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Tsai Y, Kuo T, Lin M, Shen F, Lin Y. Mandatory monthly outpatient visits could improve therapeutic inertia in patients with poorly controlled type 2 diabetes. J Diabetes Investig 2024; 15:227-236. [PMID: 37882416 PMCID: PMC10804897 DOI: 10.1111/jdi.14102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/30/2023] [Accepted: 10/10/2023] [Indexed: 10/27/2023] Open
Abstract
AIMS/INTRODUCTION Delayed intensification of treatment, or therapeutic inertia, increases the risk of diabetic complications and death. The aim of this study was to determine the effect of mandatory monthly outpatient visits on therapeutic inertia in patients with suboptimal control of type 2 diabetes. MATERIALS AND METHODS This retrospective cohort study used data from the Chang Gung Research Database and defined two study periods: the baseline period and the intervention period. The intervention period began when the Kaohsiung branch initiated a mandatory monthly outpatient visits program. Type 2 diabetes patients with baseline glycated hemoglobin (HbA1c) >7% and a follow-up HbA1c measurement were enrolled in each period, and divided into a Kaohsiung branch (intervention) group and the other branches (control) group. Therapy intensification was evaluated by comparing prescriptions after the follow-up HbA1c measurement with the prescriptions after the baseline HbA1c measurement. RESULTS A total of 5,045 patients at the Kaohsiung branch and 13,400 participants at other branches were enrolled in the baseline period; and 5,573 and 15,603 patients, respectively, were enrolled in the intervention period. The adjusted odds ratio (AOR) for therapy intensification in patients with baseline HbA1c ≥9% was not significantly higher at 1.21 (95% CI, 1.00-1.47) in the intervention period at the Kaohsiung branch, but was significantly higher (AOR, 1.53; 95% CI, 1.02-2.30) in the subgroup with worsened HbA1c. CONCLUSIONS Mandatory monthly outpatient visits could improve therapeutic inertia in patients with poorly controlled type 2 diabetes, especially in those with worsened control. The trajectory of HbA1c could significantly influence the assessment of the prevalence of therapeutic inertia.
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Affiliation(s)
- Ying‐Ying Tsai
- Division of Traditional Chinese MedicineChang Gung Memorial Hospital Chiayi BranchChiayiTaiwan
| | - Ting‐Yu Kuo
- Health Information and Epidemiology LaboratoryChang Gung Memorial Hospital Chiayi BranchChiayiTaiwan
| | - Meng‐Hung Lin
- Health Information and Epidemiology LaboratoryChang Gung Memorial Hospital Chiayi BranchChiayiTaiwan
| | - Feng‐Chih Shen
- Division of Endocrinology and Metabolism, Department of Internal MedicineKaohsiung Chang Gung Memorial HospitalKaohsiungTaiwan
| | - Yung‐Hsiang Lin
- Division of Endocrinology and MetabolismChang Gung Memorial Hospital Chiayi BranchChiayiTaiwan
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Fava MC, Reiff S, Azzopardi J, Fava S. Time trajectories of key cardiometabolic parameters and of cardiovascular risk in subjects with diabetes in a real world setting. Diabetes Metab Syndr 2023; 17:102777. [PMID: 37216853 DOI: 10.1016/j.dsx.2023.102777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 04/07/2023] [Accepted: 04/28/2023] [Indexed: 05/24/2023]
Abstract
BACKGROUND AND AIMS Diabetes is associated with increased cardiovascular risk. Glycated haemoglobin (HbA1c), lipid parameters and blood pressure are known risk factors for adverse outcome. The aim of the study was to explore the time trajectories of these key parameters and of the associated cardiovascular risk. METHODS We linked the diabetes electronic health records to the laboratory information system so as to investigate the trajectories of key metabolic parameters from 3 years prior to the diagnosis of diabetes to 10 years after diagnosis. We calculated the cardiovascular risk at the different time points during this period using the United Kingdom Prospective Study (UKPDS) risk engine. RESULTS The study included 21,288 patients. The median age at diagnosis was 56 years and 55.3% were male. There was a sharp decrease in HbA1c after diagnosis of diabetes, but there was a progressive rise thereafter. All lipid parameters after diagnosis also improved in the year of diagnosis, and these improvements persisted even up to 10 years post-diagnosis. There was no discernible trend in mean systolic or diastolic blood pressures following diagnosis of diabetes. There was a slight decrease in the UKPDS-estimated cardiovascular risk after diagnosis of diabetes followed by a progressive increase. Estimated glomerular filtration rate declined at an average rate of 1.33 ml/min/1.73 m2/year. CONCLUSIONS Our data suggest that lipid control should be tightened with increasing duration of diabetes since this is more readily achievable than HbA1c lowering and since other factors such as age and duration of diabetes are unmodifiable.
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Affiliation(s)
| | | | | | - Stephen Fava
- Mater Dei Hospital, Malta; University of Malta Medical School, Malta.
