Skip to main content

Clinical factors associated with recent medical care visits in nursing homes: a multi-site cross-sectional study

Abstract

Objectives

We examined which resident-level clinical factors influence the provision of a recent medical care visit in nursing homes (NHs).

Design

Multi-site cross-sectional.

Setting and participants

We extracted data on 3,556 NH residents from 18 NH facilities in Ontario, Canada, who received at minimum, an admission and first-quarterly assessment with the Resident Assessment Instrument Minimum Data Set (MDS) 2.0 between November 1, 2009, and October 31, 2017.

Methods

We conducted a secondary analysis of routinely collected MDS 2.0 data. The provision of a recent medical care visit by a physician (or authorized clinician) was assessed in the 14-day period preceding a resident’s first-quarterly MDS 2.0 assessment. We utilized best-subset multivariable logistic regression to model the adjusted associations between resident-level clinical factors and a recent medical care visit.

Results

Two thousand eight hundred fifty nine (80.4%) NH residents had one or more medical care visits prior to their first-quarterly MDS 2.0 assessment. Six clinically relevant factors were identified to be associated with recent medical care visits in the final model: exhibiting wandering behaviours (OR = 1.34, 95% CI 1.09 – 1.63), presence of a pressure ulcer (OR = 1.37, 95% CI 1.05 – 1.78), a urinary tract infection (UTI) (OR = 1.52, 95% CI 1.06 – 2.18), end-stage disease (OR = 9.70, 95% CI 1.32 – 71.02), new medication use (OR = 1.31, 95% CI 1.09 – 1.57), and analgesic use (OR = 1.24, 95% CI 1.03 – 1.49).

Conclusions and implications

Our findings suggest that resident-level clinical factors drive the provision of medical care visits following NH admission. Clinical factors associated with medical care visits align with the minimum competencies expected of physicians in NH practice, including managing safety risks, infections, medications, and death. Ensuring that NH physicians have opportunities to acquire and strengthen these competencies may be transformative to meet the ongoing needs of NH residents.

Peer Review reports

Introduction

The COVID-19 pandemic has highlighted the essential services provided within nursing homes (NHs) by multidisciplinary clinical teams [1]. While nursing staff and personal support workers provide the majority of bedside care, the complexity of NH residents warrants the need for physician care and mobile medical services [2,3,4]. In both Canada and the United States, a Most Responsible Provider (MRP) is assigned to each NH resident to provide routine medical assessments, ambulatory care visits, and determine clinical treatments and plans of care [5, 6]. MRPs exhibit knowledge, skills, and expertise in identifying and managing frailty, multimorbidity, polypharmacy, cognitive or behavioural conditions, and transitions of care [7].

Across North America, the frequency and pattern of NH resident visit schedules vary greatly based on current funding mandates [8]. In Ontario, Canada, MRPs provide routine services according to the Ontario Health Insurance Plan (OHIP) Fee Schedule, including admission and annual assessments, chronic disease management, monitoring changing health needs, ambulatory care visits, pre-procedural assessments, and palliative care services [6, 9]. Monthly Management fees account for the majority of physician claims in NHs, whereby MRPs are expected to provide a minimum of two assessments or progress notes, on average, per calendar month for routine medical care, management, or supervision of a NH resident [9]. In addition, NH physicians provide services such as engaging in shared-decision making with a resident’s family and care team, completing medication reviews, and participating in telephone consults [9]. The current Fee Schedule and mandates in Ontario do not strongly incentivize emergent and unscheduled medical care visits in NHs.

Prior work has attempted to make physician care in NHs more responsive to resident needs and complexity [10, 11], including same-day access to physician care [5]. However, little is known about what resident-level clinical factors influence medical care visits. Given that the vast majority of NH physicians in Ontario participate in a billing model that does not incentivize emergent and unscheduled medical care, it is an ideal sample to investigate the responsiveness of medical care to identifiable need. Our objective was to examine and determine what NH resident-level clinical factors influence the odds of receiving a medical care visit prior to a resident’s scheduled first-quarterly assessment.

