Rise in Malaria Incidence Rates in South Africa: A Small-Area Spatial Analysis of Variation in Time Trends

Using Bayesian statistical models, the authors investigated spatial and temporal variations in small-area malaria incidence rates for the period mid-1986 to mid-1999 for two districts in northern KwaZulu Natal, South Africa. Maps of spatially smoothed incidence rates at different time points and spatially smoothed time trends in incidence gave a visual impression of the highest increase in incidence occurring where incidence rates previously had been lowest. This was confirmed by conditional autoregressive models, which showed that there was a significant negative association between time trends and smoothed baseline incidence before the steady rise in caseloads began. Growth rates also appeared to be higher in the areas close to the Mozambican border. The main findings of this analysis were that: 1) the spatial distribution of the rise in malaria incidence is uneven and strongly suggests a geographic expansion of high-malaria-risk areas; 2) there is evidence of a stabilization of incidence in areas that had the highest rates before the current escalation of rates began; and 3) areas immediately adjoining the Mozambican border appear to have undergone larger increases in incidence, in contrast to the general pattern of low growth in the more northern, high-baseline-incidence areas, but this was not confirmed by modeling. Smoothing of small-area maps of incidence and growth in incidence (trend) is important for interpretation of the spatial distribution of disease incidence and the spatial distribution of rapid changes in disease incidenc

El Niño Southern Oscillation (ENSO) and annual malaria incidence in Southern Africa

We evaluated the association between annual malaria incidence and El Niño Southern Oscillation (ENSO) as measured by the Southern Oscillation Index (SOI) in five countries in Southern Africa from 1988 to 1999. Below normal incidence of malaria synchronised with a negative SOI (El Niño) and above normal incidence with a positive SOI (La Niña), which lead to dry and wet weather conditions, respectively. In most countries there was a positive relationship between SOI and annual malaria incidence, especially where Anopheles arabiensis is a major vector. This mosquito breeds in temporary rain pools and is highly sensitive to fluctuations in weather conditions. South Africa and Swaziland have the most reliable data and showed the strongest associations, but the picture there may also be compounded by the moderating effect of other oscillatory systems in the Indian Ocean. The impact of ENSO also varies over time within countries, depending on existing malaria control efforts and response capacity. There remains a need for quantitative studies that at the same time consider both ENSO-driven climate anomalies and non-ENSO factors influencing epidemic risk potential to assess their relative importance in order to provide an empirical basis for malaria epidemic forecasting model

Comparative analysis of two methods for measuring sales volumes during malaria medicine outlet surveys.

BACKGROUND: There is increased interest in using commercial providers for improving access to quality malaria treatment. Understanding their current role is an essential first step, notably in terms of the volume of diagnostics and anti-malarials they sell. Sales volume data can be used to measure the importance of different provider and product types, frequency of parasitological diagnosis and impact of interventions. Several methods for measuring sales volumes are available, yet all have methodological challenges and evidence is lacking on the comparability of different methods. METHODS: Using sales volume data on anti-malarials and rapid diagnostic tests (RDTs) for malaria collected through provider recall (RC) and retail audits (RA), this study measures the degree of agreement between the two methods at wholesale and retail commercial providers in Cambodia following the Bland-Altman approach. Relative strengths and weaknesses of the methods were also investigated through qualitative research with fieldworkers. RESULTS: A total of 67 wholesalers and 107 retailers were sampled. Wholesale sales volumes were estimated through both methods for 62 anti-malarials and 23 RDTs and retail volumes for 113 anti-malarials and 33 RDTs. At wholesale outlets, RA estimates for anti-malarial sales were on average higher than RC estimates (mean difference of four adult equivalent treatment doses (95% CI 0.6-7.2)), equivalent to 30% of mean sales volumes. For RDTs at wholesalers, the between-method mean difference was not statistically significant (one test, 95% CI -6.0-4.0). At retail outlets, between-method differences for both anti-malarials and RDTs increased with larger volumes being measured, so mean differences were not a meaningful measure of agreement between the methods. Qualitative research revealed that in Cambodia where sales volumes are small, RC had key advantages: providers were perceived to remember more easily their sales volumes and find RC less invasive; fieldworkers found it more convenient; and it was cheaper to implement than RA. DISCUSSION/CONCLUSIONS: Both RA and RC had implementation challenges and were prone to data collection errors. Choice of empirical methods is likely to have important implications for data quality depending on the study context

