The Spatial Distribution of Hepatitis C Virus Infections and Associated Determinants--An Application of a Geographically Weighted Poisson Regression for Evidence-Based Screening Interventions in Hotspots

Insights into the spatial epidemiology of hepatitis C infection: systematic synthesis of area-level determinants and spatiotemporal analyses

BACKGROUND

Hepatitis C virus (HCV) stands at the forefront of global elimination endeavors by 2030, highlighting the need for a nuanced exploration into disparities and vulnerabilities using innovative spatial epidemiological approaches. This study aimed to systematically review existing literature to summarize area-level factors associated with HCV infection and to examine the application of spatial and spatiotemporal analyses in HCV research.

METHODS

A systematic search following PRISMA guidelines was conducted for peer-reviewed literature published between 2000 and 2023 using PubMed, Web of Science, Scopus, and Embase databases. The synthesis of area-level factors was organized according to four distinct categories of risk environments: social, economic, policy, and physical environments.

RESULTS

Sixty-five studies were selected for this systematic review. 60% of the studies focused on the general population, while 20% of the studies targeted people who inject drugs. The area-level factors explored predominantly were characteristics of the social and economic risk environments. For instance, areas with a higher level of socioeconomic disadvantage, lower education attainment, higher population density, and located more remotely were associated with higher HCV infection rates. Additionally, some studies noted a significant correlation between the accessibility to harm reduction and healthcare services and HCV occurrence, testing, and treatment rates. Furthermore, spatial data exploration and cluster analysis methods were the predominant methods used to explore the nuanced spatial distribution of HCV infection.

CONCLUSION

This review emphasizes the imperative of deciphering the complex interplay of area-level factors in HCV infection dynamics. Understanding the potential risk environment landscape of HCV could facilitate identifying vulnerable areas and communities. Additionally, the limited application of spatial analytics in HCV research highlights the untapped potential, emphasizing the need for enhanced spatial techniques to pinpoint priority areas for intervention.

Exploring regional and sociodemographic disparities associated with unenrollment for the disease management program for type 2 Diabetes Mellitus using Bayesian spatial modelling

BACKGROUND

The disease management program (DMP) for type 2 Diabetes Mellitus (T2DM) is the largest DMP in Germany. Our goal was to analyze regional differences in unenrollment rates, suggest areas for intervention and provide background information, which population groups in which locations are currently not enrolled in the DMP for T2DM.

METHODS

In this study, we used data of the 1.7 mil. insurants of the AOK Nordost health insurance. For the visualization of enrollment potential, we used the Besag-York-Mollie model (BYM). The spatial scan statistic (SaTScan) was used to detect areas of unusually high rates of unenrolled diabetics to prioritize areas for intervention. To explore sociodemographic associations, we used Bayesian spatial global regression models. A Spatially varying coefficient model (SVC) revealed in how far the detected associations vary over space.

RESULTS

The proportion of diabetics currently not enrolled in the DMP T2DM was 36.8% in 2019 and varied within northeastern Germany. Local clusters were detected mainly in Mecklenburg-West-Pomerania and Berlin. The main sociodemographic variables associated with unenrollment were female sex, younger age, being unemployed, foreign citizenship, small household size and the proportion of persons commuting to work outside their residential municipality. The SVC model revealed important spatially varying effects for some but not all associations.

CONCLUSION

Lower socioeconomic status and foreign citizenship had an ubiquitous effect on not being enrolled. The DMP T2DM therefore does currently not reach those population groups, which have a higher risk for secondary diseases and possible avoidable hospitalizations. Logically, future interventions should focus on these groups. Our methodology clearly suggests areas for intervention and points out, which population group in which locations should be specifically approached.

Analysis of the association between areal socioeconomic deprivation levels and viral hepatitis B and C infections in Japanese municipalities

Background

We investigated the association between municipal socioeconomic deprivation levels and the positivity of hepatitis B surface antigen (HBsAg) and the prevalence of hepatitis C virus (HCV) among individuals who have never participated in hepatitis screening using Japanese national screening data.

Methods

The hepatitis virus screening data analyzed included the 5-year age group-specific number of participants aged 40 years or older, number of HBsAg-positive persons, and number of HCV carriers for each municipality from 2013 to 2017. Principal component analysis was used to derive a socioeconomic deprivation level using the socioeconomic characteristics of municipalities. Bayesian spatial Poisson regression analysis was conducted to investigate the association between the socioeconomic deprivation level and the results of screening. Data on 1,660 municipalities were used in the analysis.

Results

The data of 4,233,819 participants in the HBV screening and 4,216,720 in the HCV screening were used in the analysis. A principal component interpreted as level of rurality (principal component 1) and another principal component interpreted as level of low socioeconomic status among individuals (principal component 2) were extracted as the major principal components. Their principal component scores were used as the deprivation levels of municipalities. Spatial regression analysis showed that the deprivation level derived from the sum of the scores of principal components 1 and 2 was significantly and positively associated with HBsAg positivity and HCV prevalence. In addition, the deprivation level derived only from the score of principal component 2 was also significantly and positively associated with the outcomes. Conversely, the deprivation level derived only from the score of principal component 1 was not associated with the outcomes. Moreover, population density was significantly and positively associated with HBsAg positivity and HCV prevalence.

Conclusions

This study suggested that participation in hepatitis virus screening is important and meaningful, particularly for areas with a higher lower socioeconomic level in Japan.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13089-w.

COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study (Preprint)

Background

Public health scientists have used spatial tools such as web-based Geographical Information System (GIS) applications to monitor and forecast the progression of the COVID-19 pandemic and track the impact of their interventions. The ability to track SARS-CoV-2 variants and incorporate the social determinants of health with street-level granularity can facilitate the identification of local outbreaks, highlight variant-specific geospatial epidemiology, and inform effective interventions. We developed a novel dashboard, the University of Massachusetts’ Graphical user interface for Geographic Information (MAGGI) variant tracking system that combines GIS, health-associated sociodemographic data, and viral genomic data to visualize the spatiotemporal incidence of SARS-CoV-2 variants with street-level resolution while safeguarding protected health information. The specificity and richness of the dashboard enhance the local understanding of variant introductions and transmissions so that appropriate public health strategies can be devised and evaluated.

Objective

We developed a web-based dashboard that simultaneously visualizes the geographic distribution of SARS-CoV-2 variants in Central Massachusetts, the social determinants of health, and vaccination data to support public health efforts to locally mitigate the impact of the COVID-19 pandemic.

Methods

MAGGI uses a server-client model–based system, enabling users to access data and visualizations via an encrypted web browser, thus securing patient health information. We integrated data from electronic medical records, SARS-CoV-2 genomic analysis, and public health resources. We developed the following functionalities into MAGGI: spatial and temporal selection capability by zip codes of interest, the detection of variant clusters, and a tool to display variant distribution by the social determinants of health. MAGGI was built on the Environmental Systems Research Institute ecosystem and is readily adaptable to monitor other infectious diseases and their variants in real-time.

