Abstract
Background:
Elder abuse (EA) is a global public health issue. However, no prior longitudinal research has quantified the incidence of EA, which is critical to understand risk factors and future prevention strategies.
Methods:
The study is based on a longitudinal cohort design. We followed 2,713 U.S. Chinese older adults who agreed to participate in the study within 2011 to 2015. A structured questionnaire was used to collect data regarding the 2-year incidence of EA and its subtypes. We employed multiple logistic regression analyses to examine the associations between the sociodemographic characteristics and incident EA.
Results:
The incidence of overall EA was 8.8% with 4.8% for psychological, 2.9% for financial, 0.5% for physical, 0.1% for sexual abuse, and 1.1% for caregiver neglect. Age, gender, duration of residence, language preference and health status change were associated with incident EA. Self-perceived worsened health was positively associated with overall EA (odds ratio [OR] 1.28 (1.01, 1.62). Women (OR 2.98 [1.10, 8.11]) and older individuals (OR 1.06 [1.00, 1.13]) had an increased risk of caregiver neglect. Older adults who have lived in the U.S. longer had a higher risk of financial exploitation (OR 1.02 [1.00, 1.05]). Individuals who prefer to speak Mandarin or English were more likely to experience EA (OR 2.08 [1.21, 3.58]) and sexual or physical abuse (OR 3.91 [1.01, 15.17]). No significant association was observed between education, income, marital status, number of children, country of origin, overall health, life quality, and incident EA.
Conclusion:
This study presents the first illustration of EA incidence in a longitudinal cohort study, the findings of which verify and challenge prior fundamental assumptions of risk factors associated with EA, and are relevant to future prevention strategies.
Keywords: Elder abuse, Longitudinal study, Risk factors
Elder abuse (EA) is a fatal and costly global public issue that crosses all social-demographic and socioeconomic strata. Common types of EA are psychological, physical and sexual abuse, caregiver neglect, and financial exploitation (1). In part due to heterogeneity in definitions, methodologies, study design, and setting, the prevalence of EA ranges between 3.2% to 27.5 according to a systematic review of rigorous studies related to EA worldwide (2). In the United States, recent large population studies (n = 3,159, 4,156, and 5,777) suggested the EA prevalence of 15.0%, 14.1%, and 10.0% (3–5), with disproportionately higher prevalence among minority populations (4,6,7). Moreover, previous studies suggest that EA is associated with increased risk of morbidity and premature mortality, cognitive and functional impairment, psychosocial distress, and increased health care costs (5,8–13). However, all existing epidemiological studies of EA utilize data regarding prevalent EA cases, and we are not aware of any population-based studies examining the incidence of EA.
More accurate illustrations of the incidence of EA are necessary to understand causes and antecedents of abuse, which thereby would advance our knowledge of risk or protective factors and devise prevention and intervention strategies. While important, prior cross-sectional studies could not adequately elucidate risk factors leading to EA because of the inability to detect causality. Therefore, longitudinal research is needed to demonstrate risk factors through the establishment of temporal sequence between associated variables. In the current literature, although findings of associated factors of abuse are inconsistent, many researchers have indicated that older age, female gender, and lower levels of socioeconomic status have been associated with prevalent EA. However, their relationships with incident EA remain unknown. Additionally, Chinese is the fastest growing ethnic minority population in the United States (14), and the older U.S. Chinese people might be most susceptible to EA because of cultural obstacles and socioeconomic disparities, but vast knowledge gaps exist in this regard (15,16).
Building on the prior work of EA prevalence research, this study aims to quantify the incidence of EA, and to examine the associations between sociodemographic characteristics and incident EA, within the context of a longitudinal population-based cohort of Chinese in the Greater Chicago area.
Methods
Settings
The study used 2 waves of longitudinal data from the Population-based Study of Chinese Elderly (PINE) project. Baseline (time 1) data was collected from 2011 to 2013; time 2 data was collected from 2013 to 2015. The follow-up period was approximately 2 years for each participant. The PINE project is a community-engaged and population-based study that is representative of the Chinese aging population (over 60) in the Greater Chicago area (17). It was initiated by synergistic community-academic collaboration among the Rush Institute for Healthy Aging, Northwestern University, and many community-based social services agencies and organizations in the Greater Chicago area.
