Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ASCO Annual Meeting 2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

2024 ASCO Annual Meeting

Keep up to date with all the top stories and latest evidence with our hub page, including official congress videos and expert takeaways.
ADVANCED SEARCH
Log in
Top
BMC Health Services Research
Published in: BMC Health Services Research 1/2023

Open Access 01-12-2023 | Care | Research

Patient-level predictors of temporal regularity of primary care visits

Authors: Adam J. Rose, Wiessam Abu Ahmad, Faige Spolter, Maram Khazen, Avivit Golan-Cohen, Shlomo Vinker, Ilan Green, Ariel Israel, Eugene Merzon

Published in: BMC Health Services Research | Issue 1/2023

Login to get access

Abstract

Background

Patients with chronic diseases should meet with their primary care doctor regularly to facilitate proactive care. Little is known about what factors are associated with more regular follow-up.

Methods

We studied 70,095 patients age 40 + with one of three chronic conditions (diabetes mellitus, heart failure, chronic obstructive pulmonary disease), cared for by Leumit Health Services, an Israeli health maintenance organization. Patients were divided into the quintile with the least temporally regular care (i.e., the most irregular intervals between visits) vs. the other four quintiles. We examined patient-level predictors of being in the least-temporally-regular quintile. We calculated the risk-adjusted regularity of care at 239 LHS clinics with at least 30 patients. For each clinic, compared the number of patients with the least temporally regular care with the number predicted to be in this group based on patient characteristics.

Results

Compared to older patients, younger patients (age 40–49), were more likely to be in the least-temporally-regular group. For example, age 70–79 had an adjusted odds ratio (AOR) of 0.82 compared to age 40–49 (p < 0.001 for all findings discussed here). Males were more likely to be in the least-regular group (AOR 1.18). Patients with previous myocardial infarction (AOR 1.07), atrial fibrillation (AOR 1.08), and current smokers (AOR 1.12) were more likely to have an irregular pattern of care. In contrast, patients with diabetes (AOR 0.79) or osteoporosis (AOR 0.86) were less likely to have an irregular pattern of care. Clinic-level number of patients with irregular care, compared with the predicted number, ranged from 0.36 (fewer patients with temporally irregular care) to 1.71 (more patients).

