Patient characteristics and degrees of discontinuity of care in Danish general practice: a cohort study

Ditte Elschner Rimestad; Peder Ahnfeldt-Mollerup; Troels Kristensen

doi:10.3399/BJGP.2024.0570

Abstract

Background Continuity of care is crucial for effective health care, but patients often experience discontinuity of care. Understanding the patterns and associated patient characteristics can guide interventions to improve continuity of care.

Aim To investigate longitudinal provider discontinuity of care to identify disparities in patient characteristics across degrees of discontinuity of care in Danish general practice.

Design & setting Cohort study using data from all Danish general practice patients aged ≥12 years, who were alive between 2007 and 2018.

Method Logistic regression was employed to estimate discontinuity of care at level 0 versus: 1–2 shifts, 3–4 shifts, 5–6 shifts, 7–8 shifts, and ≥9 shifts. Five regression models were used to analyse the odds of different levels of discontinuity of care relative to demographic, regional, municipal, socioeconomic, and morbidity factors.

Results The majority of males were likely to have lower levels of discontinuity of care than females; however, males in the subgroup with the most shifts had a higher likelihood of experiencing the highest level of discontinuity. Younger adults aged 25–44 years had a higher likelihood of moderate-to-high discontinuity of care compared to those aged 12–24 years, whereas older adults had a lower probability. Discontinuity of care was higher among residents of the Capital and Zealand Regions, and varied by municipality type — being lowest in intermediate areas and highest in peripheral and rural municipalities at the most severe levels. Individuals who were unemployed, those in the lower income quartiles, and those classified as being of ‘other ethnicities’ and single status had increased probability of discontinuity of care. Patients with lower or moderate-to-high morbidity were also more likely to experience discontinuity of care than patients with no chronic diseases.

Conclusion This study revealed disparities in discontinuity of care linked to lower socioeconomic status and higher morbidity, with varying odds by region, municipality type, age, and gender.

Introduction

Continuity of care (COC) is recognised as a cornerstone of effective health care, which ensures that patients receive consistent and coordinated services.^1–4 Long-term COC is crucial for good patient outcomes, and promotes consistency, understanding, and better chronic disease management.⁵ However, patients encounter discontinuity of care, which can negatively affect health outcomes.^4,6,7 Discontinuity of care occurs when there are breaks in a patient’s care pathway, leading to fragmented services.³ Previous literature has suggested that achieving COC for patients with complex care needs is facilitated by professional and cross-disciplinary cooperation;^8–10 as such, it is crucial to develop provider COC, ensuring that patients remain under the care of the same clinical team for a longer period.^11,12

There are two main perspectives on COC in general practice: one focuses on the patient’s experience of a continuous, caring relationship with a health professional; the other emphasises the provider’s role in delivering seamless services through coordination and information sharing among healthcare staff. This study focused on discontinuity of care at the provider level and the degrees of site discontinuity, which refers to the number of changes in registration between general practice providers over an extended analysis period.^3,11 1

How this fits in

Continuity of care is a well-established component of high-quality general practice, but research on discontinuity remains limited. Most studies focus on personal continuity, use small samples or short follow-up periods, and treat discontinuity simply as the absence of continuity. Although recent work has introduced more nuanced continuity measures and examined patient characteristics, variation in the degree and distribution of discontinuity across patient groups remains underexplored. Few studies have examined discontinuity at the clinic level, particularly in the form of shifts between providers over time. This study addresses that gap by investigating longitudinal clinic-level discontinuity in a population-based setting, with particular focus on variation by individual characteristics. It reveals disparities linked to gender, age, geography, socioeconomic status, and morbidity burden, highlighting the need for targeted interventions to improve continuity of care.

The nature and extent of discontinuity of care across patient factors is not well known.¹³ The literature has suggested that demographic, geographic, socioeconomic, and morbidity factors may be linked to the degree of discontinuity of care,^14–19 but findings have been inconsistent.¹⁸ The present study examined whether these links hold in Danish general practice. The authors hypothesised that older patients with ≥1 chronic condition would exhibit a higher degree of COC compared to younger and healthier individuals,^14,20 and that there would be greater discontinuity of care among patients with lower socioeconomic status and those living in urban municipalities.^19–22 This study aimed to examine levels of discontinuity of care in general practice and key patient disparities with respect to patient characteristics for the entire Danish population.

Method

Setting

Danish GPs operate within a public healthcare system that provides free access to services for all residents. GPs serve as the cornerstone, acting as gatekeepers to secondary care and specialists. Registration with a practice ensures access to care and serves as a structural basis for fostering continuity and long-term relationships between patient and provider. Most GPs are self-employed and work in private practices under contracts with one of five regions. In total, 36% of practices are single doctor, while 64% are multidoctor.²³ General practices typically include support staff — such as nurses, medical secretaries, and medical students — and collaborate with public workers, such as home nurses, social workers, and midwives.

Approaches

This cohort study was based on data from the Danish list system, in which everyone is listed at provider level.¹² The authors used a population dataset on provider COC in Danish general practice, dating from 1 January 2007 until 31 December 2018, to analyse population differences and odds for discontinuity of care. The cohort included all listed patients aged ≥12 years, who were alive during the analysis period.¹¹ By utilising population data, this study allowed for the analysis of discontinuity of care with minimal concerns about sampling errors.

