IntroductionPurpose of this course and plans of assessmentAfter completion of this course you will learn:How to understand your environment from a public health perspectiveHow to investigate environmental health risksKey terms such as precautionary principles, risks, & hazardsHow will I assess you in this courseWrite a Memo (50%, out of 50 marks)Select an Environmental Issue in New ZealandResearch that environmental problem to find out what health effects can result from such environmental exposurePrepare a memo based on that understandingAddress the memo to your local councillor or a public health authorityThe structure of a memo will consist of the following:The Name and address of the person to whom you addressThe title of the topicthe body of the memo within 1500 words The word count excludes words contained in images, tables, and references, all other words including headings will countYou will submit the memo in the form of a PDF file through Learn onlyTake a 50-point Quiz (50%, out of 50 marks)This will be an open book quizThere will be 50 questionsEach question will be a multiple choice questionTime limit: 2 hours (1-3 PM usual class hour on the last day of the course)What is meant by Environmental and Occupational Health?Environmental health refers to the study of the effects of specific human-engendered environment on our physical,  mental, social well-being. The keyword here is "human-engendered" environmental changes, rather than natural events alone. Most environmental changes are human-made. This includes air pollution,

IntroductionEvidence based health is useful as it lets us ask and answer questions on effectiveness of care, but often we are interested to know whether we should use a specific care or intervention for managing a particular illness or for improving health. This decision, whether we should recommend or develop guideline for managing a disease and use an evidence based approach to do so, is more complex and requires a different approach where we need to balance the benefits and harms within the scope of evidence based practice. Here, we need to grade evidence with respect to outcomes we are interested. In this tutorial, we shall teach you how you can use grading of evidence approach as used in GRADE and develop an evidence portfolio based on outcomes, and on that basis, develop a decision framework whether you can recommend a particular intervention for a specific disease management, or improvement of health state, all things considered.David Sackett, considered the "father figure" for evidence based medicine defined Evidence Based Medicine as the judicious, explicit process of arriving at a decision about medical care based on available research and in collaboration with the patient and the practitioner (citation??). Accordingly, evidence based health practitioners follow a stepwise process where:They frame an answerable questionThen they search scholarly literature databases for studies that have answered this questionThen they abstract information from these studiesThey critically appraise these studies and test for internal and external validityThen they decide how they should evaluate the evidence they have with them.Using this stepwise process, the evidence based health care practitioner grades or puts a mark on the available evidence, that will help him to choose a course of action.  When we discussed systematic reviews and meta analyses, we discussed that in meta-analysis too, we follow these similar steps. We select a question, search literature, retrieve studies, critically appraise studies, and arrive at a summary estimate. Although evidence based processes and meta-analyses help us assimilate evidence and suggests cause and effect associations between exposure and outcomes, or whether a treatment is effective, we cannot decide whether this treatment is useful or not. We decide this using guidelines and here, grading of evidence where we put the focus on outcomes is important. GRADE (Grading, recommendations, assessment, development, and evaluation) is a process of assigning quality scores to evidence and GRADEpro is a tool that we will use in this context to compare two competing interventions. Principles of GRADEWhen we use GRADE, we assign quality scores to the evidence for outcomes. For example, let's say we want to know if coffee drinking benefits those with depression, and whether we can recommend coffee drinking to someone with depression. Therefore, in this case, coffee drinking is an intervention, and we will use non-coffee drinking or no coffee consumption or consumption of other beverages as comparisons. We will select  depression as an outcome;  we also know that coffee drinking causes anxiety or is associated with anxiety (cite??), therefore anxiety will be an adverse effect that we will have to consider in order for us to arrive at a decision weighing the pros and cons of coffee drinking for depression. This kind of scenarios are good candidates for GRADE based evidence appraisal. In conducting a GRADE appraisal, we will select these two outcomes and identify as many studies as we can obtain. Then for each outcome, i.e., depression and anxiety in this case, we will appraise each study or combinations of the studies, such as a meta-analysis, and assign a grade to that body of evidence per outcome. If we find that the quality of evidence is higher for the desirable outcome, that is relief of depression, than for the undesirable or adverse outcome, then we will recommend coffee drinking for those with depression. This decision is not final, however; we must be open to revise our decision should new evidence emerge. We assign quality scores based on several criteria and in turn tabulate summary of findings and prepare an evidence portfolio that we will now describe.We assign quality scores to individual outcomes based on the studies we access and appraise. After assessing the quality of evidence for each outcome, we assign one of the following "plus" signs to the "evidence" that we appraise for each outcome. These plus signs are as follows (Table 1):

