Spatiotemporal Regression Model Research Articles

Improving the representativeness of UK’s national COVID-19 Infection Survey through spatio-temporal regression and post-stratification

Population-representative estimates of SARS-CoV-2 infection prevalence and antibody levels in specific geographic areas at different time points are needed to optimise policy responses. However, even population-wide surveys are potentially impacted by biases arising from differences in participation rates across key groups. Here, we used spatio-temporal regression and post-stratification models to UK’s national COVID-19 Infection Survey (CIS) to obtain representative estimates of PCR positivity (6,496,052 tests) and antibody prevalence (1,941,333 tests) for different regions, ages and ethnicities (7-December-2020 to 4-May-2022). Not accounting for vaccination status through post-stratification led to small underestimation of PCR positivity, but more substantial overestimations of antibody levels in the population (up to 21 percentage points), particularly in groups with low vaccine uptake in the general population. There was marked variation in the relative contribution of different areas and age-groups to each wave. Future analyses of infectious disease surveys should take into account major drivers of outcomes of interest that may also influence participation, with vaccination being an important factor to consider.

Multi-scale Driving Mechanism of Urbanization on PM2.5 Concentration in Urban Agglomeration in the Middle Reaches of the Yangtze River

Exploring the nonlinear effect of urbanization on PM2.5 concentration and its driving mechanism is crucial for controlling urban air pollution. Based on remote sensing data and statistical data from 2002 to 2020, spatial autocorrelation, systematic dynamic panel regression, and spatio-temporal geographical weighted regression models were used to analyze the spatio-temporal evolution of PM2.5 concentration in the urban agglomeration of the middle reaches of the Yangtze River and explore the driving mechanism of urbanization on PM2.5 concentration at different spatial scales. The results showed that:① PM2.5 concentration in the middle reaches of the Yangtze River showed an overall decreasing trend from 2002 to 2020, with a spatial distribution pattern of "high in the north and low in the south." ② Hot spot cities expanded towards the western part of the urban agglomeration, whereas cold spot cities showed enhanced spatial correlation. ③ The relationship between PM2.5 concentration and economic, land, and population urbanization followed N-shaped, U-shaped, and U-shaped curves, respectively. Secondary industry and energy consumption significantly promoted the change in PM2.5 concentration, and precipitation and vegetation helped mitigate PM2.5 pollution. ④ The overall driving effects of all urbanization factors in local areas were transformed, and the main areas of influence were concentrated in the southeast, northwest, and southwest of the study area. Considering the current urban development status and regional characteristics of the urban agglomeration in the middle reaches of the Yangtze River, promoting green industrial transformation, rational planning of urban spatial distribution and population distribution, and enhancing infrastructure construction will facilitate the coordinated development between urbanization and environmental protection.

The association of in-utero exposure to air pollution and atherogenic index of plasma in newborns

BackgroundPrenatal exposure to particulate matter (PM) and traffic was associated with the programming of cardiovascular diseases (CVDs) in early life. However, the exact underlying mechanisms are not fully understood. Therefore, we aimed to evaluate the association between in-utero exposure to PMs and traffic indicators with the atherogenic index of plasma (AIP) in newborns, which is a precise index reflecting an enhancement of lipid risk factors for CVDs.MethodsIn this cross-sectional study, a total of 300 mother-newborn pairs were enrolled in Sabzevar, Iran. Spatiotemporal land-use regression models were used to estimate the level of PM1, PM2.5 and PM10 at the mother's residential address. The total length of streets in different buffers (100,300 and 500m) and proximity to major roads were calculated as indicators of traffic. The AIP of cord blood samples was calculated using an AIP calculator. Multiple linear regression models were used to examine the association of PM concentrations as well as traffic indicators with AIP controlled for relevant covariates.ResultsPM2.5 exposure was significantly associated with higher levels of AIP in newborns. Each interquartile range (IQR) increment of PM2.5 concentration at the mothers' residential addresses was associated with a 5.3% (95% confidence interval (CI): 0.0, 10.6%, P = 0.04) increase in the AIP. Associations between PM1, PM10 and traffic indicators with cord blood level of AIP were positive but not statistically significant.ConclusionOur findings showed that in utero exposure to PM2.5 may be associated with CVDs programming through the increase of atherogenic lipids.

