About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ > million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.
Citation: Carlos H. L. Cavalcante, Pedro E. O. Primo, Carlos A. F. Sales, Weslley L. Caldas, João H. M. Silva, Amauri H. Souza, Emmanuel S. Marinho, Roberto C. Pedrosa, João A. L. Marques, Hélcio S. Santos, João P. V. Madeiro. Sudden cardiac death multiparametric classification system for Chagas heart disease's patients based on clinical data and 24-hours ECG monitoring[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 9159-9178. doi: 10.3934/mbe.2023402
| [1] | XinQi Dong, Manrui Zhang . The Prevalence of Neurological Symptoms among Chinese Older Adults in the Greater Chicago Area. AIMS Medical Science, 2015, 2(1): 35-50. doi: 10.3934/medsci.2015.1.35 |
| [2] | Xinqi Dong, E-Shien Chang, Stephanie Bergren . The Prevalence of Musculoskeletal Symptoms among Chinese older Adults in the Greater Chicago Area—Findings from the PINE Study. AIMS Medical Science, 2014, 1(2): 87-102. doi: 10.3934/medsci.2014.2.87 |
| [3] | XinQi Dong . Addressing Health and Well-being of U.S. Chinese Older Adults through Community-Based Participatory Research: Introduction to the PINE Study. AIMS Medical Science, 2015, 2(3): 261-270. doi: 10.3934/medsci.2015.3.261 |
| [4] | Xinqi Dong, Manrui Zhang, Melissa A. Simon . Self-mastery among Chinese Older Adults in the Greater Chicago Area. AIMS Medical Science, 2014, 1(1): 57-72. doi: 10.3934/medsci.2014.1.57 |
| [5] | Xinqi Dong, E-Shien Chang, Melissa A. Simon . Sense of Community among Chinese Older Adults in the Greater Chicago Area: Findings from the PINE Study. AIMS Medical Science, 2014, 1(1): 28-39. doi: 10.3934/medsci.2014.1.28 |
| [6] | Ruijia Chen, XinQi Dong . The Prevalence and Correlates of Gambling Participation among Community-Dwelling Chinese Older Adults in the U.S.. AIMS Medical Science, 2015, 2(2): 90-103. doi: 10.3934/medsci.2015.2.90 |
| [7] | Manrui Zhang, Melissa A. Simon, Xinqi Dong . The Prevalence of Perceived Stress among U.S. Chinese Older Adults. AIMS Medical Science, 2014, 1(1): 40-56. doi: 10.3934/medsci.2014.1.40 |
| [8] | E-Shien Chang, Xinqi Dong . Personality Traits among Community-Dwelling Chinese Older Adults in the Greater Chicago Area. AIMS Medical Science, 2014, 1(2): 73-86. doi: 10.3934/Medsci.2014.2.73 |
| [9] | Ruijia Chen, Melissa A. Simon, Xinqi Dong . Gender Differences in Depressive Symptoms in U.S. Chinese Older Adults. AIMS Medical Science, 2014, 1(1): 13-27. doi: 10.3934/Medsci.2014.1.13 |
| [10] | Xinqi Dong, Ruijia Chen, Susan K. Roepke-Buehler . Characteristics Associated with Psychological, Physical, Sexual Abuse, Caregiver Neglect and Financial Exploitation in U.S. Chinese Older Adults: Findings from the Population-Based Cohort Study in the Greater Chicago Area. AIMS Medical Science, 2014, 1(2): 103-124. doi: 10.3934/medsci.2014.2.103 |
About 6.5 million people are infected with Chagas disease (CD) globally, and WHO estimates that $ > million people worldwide suffer from ChHD. Sudden cardiac death (SCD) represents one of the leading causes of death worldwide and affects approximately 65% of ChHD patients at a rate of 24 per 1000 patient-years, much greater than the SCD rate in the general population. Its occurrence in the specific context of ChHD needs to be better exploited. This paper provides the first evidence supporting the use of machine learning (ML) methods within non-invasive tests: patients' clinical data and cardiac restitution metrics (CRM) features extracted from ECG-Holter recordings as an adjunct in the SCD risk assessment in ChHD. The feature selection (FS) flows evaluated 5 different groups of attributes formed from patients' clinical and physiological data to identify relevant attributes among 57 features reported by 315 patients at HUCFF-UFRJ. The FS flow with FS techniques (variance, ANOVA, and recursive feature elimination) and Naive Bayes (NB) model achieved the best classification performance with 90.63% recall (sensitivity) and 80.55% AUC. The initial feature set is reduced to a subset of 13 features (4 Classification; 1 Treatment; 1 CRM; and 7 Heart Tests). The proposed method represents an intelligent diagnostic support system that predicts the high risk of SCD in ChHD patients and highlights the clinical and CRM data that most strongly impact the final outcome.
Grandparenting is perceived as a rewarding experience for many Chinese older adults [1]. Indeed, traditional Chinese values emphasize the importance of the multi-generational family and collective identity [2,3]. Traditional Confucian beliefs emphasize older adults and grandparents as important and valued due to their wisdom and experiences, which may lead to appreciation from children and positive grandparenting experiences [4]. Therefore, for many Chinese families, it is culturally relevant for grandparents to provide substantial care for their grandchildren [5]. For thousands of years, Chinese familial relations have been modeled off these Confucian ideas, and caring for grandchildren has traditionally been considered sacrosanct, especially for women [6]. Taking care of a grandchild is not merely a favor among family members, but often seen as necessary to bind together generations, preserving closeness and continuity of the family by maintaining reverence for the wisdom of elders [6]. Chinese elders are expected to provide care for their grandchildren because they are expected to pass down wisdom, advice, and life experiences.
However, in the United States, grandparenting is sometimes a taxing experience for older adults due to the overwhelming nature of caring for children, financial burden, diminishing time for self-care, and related psychological well-being effects [7,8]. The amount of grandparenting responsibility may be overwhelming for older adults who have less energy or preexisting health conditions [7]. Stress from grandparenting can also contribute to the worsening of preexisting health conditions [9,10,11]. Despite traditional Chinese values concerning the necessarily positive role of grandparenting in older adults’ lives, the reality may be much more complex.
In the U.S. Chinese aging population, not only do these individuals face the physical frailty that accompanies older age [12,13], but also there are social and cultural factors which contribute to their vulnerability for grandparenting burden. Though not all Chinese people immigrate to the U.S. for the same reasons, historical review has indicated that many Chinese immigrate to reunite with their families, often with expectation of co-residence and an active role in grandchild caregiving [14,15]. Migration to the U.S. may result in the erosion of Confucian values and/or disparities in cultural adherence between generations, which complicates and may increase grandparenting burden or decrease satisfaction [16,17]. As immigrants, Chinese older adults are more likely to experience intergenerational value discrepancies, as well [18]. Given their increased likelihood of living in intergenerational homes, Chinese older adults may be more vulnerable to grandparenting burden [19], issues concerning acculturation [20], and the dissolution of traditional Confucian values which emphasize reverence toward the elderly and grandparents [18]. Furthermore, there is evidence of these discrepancies and lack of adequate social support in Chinese older adult population [21,22,23].
Chinese older adults often make professional or social sacrifices to care for their grandchildren and thus may have an even more limited community support network [5]. Among grandparent caregivers in Taiwan, a majority of caregivers reported psychological distress, even though most had support from family members [24]. Asian Americans have described their caregiving responsibilities as confining, making them prone to isolation [25]. Despite these Confucian values emphasizing the positive reciprocity of grandparent care, it is unknown if these values are still germane to all generations involved in grandparent caregiving in the U.S.
Furthermore, the level of grandparenting burden among Chinese older adults in China, Taiwan, and Hong Kong is relatively unknown, despite extensive literature about grandparenting in China and its relationship to health [4,26]. Of quantitative research conducted in China, a few studies have indicated that grandparents provide substantial care for grandchildren [28,29,30]. Another quantitative study found that urban Chinese grandmothers did not endorse better well-being, perhaps due to accompanying caregiver burden [28]. An ethnographic study of grandparents in Xiamen, China highlighted diminishing rewards and prominent physical exhaustion despite a strong desire to adhere to cultural values of familial obligation [27]. Among grandparents in Hong Kong, researchers found significant endorsement of perceived level of grandparenthood stress related to relationship-oriented and grandparent role issues [31]. Also, in a comparison of grandparenting role “difficulties” between grandparents in the U.S. and China, Chinese grandparents are more likely to rate the grandparenting experience much lower than American grandparents [32]. In sum, though there is a growing body of research concerning grandparenting in China, grandparenting burden is more alluded to, than examined directly [33].
In addition, there is a dearth of research which examines the Chinese older adult grandparenting burden in the United States. Though there have been many studies examining negative outcomes of grandparenting, most quantitative studies examine African-American and Caucasian grandparents, especially in regard to custodial grandparenting roles [32,34,35]. There is very sparse knowledge regarding grandparenting burden among older Asian Americans [36]. Research about Asian American older adults and their grandparenting experience has highlighted both reward and burden, though through qualitative and ethnographic study design [5,25,37]. Given our knowledge of the unique immigration patterns, acculturative processes, and demographics of Chinese older adults in the U.S., we do not expect there to be an apt comparison in quantitative data between these listed populations and our study population.
