Association Between Duration of Usual Source of Care and Patient-reported Primary Care Quality in Japan: A Nationwide Cross-sectional Study

Abstract

Objectives:

High-quality primary care is associated with improved patient experience (PX). Continuity of care is a driver of PX but often relies on complex indices. Whether a simple measure—years of attendance—captures PX remains unclear, especially in free-access systems lacking formal registration such as Japan, South Korea, and Germany. We aimed to investigate the association between “years-of-attendance” and PX, measured by the validated Japanese Person-Centered Primary Care Measure (PCPCM), among adults with a usual source of care (USC).

Methods:

In 2022, we conducted a nationwide, cross-sectional, web survey of Japanese adults (20-74 years). Of 800 respondents (response rate: 71.9%), we analyzed data from the 423 participants who reported having a USC. Exposures were log-transformed years since respondents began visiting their usual institution and physician. The outcome was the mean PCPCM score (11 domains, range: 1-4). Multivariable linear regression adjusted for age, sex, education, household income, self-rated health, number of chronic conditions, type of USC (clinic vs hospital), and rurality. We conducted stratified analyses by type of USC (clinic vs hospital) and rurality (urban vs rural/remote) to assess contextual robustness.

Results:

The median PCPCM was 2.47. Longer continuity showed a positive, dose-responsive association with PCPCM scores. The effect was strongest for physician-level continuity (β = .20, 95% CI: 0.09-0.31, P < .001). Stratified analyses revealed that the continuity-PX gradient was steepest for clinics in urban settings and hospitals in rural or remote settings.

Conclusions:

Among Japanese adults with a USC, longer continuity correlates with better PX. “Years of attendance” works as a practical quality indicator, with interpersonal continuity mattering more than institutional continuity. These findings do not extend to adults without a USC and should be interpreted within this population. Future longitudinal studies are required to inform policies aimed at sustaining continuity.

Keywords

primary care patient experience continuity PCPCM cross-sectional study

Introduction

Primary care is widely recognized as the foundation of an effective, efficient, and equitable health system.¹ Robust primary care is linked to better population health and narrower inequities than specialty-focused systems.² It is central to achieving the Quintuple Aim of improved health, enhanced patient experience (PX), lower costs, clinician well-being, and equity.^3-5 The core functions—accessibility, comprehensiveness, coordination, and continuity—operate synergistically to deliver system-level quality.^6-8

This study is set in Japan’s health system, which combines universal coverage with free access, allowing patients to attend clinics and hospital outpatient departments without referral or gatekeeping. Similar “free-access” (or low-barrier) systems exist in countries such as South Korea and Germany, although their insurance and registration structures differ from Japan’s national universal coverage model. Japan lacks formal registration with a named primary care physician; patients instead self-identify a clinic physician or a hospital specialist as their usual doctor, producing heterogeneous patterns of usual source of care (USC) across urban and rural settings.^6,7,9 Hospitals often deliver front-line ambulatory care—particularly in rural or remote regions—whereas clinics more commonly provide longitudinal, person-centered care in urban areas.^10,11 These structural features may shape how continuity relates to PX by facility type and geography.

We foreground PX because it is both a core quality domain and a proximal mechanism through which primary care influences safety, utilization, equity, and costs.^6-8,12 The Person-Centered Primary Care Measure (PCPCM) parsimoniously captures high-value functions (eg, access, coordination, continuity, and comprehensiveness) that mediate downstream outcomes, making PX a theory-consistent outcome for assessing primary care quality.^13,14 Across Organization for Economic Co-operation and Development (OECD) countries, PCPCM and related patient-reported measures have been linked to preventive care uptake, avoidable hospitalizations, and health-system performance.^13-15 Studies from registration-based systems (eg, the United Kingdom (UK) and Canada) and other OECD settings have reported that having a USC and higher continuity indices are associated with better patient-reported experiences, but these analyses typically rely on claims-based continuity measures or formal patient lists and rarely use the PCPCM.^16-23

Among the 4 core functions of primary care, continuity includes interpersonal (relational), longitudinal, informational, and management components.²⁴ Interpersonal continuity is most proximal to PX because it accumulates tacit knowledge, trust, and coordination efficiency that patients directly experience.^24-29 In Japan’s non-registration, free-access context—where both clinics and hospital outpatient departments can function as USCs—variation in relationship duration offers a pragmatic lever to test whether sustained relationships map onto better person-centered care.^10,11 In such free-access systems, administrative visit-based indices (eg, the Usual Provider of Care index or Bice-Boxerman Continuity of Care Index) may be unavailable or unstable because patients can attend multiple providers ad libitum.^29-32 Patient-reported years since first visiting their usual facility or physician therefore provide a simple, comprehensible continuity indicator continuity that is directly meaningful to patients. Although tenure-based measures sacrifice some comparability with visit-based indices, they capture the lived duration of relationships in non-registration settings such as Japan, South Korea, and Germany. In this study, our physician-level exposure primarily captures interpersonal (relational) continuity, whereas the facility-level exposure also reflects longitudinal continuity at the institution level.

To our knowledge, no prior study has examined tenure-based continuity in relation to a validated, multi-domain PX measure in a nationally distributed sample from a non-registration, free-access health system.^13-15 As Japan and other countries debate policies to formalize primary care functions (eg, through primary care function reporting systems and implicit registration),¹¹ quantifying how simple continuity indicators relate to PX is timely. Therefore, this study aimed to investigate whether, among adults with a USC in Japan, longer duration with the usual facility or physician is associated with higher PX, as measured by PCPCM. We hypothesized that longer duration with the usual facility or physician would be associated with higher PCPCM scores, with the strongest signal for interpersonal (physician-level) continuity, and prespecified stratified analyses by facility type (clinic vs hospital) and rurality.