The authors compared patient characteristics across differing degrees of individual discontinuity of care (0 versus: 1–2 shifts, 3–4 shifts, 5–6 shifts, 7–8 shifts, and ≥9 shifts) at the end of the analysis period. This approach allowed the average patient characteristics within each level of discontinuity of care to be summarised. Patient characteristics included demographic markers (gender and age), regional markers (region of residence), and municipal markers (peripheral, rural, urban, and intermediate) based on 14 classification indicators.²⁴ The indicators are related to population density, infrastructure access, labour market integration, age, education, and demographic development. Municipality markers are defined using a composite rural district index ranging from 1 to 100, based on the indicators.²⁴ Municipalities with the highest index scores are classified as peripheral municipalities, while those with the lowest scores are considered urban municipalities. Intermediate and rural municipalities fall between these two extremes, being neither highly urbanised nor strongly peripheral.

Socioeconomic markers (unemployed, short education, other ethnicity, single, children at home, and income) were based on data from Statistics Denmark.²⁵ Morbidity was assessed using the Charlson Comorbidity Index (CCI), with diagnoses obtained from the Danish National Patient Register, which contains information on all hospital contacts. Disposable income was used as a key socioeconomic indicator, defined by Statistics Denmark as total income, including earned income, capital income, and transfers, after deducting personal taxes and interest payments. Due to the large sample size, differences between discontinuity of care subpopulations and baseline COC (0 shifts) were described without formal hypothesis testing.²⁶

At the end of the cohort period in 2018, logistic regression was used to assess probability across discontinuity of care degrees from 2007–2018.^27,28 Five independent models were created, each representing a different degree of discontinuity of care, with a dichotomous outcome comparing COC (no shifts) with discontinuity of care degrees. Patient characteristics that were hypothesised to be linked to discontinuity of care were included as dichotomous variables in each model. Age, income, and CCI score were categorised into age bands, income quartiles, and morbidity bands (0, 1–2, 3–4, or ≥5 chronic diseases), respectively. All variables were included simultaneously as substantive covariates. The 12–24-year-old age group and the highest income quartile (both of which are often linked to better health), the category with the lowest CCI score (the healthiest group), South Region, and urban municipalities were selected as references. Logistic regressions were adjusted for patient clustering at clinics using cluster-robust standard errors. These models assessed the odds of each discontinuity of care level relative to COC. Data analysis was conducted using Stata (version 16).

Results

Figure 1 shows the distribution of the population by COC and the degree of discontinuity of care in Danish general practice over the period of analysis. The majority of patients (61.5%) experienced varying degrees of discontinuity of care, with the most common being 1–2 shifts (45.7%) and 3–4 shifts (11.3%). Notably, 38.5% of patients maintained COC over the 12-year period.

Figure 1. Distribution of continuity of care and varying degree of discontinuity of care among patients in Danish general practice, 2007–2018.

Table 1 presents COC and discontinuity of care degrees as a percentage of the total number of patients per variable across the different patient characteristics (horizontally) and as a percentages of the individual shift group (vertically). The proportion of males increased with higher discontinuity: males made up 49.3% of the 0-shift group and 56.5% of the ≥9-shift group. The 25–34-year-old age group had the lowest share of COC (14.1%) of all the age groups, and the highest percentages for 3–4 shifts. From age 45–84 years, the percentage of patients with COC increased with age across each successive age group, but this trend was not seen in the oldest group — those aged ≥85 years.

View this table:

Table 1. Continuity of care and varying degrees of discontinuity of care, by patient characteristic, 2007–2018

Patients from the Capital Region had the lowest COC and in most groups the highest discontinuity of care percentages, while those in intermediate municipalities had the highest COC and the lowest discontinuity of care percentages.

The percentage of individuals in each degree group who were unemployed increased with higher degrees of discontinuity of care (Table 1). Patients with the highest income (quartile 4) had the highest COC and in most groups the lowest discontinuity of care, whereas those in the lower income quartiles (1 and 2) had relatively higher percentages of greater degrees of discontinuity of care.

Figure 2 shows the distribution of patient subgroups within each shift category as a proportion of total shifts by income.

Figure 2. Continuity of care and varying degrees of discontinuity of care among patients in Danish general practice by patient income, 2007–2018.

Table 2 presents the odds of discontinuity of care levels compared with baseline COC (n = 1 758 753), by patient characteristic, from 2007 to 2018. Males had slightly reduced odds for lower discontinuity of care levels compared with females, but increased odds for the highest discontinuity of care levels. Younger adults (aged 25–34 years) and middle-aged adults (aged 35–44 years) had higher discontinuity of care odds compared with the reference group (aged 12–24 years), with the 25–34-year-old age group showing the highest relative odds. For both age groups, the odds of discontinuity of care increased as the number of shifts increased. Patients aged ≥45 years had lower discontinuity of care odds than the reference group, and odds declined across discontinuity of care degrees for those aged ≥65 years until the 75-84-year-old age group. The oldest age group had slightly higher odds than the second oldest at most levels, although these were still low overall.