Furthermore, it may be difficult to make claims about prevalence of grandparenting burden among Chinese older adults in the U.S. because of lack of data and incomparable measurements. There is a large variety of measurements used in grandparenting burden research, many of which indirectly measure burden. A few scales have been used to examine grandparenting burden: the Zarit Burden Interview [7] and a 9 item scale developed by Lawton and colleagues [8,38], both of which were used in Caucasian and African American populations. Other studies have looked at life satisfaction or quality of life among Chinese older adults in China and Taiwan to make inferences about grandparenting burden [24,33]. Stress has also been examined in relationship to grandparenting in Hong Kong [31], but stress has specific psychological implications which should not be inferred in examining perceived burden of grandparenting. In order to counter or validate the perception of universally positive grandparenting experiences among Chinese older adults in the U.S., it is necessary to examine the existence of any grandparenting burden using culturally relevant measurements.
To provide an overall approximation of grandparenting burden among Chinese older adults in the U.S., this study aims to 1) evaluate the prevalence of grandparenting burden and 2) examine the correlations between grandparenting burden, hours spent on grandparenting, socio-demographic characteristics, and self-reported health and quality of life measures.
The Population Study of Chinese Elderly in Chicago (PINE) is a population-based epidemiological study of U.S. Chinese older adults aged 60 and over in the greater Chicago area. The purpose of the PINE study is to collect community-level data of U.S. Chinese older adults to examine the key cultural determinants of health and well-being. The project was initiated by a synergistic community-academic collaboration among Rush Institute for Healthy Aging, Northwestern University, and many community-based social services agencies and organizations throughout the greater Chicago area [39].
In order to ensure study relevance to the well-being of the Chinese community and enhance community participation, the PINE study implemented culturally and linguistically appropriate community recruitment strategies strictly guided by a community-based participatory research (CBPR) approach. A Community Advisory Board (CAB) assisted in the recruitment of participants and determining the cultural and linguistic relevancy of study materials and helped to overcome related barriers to effective research [40,41,42]. Workshops were conducted to gain insight on relevant health topics among Chinese older adults [43]. Over twenty social services agencies, community centers, health advocacy agencies, faith-based organizations, senior apartments and social clubs serving as study recruitment sites, where eligible participants were approached during routine social service and outreach efforts serving Chinese Americans families in the Chicago city and suburban areas. All participants consented and were interviewed by trained bicultural research assistants in English or Chinese dialects, including Mandarin, Cantonese, Toisanese, and Teochow, according to respondents’ preference [44]. Out of 3, 542 eligible participants, 3, 159 agreed to participate in the study, yielding a response rate of 91.9%. The collected data on grandparenting burden items comes only from the 2, 146 participants who have grandchildren.
Based on the available census data drawn from U.S. Census 2010 and a random block census project conducted in the Chicago’s Chinese community, the PINE study is representative of the Chinese aging population in the greater Chicago area with respect to key demographic attributes [45], including age, sex, income, education, number of children, and country of origin. The study was approved by the Institutional Review Boards of the Rush University Medical Center.
Basic demographic information was collected, including age (in years), sex, education level, annual income (in USD), marital status, number of children, number of grandchildren, living arrangement, and country of origin. Immigration data relating to participants’ years spent living in the U.S. and years residing in their current community were also collected. Education level was assessed by asking participants their highest educational level completed in years. We created a dichotomous country of origin variable by categorizing respondents into the “China” group if they were born in mainland China and “other” group for other countries and regions. Living arrangement was assessed by asking participants how many people live in their household besides themselves and was categorized into four groups: (1) living alone; (2) living with 1-2 persons; (3) living with 2-3 persons; (4) living with 4 more persons. Self reported annual income included income from all sources, such as wages, salaries, social security or retirement benefits, help from relatives, rent from property, etc. Annual income was categorized into four groups: (1) 0−4, 999 per year; (2) 5,000−9, 999 per year; (3) 10,000−14,999peryear;and(4)morethan15, 000 per year. Language was assessed by ability to speak and preference of English, Cantonese, Mandarin, or Toisanese.
Overall health status was measured by “In general, how would you rate your health?” on a four point scale (1 = poor, 2 = fair, 3 = good, 4 = very good). Quality of life was assessed by asking “In general, how would you rate your quality of life?” on a four point scale (1 = poor, 2 = fair, 3 = good, 4 = very good). Health change in last year was measured by the question “Compared to one year ago, how would you rate your health now?” on a five point scale (1 = much worse; 2 = somewhat worse; 3 = about the same; 4 = somewhat better; and 5 = much better than one year ago). Health changes were then categorized into three groups: (1) improved health; (2) same health; and (3) worsened health.
Our study administered a four-question scale to assess grandparenting burden. This scale was co-developed by our bilingual research team and CAB members based on extensive literature search, as well as knowledge of the community health and cultural issues. The questions were first developed in Chinese and then translated into English. The term “burden” is used to contrast this negative grandparenting experience with grandparenting reward. Further,“burden” differs from “stress” due to the psychological implications of the latter. The questions have been developed to be culturally relevant to Chinese older adults in the U.S. We chose to ask “how often” rather than “do you feel” because we believe a binary of “yes” or “no” options would encourage Chinese older adults to say “no,” due to cultural values of saving face [13].
First, we asked how many hours were spent caring for grandchildren with the question,“On average, how many hours a week do you spend taking care of grandchildren?” and an integer amount was recorded. Then, we used a series of four questions to determine grandparenting burden among our participants on a five point Likert-type scale. Pressure to take care of grandchildren was measured by the question “How often do you feel pressured by your sons/daughters to take care of their children?” (0 = never, 1 = little, 2 = sometimes, 3 = often, 4 = always). Caregiving burden was measured by the question “How often do you feel it is a burden to take care of your grandchildren?” (0 = never, 1 = little, 2 = sometimes, 3 = often, 4 = always). Grandparenting effects on health were measured by the question “How often do you feel that your own health is negatively affected as a result of taking care of your grandchildren?” (0 = never, 1 = little, 2 = sometimes, 3 = often, 4 = always). Lastly, being prevented from seeing grandchildren was measured by the question “How often do you feel it that your son, son-in-law, daughter and/or daughter-in-law are intentionally preventing you from seeing/talking to your grandchildren?” (0 = never, 1 = little, 2 = sometimes, 3 = often, 4 = always).
Descriptive univariate statistics were used to summarize socio-demographic characteristics and the presence of prevalence of grandparenting burden among the sample population. Chi-squared tests were used to compare the bivariate socio-demographic differences between no grandparenting burden group and any grandparenting burden group. The Pearson Correlation coefficients were used to examine the correlations between socio-demographic variables, hours spent caring for grandchildren, and prevalence of grandparenting burden. Statistical analyses were conducted using SAS, Version 9.2 (SAS Institute Inc., Cary, NC).
Among 3, 159 participants enrolled in the PINE Study, 58.9% were female, 71.3% were married, and 85.1% had an annual income below 10,000.Outofthose3,159participants,2,146hadgrandchildren.Outoftheseparticipants,59.810, 000. Most participants (64.7%) did not spend any time caregiving for grandchildren; 9.0% of participants spent 1-10 hours taking care of grandchildren; 7.7% spent 11-20 hours taking care of grandchildren; 4.2% spent 21-30 hours taking care of grandchildren; and 14.4% spent 31 or more hours taking care of grandchildren. Around one in five (22%) of participants reported feeling any grandparenting burden as shown in Table 1. The highest prevalence among participants who experienced any grandparenting burden was exhibited in those ages 60-64 (22.3%,P < 0.001), who live with four or more people (34.0%,P < 0.001), who have lived 11-20 years in the US (33.5%,P < 0.001), born in China (96.6%,P < 0.001), and with a language preference of Cantonese (53.4%,P < 0.001). Those with a poor health status and those with a fair or poor quality of life constituted a higher percentage of participants with grandparenting burden versus those without (22.6% vs. 19.6%,P < 0.001 and 57.7% vs. 48.0%,P < 0.001, respectively).
