Latent Class Analysis and Influencing Factors of Medication Adherence in Multiple Chronic Conditions Patients
Background The cases of multiple chronic conditions are increasing yearly, yet their medication adherence is unsatisfactory though taking medication as prescribed is recognized as the most effective measure to manage chronic diseases. To improve the prevention and control of chronic diseases, it is...
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| Format: | Article |
| Language: | zho |
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Chinese General Practice Publishing House Co., Ltd
2022-11-01
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| Series: | Zhongguo quanke yixue |
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| Online Access: | https://www.chinagp.net/fileup/1007-9572/PDF/zx20220340.pdf |
| _version_ | 1797216909726318592 |
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| author | ZHANG Zhenxiang, HE Fupei, ZHANG Chunhui, LIN Beilei, PING Zhiguang, GUO Huijuan |
| author_facet | ZHANG Zhenxiang, HE Fupei, ZHANG Chunhui, LIN Beilei, PING Zhiguang, GUO Huijuan |
| author_sort | ZHANG Zhenxiang, HE Fupei, ZHANG Chunhui, LIN Beilei, PING Zhiguang, GUO Huijuan |
| collection | DOAJ |
| description | Background The cases of multiple chronic conditions are increasing yearly, yet their medication adherence is unsatisfactory though taking medication as prescribed is recognized as the most effective measure to manage chronic diseases. To improve the prevention and control of chronic diseases, it is crucial to identify the causes and influencing factors of non-compliance in multiple chronic conditions patients. Objective To classify the medication adherence and to identify the associated factors of each class of medication adherence in multiple chronic conditions patients. Methods This investigation was conducted between July and September 2021 with a convenience sample of 267 inpatients from two tertiary A general hospitals of Henan Province using the Chinese version of Beliefs about Medicines (BMQ-C) , the Chinese version of 8-item Morisky Medication Adherence Scale (MMAS-8-C) , and the Medication Knowledge Scale (MKS) . Latent class analysis was used to classify the medication adherence. Demographic characteristics, medication use, medication knowledge and medication beliefs were compared by the class of medication adherence. Multiple Logistic regression was used to explore the associated factors of each class of medication adherence. Results The medication adherence of the participants was divided into three latent classes, namely subjective poor medication adherence, overall poor medication adherence, and overall good medication adherence, and the prevalence of the three classes was 18.0%, 34.4% and 47.6%, respectively. The education level, occupational status after an illness, living situation, household monthly income per person, financial resources, prevalence of having pharmacist guidance, number of medications, frequency of taking medication, years of taking medication, the BMQ-C score, and MKS score in the participants differed significantly by the class of medication adherence (P<0.05) . By multiple Logistic regression analysis, compared with patients with subjective poor medication adherence, those with overall good medication adherence had higher prevalence of having pharmacist guidance, and higher average scores of BMQ-C and MKS, and lower prevalence of retirement due to illness and offspring's support as the only financial resource (P<0.05) . Compared with those with overall poor medication adherence, those with overall good medication adherence had higher prevalence of retirees, taking medication once a day, and having pharmacist guidance, as well as higher average scores of BMQ-C and MKS (P<0.05) . Conclusion The medication adherence in these multiple chronic conditions patients could be classified into three latent classes. More attention should be given to those who were retired due to illness or financially supported by their children, because they were prone to having poor medication adherence. Those who had lower frequency of medication use, medication guidance from a pharmacist, and higher levels of medication knowledge and beliefs were prone to having good medication adherence. |
| first_indexed | 2024-04-24T11:53:27Z |
| format | Article |
| id | doaj.art-945fc42e23774d779007f757bbcf7e82 |
| institution | Directory Open Access Journal |
| issn | 1007-9572 |
| language | zho |
| last_indexed | 2024-04-24T11:53:27Z |
| publishDate | 2022-11-01 |
| publisher | Chinese General Practice Publishing House Co., Ltd |
| record_format | Article |
| series | Zhongguo quanke yixue |