Concepts of Meta AnalysesWe face evidence based information and published primary research every day; while this lets us access a wealth of health information, as consumers and health care practitioners, we also have to deal with studies on health care interventions and risks that frequently contradict each other: besides, given this glut of information, we can find it overwhelming as to what should be our basis of decision making. As a result, unless we can resolve this diversity and plethora of information, we can be drowned in the information deluge and end up making wrong decisions.  We can tide over if we can closely examine the studies and derive summary estimates to guide our assessments;  this  we can obtain using a meta analysis. Meta analysis refers to a process where we integrate the results of many studies to arrive at an evidence synthesis \cite{normand1999tutorial} . This evidence synthesis is a form of systematic review; however, in addition to narrative summary that is characteristic of systematic review, in meta analysis, we numerically pool the results of the studies and arrive at a summary estimate. The purpose of this paper is to describe how to conduct a meta analysis. We will describe the processes of framing a question, obtaining studies, assessing their heterogeneity, obtaining summary estimates, assessment of publication bias, and analysis of subgroups of data from the sets of studies in R with an example of a study.Nine Steps to Meta AnalysesThe following nine steps are part of meta analyses.Frame a question (based on a theory)Run a search (on Pubmed/Medline, Google Scholar, other sources)Read the abstract and title of the individual papers.Abstract information from the selected set of final articles.Determine the quality of the information in these articles. This is done using a judgment of their internal validity but also using the GRADE criteriaDetermine the extent to which these articles are heterogeneousEstimate the summary effect size in the form of Odds Ratio and using both fixed and random effects models and construct a forest plotDetermine the extent to which these articles have publication bias and run a funnel plotConduct subgroup analyses and meta regression to test if there are subsets of research that capture the summary effectsStep I: Frame a QuestionFor framing an answerable question in a meta analysis, use the PICO framework \cite{schardt2007utilization}. PICO is an acronym for ”Participant-Intervention-Comparator-Outcomes”. "Participant" here refers to the individuals or population who will be studied and whose profiles are of interest to the analyst. For example, if we are interested to study the effectiveness of a drug such as nedocromil on bronchoconstriction (narrowing of air passages) among adult asthma patients, then we shall include only adult asthmatics for our study, not children or older adults (if such individuals are not of our interest); if we are interested to study the effectiveness of mindfulness meditation for anxiety for adults, then again adult age group would be our interest; we could further narrow down the age band to our interest.Intervention too, needs to be as broadly or as narrowly defined keeping only the interventions of our interest. Usually, meta analyses are done in assimilating studies that are RCTs or quasi-experimental studies where pairs of interventions (intervention versus placebo or interventions versus conventional treatment or interventions and no treatment) are compared \cite{normand1999tutorial}. However, meta analyses are not necessarily restricted only to randomised controlled trials, these are now increasingly applied to observational study designs as well for example cohort and case control studies; in these situations, we refer to the specific expsoure variables of our interest \cite{stroup2000meta} . Meta-analyses are also conducted for diagnostic and screening studies \cite{hasselblad1995meta}.Let's say we are interested to study if consumption of plant based diets is associated with reduced risk of cardiovascular illnesses and myocardial infarction. You can see that for ethical reasons, it is not possible to conduct randomised controlled trials so that one group will be forced to consume plant based diet and the other group will be forced to consume non-plant based diet, but it is possible to obtain that information about heart diseases from two groups of people who have consumed and not consumed certain levels of vegetarian items in their diets. Such studies are observational epidemiological studies and using observational studies such as cohort and case control studies. In such situations, it is useful to summarise findings of cohort and case control studies. Intervention then is not appropriate; however, we use the term ”Exposure”. Likewise, the comparison group is important as well. The comparison group can be ”no intervention”, or ”placebo”, or ”usual treatment”.The outcomes that we are interested can be narrowly or broadly defined based on the objective of the meta analysis. If the outcome is narrowly defined, then the meta analysis is only restricted to that outcome, for instance, if we are interested to study the effectiveness of mindfulness meditation on anxiety then, anxiety is our outcome; we are not interested to find out if mindfulness is effective for depression. On the other hand, if the objective of hte study is to test if mindfulness meditation is useful for ”any health outcome”, then the scope of the search is much wider. So, after you have set up your theory and your question, now is the time to rewrite the question and reframe it as a PICO formatted question. Say we are interested to find out if minduflness meditation is effective for anxiety, then we may state the question in PICO as follows:P: Adults (age 18 years and above), both sexes, all ethnicity, all nationalityI: Mindfulness MeditationC: Placebo, Or No Intervention, or Anxiolytics Or Traditional Approaches, or Drug Based Approaches, or Other Cognitive Behavioural TherapyO: Anxiety Symptom Scores, or Generalised AnxietyThen, on the basis of PICO, we reframe the question as follows: ”Among Adults, compared with all other approaches, what is the effectiveness of Mindfulness Meditation for the relief of Anxiety?”Step II: Conduct a Search of the Literature DatabasesAfter you have decided the PICO, you will conduct a search of the literature databases. This will help you to identify the appropriate search terms. These search terms are arranged using Boolean Logic, fuzzy logic, specific search related controlled vocabulary, symbols of trucation or expansion, and placement of the terms in different sections of a reported study \cite{tuttle2009pubmed} . In Boolean Logic, you use the keywords, ”AND”, ”OR”, and ”NOT” in various combinations to expand or narrow down search results and findings. For example,”Adults” AND ”Mindfulness Meditation” will find only those articles that have BOTH adults AND mindfulness meditation as their subject topics. While,”Adults” OR ”Mindfulness Meditation” will find all articles that have EITHER ”Adults” OR ”Mindfulness Meditation” in their subject topics, so the number of results returned will be larger.”Adults” NOT ”Mindfulness Meditation” will find only those articles that contain ”Adults” but will exclude all articles that have ”Mindfulness