Methods

We conducted a nationally distributed, cross-sectional web survey (November to December 2022) administered by RJC Research (n.d.), which maintains a profiled panel (≈1.5 million adults) spanning all 47 prefectures with demographic and geographic quotas.^6,7,9,33 We followed STROBE guidance (EQUATOR).

Target Population and Analytic Sample

Using sex- and age-stratified random sampling, we invited 1112 adults aged 20 to 74 years (the PCPCM validation range) via automated e-mail from the survey company’s secure platform; investigators had no direct contact with potential participants.⁹ Responses were submitted through anonymized online forms, and the research team received de-identified data only. Respondents were first asked, “Do you have a medical facility that you usually visit when ill or for health advice?” Those answering yes (USC = 1; n = 423) proceeded to the PCPCM and comprised the analytic sample for outcome models. Those answering no (USC = 0; n = 377) did not receive PCPCM items and were excluded from outcome analyses, consistent with recommended gating in non-registration systems.^8,9 Within the analytic sample (USC = 1), there were no missing data on exposure or outcome.

Exposure: Duration-Based Continuity

Continuity was measured as years since first attending the usual institution and years since first consulting the usual physician, reported by USC = 1 respondents. Relationship duration has been used as a patient-centered proxy for interpersonal continuity with supportive associations to preventive care, utilization, and mortality.^29-32 When respondents had more than 1 potential USC, instructions specified the facility used most often for general health problems; ties were resolved by selecting the most recently visited facility. To accommodate 0 years (<1 year) and reduce right-skew, we modeled ln(years+1) (“log1p”) as a continuous predictor, as illustrated in Figure 1.

Figure 1.

Distributions of years since starting to visit: (a) a regular medical institution (“Years with regular institution”), (b) natural log-transformed “years with regular institution,” (c) a regular physician (“Years with regular physician”), and (d) natural log-transformed “years with regular physician.”

Outcome: PX

PX was assessed using the validated Japanese PCPCM (11 domains; each 1-4).⁹ Consistent with prior PCPCM studies,^13,14 the composite score used in analyses was the mean of the 11 items (range: 1-4), not the sum; thus, values in figures and tables represent average item scores. The eleven items cover accessibility, comprehensiveness, integration, coordination, relationship, continuity, advocacy, family and community context, goal-orientation, and health promotion. The continuity item (“My doctor and I have been through a lot together”) does not include relationship length. The Japanese and original versions of all items are provided in Supplemental Tables S1 and S2.

Covariates

Based on prior associations with continuity and PX, we adjusted for age, sex, education, household income, marital status, occupation, self-rated health, multimorbidity (count of 20 chronic conditions), USC type (clinic vs hospital), and rurality using the Rurality Index for Japan (RIJ; 1-100; higher = rural/remote).^10,34-37 Age was grouped into 5-year bands to align with sampling strata and reduce functional form misspecification. Multimorbidity was defined as the count of 20 self-reported chronic conditions (eg, hypertension, diabetes, dyslipidemia, chronic lung disease, mental illness), and entered as a categorical covariate (0, 1, and ≥2 conditions) in the main models to reflect overall burden while avoiding over-interpretation of specific disease combinations sharing the same count.

Sample Size Planning

From prior studies we anticipated a USC prevalence of ~55% and a PCPCM standard deviation of ~0.6.^9,14,15 Power calculations targeting a small effect for the continuity term (Cohen’s f² = 0.02) in multiple regression with ~12 to 14 covariates (α = .05, 1 − β = .80) indicated the need for ≈380 to 420 USC respondents, using the standard sample-size formula for multiple linear regression based on Cohen’s f². Assuming ~70% response and 55% USC prevalence, we issued ≈1100 invitations to reach this target while satisfying the ≥10 observations-per-parameter heuristic.

Statistical Analysis

Categorical variables were summarized as counts and percentages; continuous variables as medians and interquartile ranges. We first fitted multivariable linear regression models of PCPCM on ln(years+1) continuity, separately for institution- and physician-level exposures, adjusting for covariates: age, sex, education, household income, marital status, occupation, self-rated health, multimorbidity, USC type, and RIJ. In the primary specification, multimorbidity was included as a categorical variable (0, 1, and ≥2 conditions). As a sensitivity analysis, we re-estimated all models with multimorbidity entered as a continuous count variable (0-20 conditions) to assess the robustness of continuity estimates to this modelling choice. The PCPCM mean score was treated as a continuous outcome. Model assumptions (linearity, homoscedasticity, normality of residuals) were checked using diagnostic plots, and variance inflation factors (VIFs) were examined (threshold 2.5).^38,39 Two-sided P < .05 denoted statistical significance. Analyses used Stata/SE 17.0.

Stratified Analysis

To examine potential effect modification and contextual robustness, we stratified the main models by (a) USC type (clinic vs hospital) and (b) rurality (RIJ 1-20 vs RIJ 21-100, corresponding to urban vs rural/remote). Facility type and geography are known to shape primary care delivery patterns, resource availability, and patient-provider relationships in Japan.^10,11

Sensitivity Analyses

Sensitivity analyses addressed potential construct overlap, alternative exposure specifications, nonlinearity, heteroskedasticity and outliers, missing data, and a negative-control outcome. To address potential construct overlap, we recomputed the PCPCM composite after excluding the continuity item (No. 6) and repeated the primary models. Details of all sensitivity-analysis specifications, including categorical exposures, trimming/winsorization, spline modeling, and missing-data handling, are provided in the Supplemental Material. We also repeated all models with multimorbidity entered as a continuous count variable (0-20 chronic conditions) instead of categories; continuity coefficients were very similar, indicating robustness to this modelling choice. Unless otherwise noted, models used heteroskedasticity-consistent covariance estimator type 3 (HC3) robust standard errors.

Ethical Approval and Consent to Participate

The study was performed in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Yokohama City University (approval number: F220900057). All participants provided electronic informed consent and were allowed to terminate the survey at any time without penalty.

