Current Status and Hospital-Level Differences in Care and Outcomes of Patients With Acute Non-ST-Segment Elevation Myocardial Infarction in China: Insights From China Acute Myocardial Infarction Registry

BackgroundWith the growing burden of non-ST-elevation myocardial infarction (NSTEMI), developing countries face great challenges in providing equitable treatment nationwide. However, little is known about hospital-level disparities in the quality of NSTEMI care in China. We aimed to investigate the variations in NSTEMI care and patient outcomes across the three hospital levels (province-, prefecture- and county-level, with decreasing scale) in China.MethodsData were derived from the China Acute Myocardial Infarction Registry on patients with NSTEMI consecutively registered between January 2013 and November 2016 from 31 provinces and municipalities throughout mainland China. Patients were categorized according to the hospital level they were admitted to. Multilevel generalized mixed models were fitted to examine the relationship between the hospital level and in-hospital mortality risk.ResultsIn total, 8,054 patients with NSTEMI were included (province-level: 1,698 patients; prefecture-level: 5,240 patients; county-level: 1,116 patients). Patients in the prefecture- and county-level hospitals were older, more likely to be female, and presented worse cardiac function than those in the province-level hospitals (P <0.05). Compared with the province-level hospitals, the rate of invasive strategies was significantly lower in the prefecture- and county-level hospitals (65.3, 43.3, and 15.4%, respectively, P <0.001). Invasive strategies were performed within the guideline-recommended timeframe in 25.4, 9.7, and 1.7% of very-high-risk patients, and 16.4, 7.4, and 2.4% of high-risk patients in province-, prefecture- and county-level hospitals, respectively (both P <0.001). The use of dual antiplatelet therapy in the county-level hospitals (87.2%) remained inadequate compared to the province- (94.5%, P <0.001) and prefecture-level hospitals (94.5%, P <0.001). There was an incremental trend of in-hospital mortality from province- to prefecture- to county-level hospitals (3.0, 4.4, and 6.9%, respectively, P-trend <0.001). After stepwise adjustment for patient characteristics, presentation, hospital facilities and in-hospital treatments, the hospital-level gap in mortality risk gradually narrowed and lost statistical significance in the fully adjusted model [Odds ratio: province-level vs. prefecture-level: 1.23 (0.73–2.05), P = 0.441; province-level vs. county-level: 1.61 (0.80–3.26), P = 0.182; P-trend = 0.246].ConclusionsThere were significant variations in NSTEMI presentation and treatment patterns across the three hospital levels in China, which may largely explain the hospital-level disparity in in-hospital mortality. Quality improvement initiatives are warranted, especially among lower-level hospitals.

Hospital management practices in county-level hospitals in rural China and international comparison

Background Although management is important in healthcare, low- and middle-income countries (LMICs) have little experience measuring the competence of hospital management. While improving hospital management is the main focus of hospital reform in China, few studies have empirically documented the baseline situation to inform policy design. Methods We assessed the management practices of county-level hospitals in Guizhou in southwest China during 2015. We used the Development World Management Survey (D-WMS) instrument to interview 273 managers in 139 hospitals. We scored the management practices of the sampled hospitals, overall and in four dimensions (operations, monitoring, targets, personnel management) and three processes (implementation, usage, monitoring). We then converted the scores to the WMS scale and compared these with data from two other LMICs and seven high-income countries (HICs). Results On a scale of 1 (‘worst practice’) to 5 (‘best practice’), the mean (SD) hospital D-WMS scores were 2.57 (0.46) overall; 2.71 (0.48), 2.64 (0.58), 2.40 (0.64), and 2.56 (0.40) for operation, monitoring, target, and personnel, respectively; and 2.43 (0.48), 2.62 (0.48), and 2.66 (0.47) for implementation, usage, and monitoring, respectively. After conversion to WMS scores, China ranked seventh of 10 countries, after six HICs and higher than one HIC and two other LMICs (Brazil and India). China ranked higher than the two LMICs in each of the four dimensional scores. Conclusions Chinese county-level hospitals should improve their low quality of management by prioritizing target-setting and process implementation, particularly in personnel management. Meanwhile, modern management training should be given to most clinical managers.

Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study

Background Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. Methods We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. Results Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90th and 10th percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. Conclusions County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.

Covid-19 Space-time Cluster Detection Using Retrospective Analysis

Background: As of the 31st of January 2021, there had been 102,399,513 confirmed cases of COVID-19 worldwide, with 2,217,005 deaths reported to WHOThe goal of this study is to uncover the spatiotemporal patterns of COVID 19 in Ethiopia, which will aid in the planning and implementation of essential preventative measures. Methods We obtained data on COVID 19 cases reported in Ethiopia from November 23 to December 29, 2021, from an Ethiopian health data website that is open to the public.Kulldorff's retrospective space-time scan statistics were utilized to detect the temporal, geographical, and spatiotemporal clusters of COVID 19 at the county level in Ethiopia, using the discrete Poisson probability model. Results: In Ethiopia, between November 23 and December 29, 2021, a total of 22,199 COVID 19 cases were reported.The COVID 19 cases in Ethiopia were strongly clustered in spatial, temporal, and spatiotemporal distribution, according to the results of Kulldorff's scan. statisticsThe most likely Spatio-temporal cluster (LLR = 70369.783209, RR = 412.48, P 0.001) was mostly concentrated in Addis Ababa and clustered between 2021/11/1 and 2021/11/30.Conclusion: From November 23 to December 29, 2021, this study found three large COVID 19 space-time clusters in Ethiopia, which could aid in future resource allocation in high-risk locations for COVID 19 management and prevention.