View this table:

Table 2. Levels of discontinuity of care in Danish general practice patients by patient characteristic, 2007–2018

Patients in Zealand Region and Capital Region were more likely to experience discontinuity of care than the reference group. Patients from intermediate municipalities were least likely to experience discontinuity of care, with odds ratios ranging from 0.571 (COC) to 0.771 (greatest degree of discontinuity of care) compared with urban residents. Peripheral and rural municipalities followed a similar trend to urban areas for lower degrees of discontinuity of care, but had a greater risk of the highest discontinuity of care (Table 2).

The results relating to socioeconomic data indicate a negative gradient in unemployment and income: patients who were unemployed and those with lower incomes had higher, and increasing, odds of experiencing greater discontinuity of care compared with the reference (income quartile 4). Patients categorised as being of a minority ethnicity had higher odds of discontinuity of care than the general population, peaking at 3–4 shifts and 5–6 shifts before returning to the initial level at the highest degree of discontinuity of care. Contrary to the authors’ hypothesis, patients with moderate morbidity (CCI score 1–2 and 3–4) had higher discontinuity of care odds than those without chronic disease (CCI score 0), with only a slight drop for CCI 3–4 in model 5 (Table 2).

Discussion

Summary

Whereas most previous studies have emphasised personal COC and its effects on patient outcomes,²⁹ this study focused on discontinuity of care when patients are registered with a clinic, rather than an individual physician. Understanding the relative strength of association between physician- and practice-level discontinuity can inform decisions related to quality and outcome reforms,^11,29 and highlights aspects of the quality of care delivered by a team within a general practice clinic. The present study identified demographic, geographic, socioeconomic, and morbidity-related factors associated with degrees of discontinuity of care. Discontinuity of care, measured by practice shifts, was higher among younger adults, subgroups of males, urban residents, those in Zealand Region and Capital Region, lower socioeconomic groups, and patients with higher morbidity. These findings highlight inequality in discontinuity of care among subpopulations, especially those with low income, unemployment, and chronic conditions.

Strengths and limitations

Using longitudinal population data offers several strengths, including complete coverage, increased statistical power, generalisability/minimised sampling error, and the ability to provide longitudinal insights and detailed patient analyses.^26,30 These strengths make the findings presented here more robust and potentially applicable to a range of general practice settings.

The large sample size allowed for the omission of traditional hypothesis testing between subpopulations of discontinuity of care and baseline COC (0 shifts), as even minor differences yielded statistically significant results.²⁶ However, it is a limitation that the study’s findings are specific to the Danish general practice system, including the list system based on site registration and gatekeeping organisation.³¹ The monocentric results may not be transferable to all other countries with different general practice list systems and structures.^12,31

Although the focus on patient characteristics provides valuable insights into those characteristics that influence discontinuity of care, it does not account for the potential impact of provider-level variables — such as provider type — on discontinuity of care . Furthermore, there is no information on the data quality. Validation of registry data is often limited by the lack of an established gold standard.^26,30 Still, the data extracted from routine practice in the Danish regions are collected autonomously and used as the basis for patient management.²⁶

As only hospital diagnoses were included in the CCI score calculation, the proportion of individuals with no recorded conditions (CCI score 0) may have been overestimated. The applied regression analysis is effective for modeling binary outcomes, providing clear interpretability through odds ratios, but it is important to be aware that logistic regression is also based on assumptions and can be sensitive to outliers.²⁸ Also, it should be noted that, when a solo general practice closes, a shift is recorded more often than when a patient is reassigned a GP within a large practice.¹¹

Comparison with existing literature

The finding of lower odds for moderate degrees of discontinuity of care, and higher odds for more severe discontinuity among males, is mixed compared to previous literature, which has reported either no gender difference or greater discontinuity among both male and females.^14,32,33 The evidence that patients aged ≥45 years had lower discontinuity of care than the reference group, align with literature showing lower discontinuity of care across age.^14,15,34 Excluding the oldest age group (≥85 years), patients aged ≥65 years were less likely to have higher discontinuity of care than 45–64 year olds, supporting the initial hypothesis that older patients experience greater COC.¹⁴ This was based on evidence that older patients were found to experience significantly greater COC in general practice compared to younger age groups.¹⁴

Patients in rural areas have more stable care relationships, while urban patients experience more discontinuity due to circumstances such as higher physican turnover, mobility, and GP demand.³⁴ Compared to the latter literature, the higher probability of discontinuity of care in Zealand Region and Capital Region, with more urbanisation and related regional health system structure, is consistent with this.¹⁸ Berghofer et al found that patients living in urban areas have a significantly higher risk of treatment discontinuity compared to those in rural areas.¹⁸

The low discontinuity in rural areas is consistent with the result for non-urban municipalities.^18,34 The increased odds of discontinuity of care in peripheral and rural municipalities with the highest degree of discontinuity of care may stem from the greater prevalence of solo practitioners and unstable general practice capacity.¹⁹

The socioeconomic results align with most existing literature,^18,19,21,34 although some findings remain ambiguous. Berghofer et al found that unemployment and low education, as proxies for socioeconomic status, were significantly associated with higher discontinuation of treatment in outpatient mental health care.¹⁸ Olah et al found reduced access to care for low socioeconomic status individuals.²¹ They interpret this as socioeconomic discrimination, suggesting that bias in appointment screening by clinic staff contributes to inequalities in COC. This structural discrimination can play a role, resulting in less attention or lower-quality care for patients of minority ethnicity.^15,21 Other ethnicity individuals are associated with higher discontinuation, likely due to cultural or linguistic barriers.^15,18