Results

Of 1112 invitees, 800 completed the survey (response 71.9%); 399 were men (49.8%) and 400 women (50.0%). A USC was reported by 423 respondents (52.9%); this group formed the analytic sample for all PCPCM analyses. Participant characteristics for those with and without a USC are presented separately in Table 1, together with P-values for between-group comparisons. Among USC holders, ≈70% used clinics and ≈30% hospitals as their USC. All VIFs were <2.5, and residual diagnostics did not suggest marked heteroscedasticity, and we reported HC3-robust standard errors.

Table 1.

Participant Characteristics (N = 800).

		Without USC N = 377	With USC N = 423	P-value
	Overall	n (%)	n (%)	n (%)
Sex				.055
Male	399 (49.8)	203 (53.8)	196 (46.3)
Female	400 (50.0)	173 (45.9)	227 (53.7)
Others	1 (0.1)	1 (0.3)	0 (0)
Age (years)				<.001
Mean [SD]	48.4 [15.1]	44.9 [14.4]	51.5 [15.0]
20-24	41 (5.1)	22 (5.8)	19 (4.5)
25-29	86 (10.8)	55 (14.6)	31 (7.3)
30-34	51 (6.4)	34 (9)	17 (4.0)
35-39	75 (9.4)	41 (10.9)	34 (8.0)
40-44	85 (10.6)	47 (12.5)	38 (9.0)
45-49	81 (10.1)	32 (8.5)	49 (11.6)
50-54	78 (9.8)	36 (9.5)	42 (9.9)
55-59	74 (9.2)	38 (10.1)	36 (8.5)
60-64	83 (10.4)	34 (9)	49 (11.6)
65-69	61 (7.6)	10 (2.7)	51 (12.1)
70-74	85 (10.6)	28 (7.4)	57 (13.5)
Education				.87
<High school	13 (1.6)	6 (1.6)	7 (1.7)
High school	238 (29.8)	111 (29.4)	127 (30.0)
Junior college	174 (21.8)	78 (20.7)	96 (22.7)
≥College	375 (46.9)	182 (48.3)	193 (45.6)
Annual household income (million JPY)				.056
<3.00 (≒20 000 US dollar)	166 (20.8)	88 (23.3)	78 (18.4)
3.00-5.99	208 (26.0)	80 (21.2)	128 (30.3)
6.00-8.99	124 (15.5)	59 (15.6)	65 (15.4)
9.00-11.99	77 (9.6)	38 (10.1)	39 (9.2)
12.00-14.99	35 (4.4)	18 (4.8)	17 (4.0)
≥15.00	24 (3.0)	8 (2.1)	16 (3.8)
Unknown	166 (20.8)	86 (22.8)	80 (18.9)
Self-rated health status				.39
Excellent	39 (4.9)	19 (5)	20 (4.7)
Good	110 (13.8)	53 (14.1)	57 (13.5)
Neutral	228 (28.5)	116 (30.8)	112 (26.5)
Poor	309 (38.6)	144 (38.2)	165 (39.0)
Very poor	114 (14.2)	45 (11.9)	69 (16.3)
Marital status				<.001
Married	395 (49.4)	158 (41.9)	237 (56.0)
Divorced	81 (10.1)	38 (10.1)	43 (10.2)
Never married	304 (38.0)	173 (45.9)	131 (31.0)
Widowed	18 (2.2)	7 (1.9)	11 (2.6)
Others	2 (0.2)	1 (0.3)	1 (0.2)
Employment status				.002
Full-time employee	345 (43.1)	189 (50.1)	156 (36.9)
Part-time employee	155 (19.4)	66 (17.5)	89 (21.0)
Self-employed	47 (5.9)	20 (5.3)	27 (6.4)
Unemployed	236 (29.5)	92 (24.4)	144 (34.0)
Others	17 (2.1)	10 (2.7)	7 (1.7)
Number of chronic conditions				<.001
Mean [SD]	0.69 [1.05]	0.31 [0.73]	1.02 [1.18]
0	472 (59.0)	301 (79.8)	171 (40.4)
1	202 (25.2)	54 (14.3)	148 (35.0)
2	78 (9.8)	13 (3.4)	65 (15.4)
3	26 (3.2)	4 (1.1)	22 (5.2)
4	15 (1.9)	4 (1.1)	11 (2.6)
5	4 (0.5)	1 (0.3)	3 (0.7)
6	1 (0.1)	0 (0)	1 (0.2)
7	2 (0.2)	0 (0)	2 (0.5)
RIJ (1-100)				.58
1-20	465 (58.1)	223 (59.2)	242 (57.2)
21-100	335 (41.9)	154 (40.8)	181 (42.8)

Abbreviations: RIJ, Rurality Index for Japan; USC, usual source of care.

“Others” indicates respondents who selected the “Other” option because they did not fit the specific categories listed.

The distributions of years since first attending the usual institution and years since first consulting the usual physician peaked around 5 to 10 years, with some relationships extending beyond 40 years (Figure 1). After log transformation, distributions approximated normality. PCPCM scores centered between 2 and 3; the median was 2.47 (interquartile range = 2.00-3.00; Figure 2).

Figure 2.

Distribution of PCPCM mean score (average of 11 PCPCM items; each item 1-4, so composite range: 1-4).

In adjusted models restricted to USC holders, longer continuity was associated with higher PCPCM scores at both levels (Tables 2 and 3). The association was strongest for physician-level continuity (β = .20; 95% CI: 0.09-0.31; P < .001), with a smaller but positive association for institution-level continuity. Given the log-transformed exposure, this coefficient implies that, holding covariates constant, increasing continuity with the same physician from 1 to 10 years is associated with an estimated 0.34-point higher PCPCM score on the 1 to 4 scale—approximately half a standard deviation—suggesting a clinically meaningful gradient rather than a trivial effect.

