Assessment of Rural-Urban Differences in Health Care Use and Survival Among Medicare Beneficiaries With Alzheimer Disease and Related Dementia

Rahman, Momotazur; White, Elizabeth M.; Thomas, Kali S.; Jutkowitz, Eric

doi:10.1001/jamanetworkopen.2020.22111

Cited by 42 publications

(51 citation statements)

References 48 publications

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“…These include the higher prevalence of dementia than urban areas, low dementia screening coverage in rural areas, and results from preliminary studies points out dementia as the most common geriatric problem in rural areas. 9,10,11 We obtained a low sensitivity and high specificity for the AMT questionnaire. This finding was similar to previous studies, which observed a wide sensitivity variation (12.8-99%) but consistently high specificity (84-100%) for AMT.…”

Section: Discussionmentioning

confidence: 89%

Are Abbreviated Mental Test (AMT) and Ascertain Dementia 8 Indonesia (AD8-INA) Questionnaires More Superior than Mini-Mental State Examination (MMSE) as Dementia Screening Instrument among Elderly in Rural Areas?

Swatan

Haryono

2022

AKSONA : Aksona: Journal of Neurology and Neuroscience

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Introduction: Dementia screening provides numerous benefits to its users. However, current screening methods have several limitations regarding applicability and accuracy, making it difficult to accommodate the results. Objective: To describe whether the Abbreviated Mental Test (AMT) and Ascertain Dementia 8 Indonesia (AD8-INA) questionnaire is superior to Mini-Mental State Examination (MMSE) questionnaire as a dementia screening instrument for the elderly in rural areas. Methods: A cross-sectional study was conducted in February 2020 at Banyuwangi residence. Dementia screening was conducted among elderly respondents using MMSE, AMT, and AD8-INA questionnaires. Sensitivity and specificity of AMT, AD8-INA and combined AMT+AD8-INA were compared with the MMSE questionnaire using crosstabs. Comparison of time required to complete each questionnaire was analyzed using Wilcoxon Signed Rank Test. Results: Mean age among 59 respondents was 68.44 years. The average MMSE score was 24.54. Compared with MMSE, the AMT questionnaire had a sensitivity of 47.37% and specificity of 100% (X2 = 22.36, p <0.001). Meanwhile, the AD8-INA questionnaire had a sensitivity of 63.16% and specificity of 45% (X2 = 24.64, p <0.001). The average questionnaire completion time of AMT, AD8-INA, and combined AMT+AD8-INA each was significantly shorter than MMSE (122.59, 121.17, and 243.76 seconds vs 319.83 seconds, p<0.001, <0.001, and <0.001, respectively). Conclusion: This study found that the combined AMT+AD8-INA questionnaire could be used as a dementia screening instrument among the elderly in rural areas with considerable sensitivity and shorter administration time.

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Section: Discussionmentioning

confidence: 89%

Are Abbreviated Mental Test (AMT) and Ascertain Dementia 8 Indonesia (AD8-INA) Questionnaires More Superior than Mini-Mental State Examination (MMSE) as Dementia Screening Instrument among Elderly in Rural Areas?

Swatan

Haryono

2022

AKSONA : Aksona: Journal of Neurology and Neuroscience

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“…It should be noted that our study does have some limitations. In particular, previous studies have indicated that risks in addition to genetic risk can lead to AD (Durazzo et al, 2014;Rahman et al, 2020;Thomas et al, 2020). For example, a recent study showed that individuals with diabetes and the ɛ4 allele demonstrated a faster functional decline than those without diabetes (Thomas et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

The link between static and dynamic brain functional network connectivity and genetic risk of Alzheimer’s disease

Zendehrouh

et al. 2021

Preprint

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Apolipoprotein E (APOE) polymorphic alleles are genetic factors associated with Alzheimer's disease (AD) risk. Although previous studies have explored the link between AD genetic risk and static functional network connectivity (sFNC), to the best of our knowledge, no previous studies have evaluated the association between dynamic FNC (dFNC) and AD genetic risk. Here, we examined the link between sFNC, dFNC, and AD genetic risk with a reproducible, data-driven approach. We used rs-fMRI, demographic, and APOE data from cognitively normal individuals (N=894) between 42 to 95 years of age (mean = 70 years). We divided individuals into low, moderate, and high-risk groups. Using Pearson correlation, we calculated sFNC across seven brain networks. We also calculated dFNC with a sliding window and Pearson correlation. The dFNC windows were partitioned into three distinct states with k-means clustering. Next, we calculated the amount of time each subject spent in each state, called occupancy rate or OCR. We compared both sFNC and OCR, estimated from dFNC, across individuals with different genetic risk and found that both sFNC and dFNC are related to AD genetic risk. We found that higher AD risk reduces within-visual sensory network (VSN) sFNC and that individuals with higher AD risk spend more time in a state with lower within-VSN dFNC. Additionally, we found that AD genetic risk affects whole-brain sFNC and dFNC in women but not in men. In conclusion, we presented novel insights into the links between sFNC, dFNC, and AD genetic risk.

