Spatiotemporal Modeling of Cholera, Uvira, Democratic Republic of the Congo, 2016-2020.

We evaluated the spatiotemporal clustering of rapid diagnostic test-positive cholera cases in Uvira, eastern Democratic Republic of the Congo. We detected spatiotemporal clusters that consistently overlapped with major rivers, and we outlined the extent of zones of increased risk that are compatible with the radii currently used for targeted interventions.

Prefiltered component-based greedy (PreCoG) scan method.

The spatial distribution of disease cases can provide important insights into disease spread and its potential risk factors. Identifying disease clusters correctly can help us discover new risk factors and inform interventions to control and prevent the spread of disease as quickly as possible. In this study, we propose a novel scan method, the Prefiltered Component-based Greedy (PreCoG) scan method, which efficiently and accurately detects irregularly shaped clusters using a prefiltered component-based algorithm. The PreCoG scan method's flexibility allows it to perform well in detecting both regularly and irregularly-shaped clusters. Additionally, it is fast to apply while providing high power, sensitivity, and positive predictive value for the detected clusters compared to other scan methods. To confirm the effectiveness of the PreCoG method, we compare its performance to many other scan methods. Additionally, we have implemented this method in the smerc R package to make it publicly available to other researchers. Our proposed PreCoG scan method presents a unique and innovative process for detecting disease clusters and can improve the accuracy of disease surveillance systems.

A call for caution when using network methods to study multimorbidity: an illustration using data from the Canadian Longitudinal Study on Aging.

OBJECTIVES: To examine the impact of two key choices when conducting a network analysis (clustering methods and measure of association) on the number and type of multimorbidity clusters. STUDY DESIGN AND SETTING: Using cross-sectional self-reported data on 24 diseases from 30,097 community-living adults aged 45-85 from the Canadian Longitudinal Study on Aging, we conducted network analyses using 5 clustering methods and 11 association measures commonly used in multimorbidity studies. We compared the similarity among clusters using the adjusted Rand index (ARI); an ARI of 0 is equivalent to the diseases being randomly assigned to clusters, and 1 indicates perfect agreement. We compared the network analysis results to disease clusters independently identified by two clinicians. RESULTS: Results differed greatly across combinations of association measures and cluster algorithms. The number of clusters identified ranged from 1 to 24, with a low similarity of conditions within clusters. Compared to clinician-derived clusters, ARIs ranged from -0.02 to 0.24, indicating little similarity. CONCLUSION: These analyses demonstrate the need for a systematic evaluation of the performance of network analysis methods on binary clustered data like diseases. Moreover, in individual older adults, diseases may not cluster predictably, highlighting the need for a personalized approach to their care.

Risk of human leptospirosis in Colombia: spatiotemporal analysis and related hydroclimatic factors.

BACKGROUND: Leptospirosis is an endemic zoonosis in tropical areas that is mainly related to rural activities; nevertheless, human leptospirosis (HL) outbreaks differ among regions. In Colombia, HL notifications are mandatory. Our objective was to determine the spatiotemporal distribution of HL in Colombia during 2007-2018 and its relationship with the main hydroclimatic variables. METHODS: We determined the estimated incidence and lethality of HL according to department and year. The Bayesian spatiotemporal analysis of an autoregressive model (STAR) model included HL cases and hydroclimatic factors (average temperature, rainfall and relative humidity) for quarterly periods. RESULTS: During the study period, 10 586 HL cases were registered (estimated incidence: 1.75 cases x 105) and 243 deaths by HL (lethality 2.3%). The STAR model found association of HL risk with temperature (RR:6.80; 95% CI 3.57 to 12.48) and space. Quindío and three other Amazonian departments (Guainía, Guaviare and Putumayo) had a positive relationship with a significant number of HL cases, adjusted for quarterly precipitation and humidity. CONCLUSION: Spatial analysis showed a high risk of HL in departments of the western Andean Colombian regions. By contrast, in the spatiotemporal model, a higher HL risk was associated with temperature and departments of the North Colombian Amazon regions and Quindío in the Colombian Andean region.