Predicting subnational incidence of COVID-19 cases and deaths in EU countries

BackgroundRecurring COVID-19 waves highlight the need for tools able to quantify transmission risk, and identify geographical areas at risk of outbreaks. Local outbreak risk depends on complex immunity patterns resulting from previous infections, vaccination, waning and immune escape, alongside other factors (population density, social contact patterns). Immunity patterns are spatially and demographically heterogeneous, and are challenging to capture in country-level forecast models.MethodsWe used a spatiotemporal regression model to forecast subnational case and death counts and applied it to three EU countries as test cases: France, Czechia, and Italy. Cases in local regions arise from importations or local transmission. Our model produces age-stratified forecasts given age-stratified data, and links reported case counts to routinely collected covariates (e.g. test number, vaccine coverage). We assessed the predictive performance of our model up to four weeks ahead using proper scoring rules and compared it to the European COVID-19 Forecast Hub ensemble model. Using simulations, we evaluated the impact of variations in transmission on the forecasts. We developed an open-source RShiny App to visualise the forecasts and scenarios.ResultsAt a national level, the median relative difference between our median weekly case forecasts and the data up to four weeks ahead was 25% (IQR: 12–50%) over the prediction period. The accuracy decreased as the forecast horizon increased (on average 24% increase in the median ranked probability score per added week), while the accuracy of death forecasts was more stable. Beyond two weeks, the model generated a narrow range of likely transmission dynamics. The median national case forecasts showed similar accuracy to forecasts from the European COVID-19 Forecast Hub ensemble model, but the prediction interval was narrower in our model. Generating forecasts under alternative transmission scenarios was therefore key to capturing the range of possible short-term transmission dynamics.DiscussionOur model captures changes in local COVID-19 outbreak dynamics, and enables quantification of short-term transmission risk at a subnational level. The outputs of the model improve our ability to identify areas where outbreaks are most likely, and are available to a wide range of public health professionals through the Shiny App we developed.

A Bayesian Aoristic Logistic Regression to Model Spatio-Temporal Crime Risk Under the Presence of Interval-Censored Event Times

PurposeCrime data analysis has gained significant interest due to its peculiarities. One key characteristic of property crimes is the uncertainty surrounding their exact temporal location, often limited to a time window.MethodsThis study introduces a spatio-temporal logistic regression model that addresses the challenges posed by temporal uncertainty in crime data analysis. Inspired by the aoristic method, our Bayesian approach allows for the inclusion of temporal uncertainty in the model.ResultsTo demonstrate the effectiveness of our proposed model, we apply it to both simulated datasets and a dataset of residential burglaries recorded in Valencia, Spain. We compare our proposal with a complete cases model, which excludes temporally-uncertain events, and also with alternative models that rely on imputation procedures. Our model exhibits superior performance in terms of recovering the true underlying crime risk.ConclusionsThe proposed modeling framework effectively handles interval-censored temporal observations while incorporating covariate and space–time effects. This flexible model can be implemented to analyze crime data with uncertainty in temporal locations, providing valuable insights for crime prevention and law enforcement strategies.

A flexible likelihood-based neural network extension of the classic spatio-temporal model

The inclusion of the geographic information into regression models is becoming increasingly popular due to the increased availability of corresponding geo-referenced data. In this paper, a novel framework for combining spatio-temporal regression techniques and artificial neural network (ANN) regression models is presented. The key idea is to use the universal approximation property of the ANN function to account for an arbitrary spatial pattern in the dependent variable by including geographic coordinate variables as regressors. Moreover, the implicit location-specific effects are allowed to exhibit arbitrary interaction effects with other regressors such as a time variable. In contrast to other machine learning approaches for spatio-temporal data, the likelihood framework of the classic (linear) spatio-temporal regression model is preserved. This allows, inter alia, for inference regarding marginal effects and associated confidence. The framework also allows for non-normal conditional distributions, conditional spatial correlation, arbitrary trend and seasonality. These features are demonstrated in a simulation section and two data examples, using linear spatio-temporal models as a reference.