| Any BurdenN = 470 | No BurdenN = 1676 | χ2 | d.f | P | |
| Age, N (%) | |||||
| 60–64 | 105 (22.3) | 270 (16.1) | |||
| 65–69 | 104 (22.1) | 306 (18.3) | |||
| 70–74 | 104 (22.1) | 345 (20.6) | |||
| 75–79 | 85 (18.1) | 325 (19.4) | |||
| 80+ | 72 (15.3) | 430 (25.7) | 28.5 | 4 | < 0.001 |
| Sex, N (%) | |||||
| Male | 170 (36.2) | 692 (41.3) | |||
| Female | 300 (63.8) | 984 (58.7) | 4.0 | 1 | 0.045 |
| Education (years), N (%) | |||||
| 0 | 46 (9.9) | 108 (6.5) | |||
| 1–6 | 188 (40.3) | 679 (41.7) | |||
| 7–12 | 163 (34.9) | 518 (31.0) | |||
| 13–16 | 63 (13.5) | 303 (18.1) | |||
| 17+ | 7 (1.5) | 44 (2.6) | 14.3 | 4 | 0.006 |
| Income (USD), N (%) | |||||
| 0–4, 999 | 173 (37.0) | 559 (33.6) | |||
| 5,000–9, 999 | 254 (54.3) | 889 (53.4) | |||
| 10,000–14, 999 | 29 (6.2) | 161 (9.7) | |||
| 15,000–19, 999 | 3 (0.6) | 27 (1.6) | |||
| $20, 000 and over | 9 (1.9) | 30 (1.8) | 8.8 | 4 | 0.07 |
| Marital Status, N (%) | |||||
| Married | 351 (74.7) | 1, 172 (70.1) | |||
| Separated | 8 (1.7) | 24 (1.4) | |||
| Divorced | 9 (1.9) | 34 (2.0) | |||
| Widowed | 102 (21.7) | 443 (26.5) | 4.6 | 3 | 0.20 |
| Living Arrangement, N (%) | |||||
| Living alone | 85 (18.1) | 354 (21.1) | |||
| 1 | 178 (37.9) | 703 (42.3) | |||
| 2–3 | 47 (10.0) | 210 (12.5) | |||
| 4 or more | 160 (34.0) | 402 (24.0) | 19.5 | 3 | < 0.001 |
| Number of Children, N (%) | |||||
| 0 | 10 (2.1) | 44 (2.6) | |||
| 1–2 | 155 (33.0) | 541 (32.3) | |||
| 3 and more | 305 (64.9) | 1, 091 (65.1) | 0.4 | 2 | 0.81 |
| Number of Grandchildren, N(%) | |||||
| 1–2 | 75 (16.0) | 279 (16.7) | |||
| 3 and more | 395 (84.0) | 1, 397 (83.4) | 0.1 | 1 | 0.72 |
| Years in the US, N (%) | |||||
| 0–10 | 112 (23.9) | 445 (27.3) | |||
| 11–20 | 157 (33.5) | 503 (23.5) | |||
| 21–30 | 114 (30.7) | 403 (24.1) | |||
| 31+ | 56 (11.9) | 309 (18.5) | 18.2 | 3 | < 0.001 |
| Years in the Community, N(%) | |||||
| 0–10 | 283 (60.5) | 984 (58.8) | |||
| 11–20 | 106 (22.7) | 355 (21.2) | |||
| 21–30 | 60 (12.8) | 216 (12.9) | |||
| 31+ | 19 (4.1) | 118 (7.1) | 5.6 | 3 | 0.13 |
| Country of Origin, N (%) | |||||
| China | 454 (96.6) | 1557 (92.9) | |||
| Other | 16 (3.4) | 119 (7.1) | 18.8 | 1 | < 0.001 |
| Language preference, N (%) | |||||
| English | 1 (0.2) | 13 (0.8) | |||
| Cantonese | 251 (53.4) | 855 (51.01) | |||
| Mandarin | 68 (14.5) | 403 (24.1) | |||
| Toisanese | 250 (31.9) | 405 (24.2) | 26.1 | 3 | < 0.001 |
| Overall Health Status, N (%) | |||||
| Very good | 7 (1.5) | 74 (4.4) | |||
| Good | 134 (28.5) | 619 (36.9) | |||
| Fair | 223 (47.5) | 654 (39.0) | |||
| Poor | 106 (22.6) | 329 (19.6) | 23.7 | 3 | < 0.001 |
| Quality of Life, N (%) | |||||
| Very good | 14 (3.0) | 114 (6.8) | |||
| Good | 185 (39.4) | 757 (45.2) | |||
| Fair | 258 (54.9) | 762 (45.5) | |||
| Poor | 13 (2.8) | 42 (2.5) | 18.8 | 3 | < 0.001 |
| Health Changes over the Last | |||||
| Year, N (%) | |||||
| Improved | 40 (8.5) | 134 (8.0) | |||
| Same | 198 (42.1) | 810 (48.4) | |||
| Worsened | 232 (49.4) | 731 (43.6) | 5.8 | 2 | 0.05 |
DownLoad: CSVGrandparenting burden scale item correlations were presented in Table 2. For all items the correlation coefficient (r) was between 0.07 and 0.56. The pressure to take care of grandchildren was correlated to feeling like it was a burden to take care of grandchildren at r = 0.56,P < 0.001. Feeling like it was a burden to take care of grandchildren was correlated to feeling like children or children’s spouses prevented participants from seeing their grandchildren at r = 0.07,P < 0.01. The raw alpha was α = 0.66
| Alpha if item deleted | 1 | 2 | 3 | 4 | |
| Alpha = 0.66, *P < 0.05, **P < 0.01, ***P < 0.001 | |||||
| 1. Felt pressured by children to take care of grandchildren | 0.50 | 1.0 | 1.0 | 1.0 | 1.0 |
| 2. Felt it is a burden to take care of grandchildren | 0.47 | 0.56*** | |||
| 3. Felt that health is negatively affected as a result of taking care of grandchildren | 0.52 | 0.44*** | 0.52*** | ||
| 4. Felt that children and/or children-in-law intentionally prevented you from seeing or talking to grandchildren | 0.74 | 0.07*** | 0.07** | 0.08*** | |
DownLoad: CSVThe frequency of the grandparenting burden items was presented in Table 3. Nearly one in five (18.9%) participants felt it was a burden to take care of grandchildren. Around one in ten (9.9%) participants felt pressured by children to take care of grandchildren. Similarly, around one in ten (10.2%) participants felt their health was negatively affected by taking care of their grandchildren. However, only 1% of participants felt like their children or children’s spouses intentionally prevented them from seeing or talking to grandchildren.
| Alpha if item deleted | 1 | 2 | 3 | 4 | |
| 1. Felt pressured by children to take care of grandchildren | 0.50 | 1.0 | 1.0 | 1.0 | 1.0 |
| 2. Felt it is a burden to take care of grandchildren | 0.47 | 0.56*** | |||
| 3. Felt that health is negatively affected as a result of taking care of grandchildren | 0.52 | 0.44*** | 0.52*** | ||
| 4. Felt that children and/or children-in-law intentionally prevented you from seeing or talking to grandchildren | 0.74 | 0.07*** | 0.07** | 0.08*** |
DownLoad: CSVFactors including age, living arrangement, country of origin, overall health status and quality of life were significantly correlated with grandparenting burden in Table 4. Older age (r = 0.12,P < 0.001), higher overall health status (r = 0.09,P < 0.001), higher quality of life (r = 0.09,P < 0.001) were associated with lower prevalence of grandparenting burden. Living with more people (r = 0.09,P < 0.001), being born in China (r = 0.06,P < 0.01), and preferring to speak Cantonese (r = 0.07,P < 0.01) were associated with higher prevalence of grandparenting burden. Greater number of hours spent caring for grandchildren (r = 0.37,P < 0.001) was correlated with higher prevalence of grandparenting burden.
| Age | Sex | Edu | Inc | MS | Living | Child | Grand Ch | Yrs in U.S. | Yrs in com | Origin | Lang Pref | OHS | QOL | HC | Grand | Hours | |
| Age | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| Sex | 0.01 | ||||||||||||||||
| Edu | -0.12*** | -0.21*** | |||||||||||||||
| Inc | 0.05** | 0.00 | 0.01 | ||||||||||||||
| MS | -0.33*** | -0.32*** | 0.22 | -0.03 | |||||||||||||
| Living | -0.35*** | -0.07*** | 0.02 | 0.16*** | 0.24 *** | ||||||||||||
| Child | 0.32*** | 0.09*** | -0.38*** | 0.00 | -0.13*** | -0.07*** | |||||||||||
| Grand Ch | 0.43*** | 0.12*** | -0.39*** | -0.02 | -0.18*** | -0.10*** | 0.72*** | ||||||||||
| Yrs in U.S. | 0.35*** | 0.03 | -0.10*** | 0.35*** | -0.2*** | -0.31*** | 0.15*** | 0.17*** | |||||||||
| Yrs in com | 0.23*** | 0.02 | -0.11*** | 0.24*** | -0.13*** | -0.18*** | 0.10*** | 0.10*** | 0.66*** | ||||||||
| Origin | 0.04* | -0.01 | -0.08*** | -0.20 | 0.05** | 0.05** | 0.04* | 0.06*** | -0.2*** | -0.15*** | |||||||
| Lang Pref | -0.02 | -0.01 | -0.56*** | -0.03 | -0.06*** | 0.06*** | 0.27*** | 0.25*** | 0.18*** | 0.20*** | 0.06** | ||||||
| OHS | 0.08*** | 0.06** | -0.06*** | -0.12*** | -0.05** | 0.00 | 0.00 | -0.02 | 0.01 | -0.05* | 0.03 | 0.01 | |||||
| QOL | -0.06*** | -0.05** | -0.09*** | -0.08*** | 0.03 | 0.01 | -0.04* | 0.05** | 0.00 | 0.02 | 0.04* | -0.12*** | 0.32*** | ||||
| HC | 0.11*** | 0.03 | -0.02 | -0.05** | -0.07*** | -0.01 | 0.02 | -0.05 | 0.04* | -0.03 | 0.00 | 0.03 | 0.35*** | 0.15*** | |||
| Grand | -0.12*** | 0.04* | -0.05* | -0.04 | 0.04* | 0.09*** | -0.01 | 0.04 | -0.03 | -0.04 | 0.06** | 0.07** | -0.09*** | -0.09*** | -0.03 | ||
| Hours | -0.32*** | 0.04 | 0.01 | -0.14*** | 0.09*** | 0.34*** | -0.10*** | -0.04* | -0.17*** | -0.11*** | 0.04* | 0.02 | 0.04* | 0.04* | 0.07*** | 0.37*** |
DownLoad: CSVAs the first population-based epidemiological study that reported the experience of grandparenting burden among U.S. Chinese older adults, the PINE Study indicates that grandparenting burden is experienced by one in five (22%) Chinese older adults in the Greater Chicago area. As reported, Chinese older adults are more likely to experience grandparenting burden if they are younger, live with more people, were born in China, have a poorer overall health status, a poorer quality of life, and spend more time caring for grandchildren. Among our study participants who are grandparents, nearly 65% do not spend any time caring for children, which is significantly more than a study among Chinese older adults in China has reported [29]. In a study of 90 grandmothers in the U.S., ages 39 to 82 years, who lived with at least one grandchild and participated in their care, 38% of grandmothers scored within the 90th percentile on the Parental Stress Index, indicating a clinically significant stress level [46]. However, current literature does not give comparable estimates about grandparenting burden in either the general US older population or specific ethnic/racial groups. In one of the few studies to directly measure burden, a majority of Caucasian and African American custodial grandmothers in skipped generation households indicated that their grandparenting responsibilities affected their social life, privacy, and energy levels [38]. However, since this study examined custodial grandmothers, and not a gradient of grandparenting among both genders, the lack of similar data indicates the need for research to evaluate the prevalence of grandparenting burden among general populations as well as specific ethnic groups across the U.S. However, it is known that there is a normative cultural expectation of finding grandparent caregiving necessarily rewarding [4,25,37], and our findings indicate a contradictory lived experience.