Efficacy and safety of Infliximab in systemic sarcoidosis according to GenPhenReSa organ-involvement phenotype: a retrospective study of 55 patients.

BACKGROUND: Infliximab is currently recommended as a third-line treatment for refractory sarcoidosis. Data in function of clinical phenotype are currently lacking. We evaluated patients' characteristics and responses to infliximab according to their GenPhenReSa cluster. METHODS: We evaluated clinical and biological characteristics of patients diagnosed with sarcoidosis who received infliximab between September 2008 and April 2019 at our centre. RESULTS: Fifty-five patients (median disease duration, 87 months) received infliximab: 48 (87%) as a second- or third-line treatment, and 7 (13%) as a first-line treatment. After a median duration of 12 months, 24 (45%) and 14 (25%) patients achieved complete and partial responses, respectively, together with a significant decrease in the number of affected organs and tapering of steroid doses. All patients with neurosarcoidosis (OR 17), 90% in group 2 (ocular-cardiac-cutaneous-CNS, OR 7.4), and approximately two-thirds of those in groups 1 (abdominal organs), 4 (pulmonary-lympho-nodal), and 5 (extrapulmonary), achieved a response, whereas patients in group 3 (musculoskeletal-cutaneous) had a treatment-failure OR of 9. Infliximab could be stopped after complete remission was achieved in 7 patients: 4 relapsed after a median of 6 months. Overall, 36% of patients experienced serious adverse events, mainly infections, which led to treatment cessation in 29% of patients and caused two deaths. CONCLUSIONS: Other than patients with musculoskeletal-cutaneous involvement (group 3), infliximab led to a good response for patients with CNS (group 2) and liver (group 1) organ-predominant sarcoidosis. However, it led to serious infections and merely suspended sarcoidosis, so further research on factors predictive of relapse is needed.

Temporal, spatial and space-time distribution of infections caused by five major enteric pathogens, Ontario, Canada, 2010-2017.

AIMS: In Canada, enteric diseases pose substantial health and economic burdens. The distribution of these diseases is uneven across both geography and time and understanding these patterns is therefore important for the prevention of future outbreaks. We evaluated temporal, spatial and space-time clustering of laboratory-confirmed cases of Campylobacter spp. (n = 28,728), non-typhoidal Salmonella spp. (n = 22,640), Shiga toxin-producing Escherichia coli (STEC; n = 1340), Yersinia spp. (n = 1674) and Listeria monocytogenes (n = 471) infections, reported between 2010 and 2017 inclusive in Ontario, the most populous province in Canada (population ~ 13,500,000 in 2016). METHODS AND RESULTS: For each enteric pathogen, we calculated the mean incidence rates (IRs) for Ontario's 35 public health unit (PHU) areas and visualized them using choropleth maps. We identified temporal, spatial and space-time high infection rate clusters using retrospective Poisson scan statistics. Campylobacter and Salmonella infections had the highest IRs, while Listeria infections had the lowest. Campylobacter, Salmonella, STEC and Listeria mostly clustered temporally in the spring/summer and sometimes extended into fall, while Yersinia showed a less clear seasonal pattern. The IR visualizations and spatial and space-time scan statistics showed geographic heterogeneity of infection rates with high infection rate clusters detected mainly in PHUs across the southwestern and central-western regions of Ontario for Campylobacter, Salmonella and STEC infections, and mainly in PHUs located in the central-eastern regions for Yersinia and Listeria. A high proportion of cases in some of the significant Salmonella, STEC and Listeria infection clusters were linked to disease outbreaks. CONCLUSIONS: Results from this study will inform heightened public health surveillance, and prevention and control programmes, in populations and regions of high infection rates. Further research is needed to determine the pathogen-specific socioeconomic, environmental and agricultural risk factors that may be related to the temporal and spatial disease patterns we observed in our study.