Meditation” as their topic area.In addition to the use of Boolean logic, you can also use ”fuzzy logic” to search for specific articles. When you use fuzzy logic, you use search terms where you use words like ”Adults” NEAR ”Mindfulness” or ”Adults” WITHIN 5 Words of ”Mindfulness” to search for articles that are very specific. These can be combined in many different ways.Many databases, such as Pubmed/Medline, contain MeSH (Medical Subject Headings) as controlled vocabulary where hte curators of thse databses maintain or archive difernet articles under specific search terms \cite{robinson2002development} . When you search Medline or Pubmed, you can use MeSH terms to search for your studies. You can use or combine MeSH terms along with other terms to search more widely or more comprehensively.Besides these, you will use specific symbols such as asterisk (*) marks and dollar signs to indicate truncation or find related terms to find out articles. For example, if you use something like ”Meditat$” in a search term, then you can find articles that use the terms ”meditating”, or ”meditation”, or ”meditative” or ”Meditational”; you will find list of such symbols in the documentation section of the database that you intend to search \cite{robinson2002development} .Finally, search terms can occur in many different sections and parts of a study report. One way to search is to search the title and abstract of most studies. Another way to search place to search is within the entire body of the article. Thus, combining these various strategies, you can run a comprehensive search of the publications or research that will contain data that you can use for your meta-analysis.Step III: Select the articles for meta analysis by reading Titles and Abstracts and full textsFirst, read the titles and abstracts of all relevant searched papers. But before you do so, set up a scheme where you will decide that you will select and reject articles for your meta analysis. For example, you can set up a scheme where you can write:The article is irrelevant for the study questionThe article does not have the relevant populationThe article does not have the relevant intervention (or exposure)The article does not have a relevant comparison groupThe article does not discuss the outcome that is of interest to this researchThe article is published in a non-standard format and not suitable for reviewThe article is published in a foreign language and cannot be translatedThe article is published outside of the date rangesThe article is a duplicate of another article (same publication published twice)Use this scheme to go through each and every article you retrieved initially on the basis of reading their titles and abstracts. Usually only one clause is good enough to reject a study and note it that study got rejected on that criterion, and the first clause that rejects the study is noted down as the main cause. So, even if a study can be rejected on two clauses, the first one that rejects the study is mentioned as the main clause of rejection; you will need to put together a process diagram to indicate which articles were rejected and why. Such a process diagram is referred to as PRISMA (Preferred Reporting Items of Systematic Reviews and Meta Analyses) chart \cite{moher2009preferred}. After you have run through this step and have identified a certain number of studies which must be included in the meta-analysis, obtain their full texts. Then read the full text once more and conduct this rejection exercise and note the numbers. As may be expected, you will reject fewer articles in this round. Then, read the full text and hand search the reference lists of these articles to widen your research. This step is critical. Often, in this step, you will find out sources that you must search, or identify authors whose work you must read to get a full list of all works and researches that have been conducted on this topic. Do not skip this step. In this step, you will note that some authors feature prominently, and some research groups surface; take a note of them; you may have to write to a few authors to identify if they have published more research. All this is needed to run a thorough search of the studies so that you will not miss any study that may be relevant for this meta analysis. Step IV: Abstract information from these articlesOnce you know that you have a set of studies that you will work with, you will need to work with, you will now need to abstract data from them for your study. At the minimum, you must obtain the following information for each study included in you analysis:The name of the first authorThe year the article was publishedThe population on whom the study was conductedThe type of research (was it an RCT? Or if observational, what type of study was it?)What was the intervention exactly? (A brief description of the intervention)The comparison condition (what was it compared with?)What was the outcome and how was it measured?How many individuals were in the intervention (Ne)?How many people were included in the control arm (Nc)?If the outcomes were measured in a continuous scale, what was the mean value of the outcome among those in the intervention arm?If the outcome was meausured on a continuous scale, the mean of the outcome among those in the comparison conditionIf the outcome was measured on a continuous scale, what was the standard deviation of the measure for the exposure or intervention?If the outcome was measured on a continuous scale, what was the standard deviation of the measure for the comparison arm?If the outcome was measured on a binary scale (more on this later), the number of people with the outcome in the intervention armIf the outcome was measured on a binary scale, the number of people with the outcome on the comparison armA quality score or a note on the quality or crticial appraisal of each studyThis is just a suggestion; I do not recommend a fixed set of variables and you will determine what variables you need for each meta analysis. If you use a software such as Revman, then that will guide you with the process of abstraction of data from each article and you should follow the steps there. Note that in this case, we are only considering tabulation of these information per article. Also note that in this case, we will work with one intervention and one outcome in each table. You may have more than one outcome in the paper; in that case, you will need to set up different tables. Enter this information on a spreadsheet, and export the spreadsheet in the form of a csv file that you can input into R. In this exercise we will use R for statistical computing \cite{Rcite}Step V: Determine the quality of information of these articlesFor each article, you will need to critically appraise the information contained within it and decide if the publication you are considering for your review meets the internal validity criteria. At the minimum you will need to identify the following:What is the theory and the hypotheses this research is about?Is the sample size adequate for the research question? is this study underpowered?To what extent did the authors eliminate biases in the study? Even if it is an RCT, was there blinding? How confident are you that the authors conducted an appropriate randomisation procedure? What is the likelihood that the