Study Design and Sample
To increase community engagement, the research team implemented extensively culturally and linguistically appropriate recruitment strategies guided by a community-based participatory research approach (18). Twenty-two community centers were engaged as recruiting sites, which included social services agencies, community centers, health advocacy agencies, faith-based organizations, senior apartments, and social clubs. Eligibility criteria were (a) community-dwelling older adults aged 60 years and over; (b) who were self-identified as Chinese. Individuals who were unable to consent or speak either of English or Chinese did not complete the survey. Out of 3,542 eligible older adults approached, 3,157 agreed to participate in the study. Of those, 2,713 completed a follow-up interview 2 years later. Demographic characteristics of the PINE sample were comparable to those available from the 2010 U.S. Census and a random street-block census of the Chinese community in Chicago (17). Written informed consent was obtained from all participants. Details of the PINE study design have been published elsewhere (19). Multilingual research assistants recruited through community partners conducted face-to-face interviews with participants. Data were collected with the use of the state-of-science innovative web-based software that recorded English and Chinese traditional and simplified characters simultaneously. The study was approved by the Institutional Review Board of the Rush University Medical Center.
Measurements
Sociodemographic Characteristics
The sociodemographic variables (baseline) considered herein are age (in years), sex (male and female), education (completed years of school), annual personal income (categories in U.S. dollars), number of children, years in the United States, country of origin, marital status, language preference, and three brief self-evaluated question of overall health status, quality of life, and health change over the last year.
Elder Abuse Incidence
Primary outcomes were 2-year self-reported incident EA and its subtypes including (a) psychological abuse, (b) physical abuse, (c) financial exploitations, (d) sexual abuse, and (e) caregiver neglect. The study used separate measurements to assess general abuse, and each of these subtypes by asking questions directly from the participants.
To assess general EA incidence, we used a 10-question instrument modified from the Hwalek-Sengstok Elder Abuse Screening Test (H-S/EAST) (20) and the Vulnerability to Abuse Screening Scale (VASS) (21). Prior studies have shown favorable face and construct validity of the VASS and the H-S/EAST measurements (21,22), and the scale demonstrated good reliability in this study sample (Cronbach’s alpha 0.80) (23). The modified VASS has been administered to Chinese elderly populations both in Mainland China and in the United States (24,25).
Detailed subtypes of EA were assessed through another 46-item scale (3). We used an 8-item modified Conflict Tactic Scale (CTS) (26) to assess psychological abuse, which involves interpersonal contact that is likely to cause mental pain, anguish, or distress on older adults through verbal or nonverbal acts such as humiliating, intimidating, or threatening (27). Physical abuse was evaluated by a 10-item instrument derived from CTS (26). It involves physical pain or injury such as slapping, bruising, or restraining by physical or chemical means. Financial exploitations are illegal taking, misuse, or concealment of funds, property, or assets of a senior, which was measured with a 17-item instrument (28). Neglect is failure of a responsible caregiver providing food, shelter, health care, or protection to an older adult that is of needs. We assessed caregiver neglect through a 20-item unmet needs in key activities of daily living (29,30). Sexual abuse was asked by a single item regarding any unwanted sexual contact or activity. A full list of the 56 items was presented in anther publication (3).
Data Analysis
New cases of EA were identified when respondents reported negative at time 1 and positive to any item of the brief screener or detailed assessment and EA subtypes measurements at time 2. We used descriptive statistics to summarize the sociodemographic characteristics of the older persons. Chi-square statistics were used to compare these characteristics between groups with or without any EA incidence case. We employed multiple logistic regression analyses to examine the associations between the sociodemographic characteristics and EA incidence. Analyses were conducted controlling for age, sex (male vs female), education, income, marital status (married vs divorced/separated/widowed), number of children, duration of residence in the United States, country of origin (China vs not China), language preference (Mandarin/English vs Cantonese/Taishanese), overall health status, life quality, and health change. We also controlled for time, defined as the intervals between time 1 and time 2 interviews (all approximate 2 years), to assess its influence. Data analyses were conducted using SAS version 9.2 software (SAS Institute Inc).
Results
Incidence Estimates of EA and Subtypes
The 2-year incidence estimates of EA and subtypes among the Chinese older adults are presented in Table 1. The overall incident EA cases were 174 (8.8%), with 127 (5.6%) identified by the 10-question EA brief screener, 118 (4.8%) for psychological abuse, 71 (2.9%) for financial exploitation, 23 (1.1%) for caregiver neglect, 13 (0.5%) for physical abuse, and 3 (0.1%) for sexual abuse.
Table 1.
