scholarly journals 862. Spatial Distribution of HIV Transmission in Ethiopia and Characteristics of HIV Clusters

2018 ◽  
Vol 5 (suppl_1) ◽  
pp. S21-S21
Author(s):  
Roger Ying ◽  
Lelisa Fekadu ◽  
Bruce R Schackman ◽  
Stéphane Verguet
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Hiv Transmission ◽  
Addis Ababa ◽  
Temporal Trends ◽  
Temporal Distribution ◽  
Spatial Scan Statistic ◽  
Targeted Interventions ◽  
Unsafe Injections ◽  
Spatial And Temporal Trends

Abstract Background HIV prevalence in Ethiopia has decreased by nearly 75% in the past 20 years with the implementation of antiretroviral therapy, but HIV transmission continues in certain high-risk regions around the country. Identification of the spatial and temporal trends of these transmission clusters, as well as their epidemiologic correlates, can lead to refinement of targeted interventions. Methods We used data from the 2005, 2011, and 2016 Ethiopia Demographic and Health Survey program (DHS). The spatial-temporal distribution of HIV was estimated using the Kuldorff spatial scan statistic, which determines the likelihood ratio of HIV within possible circular clusters across the country. Significant clusters (P < 0.05) were identified and compared based on known HIV risk factors using descriptive statistics to compare them to the noncluster area of the country. All analyses were conducted in SaTScan and R. Results Data from 11,383, 29,812, and 26,753 individuals with HIV were included in the 2005, 2011, and 2016 DHS, respectively. Four HIV clusters were identified consistently over the 3 time points, with the clusters representing 17% of the total population and 47% of all HIV cases. The 4 clusters were centered on the Addis Ababa, Afar, Dire Dawa/Harare, and Gambella regions, respectively. Cluster 1 is characterized by higher levels of unsafe injections (4.9% vs. 2.2%, P < 0.001) and high-risk occupations, such as truck drivers (5.7% vs. 1.7%, P < 0.001), when compared with noncluster regions, but by lower levels of transactional sex (18.6% vs. 23.0%, P < 0.001). Cluster 2 is also characterized by higher levels of high-risk occupations (2.8% vs. 1.7%, P < 0.01), whereas cluster 4 is characterized by a lower prevalence of circumcised men (59.1% vs. 91.3%, P < 0.01). No cluster had significantly higher levels of having more than one sexual partner in the last 12 months, although cluster 3 had a significantly lower level (0% vs. 1.7%, P < 0.001). Conclusion HIV in Ethiopia is composed of heterogeneous clusters of HIV transmission that appear to be driven by different risk factors. Further decreasing the HIV burden will likely require targeted and prioritized interventions in specific regions rather than uniform national policies. Disclosures All authors: No reported disclosures.

scholarly journals The study of gene–environment interactions in psychiatry: limited gains at a substantial cost?

2009 ◽  
Vol 40 (5) ◽  
pp. 711-716 ◽  
Author(s):  
S. Zammit ◽  
N. Wiles ◽  
G. Lewis
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Risk Groups ◽  
Substantial Cost ◽  
Targeted Interventions ◽  
Gene Environment ◽  
The Future ◽  
Widespread Belief ◽  
Novel Risk Factors

There is an ever-increasing body of literature examining gene–environment interactions in psychiatry, reflecting a widespread belief that such studies will aid identification of novel risk factors for disease, increase understanding about underlying pathological mechanisms, and aid identification of high-risk groups for targeted interventions. In this article we discuss to what extent studies of gene–environment interactions are likely to lead to any such benefits in the future.

scholarly journals Spatio-temporal trends and distribution patterns of typhoid disease in Uganda from 2012 to 2017

2021 ◽  
Vol 15 (2) ◽  
Author(s):  
Kamukama Ismail ◽  
Gilbert Maiga ◽  
Denis Ssebuggwawo ◽  
Peter Nabende ◽  
Ali Mansourian
Keyword(s):  
Geographical Information Systems ◽  
Local Population ◽  
Temporal Trends ◽  
Distribution Patterns ◽  
Geographical Information ◽  
Global Public Health ◽  
Spatial Scan Statistic ◽  
Public Health Burden ◽  
Targeted Interventions ◽  
Populations At Risk