Financial Inclusion and Carbon Reduction: Evidence From Chinese Counties

With the improvement of inclusive financial system, China’s economy has made significant development and growth. It worth in-depth investigation on environmental impact of financial inclusion, since growing GDP usually accompanied by more intensive carbon emission. This paper aims to reveal whether financial inclusion contributes to the carbon reduction in China using county-level dataset. A fixed-effect panel regression approach is adopted to examine the impact of financial inclusion on county-level regional carbon emissions. The estimation results imply that financial inclusion plays an important role in reducing carbon emissions. The mediation effect analysis reveals two channels through which financial inclusion imposes negative impact on the level of regional carbon emissions. One is to elevate the carbon sequestration capacity by increasing vegetation coverage, and the other is to improve the industrial structure through enhanced financial support. In addition to being a bridge between economic opportunity and output, financial inclusion can also act as an effective measure for addressing climate change.

Beginning farmers' entry and exit: evidence from county level data

PurposeThis paper identifies factors that affect entry and exit of beginning, young and women farmers and ranchers.Design/methodology/approachThe empirical framework is fixed effects regression analysis that uses county level data to evaluate how barriers to entry, access to and use of credit, local economic environment, and climate affect entry and exit of Beginning Farmers and Ranchers (BFRs). The dataset is assembled from several sources matching the Census of Agriculture years for the period of 1997–2017.FindingsResults show that new farmers are more likely to enter in counties with more and smaller farms and with lower farm productivity, indicating that BFRs have the potential to improve the overall productivity in such counties if able to grow and succeed. The results also indicate that the high capital intensity nature of farming is an effective barrier to entry. BFRs are more likely to do better in counties where agriculture is more important to the economy and with more off-farm work opportunities. The net entry is positively associated with higher input/output price index and the use of insurance but is unaffected by government payments and farm and off-farm income. The authors observe substitutability between farming and alternative self-employment for more entrepreneurial young people. Net entry increases with availability of non-real-estate loans but decreases with real estate credit. Thus, for BFRs to acquire the assets needed to reach optimal scale, access to credit remains essential.Originality/valueThe authors are not aware of other work that estimates how barriers to entry and other economic factors including access to credit affect entry and exit of BFRs of various ages and young and women farmers using the Census of Agriculture data up to 2017.

Gene–environment interactions increase the risk of pediatric-onset multiple sclerosis associated with ozone pollution

Background: We previously reported a relationship between air pollutants and increased risk of pediatric-onset multiple sclerosis (POMS). Ozone is an air pollutant that may play a role in multiple sclerosis (MS) pathoetiology. CD86 is the only non-HLA gene associated with POMS for which expression on antigen-presenting cells (APCs) is changed in response to ozone exposure. Objectives: To examine the association between county-level ozone and POMS, and the interactions between ozone pollution, CD86, and HLA- DRB1*15, the strongest genetic variant associated with POMS. Methods: Cases and controls were enrolled in the Environmental and Genetic Risk Factors for Pediatric MS study of the US Network of Pediatric MS Centers. County-level-modeled ozone data were acquired from the CDC’s Environmental Tracking Network. Participants were assigned ozone values based on county of residence. Values were categorized into tertiles based on healthy controls. The association between ozone tertiles and having MS was assessed by logistic regression. Interactions between tertiles of ozone level and the GG genotype of the rs928264 (G/A) single nucleotide polymorphism (SNP) within CD86, and the presence of DRB1*15:01 ( DRB1*15) on odds of POMS were evaluated. Models were adjusted for age, sex, genetic ancestry, and mother’s education. Additive interaction was estimated using relative excess risk due to interaction (RERI) and attributable proportions (APs) of disease were calculated. Results: A total of 334 POMS cases and 565 controls contributed to the analyses. County-level ozone was associated with increased odds of POMS (odds ratio 2.47, 95% confidence interval (CI): 1.69–3.59 and 1.95, 95% CI: 1.32–2.88 for the upper two tertiles, respectively, compared with the lowest tertile). There was a significant additive interaction between high ozone tertiles and presence of DRB1*15, with a RERI of 2.21 (95% CI: 0.83–3.59) and an AP of 0.56 (95% CI: 0.33–0.79). Additive interaction between high ozone tertiles and the CD86 GG genotype was present, with a RERI of 1.60 (95% CI: 0.14–3.06) and an AP of 0.37 (95% CI: 0.001–0.75) compared to the lowest ozone tertile. AP results indicated that approximately half of the POMS risk in subjects can be attributed to the possible interaction between higher county-level ozone carrying either DRB1*15 or the CD86 GG genotype. Conclusions: In addition to the association between high county-level ozone and POMS, we report evidence for additive interactions between higher county-level ozone and DRB1*15 and the CD86 GG genotype. Identifying gene–environment interactions may provide mechanistic insight of biological processes at play in MS susceptibility. Our work suggests a possible role of APCs for county-level ozone-induced POMS risk.