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Zhang L, Liu H, Zou Z, Su S, Ong JJ, Ji F, Cui F, Chan PL, Ning Q, Li R, Shen M, Fairley CK, Liu L, Seto WK, Wong WC. Shared-care models are highly effective and cost-effective for managing chronic hepatitis B in China: reinterpreting the primary care and specialty divide. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2023; 35:100737. [PMID: 37424676 PMCID: PMC10326699 DOI: 10.1016/j.lanwpc.2023.100737] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 02/17/2023] [Accepted: 02/23/2023] [Indexed: 03/22/2023]
Abstract
Background We evaluate the impact and cost-effectiveness of shared primary-specialty chronic hepatitis B (CHB) care models in China. Methods We constructed a decision-tree Markov model to simulate hepatitis B virus (HBV) disease progression in a cohort of 100,000 CHB individuals aged ≥18 years over their lifetime (aged 80). We evaluated the population impacts and cost-effectiveness in three scenarios: (1) status quo; (2) shared-care model with HBV testing and routine CHB follow-ups in primary care and antiviral treatment initiation in specialty care; and (3) shared-care model with HBV testing, treatment initiation and routine CHB follow-up in primary care and treatment for predetermined conditions in specialty care. We evaluated from a healthcare provider's perspective with 3% discounting rate and a willingness-to-pay (WTP) threshold of 1-time China's GDP. Findings Compared with status quo, scenario 2 would result in an incremental cost of US$5.79-132.43m but a net gain of 328-16,993 quality-adjusted life years (QALYs) and prevention of 39-1935 HBV-related deaths over cohort's lifetime. Scenario 2 was not cost-effective with a WTP of 1-time GDP per capita, but became cost-effective when treatment initiation rate increased to 70%. In contrast, compared with status quo, secnario 3 would save US$144.59-192.93m in investment and achieve a net gain of 23,814-30,476 QALYs and prevention of 3074-3802 HBV-related deaths. Improving HBV antiviral treatment initiation among eligible CHB individuals substantially improved the cost-effectiveness of the shared-care models. Interpretation Shared-care models with HBV testing, follow up and referring of predetermined conditions to specialty care at an appropriate time, especially antiviral treatment initiation in primary care, are highly effective and cost-effective in China. Funding National Natural Science Foundation of China.
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Affiliation(s)
- Lei Zhang
- China–Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, China
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
| | - Hanting Liu
- China–Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, China
| | - Zhuoru Zou
- China–Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, China
| | - Shu Su
- Clinical Research Management Office, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jason J. Ong
- China–Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, China
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
| | - Fanpu Ji
- Department of Infectious Diseases, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Fuqiang Cui
- School of Public Health, Peking University, 38 Xueyuan Road, Haidian District, Beijing, China
| | - Po-lin Chan
- Division of Communicable Disease, World Health Organization Western Pacific Regional Office, Manila, Philippines
| | - Qin Ning
- Department and Institute of Infectious Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Rui Li
- China–Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, China
| | - Mingwang Shen
- China–Australia Joint Research Centre for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Centre, Xi'an, China
- Key Laboratory for Disease Prevention and Control and Health Promotion of Shaanxi Province, Xi'an, Shaanxi, China
| | - Christopher K. Fairley
- Central Clinical School, Faculty of Medicine, Monash University, Melbourne, VIC, Australia
- Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health, Melbourne, VIC, Australia
| | - Lan Liu
- Chinese Medical Association Publishing House, 69 Dongheyanjie Street, XiCheng District, Beijing, China
| | - Wai-Kay Seto
- Department of Medicine, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Medicine, School of Clinical Medicine and State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong, China
| | - William C.W. Wong
- Department of Family Medicine and Primary Care, The University of Hong Kong–Shenzhen Hospital, Shenzhen, China
- Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China
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Predictors of Clinical Inertia and Type 2 Diabetes: Assessment of Primary Care Physicians and Their Patients. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19084436. [PMID: 35457303 PMCID: PMC9031531 DOI: 10.3390/ijerph19084436] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/03/2022] [Accepted: 04/05/2022] [Indexed: 11/24/2022]
Abstract
With the growing prevalence and complex pathophysiology of type 2 diabetes, many patients fail to achieve treatment goals despite guidelines and possibilities for treatment individualization. One of the identified root causes of this failure is clinical inertia. We explored this phenomenon, its possible predictors, and groups of patients affected the most, together with offering potential paths for intervention. Our research was a cross-sectional study conducted during 2021 involving 52 physicians and 543 patients of primary healthcare institutions in Belgrade, Serbia. The research instruments were questionnaires based on similar studies, used to collect information related to the factors that contribute to developing clinical inertia originating in both physicians and patients. In 224 patients (41.3%), clinical inertia was identified in patients with poor overall health condition, long diabetes duration, and comorbidities. Studying the changes made to the treatment, most patients (53%) had their treatment adjustment more than a year ago, with 19.3% of patients changing over the previous six months. Moreover, we found significant inertia in the treatment of patients using modern insulin analogues. Referral to secondary healthcare institutions reduced the emergence of inertia. This assessment of primary care physicians and their patients pointed to the high presence of clinical inertia, with an overall health condition, comorbidities, diabetes duration, current treatment, last treatment change, glycosylated hemoglobin and fasting glucose measuring frequency, BMI, patient referral, diet adjustment, and physician education being significant predictors.
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