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A novel approach for measuring the burden of uncomplicated Plasmodium falciparum malaria: application to data from Zambia.

Crowell V, Yukich JO, Briët OJ, Ross A, Smith TA - PLoS ONE (2013)

Bottom Line: The use of burden estimates that do not consider effects of treatment, leads to under-estimation of the impact of improvements in case management.Official estimates of burden very likely massively underestimate the impact of the roll-out of ACT as first-line therapy across Africa.The estimates of recall bias, and of the numbers of days with illness contributing to single illness recalls, could be applied more generally.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.

ABSTRACT
Measurement of malaria burden is fraught with complexity, due to the natural history of the disease, delays in seeking treatment or failure of case management. Attempts to establish an appropriate case definition for a malaria episode has often resulted in ambiguities and challenges because of poor information about treatment seeking, patterns of infection, recurrence of fever and asymptomatic infection. While the primary reason for treating malaria is to reduce disease burden, the effects of treatment are generally ignored in estimates of the burden of malaria morbidity, which are usually presented in terms of numbers of clinical cases or episodes, with the main data sources being reports from health facilities and parasite prevalence surveys. The use of burden estimates that do not consider effects of treatment, leads to under-estimation of the impact of improvements in case management. Official estimates of burden very likely massively underestimate the impact of the roll-out of ACT as first-line therapy across Africa. This paper proposes a novel approach for estimating burden of disease based on the point prevalence of malaria attributable disease, or equivalently, the days with malaria fever in unit time. The technique makes use of data available from standard community surveys, analyses of fever patterns in malaria therapy patients, and data on recall bias. Application of this approach to data from Zambia for 2009-2010 gave an estimate of 2.6 (95% credible interval: 1.5-3.7) malaria attributable fever days per child-year. The estimates of recall bias, and of the numbers of days with illness contributing to single illness recalls, could be applied more generally. To obtain valid estimates of the overall malaria burden using these methods, there remains a need for surveys to include the whole range of ages of hosts in the population and for data on seasonality patterns in confirmed case series.

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Branching process of events underlying cross-sectionally recorded outcomes.p is the probability of an RDT being positive; m is the probability of clinical malaria during any two week period, conditional on infection; n is the probability of non-malaria fever during any two week period; t is the probability of treatment with an antimalarial conditional on being both infected and febrile during the two-week period; and r is the probability that an untreated fever is reported.
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pone-0057297-g004: Branching process of events underlying cross-sectionally recorded outcomes.p is the probability of an RDT being positive; m is the probability of clinical malaria during any two week period, conditional on infection; n is the probability of non-malaria fever during any two week period; t is the probability of treatment with an antimalarial conditional on being both infected and febrile during the two-week period; and r is the probability that an untreated fever is reported.

Mentions: In this approach, the survey data from Luangwa, Zambia (Table 2) were used to separately estimate the malaria prevalence among fever recalls, pe, the period prevalence of reported fever, pf, and the relative risk of fever associated with malaria, RR, in each case without accounting for reporting bias, using formulae given in Table 3. The population attributable fraction of fever, PAF, and period prevalence of reported malaria attributable fever, pmf, were then obtained by substituting the estimates of pe, pf, and RR into further formulae given in Table 3. The point estimate of pmf thus obtained was then mapped on an estimate of the period prevalence of malaria fever allowing for reporting bias, , using further equations in the recall probability, for which the point estimate of (described above) was substituted for r (formulae also in Table 3).


A novel approach for measuring the burden of uncomplicated Plasmodium falciparum malaria: application to data from Zambia.

Crowell V, Yukich JO, Briët OJ, Ross A, Smith TA - PLoS ONE (2013)

Branching process of events underlying cross-sectionally recorded outcomes.p is the probability of an RDT being positive; m is the probability of clinical malaria during any two week period, conditional on infection; n is the probability of non-malaria fever during any two week period; t is the probability of treatment with an antimalarial conditional on being both infected and febrile during the two-week period; and r is the probability that an untreated fever is reported.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3585385&req=5

pone-0057297-g004: Branching process of events underlying cross-sectionally recorded outcomes.p is the probability of an RDT being positive; m is the probability of clinical malaria during any two week period, conditional on infection; n is the probability of non-malaria fever during any two week period; t is the probability of treatment with an antimalarial conditional on being both infected and febrile during the two-week period; and r is the probability that an untreated fever is reported.
Mentions: In this approach, the survey data from Luangwa, Zambia (Table 2) were used to separately estimate the malaria prevalence among fever recalls, pe, the period prevalence of reported fever, pf, and the relative risk of fever associated with malaria, RR, in each case without accounting for reporting bias, using formulae given in Table 3. The population attributable fraction of fever, PAF, and period prevalence of reported malaria attributable fever, pmf, were then obtained by substituting the estimates of pe, pf, and RR into further formulae given in Table 3. The point estimate of pmf thus obtained was then mapped on an estimate of the period prevalence of malaria fever allowing for reporting bias, , using further equations in the recall probability, for which the point estimate of (described above) was substituted for r (formulae also in Table 3).

Bottom Line: The use of burden estimates that do not consider effects of treatment, leads to under-estimation of the impact of improvements in case management.Official estimates of burden very likely massively underestimate the impact of the roll-out of ACT as first-line therapy across Africa.The estimates of recall bias, and of the numbers of days with illness contributing to single illness recalls, could be applied more generally.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland.

ABSTRACT
Measurement of malaria burden is fraught with complexity, due to the natural history of the disease, delays in seeking treatment or failure of case management. Attempts to establish an appropriate case definition for a malaria episode has often resulted in ambiguities and challenges because of poor information about treatment seeking, patterns of infection, recurrence of fever and asymptomatic infection. While the primary reason for treating malaria is to reduce disease burden, the effects of treatment are generally ignored in estimates of the burden of malaria morbidity, which are usually presented in terms of numbers of clinical cases or episodes, with the main data sources being reports from health facilities and parasite prevalence surveys. The use of burden estimates that do not consider effects of treatment, leads to under-estimation of the impact of improvements in case management. Official estimates of burden very likely massively underestimate the impact of the roll-out of ACT as first-line therapy across Africa. This paper proposes a novel approach for estimating burden of disease based on the point prevalence of malaria attributable disease, or equivalently, the days with malaria fever in unit time. The technique makes use of data available from standard community surveys, analyses of fever patterns in malaria therapy patients, and data on recall bias. Application of this approach to data from Zambia for 2009-2010 gave an estimate of 2.6 (95% credible interval: 1.5-3.7) malaria attributable fever days per child-year. The estimates of recall bias, and of the numbers of days with illness contributing to single illness recalls, could be applied more generally. To obtain valid estimates of the overall malaria burden using these methods, there remains a need for surveys to include the whole range of ages of hosts in the population and for data on seasonality patterns in confirmed case series.

Show MeSH
Related in: MedlinePlus