The spatial distribution of leprosy cases during 15 years of a leprosy control program in Bangladesh: an observational study

Unmasking the Hidden Burden: A Delayed Diagnosis of Leprosy Patients With Grade 2 Disability and Its Effects on the Healthcare System in Bangladesh

Introduction Leprosy remains a significant cause of preventable disability worldwide. Early diagnosis and treatment of leprosy are critical not only to stop its spread but also to prevent physical and social complications and reduce the disease burden. Objectives The study aims to evaluate the factors that lead to a delayed leprosy diagnosis. Methods This study was conducted in the outpatient departments of Leprosy Control Institute and Hospital, Dhaka, Bangladesh, and at Medical College for Women and Hospital, Dhaka, Bangladesh, from March 2023 to June 2023. A total number of 252 male (148) and female (104) patients were selected with any sign of leprosy, including disability, age ranging from 15 to 74 years. Data was collected in a pre-designed structured questionnaire by the researchers. To assess the risk of independent exposures of Grade 2 leprosy disabilities, we used a logistic regression model. A chi-square test showed the association between significant effects and leprosy disabilities. A p-value of 0.05 was considered as significant. For statistical analysis, STATA version 15 (StataCorp LLC, College Station, Texas, USA) was used. Results The study participants exhibited a higher percentage of disability, with a rate of 25.8% for Grade 2 disabilities. In addition to this, males represented a more considerable proportion, 58.7%, than females among leprosy and disability patients across all levels of disability. In our study, lack of money and painless symptoms showed a significant association (p<0.001) with Grade 2 disability. Conclusion The study reveals that Grade 2 disabilities are more common in males and are particularly prevalent in lower socioeconomic groups.

Identifying clusters of leprosy patients in India: A comparison of methods

BACKGROUND

Preventive interventions with post-exposure prophylaxis (PEP) are needed in leprosy high-endemic areas to interrupt the transmission of Mycobacterium leprae. Program managers intend to use Geographic Information Systems (GIS) to target preventive interventions considering efficient use of public health resources. Statistical GIS analyses are commonly used to identify clusters of disease without accounting for the local context. Therefore, we propose a contextualized spatial approach that includes expert consultation to identify clusters and compare it with a standard statistical approach.

METHODOLOGY/PRINCIPAL FINDINGS

We included all leprosy patients registered from 2014 to 2020 at the Health Centers in Fatehpur and Chandauli districts, Uttar Pradesh State, India (n = 3,855). Our contextualized spatial approach included expert consultation determining criteria and definition for the identification of clusters using Density Based Spatial Clustering Algorithm with Noise, followed by creating cluster maps considering natural boundaries and the local context. We compared this approach with the commonly used Anselin Local Moran's I statistic to identify high-risk villages. In the contextualized approach, 374 clusters were identified in Chandauli and 512 in Fatehpur. In total, 75% and 57% of all cases were captured by the identified clusters in Chandauli and Fatehpur, respectively. If 100 individuals per case were targeted for PEP, 33% and 11% of the total cluster population would receive PEP, respectively. In the statistical approach, more clusters in Chandauli and fewer clusters in Fatehpur (508 and 193) and lower proportions of cases in clusters (66% and 43%) were identified, and lower proportions of population targeted for PEP was calculated compared to the contextualized approach (11% and 11%).

CONCLUSION

A contextualized spatial approach could identify clusters in high-endemic districts more precisely than a standard statistical approach. Therefore, it can be a useful alternative to detect preventive intervention targets in high-endemic areas.

Geospatial epidemiology of leprosy in northwest Bangladesh: a 20-year retrospective observational study

Background

Leprosy is known to be unevenly distributed between and within countries. High risk areas or ‘hotspots’ are potential targets for preventive interventions, but the underlying epidemiologic mechanisms that enable hotspots to emerge, are not yet fully understood. In this study, we identified and characterized leprosy hotspots in Bangladesh, a country with one of the highest leprosy endemicity levels globally.

Methods

We used data from four high-endemic districts in northwest Bangladesh including 20 623 registered cases between January 2000 and April 2019 (among ~ 7 million population). Incidences per union (smallest administrative unit) were calculated using geospatial population density estimates. A geospatial Poisson model was used to detect incidence hotspots over three (overlapping) 10-year timeframes: 2000–2009, 2005–2014 and 2010–2019. Ordinal regression models were used to assess whether patient characteristics were significantly different for cases outside hotspots, as compared to cases within weak (i.e., relative risk (RR) of one to two), medium (i.e., RR of two to three), and strong (i.e., RR higher than three) hotspots.