Multimorbidity and Complexity Among Patients with Cancer in Ontario: A Retrospective Cohort Study Exploring the Clustering of 17 Chronic Conditions with Cancer.

BACKGROUND: Multimorbidity is a concern for people living with cancer, as over 90% have at least one other condition. Multimorbidity complicates care coming from multiple providers who work within separate, siloed systems. Information describing high-risk and high-cost disease combinations has potential to improve the experience, outcome, and overall cost of care by informing comprehensive care management frameworks. This study aimed to identify disease combinations among people with cancer and other conditions, and to assess the health burden associated with those combinations to help healthcare providers more effectively prioritize and coordinate care. METHODS: We used a population-based retrospective cohort design including adults with a cancer diagnosis between March-2003 and April-2013, followed-up until March 2018. We used observed disease combinations defined by level of multimorbidity and partitive (k-means) clusters, ie groupings of similar diseases based on the prevalence of each condition. We assessed disease combination-associated health burden through health service utilization, including emergency department visits, primary care visits and hospital admissions during the follow-up period. RESULTS: 549,248 adults were included in the study. Anxiety, diabetes mellitus, hypertension, and osteoarthritis co-occurred with cancer 1.1 to 5.3 times more often than expected by chance. Disease combinations varied by cancer type and age but were similar between sexes. The largest partitive cluster included cancer and anxiety, with at least 25% of individuals also having osteoarthritis. Cancer also tended to co-occur with hypertension (8.0%) or osteoarthritis (6.2%). There were differences between clusters in healthcare utilization, regardless of the number of disease combinations or clustering approach used. CONCLUSION: Researchers, clinicians, policymakers, and other stakeholders can use the clustering information presented here to improve the healthcare system for people with cancer multimorbidity by developing cluster-specific care management and clinical guidelines for common disease combinations.

Epidemiological characteristics of hand, foot, and mouth disease clusters during 2016-2020 in Beijing, China.

Hand, foot, and mouth disease (HFMD) is an infectious disease that usually occurs in children under 5 years and is caused by a group of enteroviruses. This study aimed to investigate the epidemiological characteristics of HFMD clusters from 2016 to 2020 in Tongzhou, Beijing, and explored the genetic evolution of CV-A6. The HFMD case information came from the Information System of China Center for Disease Control and Prevention (CDC), as well as the clusters information verification and on-site investigation by Tongzhou CDC. ARIMA model was applied to forecast HFMD clusters in 2020. Totally 440 HFMD clusters were reported during 2016-2020. The large peak of the clusters occurred in April-July, followed by a smaller peak in October-November during 2016-2019. However, in 2020, the two peaks disappeared. The main site of HFMD clusters was childcare facilities (65.0%) and mostly occurred in urban areas (46.1%). The detection rate of CV-A6 was the highest (36.1%), and cases with CV-A6 infection had the highest proportion of fever. The phylogenetic analysis based on CV-A6 VP1 gene showed that the predominant strains mainly located in Group F during 2016-2017, while changed into Group A during 2018-2020. HFMD clusters presented seasonality, mainly located in childcare facilities and urban areas, and CV-A6 was the major causative agent. Targeted prevention and control measures should be taken to reduce HFMD clusters.

Multimorbidity patterns in South Africa: A latent class analysis.