Methods

Study design

We conducted a cross-sectional study of residents across a chain of 18 for-profit NHs in Ontario, Canada. Ethics approval was granted by the Hamilton Integrated Research Ethics Board (#2018–0739) for secondary analysis of the administrative and health data analyzed. We satisfied data security standards related to privacy and reporting, and we received a waiver of informed consent based on the absolute impractically of obtaining informed consent where the data were de-identified. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement was used to report the results of this study [12].

Data source

Data on residents’ clinical characteristics and outcomes were obtained from the Resident Assessment Instrument Minimum Data Set (MDS) 2.0 assessment. The MDS 2.0 is a comprehensive and standardized clinical assessment instrument that examines over 300 items from 15 health domains, including physical and cognitive functioning, medical diagnoses, social support, medication use, and health service use [13,14,15]. MDS 2.0 assessments were conducted by trained NH nursing staff as the standard practice [16].

The majority of Canadian provinces and territories use the MDS 2.0, or its successor, the interRAI Long-Term Care Facilities (LTCF) instrument, to assess all NH residents at admission and quarterly thereafter, until death or discharge [17]. The MDS is also mandated for use in the United States; however, the MDS 3.0 version is most commonly used [18]. Data for this study were obtained from an anonymized, population-level health administrative database.

Setting and participants

Our cohort comprised NH residents admitted to any facility within a large for-profit chain who received a minimum of two routine MDS 2.0 assessments (i.e., on intake to facility and first-quarterly) between November 1, 2009, and October 31, 2017. All residents were included in our analysis, regardless of pre-existing health conditions, resident complexity, or clinical factors. To examine the relationship between resident health and MRP encounters, we examined each NH resident’s first-quarterly MDS 2.0 assessment record which is routinely conducted 90 days after admission to a NH. The first-quarterly MDS 2.0 assessment was of interest to rule out the additional variance introduced by greater health instability and increased emergency department (ED) use during the transitory admission period [19].

Variables and measurement

Recent medical care visits in NHs may be impacted by many clinical factors related to the resident’s complexity or medical acuity. A total of 42 independent measures (see Additional file 1) that are routinely collected in MDS 2.0 assessments were selected as candidate predictors a priori, based on their relevance to newly developed primary care provider quality indicators for NH care and The Society for Post-Acute and Long-Term Care Medicine (AMDA) physician competency domains [20, 21].

We examined demographic (age and sex) and a series of clinical factors including, but not limited to: cognition, mood, and behaviour; falls, mobility, and pressure ulcers; pain management; urinary; medication use; special treatments and procedures; and clinical symptoms. Unless otherwise mentioned, all variables were left in their original measurement form (see Additional file 1).

Outcome measure

The primary outcome was a documented medical care visit by a physician (or authorized assistant or practitioner) in the 14 days preceding a resident’s first-quarterly MDS 2.0 assessment. Medical care visits were defined as partial or full examinations at the NH facility or in the physician’s office – excluding exams conducted in the ED [13]. Medical care visits included those conducted by medical doctors, osteopaths, podiatrists, or dental experts, who served as either the primary or consulting clinician. Authorized physician assistants and nurse practitioners working in collaboration were also included. The availability of data captured in the MDS 2.0 restricted our timeframe of analysis to medical care visits occurring 14 days prior to the first-quarterly assessment.