Evaluation of skin cancer screening in Germany – Spatiotemporal associations between skin cancer screening and skin cancer mortality based on ambulatory claims data

SummaryBackgroundThere are regional differences in skin cancer screening uptake in Germany. So far, it is unclear whether a high uptake of screening services leads to a reduction in mortality. This article presents study results on the investigation of spatiotemporal associations between skin cancer screening and mortality. The methods used are discussed regarding their suitability.Material and methodsThe basis is ambulatory claims data on the utilization of early skin cancer detection as well as data on skin cancer mortality from the cause‐of‐death statistics of the years 2011–2015 at county level in Germany. In addition to a descriptive evaluation, spatiotemporal cluster analyses and regression models were used to investigate the relationship between the uptake of early detection and mortality. In addition to age, adjustments were also made for other selected socio‐economic and socio‐graphical variables.ResultsThe descriptive results show striking spatial patterns of skin cancer screening and mortality. Cluster analyses identified regions with significantly higher and lower cases of early detection and skin cancer mortality. The spatiotemporal regression analyses show no clear association. Only early detection by a dermatologist, adjusted for age, shows an association with mortality.ConclusionsNo clear association between early skin cancer detection and mortality can be derived from the results. However, the study design used with a spatiotemporal cluster and regression analysis has shown that these methods allow in‐depth statements about the relationship between early skin cancer detection and mortality.

Development of Spatio-Temporal Land Use Regression Models for Fine Particulate Matter and Wood-Burning Tracers in Temuco, Chile.

Biomass burning is common in much of the world, and in some areas, residential wood-burning has increased. However, air pollution resulting from biomass burning is an important public health problem. A sampling campaign was carried out between May 2017 and July 2018 in over 64 sites in four sessions, to develop a spatio-temporal land use regression (LUR) model for fine particulate matter (PM) and wood-burning tracers levoglucosan and soluble potassium (Ksol) in a city heavily impacted by wood-burning. The mean (sd) was 46.5 (37.4) μg m-3 for PM2.5, 0.607 (0.538) μg m-3 for levoglucosan, and 0.635 (0.489) μg m-3 for Ksol. LUR models for PM2.5, levoglucosan, and Ksol had a satisfactory performance (LOSOCV R2), explaining 88.8%, 87.4%, and 87.3% of the total variance, respectively. All models included sociodemographic predictors consistent with the pattern of use of wood-burning in homes. The models were applied to predict concentrations surfaces and to estimate exposures for an epidemiological study.

Effects of short-term air pollution exposure on symptoms development in the course of idiopathic pulmonary fibrosis

ABSTRACT Background Lately a potential detrimental effect of air pollution to idiopathic pulmonary fibrosis emerged. We aimed to assess the effects of short-term air pollution exposure to the clinical course of IPF. Research design and methods IPF patients were followed intensively for four nonconsecutive study periods between 13 July 2020 and 5 September 2021. Short-term exposure to O3, NO2 and PM10 concentrations was estimated using spatio-temporal land use regression models. Associations among symptoms, lung function, oxygen saturation, and short-term personal air pollutant exposure were assessed through multiple mixed effects logistic regression models. Results Data for up to 24 IPF patients (mean age: 72.2 ± 7.6 years) were analyzed. We detected positive significant associations between cough and a 10 μg/m3 increase in same day mean level of NO2 (OR = 1.59, 95%CI: 1.00–2.53), PM10 (OR = 2.42, 95%CI: 1.54–3.79), and O3 (OR = 1.63, 95%CI: 1.14–2.32). A 10 μg/m3 increase in same day mean level of NO2 was also associated with the risk of appearance of wheezing (OR = 3.01, 95%CI: 1.00–9.04), while exposure to O3 was associated with common cold (OR = 6.30, 95%CI: 3.59–11.07). No significant associations were detected between short-term exposure to air pollutants and forced vital capacity or saturation of oxygen. Conclusions Short-term exposure to increased concentrations of air pollutants is an independent risk factor for IPF symptoms' aggravation.