Introduction: South Africa has the largest burden of HIV worldwide and has a growing burden of non-communicable diseases; the combination of which may lead to diseases clustering in ways that are not seen in other regions. This study sought to identify common disease classes and sociodemographic and lifestyle factors associated with each disease class. Methods: Data were analyzed from the South African Demographic and Health Survey 2016. A latent class analysis (LCA) was conducted using nine disease conditions. Sociodemographic and behavioral factors associated with each disease cluster were explored. All analysis was conducted in Stata 15 and the LCA Stata plugin was used to conduct the latent class and regression analysis. Results: Multimorbid participants were included (n = 2 368). Four disease classes were identified: (1) HIV, Hypertension and Anemia (comprising 39.4% of the multimorbid population), (2) Anemia and Hypertension (23.7%), (3) Cardiovascular-related (19.9%) and (4) Diabetes and Hypertension (17.0%). Age, sex, and lifestyle risk factors were associated with class membership. In terms of age, with older adults were less likely to belong to the first class (HIV, Hypertension and Anemia). Males were more likely to belong to Class 2 (Anemia and Hypertension) and Class 4 (Diabetes and Hypertension). In terms of alcohol consumption, those that consumed alcohol were less likely to belong to Class 4 (Diabetes and Hypertension). Current smokers were more likely to belong to Class 3 (Cardiovascular-related). People with a higher body mass index tended to belong to Class 3 (Cardiovascular-related) or the Class 4 (Diabetes and Hypertension). Conclusion: This study affirmed that integrated care is urgently needed, evidenced by the largest disease class being an overlap of chronic infectious diseases and non-communicable diseases. This study also highlighted the need for hypertension to be addressed. Tackling the risk factors associated with hypertension could avert an epidemic of multimorbidity.

Recent Malignant Melanoma Epidemiology in Upper Silesia, Poland. A Decade-Long Study Focusing on the Agricultural Sector.

The aim of the present study was to create spatial and spatio-temporal patterns of cutaneous malignant melanoma (MM) incidence in Upper Silesia, Poland, using the largest MM database (<4K cases) in Central Europe, focusing on the agricultural sector. The data comprised all the registered cancer cases (C43, according to the International Classification of Diseases after the 10th Revision) between the years 2004-2013 by the Regional Cancer Registries (RCRs) in Opole and Gliwice. The standardized incidence ratios (SIRs), spatio-temporal growth rates (GRs), and disease cluster relative risks (RRs) were estimated. Based on the regression coefficients, we have indicated irregularities of spatial variance in cutaneous malignant melanoma, especially in older women (≥60), and a possible age-migrating effect of agricultural population density on the risk of malignant melanoma in Upper Silesia. All the estimates were illustrated in choropleth thematic maps.

Comparisons of disease cluster patterns, prevalence and health factors in the USA, Canada, England and Ireland.

BACKGROUND: Identification of those who are most at risk of developing specific patterns of disease across different populations is required for directing public health policy. Here, we contrast prevalence and patterns of cross-national disease incidence, co-occurrence and related risk factors across population samples from the U.S., Canada, England and Ireland. METHODS: Participants (n = 62,111) were drawn from the US Health and Retirement Study (n = 10,858); the Canadian Longitudinal Study on Ageing (n = 36,647); the English Longitudinal Study of Ageing (n = 7938) and The Irish Longitudinal Study on Ageing (n = 6668). Self-reported lifetime prevalence of 10 medical conditions, predominant clusters of multimorbidity and their specific risk factors were compared across countries using latent class analysis. RESULTS: The U.S. had significantly higher prevalence of multimorbid disease patterns and nearly all diseases when compared to the three other countries, even after adjusting for age, sex, BMI, income, employment status, education, alcohol consumption and smoking history. For the U.S. the most at-risk group were younger on average compared to Canada, England and Ireland. Socioeconomic gradients for specific disease combinations were more pronounced for the U.S., Canada and England than they were for Ireland. The rates of obesity trends over the last 50 years align with the prevalence of eight of the 10 diseases examined. While patterns of disease clusters and the risk factors related to each of the disease clusters were similar, the probabilities of the diseases within each cluster differed across countries. CONCLUSIONS: This information can be used to better understand the complex nature of multimorbidity and identify appropriate prevention and management strategies for treating multimorbidity across countries.

Classification of ANCA-associated vasculitis: differences based on ANCA specificity and clinicopathologic phenotype.

