4. You will read the full text of the studies and then either further exclude them OR, you will read their reference lists and hand search other studies and in this way, iteratively, you will build a database of studies to be included in the meta-analysis.
5. In this step, you will abstract information from individual studies included in the meta-analysis. This will include information on authors, years of study, the population studied, the sample size, study type, the effect size or point estimate, the 95% confidence interval, the p-value
6.Based on the information you have collected in Step 5, you will first conduct a forest plot to show the distribution of the effect size of the different studies.
7.Based on the information you collected in step 5, you will first test the heterogeneity of the studies. You will test heterogeneity of the studies using a variation of chi-square test based on a pooled estimate, the effect estimate of each individual study, and the number of studies. This test is referred to as Q statistic and you will note the associated p-value, and evaluate it at 0.05 for the null hypothesis. Your null hypothesis will be that the results of the studies are similar to each other or that there is no difference in the results of the studies included in the meta analysis. The alternative hypothesis is that, the results of the studies differ from each other. If your p-value rejects the null hypothesis, then your studies are heterogeneous; if your p-value fails to reject the null hypothesis, then you will conclude that the studies are homogeneous. If the studies are homogeneous, then you will pool the results of the studies together and report two types of estimates: (1) fixed effects estimate based on the assumption that the studies that you have included in your research form an exhaustive set of studies; and (2) a random effect estimate where you will assume that the set of studies you have included in your analysis form a 'sample' or random sample of studies of 'all possible studies'.
8.Conduct the pooled estimate as mentioned in step 7 and report the effect estimate.
9. You will test for publication bias. Publication bias refers to a bias that occurs due to the fact that smaller studies and those with "equivocal estimates" (that is estimates that are inconclusive or those studies with negative estimates) are less likely to be published and therefore less likely to be captured in your meta analysis than those studies that are large and have positive findings. If you plot the variance of the study estimates (variance of the effect estimate of a study is a function of its sample size) and the effect estimate itself, you will see that the cloud of points may define a funnel. The base of the funnel will be formed by studies that are small in size (hence large variance) and the effect estimates wil vary all around the point estimate; the apex or peak of the funnel will be formed by those studies that are large sized (hence low variance) and all the estimates will clouded around the point estimate you obtained in the meta analysis. If part of the funnel is missing, then that indicates that there was publication bias. This is referred to as the funnel plot. There are other tests, such as "Egger's Test" that can statistically report the extent of publication bias. (Find out about them and write here). You cannot do much to remedy publication bias other than searching for 'fugitive literature' and contacting the research groups and others who can have studies that are small and remained unpublished or obtain the raw data from different sources.
10. You will test for meta-regression or subgroup analyses. In this analysis, you will subgroup your data and analyse them separately using a regression model. You will test if the estimates are different for those in developing versus developed countries, and also for those with different types of source apportionment. Source apportionment refers to the phenomenon that different sources will contribute differently to air pollution. For example, do sources such as vehicle exhausts lead to higher admission rates than say coal burning plants? Is the association between PM10 and hospital admissions different for developing countries than for developed countries?