Cancer patterns in Iran: a gender-specific spatial modelling of cancer incidence during 2014-2017.

BACKGROUND: Cancer is a significant public health concern and the second leading cause of death. This study aims to visualize spatial patterns of top common cancer types and identify high-risk and low-risk counties for these cancers in Iran from 2014 to 2017. METHODS: In this study, we analyzed 482,229 newly diagnosed cancer cases recorded by the Iranian National Population-Based Cancer Registry from 2014 to 2017. We employed a purely spatial scanning model and local Moran I analysis to explore spatial patterns across Iran. RESULTS: Approximately 53% of all cases were male. The average age of cancer diagnosis was 62.58 ± 17.42 years for males and 56.11 ± 17.33years for females. Stomach cancer was the most common cancer in men. The northern and northwestern regions of Iran were identified as high-risk areas for stomach cancer in both genders, with a relative risk (RR) ranging from 1.26 to 2.64 in males and 1.19 to 3.32 in females. These areas recognized as high-risk areas for trachea, bronchus, and lung (TBL) cancer specifically in males (RR:1.15-2.02). Central regions of Iran were identified as high-risk areas for non-melanoma skin cancers in both genders, ranking as the second most common cancer (RR:1.18-5.93 in males and 1.24-5.38 in females). Furthermore, bladder cancer in males (RR:1.32-2.77) and thyroid cancer in females (RR:1.88-3.10) showed concentration in the central part of Iran. Breast cancer, being the most common cancer among women (RR:1.23-5.54), exhibited concentration in the northern regions of the country. Also, northern regions of Iran were identified as high-risk clusters for colon cancer (RR:1.31-3.31 in males and 1.33-4.13 in females), and prostate cancer in males (RR:1.22-2.31). Brain, nervous system cancer, ranked sixth among women (RR:1.26-5.25) in central areas. CONCLUSIONS: The study's revelations on the spatial patterns of common cancer incidence in Iran provide crucial insights into the distribution and trends of these diseases. The identification of high-risk areas equips policymakers with valuable information to tailor targeted screening programs, facilitating early diagnosis and effective disease control strategies.

Spatiotemporal epidemiology of substance-related accidental acute toxicity deaths in Canada from 2016 to 2017.

OBJECTIVES: In Canada, substance-related accidental acute toxicity deaths (AATDs) continue to rise at the national and sub-national levels. However, it is unknown if, where, when, and to what degree AATDs cluster in space, time, and space-time across the country. The objectives of this study were to 1) assess for clusters of AATDs that occurred in Canada during 2016 and 2017 at the national and provincial/territorial (P/T) levels, and 2) examine the substance types detected in AATD cases within each cluster. METHODS: Two years of person-level data on AATDs were abstracted from coroner and medical examiner files using a standardized data collection tool, including the decedent's postal code and municipality information on the places of residence, acute toxicity (AT) event, and death, and the substances detected in the death. Data were combined with Canadian census information to create choropleth maps depicting AATD rates by census division. Spatial scan statistics were used to build Poisson models to identify clusters of high rates (p < 0.05) of AATDs at the national and P/T levels in space, time, and space-time over the study period. AATD cases within clusters were further examined for substance types most present in each cluster. RESULTS: Eight clusters in five regions of Canada at the national level and 24 clusters in 15 regions at the P/T level were identified, highlighting where AATDs occurred at far higher rates than the rest of the country. The risk ratios of identified clusters ranged from 1.28 to 9.62. Substances detected in clusters varied by region and time, however, opioids, stimulants, and alcohol were typically the most commonly detected substances within clusters. CONCLUSION: Our findings are the first in Canada to reveal the geographic disparities in AATDs at national and P/T levels using spatial scan statistics. Rates associated with substance types within each cluster highlight which substance types were most detected in the identified regions. Findings may be used to guide intervention/program planning and provide a picture of the 2016 and 2017 context that can be used for comparisons of the geographic distribution of AATDs and substances with different time periods.