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“…18 For purposes of this study, three categories are defined: urban, urban-adjacent rural (UAR), and nonurban-adjacent rural (NAR). 19,20 This classification has been commonly used to attribute rurality based on census tract or ZIP Code. 21,22 To validate representativeness of the cohort population with the overall US population, we compared the population percentages for each category in N3C with the US population using public datasets.…”

Section: Cohort Identificationmentioning

confidence: 99%

Higher hospitalization and mortality rates among SARS-CoV-2 infected Persons in Rural America

Anzalone

Horswell

Hendricks

et al. 2021

Preprint

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IMPORTANCE: Rural communities are among the most underserved and resource-scarce populations in the United States (US), yet there are limited data on COVID-19 mortality in rural America. Furthermore, rural data are rarely centralized, precluding comparability across urban and rural regions. OBJECTIVE: The purpose of this study is to assess hospitalization rates and all-cause inpatient mortality among persons with definitive COVID-19 diagnoses residing in rural and urban areas. DESIGN, SETTINGS, AND PARTICIPANTS: This retrospective cohort study from the National COVID Cohort Collaborative (N3C) examines a cohort of 573,018 patients from 27 US hospital systems presenting with SARS-CoV-2 infection between January 2020 and March 2021, of whom 117,897 were hospitalized. A sample of 450,725 hospitalized persons without COVID-19 diagnoses was identified for comparison. EXPOSURES: ZIP Codes provided by source hospital systems were classified by urban-rural gradient through a crosswalk to the US Department of Agriculture Rural-Urban Commuting Area Codes. MAIN OUTCOMES AND MEASURES: Primary outcomes were hospitalization and all-cause mortality among hospitalized patients. Kaplan-Meier analysis and mixed effects logistic regression were used to estimate 30-day survival in hospitalized patients and associations between rurality, hospitalization, and inpatient mortality while controlling for major risk factors. RESULTS: Rural patients were more likely to be older, white, have higher body mass index, and diagnosed with SARS-CoV-2 later in the pandemic compared with their urban counterparts. Rural compared with urban inhabitants had higher rates of hospitalization (23% vs. 19%) and all-cause mortality among hospitalized patients (16% vs. 11%). After adjustment for demographic and baseline differences, rural residents (both urban adjacent and non-adjacent) with COVID-19 were more likely to be hospitalized (Adjusted Odds Ratio (AOR) 1.41, 95% Confidence Interval (CI), 1.37-1.45 and AOR 1.42, CI 1.35-1.50) and to die or be transferred to hospice (AOR 1.62, CI 1.30-1.49 and 1.38, CI 1.30-1.49), respectively. Similar differences in mortality were noted for hospitalized patients without SARS-CoV-2 infection. CONCLUSIONS: Hospitalization and inpatient mortality are higher among rural compared with urban persons with COVID-19, even after adjusting for several factors, including age and comorbidities. Further research is needed to understand the factors that drive health disparities in rural populations.

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Assessment of Rural-Urban Differences in Health Care Use and Survival Among Medicare Beneficiaries With Alzheimer Disease and Related Dementia

Abstract: This cohort study uses Medicare claims data from nursing home and home health assessments from all US counties to investigate how health care use and survival compares between individuals with Alzheimer disease and related dementia in rural vs urban settings.

Cited by 42 publications

References 48 publications

Are Abbreviated Mental Test (AMT) and Ascertain Dementia 8 Indonesia (AD8-INA) Questionnaires More Superior than Mini-Mental State Examination (MMSE) as Dementia Screening Instrument among Elderly in Rural Areas?

Are Abbreviated Mental Test (AMT) and Ascertain Dementia 8 Indonesia (AD8-INA) Questionnaires More Superior than Mini-Mental State Examination (MMSE) as Dementia Screening Instrument among Elderly in Rural Areas?

The link between static and dynamic brain functional network connectivity and genetic risk of Alzheimer’s disease

Higher hospitalization and mortality rates among SARS-CoV-2 infected Persons in Rural America

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