Predicting the detection of leprosy in a hyperendemic area of Brazil: Using time series analysis.

BACKGROUND: Brazil has the second highest prevalence of leprosy worldwide. Autoregressive integrated moving average models are useful tools in surveillance systems because they provide reliable forecasts from epidemiological time series. AIM: To evaluate the temporal patterns of leprosy detection from 2001 to 2015 and forecast for 2020 in a hyperendemic area in northeastern Brazil. METHODS: A cross-sectional study was conducted using monthly leprosy detection from the Brazil information system for notifiable diseases. The Box-Jenkins method was applied to fit a seasonal autoregressive integrated moving average model. Forecasting models (95% prediction interval) were developed to predict leprosy detection for 2020. RESULTS: A total of 44,578 cases were registered with a mean of 247.7 cases per month. The best-fitted model to make forecasts was the seasonal autoregressive integrated moving average ((1,1,1); (1,1,1)). It was predicted 0.32 cases/100,000 inhabitants to January of 2016 and 0.38 cases/100,000 inhabitants to December of 2020. LIMITATIONS: This study used secondary data from Brazil information system for notifiable diseases; hence, leprosy data may be underreported. CONCLUSION: The forecast for leprosy detection rate for December 2020 was < 1 case/100,000 inhabitants. Seasonal autoregressive integrated moving average model has been shown to be appropriate and could be used to forecast leprosy detection rates. Thus, this strategy can be used to facilitate prevention and elimination programmes.

Spatial analysis and privation index to identify urban areas with a high risk of lymphatic filariasis.

OBJECTIVE: To evaluate composite living conditions as indicators of urban areas with a higher risk of filariasis transmission. METHODS: This was an ecological study in the municipality of Jaboatão dos Guararapes, in Brazil. The analysis units were census tracts. The study was divided into three phases. First, data gathered during an epidemiological investigation were analysed. Secondly, living condition indicators were drawn up and the relationship between these indicators and microfilaremia prevalence rates was analysed. Thirdly, positive cases were georeferenced with a view to identifying spatial concentration using kernel intensity estimates. Two composite living condition indicators were calculated: a socio-environmental risk index (in the form of scores) and a social deprivation index (through principal-component factor analysis). RESULTS: Of 23,673 individuals examined, 1.4% had microfilaremia. According to the two indicators, greater prevalence was found in the high-risk strata, and this association was confirmed by the kernel intensity estimates. CONCLUSIONS: Classification of census tracts into risk strata showed the relevance of socio-economic factors and environmental conditions in identifying priority areas in urban spaces for interventions by the surveillance services and in planning filariasis control. Spatial analysis also proved to be an important tool for building up a territorially based surveillance system. These indicators, used in association with spatial analysis, are an instrument to be used by the Global Programme to Eliminate Lymphatic Filariasis.

Social deprivation index and lymphatic filariasis: a tool for mapping urban areas at risk in northeastern Brazil.

This paper describes the construction and application of a social deprivation index that was created to explore the relationship between lymphatic filariasis and socioenvironmental variables in the municipality of Jaboatão dos Guararapes, Pernambuco, Brazil, thereby contributing towards identifying priority areas for interventions. This indicator was obtained from principal-component factor analysis. Variables available from the national census representing socioenvironmental conditions, household characteristics and urban services were used. Epidemiological data came from a parasitological survey on lymphatic filariasis. 23 673 individuals were examined and 323 were positive (1.4%). Two factors that together explained 80.61% of the total variance were selected. The social deprivation strata were capable of indicating a risk gradient, with 74.9% of the microfilaremia cases situated in the high-risk stratum. Principal-component factor analysis was shown to be sensitive for selecting indicators associated with the risk of lymphatic filariasis transmission and for detecting areas potentially at risk. The capacity of the social deprivation index for picking up social inequalities qualifies it as a new tool for use in planning interventions aimed at controlling lymphatic filariasis in urban spaces.