Results

New case detection rates dropped from 44/100 000 in 2000 to 10/100 000 in 2019. Statistically significant hotspots were identified during all timeframes and were often located at areas with high population densities. The RR for leprosy was up to 12 times higher for inhabitants of hotspots than for people living outside hotspots. Within strong hotspots (1930 cases among less than 1% of the population), significantly more child cases (i.e., below 15 years of age) were detected, indicating recent transmission. Cases in hotspots were not significantly more likely to be detected actively.

Conclusions

Leprosy showed a heterogeneous distribution with clear hotspots in northwest Bangladesh throughout a 20-year period of decreasing incidence. Findings confirm that leprosy hotspots represent areas of higher transmission activity and are not solely the result of active case finding strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40249-021-00817-4.

Socioeconomic determinants of leprosy new case detection in the 100 Million Brazilian Cohort: a population-based linkage study

BACKGROUND

Although leprosy is recognised as a disease of poverty, there is little evidence on the specific socioeconomic factors associated with disease risk. To inform targeted strategies for disease elimination, we investigated socioeconomic markers of leprosy risk in Brazil.

METHODS

Socioeconomic data from the 100 Million Brazilian Cohort were linked to the Brazilian national disease registry (Sistema de Informação de Agravos de Notificação) for leprosy from Jan 1, 2007, to Dec 31, 2014. Using Poisson regression, we assessed the association of socioeconomic factors with risk of incident leprosy in the full cohort and in children (aged 0-15 years), by leprosy subtype and region of residence.

FINDINGS

In an analysis of 23 899 942 individuals including 18 518 patients with leprosy, increased levels of deprivation were associated with an increased risk of leprosy in Brazil. Directions of effect were consistent in children younger than 15 years and across disease subtypes. Individuals residing in regions with the highest poverty in the country (central-west, north, and northeast regions) had a risk of leprosy incidence five-to-eight times greater than did other individuals. Decreased levels of income and education and factors reflecting unfavourable living conditions were associated with an up to two-times increase in leprosy incidence (incidence rate ratio 1·46, 95% CI 1·32-1·62, for lowest vs highest quartile of income per capita; 2·09, 95% CI 1·62-2·72, for lowest vs highest level of education).

INTERPRETATION

Within the poorest half of the Brazilian population, the most deprived individuals have the greatest risk of leprosy. Strategies focusing on early detection and treatment in the poorest populations could contribute substantially to global disease control.

FUNDING

Medical Research Council, Wellcome Trust, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Brazil), the Conselho Nacional das Fundações Estaduais de Amparo à Pesquisa, Economic and Social Research Council, Biotechnology and Biological Sciences Research Council, Conselho Nacional de Desenvolvimento Científico e Tecnológico, and Fundação de Apoio à Pesquisa do Distrito Federal.

Spatial clustering, social vulnerability and risk of leprosy in an endemic area in Northeast Brazil: an ecological study

BACKGROUND

Despite the global decline in the detection of leprosy cases, its incidence has remained unchanged in certain settings and requires the determination of the factors linked to its persistence. We examined the spatial and space-time distribution of leprosy and the influence of social vulnerability on the occurrence of the disease in an endemic area of Northeast Brazil.

METHODS

We performed an ecological study of all leprosy cases reported by Sergipe state, Northeast Brazil from 2001 to 2015, to examine the association of the Social Vulnerability Index and the prevalence and persistence of leprosy among the State's municipalities. Socio-economic and leprosy surveillance information was collected from the Brazilian information systems, and a Bayesian empirical local model was used to identify fluctuations of the indicators. Spatial and space-time clusters were identified using scan spatial statistic tests and to measure the municipalities' relative risk of leprosy.

RESULTS

Leprosy clusters and burden of disease had a strong statistical association with the municipalities' Social Vulnerability Index. Municipalities with a high social vulnerability had higher leprosy incidence, multibacillary leprosy and newly diagnosed cases with grade 2 disability than areas with low social vulnerability.