Optimizing the maximum reported cluster size for the multinomial-based spatial scan statistic.

BACKGROUND: Correctly identifying spatial disease cluster is a fundamental concern in public health and epidemiology. The spatial scan statistic is widely used for detecting spatial disease clusters in spatial epidemiology and disease surveillance. Many studies default to a maximum reported cluster size (MRCS) set at 50% of the total population when searching for spatial clusters. However, this default setting can sometimes report clusters larger than true clusters, which include less relevant regions. For the Poisson, Bernoulli, ordinal, normal, and exponential models, a Gini coefficient has been developed to optimize the MRCS. Yet, no measure is available for the multinomial model. RESULTS: We propose two versions of a spatial cluster information criterion (SCIC) for selecting the optimal MRCS value for the multinomial-based spatial scan statistic. Our simulation study suggests that SCIC improves the accuracy of reporting true clusters. Analysis of the Korea Community Health Survey (KCHS) data further demonstrates that our method identifies more meaningful small clusters compared to the default setting. CONCLUSIONS: Our method focuses on improving the performance of the spatial scan statistic by optimizing the MRCS value when using the multinomial model. In public health and disease surveillance, the proposed method can be used to provide more accurate and meaningful spatial cluster detection for multinomial data, such as disease subtypes.

Spatio-temporal modeling of human leptospirosis prevalence using the maximum entropy model.

BACKGROUND: Leptospirosis, a zoonotic disease, stands as one of the prevailing health issues in some tropical areas of Iran. Over a decade, its incidence rate has been estimated at approximately 2.33 cases per 10,000 individuals. Our research focused on analyzing the spatiotemporal clustering of Leptospirosis and developing a disease prevalence model as an essential focal point for public health policymakers, urging targeted interventions and strategies. METHODS: The SaTScan and Maximum Entropy (MaxEnt) modeling methods were used to find the spatiotemporal clusters of the Leptospirosis and model the disease prevalence in Iran. We incorporated nine environmental covariates by employing a spatial resolution of 1 km x 1 km, the finest resolution ever implemented for modeling Human Leptospirosis in Iran. These covariates encompassed the Digital Elevation Model (DEM), slope, displacement areas, water bodies, and land cover, monthly recorded Normalized Difference Vegetation Index (NDVI), monthly recorded precipitation, monthly recorded mean and maximum temperature, contributing significantly to our disease modeling approach. The analysis using MaxEnt yielded the Area Under the Receiver Operating Characteristic Curve (AUC) metrics for the training and test data, to evaluate the accuracy of the implemented model. RESULTS: The findings reveal a highly significant primary cluster (p-value < 0.05) located in the western regions of the Gilan province, spanning from July 2013 to July 2015 (p-value < 0.05). Moreover, there were four more clusters (p-value < 0.05) identified near Someh Sara, Neka, Gorgan and Rudbar. Furthermore, the risk mapping effectively illustrates the potential expansion of the disease into the western and northwestern regions. The AUC metrics of 0.956 and 0.952 for the training and test data, respectively, underscoring the robust accuracy of the implemented model. Interestingly, among the variables considered, the influence of slope and distance from water bodies appears to be minimal. However, altitude and precipitation stand out as the primary determinants that significantly contribute to the prevalence of the disease. CONCLUSIONS: The risk map generated through this study carries significant potential to enhance public awareness and inform the formulation of impactful policies to combat Leptospirosis. These maps also play a crucial role in tracking disease incidents and strategically directing interventions toward the regions most susceptible.

Spatiotemporal patterns of drug use disorder in Sweden assessed using population-based registries.