The burden of disease attributable to indoor air pollutants in China from 2000 to 2017

High-level exposure to indoor air pollutants (IAPs) and their corresponding adverse health effects have become a public concern in China in the past 10 years. However, neither national nor provincial level burden of disease attributable to multiple IAPs has been reported for China. This is the first study to estimate and rank the annual burden of disease and the financial costs attributable to targeted residential IAPs at the national and provincial level in China from 2000 to 2017. We first did a systematic review and meta-analysis of 117 articles from 37 231 articles identified in major databases, and obtained exposure-response relationships for the candidate IAPs. The exposure levels to these IAPs were then collected by another systematic review of 1864 articles selected from 52 351 articles. After the systematic review, ten IAPs with significant and robust exposure-response relationships and sufficient exposure data were finally targeted: PM2·5, nitrogen dioxide, sulphur dioxide, ozone, carbon monoxide, radon, formaldehyde, benzene, toluene, and p-dichlorobenzene. The annual exposure levels in residences were then evaluated in all 31 provinces in mainland China continuously from 2000 to 2017, using the spatiotemporal Gaussian process regression model to analyse indoor originating IAPs, and the infiltration factor method to analyse outdoor originating IAPs. The disability-adjusted life-years (DALYs) attributable to the targeted IAPs were estimated at both national and provincial levels in China, using the population attributable fraction method. Financial costs were estimated by an adapted human capital approach. From 2000 to 2017, annual DALYs attributable to the ten IAPs in mainland China decreased from 4620 (95% CI 4070-5040) to 3700 (3210-4090) per 100 000. Nevertheless, in 2017, IAPs still ranked third among all risk factors, and their DALYs and financial costs accounted for 14·1% (95% CI 12·3-15·6) of total DALYs and 3·45% (3·01-3·82) of the gross domestic product. Specifically, the rank of ten targeted IAPs in order of their contribution to DALYs in 2017 was PM2·5, carbon monoxide, radon, benzene, nitrogen dioxide, ozone, sulphur dioxide, formaldehyde, toluene, and p-dichlorobenzene. The DALYs attributable to IAPs were 9·50% higher than those attributable to outdoor air pollution in 2017. For the leading IAP, PM2·5, the DALYs attributable to indoor origins are 18·3% higher than those of outdoor origins. DALYs attributed to IAPs in China have decreased by 20·0% over the past two decades. Even so, they are still much higher than those in the USA and European countries. This study can provide a basis for determining which IAPs to target in various indoor air quality standards and for estimating the health and economic benefits of various indoor air quality control approaches, which will help to reduce the adverse health effects of IAPs in China. The National Key Research and Development Program of China and the National Natural Science Foundation of China.

Insighting Drivers of Population Exposure to Ambient Ozone (O3) Concentrations across China Using a Spatiotemporal Causal Inference Method

