Article type
Year
Abstract
Background:
The policy-makers and epidemiologists usually estimate the infectious disease burdens due to a specific organism by using statistical remodelling or extrapolation of the data from different settings. Though proven for usefulness, these are complex and difficult to replicate. For this reason there is a need to create strategies to make it simple and easy to replicate.
Objectives:
To describe a simpler method of using the data available from surveillance of infectious diseases.
Methods:
A systematic review and meta-analysis was carried out on surveillance studies to discuss the impact of the clinical evidence available. The proportions were calculated from individual studies: a) proportion of invasive pneumococcal diseases (IPD) from suspected children (< 12 years), b) proportion of invasive pneumococcal disease from confirmed invasive bacterial disease, and c) meta-regression analysis was carried to find out the effect of confounding factors using Stata/MP-12 2 core. Pooling the proportions separately obtained provided an estimate size to inform about the proportion of patients affected with IPD.
Results:
The pooled proportions from 22 included studies show an effect size 3.57 (95% CI 2.66 to 4.49), when the proportions of IPD from suspected patients were pooled. An effect size of 23.74 (95% CI 17.47 to 30.01) was obtained when the proportion of IPD among confirmed bacterial diseases were pooled. The results thus obtained show that a high proportion of bacterial diseases are pneumococcal diseases. The meta-regression showed a trend towards significance when different diagnostic methods were used (OR 1.09, 95% CI 0.99 to 1.21).
Conclusions:
The meta-analysis of surveillance studies could be a useful method for policy-makers in cases where data from clinical trials are not available.
Acknowledgement: Indian Council of Medical Research, New Delhi, India
The policy-makers and epidemiologists usually estimate the infectious disease burdens due to a specific organism by using statistical remodelling or extrapolation of the data from different settings. Though proven for usefulness, these are complex and difficult to replicate. For this reason there is a need to create strategies to make it simple and easy to replicate.
Objectives:
To describe a simpler method of using the data available from surveillance of infectious diseases.
Methods:
A systematic review and meta-analysis was carried out on surveillance studies to discuss the impact of the clinical evidence available. The proportions were calculated from individual studies: a) proportion of invasive pneumococcal diseases (IPD) from suspected children (< 12 years), b) proportion of invasive pneumococcal disease from confirmed invasive bacterial disease, and c) meta-regression analysis was carried to find out the effect of confounding factors using Stata/MP-12 2 core. Pooling the proportions separately obtained provided an estimate size to inform about the proportion of patients affected with IPD.
Results:
The pooled proportions from 22 included studies show an effect size 3.57 (95% CI 2.66 to 4.49), when the proportions of IPD from suspected patients were pooled. An effect size of 23.74 (95% CI 17.47 to 30.01) was obtained when the proportion of IPD among confirmed bacterial diseases were pooled. The results thus obtained show that a high proportion of bacterial diseases are pneumococcal diseases. The meta-regression showed a trend towards significance when different diagnostic methods were used (OR 1.09, 95% CI 0.99 to 1.21).
Conclusions:
The meta-analysis of surveillance studies could be a useful method for policy-makers in cases where data from clinical trials are not available.
Acknowledgement: Indian Council of Medical Research, New Delhi, India