Attendance versus compliance with tuberculosis treatment in an occupational setting a pilot study

Aim. To determine the prevalence of non-compliance with tuberculosis treatment at Freegold Mines.Objectives. 1. To establish the rates of attendance and collection of anti-tuberculosis drugs. 2. To detennine prevalence of non-compliance by means of urine tests.Design. A cross-sectional study conducted over 2 weeks at mine medical stations.Method. Urine samples were collected from tuberculosis patients 3 hours after drug ingestion. Non-compliance was established by testing these samples for rifampicin and/or isoniazid (INH) metabolites. Non-compliance was defined as a negative urine test result for these drugs in participants whose treatment regimens included one or both. Daily attendance and collection of drugs statistics are recorded in the medical station tuberculosis register. The patient rate of adherence was calculated as the observed number of days on which medication had been collected over the expected treatment days in a given period.Results. Urine test results showed an overall prevalence of non-compliance of 14.6 ± 3.3%, The study showed that non-compliance with tuberculosis treatment was underestimated by the surveillance data, The rate of nonadherence with treatment established from the formal surveillance procedure was 0.2%. The poor response rate of patients was found to be a major problem and fewer than 40% per day returned to bring urine specimens. The mean prevalences of non-compliance established by rifampicin and INH tests were 19.5 ± 5.3% and 9.8 ± 3.9%, respectively, and these were significantly different (x2 = 7.44; P < 0.05). The proportion of false-positive results for INH and rifampicin urine tests were 21% (11/53) and 35%   (17/48), respectively, showing that some patients were taking the wrong treatment.Conclusions. It is clear that attendance at the clinics does not accurately reflect compliance. 80th programme compliance (dispensing of the correct treatment) and patient compliance need to be improved. This has important implications for the new national tuberculosis control policy adopted by the South African government that stresses the importance of directly observed therapy, short-course (DOTS) and a patient-centred approach

Modelling the effect of malaria endemicity on spatial variations in childhood fever, diarrhoea and pneumonia in Malawi

BACKGROUND: Co-morbidity with conditions such as fever, diarrhoea and pneumonia is a common phenomenon in tropical Africa. However, little is known about geographical overlaps in these illnesses. Spatial modelling may improve our understanding of the epidemiology of the diseases for efficient and cost-effective control. METHODS: This study assessed subdistrict-specific spatial associations of the three conditions (fever, diarrhoea and pneumonia) in relation to malaria endemicity. We used data from the 2000 Malawi demographic and health survey which captured the history of childhood morbidities 2 weeks prior to the survey date. The disease status of each child in each area was the outcome of interest and was modelled using a trivariate logistic regression model, and incorporated random effects to measure spatial correlation. RESULTS: The risk of fever was positively associated with high and medium malaria endemicity levels relative to low endemicity level, while for diarrhoea and pneumonia we observed marginal positive association at high endemicity level relative to low endemicity level, controlling for confounding covariates and heterogeneity. A positive spatial correlation was found between fever and diarrhoea (r = 0.29); while weak associations were estimated between fever and pneumonia (r = 0.01); and between diarrhoea and pneumonia (r = 0.05). The proportion of structured spatial variation compared to unstructured variation was 0.67 (95% credible interval (CI): 0.31-0.91) for fever, 0.67 (95 % CI: 0.27-0.93) for diarrhoea, and 0.87 (95% CI: 0.62-0.96) for pneumonia. CONCLUSION: The analysis suggests some similarities in subdistrict-specific spatial variation of childhood morbidities of fever, diarrhoea and pneumonia, and might be a result of shared and overlapping risk factors, one of which is malaria endemicity

