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Lord J, Reid K, Duclos C, Mai A, Odoi A. Investigation of predictors of severity of diabetes complications among hospitalized patients with diabetes in Florida, 2016-2019. BMC Public Health 2023; 23:2424. [PMID: 38053065 PMCID: PMC10698929 DOI: 10.1186/s12889-023-17288-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 11/22/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND Severe diabetes complications impact the quality of life of patients and may lead to premature deaths. However, these complications are preventable through proper glycemic control and management of risk factors. Understanding the risk factors of complications is important in guiding efforts to manage diabetes and reduce risks of its complications. Therefore, the objective of this study was to identify risk factors of severe diabetes complications among adult hospitalized patients with diabetes in Florida. METHODS Hospital discharge data from 2016 to 2019 were obtained from the Florida Agency for Health Care Administration through a Data Use Agreement with the Florida Department of Health. Adapted Diabetes Complications Severity Index (aDCSI) scores were computed for 1,061,140 unique adult patients with a diagnosis of diabetes. Severe complications were defined as those with an aDCSI ≥ 4. Population average models, estimated using generalized estimating equations, were used to identify individual- and area-level predictors of severe diabetes complications. RESULTS Non-Hispanic Black patients had the highest odds of severe diabetes complications compared to non-Hispanic White patients among both males (Odds Ratio [OR] = 1.20, 95% Confidence Interval [CI]: 1.17, 1.23) and females (OR = 1.27, 95% CI: 1.23, 1.31). Comorbidities associated with higher odds of severe complications included hypertension (OR = 2.30, 95% CI: 2.23, 2.37), hyperlipidemia (OR = 1.29, 95% CI: 1.27, 1.31), obesity (OR = 1.24, 95% CI: 1.21, 1.26) and depression (OR = 1.09, 95% CI: 1.07, 1.11), while the odds were lower for patients with a diagnosis of arthritis (OR = 0.81, 95% CI: 0.79, 0.82). Type of health insurance coverage was associated with the severity of diabetes complications, with significantly higher odds of severe complications among Medicare (OR = 1.85, 95% CI: 1.80, 1.90) and Medicaid (OR = 1.83, 95% CI: 1.77, 1.90) patients compared to those with private insurance. Residing within the least socioeconomically deprived ZIP code tabulation areas (ZCTAs) in the state had a protective effect compared to residing outside of these areas. CONCLUSIONS Racial, ethnic, and socioeconomic disparities in the severity of diabetes complications exist among hospitalized patients in Florida. The observed disparities likely reflect challenges to maintaining glycemic control and managing cardiovascular risk factors, particularly for patients with multiple chronic conditions. Interventions to improve diabetes management should focus on populations with disproportionately high burdens of severe complications to improve quality of life and decrease premature mortality among adult patients with diabetes in Florida.
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Affiliation(s)
- Jennifer Lord
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, The University of Tennessee, Knoxville, TN, USA
| | - Keshia Reid
- Florida Department of Health, Tallahassee, FL, USA
| | - Chris Duclos
- Florida Department of Health, Tallahassee, FL, USA
| | - Alan Mai
- Florida Department of Health, Tallahassee, FL, USA
| | - Agricola Odoi
- Department of Biomedical and Diagnostic Sciences, College of Veterinary Medicine, The University of Tennessee, Knoxville, TN, USA.