In our study, Chinese older adults experience more grandparenting burden if they are younger. This may occur for a variety of reasons: younger grandparents may have younger grandchildren who require more care, or caregiving responsibilities may interfere more with their livelihood/occupation. Indeed, there has been a growing trend among Chinese grandmothers in China to take up hobbies or new jobs in retirement, instead of solely taking care of grandchildren; this signals a trend that able-bodied, younger Chinese older adults have values beyond traditional Confucianism [6]. Our findings both support and contrast current literature. In a population study of over 13, 000 grandparents in the U.S., younger age was found to be an indicator of starting caregiving practices [47]. Younger grandparents may be more inexperienced and therefore, have greater difficulties caring for grandchildren. In a multi-generational study of Chinese families living in Taiwan, Chinese older grandparents aged 60 and up, compared to younger Chinese grandparents aged 59 or younger, rated themselves as being more successful at grandparenting, likely due to level of experience [32]. However, in a study of Hong Kong grandparents, stress was not significantly correlated with age of the grandparent [31]. As a result, future research should examine age in relationship to burden, taking into account occupational/social sacrifices and grandchildren’s ages, among Chinese older adults in the United States in order to ascertain a clearer picture of grandparenting burden prevalence and causation.
We found that Chinese older adults are more likely to experience grandparenting burden if they live in a household with at least four other people. Existing literature does not directly compare grandparenting burden with living arrangement by number of people. However, literature does indicate that grandparents who reside with their grandchildren experience higher levels of grandparenting stress [7,48]. We did not collect data on household composition, so we are unable to determine if grandparenting burden is related to co-residence with grandchildren. Still, given the traditional Chinese value of multigenerational households, it would be unsurprising if a living arrangement of four or more people reflected a three-generation household. U.S. Census reports have also indicated that Chinese older adults live with grandchildren at higher rates than African-American, Hispanic, Caucasian, and Japanese older adults [14]. Further, a study in urban China of grandparents found that co-residence and grandparenting responsibilities greatly limited their freedom and increased feelings of burden, despite a self-imposed sense of obligation [27].
Additionally, many Chinese older adults immigrate to the U.S. in order to be reunited and live with family [15], but experience ambivalence about their new roles. As reported by a study of Chinese American grandmothers in the U.S., over 40% of those participants reported experiencing language barriers with their grandchildren [37]. These intergenerational issues with grandchildren or children as a result of caregiving may be exacerbated if the individual resides with their grandchildren or children. These intergenerational, immigration-related barriers are especially relevant given that nearly all of our participants are immigrants. Future research should examine whether household composition, more specifically co-residing with grandchildren, influences levels of grandparenting burden among Chinese older adults and how this may be tied to intergenerational conflict or acculturative stress.
Our study found that Chinese older adults who reported poorer health status and quality of life are more likely to experience grandparenting burden. Our findings both confirm and conflict with existing literature about health and grandparenting correlations. In a study of Chinese grandparents in China who care for grandchildren, high intensity care for younger grandchildren accelerated health declines, while lighter levels of care had a protective effect on health [4]. In a longitudinal study of Taiwanese grandparents in Taiwan, caring for grandchildren showed improvements in self-rated health, especially for long-term caregivers [49]. Among the general U.S. older adult population, grandparents who care for their grandchildren were significantly more likely to report poorer mental health [50]. In one of the few studies of Chinese American grandmothers and the psychological effect of grandmothering, researchers found the presence of grandparenting challenges regarding isolation and generational differences [37]. A population study of grandparents in the U.S. found that effects of grandparenting caregiving on health were contingent on the context and circumstances of the care [51]. Given these diverse findings, future research should examine the mental and physical health of Chinese grandparents in the U.S. on multiple frequencies of care levels, using a longitudinal study design to note any possible changes due to differing grandparenting responsibility.
Lastly, our study found that Chinese older adults who spend more time caring for grandchildren are more likely to experience grandparenting-related burden. This is consistent with previous literatures. However, it is likely that amount of hours spent caring for grandchildren in relationship to grandparenting burden is not linear. As previous literatures about African American grandparents have suggested, there may be a cutoff point or ideal amount of time spent with grandchildren where rewards outweigh stresses [7,52,53]. Existing literature has yet to suggest or deny that this is a similar experience for Chinese grandparents. Future research should focus not only on older adults who provide extensive care for their grandchildren in relationship to burden-related outcomes, but also look at the spectrum of grandparent caregiving to determine correlations and causations between hours spent caregiving and mental health outcomes.
Our study is not without limitations. First, our study is representative of Chinese older adults in the US, but these findings may not be generalizable to the U.S. Chinese population or Chinese in Asia. There is a high degree of intra-group diversity within the Chinese ethnicity. Second, we did not record the ages of the grandchildren or household composition, which limits our knowledge about the type and intensity of caregiving Chinese older adults provide. Furthermore, we did not inquire about reasons why grandparents care for their grandchildren, whether it is due to traditional Chinese roles or financial difficulties, or other reasons. Even though research indicates cultural relevance of filial piety and grandparenting among Chinese immigrants, we did not ask participants about their individual expectations, family’s expectations, or cultural expectations of grandparenting responsibility. Also, given the cross-sectional nature of this study, we are both unable to provide causations of grandparent burden and examine if grandparent burden changes over time, or due to which factors. Grandparenting burden and reward are not exclusive [31], and in-depth qualitative research is likely necessary to have a more complete understanding of the grandparenting experience of Chinese older adults in the U.S.
Still, our study’s findings present important implications for future research. This study challenges the common perception that grandparenting is a universally rewarding experience for Chinese older adults due to cultural values. This indicates that research should focus on finding causation for grandparenting burden and the circumstances which differentiate grandparenting burden from grandparenting reward, which likely include criteria like living arrangement, age, and time spent caring for grandchildren. Given the majority of Chinese older adults are immigrants, the immigration and acculturation processes in relationship to grandparenting burden and responsibilities needs to be more closely examined. Further, our study indicates that health status, quality of life, and grandparenting burden are related, indicating that research should focus on the psychological, physical, and social effects of grandparenting burden.
Grandparenting within the Chinese older adult community is not a necessarily rewarding experience. Our study found that grandparenting burden is experienced by 1 in 5 Chinese older adults with grandchildren. Due to the deleterious effects of feelings of burden and stress on psychological and physical well-being, this is an area of concern. Our study found that certain subsets of the Chinese older adult population are more likely to experience grandparenting burden, including younger grandparents, those living with many people, those who spend more time caring for grandchildren, those who have poorer health, and those with a lower quality of life. These vulnerable populations should be the focus of future research to determine the causes of grandparenting burden, its specific effects on the health, and wellbeing of older adults.
We are grateful to Community Advisory Board members for their continued effort in this project. Particular thanks are extended to Bernie Wong, Vivian Xu, and Yicklun Mo with the Chinese American Service League (CASL); Dr. David Lee with the Illinois College of Optometry; David Wu with the Pui Tak Center; Dr. Hong Liu with the Midwest Asian Health Association; Dr. Margaret Dolan with John H. Stroger Jr. Hospital; Mary Jane Welch with the Rush University Medical Center; Florence Lei with the CASL Pine Tree Council; Julia Wong with CASL Senior Housing; Dr. Jing Zhang with Asian Human Services; Marta Pereya with the Coalition of Limited English Speaking Elderly; and Mona El-Shamaa with the Asian Health Coalition.
Dr. Dong was supported by National Institute on Aging grants (R01 AG042318, R01 MD006173, R01 CA163830, R34MH100443, R34MH100393 & RC4AG039085), Paul B. Beeson Award in Aging, The Starr Foundation, American Federation for Aging Research, John A. Hartford Foundation and The Atlantic Philanthropies.