BACKGROUND: Drug Use Disorder (DUD) is a major contributor to world-wide morbidity and mortality. The extensive national registers in Sweden provide the basis for a study of spatial and temporal patterns of DUD onset and recurrence in Sweden from 2001-2015. METHODS: To identify patterns of DUD over space, time and gender for Swedish individuals aged 15-35, space-time clustering using SaTScan was applied. We used yearly information on residential locations in Demographic Statistical Areas (DeSO) for all of Sweden. The clustering analysis used a Poisson probability model and a null hypothesis that the expected number of cases in each DeSO was proportional to the population size of DeSOs. As SaTScan results can be unstable, steps were taken to determine stable clusters and to refine and optimize cluster size. Results for each gender-register combination were compared to the results of spatial clustering using Gi* statistics. The space-time scanning model was also run with an adjustment for neighborhood socioeconomic status to determine DUD prevalence as it relates to education, income, unemployment and receipt of social welfare. RESULTS: DUD prevalence increased over time. Males yielded more significant clusters than females for both criminal and medical registers. Female DUD prevalence rates increased over time, especially after 2012. Higher correlations in DUD rates existed across the two registers than across gender. Male clusters were present from 2004 onwards while female-criminal clusters appeared after 2007, and female-medical clusters not until 2010. By 2013, clusters existed for all gender-register combinations. Male-criminal clusters were concentrated in Stockholm, Göteborg and Malmö as were male and female-medical clusters. Neighborhood SES was more highly related to the distribution of criminal than medical DUD clusters. A persistent gap in core clusters was identified in Stockholm in an area with notably high SES. CONCLUSIONS: Persistent hotspots of DUD in Sweden were confirmed as well as new and emerging hotspots, especially in Stockholm, Göteborg and Malmö. Higher correlations existed in DUD rates across registers than across gender. The findings are useful for monitoring the current drug problem and for identifying drivers underlying patterns of spread and important causal pathways to DUD.

Relationship between Clonorchis sinensis Infection and Cholangiocarcinoma in Korea.

This study provides an overview of the current status of clonorchiasis and cholangiocarcinoma (CCA), and their relationship in Korea during 2012-2020. Data were obtained from the Health Insurance Review & Assessment Service of Korea. Cluster, trend, and correlation analyses were performed. Gyeongsangnam-do and Seoul had the highest average number of cases (1,026 and 4,208) and adjusted rate (306 and 424) for clonorchiasis and CCA, respectively. The most likely clusters (MLC) for clonorchiasis and CCA were Busan/Gyeongsangnam-do/Ulsan/Daegu/Gyeongsangbuk-do (Relative Risk; RR = 4.55, Likelihood Ratio; LLR = 9,131.115) joint cluster and Seoul (RR = 2.29, LLR = 7,602.472), respectively. The MLC for clonorchiasis was in the southeastern part of Korea, while that for CCA was in the southern part. Clonorchiasis showed a decreasing trend in the southeastern districts, while increased in the southwestern districts. Cities in the central region had a decreasing trend, while the western districts had an increasing trend. In most adults (30-59), infection rate of clonorchiasis showed a significant decrease until 2018, while thereafter increased, although not significant. CCA showed a sharply decreasing tendency. The incidence of clonorchiasis and CCA were positively correlated. In general, the correlation was weak (r = 0.39, P < 0.001), but it was strongly positive around the 4 river basins (r = 0.74, P < 0.001). This study might provide an analytic basis for developing an effective system against clonorchiasis and CCA.

Spatiotemporal Clusters and Trend of Trichomonas vaginalis Infection in Korea.

This study was done to provide an overview of the latest trichomoniasis status in Korea by finding disease clusters and analyzing temporal trends during 2012-2020. Data were obtained from the Health Insurance Review & Assessment Service (HIRA) of Korea. SaTScan and Joinpoint programs were used for statistical analyses. Gyeonggi-do had the highest average population and highest number of cases. The high incidence of T. vaginalis infections were observed among women aged 40-49 and 30-39 years (33,830/year and 33,179/year, respectively). Similarly, the 40-49 and 30-39 age group in men showed the highest average cases (1,319/year and 1,282/year, respectively). Jeollabuk-do was the most likely cluster, followed by Busan/Gyeongsangnam-do/Ulsan/Daegu and Jeju-do and Gwangju. Urban and rural differences were prominent. Trichomoniasis has decreased significantly in most clusters, except for Incheon. Trichomoniasis was decreasing in women recently after peaking around 2014. Men showed different trends according to age. Trichomoniasis was increasing in the 10-39 age groups, but decreasing in the 40-59 age groups. This study might provide an analytic basis for future health measures, policy-makers, and health authorities in developing effective system for prevention of trichomoniasis.

