Change in case management outcomes after early rollout of WHO 2010 guidelines in Tanzania.

<p>The absolute percentage point change (grey bar) and percentage change relative to the baseline scenario (orange bar) following the introduction of the WHO 2010 case management guidelines advocating diagnostic-led treatment for all ages in Tanzania in 1) estimated proportion of attending cases correctly treated (both malaria and NMFI); 2) proportion of malaria cases correctly treated and 3) proportion of NMFI cases given an ACT. Data used were collected during the early period of the rollout of the new guidance and thus may not reflect more recent improvements in case management.</p

Decision tree modelling approach to malaria case management in the public sector.

<p>At the left-hand side the entry point is a febrile case seeking treatment. We next stratify on their true (unobserved) cause of fever as either malaria or non-malarial febrile illness (NMFI). The case management process then involves five steps – the availability of an RDT, whether the RDT is used, the outcome of the RDT given the true underlying cause of fever (based on the sensitivity and specificity of the diagnostic), whether an ACT is stock, and whether an ACT is prescribed given the RDT result or clinical diagnosis. This leads to four outcomes: correct treatment for malaria or for NMFI (shown as a green circle), under-treatment of malaria (shown as a purple circle), or overtreatment of an NMFI for malaria (shown as a red circle). In a perfect case management system there would be no under- or over-treatment.</p

Parameter estimates for each process in the cascade and decision-tree models.

<p>The values are stratified by whether the data were collected before or after the introduction of WHO diagnostic policy recommending universal diagnostic-led treatment for malaria. Values specific to a Tanzanian case study are also shown. The median and interquartile range from the published studies is presented. For the probability of seeking treatment at the public sector clinic, diagnostic sensitivity and diagnostic specificity, separate values for Tanzania were not available and so the general parameters were used. The probability of fever being due to malaria was assumed the same in the aggregated analysis but set to reflect the reduction in malaria incidence seen in Tanzania. In the Tanzanian case study, the probability of at least one dose of ACT being in stock was used rather than the probability of all doses of ACT being in stock due to limited data on the latter.</p

Estimated proportion of malaria cases at each case management point in the systems effectiveness pathway.

<p>The grey bars show the probabilities for each step for malaria case management whilst the orange line and values show the cumulative probability along this pathway. Data here is taken from studies published across sub-Saharan Africa prior to the rollout of the WHO guidelines on universal rational management.</p

Modelled impact on treatment gap and treatment excess in Tanzania.

<p>Figure depicting the % treatment gap and % overtreatment (treatment excess) of all febrile patients attending health facilities using Tanzania as a case study, in the scenarios defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069654#pone-0069654-t002" target="_blank">Table 2</a>. Desirable outcomes, namely malaria cases receiving ACTs and NMFI cases not being treated with ACTs are depicted in green. The % treatment gap, i.e. cases that need ACTs but that do not receive ACTs are depicted in purple. The % treatment excess i.e. cases that do not need antimalarials but are given ACTs unnecessarily are depicted in red.</p

Results from the decision tree model for cases attending the health facility.

<p>A) % of malaria cases correctly treated with an ACT (grey bars) and % of non-malarial febrile illness (NMFI) overtreated with an ACT (orange bars) in a variety of scenarios as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069654#pone-0069654-t002" target="_blank">Table 2</a> B) % change from baseline of malaria cases correctly treated with an ACT (grey bars) and % of non-malarial febrile illness (NMFI) overtreated with an ACT (orange bars) in each of the scenarios depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069654#pone-0069654-g003" target="_blank">Figure 3A</a> and defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069654#pone-0069654-t002" target="_blank">Table 2</a>.</p

Scenarios for improved malaria case management.

<p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069654#pone-0069654-t002" target="_blank">Table 2</a> describes the scenarios used in the model to investigate the impact of improving case management at different points along the patient care-pathway. Scenarios of individual interventions e.g. 100% ACT stock were first considered and then combinations of interventions were studied. The health system parameters that are perfected in each scenario are defined here. The results from the decision tree model for each of these scenarios are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069654#pone-0069654-g003" target="_blank">Figures 3</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069654#pone-0069654-g004" target="_blank">4</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0069654#pone-0069654-g005" target="_blank">5</a>.</p

Characteristics of studies included in the review (by author) for studies with clinical outcomes which also report adherence.

<p>¹ Duration of drug regimen in days not given for household surveys;</p><p>² See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084555#pone-0084555-t004" target="_blank">Table 4</a> for definitions;</p><p>³ Not incorporated into adherence definition</p

Approaches to assessing patient adherence across studies.

<p>¹ All studies are included if adherence is reported by at least one of these five approaches (n = 52 studies) and are included more than once if multiple approaches were used.</p

Characteristics of studies included in the review (by author) for descriptive studies.

<p>¹ Duration of drug regimen in days not given for household surveys;</p><p>² See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084555#pone-0084555-t004" target="_blank">Table 4</a> for definitions of approaches;</p><p>³ Not incorporated into approach to assessing adherence.</p