Incidence of Elder Abuse (EA) in Chinese Older Adults: The Longitudinal PINE Study
| EA positive at time 2, No. (%) | EA negative at time 2, No. (%) | |
|---|---|---|
| Elder abuse measures | (n = 2,713) | |
| Overall | 174 (8.8) | 1808 (91.2) |
| 10-item brief EA acreener | 127 (5.6) | 2153 (94.4) |
| Psychological abuse | 118 (4.8) | 2322 (95.2) |
| Financial exploitations | 71 (2.9) | 2380 (97.1) |
| Caregiver neglect | 23 (1.1) | 2123 (98.9) |
| Physical abuse | 13 (0.5) | 2662 (99.5) |
| Sexual abuse | 3 (0.1) | 2691 (99.8) |
Sample Characteristics
Table 2 presents the baseline sociodemographics of the participants by EA incidence estimates. In general, compared to the group without incident EA, older adults who self-reported any EA overall were more likely to have higher education (more than 17 years versus no completed years of education; 24.4% vs 8.0%, p < 0.001), preferred to speak Mandarin versus Cantonese or Taishanese (13.6% vs 8.0/7.5%, p < 0.004), and had worsened rather than improved or same health status (10.6% vs 9.8/7.3%, p < 0.048). Observed trends vary by types of abuse. Older adults who reported psychological abuse were more likely to have higher education and have 1 to 2 children; participants who reported financial abuse had a greater proportion of participants who were men, had higher education, and preferred to speak Mandarin or English to Cantonese or Taishanese.
Table 2.
Incidence of Elder Abuse Across Characteristics of PINE Study Participantsa,b
| Characteristics | Any EA overall | 10-item screener | Psychological | Financial | Neglect | Physical | Sexual | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | Yes | No | |
| Age | ||||||||||||||
| 60–64 | 32 (7.1) | 421 (92.9) | 29 (5.7) | 482 (94.3) | 30 (5.6) | 508 (94.4) | 16 (2.9) | 531 (97.1) | 4 (0.7) | 557 (99.3) | 3 (0.5) | 583 (99.5) | 2 (0.3) | 589 (99.7) |
| 65–69 | 40 (10.1) | 356 (89.9) | 28 (6.2) | 426 (93.8) | 31 (6.3) | 460 (93.7) | 17 (3.5) | 475 (96.5) | 7 (1.4) | 479 (98.6) | 4 (0.7) | 543 (99.3) | 0 (0.0) | 547 (100.0) |
| 70–74 | 26 (7.0) | 345 (93.0) | 24 (5.5) | 410 (94.5) | 16 (3.4) | 455 (96.6) | 11 (2.3) | 464 (97.7) | 2 (0.5) | 424 (99.5) | 2 (0.4) | 522 (99.6) | 0 (0.0) | 532 (100.0) |
| 75–79 | 39 (11.6) | 298 (88.4) | 26 (6.5) | 373 (93.5) | 24 (5.6) | 406 (94.4) | 15 (3.5) | 420 (96.6) | 5 (1.4) | 353 (98.6) | 1 (0.2) | 476 (99.8) | 0 (0.0) | 480 (100.0) |
| ≥80 | 37 (8.8) | 388 (91.3) | 20 (4.2) | 462 (95.9) | 17 (3.4) | 493 (96.7) | 12 (2.4) | 490 (97.6) | 5 (1.6) | 310 (98.4) | 3 (0.6) | 538 (99.4) | 1 (0.2) | 543 (99.8) |
| Sex | ||||||||||||||
| Female | 93 (8.0) | 1,076 (92.0) | 66 (5.0) | 1,252 (95.0) | 64 (4.6) | 1,342 (95.5) | 34 (2.3) | 1,423 (97.7) | 17 (1.4) | 1,190 (98.6) | 9 (0.6) | 1,552 (99.4) | 2 (0.1) | 1,568 (99.9) |
