Small area estimation of county-level U.S. HIV-prevalent cases

Areas with high HIV prevalence: A spatial analysis of nationwide claims data in Germany

OBJECTIVE

We aimed to identify spatial clusters of high HIV prevalence in Germany.

METHODS

Using nationwide outpatient claims data comprising information of about 88% of the total German population (N = 72 041 683), we examined spatial variations and spatial clusters of high HIV prevalence at the district level (N = 401). People with HIV were identified using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10 codes) B20, B22, and B24 (HIV disease) documented as 'confirmed'.

RESULTS

Among 72 041 683 people with statutory health insurance in Germany in 2021, 72 636 had diagnosed HIV, which corresponds to a prevalence of 101 per 100 000 individuals (0.10%). Of these, 56 895 were males (78%). At a district level, the HIV prevalence varied by a factor of 32 between 13 in a rural district in Bavaria and 417 per 100 000 individuals in the German capital, Berlin. The spatial autocorrelation coefficient was 0.24 (p < 0.0001, Global Moran's I). Several high-prevalence spatial clusters of different sizes were identified, mostly located in western Germany. The largest cluster comprised eight districts in the southern part of Hesse, including the city of Frankfurt and the city of Mainz in Rhineland-Palatinate. The second cluster consisted of four districts in North Rhine-Westphalia, including the cities of Cologne and Düsseldorf. Two districts in southern Germany (Mannheim and Ludwigshafen) formed the third cluster. Only urban districts were observed in spatial clusters of high HIV prevalence.

CONCLUSIONS

The current study identified for the first time spatial clusters with high HIV prevalence in Germany. This understanding is of particular importance when planning the general and specialized medical care of patients with HIV and to support preventive measures.

Estimates of Childhood Overweight and Obesity at the Region, State, and County Levels: A Multilevel Small-Area Estimation Approach

Local-level childhood overweight and obesity data are often used to implement and evaluate community programs, as well as allocate resources to combat overweight and obesity. The most current substate estimates of US childhood obesity use data collected in 2007. Using a spatial multilevel model and the 2016 National Survey of Children's Health, we estimated childhood overweight and obesity prevalence rates at the Census regional division, state, and county levels using small-area estimation with poststratification. A sample of 24,162 children aged 10-17 years was used to estimate a national overweight and obesity rate of 30.7% (95% confidence interval: 27.0%, 34.9%). There was substantial county-to-county variability (range, 7.0% to 80.9%), with 31 out of 3,143 counties having an overweight and obesity rate significantly different from the national rate. Estimates from counties located in the Pacific region had higher uncertainty than other regions, driven by a higher proportion of underrepresented sociodemographic groups. Child-level overweight and obesity was related to race/ethnicity, sex, parental highest education (P < 0.01 for all), county-level walkability (P = 0.03), and urban/rural designation (P = 0.02). Overweight and obesity remains a vital issue for US youth, with substantial area-level variability. The additional uncertainty for underrepresented groups shows surveys need to better target diverse samples.

What is the place for space in epidemiology?

At the heart of spatial epidemiology is the need to describe and understand variation in population health. In this review and introduction to the themed issue on "Spatial Analysis and GIS in Epidemiology," we present theoretical foundations and methodological developments in spatial epidemiology, discuss spatial analytical techniques and their public health applications, and identify novel data sources and applications with the potential to make epidemiology more consequential. Challenges with using georeferenced data are also explored, including dealing with small sample sizes, missingness, generalizability, and geographic scale. Given the increasing availability of spatial data and visualization tools, we have an opportunity to overcome traditionally siloed fields and practice settings to advance knowledge and more appropriately respond to emerging public health crises.