Authors report no conflict of interest.
| [1] |
A. F. Members, S. G. Priori, C. Blomström-Lundqvist, A. Mazzanti, N. Blom, M. Borggrefe, et al., 2015 esc guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The task force for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death of the european society of cardiology (esc) endorsed by: Association for european paediatric and congenital cardiology (aepc), EP Europace, 17 (2015), 1601–1687. https://doi.org/10.1093/europace/euv319 doi: 10.1093/europace/euv319
|
| [2] |
A. S. Adabag, R. V. Luepker, V. L. Roger, B. J. Gersh, Sudden cardiac death: epidemiology and risk factors, Nat. Rev. Cardiol., 7 (2010), 216–225. https://doi.org/10.1038/nrcardio.2010.3 doi: 10.1038/nrcardio.2010.3
|
| [3] | World Health Organization, Fourth who report on neglected tropical diseases: Integrating neglected tropical diseases into global health and development, IV WHO Report on Neglected Tropical Diseases, 2017. Available from: https://apps.who.int/iris/handle/10665/255011. |
| [4] |
A. Rassi Jr, A. Rassi, W. C. Little, S. S. Xavier, S. G. Rassi, A. G. Rassi, et al., Development and validation of a risk score for predicting death in chagas' heart disease, N. Engl. J. Med., 355 (2006), 799–808. https://doi.org/10.1056/NEJMoa053241 doi: 10.1056/NEJMoa053241
|
| [5] | World Health Organization, Chagas Disease (also known as American Trypanosomiasis), 2021. Available from: https://www.who.int/news-room/fact-sheets/detail/chagas-disease-(american-trypanosomiasis), Accesse date: 10 January 2022. |
| [6] |
R. C. Pedrosa, Dysautonomic arrhythmogenesis: A working hypothesis in chronic chagas cardiomyopathy, Int. J. Cardiovasc. Sci., 33 (2020), 713–720. https://doi.org/10.36660/ijcs.20200169 doi: 10.36660/ijcs.20200169
|
| [7] |
J. A. Marin-Neto, E. Cunha-Neto, B. C. Maciel, M. V. Simões, Pathogenesis of chronic chagas heart disease, Circulation, 115 (2007), 1109–1123. https://doi.org/10.1161/CIRCULATIONAHA.106.624296 doi: 10.1161/CIRCULATIONAHA.106.624296
|
| [8] |
A. C. J. de Souza, G. Salles, A. M. Hasslocher-Moreno, A. S. de Sousa, P. E. A. A. do Brasil, R. M. Saraiva, et al., Development of a risk score to predict sudden death in patients with chaga's heart disease, Int. J. Cardiol., 187 (2015), 700–704. https://doi.org/10.1016/j.ijcard.2015.03.372 doi: 10.1016/j.ijcard.2015.03.372
|
| [9] |
J. A. Pérez-Molina, I. Molina, Chagas disease cardiomyopathy treatment remains a challenge–authors' reply, Lancet, 391 (2018), 2209–2210. https://doi.org/10.1016/S0140-6736(18)30776-1 doi: 10.1016/S0140-6736(18)30776-1
|
| [10] |
F. M. Rassi, L. Minohara, A. Rassi, L. C. L. Correia, J. A. Marin-Neto, A. Rassi, et al., Systematic review and meta-analysis of clinical outcome after implantable cardioverter-defibrillator therapy in patients with chagas heart disease, JACC: Clin. Electrophysiol., 5 (2019), 1213–1223. https://doi.org/10.1016/j.jacep.2019.07.003 doi: 10.1016/j.jacep.2019.07.003
|
| [11] |
F. Lopez-Jimenez, Z. Attia, A. M. Arruda-Olson, R. Carter, P. Chareonthaitawee, H. Jouni, et al., Artificial intelligence in cardiology: present and future, Mayo Clin. Proc., 95 (2020), 1015–1039. https://doi.org/10.1016/j.mayocp.2020.01.038 doi: 10.1016/j.mayocp.2020.01.038
|
| [12] |
F. Ali, S. El-Sappagh, S. R. Islam, D. Kwak, A. Ali, M. Imran, et al., A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion, Inf. Fusion, 63 (2020), 208–222. https://doi.org/10.1016/j.inffus.2020.06.008 doi: 10.1016/j.inffus.2020.06.008
|
| [13] |
J. Barker, X. Li, S. Khavandi, D. Koeckerling, A. Mavilakandy, C. Pepper, et al., Machine learning in sudden cardiac death risk prediction: a systematic review, Europace, 24 (2022), 1777–1787. https://doi.org/10.1093/europace/euac135 doi: 10.1093/europace/euac135
|
| [14] |
P. Pace, G. Aloi, R. Gravina, G. Caliciuri, G. Fortino, A. Liotta, An edge-based architecture to support efficient applications for healthcare industry 4.0, IEEE Trans. Ind. Inf., 15 (2018), 481–489. https://doi.org/10.1109/TII.2018.2843169 doi: 10.1109/TII.2018.2843169
|
| [15] |
G. Alfian, M. Syafrudin, M. F. Ijaz, M. A. Syaekhoni, N. L. Fitriyani, J. Rhee, A personalized healthcare monitoring system for diabetic patients by utilizing ble-based sensors and real-time data processing, Sensors, 18 (2018), 2183. https://doi.org/10.3390/s18072183 doi: 10.3390/s18072183
|
| [16] |
J. A. L. Marques, T. Han, W. Wu, J. P. do Vale Madeiro, A. V. L. Neto, R. Gravina, et al., Iot-based smart health system for ambulatory maternal and fetal monitoring, IEEE Internet Things J., 8 (2020), 16814–16824. https://doi.org/10.1109/JIOT.2020.3037759 doi: 10.1109/JIOT.2020.3037759
|
| [17] |
D. L. T. Wong, J. Yu, Y. Li, C. J. Deepu, D. H. Ngo, C. Zhou, et al., An integrated wearable wireless vital signs biosensor for continuous inpatient monitoring, IEEE Sens. J., 20 (2020), 448–462. https://doi.org/10.1109/JSEN.2019.2942099 doi: 10.1109/JSEN.2019.2942099
|
| [18] |
J. C. P. Dias, A. N. Ramos, E. D. Gontijo, A. Luquetti, M. A. Shikanai-Yasuda, J. R. Coura, et al., 2nd brazilian consensus on chagas disease, 2015, Rev. Soc. Bras. Med. Trop., 49 (2016), 03–60. https://doi.org/10.1590/0037-8682-0505-2016 doi: 10.1590/0037-8682-0505-2016
|
| [19] | M. C. P. Nunes, A. Z. Beaton, H. Acquatella, C. Bern, A. F. Bolger, L. E. Echeverría, et al., Circulation, 138 (2018), e169–e209. https://doi.org/10.1161/CIR.0000000000000599 |
| [20] |
R. J. Moll-Bernardes, P. H. Rosado-de Castro, G. C. Camargo, F. S. N. S. Mendes, A. S. Brito, A. S. Sousa, New imaging parameters to predict sudden cardiac death in chagas disease, Trop. Med. Infect. Dis., 5 (2020), 74. https://doi.org/10.3390/tropicalmed5020074 doi: 10.3390/tropicalmed5020074
|
| [21] | N. Sharma, K. Saroha, Study of dimension reduction methodologies in data mining, in International Conference on Computing, Communication & Automation, IEEE, (2015), 133–137. https://doi.org/10.1109/CCAA.2015.7148359 |
| [22] |
S. Velliangiri, S. Alagumuthukrishnan, I. T. J. Swamidason, A review of dimensionality reduction techniques for efficient computation, Procedia Comput. Sci., 165 (2019), 104–111. https://doi.org/10.1016/j.procs.2020.01.079 doi: 10.1016/j.procs.2020.01.079
|
| [23] |
C. Antzelevitch, S. Sicouri, J. M. Di Diego, A. Burashnikov, S. Viskin, W. Shimizu, et al., Does tpeak–tend provide an index of transmural dispersion of repolarization, Heart Rhythm, 4 (2007), 1114–1116. https://doi.org/10.1016/j.hrthm.2007.05.028 doi: 10.1016/j.hrthm.2007.05.028
|
| [24] | A. A. Fossa, M. Zhou, Assessing QT prolongation and electrocardiography restitution using a beat-to-beat method, Cardiol. J., 17 (2010), 230–243. |
| [25] |
W. B. Nicolson, G. P. McCann, M. I. Smith, A. J. Sandilands, P. J. Stafford, F. S. Schlindwein, et al., Prospective evaluation of two novel ecg-based restitution biomarkers for prediction of sudden cardiac death risk in ischaemic cardiomyopathy, Heart, 100 (2014), 1878–1885. http://dx.doi.org/10.1136/heartjnl-2014-305672 doi: 10.1136/heartjnl-2014-305672
|
| [26] |
A. C. Alberto, R. C. Pedrosa, V. Zarzoso, J. Nadal, Association between circadian holter ecg changes and sudden cardiac death in patients with chagas heart disease, Physiol. Meas., 41 (2020), 025006. https://doi.org/10.1088/1361-6579/ab6ebc doi: 10.1088/1361-6579/ab6ebc
