Dataset Information

Childhood malaria admission rates to four hospitals in Malawi between 2000 and 2010.

ABSTRACT:

Introduction

The last few years have witnessed rapid scaling-up of key malaria interventions in several African countries following increases in development assistance. However, there is only limited country-specific information on the health impact of expanded coverage of these interventions.

Methods

Paediatric admission data were assembled from 4 hospitals in Malawi reflecting different malaria ecologies. Trends in monthly clinical malaria admissions between January 2000 and December 2010 were analysed using time-series models controlling for covariates related to climate and service use to establish whether changes in admissions can be related to expanded coverage of interventions aimed at reducing malaria infection.

Results

In 3 of 4 sites there was an increase in clinical malaria admission rates. Results from time series models indicate a significant month-to-month increase in the mean clinical malaria admission rates at two hospitals (trend P<0.05). At these hospitals clinical malaria admissions had increased from 2000 by 41% to 100%. Comparison of changes in malaria risk and ITN coverage appear to correspond to a lack of disease declines over the period. Changes in intervention coverage within hospital catchments showed minimal increases in ITN coverage from <6% across all sites in 2000 to maximum of 33% at one hospital site by 2010. Additionally, malaria transmission intensity remained unchanged between 2000-2010 across all sites.

Discussion

Despite modest increases in coverage of measures to reduce infection there has been minimal changes in paediatric clinical malaria cases in four hospitals in Malawi. Studies across Africa are increasingly showing a mixed set of impact results and it is important to assemble more data from more sites to understand the wider implications of malaria funding investment. We also caution that impact surveillance should continue in areas where intervention coverage is increasing with time, for example Malawi, as decline may become evident within a period when coverage reaches optimal levels.

SUBMITTER: Okiro EA

PROVIDER: S-EPMC3637378 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Childhood malaria admission rates to four hospitals in Malawi between 2000 and 2010.

Okiro Emelda A EA Kazembe Lawrence N LN Kabaria Caroline W CW Ligomeka Jeffrey J Noor Abdisalan M AM Ali Doreen D Snow Robert W RW

PloS one 20130426 4

<h4>Introduction</h4>The last few years have witnessed rapid scaling-up of key malaria interventions in several African countries following increases in development assistance. However, there is only limited country-specific information on the health impact of expanded coverage of these interventions.<h4>Methods</h4>Paediatric admission data were assembled from 4 hospitals in Malawi reflecting different malaria ecologies. Trends in monthly clinical malaria admissions between January 2000 and Dec ...[more]

PMID: 23638008

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Project description:BackgroundAgainst a backdrop of rising healthcare costs, variability in care provision and an increased emphasis on patient satisfaction, the need for effective interventions to improve quality of care has come to the fore. This is the first ten year (2000-2010) systematic review of interventions which sought to improve quality of care in a hospital setting. This review moves beyond a broad assessment of outcome significance levels and makes recommendations for future effective and accessible interventions.MethodsTwo researchers independently screened a total of 13,195 English language articles from the databases PsychInfo, Medline, PubMed, EmBase and CinNahl. There were 120 potentially relevant full text articles examined and 20 of those articles met the inclusion criteria.ResultsIncluded studies were heterogeneous in terms of approach and scientific rigour and varied in scope from small scale improvements for specific patient groups to large scale quality improvement programmes across multiple settings. Interventions were broadly categorised as either technical (n = 11) or interpersonal (n = 9). Technical interventions were in the main implemented by physicians and concentrated on improving care for patients with heart disease or pneumonia. Interpersonal interventions focused on patient satisfaction and tended to be implemented by nursing staff. Technical interventions had a tendency to achieve more substantial improvements in quality of care.ConclusionsThe rigorous application of inclusion criteria to studies established that despite the very large volume of literature on quality of care improvements, there is a paucity of hospital interventions with a theoretically based design or implementation. The screening process established that intervention studies to date have largely failed to identify their position along the quality of care spectrum. It is suggested that this lack of theoretical grounding may partly explain the minimal transfer of health research to date into policy. It is recommended that future interventions are established within a theoretical framework and that selected quality of care outcomes are assessed using this framework. Future interventions to improve quality of care will be most effective when they use a collaborative approach, involve multidisciplinary teams, utilise available resources, involve physicians and recognise the unique requirements of each patient group.