CONCLUSION

Social vulnerability is strongly associated with leprosy transmission and maintenance of disease incidence. Leprosy control programmes should be targeted to the populations with high social vulnerability.

Exploring Spatial Influence of Remotely Sensed PM2.5 Concentration Using a Developed Deep Convolutional Neural Network Model

Currently, more and more remotely sensed data are being accumulated, and the spatial analysis methods for remotely sensed data, especially big data, are desiderating innovation. A deep convolutional network (CNN) model is proposed in this paper for exploiting the spatial influence feature in remotely sensed data. The method was applied in investigating the magnitude of the spatial influence of four factors—population, gross domestic product (GDP), terrain, land-use and land-cover (LULC)—on remotely sensed PM2.5 concentration over China. Satisfactory results were produced by the method. It demonstrates that the deep CNN model can be well applied in the field of spatial analysing remotely sensed big data. And the accuracy of the deep CNN is much higher than of geographically weighted regression (GWR) based on comparation. The results showed that population spatial density, GDP spatial density, terrain, and LULC could together determine the spatial distribution of PM2.5 annual concentrations with an overall spatial influencing magnitude of 97.85%. Population, GDP, terrain, and LULC have individual spatial influencing magnitudes of 47.12% and 36.13%, 50.07% and 40.91% on PM2.5 annual concentrations respectively. Terrain and LULC are the dominating spatial influencing factors, and only these two factors together may approximately determine the spatial pattern of PM2.5 annual concentration over China with a high spatial influencing magnitude of 96.65%.

Analysis of the epidemiological situation of leprosy in an endemic area in Brazil: spatial distribution in the periods 2001 - 2003 and 2010 - 2012

INTRODUCTION

In Brazil, the spatial distribution of leprosy is heterogeneous. Areas with high transmission of the disease remain in the North, Center-west and Northeast. Areas with high transmission of the disease remain in the Northern, Central-Western and Northeastern regions of the country.

OBJECTIVE

to describe the spatial distribution of leprosy in municipalities with high risk of transmission, in the periods from 2001 - 2003 and 2010 - 2012.

METHODS

This was an ecological study using data from the Notifiable Diseases Information System (SINAN). They included all municipalities in the states of Mato Grosso, Tocantins, Rondônia, Pará and Maranhão. The following leprosy indicators were calculated per 100,000 inhabitants: incidence rate of leprosy, incidence rate in children aged less than 15 years and rate of new cases with grade 2 disabilities. The spatial scan statistic was used to detect significant clusters (p ≤ 0.05) in the study area.

RESULTS

In the period 2001 - 2003, the scan spatial statistics identified 44 significant clusters for the leprosy incidence rate, and 42 significant clusters in the period 2010 - 2012. In the period 2001 - 2003, it was possible to identify 20 significant clusters to the incidence rate in children aged less than 15, and 14 significant clusters in the period 2010 - 2012. For the rate of new cases with grade 2 disability, the scan statistics identified 19 significant clusters in the period 2001 - 2003, and 14 significant clusters in the period 2010 - 2012.

CONCLUSIONS

Despite the reduction in the detection of leprosy cases, there is a need intensify disease control actions, especially in the clusters identified.

Socioeconomic risk markers of leprosy in high-burden countries: A systematic review and meta-analysis