Geographically Weighted Regression Modeling of Spatial Clustering and Determinants of Focal Typhoid Fever Incidence.

BACKGROUND: Typhoid is known to be heterogenous in time and space, with documented spatiotemporal clustering and hotspots associated with environmental factors. This analysis evaluated spatial clustering of typhoid and modeled incidence rates of typhoid from active surveillance at 4 sites with child cohorts in India. METHODS: Among approximately 24 000 children aged 0.5-15 years followed for 2 years, typhoid was confirmed by blood culture in all children with fever >3 days. Local hotspots for incident typhoid cases were assessed using SaTScan spatial cluster detection. Incidence of typhoid was modeled with sociodemographic and water, sanitation, and hygiene-related factors in smaller grids using nonspatial and spatial regression analyses. RESULTS: Hotspot households for typhoid were identified at Vellore and Kolkata. There were 4 significant SaTScan clusters (P < .05) for typhoid in Vellore. Mean incidence of typhoid was 0.004 per child-year with the highest incidence (0.526 per child-year) in Kolkata. Unsafe water and poor sanitation were positively associated with typhoid in Kolkata and Delhi, whereas drinking untreated water was significantly associated in Vellore (P = .0342) and Delhi (P = .0188). CONCLUSIONS: Despite decades of efforts to improve water and sanitation by the Indian government, environmental factors continue to influence the incidence of typhoid. Hence, administration of the conjugate vaccine may be essential even as efforts to improve water and sanitation continue.

Optimizing the maximum reported cluster size in the spatial scan statistic for survival data.

BACKGROUND: The spatial scan statistic is a useful tool for cluster detection analysis in geographical disease surveillance. The method requires users to specify the maximum scanning window size or the maximum reported cluster size (MRCS), which is often set to 50% of the total population. It is important to optimize the maximum reported cluster size, keeping the maximum scanning window size at as large as 50% of the total population, to obtain valid and meaningful results. RESULTS: We developed a measure, a Gini coefficient, to optimize the maximum reported cluster size for the exponential-based spatial scan statistic. The simulation study showed that the proposed method mostly selected the optimal MRCS, similar to the true cluster size. The detection accuracy was higher for the best chosen MRCS than at the default setting. The application of the method to the Korea Community Health Survey data supported that the proposed method can optimize the MRCS in spatial cluster detection analysis for survival data. CONCLUSIONS: Using the Gini coefficient in the exponential-based spatial scan statistic can be very helpful for reporting more refined and informative clusters for survival data.

Spatio-temporal epidemiology of the tuberculosis incidence rate in Iran 2008 to 2018.

BACKGROUND: Effective reduction of tuberculosis (TB) requires information on the distribution of TB incidence rate across time and location. This study aims to identify the spatio-temporal pattern of TB incidence rate in Iran between 2008 and 2018. METHODS: This cross-sectional study was conducted on aggregated TB data (50,500 patients) at the provincial level provided by the Ministry of Health in Iran between 2008 and 2018. The Anselin Local Moran's I and Getis-Ord Gi* were performed to identify the spatial variations of the disease. Furthermore, spatial scan statistic was employed for purely temporal and spatio-temporal analyses. In all instances, the null hypothesis of no clusters was rejected at p ≤ 0.05. RESULTS: The overall incidence rate of TB decreased from 13.46 per 100,000 (95% CI: 13.19-13.73) in 2008 to 10.88 per 100,000 (95% CI: 10.65-11.11) in 2018. The highest incidence rate of TB was observed in southeast and northeast of Iran for the whole study period. Additionally, spatial cluster analysis discovered Khuzestan Province, in the West of the country, having significantly higher rates than neighbouring provinces in terms of both total TB and smear-positive pulmonary TB (SPPTB). Purely temporal analysis showed that high-rate and low-rate clusters were predominantly distributed in the time periods 2010-2014 and 2017-2018. Spatio-temporal results showed that the statistically significant clusters were mainly distributed from centre to the east during the study period. Some high-trend TB and SPPTB statistically significant clusters were found. CONCLUSION: The results provided an overview of the latest TB spatio-temporal status In Iran and identified decreasing trends of TB in the 2008-2018 period. Despite the decreasing incidence rate, there is still need for screening, and targeting of preventive interventions, especially in high-risk areas. Knowledge of the spatio-temporal pattern of TB can be useful for policy development as the information regarding the high-risk areas would contribute to the selection of areas needed to be targeted for the expansion of health facilities.