The classification of vasculitis according to a schema with universal acceptance is challenging, given the heterogeneous and protean nature of these diseases. Formal nomenclature and classification criteria for anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) have suffered several changes since their first description; none provides comprehensive diagnostic and classification criteria. Different factors account for the difficulties in the classification of vasculitis, including the incomplete understanding of the pathogenesis, the multisystemic nature of the disease, the non-specific patterns of vascular involvement, the overlap between entities, and the presence of various classification systems. The present article reviews the classification of AAV considering different points of view, including clinical, serologic, pathogenetic, organ predilection, therapeutic, and prognostic factors, and provides perspectives on future challenges in the understanding of AAV. There is an unmet need for a unifying view of the disease spectrum that considers the constantly evolving paradigms.

Estimating the optimal population upper bound for scan methods in retrospective disease surveillance.

Correctly and quickly identifying disease patterns and clusters is a vital aspect of public health and epidemiology so that disease outbreaks can be mitigated as effectively as possible. The circular scan method is one of the most commonly used methods for detecting disease outbreaks and clusters in retrospective and prospective disease surveillance. The circular scan method requires a population upper bound in order to construct the set of candidate zones to be scanned, which is usually set to 50% of the total population. The performance of the circular scan method is affected by the choice of the population upper bound, and choosing an upper bound different from the default value can improve the method's performance. Recently, the Gini coefficient based on the Lorenz curve, which was originally used in economics, was proposed to determine a better population upper bound. We present the elbow method, a new method for choosing the population upper bound, which seeks to address some of the limitations of the Gini-based method while improving the performance of the circular scan method over the default value. To evaluate the performance of the proposed approach, we evaluate the sensitivity and positive predictive value of the circular scan method for publicly-available benchmark data for the default value, the Gini coefficient method, and the elbow method.

Rising burden of multimorbidity and related socio-demographic factors: a repeated cross-sectional study of Ontarians.

OBJECTIVE: This study aimed to provide population-level data regarding trends in multimorbidity over 13 years. METHODS: We linked provincial health administrative data in Ontario, Canada, to create 3 cross-sectional panels of residents of any age in 2003, 2009, and 2016 to describe: (i) 13-year trends in multimorbidity prevalence and constellations among residents and across age, sex, and income; and (ii) chronic condition clusters. Multimorbidity was defined as having at least any 2 of 18 selected conditions, and further grouped into levels of 2, 3, 4, or 5 or more conditions. Age-sex standardized multimorbidity prevalence was estimated using the 2009 population as the standard. Clustering was defined using the observed combinations of conditions within levels of multimorbidity. RESULTS: Standardized prevalence of multimorbidity increased over time (26.5%, 28.8%, and 30.0% across sequential panels), across sex, age, and area-based income. Females, older adults and those living in lower income areas exhibited higher rates in all years. However, multimorbidity increased relatively more among males, younger adults, and those with 4 or 5 or more conditions. We observed numerous and increasing diversity in disease clusters, namely at higher levels of multimorbidity. CONCLUSION: Our study provides relevant and needed population-based information on the growing burden of multimorbidity, and related socio-demographic risk factors. Multimorbidity is markedly increasing among younger age cohorts. Also, there is an increasing complexity and lack of common clustering patterns at higher multimorbidity levels.

RéSUMé: OBJECTIF: Cette étude a pour but d'offrir des données populationnelles sur la multimorbidité et les tendances sur 13 années. MéTHODES: Cette étude transversale utilise des données administratives provinciales, incluant trois panels d'individus de tous âges, en 2003, 2009 et 2016, pour décrire : (i) les tendances de la multimorbidité en Ontario, et les différences entre âge, sexe et niveaux de revenus; ainsi que (ii) les combinaisons de maladies chroniques. La multimorbidité a été définie comme ayant au moins deux des 18 maladies chroniques sélectionnées, et ensuite groupée par niveau de 2, 3, 4 ou 5 maladies ou plus. Les taux de prévalence standardisés ont été estimés à partir de la population de 2009. Les combinaisons fréquentes de maladies chroniques observées par niveau de multimorbidité sont également arborées. RéSULTATS: La prévalence standardisée de multimorbidité a augmenté au fil des années (26,5 %, 28,8 % et 30,0 %). Elle était plus élevée chez les femmes, les personnes âgées, et celles vivant dans les endroits à faible revenus, peu importe l'année. Toutefois l'augmentation dans le temps était plus importante chez les hommes, les jeunes adultes, et pour les niveaux élevés de multimorbidité (4, 5 ou plus). Nous avons observé un nombre élevé de combinaisons de maladies, et une diversité grandissante spécialement pour les niveaux de multimorbidité élevés. CONCLUSION: Cette étude fournit des données épidémiologiques probantes sur le problème grandissant de la multimorbidité, notamment au sein des jeunes cohortes, et les facteurs sociodémographiques associés. Il existe également une complexité grandissante et pas de profils communs dans les combinaisons de maladies, aux niveaux élevés de multimorbidité.

