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Ridosh MM, Adams W, Magaña F, Sawin KJ, Holmbeck GN. Trajectories of self-management and independence in youth with spina bifida: Demographic predictors of growth. Child Care Health Dev 2023; 49:508-517. [PMID: 36206540 PMCID: PMC10079778 DOI: 10.1111/cch.13065] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/30/2022] [Indexed: 11/26/2022]
Abstract
AIM The purpose of this study was to examine the trajectories of condition and independent living self-management in youth with spina bifida (SB). METHODS A diverse sample of adolescents and young adults (AYAs) with SB completed the Adolescent/Young Adult Self-Management and Independence Scale (AMIS-II) across four time points. Parents reported on demographic characteristics including age, sex, race/ethnicity, and family income. Growth in self-management and its subscales (condition and independent living) were estimated using linear mixed-effect models as a function of respondents' demographics. RESULTS This study included 99 respondents age 18 to 27 years old. About half were female (52.5%) and White (52.5%); 15.2% were Black, and about a third were Hispanic/Latino (32.3%). Eighty-seven AYAs (87.9%) had myelomeningocele. The lesion level was 31.3% sacral, 48.5% lumbar and 18.2% thoracic. A third of the families earned less than 50K. Overall, self-management growth was dependent on age, sex, and race/ethnicity, but not income. Growth in condition self-management depended on sex; only males demonstrated increasing growth ( β ̂ = 0.11, p < 0.001). Black participants endorsed higher increasing total and condition self-management when compared with White ( β ̂ diff = 0.17 and 0.17, respectively, both p < 0.05) and Hispanic/Latino ( β ̂ diff = 0.18 and 0.21, respectively, both p = 0.02) respondents. CONCLUSION This study provides evidence of differences in growth of self-management by demographic/social determinants of health. Possible reasons for differences are discussed. Predictors of changes in self-management behaviours over time in young adults with SB can identify subgroups in need of further study.
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Affiliation(s)
- Monique M Ridosh
- Marcella Niehoff School of Nursing, Loyola University Chicago, Chicago, Illinois, USA
| | - William Adams
- Stritch School of Medicine, Loyola University Chicago, Chicago, Illinois, USA
| | - Fabiola Magaña
- Marcella Niehoff School of Nursing, Loyola University Chicago, Chicago, Illinois, USA
| | - Kathleen J Sawin
- Department of Nursing Research and Evidence-Based Practice, Children's Wisconsin, Milwaukee, Wisconsin, USA
- Self-Management Science Center, College of Nursing, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA
| | - Grayson N Holmbeck
- Department of Psychology, Loyola University Chicago, Chicago, Illinois, USA
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Bi Y, Wang Z, Feng S, Wang Y, Zhao Y, Li H, Yu J, Liu Q, Zhu C, Li M. Dynamic trajectory of platelet counts after the first cycle of induction chemotherapy in AML patients. BMC Cancer 2022; 22:477. [PMID: 35501722 PMCID: PMC9059911 DOI: 10.1186/s12885-022-09601-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 04/21/2022] [Indexed: 12/24/2022] Open
Abstract
Background Platelet counts varied over time after induction chemotherapy. We aimed to investigate the different trajectories of platelet counts after the first cycle of induction chemotherapy in patients newly diagnosed with acute myeloid leukemia. Methods and results In total, 149 individuals were included in this study. We identified four distinct trajectories using a group-based trajectory model: low- stability group (n = 27, 18.12%), low-level decrease–medium elevation group (n = 42, 28.19%), low-level decrease–high elevation group (n = 60, 40.27%), and high-level decrease–medium elevation group (n = 20, 13.42%). The baseline characteristics of the high-level decrease–medium elevation group included higher platelet count, lower white blood cell count, lower percentage of bone marrow blasts, and lower rates of complete remission after the first cycle of induction chemotherapy. Compared with the low-stability group, the hazard ratios were 0.32 (95% confidence interval, 0.15–0.68) for the low-level decrease–medium elevation group, 0.31 (95% confidence interval, 0.15–0.63) for the low-level decrease–high elevation group, and 0.35 (95% confidence interval, 0.13–0.89) for the high-level decrease–medium elevation group after adjustment for age and gender by Cox proportional hazard regression. Compared with the low-stability group, the hazard ratios were 0.33 (95% confidence interval, 0.14–0.77) for the low-level decrease–medium elevation group and 0.31 (95% confidence interval, 0.14–0.67) for the low-level decrease–high elevation group after adjustment for age, gender, white blood cell count, and bone marrow blasts. These associations persisted after adjusting for age, gender, white blood cell count, bone marrow blasts, and platelet count. Conclusion The dynamic trajectory of platelet counts after the first cycle of induction chemotherapy is a significant predictor of all-cause mortality in patients with acute myeloid leukemia. Timely intervention should be considered for the low-stability group. The low-level decrease–medium elevation and low-level decrease-high elevation groups were independent protective factors for all-cause mortality.
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Affiliation(s)
- Yazhen Bi
- Shandong Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.,Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, Shandong, China
| | - Zhaohui Wang
- Department of Hematology, Haici Medical Group Qingdao, Qingdao, Shandong, China
| | - Saran Feng
- Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, Shandong, China
| | - Yan Wang
- Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, Shandong, China
| | - Yang Zhao
- Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China
| | - Hong Li
- Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, Shandong, China
| | - Jingyi Yu
- Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, Shandong, China
| | - Qian Liu
- Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, Shandong, China
| | - Chuansheng Zhu
- Shandong Qianfoshan Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China. .,Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, Shandong, China.
| | - Mingzhuo Li
- Center for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, China.
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