Ground-level ozone (O3) is a well-known atmospheric pollutant aside from particulate matter. China as a global populous country is facing serious surface O3 pollution. To detect the complex spatiotemporal transformation of the population exposure to ambient O3 pollution in China from 2005 to 2019, the Bayesian multi-stage spatiotemporal evolution hierarchy model was employed. To insight the drivers of the population exposure to ambient O3 pollution in China, a Bayesian spatiotemporal LASSO regression model (BST-LASSO-RM) and a spatiotemporal propensity score matching (STPSM) were firstly applied; then, a spatiotemporal causal inference method integrating the BST-LASSO-RM and STPSM was presented. The results show that the spatial pattern of the annual population-weighted ground-level O3 (PWGLO3) concentrations, representing population exposure to ambient O3, in China has transformed since 2014. Most regions (72.2%) experienced a decreasing trend in PWGLO3 pollution in the early stage, but in the late stage, most areas (79.3%) underwent an increasing trend. Some drivers on PWGLO3 concentrations have partial spatial spillover effects. The PWGLO3 concentrations in a region can be driven by this region’s surrounding areas’ economic factors, wind speed, and PWGLO3 concentrations. The major drivers with six local factors in 2005–2014 changed to five local factors and one spatial adjacent factor in 2015–2019. The driving of the traffic and green factors have no spatial spillover effects. Three traffic factors showed a negative driving effect in the early stage, but only one, bus ridership per capita (BRPC), retains the negative driving effect in the late stage. The factor with the maximum driving contribution is BRPC in the early stage, but PM2.5 pollution in the late stage, and the corresponding driving contribution is 17.57%. Green area per capita and urban green coverage rates have positive driving effects. The driving effects of the climate factors intensified from the early to the later stage.

The Spatio-Temporal Evolution and Driving Factors of High-Quality Development in the Yellow River Basin during the Period of 2010–2022

The Yellow River Basin is an important ecological barrier and economic development area in China, but it faces some problems such as the degradation of its ecological quality and a lagging economic level. Promoting the high-quality development of the Yellow River Basin is the only way for China’s economic construction to enter into high-quality development, and an objective evaluation of the development quality of the study area is the premise for effectively improving this development quality. Based on panel data during the period of 2010–2022, a framework of drivers, pressures, state, impact, and a response model was used to build an index system. The index of high-quality development for each province in the Yellow River Basin was calculated using the entropy TOPSIS model. Further, the descriptive statistics method and standard deviation ellipse were applied to analyze the spatio-temporal characteristics of high-quality development in the study area, and the geographical detector and spatio-temporal geographical weighted regression model were employed to reveal the driving factors for this high-quality development in the Yellow River Basin. The results showed that (1) the high-quality development index of the Yellow River Basin was steadily improved over the study period, with an average annual growth rate of 3.024%. (2) The high-value area of the high-quality development level in the study area was distributed from northwest to southeast, with the high values of each subsystem tending to be spatially stable, as well as the spatial differences of the subsystems increasing. (3) The proportion of tertiary industry, per capita disposable income, rural–urban income ratio, per capita GDP, per capita highway mileage, and population were the main factors affecting the spatio-temporal evolution of high-quality development level in the Yellow River Basin, with average q values of 0.867, 0.938, 0.852, 0.781, 0.842, and 0.763, respectively. (4) Except for the negative effect of per capita GDP, the other five driving factors all had positive effects on the high-quality development level, with average values of 0.044, 0.068, 0.227, 0.064, and 0.215, respectively.

The utility of a Bayesian predictive model to forecast neuroinvasive West Nile virus disease in the United States of America, 2022.

Arboviruses (arthropod-borne-viruses) are an emerging global health threat that are rapidly spreading as climate change, international business transport, and landscape fragmentation impact local ecologies. Since its initial detection in 1999, West Nile virus has shifted from being a novel to an established arbovirus in the United States of America. Subsequently, more than 25,000 cases of West Nile neuro-invasive disease have been diagnosed, cementing West Nile virus as an arbovirus of public health importance. Given its novelty in the United States of America, high-risk ecologies are largely underdefined making targeted population-level public health interventions challenging. Using the Centers for Disease Control and Prevention ArboNET neuroinvasive West Nile virus data from 2000-2021, this study aimed to predict neuroinvasive West Nile virus human cases at the county level for the contiguous USA using a spatio-temporal Bayesian negative binomial regression model. The model includes environmental, climatic, and demographic factors, as well as the distribution of host species. An integrated nested Laplace approximation approach was used to fit our model. To assess model prediction accuracy, annual counts were withheld, forecasted, and compared to observed values. The validated models were then fit to the entire dataset for 2022 predictions. This proof-of-concept mathematical, geospatial modelling approach has proven utility for national health agencies seeking to allocate funding and other resources for local vector control agencies tackling West Nile virus and other notifiable arboviral agents.