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Stokes EK, Pickens CM, Wilt G, Liu S, David F. County-level social vulnerability and nonfatal drug overdose emergency department visits and hospitalizations, January 2018-December 2020. Drug Alcohol Depend 2023; 247:109889. [PMID: 37148633 DOI: 10.1016/j.drugalcdep.2023.109889] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/13/2023] [Accepted: 04/16/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND Nonfatal drug overdoses (NFODs) are often attributed to individual behaviors and risk factors; however, identifying community-level social determinants of health (SDOH) associated with increased NFOD rates may allow public health and clinical providers to develop more targeted interventions to address substance use and overdose health disparities. CDC's Social Vulnerability Index (SVI), which aggregates social vulnerability data from the American Community Survey to produce ranked county-level vulnerability scores, can help identify community factors associated with NFOD rates. This study aims to describe associations between county-level social vulnerability, urbanicity, and NFOD rates. METHODS We analyzed county-level 2018-2020 emergency department (ED) and hospitalization discharge data submitted to CDC's Drug Overdose Surveillance and Epidemiology system. Counties were ranked in vulnerability quartiles based on SVI data. We used crude and adjusted negative binomial regression models, by drug category, to calculate rate ratios and 95% confidence intervals comparing NFOD rates by vulnerability. RESULTS Generally, as social vulnerability scores increased, ED and hospitalization NFOD rates increased; however, the magnitude of the association varied across drugs, visit type, and urbanicity. SVI-related theme and individual variable analyses highlighted specific community characteristics associated with NFOD rates. CONCLUSIONS The SVI can help identify associations between social vulnerabilities and NFOD rates. Development of an overdose-specific validated index could improve translation of findings to public health action. The development and implementation of overdose prevention strategies should consider a socioecological perspective and address health inequities and structural barriers associated with increased risk of NFODs at all levels of the social ecology.
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Affiliation(s)
- Erin K Stokes
- Centers for Disease Control and Prevention, Division of Overdose Prevention, 4770 Buford Hwy MS-S106-8, Atlanta, GA30341, USA.
| | - Cassandra M Pickens
- Centers for Disease Control and Prevention, Division of Overdose Prevention, 4770 Buford Hwy MS-S106-8, Atlanta, GA30341, USA
| | - Grete Wilt
- Agency for Toxic Substances and Disease Registry, 4470 Buford Hwy NE, Atlanta, GA30341, USA
| | - Stephen Liu
- Centers for Disease Control and Prevention, Division of Overdose Prevention, 4770 Buford Hwy MS-S106-8, Atlanta, GA30341, USA
| | - Felicita David
- Centers for Disease Control and Prevention, Division of Overdose Prevention, 4770 Buford Hwy MS-S106-8, Atlanta, GA30341, USA
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Chen ZY, Deng XY, Zou Y, He Y, Chen SJ, Wang QT, Xing DG, Zhang Y. A Spatio-temporal Bayesian model to estimate risk and influencing factors related to tuberculosis in Chongqing, China, 2014-2020. Arch Public Health 2023; 81:42. [PMID: 36945028 PMCID: PMC10031926 DOI: 10.1186/s13690-023-01044-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 02/16/2023] [Indexed: 03/23/2023] Open
Abstract
BACKGROUND Tuberculosis (TB) is a serious infectious disease that is one of the leading causes of death worldwide. This study aimed to investigate the spatial and temporal distribution patterns and potential influencing factors of TB incidence risk, and to provide a scientific basis for the prevention and control of TB. METHODS We collected reported cases of TB in 38 districts and counties in Chongqing from 2014 to 2020 and data on environment, population characteristics and economic factors during the same period. By constructing a Bayesian spatio-temporal model, we explored the spatio-temporal distribution pattern of TB incidence risk and potential influencing factors, identified key areas and key populations affected by TB, compared the spatio-temporal distribution characteristics of TB in populations with different characteristics, and explored the differences in the influence of various social and environmental factors. RESULTS The high-risk areas for TB incidence in Chongqing from 2014 to 2020 were mainly concentrated in southeastern and northeastern regions of Chongqing, and the overall relative risk (RR) of TB showed a decreasing trend during the study period, while RR of TB in main urban area and southeast of Chongqing showed an increasing trend. The RR of TB was relatively high in the main urban area for the female population and the population aged 0-29 years, and the RR of TB for the population aged 30-44 years in the main urban area and the population aged 60 years or older in southeast of Chongqing had an increasing trend, respectively. For each 1 μg/m3 increase in SO2 and 1% increase in the number of low-income per 1000 non-agricultural households (LINA per 1000 persons), the RR of TB increased by 0.35% (95% CI: 0.08-0.61%) and 0.07% (95% CI: 0.05-0.10%), respectively. And LINA per 1000 persons had the greatest impact on the female population and the over 60 years old age group. Although each 1% increase in urbanization rate (UR) was associated with 0.15% (95% CI: 0.11-0.17%) reduction in the RR of TB in the whole population, the RR increased by 0.18% (95% CI: 0.16-0.21%) in the female population and 0.37% (95% CI: 0.34-0.45%) in the 0-29 age group. CONCLUSION This study showed that high-risk areas for TB were concentrated in the southeastern and northeastern regions of Chongqing, and that the elderly population was a key population for TB incidence. There were spatial and temporal differences in the incidence of TB in populations with different characteristics, and various socio-environmental factors had different effects on different populations. Local governments should focus on areas and populations at high risk of TB and develop targeted prevention interventions based on the characteristics of different populations.