The increasing odds of greater degrees of discontinuity of care in lower-income quartiles (quartile 3 and quartile 2) are expected.^19–21 It has been found that poorer communities had less access to qualified doctors and medical resources.^19–21 In contrast, wealthier areas attract better resourced providers, resulting in a mismatch between medical need and care availability.^19,20 Mold et al found that higher income and educational attainment were significantly associated with longer COC, indicating that socioeconomic advantage promotes COC, likely through better system navigation and access to transportation.³⁴

Discontinuity of care has been shown to be associated with a significant increase in morbidity, particularly among those with chronic conditions, driven by reduced access to primary care and a susequent rise in hospital admissions.^3,4,7,35,36 The findings align with the presently observed increased risk of discontinuity of care among individuals with low, moderate, and high comorbidity burdens compared to no chronic disease status, indicating that even intermediate and high morbidity levels are associated with discontinuity of care. The evidenced associations indicate that poor access to health care is linked to morbidity, as explained by the inverse care law.^19,22 While this study assesses discontinuity of care through longitudinal changes in GP provider, most other studies use visit-based or patient-reported measures over shorter timeframes.^1,10 The present study approach is complementary, capturing broader structural patterns in care delivery that are typically overlooked by these methods.

Implications for practice

This study highlights the need for general practice clinicians to identify and support patients at increased risk of discontinuity — particularly those with lower income or unemployment, minority ethnic backgrounds, or chronic conditions. Although the majority of females overall had slightly higher odds for discontinuity, a small subgroup of males experienced the greatest odds for discontinuity, underscoring the importance of targeting this overlooked population. Timely recognition of these risk groups should enable interventions that strengthen patient–provider relationships and reduce negative impacts on care quality and outcomes. Notably, even those with moderate chronic illness had high levels of discontinuity, indicating that continuity strategies must extend beyond patients with complex needs. Routine chronic disease reviews are a practical opportunity to reinforce stable therapeutic relationships and ensure consistent care delivery. In addition, the increased risk for discontinuity of care in terms of many shifts in peripheral and rural municipalities likely highlights the need for targeted interventions, such as incentivising GP retention. In contrast, the lower odds of discontinuity of care in intermediate municipalities may underscore the benefits of these municipalities. Policymakers could focus on replicating conditions in the best-performing regions and municipalities to reduce discontinuity of care disparities. In contexts with frequent provider turnover — such as urban or rural areas — clinicians must adopt structured handover procedures and maintain up-to-date, shared clinical records to preserve continuity despite changes in personnel. By actively addressing these risk factors in everyday practice, GPs can foster more equitable, coordinated care and contribute to improved patient outcomes and satisfaction.

Notes

Funding

The work was supported by University of Southern Denmark, Fonden for Almen Praksis (Foundation for General Practice), and the Danish Regions.

Ethical approval

Complying with European data protection rules, the legal services at University of Southern Denmark (Research and Innovation Organisation) approved the data processing activities regarding this project (journal number: 10.151). Approval from the ethics committee and informed consent is not required according to section 14.2 of the Act on Research Ethics Review of Health Research Projects because the study is based solely on register data.

Data

Data are protected by Statistics Denmark. Application to access these data must be made to Danish Regions and Statistics Denmark in collaboration with a Danish research institution.

Acknowledgements

The authors gratefully acknowledge the two reviewers for their constructive feedback, which significantly improved the manuscript.

Competing interests

The authors declare that no competing interests exist.

Received September 10, 2024.
Revision received December 3, 2024.
Accepted March 20, 2025.

http://creativecommons.org/licenses/by/4.0/

This article is Open Access: CC BY 4.0 licence (http://creativecommons.org/licenses/by/4.0/).