Typhoid disease continues to be a global public health burden. Uganda is one of the African countries characterized by high incidences of typhoid disease. Over 80% of the Ugandan districts are endemic for typhoid, largely attributable to lack of reliable knowledge to support disease surveillance. Spatial-temporal studies exploring major characteristics of the disease within the local population have remained limited in Uganda. The main goal of the study was to reveal spatial-temporal trends and distribution patterns of typhoid disease in Uganda for the period 2012 to 2017. Spatial-temporal statistics revealed monthly and annual trends of the disease at both regional and national levels. Results show that outbreaks occurred during 2015 and 2017 in central and eastern regions, respectively. Spatial scan statistic using the discrete Poisson model revealed spatial clusters of the disease for each of the years from 2012 to 2017, together with populations at risk. Most of the disease clustering was in the central region, followed by western and eastern regions (P <0.01). The northern region was the safest throughout the study period. This knowledge helps surveillance teams to i) plan and enforce preventive measures; ii) effectively prepare for outbreaks; iii) make targeted interventions for resource optimization; and iv) evaluate effectiveness of the intervention methods in the study period. This exploratory research forms a foundation of using Geographical Information Systems (GIS) in other related subsequent research studies to discover hidden spatial patterns that are difficult to discover with conventional methods.

scholarly journals The secondary transmission pattern of COVID-19 based on contact tracing in Rwanda

2021 ◽  
Vol 6 (6) ◽  
pp. e004885
Author(s):  
Muhammed Semakula ◽  
FranÇois Niragire ◽  
Angela Umutoni ◽  
Sabin Nsanzimana ◽  
Vedaste Ndahindwa ◽  
...  
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Disease Transmission ◽  
Index Case ◽  
Conditional Logistic Regression ◽  
Contact Tracing ◽  
African Countries ◽  
Targeted Interventions ◽  
The Impact ◽  
Mapping Models

IntroductionCOVID-19 has shown an exceptionally high spread rate across and within countries worldwide. Understanding the dynamics of such an infectious disease transmission is critical for devising strategies to control its spread. In particular, Rwanda was one of the African countries that started COVID-19 preparedness early in January 2020, and a total lockdown was imposed when the country had only 18 COVID-19 confirmed cases known. Using intensive contact tracing, several infections were identified, with the majority of them being returning travellers and their close contacts. We used the contact tracing data in Rwanda for understanding the geographic patterns of COVID-19 to inform targeted interventions.MethodsWe estimated the attack rates and identified risk factors associated to COVID-19 spread. We used Bayesian disease mapping models to assess the spatial pattern of COVID-19 and to identify areas characterised by unusually high or low relative risk. In addition, we used multiple variable conditional logistic regression to assess the impact of the risk factors.ResultsThe results showed that COVID-19 cases in Rwanda are localised mainly in the central regions and in the southwest of Rwanda and that some clusters occurred in the northeast of Rwanda. Relationship to the index case, being male and coworkers are the important risk factors for COVID-19 transmission in Rwanda.ConclusionThe analysis of contact tracing data using spatial modelling allowed us to identify high-risk areas at subnational level in Rwanda. Estimating risk factors for infection with SARS-CoV-2 is vital in identifying the clusters in low spread of SARS-CoV-2 subnational level. It is imperative to understand the interactions between the index case and contacts to identify superspreaders, risk factors and high-risk places. The findings recommend that self-isolation at home in Rwanda should be reviewed to limit secondary cases from the same households and spatiotemporal analysis should be introduced in routine monitoring of COVID-19 in Rwanda for policy making decision on real time.

Post-operative delirium in older patients following cancer related and other high-risk surgeries.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e24024-e24024
Author(s):  
Rawad Elias ◽  
Ilene Staff ◽  
Stephen Thompson ◽  
Christine Waszynski ◽  
Jennifer Zanchi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Logistic Regression ◽  
High Risk ◽  
Tertiary Care ◽  
Confusion Assessment Method ◽  
Assessment Method ◽  
Chi Square ◽  
Targeted Interventions ◽  
Increased Risk