Table 2.

Multivariable Linear Regression of PCPCM Score on Natural Log-transformed Years With a Regular Institution: Overall and Subgroup Analyses (Hospital vs Clinic).

	With USC, overall	Type of USC: hospital	Type of USC: clinic
	N = 423	N = 126	N = 297
	Coefficient (95% CI)	Coefficient (95% CI)	Coefficient (95% CI)
Natural log-transformed years with a regular institution	0.124 (0.042 to 0.206)	0.101 (−0.066 to 0.269)	0.152 (0.051 to 0.253)
Sex
Male	Reference	Reference	Reference
Female	−0.032 (−0.186 to 0.121)	−0.105 (−0.431 to 0.221)	0.065 (−0.128 to 0.259)
Age (years)
20-24	Reference	Reference	Reference
25-29	−0.151 (−0.565 to 0.262)	0.177 (−0.578 to 0.933)	−0.297 (−0.822 to 0.229)
30-34	0.105 (−0.370 to 0.580)	0.062 (−0.776 to 0.899)	0.084 (−0.529 to 0.697)
35-39	−0.163 (−0.576 to 0.250)	−0.488 (−1.207 to 0.230)	0.012 (−0.545 to 0.569)
40-44	−0.093 (−0.502 to 0.315)	−0.392 (−1.218 to 0.433)	−0.062 (−0.569 to 0.445)
45-49	−0.075 (−0.468 to 0.318)	−0.173 (−1.007 to 0.661)	−0.093 (−0.586 to 0.399)
50-54	−0.069 (−0.472 to 0.333)	−0.061 (−0.803 to 0.681)	−0.178 (−0.692 to 0.335)
55-59	−0.095 (−0.512 to 0.323)	0.252 (−0.515 to 1.020)	−0.372 (−0.902 to 0.158)
60-64	−0.180 (−0.585 to 0.224)	−0.193 (−0.952 to 0.565)	−0.224 (−0.748 to 0.300)
65-69	−0.195 (−0.616 to 0.227)	−0.399 (−1.175 to 0.377)	−0.155 (−0.700 to 0.390)
70-74	−0.226 (−0.649 to 0.197)	−0.014 (−0.885 to 0.856)	−0.425 (−0.964 to 0.114)
Education
<High school	Reference	Reference	Reference
High school	0.545 (0.004 to 1.086)	0.409 (−0.758 to 1.576)	0.875 (0.201 to 1.548)
Junior college	0.678 (0.132 to 1.224)	0.667 (−0.534 to 1.868)	0.905 (0.228 to 1.583)
≥College	0.485 (−0.056 to 1.026)	0.441 (−0.768 to 1.650)	0.677 (0.013 to 1.342)
Annual household income (million JPY)
<3.00 (≒20 000 US dollar)	Reference	Reference	Reference
3.00-5.99	0.063 (−0.144 to 0.270)	0.093 (−0.350 to 0.535)	0.018 (−0.232 to 0.269)
6.00-8.99	−0.026 (−0.282 to 0.231)	0.205 (−0.323 to 0.733)	−0.092 (−0.400 to 0.215)
9.00-11.99	0.147 (−0.146 to 0.440)	0.335 (−0.240 to 0.910)	0.064 (−0.310 to 0.439)
12.00-14.99	0.328 (−0.064 to 0.720)	0.633 (−0.173 to 1.440)	0.426 (−0.073 to 0.925)
≥15.00	0.332 (−0.087 to 0.751)	0.747 (−0.570 to 2.064)	0.303 (−0.174 to 0.781)
Unknown	−0.076 (−0.301 to 0.148)	−0.087 (−0.532 to 0.357)	−0.152 (−0.435 to 0.130)
Self-rated health status
Excellent	Reference	Reference	Reference
Good	0.181 (−0.193 to 0.556)	0.624 (−0.270 to 1.519)	0.179 (−0.289 to 0.647)
Neutral	−0.025 (−0.375 to 0.326)	0.766 (−0.116 to 1.647)	−0.147 (−0.579 to 0.285)
Poor	−0.390 (−0.739 to −0.041)	0.166 (−0.714 to 1.046)	−0.465 (−0.885 to −0.044)
Very poor	−0.576 (−0.960 to −0.192)	0.025 (−0.918 to 0.967)	−0.654 (−1.123 to −0.185)
Marital status
Married	Reference	Reference	Reference
Divorced	0.110 (−0.126 to 0.346)	0.200 (−0.301 to 0.702)	0.034 (−0.256 to 0.324)
Never married	0.063 (−0.116 to 0.243)	0.342 (−0.056 to 0.740)	−0.020 (−0.242 to 0.201)
Widowed	−0.338 (−0.772 to 0.097)	−0.326 (−1.409 to 0.758)	−0.349 (−0.842 to 0.144)
Others	1.308 (−0.246 to 2.862)	1.368 (−0.279 to 3.015)	0.000 (0.000 to 0.000)
Employment status
Full-time employee	Reference	Reference	Reference
Part-time employee	0.037 (−0.173 to 0.246)	0.267 (−0.147 to 0.680)	−0.065 (−0.321 to 0.190)
Self-employed	0.179 (−0.132 to 0.489)	0.683 (0.079 to 1.287)	−0.146 (−0.540 to 0.248)
Unemployed	0.219 (−0.001 to 0.438)	0.470 (0.016 to 0.924)	0.172 (−0.101 to 0.445)
Others	0.199 (−0.405 to 0.803)	−1.314 (−3.057 to 0.429)	0.302 (−0.440 to 1.045)
Number of chronic conditions
0	Reference	Reference	Reference
1	0.202 (0.036 to 0.368)	0.007 (−0.340 to 0.354)	0.243 (0.043 to 0.443)
2	0.234 (0.014 to 0.454)	0.183 (−0.292 to 0.659)	0.235 (−0.031 to 0.502)
3	0.274 (−0.051 to 0.598)	0.358 (−0.439 to 1.155)	0.234 (−0.141 to 0.608)
4	0.368 (−0.078 to 0.814)	0.003 (−0.894 to 0.899)	0.590 (0.030 to 1.149)
5	1.296 (0.469 to 2.122)	3.627 (1.295 to 5.958)	0.848 (−0.175 to 1.871)
6	−0.071 (−1.477 to 1.335)	0.074 (−1.358 to 1.505)	0.000 (0.000 to 0.000)
7	0.123 (−0.892 to 1.138)	−0.049 (−1.097 to 0.999)	0.000 (0.000 to 0.000)
RIJ (1-100)
1-20	Reference	Reference	Reference
21-100	−0.077 (−0.218 to 0.064)	0.070 (−0.230 to 0.370)	−0.140 (−0.311 to 0.031)