Over 200,000 new cases of leprosy are detected each year, of which approximately 7% are associated with grade-2 disabilities (G2Ds). For achieving leprosy elimination, one of the main challenges will be targeting higher risk groups within endemic communities. Nevertheless, the socioeconomic risk markers of leprosy remain poorly understood. To address this gap we systematically reviewed MEDLINE/PubMed, Embase, LILACS and Web of Science for original articles investigating the social determinants of leprosy in countries with > 1000 cases/year in at least five years between 2006 and 2016. Cohort, case-control, cross-sectional, and ecological studies were eligible for inclusion; qualitative studies, case reports, and reviews were excluded. Out of 1,534 non-duplicate records, 96 full-text articles were reviewed, and 39 met inclusion criteria. 17 were included in random-effects meta-analyses for sex, occupation, food shortage, household contact, crowding, and lack of clean (i.e., treated) water. The majority of studies were conducted in Brazil, India, or Bangladesh while none were undertaken in low-income countries. Descriptive synthesis indicated that increased age, poor sanitary and socioeconomic conditions, lower level of education, and food-insecurity are risk markers for leprosy. Additionally, in pooled estimates, leprosy was associated with being male (RR = 1.33, 95% CI = 1.06-1.67), performing manual labor (RR = 2.15, 95% CI = 0.97-4.74), suffering from food shortage in the past (RR = 1.39, 95% CI = 1.05-1.85), being a household contact of a leprosy patient (RR = 3.40, 95% CI = 2.24-5.18), and living in a crowded household (≥5 per household) (RR = 1.38, 95% CI = 1.14-1.67). Lack of clean water did not appear to be a risk marker of leprosy (RR = 0.94, 95% CI = 0.65-1.35). Additionally, ecological studies provided evidence that lower inequality, better human development, increased healthcare coverage, and cash transfer programs are linked with lower leprosy risks. These findings point to a consistent relationship between leprosy and unfavorable economic circumstances and, thereby, underscore the pressing need of leprosy control policies to target socially vulnerable groups in high-burden countries.

Geographic weighted regression: applicability to epidemiological studies of leprosy

INTRODUCTION

Geographic information systems (GIS) enable public health data to be analyzed in terms of geographical variability and the relationship between risk factors and diseases. This study discusses the application of the geographic weighted regression (GWR) model to health data to improve the understanding of spatially varying social and clinical factors that potentially impact leprosy prevalence.

METHODS

This ecological study used data from leprosy case records from 1998-2006, aggregated by neighborhood in the Duque de Caxias municipality in the State of Rio de Janeiro, Brazil. In the GWR model, the associations between the log of the leprosy detection rate and social and clinical factors were analyzed.

RESULTS

Maps of the estimated coefficients by neighborhood confirmed the heterogeneous spatial relationships between the leprosy detection rates and the predictors. The proportion of households with piped water was associated with higher detection rates, mainly in the northeast of the municipality. Indeterminate forms were strongly associated with higher detections rates in the south, where access to health services was more established.

CONCLUSIONS

GWR proved a useful tool for epidemiological analysis of leprosy in a local area, such as Duque de Caxias. Epidemiological analysis using the maps of the GWR model offered the advantage of visualizing the problem in sub-regions and identifying any spatial dependence in the local study area.

Global elimination of leprosy by 2020: are we on track?

BACKGROUND

Every year more than 200,000 new leprosy cases are registered globally. This number has been fairly stable over the past 8 years. WHO has set a target to interrupt the transmission of leprosy globally by 2020. The aim of this study is to investigate whether this target, interpreted as global elimination, is feasible given the current control strategy. We focus on the three most important endemic countries, India, Brazil and Indonesia, which together account for more than 80 % of all newly registered leprosy cases.

METHODS

We used the existing individual-based model SIMCOLEP to predict future trends of leprosy incidence given the current control strategy in each country. SIMCOLEP simulates the spread of M. leprae in a population that is structured in households. Current control consists of passive and active case detection, and multidrug therapy (MDT). Predictions of leprosy incidence were made for each country as well as for one high-endemic region within each country: Chhattisgarh (India), Pará State (Brazil) and Madura (Indonesia). Data for model quantification came from: National Leprosy Elimination Program (India), SINAN database (Brazil), and Netherlands Leprosy Relief (Indonesia).

RESULTS

Our projections of future leprosy incidence all show a downward trend. In 2020, the country-level leprosy incidence has decreased to 6.2, 6.1 and 3.3 per 100,000 in India, Brazil and Indonesia, respectively, meeting the elimination target of less than 10 per 100,000. However, elimination may not be achieved in time for the high-endemic regions. The leprosy incidence in 2020 is predicted to be 16.2, 21.1 and 19.3 per 100,000 in Chhattisgarh, Pará and Madura, respectively, and the target may only be achieved in another 5 to 10 years.

CONCLUSIONS

Our predictions show that although country-level elimination is reached by 2020, leprosy is likely to remain a problem in the high-endemic regions (i.e. states, districts and provinces with multimillion populations), which account for most of the cases in a country.