Results

We created a geo-referenced database and added sociodemographic and viral genomic data to the ArcGIS dashboard that interactively displays Central Massachusetts’ spatiotemporal variants distribution. Genomic epidemiologists and public health officials use MAGGI to show the occurrence of SARS-CoV-2 genomic variants at high geographic resolution and refine the display by selecting a combination of data features such as variant subtype, subject zip codes, or date of COVID-19–positive sample collection. Furthermore, they use it to scale time and space to visualize association patterns between socioeconomics, social vulnerability based on the Centers for Disease Control and Prevention’s social vulnerability index, and vaccination rates. We launched the system at the University of Massachusetts Chan Medical School to support internal research projects starting in March 2021.

Conclusions

We developed a COVID-19 variant surveillance dashboard to advance our geospatial technologies to study SARS-CoV-2 variants transmission dynamics. This real-time, GIS-based tool exemplifies how spatial informatics can support public health officials, genomics epidemiologists, infectious disease specialists, and other researchers to track and study the spread patterns of SARS-CoV-2 variants in our communities.

Estimating PM2.5 concentrations via random forest method using satellite, auxiliary, and ground-level station dataset at multiple temporal scales across China in 2017

Fine particulate matter with aerodynamic diameters less than 2.5 μm (PM_2.5) poses adverse impacts on public health and the environment. It is still a great challenge to estimate high-resolution PM_2.5 concentrations at moderate scales. The current study calibrated PM_2.5 concentrations at a 1 km resolution scale using ground-level monitoring data, Aerosol Optical Depth (AOD), meteorological data, and auxiliary data via Random Forest (RF) model across China in 2017. The three ten-folded cross-validations (CV) methods including sample-based, time-based, and spatial-based validation combined with Coefficient Square (R²), Root-Mean-Square Error (RMSE), and Mean Predictive Error (MPE) have been used for validation at different temporal scales in terms of daily, monthly, heating seasonal, and non-heating seasonal. Finally, the distribution map of PM_2.5 concentrations was illustrated based on the RF model. Some findings were achieved. The RF model performed well, with a relatively high sample-based cross-validation R² of 0.74, a low RMSE of 16.29 μg × m^-3, and a small MPE of -0.282 μg × m^-3. Meanwhile, the performance of the RF model in inferring the PM_2.5 concentrations was well at urban scales except for Chengyu (CY). North China, the CY urban agglomeration, and the northwest of China exhibited relatively high PM_2.5 pollution features, especially in the heating season. The robustness of the RF model in the present study outperformed most statistical regression models for calibrating PM_2.5 concentrations. The outcomes can supply an up-to-date scientific dataset for epidemiological and air pollutants exposure risk studies across China.

Healthcare system and social trust in the fight against Covid-19: the case of France

BACKGROUND

COVID-19, like all pandemics, has territorial specificities that needs to be considered: the impact of the COVID-19 crisis strongly differs not only across countries, but also across regions, districts and municipalities within countries. There are several factors that, potentially, can contribute to the differentiated impact of COVID-19, and explain the disparities seen among areas. This study aims to contribute to this debate by analysing the role of health system and social trust in lessening the impact of the Covid-19 pandemic in French départements.

METHODS

The data used in this study have been provided by the INSEE and the French Ministry of Health. Database is made up of the 96 départements of metropolitan France. We use spatial analysis techniques to identify the groups of areas that are particularly affected, and to test the influence of local socioeconomic factors on the spread of the epidemic.

RESULTS

Our exploratory spatial analysis reveals the heterogeneity and spatial autocorrelation of the disease. The use of spatial econometric models, then, allows us to highlight the impact of emergency services, and social capital in reducing the exposition to Covid-19. Our results also report on the role of spillover effects between neighbouring areas.

CONCLUSIONS

This research shows that, although individual characteristics are important factors in explaining the probability of contracting Covid-19 desease, health care services and social trust factors also play a significant role in curbing the epidemic's outbreak. These findings should have an interest for policy makers in the prevention of future waves of Covid-19 pandemic.

Are regions equal in adversity? A spatial analysis of spread and dynamics of COVID-19 in Europe

Often presented as a global pandemic spreading all over the world, COVID-19, however, hit not only countries but also regions differently. The objective of this paper is to focus on the spatial heterogeneity in the spread of the COVID-19 pandemic and to contribute to an understanding of the channels by which it spread, focusing on the regional socioeconomic dimension. For this, we use a dataset covering 125 European regions in 12 countries. Considering that the impact of the COVID-19 crisis differed sharply not only across countries but also across regions within the same country, the empirical strategy is based, on the one hand, on an exploratory analysis of spatial autocorrelations, which makes it possible to identify regional clusters of the disease. On the other hand, we use spatial regression models to capture the diffusion effect and the role of different families of regional factors in this process. We find that the share of older people in the population, GDP per capita, distance from achieving EU objectives, and the unemployment rate are correlated with high COVID-19 death rates. In contrast, the number of medical practitioners and hospital beds and the level of social trust are correlated with low COVID-19 death rates.

Food Environments and Hepatocellular Carcinoma Incidence

Research into the potential impact of the food environment on liver cancer incidence has been limited, though there is evidence showing that specific foods and nutrients may be potential risk or preventive factors. Data on hepatocellular carcinoma (HCC) cases were obtained from the Surveillance, Epidemiology, and End Results (SEER) cancer registries. The county-level food environment was assessed using the Modified Retail Food Environment Index (mRFEI), a continuous score that measures the number of healthy and less healthy food retailers within counties. Poisson regression with robust variance estimation was used to calculate incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for the association between mRFEI scores and HCC risk, adjusting for individual- and county-level factors. The county-level food environment was not associated with HCC risk after adjustment for individual-level age at diagnosis, sex, race/ethnicity, year, and SEER registry and county-level measures for health conditions, lifestyle factors, and socioeconomic status (adjusted IRR: 0.99, 95% CI: 0.96, 1.01). The county-level food environment, measured using mRFEI scores, was not associated with HCC risk.