Modelling drivers of trawl fisheries discards using Bayesian spatio-temporal models

Effective spatial fisheries management requires a proper understanding of the spatial distribution of both target species and discards. Also, spatial modelling of fishery-dependent data is an effective tool to capture uncertainties in data-limited situations. This study analyses the drivers behind discarding by comparing the standardising properties of three different components: Total Discards, Discards Per Unit of Effort and Total Discard Ratio. These metrics were analysed by means of Bayesian hierarchical spatio-temporal Gamma regression models to correctly to identify areas with high discards values that are characterized as discards hotspots. Our results showed that Total Discards is the component which better quantified the aggregated ecological impact of discarding practices, whereas Total Discard Ratio and Discards Per Unit of Effort identify complementary issues of benefits versus loss of biomass. Spatial maps obtained by combining these three approaches are a powerful tool for the spatial management of discards.

Analysis of the injury severity of motor vehicle–pedestrian crashes at urban intersections using spatiotemporal logistic regression models

This paper conducted a comprehensive study on the injury severity of motor vehicle–pedestrian crashes at 489 urban intersections across a dense road network based on high-resolution accident data recorded by the police from 2010 to 2019 in Hong Kong. Given that accounting for the spatial and temporal correlations simultaneously among crash data can contribute to unbiased parameter estimations for exogenous variables and improved model performance, we developed spatiotemporal logistic regression models with various spatial formulations and temporal configurations. The results indicated that the model with the Leroux conditional autoregressive prior and random walk structure outperformed other alternatives in terms of goodness-of-fit and classification accuracy. According to the parameter estimates, pedestrian age, head injury, pedestrian location, pedestrian actions, driver maneuvers, vehicle type, first point of collision, and traffic congestion status significantly affected the severity of pedestrian injuries. On the basis of our analysis, a range of targeted countermeasures integrating safety education, traffic enforcement, road design, and intelligent traffic technologies were proposed to improve the safe mobility of pedestrians at urban intersections. The present study provides a rich and sound toolkit for safety analysts to deal with spatiotemporal correlations when modeling crashes aggregated at contiguous spatial units within multiple years.

Strengthening grassland carbon source and sink management to enhance its contribution to regional carbon neutrality

Grassland ecosystems are an important part of terrestrial ecosystems. It is of great significance to fully tap the potential of grassland carbon sinks to achieve the goal of carbon neutrality in the region and even worldwide. Previous studies have focused on analyzing the temporal and spatial distributions of grassland carbon sink capacity and lacked a systematic discussion of grassland carbon source/sink management. Taking three grasslands in the Inner Mongolia Autonomous Region of China as an example, this study characterized grassland carbon source/sink capacity through net ecosystem productivity (NEP), combined with a soil respiration model, Sen + MK (Mann-Kendall) trend test method and other methods, revealed the spatiotemporal distribution characteristics of NEP and carbon emissions (CEs) in the study area from 2000 to 2019, and used spatiotemporal and geographical weighted regression (GTWR) model to clarify the potential mechanism of grassland sink increase. Suggestions for grassland carbon source/sink management are also discussed. The study found that the CEs in the study area from 2000 to 2019 could be roughly divided into three stages: slow growth period (2000–2009), rapid growth period (2009–2011) and stable development period (2011–2019). Although grassland carbon sinks could not completely offset regional CEs, grassland ecosystems had a very large carbon sink potential, which could make important contributions to the realization of regional carbon neutrality goals. Grassland carbon sources/sinks were jointly affected by climate change and human activities, and the degree of impact varied from year to year, with the impact of human activities changing more significantly. On this basis, the study puts forward policy recommendations from four aspects: regional priority, dynamic monitoring and management, control of grazing intensity and implementation of ecological compensation, and it proposed a sustainable development pattern of “carbon trading-ecological compensation-grassland sink increase”. The research results have important practical guiding significance for the management of grassland sink increases and regional carbon neutral development strategies in Inner Mongolia under the background of climate change.