Abbreviations: RIJ, Rurality Index for Japan; USC, usual source of care.

“Others” indicates respondents who selected the “Other” option because they did not fit the specific categories listed.

“Overall” refers to all participants included in the institution-level continuity models (n = 423, USC holders who reported a regular institution).

Table 3.

Multivariable Linear Regression of PCPCM Score on Natural Log-transformed Years With a Regular Physician: Overall and Subgroup Analyses (Hospital vs Clinic).

	With USC, overall	Type of USC: hospital	Type of USC: clinic
	N = 315	N = 79	N = 236
	Coefficient (95% CI)	Coefficient (95% CI)	Coefficient (95% CI)
Natural log-transformed years with a regular physician	0.174 (0.077-0.271)	0.141 (−0.153 to 0.434)	0.200 (0.087-0.312)
Sex
Male	Reference	Reference	Reference
Female	0.067 (−0.110 to 0.244)	0.100 (−0.439 to 0.640)	0.087 (−0.129 to 0.302)
Age (years)
20-24	Reference	Reference	Reference
25-29	−0.144 (−0.697 to 0.409)	0.002 (−1.143 to 1.147)	−0.330 (−1.056 to 0.396)
30-34	−0.060 (−0.622 to 0.502)	−0.073 (−1.155 to 1.009)	−0.155 (−0.891 to 0.581)
35-39	−0.321 (−0.853 to 0.211)	−0.373 (−1.432 to 0.687)	−0.324 (−1.022 to 0.375)
40-44	−0.482 (−0.981 to 0.016)	−0.298 (−1.363 to 0.767)	−0.505 (−1.132 to 0.122)
45-49	−0.219 (−0.713 to 0.275)	0.285 (−0.917 to 1.487)	−0.337 (−0.960 to 0.285)
50-54	−0.352 (−0.854 to 0.151)	−0.021 (−1.320 to 1.278)	−0.515 (−1.142 to 0.112)
55-59	−0.348 (−0.864 to 0.168)	0.362 (−0.806 to 1.530)	−0.686 (−1.331 to −0.041)
60-64	−0.348 (−0.851 to 0.156)	0.006 (−1.115 to 1.128)	−0.506 (−1.138 to 0.125)
65-69	−0.539 (−1.055 to −0.023)	−0.463 (−1.505 to 0.579)	−0.610 (−1.265 to 0.045)
70-74	−0.571 (−1.088 to −0.053)	−0.168 (−1.437 to 1.101)	−0.791 (−1.436 to −0.146)
Education
<High school	Reference	Reference	Reference
High school	0.466 (−0.311 to 1.242)	0.026 (−1.841 to 1.893)	0.828 (−0.135 to 1.792)
Junior college	0.393 (−0.393 to 1.178)	−0.007 (−1.910 to 1.896)	0.724 (−0.248 to 1.697)
≥College	0.285 (−0.492 to 1.061)	−0.135 (−2.018 to 1.748)	0.567 (−0.384 to 1.519)
Annual household income (million JPY)
<3.00 (≒20 000 US dollar)	Reference	Reference	Reference
3.00-5.99	0.088 (−0.144 to 0.320)	0.260 (−0.366 to 0.886)	−0.026 (−0.302 to 0.250)
6.00-8.99	−0.080 (−0.367 to 0.206)	−0.297 (−1.152 to 0.558)	−0.110 (−0.441 to 0.220)
9.00-11.99	0.205 (−0.133 to 0.543)	0.595 (−0.361 to 1.551)	−0.067 (−0.473 to 0.338)
12.00-14.99	0.385 (−0.053 to 0.823)	0.740 (−0.353 to 1.832)	0.273 (−0.286 to 0.833)
≥15.00	0.293 (−0.182 to 0.769)	−0.560 (−3.064 to 1.944)	0.208 (−0.316 to 0.732)
Unknown	−0.195 (−0.447 to 0.056)	−0.201 (−0.824 to 0.422)	−0.330 (−0.634 to −0.027)
Self-rated health status
Excellent	Reference	Reference	Reference
Good	−0.033 (−0.473 to 0.407)	−1.269 (−3.201 to 0.663)	0.208 (−0.291 to 0.708)
Neutral	−0.213 (−0.632 to 0.205)	−1.216 (−3.223 to 0.790)	−0.211 (−0.667 to 0.245)
Poor	−0.552 (−0.967 to −0.138)	−1.675 (−3.707 to 0.356)	−0.492 (−0.936 to −0.049)
Very poor	−0.709 (−1.157 to −0.260)	−1.660 (−3.680 to 0.359)	−0.674 (−1.167 to −0.181)
Marital status
Married	Reference	Reference	Reference
Divorced	0.052 (−0.213 to 0.317)	0.248 (−0.524 to 1.020)	−0.036 (−0.346 to 0.273)
Never married	0.038 (−0.174 to 0.250)	0.278 (−0.378 to 0.933)	0.010 (−0.241 to 0.261)
Widowed	−0.306 (−0.770 to 0.158)	−0.776 (−2.084 to 0.531)	−0.214 (−0.748 to 0.320)
Others	0.972 (−0.574 to 2.517)	1.399 (−0.222 to 3.020)	0.000 (0.000 to 0.000)
Employment status
Full-time employee	Reference	Reference	Reference
Part-time employee	0.099 (−0.141 to 0.339)	0.179 (−0.469 to 0.827)	−0.015 (−0.303 to 0.272)
Self-employed	0.156 (−0.213 to 0.524)	0.536 (−0.419 to 1.490)	−0.152 (−0.600 to 0.296)
Unemployed	0.265 (0.016 to 0.514)	0.165 (−0.470 to 0.800)	0.239 (−0.067 to 0.544)
Others	0.457 (−0.188 to 1.103)	2.186 (0.408 to 3.964)	0.212 (−0.518 to 0.941)
Number of chronic conditions
0	Reference	Reference	Reference
1	0.260 (0.073 to 0.446)	0.241 (−0.247 to 0.729)	0.284 (0.065 to 0.504)
2	0.136 (−0.101 to 0.374)	0.168 (−0.489 to 0.824)	0.143 (−0.140 to 0.426)
3	0.148 (−0.178 to 0.474)	0.185 (−0.793 to 1.162)	0.164 (−0.210 to 0.537)
4	0.377 (−0.126 to 0.880)	0.087 (−1.331 to 1.504)	0.534 (−0.055 to 1.123)
5	1.115 (0.310-1.919)	0.000 (0.000-0.000)	0.715 (−0.281 to 1.710)
6	−0.167 (−1.533 to 1.199)	−0.194 (−1.584 to 1.197)	0.000 (0.000-0.000)
7	−0.207 (−1.209 to 0.795)	−0.472 (−1.512 to 0.568)	0.000 (0.000-0.000)
RIJ (1-100)
1-20	Reference	Reference	Reference
21-100	−0.140 (−0.298 to 0.018)	−0.147 (−0.579 to 0.286)	−0.119 (−0.304 to 0.066)