Recent Spatial and Temporal Trends of Malaria in Korea.

This study was done to provide an analytical overview on the latest malaria infection clusters by evaluating temporal trends during 2010-2019 in Korea. Incheon was the most likely cluster (MLC) for all cases of malaria during the total period. MLCs for P. falciparum, vivax, malariae, ovale, and clinically diagnosed malaria without parasitological confirmation were Jeollanam-do, Incheon, Gangwon-do, Gyeongsangnam-do, and Jeollabuk-do, respectively. Malaria was decreasing in most significant clusters, but Gwangju showed an increase for all cases of malaria, P. vivax and clinically diagnosed cases. Malaria overall, P. falciparum and P. vivax seem to be under control thanks to aggressive health measures. This study might provide a sound scientific basis for future control measures against malaria in Korea.

Spatiotemporal Clustering of Multidrug-Resistant and Extensively Drug-Resistant Tuberculosis Is Associated With Human Immunodeficiency Virus Status and Drug-Susceptibility Patterns in KwaZulu-Natal, South Africa.

Using an open-access spatiotemporal analytics program, we mapped spatiotemporal heterogeneity loci in tuberculosis (TB) cases (clusters) and dynamic changes, and characterized the drug-resistant TB clustering risk using routine microbiological data from KwaZulu-Natal, South Africa. The data may provide insight into transmission dynamics and support efficient deployment of public health resources.

Clustering of asymptomatic Plasmodium falciparum infection and the effectiveness of targeted malaria control measures.

BACKGROUND: Because clustering of Plasmodium falciparum infection had been noted previously, the clustering of infection was examined at four field sites in West Africa: Dangassa and Dioro in Mali, Gambissara in The Gambia and Madina Fall in Senegal. METHODS: Clustering of infection was defined by the percent of persons with positive slides for asexual P. falciparum sleeping in a house which had been geopositioned. Data from each site were then tested for spatial, temporal and spatio-temporal clustering in relation to the prevalence of infection from smear surveys. RESULTS: These studies suggest that clustering of P. falciparum infection also affects the effectiveness of control interventions. For example, the clustering of infection in Madina Fall disappeared in 2014-2016 after vector control eliminated the only breeding site in 2013. In contrast, the temporal clustering of infection in Dioro (rainy season of 2014, dry season of 2015) was consistent with the loss of funding for Dioro in the second quarter of 2014 and disappeared when funds again became available in late 2015. The clustering of infection in rural (western) areas of Gambissara was consistent with known rural-urban differences in the prevalence of infection and with the thatched roofs, open eaves and mud walls of houses in rural Gambissara. In contrast, the most intense transmission was in Dangassa, where the only encouraging observation was a lower prevalence of infection in the dry season. Taken together, these results suggest: (a) the transmission of infection was stopped in Madina Fall by eliminating the only known breeding site, (b) the prevalence of infection was reduced in Dioro after financial support became available again for malaria control in the second half of 2015, (c) improvements in housing should improve malaria control by reducing the number of vectors in rural communities such as western Gambissara, and (d) beginning malaria control during the dry season may reduce transmission in hyperendemic areas such as Dangassa. CONCLUSIONS: From a conceptual perspective, testing for spatial, temporal and spatio-temporal clustering based on epidemiologic data permits the generation of hypotheses for the clustering observed and the testing of candidate interventions to confirm or refute those hypotheses.