References

1.↵
1. Baker R,
2. Freeman GK,
3. Haggerty JL,
4. et al.
(2020) Primary medical care continuity and patient mortality: a systematic review. Br J Gen Pract doi:10.3399/bjgp20X712289, pmid:32784220.
OpenUrl Abstract/FREE Full Text
2.
1. Pereira Gray DJ,
2. Sidaway-Lee K,
3. White E,
4. et al.
(2018) Continuity of care with doctors: a matter of life and death? a systematic review of continuity of care and mortality. BMJ Open 8(6), pmid:29959146. e021161.
OpenUrl Abstract/FREE Full Text
3.↵
1. Simonsen M,
2. Skipper L,
3. Skipper N,
4. Thingholm PR
(2021) Discontinuity in care: practice closures among primary care providers and patient health care utilization. J Health Econ 80:102551, pmid:34785433.
OpenUrl PubMed
4.↵
1. Zocher K
(2024) Exiting primary care providers. Health Econ 33(5):1033–1054, pmid:38337145.
OpenUrl PubMed
5.↵
1. Starfield B,
2. Shi L,
3. Macinko J
(2005) Contribution of primary care to health systems and health. Milbank Q 83(3):457–502, pmid:16202000.
OpenUrl CrossRef PubMed
6.↵
1. Tammes P,
2. Morris RW,
3. Murphy M,
4. Salisbury C
(2021) Is continuity of primary care declining in england? practice-level longitudinal study from 2012 to 2017. Br J Gen Pract doi:10.3399/BJGP.2020.0935, pmid:33947666.
OpenUrl Abstract/FREE Full Text
7.↵
1. Sandvik H,
2. Hetlevik Ø,
3. Blinkenberg J,
4. Hunskaar S
(2022) Continuity in general practice as predictor of mortality, acute hospitalisation, and use of out-of-hours care: a registry-based observational study in norway. Br J Gen Pract doi:10.3399/BJGP.2021.0340, pmid:34607797.
OpenUrl Abstract/FREE Full Text
8.↵
1. Ljungholm L,
2. Edin-Liljegren A,
3. Ekstedt M,
4. Klinga C
(2022) What is needed for continuity of care and how can we achieve it? Perceptions among multiprofessionals on the chronic care trajectory. BMC Health Serv Res 22(1), pmid:35606787. 686.
OpenUrl CrossRef PubMed
9.
1. Ladds E,
2. Greenhalgh T
(2023) Modernising continuity: a new conceptual framework. Br J Gen Pract doi:10.3399/bjgp23X732897, pmid:37230773.
OpenUrl FREE Full Text
10.↵
1. Haggerty JL,
2. Reid RJ,
3. Freeman GK,
4. et al.
(2003) Continuity of care: a multidisciplinary review. BMJ 327(7425):1219–1221, pmid:14630762.
OpenUrl FREE Full Text
11.↵
1. Olsen JK,
2. Kristensen T
(2023) Continuity and discontinuity of care among older patients in danish general practice: a retrospective cohort study. BJGP Open doi:10.3399/BJGPO.2023.0081, pmid:37336619.
OpenUrl CrossRef PubMed
12.↵
1. Marchildon GP,
2. Brammli-Greenberg S,
3. Dayan M,
4. et al.
(2021) Achieving higher performing primary care through patient registration: a review of twelve high-income countries. Health Policy 125(12):1507–1516, pmid:34531039.
OpenUrl CrossRef PubMed
13.↵
1. Fontanella CA,
2. Guada J,
3. Phillips G,
4. et al.
(2014) Individual and contextual-level factors associated with continuity of care for adults with schizophrenia. Adm Policy Ment Health 41(5):572–587, pmid:23689992.
OpenUrl CrossRef PubMed
14.↵
1. Winkel MTT,
2. Slottje P,
3. de Kruif AJ,
4. et al.
(2022) General practice and patient characteristics associated with personal continuity: a mixed-methods study. Br J Gen Pract doi:10.3399/BJGP.2022.0038, pmid:36192355.
OpenUrl CrossRef PubMed
15.↵
1. Stafford M,
2. Bécares L,
3. Hayanga B,
4. et al.
(2023) Continuity of care in diverse ethnic groups: a general practice record study in england. Br J Gen Pract doi:10.3399/BJGP.2022.0271, pmid:36316161.
OpenUrl Abstract/FREE Full Text
16.
1. Levene LS,
2. Baker R,
3. Walker N,
4. et al.
(2018) Predicting declines in perceived relationship continuity using practice deprivation scores: a longitudinal study in primary care. Br J Gen Pract doi:10.3399/bjgp18X696209, pmid:29739778.
OpenUrl CrossRef PubMed
17.
1. Fisher R,
2. Alderwick H
(2024) The performance of general practice in the english national health service (NHS): an analysis using starfield’s framework for primary care. Health Aff Sch 2(3), pmid:38770436. qxae022.
OpenUrl CrossRef PubMed
18.↵
1. Berghofer G,
2. Schmidl F,
3. Rudas S,
4. et al.
(2002) Predictors of treatment discontinuity in outpatient mental health care. Soc Psychiatry Psychiatr Epidemiol 37(6):276–282, pmid:12111033.
OpenUrl CrossRef PubMed
19.↵
1. Tudor Hart J
(1971) The inverse care law. The Lancet 297(7696):405–412.
OpenUrl CrossRef
20.↵
1. Christakis DA,
2. Kazak AE,
3. Wright JA,
4. et al.
(2004) What factors are associated with achieving high continuity of care? Fam Med 36(1):55–60, pmid:14710331.
OpenUrl PubMed
21.↵
1. Olah ME,
2. Gaisano G,
3. Hwang SW
(2013) The effect of socioeconomic status on access to primary care: an audit study. CMAJ 185(6):E263–E269, pmid:23439620.
OpenUrl Abstract/FREE Full Text
22.↵
1. Parisi R,
2. Lau Y-S,
3. Bower P,
4. et al.
(2023) Predictors and population health outcomes of persistent high GP turnover in english general practices: a retrospective observational study. BMJ Qual Saf 32(7):394–403, pmid:36690473.
OpenUrl Abstract/FREE Full Text
23.↵
1. General Practitioners’ Organisation (PLO)
(2025) [PLO fact sheet]. [Article in Danish], accessed. https://laeger.dk/media/y05lxe1t/plo-faktaark-2025_ver1.pdf. 4 Jul 2025.
24.↵
1. Toft Kristensen I,
2. Kjeldsen C,
3. Delgaard T
(2007) [Rural district municipalities: indicators for rural district]. [Article in Danish]. accessed. https://pure.au.dk/ws/files/4594133/RapLanddistrikt2007_20070201.pdf. 9 Jul 2025.
25.↵
1. Charlson ME,
2. Pompei P,
3. Ales KL,
4. MacKenzie CR
(1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40(5):373–383, pmid:3558716.
OpenUrl CrossRef PubMed
26.↵
1. Thygesen LC,
2. Ersbøll AK
(2014) When the entire population is the sample: strengths and limitations in register-based epidemiology. Eur J Epidemiol 29(8):551–558, pmid:24407880.
OpenUrl CrossRef PubMed
27.↵
1. DeMaris A
(1995) A tutorial in logistic regression. J Marriage Fam 57(4):956–968.
OpenUrl CrossRef
28.↵
1. Stoltzfus JC
(2011) Logistic regression: a brief primer. Acad Emerg Med 18(10):1099–1104, pmid:21996075.
OpenUrl CrossRef PubMed
29.↵
1. Yang Z,
2. Ganguli I,
3. Davis C,
4. et al.
(2022) Physician‐ versus practice‐level primary care continuity and association with outcomes in medicare beneficiaries. Health Serv Res 57(4):914–929.
OpenUrl CrossRef PubMed
30.↵
1. Frank L
(2000) When an entire country is a cohort. Science 287(5462):2398–2399.
OpenUrl FREE Full Text
31.↵
1. Kringos D,
2. Boerma W,
3. Bourgueil Y,
4. et al.
(2013) The strength of primary care in europe: an international comparative study. Br J Gen Pract doi:10.3399/bjgp13X674422, pmid:24267857.
OpenUrl Abstract/FREE Full Text
32.↵
1. Valaker I,
2. Fridlund B,
3. Wentzel-Larsen T,
4. et al.
(2020) Continuity of care and its associations with self-reported health, clinical characteristics and follow-up services after percutaneous coronary intervention. BMC Health Serv Res 20(1), pmid:32005235. 71.
OpenUrl PubMed
33.↵
1. Cohen E,
2. Lindman I
(2024) Importance of continuity of care from a patient perspective - a cross-sectional study in swedish health care. Scand J Prim Health Care 42(1):195–200, pmid:38189945.
OpenUrl PubMed
34.↵
1. Mold JW,
2. Fryer GE,
3. Roberts AM
(2004) When do older patients change primary care physicians? J Am Board Fam Pract 17(6):453–460, pmid:15575037.
OpenUrl Abstract/FREE Full Text
35.↵
1. Staiger B
(2022) Disruptions to the patient-provider relationship and patient utilization and outcomes: evidence from medicaid managed care. J Health Econ 81:102574, pmid:34968786.
OpenUrl PubMed
36.↵
1. Ljungholm L,
2. Klinga C,
3. Edin-Liljegren A,
4. Ekstedt M
(2022) What matters in care continuity on the chronic care trajectory for patients and family carers? a conceptual model. J Clin Nurs 31(9–10):1327–1338, pmid:34351651.
OpenUrl PubMed