Abbreviations: RIJ, Rurality Index for Japan; USC, usual source of care.

“Others” indicates respondents who selected the “Other” option because they did not fit the specific categories listed.

“Overall” refers to all participants included in the physician-level continuity models (n = 315, USC holders who reported a usual physician).

Stratified analyses supported context dependence (Figure 3; Table 4). In urban areas (low RIJ), clinic-based continuity showed the steepest gradient, consistent with clinics’ person-centered longitudinal role. In rural/remote areas (high RIJ), hospital-based continuity showed the strongest association, reflecting hospitals’ de facto primary care role in resource-limited regions. “Overall” in Tables 2 to 4 denotes the combined USC analytic sample (USC = 1). Across sensitivity analyses—including recomputation of the PCPCM composite excluding the continuity item (pcpcm_6), alternative exposure specifications, outlier handling, nonlinearity checks, quantile regression, and multiple imputation—the direction and magnitude of the association were consistent. For example, when pcpcm_6 was excluded, coefficients were β = .12 (95% CI: 0.03-0.20) for institution-level and β = .15 (95% CI: 0.05-0.25) for physician-level continuity.

Figure 3.

Forest plot of adjusted associations between continuity (natural log-transformed years with a regular institution/physician) and PCPCM mean score.

Table 4.

Crude and Adjusted Coefficients of Natural Log-transformed Years With a Regular Institution/Physician for PCPCM.

	Natural log-transformed years with regular institution		Natural log-transformed years with regular physician
	Crude coefficient (95% CI)	Adjusted coefficient (95% CI)	Crude coefficient (95% CI)	Adjusted coefficient (95% CI)
Main analysis
All participants	0.102 (0.023 to 0.180)*	0.122 (0.040 to 0.204)*	0.112 (0.019 to 0.206)*	0.166 (0.069 to 0.262)*
Stratified analysis
Type of USC: Hospital	0.113 (−0.029 to 0.256)	0.103 (−0.063 to 0.270)	0.185 (−0.007 to 0.377)	0.129 (−0.160 to 0.419)
RIJ: 1-20	0.125 (−0.079 to 0.328)	0.052 (−0.241 to 0.345)	0.110 (−0.148 to 0.368)	−0.233 (−1.201 to 0.735)
RIJ: 21-100	0.100 (−0.105 to 0.305)	0.160 (−0.305 to 0.626)	0.318 (0.012-0.624)*	0.404 (−0.469 to 1.277)
Type of USC: Clinic	0.095 (0.000-0.190)	0.148 (0.047-0.250)*	0.097 (−0.012 to 0.206)	0.193 (0.081-0.305)*
RIJ: 1-20	0.161 (0.024-0.298)*	0.245 (0.086-0.403)*	0.169 (0.011-0.328)*	0.338 (0.152-0.523)*
RIJ: 21-100	0.019 (−0.109 to 0.148)	0.088 (−0.057 to 0.232)	0.011 (−0.133 to 0.154)	0.119 (−0.025 to 0.263)

Abbreviations: RIJ, Rurality Index for Japan; USC, usual source of care.

P < .05.

However, when we recomputed the PCPCM composite excluding the continuity item (10-item mean), the associations changed little: institution-level continuity β = .12 (95% CI: 0.03-0.20) and physician-level continuity β = .15 (95% CI: 0.05-0.25). These findings suggest that construct overlap does not fully explain the observed gradients, although some inflation of effect sizes remains possible.