Spatial clustering of suicide mortality and associated community characteristics in Kanagawa prefecture, Japan, 2011-2017.

BACKGROUND: Suicide mortality is high in Japan and early interventional strategies to solve that problem are needed. An accurate evaluation of the regional status of current suicide mortality would be useful for community interventions. A few studies in Kanagawa prefecture, located next to Tokyo and with the second largest population in Japan, have identified spatial clusters of suicide mortality at regional levels. This study examined spatial clustering and clustering over time of such events using spatial data from regional statistics on suicide deaths. METHODS: Data were obtained from regional statistics (58 regions in Kanagawa prefecture) of the National Vital Statistics of Japan from 2011 to 2017. The standardized mortality ratio (SMR) and Empirical Bayes estimator for the SMR (EBSMR) were used as measures. Spatial clusters were examined by Kulldorff's circular spatial scan statistic, Tango-Takahashi's flexible spatial scan statistic and Tango's test. Linear regression and conditional autoregressive (CAR) models were used not only to adjust for covariates but also to estimate regional effects. The analyses were conducted for each year, inclusive. RESULTS: Among male suicide deaths, being unemployed (50%) was most frequently related to suicide while among female health problem (50%) were frequent. Spatial clusters with significance detected by FlexScan, SatScan and Tango's test were few and varied somewhat according to the method used. Spatial clusters were detected in some regions including Kawasaki ward after adjustment by covariates. By the linear regression models, selected variables with significance were different between the sexes. For males, unemployment, family size, and proportion of higher education were detected for several of the years studied while for females, family size and divorce rate were detected over this period. These variables were also observed by the CAR model with 5 covariates. Regional effects were much clearer by considering the spatial parameter for both males and females and especially, Kawasaki ward was detected as a high risk region in many years. CONCLUSION: The present results detected some spatial clustering of suicide deaths within certain regions. Factors related to suicide deaths were also indicated. These results would provide important information in policy making for suicide prevention.

Investigation of geographic disparities of pre-diabetes and diabetes in Florida.

BACKGROUND: Diabetes is a leading cause of death and disability in the United States, and its precursor, pre-diabetes, is estimated to occur in one-third of American adults. Understanding the geographic disparities in the distribution of these conditions and identifying high-prevalence areas is critical to guiding control and prevention programs. Therefore, the objective of this study was to investigate clusters of pre-diabetes and diabetes risk in Florida and identify significant predictors of the conditions. METHODS: Data from the 2013 Behavioral Risk Factor Surveillance System were obtained from the Florida Department of Health. Spatial scan statistics were used to identify and locate significant high-prevalence local clusters. The county prevalence proportions of pre-diabetes and diabetes and the identified significant clusters were displayed in maps. Logistic regression was used to identify significant predictors of the two conditions for individuals living within and outside high-prevalence clusters. RESULTS: The study included a total of 34,186 respondents. The overall prevalence of pre-diabetes and diabetes were 8.2 and 11.5%, respectively. Three significant (p < 0.05) local, high-prevalence spatial clusters were detected for pre-diabetes, while five were detected for diabetes. The counties within the high-prevalence clusters had prevalence ratios ranging from 1.29 to 1.85. There were differences in the predictors of the conditions based on whether respondents lived within or outside high-prevalence clusters. Predictors of both pre-diabetes and diabetes regardless of region or place of residence were obesity/overweight, hypertension, and hypercholesterolemia. Income and physical activity level were significant predictors of diabetes but not pre-diabetes. Arthritis, sex, and marital status were significant predictors of diabetes only among residents of high-prevalence clusters, while educational attainment and smoking were significant predictors of diabetes only among residents of non-cluster counties. CONCLUSIONS: Geographic disparities of pre-diabetes and diabetes exist in Florida. Information from this study is useful for guiding resource allocation and targeting of intervention programs focusing on identified modifiable predictors of pre-diabetes and diabetes so as to reduce health disparities and improve the health of all Floridians.