groups that were compared were very different with respect to the prognosis? - If this is an RCT, did the authors conduct an intention to treat analysis?If this is an observational study, how did the authors eliminate the risks of selection bias? How much was the risks of information bias from the participants eliminated?What confounding variables were controlled for? Are these confounding variables sufficient? (This will require that you will have to know something about the biology of the relationship; if you are not confident, ask someone)A great way to ascertain the quality of each article (rather each outcome within an article) is to use the GRADE (Grading recommendations assessment, development and evaluation) criteria and use the GRADEpro software to judge the quality of the outcome-intervention pairing.Step VI: Determine the extent to which the articles are heterogeneousThink about the distinction between a systematic review and a meta analysis. A systematic review is one where the analysts follow the same steps as above (frame a question, conduct a search, identify the right type of research, extract information from the articles). Then, in a systematic review but not in a meta analysis, all studies that are fit to be included in the review get summarised and patterns of information are tabulated and itemised. This means, that all study findings for a set of outcomes and interventions are identified, tabulated and discussed in systematic reviews. On the other hand, in a meta analysis, there is an implicit assumption that the studies have come from a population that is fairly uniform across the intervention and outcomes. This may indicate one of the two issues: either that the body of the studies that you have identified are exhaustive and the estimates that you will obtain for the association between the exposure or intervention and the outcome are based on the subset of evidence that you have identified and define or estimate the true association. This is the concept of fixed effects meta analysis \cite{hunter2000fixed} . Alternatively, you can conceptualise that the studies that you have identified for this meta analysis constitute a sample that is part of a larger population of studies. That said, this subset of studies from that larger population is interchangeable  with any other study in that wider population. Hence this set of studies is just a random sample of all possible studies. This is the notion of random effects meta analysis \cite{hunter2000fixed} . So, are the studies very similar or homogeneous in the scope of the intervention or population, or outcomes? Therefore, it is important that when we conduct a meta-analysis, because if the studies are so different from each other that it is impossible to pool the results together, then we will have to abandon any notion of pooling the study findings to arrive at a summary estimate. If the findings are close enough then the studies are homogeneous and we would conclude that it would be OK to pool the study results together using what is referred to as fixed effects meta analysis. If on the other hand, we see that the studies are different by way of their results but nevertheless there are other areas (selection of the population, the intervention, and the outcomes) that are sufficiently uniform, then we can combine the results of the studies themselves but we may conclude that the apparent lack of homogeneity would arise as these studies are part of a larger wider population of all possible studies and hence we would rather report a random effects meta analysis.We will discuss two ways to measure heterogeneity of the studies. One way to test for heterogeneity is to use a statistic referred to as Cochran’s Q statistic. The Q statistic is a chi-square statistic. The assumption here is that the studies are all from the same "population" and therefore homogeneous and therefore a fixed-effects meta-analysis would be an appropriate measure to express the summary findings. Accordingly, the software first estimates a fixed-effects summary estimate. The fixed effects summary estimate is a sum of the weighted effect size. The weight of each study is determined by the variance of the effect estimate. Then, the sum of squared difference between the summary estimate and each individual estimate would have a chi-squared distribution with K-1 degrees of freedom where K = number of studies. If the chi-square value would be low, this would indicate that the studies were indeed homogeneous, otherwise, it would indicate that the studies are heterogeneous. If the studies are found to be statistically heterogeneous, the next step for you would be to test whether there are real reasons for them to be heterogeneous, i.e., the population, the intervention, and the outcomes are very different from each other. If this indeed would be the case, then, you would summarise the study findings as you would with a systematic review. On the other hand, if you find that the studies are otherwise similar, but perhaps one or more studies were to drag the summary estimate to one direction rather than another, you would assume while the studies are not homogeneous, they may be based on a larger pool of studies. Hence you may conduct a random effect meta analysis.Another measure of heterogeneity or statistical heterogeneity for meta analyses is \(\$I^{\left\{2\right\}}\) estimate. I^2 estimate is derived from another related estimate referred to as H^2, and H^2 is given by: H^2 = Q / K - 1 where K is the number of studies. Then, if Q > K - 1, then I^2 is defined as (H^2 - 1)/H^2; otherwise I^2 is given a value of 0. For example, let’s say are working with 10 studies, and the Q statistic is 36 (this will mean that the weighted sum of squared differences between the estimated fixed effect size and the individual effect size estimates in this case is 36); As Q > 9 for 10 studies (K = 10), therefore I^2 will be defined as 3/4 or 75%. On the other hand, imagine Q was 36 but this time based on 19 studies, so H^2 would be 2, and correspondingly even though Q is still greater than K-1 that is 18, I^2 would now be 50% (1/2); If the number of studies were more, say 25, then Q would still be higher than 24, but H^2 would now be 36/24 or 1.5, and I^2 would come down to something like 33% (0.5/1.5). A high I-squared statistic would mean gross heterogeneity while a low I-squared value would mean homogeneity (usually set at 30%)Step VII: Estimate summary effect estimateFirst, we shall determine the summary effect estimate assuming both fixed and random effects modelSecond, we shall construct a Forest Plot to visually inspect how the effect estimates of each individual study are distributed around a null value but also around the overall effect estimates.Fixed and random effects models refer to the two assumptions: fixed effects model assume that the populations on which these studies are based are uniform enough to determine that these set of studies are sufficient to draw conclusions about the relationship between the exposure and the outcome; random effects model assume that while we can relax the assumption that the populations from where the studies arose were same and therefore these sets of studies were sufficient to draw our conclusions, the studies themselves form part of an interchangeable body of evidence. 