| spelling | doaj.art-945fc42e23774d779007f757bbcf7e822024-04-09T06:14:02ZzhoChinese General Practice Publishing House Co., LtdZhongguo quanke yixue1007-95722022-11-0125313904391310.12114/j.issn.1007-9572.2022.0340Latent Class Analysis and Influencing Factors of Medication Adherence in Multiple Chronic Conditions PatientsZHANG Zhenxiang, HE Fupei, ZHANG Chunhui, LIN Beilei, PING Zhiguang, GUO Huijuan01.School of Nursing and Health, Zhengzhou University, Zhengzhou 450001, China;2.College of Public Health, Zhengzhou University, Zhengzhou 450001, China;3.Nursing Department, Luoyang Central Hospital Affiliated to Zhengzhou University, Luoyang 471009, ChinaBackground The cases of multiple chronic conditions are increasing yearly, yet their medication adherence is unsatisfactory though taking medication as prescribed is recognized as the most effective measure to manage chronic diseases. To improve the prevention and control of chronic diseases, it is crucial to identify the causes and influencing factors of non-compliance in multiple chronic conditions patients. Objective To classify the medication adherence and to identify the associated factors of each class of medication adherence in multiple chronic conditions patients. Methods This investigation was conducted between July and September 2021 with a convenience sample of 267 inpatients from two tertiary A general hospitals of Henan Province using the Chinese version of Beliefs about Medicines (BMQ-C) , the Chinese version of 8-item Morisky Medication Adherence Scale (MMAS-8-C) , and the Medication Knowledge Scale (MKS) . Latent class analysis was used to classify the medication adherence. Demographic characteristics, medication use, medication knowledge and medication beliefs were compared by the class of medication adherence. Multiple Logistic regression was used to explore the associated factors of each class of medication adherence. Results The medication adherence of the participants was divided into three latent classes, namely subjective poor medication adherence, overall poor medication adherence, and overall good medication adherence, and the prevalence of the three classes was 18.0%, 34.4% and 47.6%, respectively. The education level, occupational status after an illness, living situation, household monthly income per person, financial resources, prevalence of having pharmacist guidance, number of medications, frequency of taking medication, years of taking medication, the BMQ-C score, and MKS score in the participants differed significantly by the class of medication adherence (P<0.05) . By multiple Logistic regression analysis, compared with patients with subjective poor medication adherence, those with overall good medication adherence had higher prevalence of having pharmacist guidance, and higher average scores of BMQ-C and MKS, and lower prevalence of retirement due to illness and offspring's support as the only financial resource (P<0.05) . Compared with those with overall poor medication adherence, those with overall good medication adherence had higher prevalence of retirees, taking medication once a day, and having pharmacist guidance, as well as higher average scores of BMQ-C and MKS (P<0.05) . Conclusion The medication adherence in these multiple chronic conditions patients could be classified into three latent classes. More attention should be given to those who were retired due to illness or financially supported by their children, because they were prone to having poor medication adherence. Those who had lower frequency of medication use, medication guidance from a pharmacist, and higher levels of medication knowledge and beliefs were prone to having good medication adherence.https://www.chinagp.net/fileup/1007-9572/PDF/zx20220340.pdfmultiple chronic conditions|medication adherence|latent class analysis|root cause analysis |
| spellingShingle | ZHANG Zhenxiang, HE Fupei, ZHANG Chunhui, LIN Beilei, PING Zhiguang, GUO Huijuan Latent Class Analysis and Influencing Factors of Medication Adherence in Multiple Chronic Conditions Patients Zhongguo quanke yixue multiple chronic conditions|medication adherence|latent class analysis|root cause analysis |
| title | Latent Class Analysis and Influencing Factors of Medication Adherence in Multiple Chronic Conditions Patients |
| title_full | Latent Class Analysis and Influencing Factors of Medication Adherence in Multiple Chronic Conditions Patients |
| title_fullStr | Latent Class Analysis and Influencing Factors of Medication Adherence in Multiple Chronic Conditions Patients |
| title_full_unstemmed | Latent Class Analysis and Influencing Factors of Medication Adherence in Multiple Chronic Conditions Patients |
| title_short | Latent Class Analysis and Influencing Factors of Medication Adherence in Multiple Chronic Conditions Patients |
| title_sort | latent class analysis and influencing factors of medication adherence in multiple chronic conditions patients |
| topic | multiple chronic conditions|medication adherence|latent class analysis|root cause analysis |
| url | https://www.chinagp.net/fileup/1007-9572/PDF/zx20220340.pdf |
| work_keys_str_mv | AT zhangzhenxianghefupeizhangchunhuilinbeileipingzhiguangguohuijuan latentclassanalysisandinfluencingfactorsofmedicationadherenceinmultiplechronicconditionspatients |