| Male | 81 (10.0) | 732 (90.0) | 61 (6.3) | 901 (93.7) | 54 (5.2) | 980 (94.8) | 37 (3.7) | 957 (96.3)* | 6 (1.1) | 933 (99.4) | 4 (0.4) | 1,110 (99.7) | 1 (0.1) | 1,123 (99.9) |
| Education | ||||||||||||||
| 0 | 11 (8.0) | 126 (92.0) | 8 (5.2) | 145 (94.8) | 10 (6.5) | 146 (93.6) | 3 (1.90) | 156 (98.1) | 0 (0.0) | 102 (100.0) | 0 (0.0) | 164 (100.0) | 0 (0.0) | 167 (100.0) |
| 1–6 | 63 (7.5) | 785 (92.6) | 46 (5.0) | 883 (95.1) | 35 (3.6) | 940 (96.4) | 22 (2.3) | 960 (97.8) | 9 (1.1) | 805 (98.9) | 6 (0.6) | 1,029 (99.4) | 2 (0.2) | 1,037 (99.8) |
| 7–12 | 56 (8.5) | 607 (91.6) | 42 (5.4) | 737 (94.6) | 40 (4.8) | 796 (95.2) | 23 (2.8) | 809 (97.2) | 5 (21.7) | 760 (99.4) | 3 (0.3) | 913 (99.7) | 0 (0.0) | 923 (100.0) |
| 13–16 | 34 (11.9) | 251 (88.1) | 28 (7.8) | 331 (92.2) | 27 (6.7) | 379 (93.4) | 18 (4.4) | 392 (95.6) | 7 (1.8) | 388 (98.2) | 3 (0.3) | 477 (99.4) | 1 (0.2) | 483 (99.8) |
| ≥17 | 10 (24.4) | 31 (75.6)# | 3 (6.1) | 46 (93.9) | 6 (10.3) | 52 (89.7)* | 5 (8.6) | 53 (91.4)* | 2 (3.1) | 63 (96.9) | 1 (1.5) | 68 (98.6) | 0 (0.0) | 70 (100.0) |
| Income | ||||||||||||||
| $0–$4,999 | 60 (9.4) | 578 (90.6) | 46 (6.2) | 693 (93.8) | 42 (5.3) | 747 (94.7) | 21 (2.6) | 781 (97.4) | 12 (1.6) | 732 (98.4) | 2 (0.2) | 869 (99.8) | 1 (0.1) | 883 (99.9) |
| $5,000–$9,999 | 92 (8.7) | 975 (91.4) | 65 (5.4) | 1,147 (94.6) | 62 (4.8) | 1,224 (95.2) | 36 (2.8) | 1,262 (97.2) | 9 (0.9) | 1,033 (99.1) | 11 (0.8) | 1,393 (99.2) | 1 (0.1) | 1,406 (99.9) |
| $10,000–$14,999 | 14 (7.6) | 170 (92.4) | 9 (4.2) | 207 (95.8) | 6 (2.6) | 225 (97.4) | 9 (4.0) | 218 (9.2) | 2 (0.9) | 229 (99.1) | 0 (0.0) | 251 (1.00) | 0 (0.0) | 256 (100.0) |
| ≥$15,000 | 8 (10.8) | 66 (89.2) | 7 (7.6) | 85 (92.4) | 8 (7.2) | 103 (92.8) | 4 (3.9) | 98 (96.1) | 0 (0.0) | 112 (100.0) | 0 (0.0) | 123 (100.0) | 1 (0.8) | 121 (99.2) |
| Marital status | ||||||||||||||
| Married | 129 (9.2) | 1,268 (90.8) | 95 (5.9) | 1,523 (94.2) | 90 (5.2) | 1,646 (94.8) | 51 (2.9) | 1,689 (97.1) | 17 (1.1) | 1,582 (98.9) | 9 (0.5) | 1,887 (99.5) | 2 (0.1) | 1,911 (99.9) |
| Not Married | 45 (7.7) | 539 (92.3) | 32 (4.9) | 629 (95.1) | 28 (4.0) | 675 (96.0) | 20 (2.8) | 690 (97.2) | 6 (1.1) | 540 (98.9) | 4 (0.5) | 774 (99.5) | 1 (0.1) | 779 (99.9) |
| Number of children, N (%) | ||||||||||||||
| 0 | 7 (10.8) | 58 (89.2) | 6 (7.2) | 77 (92.8) | 5 (5.8) | 81 (94.2) | 2 (2.3) | 84 (97.7) | 3 (3.2) | 92 (96.8) | 0 (0.0) | 101 (100.0) | 1 (1.0) | 101 (99.0) |
| 1–2 | 74 (10.0) | 670 (90.0) | 55 (6.4) | 812 (93.7) | 59 (6.2) | 890 (93.8) | 36 (3.7) | 927 (96.3) | 12 (1.3) | 905 (98.7) | 5 (0.5) | 1,065 (99.5) | 1 (0.1) | 1,078 (99.9) |
| ≥3 | 93 (7.9) | 1,079 (92.1) | 66 (5.0) | 1,262 (95.0) | 54 (3.9) | 1,349 (96.1)* | 33 (2.4) | 1,367 (97.6) | 8 (0.7) | 1,125 (99.3) | 8 (0.5) | 1,494 (99.5) | 1 (0.1) | 1,510 (9.9)* |
| Years in the U.S., N (%) | ||||||||||||||