AbstractThe purpose of this tutorial is to show you how you can conduct health technology assessment using Gradepro -- a freely available online tool. We will first define health technology assessment and its key steps, then we shall showcase what is meant by Grade and how grade is used, and finally we shall conduct a health technolgoy assessment exercise using Gradepro tool. At the end of this exercise you will be able to test health technology assessment exercises using Grade tools.

AbstractGrowth strain is a recent addition to wood properties of interest to breeders. The splitting tests are the fastest methods for testing for growth strain, which is commonly believed to be the driver for deformation during green sawing. Unfortunately they are destructive tests, making directly testing their precision impossible . Here a method using multiple testing scenarios and a minimization scheme was used to estimate the precision of a single splitting test measurement to be \(\pm 1006\) micro-strain. It is suggested that the resulting interval makes the splitting tests only suitable for removing very poorly performing individuals to make future breeding trials more efficient.  IntroductionIn recent years wood quality parameters have started to be included in tree breeding programs \cite{Davies2017,Chauhan_2013,Apiolaza_2011}. Particular wood properties provide advantages for different applications of timber. For example, high stiffness timber is sought after for structural uses, and a premium is paid. Growth-strain has previously been identified as a wood quality parameter which reduces the value of some Eucalypt species \cite{Yamamoto2007,Chauhan2010}. Some timber species, particularly hardwood such as Eucalypts have a tendency to produce high internal strains during growth, when these strains are released during processing, the cut boards deform resulting in an excessive value recovery loss as they must be re-sawn to gain straight boards. For more details on growth-strain refer to \citet{Alm_ras_2016} and for an older but comprehensive review see \citet{Kubler1987}.Until recently the quickest growth strain test required approximately half an hour and substantially sized trees rendering growth-strain too time consuming and expensive to incorporate as part of a breeding trial particularly within an early selection trial with small trees. \citet{jacobs1945} developed the paring test (splitting stems down the pith and measuring the movement of each side)  and reported results from Eucalyptus gigantea along with a number of other species. However he did not take the final steps of calculus required to convert his deformation measurements to strains.  \citet{Chauhan2010} developed the splitting test which (although the paper makes no mention of Jacobs) uses the same method with a number of assumptions to complete the calculus required to estimate surface strain from the paring test. The updated pairing test (now refereed to as the splitting test) substantially reduced the time and cost involved in measuring growth-strain, this method was further refined for production breeding trial assessment and a pilot study conducted by \citet{Davies2017}, who showed it had potential for suitably large scale trials on Eucalypts. The splitting test essentially involves cutting a stem longitudinally along the pith and measuring the opening, along with diameter and cut length a numerical value related to the strain in the sample can be obtained.\citet{Entwistle_2014} calculated the theoretical values of strain lost due to kerf, these become negligible with stems of significant size. \cite{Chauhan_2010} took strain gauge measurements at the surface of the stem and used them to predict the test opening, which showed a reasonable correlation on large stems. The results of this analysis were misleading for two reasons; one, the strain gauges were placed on on the surface perpendicular to the cut, and therefore only indicate the reliability of the splitting test to produce results when measured in such a way as to maximize the correlation and two, were conducted on large stems which was not the intended use for the test (very early selection). Larger diameter stems (anecdotally) exhibit similar growth strain variation by angle as smaller stems, as a result higher correlations can likely be created when tested in this particular way on larger stems, as there is a higher margin for error with gauge placement. \citet{Cramer2018} analysed Eucalyptus nitens stems with a diameter ranging from 58 to 120mm under the same method as \citet{Chauhan_2010} and found correlation between strain gauge surface strain and splitting test predicted surface strain of approximately 0.27 compared to \citet{Chauhan_2010} reporting of 0.92 on Eucalyptus nitens stems with diamters between 111 and 192mm. It is suspected that one of the reasons for this marked difference in correlation between \citet{Cramer2018} and \citet{Chauhan2010} is due to the substantially smaller openings observed by \citet{Cramer2018} due