Examining the association between socio-demographic composition and COVID-19 fatalities in the European region using spatial regression approach

The socio-demographic factors have a substantial impact on the overall casualties caused by the Coronavirus (COVID-19). In this study, the global and local spatial association between the key socio-demographic variables and COVID-19 cases and deaths in the European regions were analyzed using the spatial regression models. A total of 31 European countries were selected for modelling and subsequent analysis. From the initial 28 socio-demographic variables, a total of 2 (for COVID-19 cases) and 3 (for COVID-19 deaths) key variables were filtered out for the regression modelling. The spatially explicit regression modelling and mapping were done using four spatial regression models such as Geographically Weighted Regression (GWR), Spatial Error Model (SEM), Spatial Lag Model (SLM), and Ordinary Least Square (OLS). Additionally, Partial Least Square (PLS) and Principal Component Regression (PCR) was performed to estimate the overall explanatory power of the regression models. For the COVID cases, the local R2 values, which suggesting the influences of the selected socio-demographic variables on COVID cases and death, were found highest in Germany, Austria, Slovenia, Switzerland, Italy. The moderate local R2 was observed for Luxembourg, Poland, Denmark, Croatia, Belgium, Slovakia. The lowest local R2 value for COVID-19 cases was accounted for Ireland, Portugal, United Kingdom, Spain, Cyprus, Romania. Among the 2 variables, the highest local R2 was calculated for income (R2 = 0.71), followed by poverty (R2 = 0.45). For the COVID deaths, the highest association was found in Italy, Croatia, Slovenia, Austria. The moderate association was documented for Hungary, Greece, Switzerland, Slovakia, and the lower association was found in the United Kingdom, Ireland, Netherlands, Cyprus. This suggests that the selected demographic and socio-economic components, including total population, poverty, income, are the key factors in regulating overall casualties of COVID-19 in the European region. In this study, the influence of the other controlling factors, such as environmental conditions, socio-ecological status, climatic extremity, etc. have not been considered. This could be the scope for future research.

Investigating the Relationship between the Built Environment and Relative Risk of COVID-19 in Hong Kong

Understanding the relationship between the built environment and the risk of COVID-19 transmission is essential to respond to the pandemic. This study explores the relationship between the built environment and COVID-19 risk using the confirmed cases data collected in Hong Kong. Using the information on the residential buildings and places visited for each case from the dataset, we assess the risk of COVID-19 and explore their geographic patterns at the level of Tertiary Planning Unit (TPU) based on incidence rate (R1) and venue density (R2). We then investigate the associations between several built-environment variables (e.g., nodal accessibility and green space density) and COVID-19 risk using global Poisson regression (GPR) and geographically weighted Poisson regression (GWPR) models. The results indicate that COVID-19 risk tends to be concentrated in particular areas of Hong Kong. Using the incidence rate as an indicator to assess COVID-19 risk may underestimate the risk of COVID-19 transmission in some suburban areas. The GPR and GWPR models suggest a close and spatially heterogeneous relationship between the selected built-environment variables and the risk of COVID-19 transmission. The study provides useful insights that support policymakers in responding to the COVID-19 pandemic and future epidemics.

Hepatitis C virus genotype 1 and 2 recombinant genomes and the phylogeographic history of the 2k/1b lineage

Recombination is an important driver of genetic diversity, though it is relatively uncommon in hepatitis C virus (HCV). Recent investigation of sequence data acquired from HCV clinical trials produced twenty-one full-genome recombinant viruses belonging to three putative inter-subtype forms 2b/1a, 2b/1b, and 2k/1b. The 2k/1b chimera is the only known HCV circulating recombinant form (CRF), provoking interest in its genetic structure and origin. Discovered in Russia in 1999, 2k/1b cases have since been detected throughout the former Soviet Union, Western Europe, and North America. Although 2k/1b prevalence is highest in the Caucasus mountain region (i.e., Armenia, Azerbaijan, and Georgia), the origin and migration patterns of CRF 2k/1b have remained obscure due to a paucity of available sequences. We assembled an alignment which spans the entire coding region of the HCV genome containing all available 2k/1b sequences (>500 nucleotides; n = 109) sampled in ninteen countries from public databases (102 individuals), additional newly sequenced genomic regions (from 48 of these 102 individuals), unpublished isolates with newly sequenced regions (5 additional individuals), and novel complete genomes (2 additional individuals) generated in this study. Analysis of this expanded dataset reconfirmed the monophyletic origin of 2k/1b with a recombination breakpoint at position 3,187 (95% confidence interval: 3,172–3,202; HCV GT1a reference strain H77). Phylogeography is a valuable tool used to reveal viral migration dynamics. Inference of the timed history of spread in a Bayesian framework identified Russia as the ancestral source of the CRF 2k/1b clade. Further, we found evidence for migration routes leading out of Russia to other former Soviet Republics or countries under the Soviet sphere of influence. These findings suggest an interplay between geopolitics and the historical spread of CRF 2k/1b.

The distribution and socioeconomic burden of Hepatitis C virus in South Australia: a cross-sectional study 2010-2016

BACKGROUND

Hepatitis C virus infection (HCV) is a communicable disease of increasing global importance with 1.75 million new infections and 400,000 related deaths annually. Until recently, treatment options have had low uptake and most infected people remain untreated. New Direct Acting Antiviral medications can clear the virus in around 95% of cases, with few side-effects. These medications are restricted in most countries but freely accessible in Australia, yet most people still remain untreated. This study applies a cross-sectional research design to investigate the socio-spatial distribution of HCV in South Australia, to identify vulnerable populations, and examine epidemiological factors to potentially inform future targeted strategies for improved treatment uptake.

METHOD

HCV surveillance data were sourced from South Australia's Communicable Diseases Control Branch and socio-economic population data from the Australian Bureau of Statistics from January 2010 to December 2016 inclusive. HCV cases were spatially mapped at postcode level. Multivariate logistic regression identified independent predictors of demographic risks for HCV notification and notification source.

RESULTS

HCV notifications (n = 3356) were seven times more likely to be from people residing in the poorest areas with high rates of non-employment (75%; n = 1876) and injecting drug use (74%; n = 1862) reported. Notifications among Aboriginal and Torres Strait Islander people were around six times that of non-Indigenous people. HCV notifications negatively correlated (Spearman's rho - 0.426; p < 0.001) with socio-economic status (residential postcode socio-economic resources Index). History of imprisonment independently predicted HCV diagnoses in lesser economically-resourced areas (RR1.5; p < 0.001). Independent predictors of diagnosis elsewhere than in general practices were non-employment (RR 4.6; p = 0.028), being male (RR 2.5; p < 0.001), and younger than mean age at diagnosis (RR 2.1; p = 0.006).

CONCLUSIONS

Most people diagnosed with HCV were from marginalised sub-populations. Given general practitioners are pivotal to providing effective HCV treatment for many people in Australia a most concerning finding was that non-employed people were statistically less likely to be diagnosed by general practitioners. These findings highlight a need for further action aimed at improving healthcare access and treatment uptake to help reduce the burden of HCV for marginalised people, and progress the vision of eliminating HCV as a major public health threat.