Mathematical modelling of leprosy and its control

Leprosy or Hansen's disease is an infectious disease caused by the bacterium Mycobacterium leprae. The annual number of new leprosy cases registered worldwide has remained stable over the past years at over 200,000. Early case finding and multidrug therapy have not been able interrupt transmission completely. Elimination requires innovation in control and sustained commitment. Mathematical models can be used to predict the course of leprosy incidence and the effect of intervention strategies. Two compartmental models and one individual-based model have been described in the literature. Both compartmental models investigate the course of leprosy in populations and the long-term impact of control strategies. The individual-based model focusses on transmission within households and the impact of case finding among contacts of new leprosy patients. Major improvement of these models should result from a better understanding of individual differences in exposure to infection and developing leprosy after exposure. Most relevant are contact heterogeneity, heterogeneity in susceptibility and spatial heterogeneity. Furthermore, the existing models have only been applied to a limited number of countries. Parameterization of the models for other areas, in particular those with high incidence, is essential to support current initiatives for the global elimination of leprosy. Many challenges remain in understanding and dealing with leprosy. The support of mathematical models for understanding leprosy epidemiology and supporting policy decision making remains vital.

A Review and Framework for Categorizing Current Research and Development in Health Related Geographical Information Systems (GIS) Studies

OBJECTIVES

The application of GIS in health science has increased over the last decade and new innovative application areas have emerged. This study reviews the literature and builds a framework to provide a conceptual overview of the domain, and to promote strategic planning for further research of GIS in health.

METHOD

The framework is based on literature from the library databases Scopus and Web of Science. The articles were identified based on keywords and initially selected for further study based on titles and abstracts. A grounded theory-inspired method was applied to categorize the selected articles in main focus areas. Subsequent frequency analysis was performed on the identified articles in areas of infectious and non-infectious diseases and continent of origin.

RESULTS

A total of 865 articles were included. Four conceptual domains within GIS in health sciences comprise the framework: spatial analysis of disease, spatial analysis of health service planning, public health, health technologies and tools. Frequency analysis by disease status and location show that malaria and schistosomiasis are the most commonly analyzed infectious diseases where cancer and asthma are the most frequently analyzed non-infectious diseases. Across categories, articles from North America predominate, and in the category of spatial analysis of diseases an equal number of studies concern Asia.

CONCLUSION

Spatial analysis of diseases and health service planning are well-established research areas. The development of future technologies and new application areas for GIS and data-gathering technologies such as GPS, smartphones, remote sensing etc. will be nudging the research in GIS and health.

Spatial analysis spotlighting early childhood leprosy transmission in a hyperendemic municipality of the Brazilian Amazon region

BACKGROUND

More than 200,000 new cases of leprosy were reported by 105 countries in 2011. The disease is a public health problem in Brazil, particularly within high-burden pockets in the Amazon region where leprosy is hyperendemic among children.

METHODOLOGY

We applied geographic information systems and spatial analysis to determine the spatio-temporal pattern of leprosy cases in a hyperendemic municipality of the Brazilian Amazon region (Castanhal). Moreover, we performed active surveillance to collect clinical, epidemiological and serological data of the household contacts of people affected by leprosy and school children in the general population. The occurrence of subclinical infection and overt disease among the evaluated individuals was correlated with the spatio-temporal pattern of leprosy.

PRINCIPAL FINDINGS

The pattern of leprosy cases showed significant spatio-temporal heterogeneity (p<0.01). Considering 499 mapped cases, we found spatial clusters of high and low detection rates and spatial autocorrelation of individual cases at fine spatio-temporal scales. The relative risk of contracting leprosy in one specific cluster with a high detection rate is almost four times the risk in the areas of low detection rate (RR = 3.86; 95% CI = 2.26-6.59; p<0.0001). Eight new cases were detected among 302 evaluated household contacts: two living in areas of clusters of high detection rate and six in hyperendemic census tracts. Of 188 examined students, 134 (71.3%) lived in hyperendemic areas, 120 (63.8%) were dwelling less than 100 meters of at least one reported leprosy case, 125 (66.5%) showed immunological evidence (positive anti-PGL-I IgM titer) of subclinical infection, and 9 (4.8%) were diagnosed with leprosy (8 within 200 meters of a case living in the same area).

CONCLUSIONS/SIGNIFICANCE

Spatial analysis provided a better understanding of the high rate of early childhood leprosy transmission in this region. These findings can be applied to guide leprosy control programs to target intervention to high risk areas.