Statistical analysis

Each resident acted as a single unit of analysis. General frequency and central tendency measures were computed to provide descriptive statistics. Univariable and multivariable logistic regression was employed to identify the factors associated with one or more recent medical care visits in NHs. A series of univariable logistic regression models were computed to determine the observed relationship between each clinical factor of interest and recent medical care visits. Best-subset multivariable logistic regression was used to identify the subset of clinical covariates that best defined the relationship with recent medical care visits. The Akaike information criterion (AIC) was used to evaluate model fit. A best-subset of clinically relevant factors were selected to avoid suppressor effects commonly found with stepwise methods and statistically pre-determined predictors (e.g., < 0.25) [22]. Cases with missing data were deleted within each analysis. All analyses were performed using Statistical Analysis Software (SAS), version 9.2 (SAS Institute, Inc., Cary, North Carolina). Our model, using all candidate predictors, had an event-per-variable ratio > 10, as recommended to improve the external validity of findings [22].

Results

We identified 3,556 NH residents who had a first-quarterly MDS 2.0 assessment completed between November 15, 2009, and October 20, 2017 (Table 1). Residents were of advanced age (median 85 years, IQR = 10.8) and predominantly female (62.0%). The majority of residents had moderate or severe cognitive impairments (53.5%), and many were diagnosed with Alzheimer’s disease (18.3%) or a different taxonomy of dementia (54.5%). Most residents exhibited health stability, whereby very few experienced acute episodes or flare-ups of recurrent or chronic problems (7.8%) or end-stage disease (1.3%). Some residents had an ED visit (24.3%) or an inpatient hospital stay (23.6%) before their first-quarterly assessment. Overall, 2,859 (80.4%) NH residents had one or more medical care visits in the 14 days before their first-quarterly MDS 2.0 assessment.

Table 1 Characteristics of study participants (N = 3,556)

Resident clinical factors associated with recent medical care visits

Unadjusted and adjusted odds ratios can be found in Table 2. The best multivariable model for the relationship between recent medical care visits and NH residents’ clinical factors included seven covariates. Statistically significant odds ratios were observed for six factors entered in the final model: exhibiting wandering behaviours (OR = 1.34, 95% CI 1.09 – 1.63), the presence of a pressure ulcer (OR = 1.37, 95% CI 1.05 – 1.78), a current urinary tract infection (UTI) (OR = 1.52, 95% CI 1.06 – 2.18), end-stage disease (OR = 9.70, 95% CI 1.32 – 71.02), starting new medications (OR = 1.31, 95% CI 1.09 – 1.57), and analgesic use (OR = 1.24, 95% CI 1.03 – 1.49). Statistical adjustment did not influence the statistical significance of these predictors, highlighting the robustness of these associations. The multivariable model achieved good fit using the Hosmer and Lemeshow goodness-of-fit test (x2=6.61, df = 7, p = 0.47).

Table 2 Clinical factors associated with medical care visits

Missing data

On average, 16.5% of data were missing for the 42 independent measures (n = 588 residents were missing at least one data point), while none were missing for the outcome.

Discussion

Main findings

Most NH residents received a recent medical care visit preceding their first-quarterly MDS 2.0 assessment, affirming the need for medical care visits and support during the transitionary NH admission period. NH medical care may be impacted by many resident-level factors, including resident demographics, medical complexity, and lability of clinical symptoms [10, 23, 24]. We identified six clinically important factors that increased the odds of a recent medical care visit in newly admitted NH residents, including wandering, pressure ulcers, UTIs, end-stage disease, new medication use, and analgesic use. In the context of a predominantly scheduled billing model, we found that medical care visits are sensitive to resident needs aligned with the minimum competencies expected of physicians in NH practice.

A minimum expected competency of NH physicians includes having the knowledge and skill to identify when to implement interventions as to minimize risk factors and optimize resident safety [21]. The adverse consequences of wandering – including falls and other accidents, getting lost, malnutrition, fatigue, sleep disturbance, social isolation, kinship issues, and injury, including fractures – may necessitate medical care visits [25,26,27]. Pressure ulcers are associated with considerable mortality, distress, and discomfort among residents, all of which may drive medical care visits [28]. However, we could not discern whether medical care visits were specifically related to assessing or managing pressure ulcers, or if NH residents with pressure ulcers were more likely to have other acute health changes that drive medical care visits. Due to a lack of consensus on the diagnostic criteria for UTIs in older adults, stemming from inconsistent symptomology and biomarkers, the identification and treatment often varies across NH facilities and between residents [29,30,31]. In turn, additional physician time and resources may be required to properly diagnose and treat UTIs among NH residents with atypical or complex presentations, which may drive medical care visits. The reliance on antibiotic therapy for NH residents with UTIs may also influence visitation because of the high rates of adverse effects and poor tolerance for antibiotics [30, 31].