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[1] 1.↵
Baker R,
Freeman GK,
Haggerty JL,
et al.
(2020) Primary medical care continuity and patient mortality: a systematic review. Br J Gen Pract doi:10.3399/bjgp20X712289, pmid:32784220.
OpenUrl Abstract/FREE Full Text

[2] Baker R,

[3] Freeman GK,

[4] Haggerty JL,

[5] et al.

[6] 2.
Pereira Gray DJ,
Sidaway-Lee K,
White E,
et al.
(2018) Continuity of care with doctors: a matter of life and death? a systematic review of continuity of care and mortality. BMJ Open 8(6), pmid:29959146. e021161.
OpenUrl Abstract/FREE Full Text

[7] Pereira Gray DJ,

[8] Sidaway-Lee K,

[9] White E,

[10] et al.

[11] 3.↵
Simonsen M,
Skipper L,
Skipper N,
Thingholm PR
(2021) Discontinuity in care: practice closures among primary care providers and patient health care utilization. J Health Econ 80:102551, pmid:34785433.
OpenUrl PubMed

[12] Simonsen M,

[13] Skipper L,

[14] Skipper N,

[15] Thingholm PR

[16] 4.↵
Zocher K
(2024) Exiting primary care providers. Health Econ 33(5):1033–1054, pmid:38337145.
OpenUrl PubMed

[17] Zocher K

[18] 5.↵
Starfield B,
Shi L,
Macinko J
(2005) Contribution of primary care to health systems and health. Milbank Q 83(3):457–502, pmid:16202000.
OpenUrl CrossRef PubMed

[19] Starfield B,

[20] Shi L,

[21] Macinko J

[22] 6.↵
Tammes P,
Morris RW,
Murphy M,
Salisbury C
(2021) Is continuity of primary care declining in england? practice-level longitudinal study from 2012 to 2017. Br J Gen Pract doi:10.3399/BJGP.2020.0935, pmid:33947666.
OpenUrl Abstract/FREE Full Text

[23] Tammes P,

[24] Morris RW,

[25] Murphy M,

[26] Salisbury C

[27] 7.↵
Sandvik H,
Hetlevik Ø,
Blinkenberg J,
Hunskaar S
(2022) Continuity in general practice as predictor of mortality, acute hospitalisation, and use of out-of-hours care: a registry-based observational study in norway. Br J Gen Pract doi:10.3399/BJGP.2021.0340, pmid:34607797.
OpenUrl Abstract/FREE Full Text