Discussion

In a nationally distributed sample of Japanese adults with a USC, a simple continuity indicator—years of attendance—showed a clear, dose-responsive association with better PX as measured by the PCPCM, with effect sizes that are moderate in magnitude and likely meaningful for patients. The pattern held for both institution- and physician-level continuity, with a larger coefficient for physician-level continuity (β = .20) than for institution-level continuity (β = .11), indicating that the association with PX was strongest for interpersonal (physician-level) continuity. This finding aligns with prior evidence that continuity is associated with higher satisfaction, lower utilization, and reduced mortality.^16-23 Regarding the magnitude of the association, a coefficient of approximately 0.20 on the 4-point PCPCM scale may appear small numerically. However, in the context of this measure, this effect size is meaningful. For comparison within our sample, the adjusted difference in PCPCM scores between participants reporting “Good” health versus those reporting “Poor” health was of a similar magnitude. This suggests that the “continuity effect” is as impactful on the patient’s experience of quality as their self-rated health status.

Our findings complement existing work showing that higher PCPCM scores are associated with better preventive care, reduced emergency department use, and improved system performance in other countries.^13-15 Whereas most prior studies have focused on structural or organizational determinants of PCPCM, our study highlights continuity—measured simply as relationship duration—as an important correlate of person-centered primary care quality in a non-registration system.

Adults who reported a USC were older and had a higher burden of chronic conditions than those without a USC (Table 1), consistent with evidence that people with multimorbidity value continuity more strongly than healthier adults. This selection pattern likely concentrates continuity among those with greater need, which may bias the continuity-PX gradient toward a more positive association within the USC-holding population.

System Context and International Comparison

Regarding the scope and utility of the PCPCM, validation studies in the United States and Canada have established it as a robust measure of high-value primary care functions,^13,15 and cross-national analyses across 35 OECD countries have linked national PCPCM scores to health system performance.¹⁴ Our findings suggest that the PCPCM also captures the benefits of continuity in Japan, supporting its use beyond the Western cultural and health system contexts in which it was developed.

In registration systems (eg, the UK), list-based assignment and gatekeeping structurally protect interpersonal continuity, which is associated with fewer ambulatory care-sensitive admissions and lower mortality.^20,40 In Canada, administrative data separating physician- from clinic-level continuity show stronger reductions in acute care use for the former.²³ Our results indicate that even without registration, relationship duration tracks higher PCPCM in Japan, implying that policy tools which approximate registration—such as formal designation of a family physician responsible for first-contact care, preventive services, and longitudinal panel management, together with stable provider assignment in clinics and hospital outpatient departments—could enhance PX under free access.¹¹

In Japan’s free-access system, patients can self-select both clinics and hospital outpatient departments as their USC. This self-selection may encourage those with positive experiences to maintain long-term relationships and affords patients greater autonomy over whom they regard as their usual doctor or facility. However, self-selection is unlikely to operate uniformly across demographic groups: for example, rural residents and individuals with multimorbidity may place greater value on stable relationships and therefore be more likely to identify a USC than healthy urban adults.⁴¹ These structural features of the Japanese system may thus concentrate continuity among higher-need groups and should be considered when interpreting continuity-PX gradients and designing continuity-enhancing policies.

Facility Type and Geography

The continuity-PX gradient differed by facility and rurality: clinics in urban areas and hospitals in rural/remote areas showed the steepest associations. This pattern is consistent with rural populations’ higher valuation of interpersonal relationships⁴¹ and with the reality that regional hospitals often serve as primary contact points in remote areas. In urban settings, clinics typically provide person-centered, longitudinal care with smaller panels and appointment systems, so physician continuity may be easier to maintain and more salient for PX. In contrast, in rural and remote regions of Japan, hospital outpatient departments often function as de facto primary care clinics with limited provider choice; within this context, sustained relationships with hospital-based physicians can play a similar interpersonal role, explaining the strong continuity-PX gradients we observed. These contextual differences argue against one-size-fits-all approaches and suggest facility-specific strategies to bolster continuity.

Interpretation Boundaries and Covariates

Our covariates served to control confounding; we deliberately avoid interpreting their coefficients causally to prevent over-reading adjustment variables as determinants of PX. Estimates for sex, age, income, occupation, and health status should not be over-read. For multimorbidity, we chose a simple categorical specification (0, 1, and ≥2 conditions) because very different disease combinations can share the same count, and the marginal impact of “1 additional condition” is unlikely to be strictly linear. Sensitivity analyses modelling multimorbidity as a continuous count yielded similar continuity estimates, suggesting that our conclusions are not driven by this coding choice.

Conceptual Overlap

The PCPCM includes a domain labeled continuity/relationship, which is conceptually adjacent to our exposure. This raises the possibility of partial construct overlap inflating the association. We therefore caution readers in interpreting the magnitude of effects. However, our sensitivity analysis excluding the continuity item yielded estimates that were substantively similar to the primary results (as detailed in the Results section). These findings suggest that construct overlap does not fully explain the observed gradients, although some inflation of effect sizes remains possible.

Causal Direction and Bidirectionality

Cross-sectional design limits causal inference. Better PX could promote continuity by reducing switching, while continuity could improve PX by enabling accumulated knowledge and coordination. Unmeasured preferences and local supply may shape both. Longitudinal designs with time-varying exposures, or interventional approaches (eg, dedicated attending-physician models or fixed provider assignment), are needed to disentangle directionality.

Implications

Interventions that preserve and extend continuity are expected to support better PX and, according to prior longitudinal and interventional studies, may also contribute to lower utilization and costs.^16-23 Given our cross-sectional design, these possibilities should be interpreted as hypotheses for future research rather than causal conclusions. In Japan, strengthening the Family Physician Function Reporting System and piloting stable provider assignment in hospital outpatient departments—especially in rural regions—could operationalize continuity at scale. For clinics, this could include explicitly assigning each patient a regular physician, documenting this in medical records, and aligning appointment systems and after-hours coverage to maintain that relationship. For hospitals, continuity could be supported by small, stable outpatient teams⁴² with named lead physicians, structured handovers when staffing changes, and communication to patients about which clinician is responsible for their ongoing care. Telemedicine and panel management may help sustain continuity where workforce is scarce.