Spatio-temporal estimates of HIV risk group proportions for adolescent girls and young women across 13 priority countries in sub-Saharan Africa.

The Global AIDS Strategy 2021-2026 identifies adolescent girls and young women (AGYW) as a priority population for HIV prevention, and recommends differentiating intervention portfolios geographically based on local HIV incidence and individual risk behaviours. We estimated prevalence of HIV risk behaviours and associated HIV incidence at health district level among AGYW living in 13 countries in sub-Saharan Africa. We analysed 46 geospatially-referenced national household surveys conducted between 1999-2018 across 13 high HIV burden countries in sub-Saharan Africa. Female survey respondents aged 15-29 years were classified into four risk groups (not sexually active, cohabiting, non-regular or multiple partner[s] and female sex workers [FSW]) based on reported sexual behaviour. We used a Bayesian spatio-temporal multinomial regression model to estimate the proportion of AGYW in each risk group stratified by district, year, and five-year age group. Using subnational estimates of HIV prevalence and incidence produced by countries with support from UNAIDS, we estimated new HIV infections in each risk group by district and age group. We then assessed the efficiency of prioritising interventions according to risk group. Data consisted of 274,970 female survey respondents aged 15-29. Among women aged 20-29, cohabiting (63.1%) was more common in eastern Africa than non-regular or multiple partner(s) (21.3%), while in southern countries non-regular or multiple partner(s) (58.9%) were more common than cohabiting (23.4%). Risk group proportions varied substantially across age groups (65.9% of total variation explained), countries (20.9%), and between districts within each country (11.3%), but changed little over time (0.9%). Prioritisation based on behavioural risk, in combination with location- and age-based prioritisation, reduced the proportion of population required to be reached in order to find half of all expected new infections from 19.4% to 10.6%. FSW were 1.3% of the population but 10.6% of all expected new infections. Our risk group estimates provide data for HIV programmes to set targets and implement differentiated prevention strategies outlined in the Global AIDS Strategy. Successfully implementing this approach would result in more efficiently reaching substantially more of those at risk for infections.

Spatio-Temporal Trends in the Incidence of Type 2 Diabetes in Germany-Analysis of the Claims Data of 63 Million Persons With Statutory Health Insurance From 2014 to 2019.

There are no data on recent trends in the incidence rate of type 2 diabetes (T2D) in Germany. The aim of this study was to determine the sex-, age-, and region-specific trends in the T2D incidence rate between 2014 and 2019. Based on nationwide data from statutorily insured persons in Germany, negative binomial regression models were used to analyze age- and sex-specific trends in the T2D incidence rate. Ageand sex-adjusted trends were calculated for 401 administrative districts using a Bayesian spatiotemporal regression model. During the period concerned, approximately 450 000 new cases of T2D were observed each year among some 63 million persons. Taking all age groups together, the incidence rate decreased in both women and men, from 6.9 (95% confidence interval [6.7; 7.0]) and 8.4 [8.2; 8.6] respectively per 1000 persons in 2014 to 6.1 [5.9; 6.3] and 7.7 [7.5; 8.0] per 1000 persons in 2019. This corresponds to an annual reduction of 2.4% [1.5; 3.2] for women and 1.7% [0.8; 2.5] for men. The incidence rate increased in the age group 20-39 years. The age- and sex-adjusted incidence rate decreased in almost all districts, although regional differences persisted. The T2D incidence rate should be closely monitored to see whether the decreasing trend continues. One must not forget that the prevalence can rise despite decreasing incidence. For this reason, the findings do not necessarily mean a decrease in the disease burden of T2D and the associated demand on healthcare resources.

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48–69 % and 63–71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 μg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region.

Accurate intercensal estimates of energy access to track Sustainable Development Goal 7.

The online version contains supplementary material available at 10.1140/epjds/s13688-022-00371-5.