|
| [27] |
L. E. Hinkle Jr, H. T. Thaler, Clinical classification of cardiac deaths, Circulation, 65 (1982), 457–464. https://doi.org/10.1161/01.CIR.65.3.457 doi: 10.1161/01.CIR.65.3.457
|
| [28] | DATASUS (Departamento de Informática do Sistema Único de Saúde), TabNet Win32 3.0: Morbidade Hospitalar do SUS por Causas Externas por local de internação - Brasil, 2022. Available from: http://tabnet.datasus.gov.br, Accesse date: 10 July 2022. |
| [29] |
L. Capuani, A. L. Bierrenbach, F. Abreu, P. L. Takecian, J. E. Ferreira, E. C. Sabino, Accuracy of a probabilistic record-linkage methodology used to track blood donors in the mortality information system database, Cad. Saúde Pública, 30 (2014), 1623–1632. https://doi.org/10.1590/0102-311X00024914 doi: 10.1590/0102-311X00024914
|
| [30] |
R. Laurenti, M. de Mello Jorge, S. Gotlieb, Underlying cause-of-death mortality statistics: considering the reliability of data, Pan Amer. J. Public Health, 23 (2008), 349–356. https://doi.org/10.1590/s1020-49892008000500007 doi: 10.1590/s1020-49892008000500007
|
| [31] | New York Heart Association, The Criteria Committee of the New York Heart Association, Nomenclature and Criteria for Diagnosis of Diseases of the Heart and Great Vessels, 9th edition, Boston, Massachusetts, Little, Brown & Co, 1994. |
| [32] |
J. P. de Andrade, J. A. Marin Neto, A. A. V. de Paola, F. Vilas-Boas, G. M. M. Oliveira, F. Bacal, et al., I latin american guidelines for the diagnosis and treatment of chagas' heart disease: executive summary, Arq. Bras. Cardiol., 96 (2011), 434–442. https://doi.org/10.1590/S0066-782X2011000600002 doi: 10.1590/S0066-782X2011000600002
|
| [33] | H. Liu, H. Motoda, Computational Methods of Feature Selection, CRC Press, 2007. |
| [34] |
J. C. Ang, A. Mirzal, H. Haron, H. N. A. Hamed, Supervised, unsupervised, and semi-supervised feature selection: a review on gene selection, IEEE/ACM Trans. Comput. Biol. Bioinf., 13 (2015), 971–989. https://doi.org/10.1109/TCBB.2015.2478454 doi: 10.1109/TCBB.2015.2478454
|
| [35] |
R. Zebari, A. Abdulazeez, D. Zeebaree, D. Zebari, J. Saeed, A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction, J. Appl. Sci. Technol. Trends, 1 (2020), 56–70. https://doi.org/10.38094/jastt1224 doi: 10.38094/jastt1224
|
| [36] | J. Richter, H. Kotthaus, B. Bischl, P. Marwedel, J. Rahnenführer, M. Lang, Faster model-based optimization through resource-aware scheduling strategies, in International Conference on Learning and Intelligent Optimization, Springer, (2016), 267–273. https://doi.org/10.1007/978-3-319-50349-3_22 |
| [37] |
Q. Chen, Z. Meng, X. Liu, Q. Jin, R. Su, Decision variants for the automatic determination of optimal feature subset in RF-RFE, Genes, 9 (2018), 301. https://doi.org/10.3390/genes9060301 doi: 10.3390/genes9060301
|
| [38] | P. Schwartz, M. La Rovere, E. Vanoli, Autonomic nervous system and sudden cardiac death. experimental basis and clinical observations for post-myocardial infarction risk stratification, Circulation, Suppl., 85 (1992), I77–91. |
| [39] |
J. P. V. Madeiro, E. Santos, P. C. Cortez, J. H. S. Felix, F. S. Schlindwein, Evaluating gaussian and rayleigh-based mathematical models for t and p-waves in ECG, IEEE Lat. Am. Trans., 15 (2017), 843–853. https://doi.org/10.1109/TLA.2017.7910197 doi: 10.1109/TLA.2017.7910197
|
| [40] | F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, et al., Scikit-learn: Machine learning in python, J. Mach. Learn. Res., 12 (2011), 2825–2830. |
| [41] | B. Lantz, Machine Learning with R: Expert Techniques for Predictive Modeling, Packt publishing ltd, 2019. |
| [42] | J. Han, M. Kamber, J. Pei, Data Mining: Concepts and, Techniques, 3rd edition, 2011. |
| [43] |
N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, Smote: synthetic minority over-sampling technique, J. Artif. Intell. Res., 16 (2002), 321–357. https://doi.org/10.1613/jair.953 doi: 10.1613/jair.953
|
| [44] | I. Rish, An empirical study of the naive bayes classifier, in IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, 3 (2001), 41–46. |
| [45] | A. B. Cunha, D. M. Cunha, R. C. Pedrosa, F. Flammini, A. Silva, E. A. Saad, et al., Norepinephrine and heart rate variability: a marker of dysautonomia in chronic chagas cardiopathy, Port. J. Cardiol.: Off. J. Port. Soc. Cardiol., 22 (2003), 29–52. |
| [46] |
G. A. Ng, A. Mistry, X. Li, F. S. Schlindwein, W. B. Nicolson, Lifemap: towards the development of a new technology in sudden cardiac death risk stratification for clinical use, EP Europace, 20 (2018), f162–f170. https://doi.org/10.1093/europace/euy080 doi: 10.1093/europace/euy080
|
| [47] |
G. Tse, J. Zhou, S. Lee, T. Liu, G. Bazoukis, P. Mililis, et al., Incorporating latent variables using nonnegative matrix factorization improves risk stratification in brugada syndrome, J. Am. Heart Assoc., 9 (2020), e012714. https://doi.org/10.1161/JAHA.119.012714 doi: 10.1161/JAHA.119.012714
|
| [48] |
A. Lyon, R. Ariga, A. Mincholé, M. Mahmod, E. Ormondroyd, P. Laguna, et al., Distinct ECG phenotypes identified in hypertrophic cardiomyopathy using machine learning associate with arrhythmic risk markers, Front. Physiol., 9 (2018), 213. https://doi.org/10.3389/fphys.2018.00213 doi: 10.3389/fphys.2018.00213
|
| [49] |
K. Nakajima, T. Nakata, T. Doi, H. Tada, K. Maruyama, Machine learning-based risk model using 123 i-metaiodobenzylguanidine to differentially predict modes of cardiac death in heart failure, J. Nucl. Cardiol., 29 (2020), 1–12. https://doi.org/10.1007/s12350-020-02173-6 doi: 10.1007/s12350-020-02173-6
|
| [50] |
S. Shakibfar, O. Krause, C. Lund-Andersen, A. Aranda, J. Moll, T. O. Andersen, et al., Predicting electrical storms by remote monitoring of implantable cardioverter-defibrillator patients using machine learning, EP Europace, 21 (2019), 268–274. https://doi.org/10.1093/europace/euy257 doi: 10.1093/europace/euy257
|
| [51] |
J. Atallah, M. C. G. Corcia, E. P. Walsh, Participating Members of the Pediatric and Congenital Electrophysiology Society, Ventricular arrhythmia and life-threatening events in patients with repaired tetralogy of fallot, Am. J. Cardiol., 132 (2020), 126–132. https://doi.org/10.1016/j.amjcard.2020.07.012 doi: 10.1016/j.amjcard.2020.07.012
|
| [52] |
J. Rodriguez, S. Schulz, B. F. Giraldo, A. Voss, Risk stratification in idiopathic dilated cardiomyopathy patients using cardiovascular coupling analysis, Front. Physiol., 10 (2019), 841. https://doi.org/10.3389/fphys.2019.00841 doi: 10.3389/fphys.2019.00841
|
| [53] |
P. Vergara, W. S. Tzou, R. Tung, C. Brombin, A. Nonis, M. Vaseghi, et al., Predictive score for identifying survival and recurrence risk profiles in patients undergoing ventricular tachycardia ablation: the I-VT score, Circ.: Arrhythmia Electrophysiol., 11 (2018), e006730. https://doi.org/10.1161/CIRCEP.118.006730 doi: 10.1161/CIRCEP.118.006730
|
| [54] |
S. Lee, J. Zhou, K. H. C. Li, K. S. K. Leung, I. Lakhani, T. Liu, et al., Territory-wide cohort study of brugada syndrome in hong kong: predictors of long-term outcomes using random survival forests and non-negative matrix factorisation, Open Heart, 8 (2021), e001505. http://dx.doi.org/10.1136/openhrt-2020-001505 doi: 10.1136/openhrt-2020-001505
|