Applying core theory and spatial analysis to identify hepatitis C virus infection "core areas" in British Columbia, Canada

"Core areas" of transmission for bacterial sexually transmitted infections have been identified. However, it is unclear whether core areas apply to viral infections, such as hepatitis C virus (HCV). We used geographic mapping and spatial analysis to identify distinct core areas of HCV infection in British Columbia (BC) using the BC Hepatitis Testers Cohort (BC-HTC), 1990-2013. The BC-HTC includes all BC residents tested for HCV (~1.5 million; 1990-2013). Core HCV infection areas were identified spatially and temporally for five time periods (1990-1993, 1994-1998, 1999-2003, 2004-2008 and 2009-2013) through thematic mapping, Kernel Density Estimation, Hotspot analysis and cluster analysis at the Census dissemination area level in ArcGIS and SatScan. HCV infection core areas were consistently identified. HCV core areas expanded from the downtown of major cities in different regions of BC (Metro Vancouver, Vancouver Island, and Northern BC; 1990-1998), to smaller cities in Metro Vancouver and Interior BC (2000 onwards). Statistically significant clusters, or hotspots, were also observed for downtown Vancouver, Northern BC (Prince George) and Vancouver Island from 1990 to 2008 with expansion to other urban areas in Metro Vancouver from 1990-2013. Statistically significant clusters persisted after adjustment for injection drug use, number of HCV tests, age, sex, material and social deprivation. Persistence of areas with high HCV diagnoses rates in Vancouver and Prince George supports the theory of core areas of HCV transmission. Identification of core areas can inform prevention, care and treatment programme interventions and evaluate their impact over time.

The geographic variation and spatiotemporal distribution of hepatitis C virus infection in Libya: 2007-2016

BACKGROUND

Hepatitis C Virus infection has been considered an important hidden pandemic in developing countries, particularly in Africa. It varies greatly from one region to another and even within districts of the same region. Macroscopic geospatial analysis has become an important scientific tool for identifying the density and clustering of HCV infection and provides epidemiological information for planning interventions and control strategies. The application of these parameters provides a better knowledge of the hepatitis C virus infection prevalence at the national level and can help to implement pertinent strategies to address the HCV-related burdens. This study aims to determine the geographical variability of HCV infection in Libya and to identify the hot spots within regions and districts of the country, and to analyze the population-based demographic determinants involved and outline the intervention programs needed.

METHODS

Disease mapping and spatial analysis were conducted using geographic information data available on all documented cases of HCV infections in Libya between 2007 and 2016. Spatial autocorrelation was tested using Moran's Index, which determines and measures the degree of clustering and dispersion of HCV infection in a country.

RESULTS

A total 114,928 HCV infection cases during a ten-year period with accurate geographic information were studied. Ages ranged between 16 and 50 years and the male to female ratio was 2:1. HCV infection was unevenly distributed in Libya, and its incidence increased steadily over the study period. Several hot spots and cold spots were found mainly in the southern and eastern regions of the country.

CONCLUSION

HCV infection in Libya was geographically variable, with several hot spots particularly in eastern and southern Libya associated with different demographic determinants. Future intervention planning should consider the geospatial variability and risk factors involved.

Use of Geographically Weighted Poisson Regression to examine the effect of distance on Tuberculosis incidence: A case study in Nam Dinh, Vietnam

Objectives

This study aimed to examine the potential of combining routine tuberculosis (TB) surveillance and demographic and socioeconomic variables into the Geographic Information System (GIS) to describe the geographical distribution of TB notified incidence in relation to distances to health services as well as local demographic and socioeconomic factors, including population density, urban/rural status, and household poverty rates in Nam Dinh, Vietnam. It also aimed to compare the conventional Generalized Linear Models (GLM) Poisson regression model and Geographically Weighted Poisson Regression (GWPR) models in order to determine the best fitting model that can be used to investigate the relationship between TB notified incidence and distances and the social risk factors.

Methods

The data of new and relapse patients with all forms of TB aged ≥15 years residing in Nam Dinh (Vietnam) from 2012 to 2015 were collected from the Administration of Medical Services’ (Ministry of Health of Vietnam) TB surveillance database. Data on the population and household poverty rates from 2012 to 2015 were gathered from the Nam Dinh Statistical Office. Distances between communes and the nearest TB diagnostic facilities in districts were computed. The TB notified incidence per 100,000 population was denoted by indirect age and sex standardized incidence ratio. GLM Poisson regression and GWPR were performed to assess the relationship between distance and TB incidence.

Results

The average notified TB incidence level measured from 2012 to 2015 is 82 per 100,000 population (range: 79-84/100,000). The distance to the nearest TB diagnosis presents a negative effect on TB notified incidence. By capturing spatial heterogeneity, the GWPR may be better at fitting data (corrected Aikake information criterion [AICc] = 245.71, residual deviance = 221.12) than the traditional GLM (AICc = 251.53, residual deviance = 241.21)

Conclusions

GIS technologies benefit TB surveillance system. Distances should be considered when planning methods of improving access for those who live far from TB diagnostic services, thereby improving TB detection. Additional studies must confirm the association between geographic distance and TB case detection and must explore other factors that may affect TB notified incidence.

Ambient PM2.5 air pollution exposure and hepatocellular carcinoma incidence in the United States

PURPOSE

To conduct the first epidemiologic study prospectively examining the association between particulate matter air pollution < 2.5 µm in diameter (PM2.5) exposure and hepatocellular carcinoma (HCC) risk in the U.S.

METHODS

Surveillance, Epidemiology, and End Results (SEER) provided information on HCC cases diagnosed between 2000 and 2014 from 16 population-based cancer registries across the U.S. Ambient PM2.5 exposure was estimated by linking the SEER county with a spatial PM2.5 model using a geographic information system. Poisson regression with robust variance estimation was used to calculate incidence rate ratios and 95% confidence intervals (CIs) for the association between ambient PM2.5 exposure per 10 µg/m3 increase and HCC risk adjusting for individual-level age at diagnosis, sex, race, year of diagnosis, SEER registry, and county-level information on health conditions, lifestyle, demographic, socioeconomic, and environmental factors.

RESULTS

Higher levels of ambient PM2.5 exposure were associated with a statistically significant increased risk for HCC (n = 56,245 cases; adjusted IRR per 10 µg/m3 increase = 1.26, 95% CI 1.08, 1.47; p < 0.01).

CONCLUSIONS

If confirmed in studies with individual-level PM2.5 exposure and risk factor information, these results suggest that ambient PM2.5 exposure may be a risk factor for HCC in the U.S.

Circadian Misalignment and Hepatocellular Carcinoma Incidence in the United States

Background: Circadian misalignment may increase the risk of developing hepatocellular carcinoma (HCC). The aim of this study was to examine the association between distance from time zone meridian, a proxy for circadian misalignment, and HCC risk in the United States adjusting for known HCC risk factors.Methods: Surveillance, Epidemiology, and End Results (SEER) provided information on 56,347 HCC cases diagnosed between 2000 and 2014 from 16 population-based cancer registries in the United States. Distance from time zone meridian was estimated using the location of each SEER county's Center of Population in a geographic information system. Poisson regression with robust variance estimation was used to calculate incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for the association between distance from time zone meridian and HCC risk adjusting for individual-level age at diagnosis, sex, race/ethnicity, year of diagnosis, SEER registry, and county-level prevalence of health conditions, lifestyle factors, shift work occupation, socioeconomic status, and demographic and environmental factors.Results: A 5-degree increase in longitude moving east to west within a time zone was associated with a statistically significant increased risk for HCC (IRR, 1.07; 95% CI, 1.01-1.14, P = 0.03). A statistically significant positive association was observed among those <65 years old, while no association was observed among individuals ≥65 years old (P_{for interaction} < 0.01).Conclusions: Circadian misalignment from residing in the western region of a time zone may impact hepatocarcinogenesis.Impact: Circadian misalignment may be an independent risk factor for HCC. Cancer Epidemiol Biomarkers Prev; 27(7); 719-27. ©2018 AACR.

