Characteristics of participants’ and caregivers’ influence on non-response in a cross-sectional study of dementia in an older population

Tan, Jiping; Li, Nan; Cui, Bo; Wang, Luning; Zhao, Yiming; Bao-he, Zhang; Liu, Zhiying; Zhang, Shao-gang; Sun, L. Z.; Liu, Nan; Deng, Ruixiang; Ma, Li-hua; Zhang, Yongqing

doi:10.1016/j.archger.2015.08.018

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…For studies seeking response from family members of critically ill patients, the family member’s relationship with the patient may also be an important predictor of response. Health status has been identified as a predictor of survey response in other study populations (12, 15, 45), and here, better self-assessed health among family members was associated with completion of baseline surveys. This identifies family members with health limitations as participants who may require intensive follow-up to facilitate survey return.…”

Section: Discussionsupporting

confidence: 51%

“…Poor health status and symptoms of psychological distress have been identified as predictors of non-response and attrition in a variety of patient populations (11–15), but less information is available about factors associated with study participation by family members of critically ill patients (16). Using data from a randomized trial of an intervention to improve communication between clinicians and families of critically ill patients (17), we examined patient- and family-level predictors of the return of usable study questionnaires from family members at baseline as well as at 3- and 6-month follow-up assessments.…”

Section: Introductionmentioning

confidence: 99%

“…Using data from a randomized trial of an intervention to improve communication between clinicians and families of critically ill patients (17), we examined patient- and family-level predictors of the return of usable study questionnaires from family members at baseline as well as at 3- and 6-month follow-up assessments. Based on existing evidence about predictors of non-response and attrition (11–15, 18, 19), we chose to assess family member symptoms of psychological distress, decisional independence preference, and attachment style as predictors of return of usable surveys. We hypothesized that family members with fewer symptoms of psychological distress, a preference for less decisional independence (reflecting more trust in clinicians), and secure attachment style would be more likely to return usable questionnaires at the specified time points.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Understanding Response Rates to Surveys About Family Members' Psychological Symptoms After Patients' Critical Illness

Long

Downey

Engelberg

et al. 2017

Journal of Pain and Symptom Management

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Context Achieving adequate response rates from family members of critically ill patients can be challenging, especially when assessing psychological symptoms. Objectives To identify factors associated with completion of surveys about psychological symptoms among family members of critically ill patients. Methods Using data from a randomized trial of an intervention to improve communication between clinicians and families of critically ill patients, we examined patient- and family-level predictors of the return of usable surveys at baseline, 3- and 6-months (N=181, 171, and 155, respectively). Family-level predictors included baseline symptoms of psychological distress, decisional independence preference, and attachment style. We hypothesized that family with fewer symptoms of psychological distress, a preference for less decisional independence, and secure attachment style would be more likely to return questionnaires. Results We identified several predictors of the return of usable questionnaires. Better self-assessed family-member health status was associated with a higher likelihood, and stronger agreement with a support-seeking attachment style with a lower likelihood, of obtaining usable baseline surveys. At 3 months, family-level predictors of return of usable surveys included having usable baseline surveys, status as the patient’s legal next-of-kin, and stronger agreement with a secure attachment style. The only predictor of receipt of surveys at 6 months was the presence of usable surveys at 3 months. Conclusion We identified several predictors of the receipt of surveys assessing psychological symptoms in family of critically ill patients, including family member health status and attachment style. Using these characteristics to inform follow-up mailings and reminders may enhance response rates.