Spatial analysis and mapping of malaria risk in Malawi using point-referenced prevalence of infection data

BACKGROUND: Current malaria control initiatives aim at reducing malaria burden by half by the year 2010. Effective control requires evidence-based utilisation of resources. Characterizing spatial patterns of risk, through maps, is an important tool to guide control programmes. To this end an analysis was carried out to predict and map malaria risk in Malawi using empirical data with the aim of identifying areas where greatest effort should be focussed. METHODS: Point-referenced prevalence of infection data for children aged 1–10 years were collected from published and grey literature and geo-referenced. The model-based geostatistical methods were applied to analyze and predict malaria risk in areas where data were not observed. Topographical and climatic covariates were added in the model for risk assessment and improved prediction. A Bayesian approach was used for model fitting and prediction. RESULTS: Bivariate models showed a significant association of malaria risk with elevation, annual maximum temperature, rainfall and potential evapotranspiration (PET). However in the prediction model, the spatial distribution of malaria risk was associated with elevation, and marginally with maximum temperature and PET. The resulting map broadly agreed with expert opinion about the variation of risk in the country, and further showed marked variation even at local level. High risk areas were in the low-lying lake shore regions, while low risk was along the highlands in the country. CONCLUSION: The map provided an initial description of the geographic variation of malaria risk in Malawi, and might help in the choice and design of interventions, which is crucial for reducing the burden of malaria in Malawi

Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure

<p>Abstract</p> <p>Background</p> <p>Several malaria risk maps have been developed in recent years, many from the prevalence of infection data collated by the MARA (Mapping Malaria Risk in Africa) project, and using various environmental data sets as predictors. Variable selection is a major obstacle due to analytical problems caused by over-fitting, confounding and non-independence in the data. Testing and comparing every combination of explanatory variables in a Bayesian spatial framework remains unfeasible for most researchers. The aim of this study was to develop a malaria risk map using a systematic and practicable variable selection process for spatial analysis and mapping of historical malaria risk in Botswana.</p> <p>Results</p> <p>Of 50 potential explanatory variables from eight environmental data themes, 42 were significantly associated with malaria prevalence in univariate logistic regression and were ranked by the Akaike Information Criterion. Those correlated with higher-ranking relatives of the same environmental theme, were temporarily excluded. The remaining 14 candidates were ranked by selection frequency after running automated step-wise selection procedures on 1000 bootstrap samples drawn from the data. A non-spatial multiple-variable model was developed through step-wise inclusion in order of selection frequency. Previously excluded variables were then re-evaluated for inclusion, using further step-wise bootstrap procedures, resulting in the exclusion of another variable. Finally a Bayesian geo-statistical model using Markov Chain Monte Carlo simulation was fitted to the data, resulting in a final model of three predictor variables, namely summer rainfall, mean annual temperature and altitude. Each was independently and significantly associated with malaria prevalence after allowing for spatial correlation. This model was used to predict malaria prevalence at unobserved locations, producing a smooth risk map for the whole country.</p> <p>Conclusion</p> <p>We have produced a highly plausible and parsimonious model of historical malaria risk for Botswana from point-referenced data from a 1961/2 prevalence survey of malaria infection in 1–14 year old children. After starting with a list of 50 potential variables we ended with three highly plausible predictors, by applying a systematic and repeatable staged variable selection procedure that included a spatial analysis, which has application for other environmentally determined infectious diseases. All this was accomplished using general-purpose statistical software.</p

Malaria vector control by indoor residual insecticide spraying on the tropical island of Bioko, Equatorial Guinea