Potential geographic "hotspots" for drug-injection related transmission of HIV and HCV and for initiation into injecting drug use in New York City, 2011-2015, with implications for the current opioid epidemic in the US

OBJECTIVE

We identified potential geographic "hotspots" for drug-injecting transmission of HIV and hepatitis C virus (HCV) among persons who inject drugs (PWID) in New York City. The HIV epidemic among PWID is currently in an "end of the epidemic" stage, while HCV is in a continuing, high prevalence (> 50%) stage.

METHODS

We recruited 910 PWID entering Mount Sinai Beth Israel substance use treatment programs from 2011-2015. Structured interviews and HIV/ HCV testing were conducted. Residential ZIP codes were used as geographic units of analysis. Potential "hotspots" for HIV and HCV transmission were defined as 1) having relatively large numbers of PWID 2) having 2 or more HIV (or HCV) seropositive PWID reporting transmission risk-passing on used syringes to others, and 3) having 2 or more HIV (or HCV) seronegative PWID reporting acquisition risk-injecting with previously used needles/syringes. Hotspots for injecting drug use initiation were defined as ZIP codes with 5 or more persons who began injecting within the previous 6 years.

RESULTS

Among PWID, 96% injected heroin, 81% male, 34% White, 15% African-American, 47% Latinx, mean age 40 (SD = 10), 7% HIV seropositive, 62% HCV seropositive. Participants resided in 234 ZIP codes. No ZIP codes were identified as potential hotspots due to small numbers of HIV seropositive PWID reporting transmission risk. Four ZIP codes were identified as potential hotspots for HCV transmission. 12 ZIP codes identified as hotspots for injecting drug use initiation.

DISCUSSION

For HIV, the lack of potential hotspots is further validation of widespread effectiveness of efforts to reduce injecting-related HIV transmission. Injecting-related HIV transmission is likely to be a rare, random event. HCV prevention efforts should include focus on potential hotspots for transmission and on hotspots for initiation into injecting drug use. We consider application of methods for the current opioid epidemic in the US.

Test of cure, retesting and extragenital testing practices for Chlamydia trachomatis and Neisseria gonorrhoeae among general practitioners in different socioeconomic status areas: A retrospective cohort study, 2011-2016

Background

For Chlamydia trachomatis (CT), a test of cure (TOC) within 3–5 weeks is not recommended. International guidelines differ in advising a Neisseria gonorrhoeae (NG) TOC. Retesting CT and NG positives within 3–12 months is recommended in international guidelines. We assessed TOC and retesting practices including extragenital testing in general practitioner (GP) practices located in different socioeconomic status (SES) areas to inform and optimize local test practices.

Methods

Laboratory data of 48 Dutch GP practices between January 2011 and July 2016 were used. Based on a patient’s first positive CT or NG test, the proportion of TOC (<3 months) and retests (3–12 months) were calculated. Patient- and GP-related factors were assessed using multivariate logistic regression analyses.

Results

For CT (n = 622), 20% had a TOC and 24% had a retest at the GP practice. GP practices in low SES areas were more likely to perform a CT TOC (OR:1.8;95%CI:1.1–3.1). Younger patients (<25 years) were more likely to have a CT TOC (OR:1.6;95%CI:1.0–2.4). For CT (n = 622), 2.4% had a TOC and 6.1% had a retest at another STI care provider. For NG (n = 73), 25% had a TOC and 15% had a retest at the GP practice. For NG (n = 73), 2.7% had a TOC and 12.3% had a retest at another STI care provider. In only 0.3% of the consultations patients were tested on extragenital sites.

Conclusion

Almost 20% of the patients returned for a CT TOC, especially at GP practices in low SES areas. For NG, 1 out of 4 patients returned for a TOC. Retesting rates were low for both CT (24%) and NG (15%), (re)infections including extragenital infections may be missed. Efforts are required to focus TOC and increase retesting practices of GPs in order to improve CT/NG control.

Who is where at risk for Chronic Obstructive Pulmonary Disease? A spatial epidemiological analysis of health insurance claims for COPD in Northeastern Germany

Background

Chronic obstructive pulmonary disease (COPD) has a high prevalence rate in Germany and a further increase is expected within the next years. Although risk factors on an individual level are widely understood, only little is known about the spatial heterogeneity and population-based risk factors of COPD. Background knowledge about broader, population-based processes could help to plan the future provision of healthcare and prevention strategies more aligned to the expected demand. The aim of this study is to analyze how the prevalence of COPD varies across northeastern Germany on the smallest spatial-scale possible and to identify the location-specific population-based risk factors using health insurance claims of the AOK Nordost.

Methods

To visualize the spatial distribution of COPD prevalence at the level of municipalities and urban districts, we used the conditional autoregressive Besag–York–Mollié (BYM) model. Geographically weighted regression modelling (GWR) was applied to analyze the location-specific ecological risk factors for COPD.

Results

The sex- and age-adjusted prevalence of COPD was 6.5% in 2012 and varied widely across northeastern Germany. Population-based risk factors consist of the proportions of insurants aged 65 and older, insurants with migration background, household size and area deprivation. The results of the GWR model revealed that the population at risk for COPD varies considerably across northeastern Germany.

Conclusion

Area deprivation has a direct and an indirect influence on the prevalence of COPD. Persons ageing in socially disadvantaged areas have a higher chance of developing COPD, even when they are not necessarily directly affected by deprivation on an individual level. This underlines the importance of considering the impact of area deprivation on health for planning of healthcare. Additionally, our results reveal that in some parts of the study area, insurants with migration background and persons living in multi-persons households are at elevated risk of COPD.

Exploring the small-scale spatial distribution of hypertension and its association to area deprivation based on health insurance claims in Northeastern Germany

BACKGROUND

Hypertension is one of the most frequently diagnosed chronic conditions in Germany. Targeted prevention strategies and allocation of general practitioners where they are needed most are necessary to prevent severe complications arising from high blood pressure. However, data on chronic diseases in Germany are mostly available through survey data, which do not only underestimate the actual prevalence but are also only available on coarse spatial scales. The discussion of including area deprivation for planning of healthcare is still relatively young in Germany, although previous studies have shown that area deprivation is associated with adverse health outcomes, irrespective of individual characteristics. The aim of this study is therefore to analyze the spatial distribution of hypertension at very fine geographic scales and to assess location-specific associations between hypertension, socio-demographic population characteristics and area deprivation based on health insurance claims of the AOK Nordost.

