interventions are effective to achieve cessation of smoking however the effects are short term.The purpose of this Meta Analysis was to investigate the effectiveness of Internet and Cell phone based interventions to achieve longer term cessation of smoking among adolescent and adult smokers.The analysis was based on the assessment of Randomied Controlled Trials whose interventions included Internet and Cellphone components and reported outcomes at six months or longer. Furthermore, this analysis was based on English Language articles published in the previous 10 years and whose comparison group received either any other intervention or an intervention inclusive of but not limited to Internet and Cell phone based interventions delivered at a lower frequency. Both a Fixed effects and a Random effects Meta Analysis between all studies were conducted to assess the length of abstinence.Morever the individual studies were grouped using an outcome theme and five subgroup analyses were conducted. Findings from both, the Fixed effects and Random effects analysis suggest great heterogeneity among the studies.Additionally some heterogeneity was found among the five subgroup but overall findings suggest that Internet and Cell phone based interventions used in smoking cessation are effective in achieving longer term cessation of smoking. Findings from the subgroup analyses suggest that both Internet and Cell phone based interventions combined with an additional intervention Nicotine Replacement Therapy are most effective in achieving longer term cessation of smoking among adolescent and adult smokers.
_Need for this document_ This document in the form of playbook is written mostly for my colleagues and students (I usually write preface in all my documents) to quickly get them started on this new format. This is written in Authorea itself so please fork it if you like for yourself and use it. This is not a manual on Authorea and I do not intend to write one, but a playbook of some strategies of writing collaborative texts and publishing quickly and get productive. Authorea can be used as such or can be used in conjunction with other writing tools and reference management software. It is not a tool for online analysis of statistical data, but it is possible to do transparent and open access code writing and storage of codes (this is not yet perfect but can be done). WHAT I WILL NOT COVER HERE: 1. How to create account in Authorea etc as these are available from the site itself 2. How to write in markdown or latex (you do not need to write in Latex if you use Authorea it formats it for you)