BACKGROUND: A comprehensive malaria control intervention was initiated in February 2004 on Bioko Island, Equatorial Guinea. This manuscript reports on the continuous entomological monitoring of the indoor residual spray (IRS) programme during the first two years of its implementation. METHODS: Mosquitoes were captured daily using window traps at 16 sentinel sites and analysed for species identification, sporozoite rates and knockdown resistance (kdr) using polymerase chain reaction (PCR) to assess the efficacy of the vector control initiative from December 2003 to December 2005. RESULTS: A total of 2,807 and 10,293 Anopheles funestus and Anopheles gambiae s.l. respectively were captured throughout the study period. Both M and S molecular forms of An. gambiae s.s. and Anopheles melas were identified. Prior to the first round of IRS, sporozoite rates were 6.0, 8.3 and 4.0 for An. gambiae s.s., An. melas and An. funestus respectively showing An. melas to be an important vector in areas in which it occurred. After the third spray round, no infective mosquitoes were identified. After the first spray round using a pyrethroid spray the number of An. gambiae s.s. were not reduced due to the presence of the kdr gene but An funestus and An. melas populations declined from 23.5 to 3.1 and 5.3 to 0.8 per trap per 100 nights respectively. After the introduction of a carbamate insecticide in the second round, An. gambiae s.s. reduced from 25.5 to 1.9 per trap per 100 nights and An. funestus and An. melas remained at very low levels. Kdr was found only in the M-form of An. gambiae s.s. with the highest frequency at Punta Europa (85%). CONCLUSION: All three vectors that were responsible for malaria transmission before the start of the intervention were successfully controlled once an effective insecticide was used. Continuous entomological surveillance including resistance monitoring is of critical importance in any IRS based malaria vector control programme. This paper demonstrates that sufficient resources for such monitoring should be included in any proposal in order to avoid programme failures

Estimation of the burden of active and life-time epilepsy: a meta-analytic approach.

PURPOSE: To estimate the burden of lifetime epilepsy (LTE) and active epilepsy (AE) and examine the influence of study characteristics on prevalence estimates. METHODS: We searched online databases and identified articles using prespecified criteria. Random-effects meta-analyses were used to estimate the median prevalence in developed countries and in urban and rural settings in developing countries. The impact of study characteristics on prevalence estimates was determined using meta-regression models. RESULTS: The median LTE prevalence for developed countries was 5.8 per 1,000 (5th-95th percentile range 2.7-12.4) compared to 15.4 per 1,000 (4.8-49.6) for rural and 10.3 (2.8-37.7) for urban studies in developing countries. The median prevalence of AE was 4.9 per 1,000 (2.3-10.3) for developed countries and 12.7 per 1,000 (3.5-45.5) and 5.9 (3.4-10.2) in rural and urban studies in developing countries. The estimates of burden for LTE and AE in developed countries were 6.8 million (5th-95th percentile range 3.2-14.7) and 5.7 million (2.7-12.2), respectively. In developing countries these were 45 (14-145) million LTE and 17 (10-133) million AE in rural areas and 17 (5-61) million LTE and 10 (5-17) million AE in urban areas. Studies involving all ages or only adults showed higher estimates than pediatric studies. Higher prevalence estimates were also associated with rural location and small study size. CONCLUSIONS: This study estimates the global burden of epilepsy and the proportions with AE, which may benefit from treatment. There are systematic differences in reported prevalence estimates, which are only partially explained by study characteristics

Differential effect of regional drug pressure on dihydrofolate reductase and dihydropteroate synthetase mutations in southern Mozambique.

The prevalence and frequency of the dihydrofolate reductase (dhfr) and dihydropteroate synthetase (dhps) mutations associated with sulfadoxine-pyrimethamine (SP) resistance at 13 sentinel surveillance sites in southern Mozambique were examined regularly between 1999 and 2004. Frequency of the dhfr triple mutation increased from 0.26 in 1999 to 0.96 in 2003, remaining high in 2004. The dhps double mutation frequency peaked in 2001 (0.22) but declined to baseline levels (0.07) by 2004. Similarly, parasites with both dhfr triple and dhps double mutations had increased in 2001 (0.18) but decreased by 2004 (0.05). The peaking of SP resistance markers in 2001 coincided with a SP-resistant malaria epidemic in neighboring KwaZulu-Natal, South Africa. The decline in dhps (but not dhfr) mutations corresponded with replacement of SP with artemether-lumefantrine as malaria treatment policy in KwaZulu-Natal. Our results show that drug pressure can exert its influence at a regional level rather than merely at a national level