| 0–10 | 45 (8.8) | 468 (91.2) | 33 (5.7) | 550 (94.3) | 36 (5.7) | 597 (94.3) | 13 (2.0) | 635 (98.0) | 6 (1.0) | 623 (99.1) | 2 (0.1) | 702 (99.9) | 2 (0.3) | 707 (99.7) |
| 11–20 | 62 (9.7) | 580 (90.3) | 52 (7.1) | 685 (92.9) | 39 (5.0) | 741 (95.0) | 24 (3.0) | 769 (97.0) | 7 (1.0) | 675 (99.0) | 6 (0.7) | 846 (99.3) | 0 (0.0) | 861 (100.0) |
| 21–30 | 40 (8.1) | 457 (92.0) | 27 (4.7) | 545 (95.3) | 24 (3.9) | 588 (96.1) | 15 (2.5) | 595 (97.5) | 8 (1.7) | 469 (98.3) | 6 (0.9) | 661 (99.1) | 0 (0.0) | 671 (100.0) |
| ≥31 | 27 (8.3) | 297 (91.7) | 14 (3.7) | 365 (96.3) | 19 (4.7) | 387 (95.3) | 19 (4.9) | 372 (95.1) | 2 (0.6) | 351 (99.4) | 0 (0.0) | 443 (100.0) | 1 (0.2) | 442 (99.8) |
| Country of origin, N (%) | ||||||||||||||
| China | 162 (8.7) | 1,704 (91.3) | 119 (5.6) | 2,026 (94.5) | 108 (4.7) | 2,180 (95.3) | 64 (2.8) | 2,233 (97.2) | 22 (1.10) | 1,973 (98.9) | 11 (0.4) | 2,488 (99.6) | 3 (0.1) | 2,513 (99.9) |
| Hong Kong/Macau | 4 (7.0) | 53 (93.0) | 3 (4.6) | 63 (95.5) | 5 (7.0) | 66 (93.0) | 2 (2.7) | 71 (97.3) | 1 (1.5) | 66 (98.5) | 1 (1.2) | 81 (98.8) | 0 (0.0) | 82 (100.0) |
| Taiwan | 4 (19.1) | 17 (81.0) | 3 (11.5) | 23 (88.5) | 2 (6.5) | 29 (93.6) | 3 (5.7) | 50 (94.3) | 0 (0.0) | 33 (100.0) | 1 (2.8) | 35 (97.2) | 0 (0.0) | 36 (100.0) |
| Other | 4 (10.5) | 34 (89.5) | 2 (4.7) | 41 (95.4) | 3 (6.0) | 47 (94.0) | 2 (7.1) | 26 (92.9) | 0 (0.0) | 51 (100.0) | 0 (0.0) | 58 (100.0) | 0 (0.0) | 60 (100.0) |
| Language preference, N (%) | ||||||||||||||
| Cantonese | 89 (8.0) | 1,023 (92.0) | 60 (4.8) | 1,200 (95.2) | 62 (4.7) | 1,259 (95.3) | 38 (2.9) | 1,282 (97.1) | 12 (0.9) | 1,172 (99.1) | 5 (0.4) | 1,425 (99.7) | 1 (0.1) | 1,428 (99.9) |
| Taishanese | 40 (7.5) | 493 (92.5) | 29 (4.8) | 572 (95.2) | 21 (3.4) | 595 (96.6) | 10 (1.6) | 611 (98.4) | 4 (0.9) | 453 (99.1) | 2 (0.3) | 652 (99.7) | 1 (0.2) | 589 (99.8) |
| Mandarin | 45 (13.6) | 286 (86.4) | 38 (9.3) | 372 (90.7) | 34 (6.9) | 456 (93.1) | 22 (4.4) | 476 (95.6) | 8 (1.6) | 486 (98.4) | 6 (1.0) | 571 (99.0) | 1 (0.2) | 660 (99.9) |
| English | 0 (0.0) | 6 (100.0)+ | 0 (0.0) | 9 (100.0)+ | 1 (7.7) | 12 (92.3) | 1 (8.3) | 11 (92.7)* | 0 (0.0) | 12 (100.0) | 0 (0.0) | 14 (100.0) | 0 (0.0) | 14 (100.0) |
| Overall health status, N (%) | ||||||||||||||
| Very good | 8 (9.2) | 79 (90.8) | 3 (3.0) | 97 (97.0) | 3 (2.9) | 100 (97.1) | 3 (3.2) | 91 (96.8) | 1 (1.0) | 97 (99.0) | 1 (0.9) | 108 (99.1) | 0 (0.0) | 108 (100.0) |
| Good | 65 (8.7) | 684 (91.3) | 42 (5.1) | 782 (94.9) | 48 (5.5) | 820 (94.5) | 21 (2.4) | 842 (97.6) | 5 (0.6) | 796 (99.4) | 4 (0.4) | 928 (99.6) | 0 (0.0) | 936 (100.0) |
| Fair | 80 (9.6) | 752 (90.4) | 64 (6.5) | 915 (93.5) | 46 (4.4) | 999 (95.6) | 35 (3.3) | 1,014 (96.7) | 11 (1.2) | 901 (98.8) | 6 (0.5) | 1,148 (99.5) | 2 (0.2) | 1,153 (99.8) |
| Poor | 21 (6.7) | 293 (93.3) | 18 (4.8) | 359 (95.2) | 21 (5.0) | 403 (95.1) | 12 (2.7) | 433 (97.3) | 6 (1.8) | 329 (98.2) | 2 (0.4) | 486 (99.6) | 1 (0.2) | 494 (99.8) |