Space time analysis of dengue fever diagnosed through a network of laboratories in India from 2014-2017.

BACKGROUND & OBJECTIVES: The Department of Health Research and the Indian Council of Medical Research, Government of India, have established Virus Research and Diagnostic Laboratory Network (VRDLN) to strengthen the laboratory capacity in the country for providing timely diagnosis of disease outbreaks. Fifty-one VRDLs were functional as on December 2017 and had reported about dengue fever across Indian states. The objectives of the study were to detect space time clusters and purely temporal clusters of dengue using Kulldorff's SaTScan statistics using patient level information; and to identify regions at greater risk of developing the disease using Kriging technique aggregating at district level. METHODS: A total of 211,432 patients from 51 VRDLs were investigated for IgM antibodies or NS1 antigen against dengue virus during the period from 1 January 2014 to 31 December 2017 and among them 60,096 (28.4%) were found to be positive. Kulldorff's space time analysis was used to identify significant clusters over space and time. Kriging technique was used to interpolate dengue data for areas not physically sampled using the relationship in the spatial arrangement of the data set. Maps obtained using both the methods were overlaid to identify the regions at greater risk of developing the disease. RESULTS: Kulldorff Space time Scan Statistics using the Bernoulli model with monthly precision revealed eight statistically significant clusters (P <0.001) for the time period, 1 January 2014 to 31 December 2017. Eight significant clusters identified were districts of Nagpur, Jhunjhunu, Gadag, Dakshin Kannada, Kancheepuram, Sivaganga, Ernakulam and Malda. The purely temporal clusters occurred during the last quarter of 2015 and 2016. The Kriging technique identified north eastern part of the country (Arunachal Pradesh, Nagaland and Manipur) and Gujarat. INTERPRETATION & CONCLUSION: Dengue fever has spread in all directions in the country. Hence, it is need of the hour to perform an in-depth investigation.

Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters.

Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China in December 2019, and is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a pandemic with an estimated death rate between 1% and 5%; and an estimated R 0 between 2.2 and 6.7 according to various sources. As of March 28th, 2020, there were over 649,000 confirmed cases and 30,249 total deaths, globally. In the United States, there were over 115,500 cases and 1891 deaths and this number is likely to increase rapidly. It is critical to detect clusters of COVID-19 to better allocate resources and improve decision-making as the outbreaks continue to grow. Using daily case data at the county level provided by Johns Hopkins University, we conducted a prospective spatial-temporal analysis with SaTScan. We detect statistically significant space-time clusters of COVID-19 at the county level in the U.S. between January 22nd-March 9th, 2020, and January 22nd-March 27th, 2020. The space-time prospective scan statistic detected "active" and emerging clusters that are present at the end of our study periods - notably, 18 more clusters were detected when adding the updated case data. These timely results can inform public health officials and decision makers about where to improve the allocation of resources, testing sites; also, where to implement stricter quarantines and travel bans. As more data becomes available, the statistic can be rerun to support timely surveillance of COVID-19, demonstrated here. Our research is the first geographic study that utilizes space-time statistics to monitor COVID-19 in the U.S.

A spatiotemporal analysis of invasive cervical cancer incidence in the state of Maryland between 2003 and 2012.