Project description:BACKGROUND:Malaria transmission is influenced by a complex interplay of factors including climate, socio-economic, environmental factors and interventions. Malaria control efforts across Africa have shown a mixed impact. Climate driven factors may play an increasing role with climate change. Efforts to strengthen routine facility-based monthly malaria data collection across Africa create an increasingly valuable data source to interpret burden trends and monitor control programme progress. A better understanding of the association with other climatic and non-climatic drivers of malaria incidence over time and space may help guide and interpret the impact of interventions. METHODS:Routine monthly paediatric outpatient clinical malaria case data were compiled from 27 districts in Malawi between 2004 and 2017, and analysed in combination with data on climatic, environmental, socio-economic and interventional factors and district level population estimates. A spatio-temporal generalized linear mixed model was fitted using Bayesian inference, in order to quantify the strength of association of the various risk factors with district-level variation in clinical malaria rates in Malawi, and visualized using maps. RESULTS:Between 2004 and 2017 reported childhood clinical malaria case rates showed a slight increase, from 50 to 53 cases per 1000 population, with considerable variation across the country between climatic zones. Climatic and environmental factors, including average monthly air temperature and rainfall anomalies, normalized difference vegetative index (NDVI) and RDT use for diagnosis showed a significant relationship with malaria incidence. Temperature in the current month and in each of the 3 months prior showed a significant relationship with the disease incidence unlike rainfall anomaly which was associated with malaria incidence at only three months prior. Estimated risk maps show relatively high risk along the lake and Shire valley regions of Malawi. CONCLUSION:The modelling approach can identify locations likely to have unusually high or low risk of malaria incidence across Malawi, and distinguishes between contributions to risk that can be explained by measured risk-factors and unexplained residual spatial variation. Also, spatial statistical methods applied to readily available routine data provides an alternative information source that can supplement survey data in policy development and implementation to direct surveillance and intervention efforts.

Project description:Mortality rates among hospitalized children in many government hospitals in sub-Saharan Africa are high. Pediatric emergency services in these hospitals are often sub-optimal. Timely recognition of critically ill children on arrival is key to improving service delivery. We present a simple risk score to predict inpatient mortality among hospitalized children. Between April 2010 and June 2011, the Uganda Malaria Surveillance Project (UMSP), in collaboration with the National Malaria Control Program (NMCP), set up an enhanced sentinel site malaria surveillance program for children hospitalized at four public hospitals in different districts: Tororo, Apac, Jinja and Mubende. Clinical data collected through March 2013, representing 50249 admissions were used to develop a mortality risk score (derivation data set). One year of data collected subsequently from the same hospitals, representing 20406 admissions, were used to prospectively validate the performance of the risk score (validation data set). Using a backward selection approach, 13 out of 25 clinical parameters recognizable on initial presentation, were selected for inclusion in a final logistic regression prediction model. The presence of individual parameters was awarded a score of either 1 or 2 based on regression coefficients. For each individual patient, a composite risk score was generated. The risk score was further categorized into three categories; low, medium, and high. Patient characteristics were comparable in both data sets. Measures of performance for the risk score included the receiver operating characteristics curves and the area under the curve (AUC), both demonstrating good and comparable ability to predict deathusing both the derivation (AUC =0.76) and validation dataset (AUC =0.74). Using the derivation and validation datasets, the mortality rates in each risk category were as follows: low risk (0.8% vs. 0.7%), moderate risk (3.5% vs. 3.2%), and high risk (16.5% vs. 12.6%), respectively. Our analysis resulted in development of a risk score that ably predicted mortality risk among hospitalized children. While validation studies are needed, this approach could be used to improve existing triage systems.

Dataset Information

Childhood malaria admission rates to four hospitals in Malawi between 2000 and 2010.

Introduction

Methods

Results

Discussion

Publications

Childhood malaria admission rates to four hospitals in Malawi between 2000 and 2010.

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