[28] Sandvik H,

[29] Hetlevik Ø,

[30] Blinkenberg J,

[31] Hunskaar S

[32] 8.↵
Ljungholm L,
Edin-Liljegren A,
Ekstedt M,
Klinga C
(2022) What is needed for continuity of care and how can we achieve it? Perceptions among multiprofessionals on the chronic care trajectory. BMC Health Serv Res 22(1), pmid:35606787. 686.
OpenUrl CrossRef PubMed

[33] Ljungholm L,

[34] Edin-Liljegren A,

[35] Ekstedt M,

[36] Klinga C

[37] 9.
Ladds E,
Greenhalgh T
(2023) Modernising continuity: a new conceptual framework. Br J Gen Pract doi:10.3399/bjgp23X732897, pmid:37230773.
OpenUrl FREE Full Text

[38] Ladds E,

[39] Greenhalgh T

[40] 10.↵
Haggerty JL,
Reid RJ,
Freeman GK,
et al.
(2003) Continuity of care: a multidisciplinary review. BMJ 327(7425):1219–1221, pmid:14630762.
OpenUrl FREE Full Text

[41] Haggerty JL,

[42] Reid RJ,

[43] Freeman GK,

[44] et al.

[45] 11.↵
Olsen JK,
Kristensen T
(2023) Continuity and discontinuity of care among older patients in danish general practice: a retrospective cohort study. BJGP Open doi:10.3399/BJGPO.2023.0081, pmid:37336619.
OpenUrl CrossRef PubMed

[46] Olsen JK,

[47] Kristensen T

[48] 12.↵
Marchildon GP,
Brammli-Greenberg S,
Dayan M,
et al.
(2021) Achieving higher performing primary care through patient registration: a review of twelve high-income countries. Health Policy 125(12):1507–1516, pmid:34531039.
OpenUrl CrossRef PubMed

[49] Marchildon GP,

[50] Brammli-Greenberg S,

[51] Dayan M,

[52] et al.

[53] 13.↵
Fontanella CA,
Guada J,
Phillips G,
et al.
(2014) Individual and contextual-level factors associated with continuity of care for adults with schizophrenia. Adm Policy Ment Health 41(5):572–587, pmid:23689992.
OpenUrl CrossRef PubMed

[54] Fontanella CA,

[55] Guada J,

[56] Phillips G,

[57] et al.

[58] 14.↵
Winkel MTT,
Slottje P,
de Kruif AJ,
et al.
(2022) General practice and patient characteristics associated with personal continuity: a mixed-methods study. Br J Gen Pract doi:10.3399/BJGP.2022.0038, pmid:36192355.
OpenUrl CrossRef PubMed

[59] Winkel MTT,

[60] Slottje P,

[61] de Kruif AJ,

[62] et al.

[63] 15.↵
Stafford M,
Bécares L,
Hayanga B,
et al.
(2023) Continuity of care in diverse ethnic groups: a general practice record study in england. Br J Gen Pract doi:10.3399/BJGP.2022.0271, pmid:36316161.
OpenUrl Abstract/FREE Full Text

[64] Stafford M,

[65] Bécares L,

[66] Hayanga B,

[67] et al.

[68] 16.
Levene LS,
Baker R,
Walker N,
et al.
(2018) Predicting declines in perceived relationship continuity using practice deprivation scores: a longitudinal study in primary care. Br J Gen Pract doi:10.3399/bjgp18X696209, pmid:29739778.
OpenUrl CrossRef PubMed

[69] Levene LS,

[70] Baker R,

[71] Walker N,

[72] et al.

[73] 17.
Fisher R,
Alderwick H
(2024) The performance of general practice in the english national health service (NHS): an analysis using starfield’s framework for primary care. Health Aff Sch 2(3), pmid:38770436. qxae022.
OpenUrl CrossRef PubMed

[74] Fisher R,

[75] Alderwick H

[76] 18.↵
Berghofer G,
Schmidl F,
Rudas S,
et al.
(2002) Predictors of treatment discontinuity in outpatient mental health care. Soc Psychiatry Psychiatr Epidemiol 37(6):276–282, pmid:12111033.
OpenUrl CrossRef PubMed

[77] Berghofer G,

[78] Schmidl F,

[79] Rudas S,

[80] et al.

[81] 19.↵
Tudor Hart J
(1971) The inverse care law. The Lancet 297(7696):405–412.
OpenUrl CrossRef

[82] Tudor Hart J

[83] 20.↵
Christakis DA,
Kazak AE,
Wright JA,
et al.
(2004) What factors are associated with achieving high continuity of care? Fam Med 36(1):55–60, pmid:14710331.
OpenUrl PubMed

[84] Christakis DA,

[85] Kazak AE,

[86] Wright JA,

[87] et al.