End-stage disease is one of several indicators of the MDS-Changes in Health, End-stage Disease and Symptoms and Signs (CHESS) score, a valid predictor of mortality, and is associated with greater physician involvement and use of medical treatments [32]. Medically unstable residents often have abnormal laboratory values and responsive changes in physician orders – both of which may influence medical care visits [32]. NH physicians are also expected to identify circumstances where palliative and end-of-life services may benefit the resident and family. The presence of, or progression to, end-stage disease may warrant further attention of physicians to support shared-decision making and dignity in death [21]. AMDA sets the minimum standard for NH physicians to prescribe and adjust medications prudently and evaluate resident adverse events and outcomes resulting from medication errors [21]. Older adults metabolize and respond to medications differently than their younger counterparts, and often require closer monitoring to ensure adequate therapeutic management and minimal adverse side effects [33, 34]. The high rates of polypharmacy in older adults further increases the risk of drug interactions [35]. Lastly, physicians often report challenges in recognizing and treating pain, especially for residents with cognitive impairments [36,37,38]. Interestingly, two independent measures, pain frequency and intensity, were highly prevalent in our sample but were not included in the final multivariable model. This finding aligns with prior work in that increased physician care is associated with reporting pain and being prescribed analgesics, whereas unreported pain is typically managed without medications [38].

Clinical and policy implications

In our sample, one-fifth of NH residents did not receive any medical care visits by physicians within two weeks of their first-quarterly assessment. In NHs, older adults are typically frail with multimorbidity, complex health needs, and limited ability to seek medical services outside of NH facilities [6, 23]. Consistent follow-up is crucial to ensure that NH residents are monitored and that their health concerns are managed proactively [39]. Additionally, physician competence and knowledge of the clinical factors that drive medical care visits – namely, safety risks, infections, medications, and death – are essential to support the identification and management of NH residents. COVID-19 illustrated the consequences associated with fewer medical care visits [40]. During the first wave of COVID-19 in Ontario, fewer medical care visits and care orders occurred in NHs, suggesting that in-person medical care visits were not replaced with virtual visits [40]. Our findings suggest that NH resident needs drive recent medical care visits, and supporting physician competence in regard to these clinical factors may promote the prognosis and quality of care of NH residents.

Strengths and limitations

Our study is unique in identifying resident-level clinical factors that are significantly associated with medical care visits across a large chain of NHs. The demographic and clinical profile of this cohort is similar to the broader population of NH residents in Ontario, supporting the generalizability of our results [41]. This analysis included NH facilities in a single chain, allowing for structural variables to be held constant (e.g., size, staff organization, physician visit and fee schedules) – strengthening the internal validity of our findings. Candidate predictor selection was guided by NH physician quality indicators and competency domains, allowing for the evaluation of informative assessment items concerning the frequency and need for medical care visitation in NHs. However, this study is not without its limitations.

