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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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Average number of confirmed cases by month in Luangwa District Zambia 2009–2010.The dashed line corresponds to the annual average number of cases per month and the double headed arrow to the survey period and the corresponding average incidence.
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pone-0057297-g006: Average number of confirmed cases by month in Luangwa District Zambia 2009–2010.The dashed line corresponds to the annual average number of cases per month and the double headed arrow to the survey period and the corresponding average incidence.

Mentions: Figure 6 shows the seasonality in confirmed malaria cases at all health facilities in Luangwa District, Zambia, from HMIS records. The MIS surveys in Luangwa were conducted during peak transmission season (April-May) and thus the annual burden estimate needs to be scaled by the ratio of RDT confirmed malaria fever incidence over the whole year, relative to the incidence during this period (Table 5). It is assumed that this district is representative in terms of the degree of seasonality, and the targeting of the MIS surveys to the peak malaria season. If this district is representative, this provides an estimate of the average number of days with malaria fever per person-year in Zambian children of 2.6 days (95% credible interval: 1.5–3.8) of malarial fever per person at risk per year (Table 5).


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)

Average number of confirmed cases by month in Luangwa District Zambia 2009–2010.The dashed line corresponds to the annual average number of cases per month and the double headed arrow to the survey period and the corresponding average incidence.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0057297-g006: Average number of confirmed cases by month in Luangwa District Zambia 2009–2010.The dashed line corresponds to the annual average number of cases per month and the double headed arrow to the survey period and the corresponding average incidence.
Mentions: Figure 6 shows the seasonality in confirmed malaria cases at all health facilities in Luangwa District, Zambia, from HMIS records. The MIS surveys in Luangwa were conducted during peak transmission season (April-May) and thus the annual burden estimate needs to be scaled by the ratio of RDT confirmed malaria fever incidence over the whole year, relative to the incidence during this period (Table 5). It is assumed that this district is representative in terms of the degree of seasonality, and the targeting of the MIS surveys to the peak malaria season. If this district is representative, this provides an estimate of the average number of days with malaria fever per person-year in Zambian children of 2.6 days (95% credible interval: 1.5–3.8) of malarial fever per person at risk per year (Table 5).

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