[88] 21.↵
Olah ME,
Gaisano G,
Hwang SW
(2013) The effect of socioeconomic status on access to primary care: an audit study. CMAJ 185(6):E263–E269, pmid:23439620.
OpenUrl Abstract/FREE Full Text

[89] Olah ME,

[90] Gaisano G,

[91] Hwang SW

[92] 22.↵
Parisi R,
Lau Y-S,
Bower P,
et al.
(2023) Predictors and population health outcomes of persistent high GP turnover in english general practices: a retrospective observational study. BMJ Qual Saf 32(7):394–403, pmid:36690473.
OpenUrl Abstract/FREE Full Text

[93] Parisi R,

[94] Lau Y-S,

[95] Bower P,

[96] et al.

[97] 23.↵
General Practitioners’ Organisation (PLO)
(2025) [PLO fact sheet]. [Article in Danish], accessed. https://laeger.dk/media/y05lxe1t/plo-faktaark-2025_ver1.pdf. 4 Jul 2025.

[98] General Practitioners’ Organisation (PLO)

[99] 24.↵
Toft Kristensen I,
Kjeldsen C,
Delgaard T
(2007) [Rural district municipalities: indicators for rural district]. [Article in Danish]. accessed. https://pure.au.dk/ws/files/4594133/RapLanddistrikt2007_20070201.pdf. 9 Jul 2025.

[100] Toft Kristensen I,

[101] Kjeldsen C,

[102] Delgaard T

[103] 25.↵
Charlson ME,
Pompei P,
Ales KL,
MacKenzie CR
(1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40(5):373–383, pmid:3558716.
OpenUrl CrossRef PubMed

[104] Charlson ME,

[105] Pompei P,

[106] Ales KL,

[107] MacKenzie CR

[108] 26.↵
Thygesen LC,
Ersbøll AK
(2014) When the entire population is the sample: strengths and limitations in register-based epidemiology. Eur J Epidemiol 29(8):551–558, pmid:24407880.
OpenUrl CrossRef PubMed

[109] Thygesen LC,

[110] Ersbøll AK

[111] 27.↵
DeMaris A
(1995) A tutorial in logistic regression. J Marriage Fam 57(4):956–968.
OpenUrl CrossRef

[112] DeMaris A

[113] 28.↵
Stoltzfus JC
(2011) Logistic regression: a brief primer. Acad Emerg Med 18(10):1099–1104, pmid:21996075.
OpenUrl CrossRef PubMed

[114] Stoltzfus JC

[115] 29.↵
Yang Z,
Ganguli I,
Davis C,
et al.
(2022) Physician‐ versus practice‐level primary care continuity and association with outcomes in medicare beneficiaries. Health Serv Res 57(4):914–929.
OpenUrl CrossRef PubMed

[116] Yang Z,

[117] Ganguli I,

[118] Davis C,

[119] et al.

[120] 30.↵
Frank L
(2000) When an entire country is a cohort. Science 287(5462):2398–2399.
OpenUrl FREE Full Text

[121] Frank L

[122] 31.↵
Kringos D,
Boerma W,
Bourgueil Y,
et al.
(2013) The strength of primary care in europe: an international comparative study. Br J Gen Pract doi:10.3399/bjgp13X674422, pmid:24267857.
OpenUrl Abstract/FREE Full Text

[123] Kringos D,

[124] Boerma W,

[125] Bourgueil Y,

[126] et al.

[127] 32.↵
Valaker I,
Fridlund B,
Wentzel-Larsen T,
et al.
(2020) Continuity of care and its associations with self-reported health, clinical characteristics and follow-up services after percutaneous coronary intervention. BMC Health Serv Res 20(1), pmid:32005235. 71.
OpenUrl PubMed

[128] Valaker I,

[129] Fridlund B,

[130] Wentzel-Larsen T,

[131] et al.

[132] 33.↵
Cohen E,
Lindman I
(2024) Importance of continuity of care from a patient perspective - a cross-sectional study in swedish health care. Scand J Prim Health Care 42(1):195–200, pmid:38189945.
OpenUrl PubMed

[133] Cohen E,

[134] Lindman I

[135] 34.↵
Mold JW,
Fryer GE,
Roberts AM
(2004) When do older patients change primary care physicians? J Am Board Fam Pract 17(6):453–460, pmid:15575037.
OpenUrl Abstract/FREE Full Text

[136] Mold JW,

[137] Fryer GE,

[138] Roberts AM

[139] 35.↵
Staiger B
(2022) Disruptions to the patient-provider relationship and patient utilization and outcomes: evidence from medicaid managed care. J Health Econ 81:102574, pmid:34968786.
OpenUrl PubMed

[140] Staiger B

[141] 36.↵
Ljungholm L,
Klinga C,
Edin-Liljegren A,
Ekstedt M
(2022) What matters in care continuity on the chronic care trajectory for patients and family carers? a conceptual model. J Clin Nurs 31(9–10):1327–1338, pmid:34351651.
OpenUrl PubMed

[142] Ljungholm L,

[143] Klinga C,

[144] Edin-Liljegren A,

[145] Ekstedt M

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Patient characteristics and degrees of discontinuity of care in Danish general practice: a cohort study

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Patient characteristics and degrees of discontinuity of care in Danish general practice: a cohort study

Abstract

Introduction

How this fits in

Method

Setting

Approaches

Results

Discussion

Summary

Strengths and limitations

Comparison with existing literature

Implications for practice

Notes

Funding

Ethical approval

Data

Acknowledgements

Competing interests

References

In this issue

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