While the study’s large sample size allowed for control of a broad range of clinical factors, some potential confounders were not available in the dataset and could not be controlled for. Similarly, the quality of the medical care visit was unknown and characteristics that may have been pertinent were not examined (e.g., whether the visit occurred in-person or over the phone, whether a physical assessment occurred, the type of clinician who conducted the visit, and time spent during the encounter). We were unable to differentiate in-person medical care visits associated with the Monthly Management Fee Schedule, from those occurring in response to emergent medical needs or conditions. However, the associations between medical care visits and measures of medical complexity suggest that in-person visits are largely in response to resident needs. If many of the in-person visits were routine, then that would bias in favour of no association; therefore, our associations are likely conservative. Additionally, we were unaware of whether residents received medical care visits outside the 14 days prior to their first-quarterly assessment. However, the timing in which residents received their quarterly assessment varied greatly (see Additional file 2), mitigating this risk of bias. While the 14-day timeframe specified in the MDS 2.0 may be perceived as arbitrary, this specific and measurable period reduces the potential for recall bias and makes our association more conservative.

Additionally, while pairwise deletion maximizes available data for our analysis and increases power, it assumes that all data are missing completely at random [42]. We identified that 16.5% of all data points were missing across six missing data patterns. Data may be missing because some variables are only collected in “full” versions of the MDS 2.0, which is only required at entry to NHs, annual assessments, or if significant clinical changes occur; other times, the shorter MDS 2.0 is completed. Since best-subset regression tends to overfit the data, we considered only clinically relevant candidate predictors and gave additional consideration to whether variables with a low prevalence in the sample (e.g., parenteral/IV or feeding tube and end-stage disease) were included in the multivariable model. We conducted a sensitivity analysis without end-stage disease in the final model, and the interpretations of the other variables in the multivariable model did not change (see Additional file 3).

Conclusions and implications

We identified six resident-level clinical factors that were significantly associated with recent medical care visits in NHs. NH residents exhibiting wandering behaviours, having one or more ulcers of any stage, a UTI, end-stage disease, starting new medications, or using analgesics had greater odds of receiving medical care visits. These clinical factors suggest that physician practice in NHs corresponds to competence among NH physicians to manage safety risks, infections, medications, and death. Ensuring NH physicians have opportunities to acquire and strengthen these competencies may be transformative to meet the ongoing needs of NH residents.

Conflicts of interest

This project received no formal funding, and the authors have no financial or personal conflicts of interest to disclose.

Availability of data and materials

The datasets analyzed during the current study are not publicly available due to containing personal health information. The data were used under license for the current study and so are not publicly available. The corresponding author can be contacted regarding any requests for the data from this study.

Abbreviations

NH:

Nursing home

NHs:

Nursing homes

MRP:

Most Responsible Provider

OHIP:

Ontario Health Insurance Plan

STROBE:

Strengthening the Reporting of Observational Studies in Epidemiology

MDS:

Minimum Data Set

LTCF:

Long-Term Care Facilities

ED:

Emergency department

AMDA:

The Society for Post-Acute and Long-Term Care Medicine

AIC:

Akaike information criterion

SAS:

Statistical Analysis Software

OR:

Odds ratio

CI:

Confidence interval

UTI:

Urinary tract infection

CHESS:

Changes in Health, End-stage Disease and Symptoms and Signs

References

  1. Katz PR, Ryskina K, Saliba D, Costa A, Jung H-Y, Wagner LM, et al. Medical Care Delivery in U.S. Nursing Homes: Current and Future Practice. Geron [Internet]. 2021;61(4):595–604. https://doi.org/10.1093/geront/gnaa141.

    Article  Google Scholar 

  2. Ng R, Lane N, Tanuseputro P, Mojaverian N, Talarico R, Wodchis WP, et al. Increasing Complexity of New Nursing Home Residents in Ontario, Canada: A Serial Cross-Sectional Study. J Am Geriatr Soc. 2020;68(6):1293–300.

    Article  Google Scholar 

  3. Ontario Long Term Care Association. This is Long-Term Care 2019 [Internet]. Toronto, Ontario: Ontario Long Term Care Association; 2019 Apr [cited 2020 Apr 23] p. 16. Available from: https://www.oltca.com/OLTCA/Documents/Reports/TILTC2019web.pdf

  4. Starfield B, Shi L, Macinko J. Contribution of Primary Care to Health Systems and Health. Milbank Q. 2005;83(3):457–502.

