Geographic area-based rate as a novel indicator to enhance research and precision intervention for more effective HIV/AIDS control

Ecological and Socio-Economic Determinants of Livestock Animal Leptospirosis in the Russian Arctic

Leptospirosis is a re-emerging zoonotic infectious disease caused by pathogenic bacteria of the genus Leptospira. Regional differences in the disease manifestation and the role of ecological factors, specifically in regions with a subarctic and arctic climate, remain poorly understood. We here explored environmental and socio-economic features associated with leptospirosis cases in livestock animals in the Russian Arctic during 2000–2019. Spatial analysis suggested that the locations of the majority of 808 cases were in “boreal” or “polar” climate regions, with “cropland,” “forest,” “shrubland,” or “settlements” land-cover type, with a predominance of “Polar Moist Cropland on Plain” ecosystem. The cases demonstrated seasonality, with peaks in March, June, and August, corresponding to the livestock pasturing practices. We applied the Forest-based Classification and Regression algorithm to explore the relationships between the cumulative leptospirosis incidence per unit area by municipal districts (G-rate) and a number of socio-economic, landscape, and climatic factors. The model demonstrated satisfactory performance in explaining the observed disease distribution (R² = 0.82, p < 0.01), with human population density, livestock units density, the proportion of crop area, and budgetary investments into agriculture per unit area being the most influential socio-economic variables. Climatic factors demonstrated a significantly weaker influence, with nearly similar contributions of mean yearly precipitation and air temperature and number of days with above-zero temperatures. Using a projected climate by 2100 according to the RCP8.5 scenario, we predict a climate-related rise of expected disease incidence across most of the study area, with an up to 4.4-fold increase in the G-rate. These results demonstrated the predominant influence of the population and agricultural production factors on the observed increase in leptospirosis cases in livestock animals in the Russian Arctic. These findings may contribute to improvement in the regional system of anti-leptospirosis measures and may be used for further studies of livestock leptospirosis epidemiology at a finer scale.

An Introductory Framework for Choosing Spatiotemporal Analytical Tools in Population-Level Eco-Epidemiological Research

Spatiotemporal visualization and analytical tools (SATs) are increasingly being applied to risk-based surveillance/monitoring of adverse health events affecting humans, animals, and ecosystems. Different disciplines use diverse SATs to address similar research questions. The juxtaposition of these diverse techniques provides a list of options for researchers who are new to population-level spatial eco-epidemiology. Here, we are conducting a narrative review to provide an overview of the multiple available SATs, and introducing a framework for choosing among them when addressing common research questions across disciplines. The framework is comprised of three stages: (a) pre-hypothesis testing stage, in which hypotheses regarding the spatial dependence of events are generated; (b) primary hypothesis testing stage, in which the existence of spatial dependence and patterns are tested; and (c) secondary-hypothesis testing and spatial modeling stage, in which predictions and inferences were made based on the identified spatial dependences and associated covariates. In this step-wise process, six key research questions are formulated, and the answers to those questions should lead researchers to select one or more methods from four broad categories of SATs: (T1) visualization and descriptive analysis; (T2) spatial/spatiotemporal dependence and pattern recognition; (T3) spatial smoothing and interpolation; and (T4) geographic correlation studies (i.e., spatial modeling and regression). The SATs described here include both those used for decades and also other relatively new tools. Through this framework review, we intend to facilitate the choice among available SATs and promote their interdisciplinary use to support improving human, animal, and ecosystem health.

A 4D Indicator System of Count, P Rate, G Rate and PG Rate for Epidemiology and Global Health