| Quality of life, N (%) | ||||||||||||||
| Very good | 9 (7.50) | 111 (92.5) | 5 (3.3) | 145 (96.7) | 7 (4.5) | 149 (95.5) | 2 (1.3) | 150 (98.7) | 0 (0.0) | 142 (100.0) | 1 (0.6) | 180 (99.4) | 0 (0.0) | 181 (100.0) |
| Good | 75 (8.4) | 821 (91.6) | 58 (5.7) | 958 (94.3) | 49 (4.6) | 1,031 (95.5) | 31 (2.9) | 1,047 (97.1) | 6 (0.6) | 941 (99.4) | 4 (0.3) | 1,166 (99.7) | 0 (0.0) | 1,175 (100.0) |
| Fair | 87 (9.5) | 835 (90.6) | 62 (5.9) | 999 (94.2) | 56 (4.9) | 1,082 (95.1) | 35 (3.0) | 1,120 (97.0) | 17 (1.7) | 982 (98.3) | 8 (0.6) | 1,240 (99.4) | 2 (0.2) | 1,257 (99.8) |
| Poor | 3 (7.1) | 39 (92.9) | 2 (3.9) | 49 (96.1) | 6 (9.4) | 58 (90.6) | 3 (4.6) | 62 (95.4) | 0 (0.00) | 57 (100.0) | 0 (0.0) | 74 (100.0) | 1 (1.3) | 77 (98.7)+ |
| Health changes over the last year, N (%) | ||||||||||||||
| Improved | 16 (9.8) | 147 (90.2) | 10 (5.1) | 185 (94.9) | 11 (5.2) | 202 (94.8) | 8 (3.8) | 205 (96.2) | 4 (2.1) | 188 (97.9) | 1 (0.4) | 235 (99.6) | 0 (0.0) | 240 (100.0) |
| Same | 76 (7.3) | 965 (92.7) | 55 (4.7) | 1,114 (95.3) | 53 (4.4) | 1,168 (95.7) | 32 (2.7) | 1,176 (97.4) | 7 (0.7) | 1,079 (99.4) | 7 (0.5) | 1,299 (99.5) | 1 (0.1) | 1,311 (99.9) |
| Worsened | 82 (10.6) | 696 (89.5)* | 62 (6.8) | 854 (93.2) | 54 (5.4) | 952 (94.6) | 31 (3.0) | 999 (97.0) | 12 (1.4) | 855 (98.6) | 5 (0.4) | 1,127 (99.6) | 2 (0.2) | 1,140 (99.8) |
Note: Abbreviation: PINE, Population Study of ChINnese Elderly.
aValues are presented as N (%).
b*p < 0.05, +p < 0.01, #p < 0.001.
Association Between Participant Characteristics and Incident EA
We conducted multiple logistic regression analyses to assess the association between basic sociodemographic characteristics and the incidence of EA. The results in Table 3 indicate that several participant characteristics were significantly associated with incident abuse in the 2-year follow-up period. Overall, self-perceived worsened health was associated with increased risk of EA (OR 1.28 [1.01, 1.62]), and preference of speaking English or Mandarin was associated with increased risk of EA according to the 10-question instrument (odds ratio [OR] 2.06 [1.21, 3.52]). In regards to the specific subtypes of EA, older age and female gender were associated with increased risk of caregiver neglect (OR 1.06 [1.00, 1.13]; OR 2.98 [1.10, 8.11]). Longer duration of residence in the United States was associated with increased risk of financial abuse (OR 1.02 [1.00, 1.05]). The preference of speaking English or Mandarin was associated with higher risk of sexual or physical abuse (OR 3.91 [1.01, 15.17]). Findings also suggested that several sociodemographic characteristics were not significantly associated with EA incidence in the population, including education, income, marital status, number of children, country of origin, overall health, and life quality.
Table 3.