    Article  Google Scholar 

  5. Kobewka DM, Kunkel E, Hsu A, Talarico R, Tanuseputro P. Physician Availability in Long-term Care and Resident Hospital Transfer: A Retrospective Cohort Study. J Am Med Dir Assoc. 2019;21(4):469–75.

    Article  Google Scholar 

  6. Lam JM, Anderson GM, Austin PC, Bronskill SE. Family physicians providing regular care to residents in Ontario long-term care homes: characteristics and practice patterns. Can Fam Physician. 2012;58(11):1241–8.

    PubMed  PubMed Central  Google Scholar 

  7. Katz PR. Nursing Home Physician Specialists: A Response to the Workforce Crisis in Long-Term Care. Ann Intern Med. 2009;150(6):411.

    Article  Google Scholar 

  8. Levy C, Palat S-IT, Kramer AM. Physician Practice Patterns in Nursing Homes. J Am Med Dir Assoc. 2007;8(9):558–67.

    Article  Google Scholar 

  9. Ministry of Health and Long-Term Care. Schedule of Benefits Under the Health Insurance Act [Internet]. Government of Ontario; 2021 Feb [cited 2021 Jun 15] p. 970. Available from: https://www.health.gov.on.ca/en/pro/programs/ohip/sob/

  10. Siu HY-H, White J, Sergeant M, Moore A, Patterson C. Development of a periodic health examination form for the frail elderly in long-term care. Can Fam Physician. 2016;62(2):147–55.

    PubMed  PubMed Central  Google Scholar 

  11. Dunlop S, Coyte PC, McIsaac W. Socio-economic status and the utilisation of physicians’ services: results from the Canadian National Population Health Survey. Soc Sci Med. 2000;51(1):123–33.

    Article  CAS  Google Scholar 

  12. Vandenbroucke JP, von Elm E, Altman DG, Gøtzsche PC, Mulrow CD, Pocock SJ, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration. PLoS Med. 2007 Oct 16;4(10):e297.

  13. Morris J, Hawes C, Mor V, Phillips C, Fries B, Nonemaker S, et al. Resident Assessment Instrument (RAI) RAI-MDS 2.0 User’s Manual, Canadian Version. Washington DC: interRAI; 2010.

    Google Scholar 

  14. Hirdes JP, Ljunggren G, Morris JN, Frijters DH, FinneSoveri H, Gray L, et al. Reliability of the interRAI suite of assessment instruments: a 12-country study of an integrated health information system. BMC Health Serv Res. 2008;8(1):277.

    Article  Google Scholar 

  15. Gray LC, Berg K, Fries BE, Henrard J-C, Hirdes JP, Steel K, et al. Sharing clinical information across care settings: the birth of an integrated assessment system. BMC Health Serv Res. 2009;9:71.

    Article  Google Scholar 

  16. Dash D, Heckman GA, Boscart VM, Costa AP, Killingbeck J, d’Avernas JR. Using powerful data from the interRAI MDS to support care and a learning health system: A case study from long-term care. Healthc Manage Forum. 2018;31(4):153–9.

    Article  Google Scholar 

  17. Ministry of Health and Long-Term Care. Policy: Resident Assessment Instrument Minimum Data Set 2.0 Funding [Internet]. Government of Ontario; 2013 Jun p. 6. Available from: https://www.health.gov.on.ca/en/public/programs/ltc/docs/RAI-MDS.pdf

  18. Hirdes JP, Retalic T, Muskat C, Morris JN, Katz PR. The Seniors Quality Leap Initiative (SQLI): An International Collaborative to Improve Quality in Long-Term Care. J Am Med Dir Assoc. 2020;21(12):1931–6.

    Article  Google Scholar 

  19. Gruneir A, Bell CM, Bronskill SE, Schull M, Anderson GM, Rochon PA. Frequency and Pattern of Emergency Department Visits by Long-Term Care Residents—A Population-Based Study. J Am Geriatr Soc. 2010;58(3):510–7.

