TABLE 1.
A summarised overview of the selected studies and their characteristics (Netherlands, 2024).
| ID | Study | Geographical scope | Temporal scope | Health outcome | Results | Data source |
|---|---|---|---|---|---|---|
| Antibiotic Resistance | ||||||
| [34] | Deurenberg et al. 2005 | BE, NL, DE (NUTS-3) | 1999–2004 | Methicillin-resistant Staphylococcus aureus (MRSA) | Group Q strains were predominantly SCCmec type II and ST225, with new findings in Germany revealing ST225-MRSA-II and ST241-MRSA-III, and some strains had unique SCCmec type combinations, including previously undescribed profiles, which were linked to antibiotic susceptibility | Clinical MRSA isolates from participating hospitals |
| [44] | Dequeker et al., 2024 | BE, NL (NUTS-2) | 2018–2019 | Prevalance of faecal carriage of antimicrobial resistant bacteria | Prevalence of antibiotic-resistant bacteria (ESBL-E and CipR-E) was higher in Belgium compared to the Netherlands among children in daycare centers, with antimicrobial use and hospital admissions being significantly lower in the Netherlands. Risk factors included traveling to Asia and antimicrobial use, while cleaning practices helped reduce CipR-E carriage | Primary data collection of faecal samples from children in participating day care centres |
| [35] | Dik et al. 2016 | NL, DE (postcode-level) | 2010 | Antibiotic prescription rate | Antibiotic prescription rates differed between primary care patients in northern Netherlands (29.8%) and north-west Germany (38.9%) and notably higher second-generation cephalosporin usage among German children (25%) compared to Dutch children (<0.1%) | Secondary data of pharmacist (NL: IADB) and health insurance (DE: BARMER GEK) |
| [36] | Glasner et al. 2022 | NL, DE (hospital) | 2017–2018 | Multidrug-resistant organisms (MDRO) | MDRO prevalence, including MRSA, VRE, and 3GCRE, was higher in Germany than the Netherlands, with comparable CRE prevalence, likely influenced by distinct healthcare structures | Primary data collection of nasal and rectum swabs from participating hospitals and intensive care units |
| [37] | Jurke et al. 2019 | NL, DE (NUTS-3) | 2012–2016 | MRSA | Dutch and German hospitals differed significantly, with Germany having considerably higher MRSA rates and screening rates compared to the Netherlands | Primary data collection of MRSA-surveillance data from participating hospitals using a protocol adapted from the national German Nosomial Infectious Surveillance System |
| [38] | Köck et al. 2009 | NL, DE (hospitals) | 2006 (DE), 2007 (NL) | MRSA | MRSA prevalence is higher in regional German hospitals on admission, classical risk factors are effective in identifying MRSA patients, and livestock-associated MRSA lineage ST398 is prominent in Dutch and emerging in German isolates, with frequent transmission between regional German EUREGIO hospitals | Primary data collection through nasal swabs from participating hospitals |
| [39] | Paget et al. 2015 | NL (NUTS-2), DE (NUTS-3) | 2011–2012 | MRSA | The MRSA prevalence in community outpatient populations along the Dutch-German border was low, with similar livestock-associated MRSA patterns in GP patients from both countries but distinct spa types indicating healthcare-associated MRSA in German urologist outpatients | Primary data collection through nasal swabs and questionnaires by participating general practitioners (GPs) |
| [40] | van der Donk et al. 2012 | BE, NL, DE, (hospitals) | 2009–2011 | Prevalence of a bacteria that carries antibiotic resistance genes | Resistance in Dutch, Belgian, and German isolates differed significantly, with Belgium having the highest overall resistance and the Netherlands the lowest, while Germany exhibited the highest prevalence of ESBL-producing isolates | Primary data collection of E. coli isolates from urine samples of patients from participating hospitals |
| [41] | Van Der Donk et al. 2013 | BE, NL, DE | 2009–2012 | Prevalence of a bacteria that carries antibiotic resistance genes | Resistance prevalence among E. coli isolates in the Euroregion countries was similar, but it varied notably among different patient populations, with multiple clones identified via electrophoresis analysis, indicating the spread of resistant clones throughout the entire Euregion | Primary data collection of E. coli isolates from urine samples from patients attending urology services, general practitioners’ patients, and nursing home residents |