METHODS

To visualize the spatial distribution of hypertension prevalence at very fine geographic scales, we used the conditional autoregressive Besag-York-Mollié (BYM) model. Geographically weighted regression modelling (GWR) was applied to analyze the location-specific association of hypertension to area deprivation and further socio-demographic population characteristics.

RESULTS

The sex- and age-adjusted prevalence of hypertension was 33.1% in 2012 and varied widely across northeastern Germany. The main risk factors for hypertension were proportions of insurants aged 45-64, 65 and older, area deprivation and proportion of persons commuting to work outside their residential municipality. The GWR model revealed important regional variations in the strength of the examined associations.

CONCLUSION

Area deprivation has only a significant and therefore direct influence in large parts of Mecklenburg-West Pomerania. However, the spatially varying strength of the association between demographic variables and hypertension indicates that there also exists an indirect effect of area deprivation on the prevalence of hypertension. It can therefore be expected that persons ageing in deprived areas will be at greater risk of hypertension, irrespective of their individual characteristics. The future planning and allocation of primary healthcare in northeastern Germany would therefore greatly benefit from considering the effect of area deprivation.

Detecting Hepatitis B and C by Combined Public Health and Primary Care Birth Cohort Testing

PURPOSE

Both chronic hepatitis C (HCV) and B virus (HBV) infections are generally asymptomatic, and many remain undetected or are diagnosed at a late stage. Studies that evaluate best practice hepatitis testing strategies are needed to better detect this hidden population.

METHODS

In this prospective cohort study, we aimed to determine the diagnostic yield (test uptake and rate of positive test results) of a combined public health and primary care birth cohort testing strategy in detecting hidden cases of HCV and HBV infections. We invited all patients aged between 40 and 70 years (n = 6,743) registered with 11 family practices serving 2 higher prevalence areas, or hotspots (ie, estimated HCV prevalence of 1%; national estimated prevalence is 0.1-0.4%), in the south of the Netherlands.

RESULTS

Test uptake was 50.9% (n = 3,434 patients). No active or chronic HCV infection was detected: 0.00% (95% CI, 0.00%-0.11%). Positive test rates were 0.20% (95% CI, 0.08%-0.42%) for anti-HCV (n = 7), 0.26% (95% CI, 0.12%-0.50%) for hepatitis B surface antigen (n = 9), and 4.14% (95% CI, 3.49%-4.86%) for antihepatitis B core (n = 142).

CONCLUSIONS

This best practice testing strategy was effective in achieving a high test uptake. It completely failed, however, to detect hidden chronic HCV infections and is not recommended for countries with a low prevalence of the disease.

Micro-spatial distribution of malaria cases and control strategies at ward level in Gwanda district, Matabeleland South, Zimbabwe

Background

Although there has been a decline in the number of malaria cases in Zimbabwe since 2010, the disease remains the biggest public health threat in the country. Gwanda district, located in Matabeleland South Province of Zimbabwe has progressed to the malaria pre-elimination phase. The aim of this study was to determine the spatial distribution of malaria incidence at ward level for improving the planning and implementation of malaria elimination in the district.

Methods

The Poisson purely spatial model was used to detect malaria clusters and their properties, including relative risk and significance levels at ward level. The geographically weighted Poisson regression (GWPR) model was used to explore the potential role and significance of environmental variables [rainfall, minimum and maximum temperature, altitude, Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), rural/urban] and malaria control strategies [indoor residual spraying (IRS) and long-lasting insecticide-treated nets (LLINs)] on the spatial patterns of malaria incidence at ward level.

Results

Two significant clusters (p < 0.05) of malaria cases were identified: (1) ward 24 south of Gwanda district and (2) ward 9 in the urban municipality, with relative risks of 5.583 and 4.316, respectively. The semiparametric-GWPR model with both local and global variables had higher performance based on AICc (70.882) compared to global regression (74.390) and GWPR which assumed that all variables varied locally (73.364). The semiparametric-GWPR captured the spatially non-stationary relationship between malaria cases and minimum temperature, NDVI, NDWI, and altitude at the ward level. The influence of LLINs, IRS and rural or urban did not vary and remained in the model as global terms. NDWI (positive coefficients) and NDVI (range from negative to positive coefficients) showed significant association with malaria cases in some of the wards. The IRS had a protection effect on malaria incidence as expected.

Conclusions

Malaria incidence is heterogeneous even in low-transmission zones including those in pre-elimination phase. The relationship between malaria cases and NDWI, NDVI, altitude, and minimum temperature may vary at local level. The results of this study can be used in planning and implementation of malaria control strategies at district and ward levels.

Using exploratory regression to identify optimal driving factors for cellular automaton modeling of land use change

Defining transition rules is an important issue in cellular automaton (CA)-based land use modeling because these models incorporate highly correlated driving factors. Multicollinearity among correlated driving factors may produce negative effects that must be eliminated from the modeling. Using exploratory regression under pre-defined criteria, we identified all possible combinations of factors from the candidate factors affecting land use change. Three combinations that incorporate five driving factors meeting pre-defined criteria were assessed. With the selected combinations of factors, three logistic regression-based CA models were built to simulate dynamic land use change in Shanghai, China, from 2000 to 2015. For comparative purposes, a CA model with all candidate factors was also applied to simulate the land use change. Simulations using three CA models with multicollinearity eliminated performed better (with accuracy improvements about 3.6%) than the model incorporating all candidate factors. Our results showed that not all candidate factors are necessary for accurate CA modeling and the simulations were not sensitive to changes in statistically non-significant driving factors. We conclude that exploratory regression is an effective method to search for the optimal combinations of driving factors, leading to better land use change models that are devoid of multicollinearity. We suggest identification of dominant factors and elimination of multicollinearity before building land change models, making it possible to simulate more realistic outcomes.

Spatio-temporal Analysis for New York State SPARCS Data

Increased accessibility of health data provides unique opportunities to discover spatio-temporal patterns of diseases. For example, New York State SPARCS (Statewide Planning and Research Cooperative System) data collects patient level detail on patient demographics, diagnoses, services, and charges for each hospital inpatient stay and outpatient visit. Such data also provides home addresses for each patient. This paper presents our preliminary work on spatial, temporal, and spatial-temporal analysis of disease patterns for New York State using SPARCS data. We analyzed spatial distribution patterns of typical diseases at ZIP code level. We performed temporal analysis of common diseases based on 12 years' historical data. We then compared the spatial variations for diseases with different levels of clustering tendency, and studied the evolution history of such spatial patterns. Case studies based on asthma demonstrated that the discovered spatial clusters are consistent with prior studies. We visualized our spatial-temporal patterns as animations through videos.