    Article  Google Scholar 

  20. Mays AM, Saliba D, Feldman S, Smalbrugge M, Hertogh CMPM, Booker TL, et al. Quality Indicators of Primary Care Provider Engagement in Nursing Home Care. J Am Med Dir Assoc. 2018;19(10):824–32.

    Article  Google Scholar 

  21. AMDA. Dedicated to Long Term Care Medicine (AMDA) Competencies for Post-Acute and Long-Term Care Medicine Setting of Care: SNF/NF [Internet]. AMDA; p. 3. Available from: https://www.mcknights.com/wp-content/uploads/sites/5/2018/07/amda_competencies1_13574.pdf

  22. Steyerberg EW. Clinical Prediction Models [Internet]. New York, NY: Springer New York; 2009 [cited 2020 Jun 24]. (Statistics for Biology and Health). Available from: http://link.springer.com/, https://doi.org/10.1007/978-0-387-77244-8

  23. Shaver NS, Lapenskie J, Smith GA, Hsu AT, Liddy C, Tanuseputro P. How Often, Where, and by Which Specialty Do Long-Term Care Home Residents Receive Specialist Physician Care? A Retrospective Cohort Study. J Appl Gerontol. 2020;6:073346481990125.

    Google Scholar 

  24. Arling G. Interaction effects in a multivariate model of physician visits by older people. Med Care. 1985;23(4):361–71.

    Article  CAS  Google Scholar 

  25. Cipriani G, Lucetti C, Nuti A, Danti S. Wandering and dementia. Psychogeriatrics. 2014;14(2):135–42.

    Article  Google Scholar 

  26. Volicer L, van der Steen JT, Frijters DHM. Involvement in Activities and Wandering in Nursing Home Residents With Cognitive Impairment. Alzheimer Dis Assoc Disord. 2013;27(3):272–7.

    Article  Google Scholar 

  27. Beattie ERA, Algase DL, Song J. Special Section—Behavioral symptoms of dementia: their measurement and intervention. Keeping wandering nursing home residents at the table: improving food intake using a behavioral communication intervention. Aging Ment Health. 2004;8(2):109–16.

    Article  CAS  Google Scholar 

  28. Lyder CH, Ayello EA. Pressure Ulcers: A Patient Safety Issue. In: Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2008 [cited 2021 Jun 21]. (Advances in Patient Safety). Available from: http://www.ncbi.nlm.nih.gov/books/NBK2650/

  29. Agata ED, Loeb MB, Mitchell SL. Challenges in Assessing Nursing Home Residents with Advanced Dementia for Suspected Urinary Tract Infections. J Am Geriatr Soc. 2013;61(1):62–6.

    Article  Google Scholar 

  30. Juthani-Mehta M, Tinetti M, Perrelli E, Towle V, Ness PHV, Quagliarello V. Diagnostic Accuracy of Criteria for Urinary Tract Infection in a Cohort of Nursing Home Residents*. J Am Geriatr Soc. 2007;55(7):1072–7.

    Article  Google Scholar 

  31. Midthun S, Paur R, Bruce AW, Midthun P. Urinary Tract Infections in the Elderly: A Survey of Physicians and Nurses. Geriatr Nur (Lond). 2005;26(4):245–51.

    Article  Google Scholar 

  32. Hirdes JP, Frijters DH, Teare GF. The MDS-CHESS scale: a new measure to predict mortality in institutionalized older people. J Am Geriatr Soc. 2003;51(1):96–100.

    Article  Google Scholar 

  33. Lavan A, Gallagher P. Predicting risk of adverse drug reactions in older adults. Therapeutic advances in drug safety. Ther Adv Drug Saf. 2016;7(1):11–22.

    Article  Google Scholar 

  34. American Geriatrics Society 2019. Updated AGS Beers Criteria® for Potentially Inappropriate Medication Use in Older Adults. J Am Geriatr Soc. 2019;67(4):674–94.