| [55] | M. Zoni-Berisso, D. Molini, S. Viani, G. S. Mela, L. Delfino, Noninvasive prediction of sudden death and sustained ventricular tachycardia after acute myocardial infarction using a neural network algorithm, Ital. Heart J.: Off. J. Ital. Fed. Cardiol., 2 (2001), 612–620. |
| [56] |
B. A. Goldstein, T. I. Chang, A. A. Mitani, T. L. Assimes, W. C. Winkelmayer, Near-term prediction of sudden cardiac death in older hemodialysis patients using electronic health records, Clin. J. Amer. Soc. Nephrol., 9 (2014), 82–91. http://dx.doi.org/10.2215/CJN.03050313 doi: 10.2215/CJN.03050313
|
| [57] |
M. S. Marcolino, D. M. Palhares, L. R. Ferreira, A. L. Ribeiro, Electrocardiogram and chagas disease: a large population database of primary care patients, Global Heart, 10 (2015), 167–172. https://doi.org/10.1016/j.gheart.2015.07.001 doi: 10.1016/j.gheart.2015.07.001
|
| 1. | Bei Wang, YingXiao Hua, XinQi Dong, Development and validation of a predictive index of elder self-neglect risk among a Chinese population, 2020, 1360-7863, 1, 10.1080/13607863.2020.1758903 | |
| 2. | Ling Xu, Fengyan Tang, Lydia W. Li, Xin Qi Dong, Grandparent Caregiving and Psychological Well-Being Among Chinese American Older Adults—The Roles of Caregiving Burden and Pressure, 2017, 72, 1079-5006, S56, 10.1093/gerona/glw186 | |
| 3. | Fengyan Tang, Ling Xu, Iris Chi, XinQi Dong, Psychological Well-Being of Older Chinese-American Grandparents Caring for Grandchildren, 2016, 64, 00028614, 2356, 10.1111/jgs.14455 | |
| 4. | XinQi Dong, Manrui Zhang, Religiosity Among U.S. Chinese Older Adults in the Greater Chicago Area—Findings From the PINE Study, 2015, 27, 1552-8030, 323, 10.1080/15528030.2015.1065540 | |
| 5. | Shuhong Wang, Shengxiao (Alex) Li, Wanyang Hu, Grandparenting and subjective well-being in China: The moderating effects of residential location, gender, age, and income, 2022, 315, 02779536, 115528, 10.1016/j.socscimed.2022.115528 | |
| 6. | Jeung Hyun Kim, Grandparenting, filial piety, and well-being of Chinese-American older adults, 2022, 70, 0011-3921, 860, 10.1177/00113921211059221 | |
| 7. | Yongli Wang, Yiran Zhao, Jinjin Lu, Yuan Gao, Young Children’s Digital Literacy Practices with Caregivers in the Home Environment: Voices of Chinese Parents and Grandparents, 2024, 16, 2071-1050, 3300, 10.3390/su16083300 |
Figures(3) / Tables(6)
Carlos H. L. Cavalcante, Pedro E. O. Primo, Carlos A. F. Sales, Weslley L. Caldas, João H. M. Silva, Amauri H. Souza, Emmanuel S. Marinho, Roberto C. Pedrosa, João A. L. Marques, Hélcio S. Santos, João P. V. Madeiro. Sudden cardiac death multiparametric classification system for Chagas heart disease's patients based on clinical data and 24-hours ECG monitoring[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 9159-9178. doi: 10.3934/mbe.2023402
| Any BurdenN = 470 | No BurdenN = 1676 | χ2 | d.f | P | |
| Age, N (%) | |||||
| 60–64 | 105 (22.3) | 270 (16.1) | |||
| 65–69 | 104 (22.1) | 306 (18.3) | |||
| 70–74 | 104 (22.1) | 345 (20.6) | |||
| 75–79 | 85 (18.1) | 325 (19.4) | |||
| 80+ | 72 (15.3) | 430 (25.7) | 28.5 | 4 | < 0.001 |
| Sex, N (%) | |||||
| Male | 170 (36.2) | 692 (41.3) | |||
| Female | 300 (63.8) | 984 (58.7) | 4.0 | 1 | 0.045 |
| Education (years), N (%) | |||||
| 0 | 46 (9.9) | 108 (6.5) | |||
| 1–6 | 188 (40.3) | 679 (41.7) | |||
| 7–12 | 163 (34.9) | 518 (31.0) | |||
| 13–16 | 63 (13.5) | 303 (18.1) | |||
| 17+ | 7 (1.5) | 44 (2.6) | 14.3 | 4 | 0.006 |
| Income (USD), N (%) | |||||
| 0–4, 999 | 173 (37.0) | 559 (33.6) | |||
| 5,000–9, 999 | 254 (54.3) | 889 (53.4) | |||
| 10,000–14, 999 | 29 (6.2) | 161 (9.7) | |||
| 15,000–19, 999 | 3 (0.6) | 27 (1.6) | |||
| $20, 000 and over | 9 (1.9) | 30 (1.8) | 8.8 | 4 | 0.07 |
| Marital Status, N (%) | |||||
| Married | 351 (74.7) | 1, 172 (70.1) | |||
| Separated | 8 (1.7) | 24 (1.4) | |||
| Divorced | 9 (1.9) | 34 (2.0) | |||
| Widowed | 102 (21.7) | 443 (26.5) | 4.6 | 3 | 0.20 |
| Living Arrangement, N (%) | |||||
| Living alone | 85 (18.1) | 354 (21.1) | |||
| 1 | 178 (37.9) | 703 (42.3) | |||
| 2–3 | 47 (10.0) | 210 (12.5) | |||
| 4 or more | 160 (34.0) | 402 (24.0) | 19.5 | 3 | < 0.001 |
| Number of Children, N (%) | |||||
| 0 | 10 (2.1) | 44 (2.6) | |||
| 1–2 | 155 (33.0) | 541 (32.3) | |||
| 3 and more | 305 (64.9) | 1, 091 (65.1) | 0.4 | 2 | 0.81 |
| Number of Grandchildren, N(%) | |||||
| 1–2 | 75 (16.0) | 279 (16.7) | |||
| 3 and more | 395 (84.0) | 1, 397 (83.4) | 0.1 | 1 | 0.72 |
| Years in the US, N (%) | |||||
| 0–10 | 112 (23.9) | 445 (27.3) | |||
| 11–20 | 157 (33.5) | 503 (23.5) | |||
| 21–30 | 114 (30.7) | 403 (24.1) | |||
| 31+ | 56 (11.9) | 309 (18.5) | 18.2 | 3 | < 0.001 |
| Years in the Community, N(%) | |||||
| 0–10 | 283 (60.5) | 984 (58.8) | |||
| 11–20 | 106 (22.7) | 355 (21.2) | |||
| 21–30 | 60 (12.8) | 216 (12.9) | |||
| 31+ | 19 (4.1) | 118 (7.1) | 5.6 | 3 | 0.13 |
| Country of Origin, N (%) | |||||
| China | 454 (96.6) | 1557 (92.9) | |||
| Other | 16 (3.4) | 119 (7.1) | 18.8 | 1 | < 0.001 |
| Language preference, N (%) | |||||
| English | 1 (0.2) | 13 (0.8) | |||
| Cantonese | 251 (53.4) | 855 (51.01) | |||
| Mandarin | 68 (14.5) | 403 (24.1) | |||
| Toisanese | 250 (31.9) | 405 (24.2) | 26.1 | 3 | < 0.001 |
| Overall Health Status, N (%) | |||||
| Very good | 7 (1.5) | 74 (4.4) | |||
| Good | 134 (28.5) | 619 (36.9) | |||
| Fair | 223 (47.5) | 654 (39.0) | |||
| Poor | 106 (22.6) | 329 (19.6) | 23.7 | 3 | < 0.001 |
| Quality of Life, N (%) | |||||
| Very good | 14 (3.0) | 114 (6.8) | |||
| Good | 185 (39.4) | 757 (45.2) | |||
| Fair | 258 (54.9) | 762 (45.5) | |||
| Poor | 13 (2.8) | 42 (2.5) | 18.8 | 3 | < 0.001 |
| Health Changes over the Last | |||||
| Year, N (%) | |||||
| Improved | 40 (8.5) | 134 (8.0) | |||
| Same | 198 (42.1) | 810 (48.4) | |||
| Worsened | 232 (49.4) | 731 (43.6) | 5.8 | 2 | 0.05 |
DownLoad: CSV| Alpha if item deleted | 1 | 2 | 3 | 4 | |
| Alpha = 0.66, *P < 0.05, **P < 0.01, ***P < 0.001 | |||||
| 1. Felt pressured by children to take care of grandchildren | 0.50 | 1.0 | 1.0 | 1.0 | 1.0 |
| 2. Felt it is a burden to take care of grandchildren | 0.47 | 0.56*** | |||
| 3. Felt that health is negatively affected as a result of taking care of grandchildren | 0.52 | 0.44*** | 0.52*** | ||
| 4. Felt that children and/or children-in-law intentionally prevented you from seeing or talking to grandchildren | 0.74 | 0.07*** | 0.07** | 0.08*** | |
DownLoad: CSV| Alpha if item deleted | 1 | 2 | 3 | 4 | |
| 1. Felt pressured by children to take care of grandchildren | 0.50 | 1.0 | 1.0 | 1.0 | 1.0 |
| 2. Felt it is a burden to take care of grandchildren | 0.47 | 0.56*** | |||
| 3. Felt that health is negatively affected as a result of taking care of grandchildren | 0.52 | 0.44*** | 0.52*** | ||
| 4. Felt that children and/or children-in-law intentionally prevented you from seeing or talking to grandchildren | 0.74 | 0.07*** | 0.07** | 0.08*** |
DownLoad: CSV| Age | Sex | Edu | Inc | MS | Living | Child | Grand Ch | Yrs in U.S. | Yrs in com | Origin | Lang Pref | OHS | QOL | HC | Grand | Hours | |
| Age | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| Sex | 0.01 | ||||||||||||||||
| Edu | -0.12*** | -0.21*** | |||||||||||||||
| Inc | 0.05** | 0.00 | 0.01 | ||||||||||||||
| MS | -0.33*** | -0.32*** | 0.22 | -0.03 | |||||||||||||