PURPOSE: Invasive cervical cancer (ICC) rates have tremendously declined in the United States, yet new cases consistently occur in Maryland and throughout the United States. We hypothesized that although rates have generally declined, this decline is uneven across counties and over time. METHODS: Space-time cluster detection analysis was conducted to evaluate clusters of ICC incidence at the county level within Maryland between 2003 and 2012. RESULTS: The most likely cluster was a cluster of low incidence, which included 6 counties in eastern Maryland for the period 2009-2012. A secondary cluster of low rates, comprising 2 metropolitan counties in northern Maryland, was observed for the period 2009-2012. Two of the three clusters of high ICC rates occurred in 2009-2012 in the large metropolitan area of Baltimore City and another cluster in Frederick County, in rural western Maryland. The third cluster of high rates was observed 2005-2008, in western Maryland. CONCLUSION: In recent periods, some Maryland counties have experienced anomalously high or low ICC incidence. Clusters of high incidence are not explained by differences in screening rates and may be due to failures in follow-up care for cervical abnormalities that need to be investigated. Clusters of low incidence may represent areas of successful ICC control.

Spatial and temporal dynamics of malaria in Madagascar.

BACKGROUND: Malaria is one of the primary health concerns in Madagascar. Based on the duration and intensity of transmission, Madagascar is divided into five epidemiological strata that range from low to mesoendemic transmission. In this study, the spatial and temporal dynamics of malaria within each epidemiological zone were studied. METHODS: The number of reported cases of uncomplicated malaria from 112 health districts between 2010 and 2014 were compiled and analysed. First, a Standardized Incidence Ratio was calculated to detect districts with anomalous incidence compared to the stratum-level incidence. Building on this, spatial and temporal malaria clusters were identified throughout the country and their variability across zones and over time was analysed. RESULTS: The incidence of malaria increased from 2010 to 2014 within each stratum. A basic analysis showed that districts with more than 50 cases per 1000 inhabitants are mainly located in two strata: East and West. Lower incidence values were found in the Highlands and Fringe zones. The standardization method revealed that the number of districts with a higher than expected numbers of cases increased through time and expanded into the Highlands and Fringe zones. The cluster analysis showed that for the endemic coastal region, clusters of districts migrated southward and the incidence of malaria was the highest between January and July with some variation within strata. CONCLUSION: This study identified critical districts with low incidence that shifted to high incidence and district that were consistent clusters across each year. The current study provided a detailed description of changes in malaria epidemiology and can aid the national malaria programme to reduce and prevent the expansion of the disease by targeting the appropriate areas.

Spatial overlaps in the distribution of HIV/AIDS and malaria in Zimbabwe.

BACKGROUND: In most developing economies particularly in Africa, more people are likely to die of HIV/AIDS and malaria compared to other diseases. HIV/AIDS tends to be superimposed on the long standing malaria burden particularly in sub-Saharan Africa. The detection and understanding of spatial overlaps in disease occurrence is important for integrated and targeted disease control. Integrated disease control can enhance efficiency and cost-effectiveness through the development of drugs targeting multiple infections in the same geographic space. METHODS: Using Zimbabwe as a case study, this study tests the hypothesis that malaria clusters coincide with HIV/AIDS clusters in space. Case data for the two diseases were obtained from the Ministry of Health and Child Care in Zimbabwe at district level via the District Health Information System (DHIS). Kulldorff's spatial scan statistic was used to test for spatial overlaps in clusters of high cases of HIV/AIDS and malaria at district level. The spatial scan test was used to identify areas with higher cases of HIV/AIDS and malaria than would be expected under spatial randomness. RESULTS: Results of this study indicate that primary clusters of HIV/AIDS and malaria were not spatially coincident in Zimbabwe. While no spatial overlaps were detected between primary clusters of the two diseases, spatial overlaps were detected among statistically significant secondary clusters of HIV/AIDS and malaria. Spatial overlaps between HIV/AIDS and malaria occurred in five districts in the northern and eastern regions of Zimbabwe. In addition, findings of this study indicate that HIV/AIDS is more widespread in Zimbabwe compared to malaria. CONCLUSIONS: The results of this study may therefore be used as a basis for spatially-targeted control of HIV/AIDS and malaria particularly in high disease burden areas. This is important as previous interventions have targeted the two diseases separately. Thus, targeted control could assist in resource allocation through prioritising areas in greatest need hence maximising the impact of disease control.