to the smaller tree size, which are similar to the tree size used in this study. It has been well reported that surface growth strain can vary markedly over small areas of stem \cite{Okuyama_1994,Saurat_1976}. The de-facto standard procedure for measuring surface strain on logs with CIRAD or strain gauge methods is to take 8 measurements around the perimeter \cite{cirad} this is to account for the radial variation which can occur over small distances on the log surface \cite{Fournier_1994}. The major trade-off with the speed of the splitting test is the resolution which can be achieved, surface strains near the cut are suppressed due to the geometry of the test, while the strains perpendicular to the cut are primarily responsible for the opening. Hence placing one strain gauge on each side, perpendicular to the plane of cut of the splitting test produces a misleading assessment of how accurately the splitting test predicts the average growth strain of the sample. It should be noted that the opening in the test is not solely caused by the longitudinal strain, tangential and radial strains also likely play a role. The way longitudinal, tangential and radial strains develop and interact is still unknown, with no work (to our knowledge) having been conducted to understand the underlying mechanical relationship, past work such as \cite{Chauhan2010} have reported correlations obtained from strain gauges.  Grain angle with respect to the longitudinal cut direction likely influences the results of both the splitting and strain gauge tests, however no known research has investigated this. The splitting tests are used within breeding programs \cite{Davies2017} in order to gain a quantitative measure of how much the stem would deform during green sawing. The primary objective of this paper is to present a method (and results from) for estimating how reliable splitting test measurements are when predicting this trait. A cut directly through the pith is a procedure which while not a typically used cutting strategy for eucalypt timber production, allows for (in normal circumstances) the largest manifestation of movement during cutting. Having knowledge of how much trust to place in splitting test measurements of individuals is important for tree breeders when they are deciding on selection weights for various traits. 174 samples were measured using the original splitting test \cite{Chauhan2010}, the rapid splitting test \cite{Davies2017}  and the newly proposed "quartering test". The results from each were compared, and used to estimate the reliability of the testing procedures when considered at an arbitrary angle. Method The trees used in this study were thinnings of five year old Eucalytus argophloia grown on a moderately steep east facing slope with a rainfall of approximately 700 mm per year in Marlborough New Zealand. The 176 samples from 115 individual trees were labeled and cut when they provided at least 400 mm of suitably straight stem with an estimated under bark diameter of greater than 20 mm. Under bark diameters ranged from 21 mm to 71 mm with a mean of 40 mm. The samples were cut from the stems in autumn using a chainsaw and were packed into air tight containers with excess water and transported to a cool store where they were stored at 5C until processing, which took place over four weeks, there where no signs (visual or statistical) of sample quality degradation.Testing procedures: Samples were removed individually from their containers. Bark was removed using knives to hand peel the samples, with care taken not to damage the underlying wood. The longest sufficiently straight section of sample which could be obtained from the big end was marked (and the length from the big end recorded), with a maximum length being 50mm short of the small end. The diameter of the big end was recorded as was the small end diameter at the marked point.Rapid splitting test: The Rapid Splitting test outlined in \citet{Davies_2017} involves splitting with a band saw through the pith from the big end to the marked point. The resulting opening is measured and recorded; From the diameter d, slit length L and opening o, the strain within the sample can be estimated using Equation \ref{eq:split_eq}.Splitting Test: The original splitting test, presented by \citet{Chauhan_2010} involves splitting the sample the whole way down the pith (in this case it was achieved by docking the remaining intact end from the above procedure to gain two half rounds), clamping the two halves in the centre and measuring the opening at each end. Here the method is slightly modified due to the (compared to the original paper) low openings to reduce measurement error. In this case, the small end was clamped and the opening was measured at the big end. Note that when the curvature is sufficiently low, these two methods are approximately equivalent. The opening, big end diameter, small end diameter and split length are measured and processed through Equation — to obtain strain. — inc Figure \ref{171010}