Investigation of spatio-temporal clusters of positive leptospirosis polymerase chain reaction test results in dogs in the United States, 2009 to 2016.

BACKGROUND: Leptospirosis is a zoonotic disease of concern and an investigation of recent spatio-temporal trends of leptospirosis in dogs in the United States is needed. Leptospira PCR testing has become increasingly used in veterinary clinical medicine and these data might provide information on recent trends of disease occurrence. OBJECTIVES: To identify and describe clusters of PCR-positive Leptospira test results in dogs in the United States. ANIMALS: Leptospira real-time PCR test results from dogs (n = 40 118) in the United States from IDEXX Laboratories, Inc., between 2009 and 2016 were included in the analysis. METHODS: In this retrospective study, spatio-temporal clusters for a real-time PCR-positive test were identified using the space-time permutation scan statistic and the centroid of the zip code reported for each test. A maximum spatial window of 20% of the population at risk, and a maximum temporal window of 6 months were used. RESULTS: Seven statistically significant space-time clusters of Leptospira real-time PCR-positive test results were identified across the United States: 1 each located within the states of Arizona (2016), California (2014-2015), Florida (2010), South Carolina (2015), and 1 each located within the south-central region (2015), midwest region (2014), and northeast region (2011). Clusters ranged from 3 to 108 dogs and were identified during all years under study, except 2009, 2012, and 2013. CONCLUSIONS AND CLINICAL IMPORTANCE: The spatial and temporal components of leptospirosis in dogs in this study are similar to those in previous work. However, clusters were identified in new areas, demonstrating the complex epidemiology of this disease.

Individual and Neighborhood Factors Associated With Failure to Vaccinate Against Influenza During Pregnancy.

Uptake of influenza vaccine among pregnant women remains low. We investigated whether unvaccinated pregnant women were clustered geographically and determined factors associated with failure to vaccinate using spatial and multivariate logistic regression analyses. Pregnant women who were members of Kaiser Permanente Northern California in 2015 or 2016 were included in the study. More than half (53%) of the 77,607 included pregnant women were unvaccinated. Spatial analysis identified 5 clusters with a high prevalence of unvaccinated pregnant women. The proportion of unvaccinated women ranged from 57% to 75% within clusters as compared with 51% outside clusters. In covariate-adjusted analyses, residence in a cluster was associated with a 41% increase in the odds of being unvaccinated (odds ratio (OR) = 1.41, 95% confidence interval (CI): 1.36, 1.46). The odds of being unvaccinated were greater for Black women (OR = 1.58, 95% CI: 1.49, 1.69), Hispanic women (OR = 1.15, 95% CI: 1.05, 1.25), women with subsidized health insurance (OR = 1.18, 95% CI: 1.11, 1.24), women with fewer than 5 prenatal-care visits (OR = 1.85, 95% CI: 1.60, 2.16), and neighborhoods with a high deprivation index (fourth quartile vs. first: OR = 1.14, 95% CI: 1.07, 1.21). In conclusion, unvaccinated pregnant women were clustered geographically and by key sociodemographic factors. These findings suggest that interventions to increase influenza vaccine coverage among pregnant women are needed, particularly in vulnerable populations.

The timing of geographic power.