e24024 Background: Older adults are at increased risk for postoperative delirium (POD). This risk might be higher in patients with cancer as underlying malignancy and its complications predispose individuals to develop delirium. Therefore, it is important to evaluate the onset of delirium in this patient population especially as POD is associated with increased risk of rehospitalization, decline in cognitive function, morbidity and mortality. Methods: We performed a retrospective review of patients aged ≥ 70 years admitted January 2017 through July 2019 to a tertiary care referral center for a high-risk surgery, defined as associated with a mortality risk greater than 1%. Cancer related surgeries (CRS) were identified through cross matching with Cancer Registry. Patients who had delirium assessment in the postoperative setting using the Confusion Assessment Method (CAM) were included. Chi-square tests of proportion, Wilcoxon Ranked Sum and multivariate logistic regression analyses were conducted. Results: A total of 2340 patients were included in this analysis, 315 of whom had (CRS). Overall, the age (median, IQR) of patients at surgery was 76 years (72-80) and the length of stay (LOS) was 7 (4-11) days. Patients receiving CRS were younger (75, 72-79) than those with non-CRS (76, 72-81) (p = 0.022); had a shorter post-operative LOS (4, 2-7 vs. 5, 3-8; p > 0.001), and were less likely to develop POD (7.6% vs. 16.1%; p < 0.001). Among patients receiving CSR, those who developed POD were older (78 vs. 74; p = 0.008) and had longer post-operative LOS (14.0 vs. 4.0; p < 0.001). Those having experienced radiotherapy (RT) for cancer within the year before the surgery, were more likely to develop POD (40.0% vs. 6.6% p. < 0.001). Chemotherapy in the year prior to surgery did not increase the risk of POD (6.1% vs. 7.8%; p = 0.721). Among those having non-CRS, a cancer diagnosis did not affect POD. A logistic regression predicting POD indicated that the lower likelihood of POD following CRS was independent of age or gender (OR = 0.40; p < .001); RT within one year prior was independent predictor of higher POD (OR = 5.48; p = 0.003). Our data presentation will include further analysis of POD risk factors. Conclusions: Although older adults receiving CRS were significantly less likely to develop delirium than patients with other high-risk surgeries, it is still important to evaluate POD in this population due to its impact of patients’ outcomes. Further understanding of POD risk factors, such as preoperative RT, would allow the development of targeted interventions that might lessen the risk.

Temporal trends in cardiovascular disease risk factor profiles in a population-based schizophrenia sample: a repeat cross-sectional study

2017 ◽  
Vol 72 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Maria Chiu ◽  
Farah Rahman ◽  
Simone Vigod ◽  
Andrew S Wilton ◽  
Paul Kurdyak
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
General Population ◽  
Temporal Trends ◽  
Population Based ◽  
Cvd Risk Factors ◽  
Cvd Risk ◽  
Targeted Interventions ◽  
Schizophrenia Group ◽  
Obesity And Diabetes

BackgroundPeople with schizophrenia have an increased burden of cardiovascular diseases (CVD); however, little is known about the cardiovascular risk factor profiles of non-institutionalised individuals with schizophrenia. This study estimated the prevalence of CVD risk factors in a population-based sample of Canadians with and without schizophrenia.MethodsOntario respondents of the Canadian Community Health Survey were linked to administrative health databases; using a validated algorithm, we identified 1103 non-institutionalised individuals with schizophrenia and 156 376 without schizophrenia. We examined the prevalence of eight CVD risk factors: smoking, diabetes, hypertension, obesity, physical inactivity, fruit/vegetables consumption, psychosocial stress and binge drinking. To examine temporal trends, we compared prevalence estimates from 2001–2005 to 2007–2010.ResultsThe prevalence of most CVD risk factors was significantly higher among those with schizophrenia than the general population. Obesity and diabetes prevalence increased by 39% and 71%, respectively, in the schizophrenia group vs 11% and 24%, respectively, in the non-schizophrenia group between the two time periods. Unlike the general population, smoking rates among those with schizophrenia did not decline. Almost 90% of individuals with schizophrenia had at least one CVD risk factor and almost 40% had ≥3 co-occurring risk factors.ConclusionIndividuals with schizophrenia had a greater prevalence of individual and multiple CVD risk factors compared with those without schizophrenia, which persisted over time. Our findings suggest that public health efforts to reduce the burden of CVD risk factors have not been as effective in the schizophrenia population, thus highlighting the need for more targeted interventions and prevention strategies.

scholarly journals Spatio-temporal patterns of pneumonia in Bhutan: A Bayesian analysis

2021 ◽  
Author(s):  
Kinley Wangdi ◽  
Kinley Penjor ◽  
Tsheten Tsheten ◽  
Chachu Tshering ◽  
Peter Gething ◽  
...  
Keyword(s):  
Risk Factors ◽  
Relative Humidity ◽  
Poisson Regression ◽  
Spatial Clustering ◽  
Climatic Factors ◽  
Temporal Trends ◽  
Credible Interval ◽  
Maximum Temperature ◽  
Spatio Temporal ◽  
Spatial And Temporal Trends