Spatiotemporal meta-analysis: reviewing health psychology phenomena over space and time

Individual studies of health psychology are samples taken in particular places at particular times. The results of such studies manifest multiple processes, including those associated with individual, sample, intervention, and study design characteristics. Although extant meta-analyses of health phenomena have routinely considered these factors to explain heterogeneity, they have tended to neglect the environments where studies are conducted, which is ironic, as health phenomena cluster in space and times (e.g., epidemics). The settings in which study participants live, work, and recreate can be characterised by such environmental factors such as disease, weather, local and broad economic trends, the level of stigmatisation of minority groups, and allostatic load due to all causes. We introduce spatiotemporal meta-analysis, designed to address heterogeneity in study environments. We list potential challenges in developing spatiotemporal meta-analyses, and discuss future directions for this form of systematic reviewing methodology. Logically, to the extent that relevant spatiotemporal information on environmental conditions is available and varies widely, it can help to explain variability in study results that is not explained by individual, sample, study, or intervention features.

Is the current pertussis incidence only the results of testing? A spatial and space-time analysis of pertussis surveillance data using cluster detection methods and geographically weighted regression modelling

BACKGROUND

Despite high vaccination coverage, pertussis incidence in the Netherlands is amongst the highest in Europe with a shifting tendency towards adults and elderly. Early detection of outbreaks and preventive actions are necessary to prevent severe complications in infants. Efficient pertussis control requires additional background knowledge about the determinants of testing and possible determinants of the current pertussis incidence. Therefore, the aim of our study is to examine the possibility of locating possible pertussis outbreaks using space-time cluster detection and to examine the determinants of pertussis testing and incidence using geographically weighted regression models.

METHODS

We analysed laboratory registry data including all geocoded pertussis tests in the southern area of the Netherlands between 2007 and 2013. Socio-demographic and infrastructure-related population data were matched to the geo-coded laboratory data. The spatial scan statistic was applied to detect spatial and space-time clusters of testing, incidence and test-positivity. Geographically weighted Poisson regression (GWPR) models were then constructed to model the associations between the age-specific rates of testing and incidence and possible population-based determinants.

RESULTS

Space-time clusters for pertussis incidence overlapped with space-time clusters for testing, reflecting a strong relationship between testing and incidence, irrespective of the examined age group. Testing for pertussis itself was overall associated with lower socio-economic status, multi-person-households, proximity to primary school and availability of healthcare. The current incidence in contradiction is mainly determined by testing and is not associated with a lower socioeconomic status.

DISCUSSION

Testing for pertussis follows to an extent the general healthcare seeking behaviour for common respiratory infections, whereas the current pertussis incidence is largely the result of testing. More testing would thus not necessarily improve pertussis control. Detecting outbreaks using space-time cluster detection is feasible but needs to adjust for the strong impact of testing on the detection of pertussis cases.

Integrative Spatial Data Analytics for Public Health Studies of New York State

Increased accessibility of health data made available by the government provides unique opportunity for spatial analytics with much higher resolution to discover patterns of diseases, and their correlation with spatial impact indicators. This paper demonstrated our vision of integrative spatial analytics for public health by linking the New York Cancer Mapping Dataset with datasets containing potential spatial impact indicators. We performed spatial based discovery of disease patterns and variations across New York State, and identify potential correlations between diseases and demographic, socio-economic and environmental indicators. Our methods were validated by three correlation studies: the correlation between stomach cancer and Asian race, the correlation between breast cancer and high education population, and the correlation between lung cancer and air toxics. Our work will allow public health researchers, government officials or other practitioners to adequately identify, analyze, and monitor health problems at the community or neighborhood level for New York State.

Geographic integration of hepatitis C virus: A global threat

AIM

To assess hepatitis C virus (HCV) geographic integration, evaluate the spatial and temporal evolution of HCV worldwide and propose how to diminish its burden.

METHODS

A literature search of published articles was performed using PubMed, MEDLINE and other related databases up to December 2015. A critical data assessment and analysis regarding the epidemiological integration of HCV was carried out using the meta-analysis method.

RESULTS

The data indicated that HCV has been integrated immensely over time and through various geographical regions worldwide. The history of HCV goes back to 1535 but between 1935 and 1965 it exhibited a rapid, exponential spread. This integration is clearly seen in the geo-epidemiology and phylogeography of HCV. HCV integration can be mirrored either as intra-continental or trans-continental. Migration, drug trafficking and HCV co-infection, together with other potential risk factors, have acted as a vehicle for this integration. Evidence shows that the geographic integration of HCV has been important in the global and regional distribution of HCV.

CONCLUSION

HCV geographic integration is clearly evident and this should be reflected in the prevention and treatment of this ongoing pandemic.

Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression

BACKGROUND

The provision of general practitioners (GPs) in Germany still relies mainly on the ratio of inhabitants to GPs at relatively large scales and barely accounts for an increased prevalence of chronic diseases among the elderly and socially underprivileged populations. Type 2 Diabetes Mellitus (T2DM) is one of the major cost-intensive diseases with high rates of potentially preventable complications. Provision of healthcare and access to preventive measures is necessary to reduce the burden of T2DM. However, current studies on the spatial variation of T2DM in Germany are mostly based on survey data, which do not only underestimate the true prevalence of T2DM, but are also only available on large spatial scales. The aim of this study is therefore to analyse the spatial distribution of T2DM at fine geographic scales and to assess location-specific risk factors based on data of the AOK health insurance.

METHODS

To display the spatial heterogeneity of T2DM, a bivariate, adaptive kernel density estimation (KDE) was applied. The spatial scan statistic (SaTScan) was used to detect areas of high risk. Global and local spatial regression models were then constructed to analyze socio-demographic risk factors of T2DM.

RESULTS

T2DM is especially concentrated in rural areas surrounding Berlin. The risk factors for T2DM consist of proportions of 65-79 year olds, 80 + year olds, unemployment rate among the 55-65 year olds, proportion of employees covered by mandatory social security insurance, mean income tax, and proportion of non-married couples. However, the strength of the association between T2DM and the examined socio-demographic variables displayed strong regional variations.

CONCLUSION

The prevalence of T2DM varies at the very local level. Analyzing point data on T2DM of northeastern Germany's largest health insurance provider thus allows very detailed, location-specific knowledge about increased medical needs. Risk factors associated with T2DM depend largely on the place of residence of the respective person. Future allocation of GPs and current prevention strategies should therefore reflect the location-specific higher healthcare demand among the elderly and socially underprivileged populations.

