Dataset Information

Estimates of excess medically attended acute respiratory infections in periods of seasonal and pandemic influenza in Germany from 2001/02 to 2010/11.

ABSTRACT:

Background

The number of patients seeking health care is a central indicator that may serve several different purposes: (1) as a proxy for the impact on the burden of the primary care system; (2) as a starting point to estimate the number of persons ill with influenza; (3) as the denominator data for the calculation of case fatality rate and the proportion hospitalized (severity indicators); (4) for economic calculations. In addition, reliable estimates of burden of disease and on the health care system are essential to communicate the impact of influenza to health care professionals, public health professionals and to the public.

Methodology/principal findings

Using German syndromic surveillance data, we have developed a novel approach to describe the seasonal variation of medically attended acute respiratory infections (MAARI) and estimate the excess MAARI attributable to influenza. The weekly excess inside a period of influenza circulation is estimated as the difference between the actual MAARI and a MAARI-baseline, which is established using a cyclic regression model for counts. As a result, we estimated the highest ARI burden within the last 10 years for the influenza season 2004/05 with an excess of 7.5 million outpatient visits (CI95% 6.8-8.0). In contrast, the pandemic wave 2009 accounted for one third of this burden with an excess of 2.4 million (CI95% 1.9-2.8). Estimates can be produced for different age groups, different geographic regions in Germany and also in real time during the influenza waves.

SUBMITTER: der Heiden MA

PROVIDER: S-EPMC3712969 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundFunding from external agencies for malaria control in Africa has increased dramatically over the past decade resulting in substantial increases in population coverage by effective malaria prevention interventions. This unprecedented effort to scale-up malaria interventions is likely improving child survival and will likely contribute to meeting Millennium Development Goal (MDG) 4 to reduce the < 5 mortality rate by two thirds between 1990 and 2015.MethodsThe Lives Saved Tool (LiST) model was used to quantify the likely impact that malaria prevention intervention scale-up has had on malaria mortality over the past decade (2001-2010) across 43 malaria endemic countries in sub-Saharan African. The likely impact of ITNs and malaria prevention interventions in pregnancy (intermittent preventive treatment [IPTp] and ITNs used during pregnancy) over this period was assessed.ResultsThe LiST model conservatively estimates that malaria prevention intervention scale-up over the past decade has prevented 842,800 (uncertainty: 562,800-1,364,645) child deaths due to malaria across 43 malaria-endemic countries in Africa, compared to a baseline of the year 2000. Over the entire decade, this represents an 8.2% decrease in the number of malaria-caused child deaths that would have occurred over this period had malaria prevention coverage remained unchanged since 2000. The biggest impact occurred in 2010 with a 24.4% decrease in malaria-caused child deaths compared to what would have happened had malaria prevention interventions not been scaled-up beyond 2000 coverage levels. ITNs accounted for 99% of the lives saved.ConclusionsThe results suggest that funding for malaria prevention in Africa over the past decade has had a substantial impact on decreasing child deaths due to malaria. Rapidly achieving and then maintaining universal coverage of these interventions should be an urgent priority for malaria control programmes in the future. Successful scale-up in many African countries will likely contribute substantially to meeting MDG 4, as well as succeed in meeting MDG 6 (Target 1) to halt and reverse malaria incidence by 2015.

Dataset Information

Estimates of excess medically attended acute respiratory infections in periods of seasonal and pandemic influenza in Germany from 2001/02 to 2010/11.

Background

Methodology/principal findings

Similar Datasets

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