Conclusions

Some patient characteristics are associated with more or less temporally regular patterns of primary care visits. Clinics vary widely on the number of patients with a temporally irregular pattern of care, after adjusting for patient characteristics. Health systems can use the patient-level model to identify patients at high risk for temporally irregular patterns of primary care. The next step is to examine which strategies are employed by clinics that achieve the most temporally regular care, since these strategies may be possible to emulate elsewhere.
Literature
1.
Wagner EH, Austin BT, Von Korff M. Organizing care for patients with chronic illness. Milbank Q. 1996;74:511–44.CrossRefPubMed
2.
3.
Shi L, Starfield B. Primary care, income inequality, and self-rated health in the United States: A mixed-level analysis. Int J Health Serv. 2000;30:541–55.CrossRefPubMed
4.
Shi L, Starfield B. The effect of primary care physician supply and income inequality on mortality among blacks and whites in US metropolitan areas. Am J Public Health. 2001;91:246–50.CrossRef
5.
Starfield B, Shi L. Policy relevant determinants of health: An international perspective. Health Policy. 2002;60:201–18.CrossRefPubMed
6.
7.
Bice TW, Boxerman SB. A quantitative measure of continuity of care. Med Care. 1977;43:1130–9.
8.
Breslau N, Reeb KG. Continuity of care in a university-based practice. J Med Educ. 1975;50:965–9.PubMed
9.
Dreiher J, Comaneshter DS, Rosenbluth Y, Battat E, Bitterman H, Cohen AD. The association between continuity of care in the community and health outcomes: a population-based study. Isr J Health Policy Res. 2012;1(1):21.CrossRefPubMedPubMedCentral
10.
Magill MK, Senf J. A new method for measuring continuity of care in family practice residencies. J Fam Pract. 1987;24(2):165–8.PubMed
11.
Pollack CE, Hussey PS, Rudin RS, Fox DS, Lai J, Schneider EC. Measuring care continuity: a comparison of claims-based methods. Med Care. 2016;54(5):e30-34.CrossRefPubMedPubMedCentral
12.
13.
Shen Y, Findley PA, Maney M, et al. Department of Veterans Affairs-Medicare dual beneficiaries with stroke: where do they get care? J Rehabil Res Dev. 2008;45(1):43–51.CrossRefPubMed
14.
Steinwachs DM. Measuring provider continuity in ambulatory care: an assessment of alternative approaches. Med Care. 1979;17(6):551–65.CrossRefPubMed
15.
Youens D, Harris M, Robinson S, Preen DB, Moorin RE. Regularity of contact with GPs: Measurement approaches to improve valid associations with hospitalization. Fam Pract. 2019;36(5):650–6.CrossRefPubMed
16.
Einarsdottir K, Preen DB, Emery JD, Holman CD. Regular primary care decreases the likelihood of mortality in older people with epilepsy. Med Care. 2010;48(5):472–6.CrossRefPubMed
17.
Einarsdottir K, Preen DB, Emery JD, Holman CD. Regular primary care plays a significant role in secondary prevention of ischemic heart disease in a Western Australian cohort. J Gen Intern Med. 2011;26(10):1092–7.CrossRefPubMedPubMedCentral
18.
Einarsdottir K, Preen DB, Emery JD, Kelman C, Holman CD. Regular primary care lowers hospitalisation risk and mortality in seniors with chronic respiratory diseases. J Gen Intern Med. 2010;25(8):766–73.CrossRefPubMedPubMedCentral
19.
Ha NT, Harris M, Preen D, Robinson S, Moorin R. A time-duration measure of continuity of care to optimise utilisation of primary health care: a threshold effects approach among people with diabetes. BMC Health Serv Res. 2019;19(1):276.CrossRefPubMedPubMedCentral
20.
Ha NT, Wright C, Youens D, Preen DB, Moorin R. Effect Modification of Multimorbidity on the Association Between Regularity of General Practitioner Contacts and Potentially Avoidable Hospitalisations. J Gen Intern Med. 2020;35(5):1504–15.CrossRefPubMedPubMedCentral
21.
Moorin RE, Youens D, Preen DB, Harris M, Wright CM. Association between continuity of provider-adjusted regularity of general practitioner contact and unplanned diabetes-related hospitalisation: a data linkage study in New South Wales, Australia, using the 45 and Up Study cohort. BMJ Open. 2019;9(6): e027158.CrossRefPubMedPubMedCentral
22.
Moorin RE, Youens D, Preen DB, Wright CM. The association between general practitioner regularity of care and “high use” hospitalisation. BMC Health Serv Res. 2020;20(1):915.CrossRefPubMedPubMedCentral
23.
Rose AJ, Timbie JW, Setodji C, Friedberg MW, Malsberger R, Kahn KL. Primary care visit regularity and patient outcomes: An observational study. J Gen Intern Med. 2019;34:82–9.CrossRefPubMedPubMedCentral
24.
25.
Eldor R, Merzon E, Shpigelman M, et al. Effect of a primary-care-team focused diabetes educational program project on diabetes care quality indicators in a large health maintenance organization. Diabetes Res Clin Pract. 2021;177: 108896.CrossRefPubMed