How to end the HIV/AIDS epidemic is a typical global health question since the impact of HIV/AIDS is global and it cannot be ended without collaborative global effort. In this chapter, a new measurement system is introduced to inform HIV/AIDS control cross the globe. All countries with data available on area size, total population and total number of persons living with HIV (PLWH) were included, yielding a sample of 148 countries. Four indicators, including the total count, population-based p rate, geographic area-based g rate and population and geographic area-based pg rate were used as a 4D system to describe the global HIV epidemic. The total PLWH count provided data informing resource allocation for individual countries to improve HIV/AIDS care; and the top five countries with highest PLWH count were South Africa, Nigeria, India, Kenya, and Mozambique. Information from the remaining three indicators provided a global risk profile of the HIV epidemic, supporting HIV/AIDS prevention programming strategies. Five countries with highest p rates were Swaziland, Botswana, Lesotho, South Africa, and Zimbabwe; five countries with highest g rates were Swaziland, Malawi, Lesotho, Rwanda, and Uganda; and five countries with highest pg rates were Barbados, Swaziland, Lesotho, Malta, and Mauritius. According to pg rates, two HIV hotspots (south and middle Africa and Caribbean region) and one HIV belt across Euro-Asian were identified. In addition to HIV/AIDS, the 4D measurement system can be used to describe morbidity and mortality for many diseases across the globe. We recommend the use of this measurement system in research to address significant global health and epidemiologic issues.

Comparison of spatiotemporal patterns of historic natural Anthrax outbreaks in Minnesota and Kazakhstan

Disease spread in populations is a consequence of the interaction between host, pathogen, and environment, i.e. the epidemiological triad. Yet the influences of each triad component may vary dramatically for different settings. Comparison of environmental, demographic, socio-economic, and historical backgrounds may support tailoring site-specific control measures. Because of the long-term survival of Bacillus anthracis, Anthrax is a suitable example for studying the influence of triad components in different endemic settings. We compared the spatiotemporal patterns of historic animal Anthrax records in two endemic areas, located at northern latitudes in the western and eastern hemispheres. Our goal was to compare the spatiotemporal patterns in Anthrax progression, intensity, direction, and recurrence (disease hot spots), in relation to epidemiological factors and potential trigger events. Reported animal cases in Minnesota, USA (n = 289 cases between 1912 and 2014) and Kazakhstan (n = 3,997 cases between 1933 and 2014) were analyzed using the spatiotemporal directionality test and the spatial scan statistic. Over the last century Anthrax occurrence in Minnesota was sporadic whereas Kazakhstan experienced a long-term epidemic. Nevertheless, the seasonality was comparable between sites, with a peak in August. Declining number of cases at both sites was attributed to vaccination and control measures. The spatiotemporal directionality test detected a relative northeastern directionality in disease spread for long-term trends in Minnesota, whereas a southwestern directionality was observed in Kazakhstan. In terms of recurrence, the maximum timespans between cases at the same location were 55 and 60 years for Minnesota and Kazakhstan, respectively. Disease hotspots were recognized in both settings, with spatially overlapping clusters years apart. Distribution of the spatiotemporal cluster radii between study sites supported suggestion of site-specific control zones. Spatiotemporal patterns of Anthrax occurrence in both endemic regions were attributed to multiple potential trigger events including major river floods, changes in land use, agriculture, and susceptible livestock populations. Results here help to understand the long-term epidemiological dynamics of Anthrax while providing suggestions to the design and implementation of prevention and control programs, in endemic settings.

Comparing geographic area-based and classical population-based incidence and prevalence rates, and their confidence intervals

To quantify the HIV epidemic, the classical population-based prevalence and incidence rates (P rates) are the two most commonly used measures used for policy interventions. However, these P rates ignore the heterogeneity of the size of geographic region where the population resides. It is intuitive that with the same P rates, the likelihood for HIV can be much greater to spread in a population residing in a crowed small urban area than the same number of population residing in a large rural area. With this limitation, Chen and Wang (2017) proposed the geographic area-based rates (G rates) to complement the classical P rates. They analyzed the 2000-2012 US data on new HIV infections and persons living with HIV and found, as compared with other methods, using G rates enables researchers to more quickly detect increases in HIV rates. This capacity to reveal increasing rates in a more efficient and timely manner is a crucial methodological contribution to HIV research. To enhance this newly proposed concept of G rates, this article presents a discussion of 3 areas for further development of this important concept: (1) analysis of global HIV epidemic data using the newly proposed G rates to capture the changes globally; (2) development of the associated population density-based rates (D rates) to incorporate the heterogeneities from both geographical area and total population-at-risk; and (3) development of methods to calculate variances and confidence intervals for the P rates, G rates, and D rates to capture the variability of these indices.