Inaccuracy, uncertainty and the space-time permutation scan statistic

The space-time permutation scan statistic (STPSS) is designed to identify hot (and cool) spots of space-time interaction within patterns of spatio-temporal events. While the method has been adopted widely in practice, there has been little consideration of the effect inaccurate and/or incomplete input data may have on its results. Given the pervasiveness of inaccuracy, uncertainty and incompleteness within spatio-temporal datasets and the popularity of the method, this issue warrants further investigation. Here, a series of simulation experiments using both synthetic and real-world data are carried out to better understand how deficiencies in the spatial and temporal accuracy as well as the completeness of the input data may affect results of the STPSS. The findings, while specific to the parameters employed here, reveal a surprising robustness of the method's results in the face of these deficiencies. As expected, the experiments illustrate that greater degradation of input data quality leads to greater variability in the results. Additionally, they show that weaker signals of space-time interaction are those most affected by the introduced deficiencies. However, in stark contrast to previous investigations into the impact of these input data problems on global tests of space-time interaction, this local metric is revealed to be only minimally affected by the degree of inaccuracy and incompleteness introduced in these experiments.

Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation

The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward.

Large-scale spatial population databases in infectious disease research

Modelling studies on the spatial distribution and spread of infectious diseases are becoming increasingly detailed and sophisticated, with global risk mapping and epidemic modelling studies now popular. Yet, in deriving populations at risk of disease estimates, these spatial models must rely on existing global and regional datasets on population distribution, which are often based on outdated and coarse resolution data. Moreover, a variety of different methods have been used to model population distribution at large spatial scales. In this review we describe the main global gridded population datasets that are freely available for health researchers and compare their construction methods, and highlight the uncertainties inherent in these population datasets. We review their application in past studies on disease risk and dynamics, and discuss how the choice of dataset can affect results. Moreover, we highlight how the lack of contemporary, detailed and reliable data on human population distribution in low income countries is proving a barrier to obtaining accurate large-scale estimates of population at risk and constructing reliable models of disease spread, and suggest research directions required to further reduce these barriers.

Clusters of leprosy transmission and of late diagnosis in a highly endemic area in Brazil: focus on different spatial analysis approaches

OBJECTIVE

The Brazilian National Hansen's Disease Control Program recently identified clusters with high disease transmission. Herein, we present different spatial analytical approaches to define highly vulnerable areas in one of these clusters.

METHOD

The study area included 373 municipalities in the four Brazilian states Maranhão, Pará, Tocantins and Piauí. Spatial analysis was based on municipalities as the observation unit, considering the following disease indicators: (i) rate of new cases/100,000 population, (ii) rate of cases <15 years/100,000 population, (iii) new cases with grade-2 disability/100,000 population and (iv) proportion of new cases with grade-2 disabilities. We performed descriptive spatial analysis, local empirical Bayesian analysis and spatial scan statistic.

RESULTS

A total of 254 (68.0%) municipalities were classified as hyperendemic (mean annual detection rates >40 cases/100,000 inhabitants). There was a concentration of municipalities with higher detection rates in Pará and in the center of Maranhão. Spatial scan statistic identified 23 likely clusters of new leprosy case detection rates, most of them localized in these two states. These clusters included only 32% of the total population, but 55.4% of new leprosy cases. We also identified 16 significant clusters for the detection rate <15 years and 11 likely clusters of new cases with grade-2. Several clusters of new cases with grade-2/population overlap with those of new cases detection and detection of children <15 years of age. The proportion of new cases with grade-2 did not reveal any significant clusters.

CONCLUSIONS

Several municipality clusters for high leprosy transmission and late diagnosis were identified in an endemic area using different statistical approaches. Spatial scan statistic is adequate to validate and confirm high-risk leprosy areas for transmission and late diagnosis, identified using descriptive spatial analysis and using local empirical Bayesian method. National and State leprosy control programs urgently need to intensify control actions in these highly vulnerable municipalities.

Spatial analysis of leprosy incidence and associated socioeconomic factors

OBJECTIVE

To identify clusters of the major occurrences of leprosy and their associated socioeconomic and demographic factors.