| Living | -0.35*** | -0.07*** | 0.02 | 0.16*** | 0.24 *** | ||||||||||||
| Child | 0.32*** | 0.09*** | -0.38*** | 0.00 | -0.13*** | -0.07*** | |||||||||||
| Grand Ch | 0.43*** | 0.12*** | -0.39*** | -0.02 | -0.18*** | -0.10*** | 0.72*** | ||||||||||
| Yrs in U.S. | 0.35*** | 0.03 | -0.10*** | 0.35*** | -0.2*** | -0.31*** | 0.15*** | 0.17*** | |||||||||
| Yrs in com | 0.23*** | 0.02 | -0.11*** | 0.24*** | -0.13*** | -0.18*** | 0.10*** | 0.10*** | 0.66*** | ||||||||
| Origin | 0.04* | -0.01 | -0.08*** | -0.20 | 0.05** | 0.05** | 0.04* | 0.06*** | -0.2*** | -0.15*** | |||||||
| Lang Pref | -0.02 | -0.01 | -0.56*** | -0.03 | -0.06*** | 0.06*** | 0.27*** | 0.25*** | 0.18*** | 0.20*** | 0.06** | ||||||
| OHS | 0.08*** | 0.06** | -0.06*** | -0.12*** | -0.05** | 0.00 | 0.00 | -0.02 | 0.01 | -0.05* | 0.03 | 0.01 | |||||
| QOL | -0.06*** | -0.05** | -0.09*** | -0.08*** | 0.03 | 0.01 | -0.04* | 0.05** | 0.00 | 0.02 | 0.04* | -0.12*** | 0.32*** | ||||
| HC | 0.11*** | 0.03 | -0.02 | -0.05** | -0.07*** | -0.01 | 0.02 | -0.05 | 0.04* | -0.03 | 0.00 | 0.03 | 0.35*** | 0.15*** | |||
| Grand | -0.12*** | 0.04* | -0.05* | -0.04 | 0.04* | 0.09*** | -0.01 | 0.04 | -0.03 | -0.04 | 0.06** | 0.07** | -0.09*** | -0.09*** | -0.03 | ||
| Hours | -0.32*** | 0.04 | 0.01 | -0.14*** | 0.09*** | 0.34*** | -0.10*** | -0.04* | -0.17*** | -0.11*** | 0.04* | 0.02 | 0.04* | 0.04* | 0.07*** | 0.37*** |
DownLoad: CSV| Any BurdenN = 470 | No BurdenN = 1676 | χ2 | d.f | P | |
| Age, N (%) | |||||
| 60–64 | 105 (22.3) | 270 (16.1) | |||
| 65–69 | 104 (22.1) | 306 (18.3) | |||
| 70–74 | 104 (22.1) | 345 (20.6) | |||
| 75–79 | 85 (18.1) | 325 (19.4) | |||
| 80+ | 72 (15.3) | 430 (25.7) | 28.5 | 4 | < 0.001 |
| Sex, N (%) | |||||
| Male | 170 (36.2) | 692 (41.3) | |||
| Female | 300 (63.8) | 984 (58.7) | 4.0 | 1 | 0.045 |
| Education (years), N (%) | |||||
| 0 | 46 (9.9) | 108 (6.5) | |||
| 1–6 | 188 (40.3) | 679 (41.7) | |||
| 7–12 | 163 (34.9) | 518 (31.0) | |||
| 13–16 | 63 (13.5) | 303 (18.1) | |||
| 17+ | 7 (1.5) | 44 (2.6) | 14.3 | 4 | 0.006 |
| Income (USD), N (%) | |||||
| 0–4, 999 | 173 (37.0) | 559 (33.6) | |||
| 5,000–9, 999 | 254 (54.3) | 889 (53.4) | |||
| 10,000–14, 999 | 29 (6.2) | 161 (9.7) | |||
| 15,000–19, 999 | 3 (0.6) | 27 (1.6) | |||
| $20, 000 and over | 9 (1.9) | 30 (1.8) | 8.8 | 4 | 0.07 |
| Marital Status, N (%) | |||||
| Married | 351 (74.7) | 1, 172 (70.1) | |||
| Separated | 8 (1.7) | 24 (1.4) | |||
| Divorced | 9 (1.9) | 34 (2.0) | |||
| Widowed | 102 (21.7) | 443 (26.5) | 4.6 | 3 | 0.20 |
| Living Arrangement, N (%) | |||||
| Living alone | 85 (18.1) | 354 (21.1) | |||
| 1 | 178 (37.9) | 703 (42.3) | |||
| 2–3 | 47 (10.0) | 210 (12.5) | |||
| 4 or more | 160 (34.0) | 402 (24.0) | 19.5 | 3 | < 0.001 |
| Number of Children, N (%) | |||||
| 0 | 10 (2.1) | 44 (2.6) | |||
| 1–2 | 155 (33.0) | 541 (32.3) | |||
| 3 and more | 305 (64.9) | 1, 091 (65.1) | 0.4 | 2 | 0.81 |
| Number of Grandchildren, N(%) | |||||
| 1–2 | 75 (16.0) | 279 (16.7) | |||
| 3 and more | 395 (84.0) | 1, 397 (83.4) | 0.1 | 1 | 0.72 |
| Years in the US, N (%) | |||||
| 0–10 | 112 (23.9) | 445 (27.3) | |||
| 11–20 | 157 (33.5) | 503 (23.5) | |||
| 21–30 | 114 (30.7) | 403 (24.1) | |||
| 31+ | 56 (11.9) | 309 (18.5) | 18.2 | 3 | < 0.001 |
| Years in the Community, N(%) | |||||
| 0–10 | 283 (60.5) | 984 (58.8) | |||
| 11–20 | 106 (22.7) | 355 (21.2) | |||
| 21–30 | 60 (12.8) | 216 (12.9) | |||
| 31+ | 19 (4.1) | 118 (7.1) | 5.6 | 3 | 0.13 |
| Country of Origin, N (%) | |||||
| China | 454 (96.6) | 1557 (92.9) | |||
| Other | 16 (3.4) | 119 (7.1) | 18.8 | 1 | < 0.001 |
| Language preference, N (%) | |||||
| English | 1 (0.2) | 13 (0.8) | |||
| Cantonese | 251 (53.4) | 855 (51.01) | |||
| Mandarin | 68 (14.5) | 403 (24.1) | |||
| Toisanese | 250 (31.9) | 405 (24.2) | 26.1 | 3 | < 0.001 |
| Overall Health Status, N (%) | |||||
| Very good | 7 (1.5) | 74 (4.4) | |||
| Good | 134 (28.5) | 619 (36.9) | |||
| Fair | 223 (47.5) | 654 (39.0) | |||
| Poor | 106 (22.6) | 329 (19.6) | 23.7 | 3 | < 0.001 |
| Quality of Life, N (%) | |||||
| Very good | 14 (3.0) | 114 (6.8) | |||
| Good | 185 (39.4) | 757 (45.2) | |||
| Fair | 258 (54.9) | 762 (45.5) | |||
| Poor | 13 (2.8) | 42 (2.5) | 18.8 | 3 | < 0.001 |
| Health Changes over the Last | |||||
| Year, N (%) | |||||
| Improved | 40 (8.5) | 134 (8.0) | |||
| Same | 198 (42.1) | 810 (48.4) | |||
| Worsened | 232 (49.4) | 731 (43.6) | 5.8 | 2 | 0.05 |
| Alpha if item deleted | 1 | 2 | 3 | 4 | |
| Alpha = 0.66, *P < 0.05, **P < 0.01, ***P < 0.001 | |||||
| 1. Felt pressured by children to take care of grandchildren | 0.50 | 1.0 | 1.0 | 1.0 | 1.0 |
| 2. Felt it is a burden to take care of grandchildren | 0.47 | 0.56*** | |||
| 3. Felt that health is negatively affected as a result of taking care of grandchildren | 0.52 | 0.44*** | 0.52*** | ||
| 4. Felt that children and/or children-in-law intentionally prevented you from seeing or talking to grandchildren | 0.74 | 0.07*** | 0.07** | 0.08*** | |
| Alpha if item deleted | 1 | 2 | 3 | 4 | |
| 1. Felt pressured by children to take care of grandchildren | 0.50 | 1.0 | 1.0 | 1.0 | 1.0 |
| 2. Felt it is a burden to take care of grandchildren | 0.47 | 0.56*** | |||
| 3. Felt that health is negatively affected as a result of taking care of grandchildren | 0.52 | 0.44*** | 0.52*** | ||
| 4. Felt that children and/or children-in-law intentionally prevented you from seeing or talking to grandchildren | 0.74 | 0.07*** | 0.07** | 0.08*** |
| Age | Sex | Edu | Inc | MS | Living | Child | Grand Ch | Yrs in U.S. | Yrs in com | Origin | Lang Pref | OHS | QOL | HC | Grand | Hours | |
| Age | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| Sex | 0.01 | ||||||||||||||||
| Edu | -0.12*** | -0.21*** | |||||||||||||||
| Inc | 0.05** | 0.00 | 0.01 | ||||||||||||||
| MS | -0.33*** | -0.32*** | 0.22 | -0.03 | |||||||||||||
| Living | -0.35*** | -0.07*** | 0.02 | 0.16*** | 0.24 *** | ||||||||||||
| Child | 0.32*** | 0.09*** | -0.38*** | 0.00 | -0.13*** | -0.07*** | |||||||||||
| Grand Ch | 0.43*** | 0.12*** | -0.39*** | -0.02 | -0.18*** | -0.10*** | 0.72*** | ||||||||||
| Yrs in U.S. | 0.35*** | 0.03 | -0.10*** | 0.35*** | -0.2*** | -0.31*** | 0.15*** | 0.17*** | |||||||||
| Yrs in com | 0.23*** | 0.02 | -0.11*** | 0.24*** | -0.13*** | -0.18*** | 0.10*** | 0.10*** | 0.66*** | ||||||||
| Origin | 0.04* | -0.01 | -0.08*** | -0.20 | 0.05** | 0.05** | 0.04* | 0.06*** | -0.2*** | -0.15*** | |||||||
| Lang Pref | -0.02 | -0.01 | -0.56*** | -0.03 | -0.06*** | 0.06*** | 0.27*** | 0.25*** | 0.18*** | 0.20*** | 0.06** | ||||||
| OHS | 0.08*** | 0.06** | -0.06*** | -0.12*** | -0.05** | 0.00 | 0.00 | -0.02 | 0.01 | -0.05* | 0.03 | 0.01 | |||||
| QOL | -0.06*** | -0.05** | -0.09*** | -0.08*** | 0.03 | 0.01 | -0.04* | 0.05** | 0.00 | 0.02 | 0.04* | -0.12*** | 0.32*** | ||||
| HC | 0.11*** | 0.03 | -0.02 | -0.05** | -0.07*** | -0.01 | 0.02 | -0.05 | 0.04* | -0.03 | 0.00 | 0.03 | 0.35*** | 0.15*** | |||
| Grand | -0.12*** | 0.04* | -0.05* | -0.04 | 0.04* | 0.09*** | -0.01 | 0.04 | -0.03 | -0.04 | 0.06** | 0.07** | -0.09*** | -0.09*** | -0.03 | ||
| Hours | -0.32*** | 0.04 | 0.01 | -0.14*** | 0.09*** | 0.34*** | -0.10*** | -0.04* | -0.17*** | -0.11*** | 0.04* | 0.02 | 0.04* | 0.04* | 0.07*** | 0.37*** |