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Section: Discussionsupporting

confidence: 51%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Understanding Response Rates to Surveys About Family Members' Psychological Symptoms After Patients' Critical Illness

Long

Downey

Engelberg

et al. 2017

Journal of Pain and Symptom Management

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“…When we used different methods to estimate dementia diagnoses in patients who did not respond, the prevalence of dementia increased to 13.40–14.19% in the 80–89 age group. In a previous study [ 29 ], we compared the characteristics of responders and non-responders in phase II. It showed that non-responders in phase II were older, in poorer physical health, with lower cognitive performance, suggesting that non-responders were more likely to have dementia.…”

Section: Discussionmentioning

confidence: 99%

Robust estimation of dementia prevalence from two-phase surveys with non-responders via propensity score stratification

Shen

Wang

Zhao

et al. 2023

BMC Med Res Methodol

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Background Missing diagnoses are common in cross-sectional studies of dementia, and this missingness is usually related to whether the respondent has dementia or not. Failure to properly address this issue can lead to underestimation of prevalence. To obtain accurate prevalence estimates, we propose different estimation methods within the framework of propensity score stratification (PSS), which can significantly reduce the negative impact of non-response on prevalence estimates. Methods To obtain accurate estimates of dementia prevalence, we calculated the propensity score (PS) of each participant to be a non-responder using logistic regression with demographic information, cognitive tests and physical function variables as covariates. We then divided all participants into five equal-sized strata based on their PS. The stratum-specific prevalence of dementia was estimated using simple estimation (SE), regression estimation (RE), and regression estimation with multiple imputation (REMI). These stratum-specific estimates were integrated to obtain an overall estimate of dementia prevalence. Results The estimated prevalence of dementia using SE, RE, and REMI with PSS was 12.24%, 12.28%, and 12.20%, respectively. These estimates showed higher consistency than the estimates obtained without PSS, which were 11.64%, 12.33%, and 11.98%, respectively. Furthermore, considering only the observed diagnoses, the prevalence in the same group was found to be 9.95%, which is significantly lower than the prevalence estimated by our proposed method. This suggested that prevalence estimates obtained without properly accounting for missing data might underestimate the true prevalence. Conclusion Estimating the prevalence of dementia using the PSS provides a more robust and less biased estimate.

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“…There is an association between the level of cognitive deterioration and the proportion of non-responders to self-administered questionnaires (missing data). When there is a high level of missing data, this is considered as study limitation [24] because it hinders the results interpretation.…”

Section: Discussionmentioning

confidence: 99%

Features and impact of missing values in the association of self-rated health with mortality in care homes: a longitudinal study

Rodríguez-García¹,

Ayala

Rodríguez‐Blázquez

et al. 2019

Health Qual Life Outcomes

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Background Self-rated health (SRH) is a health measure used in studies of older adults. The objective of this study is to analyze SRH as a predictor of mortality in the institutionalized older population and the characteristics of those who do not provide information about their SRH on health questionnaires. Methods This is a 15-year follow-up study of older adult residents in nursing or care homes in of Madrid, Spain. SRH was measured on a 5-point Likert type scale. The association between answering the SRH question and socio-demographic and health characteristics was evaluated through prevalence ratio (PR), estimated by Poisson regression models. Survival rates associated with SRH were studied through a multivariate Cox regression. Results The sample has a mean age of 83.4 (standard deviation, SD = 7.3), with 75.7% women. Twelve percent did not answer the SRH item. Those who did not answer showed a higher probability of disability (Barthel index, PR = 0.76, 95% confidence interval = 0.67–0.86) and/or dementia (PR = 8.03, 3.38–19.03). A trend for higher mortality was observed in those persons who did not respond (adjusted hazard ratio HR = 1.26, 0.75–2.11). The mortality rate was 32% higher for those who declared poor SRH in comparison with those who reported good SRH (adjusted HR = 1.32, 1.08–1.6). Conclusions There is an elevated number of people who do not respond to the SRH item, mainly those with disabilities and cognitive deterioration. Lack of response to SRH is a good indicator of 15-year mortality for persons institutionalized in care or nursing homes.

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Characteristics of participants’ and caregivers’ influence on non-response in a cross-sectional study of dementia in an older population

Cited by 10 publications

References 24 publications

Understanding Response Rates to Surveys About Family Members' Psychological Symptoms After Patients' Critical Illness

Understanding Response Rates to Surveys About Family Members' Psychological Symptoms After Patients' Critical Illness

Robust estimation of dementia prevalence from two-phase surveys with non-responders via propensity score stratification

Features and impact of missing values in the association of self-rated health with mortality in care homes: a longitudinal study

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