Regression of Baseline Sociodemographic Characteristics and 2-Year EA Incidencea,b
| Overall | 10-item screener | Psychological | Neglect | Financial | Physical/sexual | |
|---|---|---|---|---|---|---|
| OR (95% CI) | ||||||
| Time (intervals) | 0. 65 (0.35, 1.21) | 0.72 (0.36, 1.43) | 0.72 (0.35, 1.47) | 1.39 (0.391. 4.921) | 0.77 (0.33, 1.82) | 0.97 (0.18, 5.24) |
| Age | 1.02 (1.00, 1.04) | 0.99 (0.97, 1.02) | 0.98 (0.95, 1.01) | 1.06 (1.00, 1.13) | 0.98 (0.94, 1.02) | 0.97 (0.90, 1.04) |
| Female | 0.84 (0.61, 1.23) | 0.77 (0.52, 1.16) | 0.91 (0.60, 1.36) | 2.98 (1.10, 8.11)* | 0.64 (0.38, 1.09) | 1.38 (044, 4.32) |
| Education | 1.02 (0.98, 1.07) | 0.99 (0.94, 1.04) | 1.02 (0.97, 1.07) | 1.05 (0.94, 1.17) | 1.05 (0.99, 1.12) | 0.97 (0.85, 1.11) |
| Income | 1.06 (0.92, 1.23) | 1.03 (0.87, 1.22) | 0.99 (0.83, 1.17) | 0.53 (0.27, 1.05) | 1.03 (0.86, 1.23) | 1.02 (0.68, 1.53) |
| Married | 1.15 (0.76, 1.74) | 0.98 (0.61, 1.57) | 1.03 (0.63, 1.67) | 1.45 (0.50, 4.18) | 0.82 (0.45, 1.50) | 0.91 (0.28, 2.97) |
| Children | 0.94 (0.84, 1.07) | 0.98 (0.85, 1.14) | 0.96 (0.82, 1.11) | 0.74 (0.52, 1.05) | 0.99 (0.76, 1.12) | 1.12 (0.78, 1.61) |
| In the U.S. | 1.00 (0.98, 1.01) | 0.99 (0.97, 1.01) | 1.00 (0.98, 1.02) | 1.00 (0.97, 1.04) | 1.02 (1.00, 1.05)* | 1.01 (0.96, 1.05) |
| Chinaborn | 0.85 (0.44, 1.63) | 0.91 (0.42, 1.98) | 0.71 (0.35, 1.43) | 1.45 (0.18, 11.65) | 0.90 (0.38, 2.12) | 0.56 (0.11, 2.92) |
| Mandarin/English | 1.40 (0.87, 2.28) | 2.08 (1.21, 3.58)+ | 1.45 (0.84, 2.49) | 0.84 (0.26, 2.74) | 1.39 (0.71, 2.72) | 3.91 (1.01, 15.17)* |
| Overall health | 0.83 (0.66, 1.05) | 1.00 (0.77, 1.29) | 0.91 (0.70, 1.20) | 1.13 (0.61, 2.09) | 1.07 (0.76, 1.50) | 0.91 (0.45, 1.84) |
| Life quality | 1.23 (0.94, 1.60) | 1.08 (0.81, 1.46) | 1.23 (0.90, 1.68) | 1.94 (0.96, 3.95) | 1.27 (0.85, 1.88) | 1.96 (0.86, 4.48) |
| Health change | 1.28 (1.01, 1.62)* | 1.20 (0.93, 1.57) | 1.23 (0.86, 1.47) | 1.14 (0.65, 2.01) | 0.92 (0.66, 1.29) | 0.96 (0.49, 1.90) |
Note: aAbbreviations: OR, odds ratio; CI, confidence interval.
b*p < 0.05, +p < 0.01, #p < 0.001.
Discussion
To our knowledge, this is the first population-based study estimating EA incidence within the context of a longitudinal cohort study. The findings suggested that incident EA cases are common among the U.S. Chinese older adults. We also found that older age, female gender, longer duration of residence, preference of speaking Mandarin or English and perceived worsened health status were significantly associated with risk of abuse incidence and these relationships differ by types of EA. Our results validate and challenge prior hypotheses relating to potential risk factors of EA, as well as examine factors that have not been thoroughly investigated, such as duration of residence, language preference, and country of origin.
Consistent with previous findings about gender (4,6,31–33), our findings suggested that female gender is associated with increased risk of caregiver neglect. This may be partially explained by the traditional expectation that Chinese women provide care to family members, but are less likely to receive the same support and care from others (34). Also, women’s higher risks of caregiver neglect might be related to their observed tendency of living with fewer people in this population (35). Alternatively, men might underreport and be reluctant to disclose neglects, due to stigma regarding weakness and dependency, and masculinity ideology (36). Given that, both genders warrant attention regarding caregiver neglect, and more qualitative investigations are needed to better understand the different experiences of EA from men and women.
Consistent with findings of a previous study of Latino immigrants (37), our results indicate that the Chinese older adults who have been in the United States longer are more likely to self-report financial exploitations. Duration of residence and language acquisition are 2 important proxies for measuring the degree of acculturation to the host society in immigrant population research (38). We suspect that this association in our study might involve a complicated process of acculturation associated with changes concerning culture, emotion, interpersonal or intergenerational relationships, and socioeconomic activities, which might expose them to greater risks of exploitations. For example, lengthier residence might lead to more assets or social capitals, social interactions and financial activities, and intergenerational conflicts due to acculturation gaps (39,40).