Abstract In this document, I am going to describe the key steps in analysis of the survey data set in R and setting it up to conduct the study and write the tables for your results section. Read the document carefully and use the codes for your data set and modify them.

IntroductionWrite a brief introduction here. What shall we do:Write a section hereCreate a github repoConnect the github repowrite the rest of the paper in markdown in AtomOr write the rest of the paper in ipynb in nteractSave the paperPush the paper through gitAdd citations and tables and figures using markdown syntaxHere is an image of BuddhabuddhaToolsExplanationAuthorea A great tool Overleaf Another great toollist.files()

Goal of this courseThis course will provide students with an understanding of key quantitative methodological issue in health research from the perspective of research designs and development of current best evidence in health

IntroductionThink of Structural Equation Modelling as a combination of Factor Analysis and Linear Regression. If you have a covariance or correlation matrix, you can use this covariance or correlation matrix to develop structural equation models to find patterns in the data. Use cases of SEM are:Analysis of Questionnaires and Opinion SurveysTwin StudiesPattern identification in large matricesBuhi \cite{Buhi:2007vd} wrote a "primer" on conducting structural equation modelling and in that primer, in introducing SEM, he claimed that SEM is more popular than ANOVA in behavioural research, particularly for health behaviour as far back as 2007.  In this paper, we shall learn about the key issues involved in Structural Equation Model. We start with some explanations and definition of terms and concepts, then immediately dive into actually creating structural equation models and learn from those models how to build models. We will use the free and Open Source R software and its associated OpenMx and Onyx to build these models. Besides R, there are many other software that you can use to build SEMs (for a list, see the end of this article). Steps of SEM (From \cite{Buhi:2007vd}Specify a health behavioural model (or indeed any model)Determine the variables pertinent to the modelDecide how to measure those variablesCollect Data (use a survey instrument)Pass the data to SEM packageSoftware fits the data to model and produces model fit statistics and parameter estimatesThree critical steps for SEM:Examine the critical assumptions of Multivariate NormalityBuild and test a measurement modelBuild and test a structural modelStart with examining univariate and multivariate normalityFirst test univariate normality for all variables that will go into the model. Then test multivariate normality using QQ plot (See R codes for QQ plots and Shapiro Wilk Tests). If the multivariate normality conditions are not met, the model will be under-estimated. model parameters will be "downwardly biased" (towards the null), standard errors will be underestimated (lower than optimal and wider). Build and test a measurement modelStart with building and testing a measurement model. The purpose is to specify the indicator-construct relationship. Construct is the concept based on your theory. The construct is something you don't get to "see" and hence this is referred to as "latent" variable. Indicator variables are those variables that you measure. The following is an example of a measurement model:

Using Authorea with UlyssesUlysses is a great editor for writing markdown.the editor is spiffythe editor is feature richAdding a figure from Ulysses:cartoonAuthorea is a web based writing tool with rich text format and can be updated from markdown, latex, and html. Links are easy to insert.Tables are with codes as well:TestingStuffA B C DCitations are like

Abstract

ResourcesAchintya Rao's https://github.com/RaoOfPhysics/modern-research-publishing/blob/master/02_markdown/00_why_markdown.md

Goal The goal of this course is to develop in students skills and knowledge of critical appraisal of health research and skills of  critical thinkig while composing research proposal and research documents. In this course, we will focus on environmental health but you should be able to apply everything you learn here to your own discipline or context. After successfully completing the course, you will become a critical and an informed reader and author of research; you will be able to design your own research project and write a research proposal; you will be able to find out data sets and if you have your own data set, you will be able to analyse them, prepare reports & visualisation from that data set, and write research. 

Description of the Course  HLTH 301: Evidence in Health offers students a unique blend of ’big picture’ health topics (system level topics) along with the technical skills required to identify, evaluate, summarise and apply evidence in health care.

AbstractThe purpose of this workshop is to provide a set of tutorials to use Gradepro for conducting health technology assessment. We first discuss the principles of the concepts of grading of evidence and the concepts of using Gradepro, then we will discuss using gradepro for conducting evidence appraisal for health technology assessment. Health technology assessment is a process where appraisal of technology and tools that are relevant for achieving quality of health care delivery and improving access are routinely undertaken. At the heart of the health technology assessment process is appraisal of evidence and application of that process. Using Gradepro we will achieve the process in a stepwise manner. Besides, Gradepro allows one to write a complete documentation on conducting health technology assessment and export the document to a format that then allows for othe changes as applicable. 

The goal of this chapter is to present the key principles of Epidemiology that can be used to interpret, describe, and analyse the health effects of climate change. Epidemiology refers to the study of the distribution and determinants of health related states and use of this knowledge to bring about improvements in health states. 

Review of epigeneticsEpigenetics refer to Epi uponGenetics gene related studiesHence, while no structural change, there are functional alterations; according to Magge(2017), for some conditions identical twins can be discordant and these can be attributed to epigenetic modifications \cite{28060197}.  You can search Google to find more instances. Study the following table.Table of EpigeneticsCondition Epigenetic Modification XXX Long tail RNA YYY Methylation