| [42] | Van Der Donk et al. 2013 | NL, DE (nursing homes) | 2009–2011 | Prevalence of methicillin-susceptible Staphylococcus aureus (MSSA) and MRSA | Staphylococcus aureus prevalence was higher in German nursing homes (39%) compared to Dutch ones (30%). German MSSA isolates showed greater resistance, and MRSA rates were also higher in Germany. Two MRSA clones spread within German nursing homes, while the MSSA population structure differed significantly between the Netherlands and Germany, suggesting limited cross-border spread | Primary data collection through nasal swabs from patients residing in participating nursing homes |
| [43] | Zhou et al. 2017 | NL, DE (hospitals) | 2010–2012 (control group), 2012–2013 | Prevalence of ESBL/pAmpC-Enditerobacteriaceae | Hospitals in the Northern Dutch-German region had similar prevalence of ESBL/pAmpC-Enterobacteriaceae, with slightly higher VRE rates in German hospitals, and few epidemiologically related ESBL-E. coli and VRE cases | Primary data collection through rectal swabs from hospitalized patients |
| COVID-19/Sars-CoV-2 | ||||||
| [45] | Chilla et al. 2022 | DE, CZ (NUTS-3), PL, NL, CH, FR, DK, AT (NUTS-2), BE (NUTS-0) | 2020–2021 | Incidences of COVID-19 cases | Border incidence types were identified, including symmetric, asymmetric without spillovers, and asymmetric with spillovers, and not all border controls effectively prevented spillover effects | Secondary data from the European Centre for Disease Prevention and Control, and the Swiss Federal Office of Public Health (Open data Source) |
| [48] | Grimée et al. 2022 | CH, IT (NUTS-2) | 2020 | COVID-19 cases | A counterfactual scenario of no Swiss-Italian border closure would have nearly doubled cumulative cases, while an earlier border closure only slightly reduced cases and delayed the epidemic by a few weeks | Secondary data from the Federal Office of Public Health for Swiss data and Presidency of the Council of Ministers - Civil Protection Department for Italian data |
| [46] | Mertel et al. 2023 | DE, CZ | 2021 | COVID-19 cases | The border showed an overall inhibitory effect, with stronger inhibition from Saxony to Czechia, marked spatial variation in disease spread inhibition along the border, and the Löbau area in Saxony emerged as a hotspot for cross-border disease transmission | Secondary data from the Saxony State Government for German data and the Ministry of Health of the Czech Republic for Czech data |
| [47] | Serwin et al. 2022 | DE (NUTS-1), PL (NUTS-3) | 2021 | SARS-CoV-2 transmission | Among non-Alpha lineages, 5.05% were binational clusters, 86.63% were German, and 8.32% were Polish; for B.1.1.7|Alpha variants, 13.11% were binational, 68.44% German, and 18.45% Polish, with transmission hubs in Saxony, West Pomerania, and Lower Silesia, reflecting viral dynamics in the border area, crucial for informing cross-border pandemic intervention policies | Secondary data of SARS-CoV-2 sequences by the Global Initiative on Sharing All Influenza Data database (GISAID) |
| Further Infectious Disease | ||||||
| [49] | Kozińska et al. 2016 | PL, CZ (NUTS-2), SK (NUTS-3) | 2007–2011 | Mycobacterium tuberculosis | Identifies six potential tuberculosis transmission outbreaks among patients of different nationalities but no clear epidemiological links, and the incidence of tuberculosis in Poland did not significantly affect the incidence in the Czech Republic or Slovakia | Primary data collection of microbiological data by laboratory staff, and patient documentation by clinicians from patients treated in participating healthcare centres |
