Simple Conceptual Framework for a Pneumonia Case Management Guideline, Based on Current WHO Advice, Illustrating Some of the Areas For Which Improved Global and Local Data Could Improve Understanding of Likely Policy Effectiveness

<div><p>Contextual factors are likely to operate from the stage at which they first appear on the left-hand side of the figure right through to the far right of the figure. The list shown is illustrative and not exhaustive.</p> <p>Consider the possible effects of introducing Haemophilus influenzae type b and pneumococcal conjugate vaccines. As the relative prevalence of bacterial pneumonia declines, the positive predictive value of any imperfect clinical or diagnostic test used to assign children to antibiotic treatment will also fall, and so the population benefits of antibiotic therapy will also decline. This will increase the proportion of unnecessary treatments and enhance the importance of adverse effects at both the individual and population levels.</p> <p>Population adverse effects, for example the development of resistance, are likely to reflect total antibiotic use and can be included as additional probabilities associated with each treatment episode. For clarity these are not shown in the illustration but see [<a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.0050241#pmed-0050241-b011" target="_blank">11</a>] for an example.</p> <p>The effect of our current lack of basic data may be illustrated when we try to estimate the value of new treatments in a new setting. In the last ten years, most emerging evidence has come from comparative efficacy trials of primary treatments for pneumonia. However, while we now have data on the relative reduction in risk of treatment failure or mortality attributable to a new therapy, we cannot estimate absolute risk reduction at a specific country level because data on the risk of treatment failure or death associated with the “old” treatment in this setting are missing.</p> <p>Consider also recent calls from paediatricians in areas with a high prevalence of HIV for changes in the guidelines on treating hospitalised infants with severe pneumonia. If we know baseline risks, the incremental effectiveness of any newly proposed treatment, and the prevalence of HIV, we can estimate the aggregate outcomes of changing treatment for a given population. Additionally, the net benefits of such a change could be compared with alternative interventions elsewhere in the decision tree, such as changing the antibiotic used for outpatient treatment of pneumonia.</p></div

Additional file 1: Table S1 of Effect of enhanced feedback to hospitals that are part of an emerging clinical information network on uptake of revised childhood pneumonia treatment policy: study protocol for a cluster randomized trial

SPIRIT 2013 Checklist: recommended items to address in a clinical trial protocol and related documents. (DOC 254 kb

Additional file 2: of The influence of power and actor relations on priority setting and resource allocation practices at the hospital level in Kenya: a case study

Sample coding tree and coding process. (DOC 40 kb

The relationship between AC1 and Kappa statistics to crude agreement unadjusted for chance.

<p>The figure demonstrates the relationship between two chance-adjusted measures of agreement the AC1 and kappa statistics and the crude unadjusted agreement represented by the proportionate agreement calculated for responses from a panel of 20 international experts to a single question on a clinical sign for 104 videos.</p

Mean sensitivity and specificity of clinicians grouped by their cadre from routine hospital settings in Kenya for identification of the presence or absence of the clinical signs presented within the set of 20 high consensus videos.

<p>Mean sensitivity and specificity of clinicians grouped by their cadre from routine hospital settings in Kenya for identification of the presence or absence of the clinical signs presented within the set of 20 high consensus videos.</p

Cost-effectiveness analysis.

<p>Cost-effectiveness analysis.</p

Additional file 1: of The influence of power and actor relations on priority setting and resource allocation practices at the hospital level in Kenya: a case study

Questions used to explore power. (DOCX 93 kb

Demographic characteristics of the international (HE) and local panels (hospitals H1 to H8 and the national hospital, HN).

1<p>Most of these were consultant paediatricians.</p

Flow chart representing video selection and presentation to the different panels.

<p>Flow chart representing video selection and presentation to the different panels.</p

Expert agreement observed for specific clinical sign groups within the whole panel of examples (n = 104), for signs (n = 20 examples) selected on the basis of very high proportionate agreement (<i>P</i>o) and for signs (n = 11) selected where proportionate agreement was low (*no example with low proportionate agreement available).

<p>Expert agreement observed for specific clinical sign groups within the whole panel of examples (n = 104), for signs (n = 20 examples) selected on the basis of very high proportionate agreement (<i>P</i>o) and for signs (n = 11) selected where proportionate agreement was low (*no example with low proportionate agreement available).</p