Local spatial variations analysis of smear-positive tuberculosis in Xinjiang using Geographically Weighted Regression model

Background

The spatial interplay between socioeconomic factors and tuberculosis (TB) cases contributes to the understanding of regional tuberculosis burdens. Historically, local Poisson Geographically Weighted Regression (GWR) has allowed for the identification of the geographic disparities of TB cases and their relevant socioeconomic determinants, thereby forecasting local regression coefficients for the relations between the incidence of TB and its socioeconomic determinants. Therefore, the aims of this study were to: (1) identify the socioeconomic determinants of geographic disparities of smear positive TB in Xinjiang, China (2) confirm if the incidence of smear positive TB and its associated socioeconomic determinants demonstrate spatial variability (3) compare the performance of two main models: one is Ordinary Least Square Regression (OLS), and the other local GWR model.

Methods

Reported smear-positive TB cases in Xinjiang were extracted from the TB surveillance system database during 2004–2010. The average number of smear-positive TB cases notified in Xinjiang was collected from 98 districts/counties. The population density (POPden), proportion of minorities (PROmin), number of infectious disease network reporting agencies (NUMagen), proportion of agricultural population (PROagr), and per capita annual gross domestic product (per capita GDP) were gathered from the Xinjiang Statistical Yearbook covering a period from 2004 to 2010. The OLS model and GWR model were then utilized to investigate socioeconomic determinants of smear-positive TB cases. Geoda 1.6.7, and GWR 4.0 software were used for data analysis.

Results

Our findings indicate that the relations between the average number of smear-positive TB cases notified in Xinjiang and their socioeconomic determinants (POPden, PROmin, NUMagen, PROagr, and per capita GDP) were significantly spatially non-stationary. This means that in some areas more smear-positive TB cases could be related to higher socioeconomic determinant regression coefficients, but in some areas more smear-positive TB cases were found to do with lower socioeconomic determinant regression coefficients. We also found out that the GWR model could be better exploited to geographically differentiate the relationships between the average number of smear-positive TB cases and their socioeconomic determinants, which could interpret the dataset better (adjusted R² = 0.912, AICc = 1107.22) than the OLS model (adjusted R² = 0.768, AICc = 1196.74).

Conclusions

POPden, PROmin, NUMagen, PROagr, and per capita GDP are socioeconomic determinants of smear-positive TB cases. Comprehending the spatial heterogeneity of POPden, PROmin, NUMagen, PROagr, per capita GDP, and smear-positive TB cases could provide valuable information for TB precaution and control strategies.

Epidemiology of hepatitis C virus and genotype distribution in immigrants crossing to Europe from North and sub-Saharan Africa

BACKGROUND

The association between the prevalence of hepatitis C virus (HCV) and immigration is rarely studied, particularly for the immigrants crossing to the resettlement countries. Most of the published data are confined to those immigrants who were resident in European countries and rarely immigrated before they reach the final destination. Libya is a large country in North Africa with the longest coast of the Mediterranean Sea facing the European Union. It has been considered as the main transient station for African immigrants to Europe. The objectives of this study were to determine: (1) the prevalence of HCV in African immigrants gathered in Libya from different African countries on their way to Europe and (2) HCV genotype distribution in these immigrants and its correlation with different demographic factors.

METHODS

A total of 14 205 serum samples were collected in a 3-year period (2013-2015) from different immigrants from North and sub-Saharan Africa who resided in the African immigrant campus, Tripoli, Libya. The participants were interviewed, and relevant information was collected, including socio-demographic, ethnic, and geographic variables. Each serum sample was tested for anti-HCV antibody using ELISA. The genotypes were determined and assigned using a specific genotyping assay and correlated with demographic and potential risk factors of the recruited individuals.

RESULTS

Of the immigrants studied, 1078 (7.6%) were positive for HCV. The prevalence of HCV infection ranged from 1.4% to 18.7%; it was higher among individuals arriving from Nile river (3.6-18.7%) of North Africa, followed by those who arrived from the West African region (2.1-14.1%), Horn of Africa (HOA, 6.8-9.9%), and Maghreb countries (1.4-2.7%). The relative risk factor attributable to gender variation was not significant (95% Cl: 0.8513-1.2381). Five genotypes were detected in 911 African immigrants. Genotypic analysis showed that the predominant HCV genotypes in this group were genotypes 4, 1, and 2 that accounted for 329 (36.1%), 326 (35.8%), and 131 (14.4%) strains, respectively, followed by genotype 3 that accounted for 87 (9.5%) strains. Genotype 5 was isolated mainly from 18 HOA (2%) and 20 West African (2.2%) individuals.

CONCLUSIONS

The prevalence of HCV is considered high with a unique disparate distribution among African immigrants crossing to Europe. This indicated that the prevalence of HCV is high among these immigrants and thus may be reflected on the HCV prevalence in the guest countries. The broad genetic heterogeneity of HCV genotypes detected here may impact the efficacy of prevention and control efforts for HCV in both Europe and North and sub-Saharan Africa; hence, an integrated global policy of actions is needed.

Spatial analysis of HIV infection and associated individual characteristics in Burundi: indications for effective prevention

Background

Adequate resource allocation is critical in the battle against HIV/AIDS, especially in Africa. The determination of the location and nature of HIV services to implement must comply with the geographic, social and behavioral characteristics of patients. We therefore investigated the spatial heterogeneity of HIV prevalence in Burundi and then assessed the association of social and behavioral characteristics with HIV infection accounting for the spatial heterogeneity.

Methods

We used data from the 2010 Demographic and Health Survey. We analyzed these data with a geostatistical approach (which takes into account spatial autocorrelation) by i) interpolating HIV data using the kernel density estimation, ii) identifying the spatial clusters with high and low HIV prevalence using the Kulldorff spatial scan statistics, and then iii) performing a multivariate spatial logistic regression.

Results

Overall HIV prevalence was 1.4 %. The interpolated data showed the great spatial heterogeneity of HIV prevalence (from 0 to 10 %), independently of administrative boundaries. A cluster with high HIV prevalence was found in the capital city and adjacent areas (3.9 %; relative risk 3.7, p < 0.001) whereas a cluster with low prevalence straddled two southern provinces (0 %; p = 0.02). By multivariate spatial analysis, HIV infection was significantly associated with the female sex (posterior odds ratio [POR] 1.36, 95 % credible interval [CrI] 1.13-1.64), an older age (POR 1.97, 95 % CrI 1.26-3.08), the level of education (POR 1.50, 95 % CrI 1.22-1.84), the marital status (POR 1.86, 95 % CrI 1.23-2.80), a higher wealth index (POR 2.11, 95 % CrI 1.77-2.51), the sexual activity (POR 1.76, 95 % CrI 1.04-2.96), and a history of sexually transmitted infection (POR 2.03, 95 % CrI 1.56-2.64).

Conclusions

Our study, which shows where and towards which populations HIV resources should be allocated, could help national health policy makers develop an effective HIV intervention in Burundi. Our findings support the strategy of the Joint United Nations Programme on HIV/AIDS (UNAIDS) for country-specific, in-depth analyses of HIV epidemics to tailor national prevention responses.