    Article  Google Scholar 

  35. Guthrie B, Makubate B, Hernandez-Santiago V, Dreischulte T. The rising tide of polypharmacy and drug-drug interactions: population database analysis 1995–2010. BMC Med. 2015;13(1):74.

    Article  Google Scholar 

  36. Horgas AL, Tsai P-F. Analgesic Drug Prescription and Use in Cognitively Impaired Nursing Home Residents. Nurs Res. 1998;47(4):235–42.

    Article  CAS  Google Scholar 

  37. Sengstaken EA, King SA. The Problems of Pain and Its Detection among Geriatric Nursing Home Residents. J Am Geriatr Soc. 1993;41(5):541–4.

    Article  CAS  Google Scholar 

  38. Smalbrugge M, Jongenelis LK, Pot AM, Beekman AT, Eefsting JA. Pain among nursing home patients in the Netherlands: prevalence, course, clinical correlates, recognition and analgesic treatment – an observational cohort study. BMC Geriatr. 2007;7(1):3.

    Article  Google Scholar 

  39. Rothera I, Jones R, Harwood R, Avery A, Waite J. Health status and assessed need for a cohort of older people admitted to nursing and residential homes. Age Ageing. 2003;32(3):303–9.

    Article  Google Scholar 

  40. Canadian Institute for Health Information. The Impact of COVID-19 on Long-Term Care in Canada: Focus on the First 6 Months [Internet]. Ottawa, ON: CIHI; 2021 p. 34. Available from: https://www.cihi.ca/sites/default/files/document/impact-covid-19-long-term-care-canada-first-6-months-report-en.pdf

  41. Ministry of Health and Long-Term Care. Long-Term Care in Ontario: Sector Overview [Internet]. Health Analytics Branch, Health System Information Management and Investment Division; 2015 Sep [cited 2020 Apr 11] p. 37. Available from: http://longtermcareinquiry.ca/wp-content/uploads/Exhibit-169-Long-Term-Care-in-Ontario-Sector-overview.pdf

  42. Hosmer D, Lemeshow S, Sturdivant R. Applied logistic regression. Third. Hoboken: Wiley; 2013. p. 528.

Download references

Acknowledgements

Not applicable.

Funding

This research did not receive any funding from agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Contributions

RHC, FIM, and APC led the conceptualization of the study methodology. RHC conducted the analysis, drafted, and revised the manuscript. AJ and APC prepared the dataset. FIM and RPS assisted with the analysis and interpretation of data. DD, PRK, AM, and AVS made contributions to the design of the study methods and supported the manuscript. All authors critically revised the manuscript and approved the final version.

Corresponding author

Correspondence to Rebecca H. Correia.

Ethics declarations

Ethics approval and consent to participate

The study was performed in accordance with the principles of the Declaration of Helsinki. Ethics approval was granted by the Hamilton Integrated Research Ethics Board (#2018–0739) for secondary analysis of the health administrative data that were analyzed. We received a waiver of informed consent from the Hamilton Integrated Research Ethics Board since the data we received were de-identified and retrospective. We satisfied data security standards related to privacy and reporting, and the waiver of informed consent was granted based on the absolute impractically of obtaining informed consent where the data were de-identified.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

Table S1. Definitions and measurement of independent variables.

Additional file 2:

Figure S1. Distribution of days between admission and first-quarterly MDS 2.0 assessment.

Additional file 3:

Table S2. Clinical factors associated with medical care visits without end-stage disease in the final model (sensitivity analysis).

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Correia, R.H., Mowbray, F.I., Dash, D. et al. Clinical factors associated with recent medical care visits in nursing homes: a multi-site cross-sectional study. BMC Geriatr 22, 320 (2022). https://doi.org/10.1186/s12877-022-03011-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12877-022-03011-9

Keywords