26.
Fogelman Y, Merzon E, Vinker S, Kitai E, Blumberg G, Golan-Cohen A. The Impact of Change in Hospital Admissions When Primary Care Is Provided by a Single Primary Care Physician: A Cohort Study Among HMO Patients in Israel. Adv Exp Med Biol. 2022;1375:63–8.CrossRefPubMed
27.
Golan-Cohen A, Blumberg G, Merzon E, et al. Does a policy that requires adherence to a regular primary care physician improve the actual adherence of patients? Isr J Health Policy Res. 2021;10(1):50.CrossRefPubMedPubMedCentral
28.
Vinker-Shuster M, Eldor R, Green I, Golan-Cohen A, Manor I, Merzon E. Glycemic control and diabetes related complications in adults with type 1 Diabetes mellitus and ADHD. J Atten Disord. 2022;26(9):1235–44.CrossRefPubMed
29.
Vinker-Shuster M, Golan-Cohen A, Merhasin I, Merzon E. Attention-Deficit hyperactivity disorder in pediatric patients with type 1 diabetes mellitus: clinical outcomes and Diabetes control. J Dev Behav Pediatr. 2019;40(5):330–4.CrossRefPubMed
30.
31.
32.
Rothendler JA, Rose AJ, Reisman JI, Berlowitz DR, Kazis LE. Choices in the use of ICD-9 codes to identify stroke risk factors can affect the apparent population-level risk factor prevalence and distribution of CHADS2 scores. Am J Cardiovasc Dis. 2012;2(3):184–91.PubMedPubMedCentral
33.
Borzecki AM, Wong AT, Hickey EC, Ash AS, Berlowitz DR. Identifying hypertension-related comorbidities from administrative data: what’s the optimal approach? Am J Med Qual. 2004;19(5):201–6.CrossRefPubMed
34.
Ash AS, Ellis RP, Pope GC, et al. Using diagnoses to describe populations and predict costs. Health Care Financ Rev. 2000;21:7–28.PubMedPubMedCentral
35.
Lindgren BW. Statistical theory. New York, New York: Macmillan; 1960.
36.
Thadewald T, Buning H. Jarque-Bera Test and its Competitors for Testing Normality – A Power Comparison. J Appl Stat. 2007;34(1):87–105.CrossRef
37.
38.
Iezzoni LI, editor. Risk Adjustment for Measuring Health Care Outcomes. 3rd ed. Chicago: Health Administration Press; 2003.
39.
Rose AJ, Hylek EM, Ozonoff A, Ash AS, Reisman JI, Berlowitz DR. Risk-adjusted percent time in therapeutic range as a quality indicator for outpatient oral anticoagulation: results of the Veterans Affairs Study to Improve Anticoagulation (VARIA). Circ Cardiovasc Qual Outcomes. 2011;4(1):22–9.CrossRefPubMed
40.
Berlowitz DR, Ash AS, Brandeis GH, Brand HK, Halpern JL, Moskowitz MA. Rating long-term care facilities on pressure ulcer development: importance of case-mix adjustment. Ann Intern Med. 1996;124(6):557–63.CrossRefPubMed
41.
42.
43.
Calderon-Margalit R, Cohen-Dadi M, Opas D, et al. Trends in the performance of quality indicators for diabetes care in the community and in diabetes-related health status: an Israeli ecological study. Isr J Health Policy Res. 2018;7(1):10.CrossRefPubMedPubMedCentral
44.
Merzon E, Grossman J, Vinker S, Merhasin I, Levit S, Golan-Cohen A. Factors associated with withdrawal from insulin pump therapy: A large-population-based study. Diabetes Metab Res Rev. 2020;36(4): e3288.CrossRefPubMed
Metadata
Title
Patient-level predictors of temporal regularity of primary care visits
Authors
Adam J. Rose
Wiessam Abu Ahmad
Faige Spolter
Maram Khazen
Avivit Golan-Cohen
Shlomo Vinker
Ilan Green
Ariel Israel
Eugene Merzon
Publication date
01-12-2023
Publisher
BioMed Central
Keyword
Care
Published in
BMC Health Services Research / Issue 1/2023
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-023-09486-5

Other articles of this Issue 1/2023

BMC Health Services Research 1/2023 Go to the issue

Research

Comparing full immunisation status of children (0–23 months) between slums of Kampala City and the rural setting of Iganga District in Uganda: a cross-sectional study

Research

A multimorbidity model for estimating health outcomes from the syndemic of injection drug use and associated infections in the United States

Research

Facilitators and barriers to optimal home blood pressure management in patients with hypertensive disorders of pregnancy in a tertiary care facility in Abuja, Nigeria: a qualitative research study

Research article

Facilitators and barriers to the implementation of new critical care practices during COVID-19: a multicenter qualitative study using the Consolidated Framework for Implementation Research (CFIR)

Research

A systematic review and knowledge mapping on ICT-based remote and automatic COVID-19 patient monitoring and care

Research

One size does not fit all: an application of stochastic modeling to estimating primary healthcare needs in Ethiopia at the sub-national level