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Affiliation(s)
- Zhi-Yi Chen
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Xin-Yi Deng
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Yang Zou
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Ying He
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Sai-Juan Chen
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Qiu-Ting Wang
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China
| | - Dian-Guo Xing
- Office of Health Emergency, Chongqing Municipal Health Commission, No.6, Qilong Road, Yubei District, Chongqing, 401147, China.
| | - Yan Zhang
- School of Public Health, Chongqing Medical University, Chongqing, 400016, China.
- Research Center for Medicine and Social Development, Chongqing Medical University, Chongqing, 400016, China.
- Innovation Center for Social Risk Governance in Health, Chongqing Medical University, Chongqing, 400016, China.
- Research Center for Public Health Security, Chongqing Medical University, Chongqing, 400016, China.
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Acharya A, Izquierdo AM, Gonçalves SF, Bates RA, Taxman FS, Slawski MP, Rangwala HS, Sikdar S. Exploring county-level spatio-temporal patterns in opioid overdose related emergency department visits. PLoS One 2022; 17:e0269509. [PMID: 36584000 PMCID: PMC9803238 DOI: 10.1371/journal.pone.0269509] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 12/13/2022] [Indexed: 12/31/2022] Open
Abstract
Opioid overdoses within the United States continue to rise and have been negatively impacting the social and economic status of the country. In order to effectively allocate resources and identify policy solutions to reduce the number of overdoses, it is important to understand the geographical differences in opioid overdose rates and their causes. In this study, we utilized data on emergency department opioid overdose (EDOOD) visits to explore the county-level spatio-temporal distribution of opioid overdose rates within the state of Virginia and their association with aggregate socio-ecological factors. The analyses were performed using a combination of techniques including Moran's I and multilevel modeling. Using data from 2016-2021, we found that Virginia counties had notable differences in their EDOOD visit rates with significant neighborhood-level associations: many counties in the southwestern region were consistently identified as the hotspots (areas with a higher concentration of EDOOD visits) whereas many counties in the northern region were consistently identified as the coldspots (areas with a lower concentration of EDOOD visits). In most Virginia counties, EDOOD visit rates declined from 2017 to 2018. In more recent years (since 2019), the visit rates showed an increasing trend. The multilevel modeling revealed that the change in clinical care factors (i.e., access to care and quality of care) and socio-economic factors (i.e., levels of education, employment, income, family and social support, and community safety) were significantly associated with the change in the EDOOD visit rates. The findings from this study have the potential to assist policymakers in proper resource planning thereby improving health outcomes.
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Affiliation(s)
- Angeela Acharya
- Department of Computer Science, George Mason University, Fairfax, VA, United States of America
- * E-mail:
| | - Alyssa M. Izquierdo
- Clinical Psychology, George Mason University, Fairfax, VA, United States of America
| | | | - Rebecca A. Bates
- School of Nursing, George Mason University, Fairfax, VA, United States of America
| | - Faye S. Taxman
- Schar School of Policy and Government, George Mason University, Fairfax, VA, United States of America
| | - Martin P. Slawski
- Department of Statistics, George Mason University, Fairfax, VA, United States of America
| | - Huzefa S. Rangwala
- Department of Computer Science, George Mason University, Fairfax, VA, United States of America
| | - Siddhartha Sikdar
- Department of Bioengineering, George Mason University, Fairfax, VA, United States of America
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