METHODS

Cases of leprosy that occurred between 1998 and 2007 in São José do Rio Preto (southeastern Brazil) were geocodified and the incidence rates were calculated by census tract. A socioeconomic classification score was obtained using principal component analysis of socioeconomic variables. Thematic maps to visualize the spatial distribution of the incidence of leprosy with respect to socioeconomic levels and demographic density were constructed using geostatistics.

RESULTS

While the incidence rate for the entire city was 10.4 cases per 100,000 inhabitants annually between 1998 and 2007, the incidence rates of individual census tracts were heterogeneous, with values that ranged from 0 to 26.9 cases per 100,000 inhabitants per year. Areas with a high leprosy incidence were associated with lower socioeconomic levels. There were identified clusters of leprosy cases, however there was no association between disease incidence and demographic density. There was a disparity between the places where the majority of ill people lived and the location of healthcare services.

CONCLUSIONS

The spatial analysis techniques utilized identified the poorer neighborhoods of the city as the areas with the highest risk for the disease. These data show that health departments must prioritize politico-administrative policies to minimize the effects of social inequality and improve the standards of living, hygiene, and education of the population in order to reduce the incidence of leprosy.

Spatio-temporal analysis of malaria incidence at the village level in a malaria-endemic area in Hainan, China

BACKGROUND

Malaria incidence in China's Hainan province has dropped significantly, since Malaria Programme of China Global Fund Round 1 was launched. To lay a foundation for further studies to evaluate the efficacy of Malaria Programme and to help with public health planning and resource allocation in the future, the temporal and spatial variations of malaria epidemic are analysed and areas and seasons with a higher risk are identified at a fine geographic scale within a malaria endemic county in Hainan.

METHODS

Malaria cases among the residents in each of 37 villages within hyper-endemic areas of Wanning county in southeast Hainan from 2005 to 2009 were geo-coded at village level based on residence once the patients were diagnosed. Based on data so obtained, purely temporal, purely spatial and space-time scan statistics and geographic information systems (GIS) were employed to identify clusters of time, space and space-time with elevated proportions of malaria cases.

RESULTS

Purely temporal scan statistics suggested clusters in 2005,2006 and 2007 and no cluster in 2008 and 2009. Purely spatial clustering analyses pinpointed the most likely cluster as including three villages in 2005 and 2006 respectively, sixteen villages in 2007, nine villages in 2008, and five villages in 2009, and the south area of Nanqiao town as the most likely to have a significantly high occurrence of malaria. The space-time clustering analysis found the most likely cluster as including three villages in the south of Nanqiao town with a time frame from January 2005 to May 2007.

CONCLUSIONS

Even in a small traditional malaria endemic area, malaria incidence has a significant spatial and temporal heterogeneity on the finer spatial and temporal scales. The scan statistics enable the description of this spatiotemporal heterogeneity, helping with clarifying the epidemiology of malaria and prioritizing the resource assignment and investigation of malaria on a finer geographical scale in endemic areas.

The effects of spatial population dataset choice on estimates of population at risk of disease

Background

The spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.

Methods

The recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1 km spatial resolution), LandScan (~1 km), UNEP Global Population Databases (~5 km), and GPW3 (~5 km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets.

Results

The estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets.

Conclusions

Detailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions.

The spatial distribution of leprosy in four villages in Bangladesh: an observational study

BACKGROUND

There is a higher case-detection rate for leprosy among spatially proximate contacts such as household members and neighbors. Spatial information regarding the clustering of leprosy can be used to improve intervention strategies. Identifying high-risk areas within villages around known cases can be helpful in finding new cases.

METHODS

Using geographic information systems, we created digital maps of four villages in a highly endemic area in northwest Bangladesh. The villages were surveyed three times over four years. The spatial pattern of the compounds--a small group of houses--was analyzed, and we looked for spatial clusters of leprosy cases.

RESULTS

The four villages had a total population of 4,123. There were 14 previously treated patients and we identified 19 new leprosy patients during the observation period. However, we found no spatial clusters with a probability significantly different from the null hypothesis of random occurrence.

CONCLUSION

Spatial analysis at the microlevel of villages in highly endemic areas does not appear to be useful for identifying clusters of patients. The search for clustering should be extended to a higher aggregation level, such as the subdistrict or regional level. Additionally, in highly endemic areas, it appears to be more effective to target complete villages for contact tracing, rather than narrowly defined contact groups such as households.