| [50] | Stefanoff et al. 2014 | CZ, PL (district-level) | 1999–2008 | Tick-Borne Encephalitis and Lyme Borreliosis | Significant variations in disease incidence exist between neighbouring Czech Republic and Poland, persisting even after adjusting for natural disease gradients and population density, implying potential differences in surveillance system performance due to administrative borders not hindering zoonotic disease transmission | Secondary data by the National Institute of Public Health in Prague for Czech data and the National Institute of Public Health – National Institute of Hygiene in Warsaw for Polish data |
| Cancer Survival | ||||||
| [52] | Rudolph et al. 2021 | DE, DK (NUTS-2) | 2004–2013 | Breast cancer survival | Significant regional differences in breast cancer survival exist, with worse outcomes in Southern Denmark and Zealand compared to Schleswig-Holstein, largely attributed to variations in stage distribution and treatment administration, although these differences are expected to diminish in the future with Denmark’s national screening program and increased adjuvant cancer therapy usage | Secondary data by the Schleswig-Holstein Cancer registry for German data, and the Nordic statistical database NORDCAN and the Danish hospital for Danish data |
| [53] | Rudolph et al., 2023 | DE, DK (NUTS-2) | 2004–2016 | Colorectal cancer survival | While colorectal cancer survival improved in both the German and Danish regions from 2004 to 2016, the improvement was greater in Denmark. By 2014–2016, colon cancer survival was similar across regions, but rectal cancer survival was significantly better in Denmark | Secondary data by the Schleswig-Holstein Cancer registry for German data, and the Nordic statistical database NORDCAN and the Danish hospital for Danish data |
| [51] | Storm et al. 2015 | DE (NUTS-2), DK (NUTS-2) | 2004–2006, 2007–2009 | Colorectal cancer survival | Rectal cancer incidence and mortality rates were similar for both genders, though slightly higher in Zealand. In contrast, colon cancer was more common in Zealand, with significant differences. However, there were data quality issues in Schleswig-Holstein, highlighting the need for better patient information registration | Secondary data from the Cause of Death Register, Central Population Register and National Patient Register for Danish data, and the Statistical Office and Local Health Authorities for German data |
| Other health outcomes | ||||||
| [56] | Alkerwi et al. 2015 | LU (NUTS-0), BE (NUTS-1), FR (NUTS-2) | 2008–2012 | Physical Activity | Luxembourg had the highest adherence to physical activity recommendations (82%), with gender differences indicating more inactive women, while Lorraine and Wallonia had lower adherence compared to Luxembourg | Primary data collection through a population-based survey carried out by the NESCaV study (Nutrition, Environment and Cardiovascular Health) |
| [55] | Alonso-Sardón et al., 2023 | ES – PT (district-level) | 2020 | Prevalence of neurodegenerative diseases | Neurodegenerative diseases affected 1.85% of the population in the Spanish-Portuguese rural border region in 2020, with higher prevalence in Salamanca, Spain (2.51%), compared to Bragança (1.87%) and Guarda (1.66%) in Portugal. The prevalence was higher among females in both countries | Secondary data collection from regional health authorities. Electronic Clinical Record of Primary Care in Spain and Sistema de Informacao das Administraciones Regionais de Saude in Protugal |
| [54] | Nonnenmacher et al. 2021 | FR (NUTS-3), BE, DE, LU, CH | 2013–2018 | Perceived health and Physical health factors | Cross-border workers (CBWs) are generally healthier, with health disparities varying among CBW groups based on work destinations (with commuters to Luxembourg exhibiting the best health outcomes and those toward Germany the worst), suggesting that the spill over phenomenon assumption is not supported, and these disparities are more related to labour status than demographics | Primary data collection through Enquete Emploi, a French survey segment of the European Labour Force Survey |
| [57] | Fries et al. 2007 | BE, NL, DE (NUTS-3) | 2002 | Cardiopulmonary resuscitation (CPR) outcomes | CPR outcomes are similar among neighboring EMS systems, but neurological outcomes are influenced by various factors, and cross-border CPR assistance needs enhancement | Primary Data collection through protocol screening of EMS systems |