Abstract Pneumonia is one of the top 10 diseases by morbity in Bhutan. This study aimed to investigate the spatial and temporal trends and risk factors of pneumonia in Bhutan. A multivariable Zero-inflated Poisson regression using a Bayesian Markov chain Monte Carlo simulation was undertaken to quantify associations of age, sex, rainfall, maximum temperature and relative humidity with monthly pneumonia incidence and identify underlying spatial structure of the data. Overall pneumonia incidence was 96.5 and 4.57 per 1,000 populations over nine years in people aged < 5 years and ≥ 5 years, respectively. Children < 5 years or being a female are more like to get pneumonia than ≥ 5 years and males. A 10mm increase in rainfall and 1°C increase in maximum temperature was associated with a 7.2% (95% (credible interval [CrI] 0.7%, 14.0%) and 28.6% (95% CrI 27.2%, 30.1%) increase in pneumonia cases. A 1% increase in relative humidity was associated with a decrease in the incidence of pneumonia by 8.6% (95% CrI 7.5%, 9.7%). There was no evidence of spatial clustering after accounting for the covariates. Seasonality and spatial heterogeneity can partly be explained by the association of pneumonia risk to climatic factors including rainfall, maximum temperature and relative humidity.

scholarly journals A Spatiotemporal Analysis of Socio-Environmental Patterns in Severe Maternal Morbidity: A Retrospective Birth Cohort

2021 ◽  
Author(s):  
Stella Harden ◽  
Jennifer D Runkle ◽  
Margaret Mae Sugg
Keyword(s):  
Risk Factors ◽  
South Carolina ◽  
High Risk ◽  
Maternal Morbidity ◽  
Spatial Clustering ◽  
Spatiotemporal Analysis ◽  
Severe Maternal Morbidity ◽  
Spatial Scan Statistic ◽  
Black Patients ◽  
The Impact

Objectives. Severe Maternal Morbidity (SMM) is a group of pregnancy complications in which a woman nearly dies. Despite its increasing prevalence, there is little research that evaluates geographic patterns of SMM and the underlying social determinants that influence excess risk. This study examines the spatial clustering of SMM across South Carolina, US, and its associations with place-based social and environmental factors. Methods. Hospitalized deliveries from 1999 to 2017 were analyzed using Kulldorff's spatial scan statistic to locate areas with abnormally high rates of SMM. Patients inside and outside risk clusters were compared using Generalized Estimating Equations (GEE) to determine underlying risk factors. Results. Final models revealed that the odds of living in a high-risk cluster were 84% higher among Black patients (OR=1.84, p<.001), 30% higher among Hispanic and Latina patients (OR=1.3, p<.05), and 1.51 times more likely among women living in highly segregated and poorer minority communities (OR=1.51 p<.001). Odds for residing in a high-risk cluster were 23% higher for those who gave birth during a period with temperatures above 30.65C/87.3F (OR=1.23, p<.001). Conclusions. This study is the first to characterize the geographic clustering of SMM risk in the US. Our geospatial approach contributes a novel understanding to factors which influence SMM beyond patient-level characteristics and identifies the impact of systemic racism on maternal morbidity. Findings address an important literature gap surrounding place-based risk factors by explaining the contextual social and built environment variables that drive SMM risk.

Prevention of Preterm Birth

2018 ◽  
Vol 11 (2) ◽  
pp. 95-104
Author(s):  
Ivan D. Ivanov ◽  
Stefan A. Buzalov ◽  
Nadezhda H. Hinkova
Keyword(s):  
Risk Factors ◽  
Preterm Birth ◽  
High Risk ◽  
Medical Science ◽  
Secondary Prophylaxis ◽  
High Risk Group ◽  
Screening Methods ◽  
Perinatal Complications ◽  
Social Significance ◽  
Neonatal Deaths

Summary Preterm birth (PTB) is a worldwide problem with great social significance because it is a leading cause of perinatal complications and perinatal mortality. PTB is responsible for more than a half of neonatal deaths. The rate of preterm delivery varies between 5-18% worldwide and has not decreased in recent years, regardless of the development of medical science. One of the leading causes for that is the failure to identify the high-risk group in prenatal care. PTB is a heterogeneous syndrome in which many different factors interfere at different levels of the pathogenesis of the initiation of delivery, finally resulting in delivery before 37 weeks of gestation (wg). The various specificities of risk factors and the unclear mechanism of initiation of labour make it difficult to elaborate standard, unified and effective screening to diagnose pregnant women at high-risk for PTB correctly. Furthermore, they make primary and secondary prophylaxis less effective and render diagnostic and therapeutic measures ineffective and inappropriate. Reliable and accessible screening methods are necessary for antenatal care, and risk factors for PTB should be studied and clarified in search of useful tools to solve issues of risk pregnancies to decrease PTB rates and associated complications.

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