AbstractThe "face" of scholarly publishing and outputs are changing. it is not unusual to find journal articles that are primary studies and secondary data and opinion (well-considered, and evidence backed opinions) presented in the space of the same journal. This was not the norm even a decade ago. In addition to this, the rapid expansion of the web as a medium of expression of scholarly communication has resulted in a new format. Hence blogs and primary and secondary analyses jostle for their spaces in the same ecosystem. As much as this has expanded, the tools to produce that knowledge is still to catch up. While there are some advances in the form of entirely web based online writing that enables people to collaborate and write freely on the web, the norm still is to produce outputs for the paper version. The screen is almost like an "extension" of the paper. Happily, with the extension of the web and appearance of small screen based tablets and phones, this trend is changing as well. In this publication, we are going to explore the new authoring tool Authorea that allows a space to write online papers and presentation of those papers on the site as well. We shall review its properties in terms of integrating LaTeX and markdown as well as simple markup based writing that enables a tight integration of different forms of writing and data presentation. This article is both a musing and a "how to" for Authorea as it stands today. IntroductionChanging face of scholarly publishingThe interface of Authorea is really simple. If you opt for the new "beta" style of writing, then you are all set. All you need to do is to set up your headings, and your thoughts and you should be able to write easily. We can write almost any form of text in Authorea. One idea might be to start with Authorea and then fine tune for further distrintion or writing long, complicated pieces with Overleaf, where you can port Authorea document as a Latex document and then open in Overleaf to finish it. Mechanics of Writing In general, when you write any document, you will be able to structure your thoughts in the fom of headings, and hen then, once the headings are all in place, fill in the headings and sections that you create with paragraphs of thought that are relevant for hat that puarti particular section and continue. For a shorter text such as an article, this would be the case; for a longer form of your writing, your setions sections and headers take the shape of your chapter heaed headers. Thus, if you were to writ write something like a thesis, you will would be using the headers or the section headers as your chapter headings and continue to write. Having said that, for academic writing, we need some more e things to worry about . Every fact that you state in an academic dcu documen m document must be accounted for, or at least you should be able to identify an evidn evidence to state your fact. Second, as academic writing is vetted in fo one form or another, therefore, this form of writing is open to scrutiny of facts and interpretations that you state. Also, bea e because of this reason, in academic wrii writing, you are restricted to state facts and interpretations rather than your opinions. This essentially means that ou wi you will depend on diagrams and analyses particularly when you write tet texts in epidemiology or health sciences. Third, most academic writing involves long tracts of passae passages where he the author is engaged in discussions, debates, discuri discursive writing where he or she draws from oher other people's writings, other diagrams, his o or her own analyses, previous writings, summaries, and othe other forms of evidene evidence and sources to substantiate the text he or she should write. To make it easy for readers to in understand the key points, an authors would like to abbreviate information or abridge or paraphrs paraphrase in the form of tables, and figures that explain or inform. These fiure figures could be in the form of flowcharts, or diagrams, or even perhaps in the form of graphs and charts that are developed on the fly. Given these different requirements, it is important Need for a simple integrated system to write scholarly paperWith the need for writing academic papers involving the following elements:1. Paragraphs that woud would enable tightly linked sentences that provide the structure of the paperParagraphs that provide the structure of the ag argumentsCitations that provide links and cross linking to the sources from the where the facts are takenStructures such as lists and bullet points that help to pare down complex ideas to sipler simpler digestible unitsTanles Tables and chrt charts that provide analytical contextsDiagrams nd and Flow charts that provide explanatory context to the text being writtenA neat set of headers that help to hold the structure in placeit is not easy to see It is no Most available word processing applications by themselves are not very intuitive to meet all these requirements that are needed to write a scholarly paper. The most popular application, Word, for example, is quite elegant in its design andn very responsive in the fo way it lets a user write free text, and also how tables and figures can be imported and embedded in the document. Howeer, it is not very intuitive for a writer to writ write or include equations and special symbols nd and the writer will have to spend time and energy to locate the symbols in the crumbs presented on the softeare to identify the symbols nand buuild the equation. build the equations or insert these sumbols to make them work. Another format is LaTeX, the elegant typesetting system. While LaTeX is elegant and very powerful tpe tupe tp typesetting system and is also dependn based on lain plain text and therefore very intuitive, it is not very easy to use. The numerous symbols and characters that one need to remeber remember in order to accomplish relti relatively sm simple tasks such as write a table or insert an environment such as an image is not very easy for a novice to wlear learn and then again, it is not e very easy to remember and use on a daily basis. As a result, many new writers who have started out with the ad process of academic riting writing tend to avoid writing in LaTeX. We ba Web based and rich text formatting of LaTeX helps to overcome some of these initial hindrances, but then again, adoption level is not very high. h The third type of format that has increasingly gained adoption in web writing in particular is using markdown formatted wiritng. This is a an elegant format of writinng particularly for web and very easy to master and use. The headers setio sections can be easily used with ah hash marks or singe single and double underlines, text emphasis with bold or italics are easy to am master, the lists and of various forms such as bullet lists and enumerated lists are easy to write and intuitive to set up. Yet due to its design restrictions, pure markdown is limited s as in markdown it is not very intuitive to add citations, and making table is impossile. The only way There hae have been further development of markdown in recent years to include scholarly markdown and that community ensures that it is possible to create tables relatively easily by using bar lines an and simple t straight lines. Therefore, a truly intuitive system of writng would n integrate everyting, i.e.,It would have an intuitie interfaceIt would be platform agnosticI w It would be so easy to write that it would allow an author to just start wtiting It would alo allow for heading and sectioningit would allow for bullet oints, points, citation, tables, and figuresIt would hae have a citation management system build uil built in itGetting all of these features w under one roof is something of a holy grail of writing. So lets see what alternatives do we have at the moment. State of the science as it exists now