Of note, although the preference of speaking Mandarin and English appeared to be a risk factor associated with incident EA compared to Taishanese and Cantonese, language preference may not be a factor affecting abuse. Language preference might reflect some extents of acculturation, and given Mandarin and English are languages that are more widely used in the United States, we highly suspect that this result might be explained by previous conclusions that less-acculturated immigrant persons are less likely to acknowledge abuse (41). Alternatively, since Taishanese and Cantonese are dialects spoken by individuals originate from areas in the vicinity of Guangdong province in southern China (42), particular history, culture, or regional values might contribute to the result.
Interestingly, although decreased physical health conditions are considered to diminish older person’s ability to defend and protect them from EA, and significant associations have been consistently observed between physical health and higher risks of EA from cross-sectional studies (43,44), our findings demonstrate that poor overall health of an earlier point were not associated with the incident EA cases during the 2 years. Similarly, low income and poor life quality were suspected to be important social determinants of EA; however, they had no significant association with incident abuse in the study. Moreover, contested findings exist surrounding the associations and strengths between several other sociodemographic characteristics of victims and abuse including education, marital status, and number of children. Our study shows that these factors were not associated with incident abuse.
The findings need to be interpreted with cautions since they might not be generalized to populations other than the U.S. Chinese community-dwelling older adults, and there are several limitations. First, the data may have been vulnerable to recall-bias given the age of the participants. Second, the incidence might be underestimated especially in the face-to-face interviewing contexts. Third, due to a lack of more accurate descriptive information about the abusive events, we are unable to determine whether the observed risk factors of abuse in the study have direct causal relationship with EA. They are likely to be mediating factors between certain unmeasured factors causing EA; for instance, preferring speaking Mandarin or English predicts increased probability of abuse, but language is only one aspect of culture and acculturation process to the host society, where the root causes might lie in. Fourth, a wider array of variables in addition to sociodemographic aspects needs to be assessed. Last, we were unable to follow up with all participants due to the longitudinal design.
Despite the limitations, this study is able to offer a key contribution and implication to the field of EA research regarding the validation of putative risk factors of EA through longitudinal data. In the arenas of health care and legislations, these findings can also assist with determining the efficacy of screening vulnerability of EA through basic characteristics of victims and identify populations at higher risk to guide policy decisions. With respect to future research, leveraged efforts are needed in the following aspects to address the persisting gaps in our knowledge of EA. First, large longitudinal studies are needed to explore EA incidence in order to validate previous hypotheses relating risk factors of EA in diverse populations and settings. To substantially save resources, researchers may consider incorporating EA incidence measurements in existing large cohort longitudinal studies. Second, to adequately and systematically assess EA risk factors, a standardized theoretical framework covering variables in the domains of individual sociodemographics, cognitive function, physical function, psychological well-being, social relationships, and contextual factors should be established (45). Third, while instruments measuring EA with increased validity, reliability, and cultural sensitivity are under development, researchers may solicit data not only from EA victims, but also from multiple informants such as perpetrators, ethnography observations, and facility-level or medical records to minimize threats to data validity owing to recall bias, recognition barriers, and reluctance for disclosure. Last, this study demonstrates how profound difference in culture values might influence EA as an intricate social problem, which calls for mixed-methods research and more nuanced approaches to better understand the role of gender and culture in EA.
In sum, incident EA is common within the U.S. Chinese older adults and is associated with sociodemographic factors of the victims. To pave the way for future development of prevention strategy, practice, and policy, more longitudinal investigations are needed to examine incidence and multilevel risk factors of EA and subtypes in diverse populations.
Funding
Dr. Dong is supported by National Institute on Aging grant (R01 AG042318, R01 MD006173, R01 AG11101 & RC4 AG039085), Paul B. Beeson Award in Aging (K23 AG030944), The Starr Foundation, John A. Hartford Foundation and The Atlantic Philanthropies.
Conflict of Interest
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Acknowledgment
Dr. Dong had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. 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 Chinese American Service League (CASL), Dr. David Lee with Illinois College of Optometry, David Wu with Pui Tak Center, Dr. Hong Liu with Midwest Asian Health Association, Dr. Margaret Dolan with John H. Stroger Jr. Hospital, Mary Jane Welch with Rush University Medical Center, Florence Lei with CASL Pine Tree Council, Julia Wong with CASL Senior Housing, Dr. Jing Zhang with Asian Human Services, Marta Pereya with Coalition of Limited English Speaking Elderly, Mona El-Shamaa with Asian Health Coalition.
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