In many studies on the spatial risk of disease, investigators use geographic locations at the time of disease diagnosis in spatial models to search for individual areas of elevated risk. However, these studies often fail to find a significant spatial signal. This may be due to the misspecification of the timing and location of pertinent exposures. Environmental exposures related to cancer risk vary over space and time, and many cancers have long latencies. When these factors are considered in conjunction with a mobile population, it is likely that the spatial signal related to relevant historic environmental exposures is obscured. To investigate this hypothesis, we conducted simulation studies to characterize the effect of residential mobility on the ability of generalized additive models to detect areas of significantly elevated historic environmental exposure. We generated data based on the residential histories of participants in the National Cancer Institute Surveillance, Epidemiology, and End Results non-Hodgkin lymphoma study, and varied the duration and intensity of the environmental exposure. Results showed that the probability of detection, mean spatial sensitivity, and mean spatial specificity of models decreased steadily as the time since relevant exposure increased. This suggests that for diseases with long latencies, spatial areas of high risk due to high-intensity exposure of relatively short duration will be difficult to detect over time when using residential locations at the time of diagnosis in mobile study populations.

Spatiotemporal Patterns of Cholera Hospitalization in Vellore, India.

Systematically collected hospitalization records provide valuable insight into disease patterns and support comprehensive national infectious disease surveillance networks. Hospitalization records detailing patient's place of residence (PoR) can be utilized to better understand a hospital's case load and strengthen surveillance among mobile populations. This study examined geographic patterns of patients treated for cholera at a major hospital in south India. We abstracted 1401 laboratory-confirmed cases of cholera between 2000-2014 from logbooks and electronic health records (EHRs) maintained by the Christian Medical College (CMC) in Vellore, Tamil Nadu, India. We constructed spatial trend models and identified two distinct clusters of patient residence-one around Vellore (836 records (61.2%)) and one in Bengal (294 records (21.5%)). We further characterized differences in peak timing and disease trend among these clusters to identify differences in cholera exposure among local and visiting populations. We found that the two clusters differ by their patient profiles, with patients in the Bengal cluster being most likely older males traveling to Vellore. Both clusters show well-aligned seasonal peaks in mid-July, only one week apart, with similar downward trend and proportion of predominant O1 serotype. Large hospitals can thus harness EHRs for surveillance by utilizing patients' PoRs to study disease patterns among resident and visitor populations.

Patterns of Medication Dispensation for Multiple Comorbidities among Older Adults in Australia.

Background: The increasing burden of chronic (medical) conditions (CCs) is a major issue for healthcare systems across the world. We aimed to examine the changes in the rate of medication dispensation for multiple CCs among Australians aged ≥65 years. Methods: A repeated cross-sectional study was performed using the 2013⁻2016 Pharmaceutical Benefits Scheme (PBS) data on reimbursed prescriptions for a 10% random sample of the Australian population. Twenty-two CCs were identified via the RxRisk-V tool. The yearly changes in the proportion of older adults dispensed medications for ≥2 CCs were determined through Poisson regression modelling using 2013 as the reference year. The occurrence of CC dyads and triads for which medications were dispensed within a 180-day window were characterised, and the observed and expected rate of medication dispensation for each CC dyad or triad were calculated to identify the top 15 combinations. Results: The proportion of older adults dispensed medications for ≥2 CCs remained stable from 2013 to 2016, at >79% in each year. The proportion who were dispensed medications for multiple CCs increased with age. No gender differences in the dispensation of medications for multiple CCs were observed. Over 60% had medications dispensed for ≥3 CCs. The most frequent CC dyad and triad for which medications were dispensed were dyslipidaemia + hypertension (38.6%) and dyslipidaemia + gastroesophageal reflux disease + hypertension (18.7%), respectively. For the majority of CC dyads and all triads examined, the observed rate of medication dispensation exceeded that expected by chance. Conclusions: A high proportion of older Australians are dispensed medications for multiple CCs, suggestive of multimorbidity. The results reiterate the need for increased research into understanding the causal mechanisms of multimorbidity to inform the design of cost-effective interventions.

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.