Limitations

This study has several limitations. First, as noted above, its cross-sectional design and the potential bidirectionality of the continuity-PX association limit causal inference. Second, because PCPCM items were only administered to respondents who reported a USC, and USC holders differed from non-holders on several sociodemographic and clinical characteristics (Table 1), our analytic sample over-represents individuals who have already established a usual place of care. This likely reflects selective uptake of continuity by people with greater healthcare needs and limits the generalizability of our findings beyond adults with a USC. Third, regarding our sampling method, although we used age- and sex-stratified quotas, our web-based panel is not a probability sample. Relative to face-to-face national surveys, web-based sampling offers rapid recruitment and lower cost but is more vulnerable to selection bias related to internet access and digital literacy. Unlike administrative-data studies, our design captures patient-reported PX but cannot provide population-level estimates. Consequently, we may underrepresent digitally marginalized groups, which could lead to overestimation of continuity and PX among USC holders. Fourth, self-reported relationship duration may be affected by recall error, especially in hospitals with rotating staff.⁴³ Such error is likely to be nondifferential with respect to PCPCM scores and would therefore tend to attenuate continuity-PX associations rather than create spurious ones; however, some respondents may over-estimate very long relationships, which could modestly inflate effect estimates at the upper end of the duration distribution. Finally, as discussed above, potential conceptual overlap between our continuity exposure and the PCPCM continuity/relationship domain and our use of a simple multimorbidity count (0, 1, and ≥2 conditions) may partially inflate associations, though sensitivity analyses excluding the continuity item and modelling multimorbidity as a continuous count yielded substantively similar estimates. These limitations should be considered when interpreting the magnitude and generalizability of our findings.

Conclusion

Among Japanese adults with a usual physician or institution, longer years of attendance are associated with higher PCPCM-measured PX. The association is strongest for interpersonal physician-level continuity and varies by facility type and rurality, reflecting Japan’s free-access structure. “Years of attendance” is a practical quality indicator that can inform continuity-enhancing strategies in both clinics and hospital outpatient departments. Longitudinal and interventional studies are warranted to establish causality and guide policy.

Supplemental Material

sj-docx-1-jpc-10.1177_21501319251413952 – Supplemental material for Association Between Duration of Usual Source of Care and Patient-reported Primary Care Quality in Japan: A Nationwide Cross-sectional Study

Supplemental material, sj-docx-1-jpc-10.1177_21501319251413952 for Association Between Duration of Usual Source of Care and Patient-reported Primary Care Quality in Japan: A Nationwide Cross-sectional Study by Hironori Yamada and Makoto Kaneko in Journal of Primary Care & Community Health

Footnotes

Acknowledgements

We would like to thank all survey participants. We also would like to thank Editage () for English language editing.

ORCID iD

Hironori Yamada

Ethical Considerations

The study was performed in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Yokohama City University (approval number: F220900057).

Consent to Participate

All participants provided electronic informed consent and were allowed to terminate the survey at any time without penalty.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by JSPS KAKENHI, grant number 20 K18847, and a grant from the 2023 to 2024 Research Development Fund of Yokohama City University (grant number not applicable). The study sponsor played no role in the study design, data collection, analysis, interpretation, writing of the report, or decision to submit the article for publication.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets generated and analyzed in the current study are not publicly available as we did not receive written informed consent for data sharing.

Supplemental Material

Supplemental material for this article is available online.

References

Starfield

Primary Care: Balancing Health Needs, Services, and Technology. Oxford University Press; 1998.

Starfield

Shi

Macinko

Contribution of primary care to health systems and health. Milbank Q. 2005;83:457-502.

Nundy

Cooper

Mate

KS.

The Quintuple Aim for Health Care Improvement: a new imperative to advance health equity. JAMA. 2022;327:521-522.

National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Care Services; Committee on Implementing High-Quality Primary Care. Implementing High-quality Primary Care: Rebuilding the Foundation of Health Care. Robinson

Meisnere

Phillips

RL Jr

McCauley

, editors. National Academies Press; 2021:19-43.

Bodenheimer

Sinsky

From triple to quadruple aim: care of the patient requires care of the provider. Ann Fam Med. 2014;12:573-576.

Kaneko

Yamada

Okada

Patient experiences in primary care do not differ according to rurality: a cross-sectional study. BMC Prim Care. 2024;25:132.

Kaneko

Yamada

Okada

Higher person-centered primary care measure score is associated with better influenza vaccine uptake: a nationwide cross-sectional study. Fam Pract. 2025;42:cmaf030.

Shi

Starfield

Validating the adult primary care assessment tool. J Fam Pract. 2001;50:161-175.

Kaneko

Okada

Aoki

Inoue

Watanabe

Kuroki

Development and validation of a Japanese version of the person-centered primary care measure. BMC Prim Care. 2022;23:112.

10.

Kaneko

Ikeda

Inoue

, et al. Development and validation of a rurality index for healthcare research in Japan: a modified Delphi study. BMJ Open. 2023;13:e068800.

11.

Ministry of Health, Labour and Welfare. Examination of detailed analyses toward implementation of the system demonstrating primary care functions: materials 1. Paper presented at: Proceedings of the 5th Subcommittee on Implementation of the System Demonstrating Primary Care Functions; May 24, 2024; Tokyo, Japan. Ministry of Health, Labour and Welfare; 2024. https://www.mhlw.go.jp/content/10800000/001256411.pdf (accessed October 28, 2025).

12.

Zhong

Huang

, et al. Relationship between patient-perceived quality of primary care and self-reported hospital utilisation in China: a cross-sectional study. Eur J Gen Pract. 2024;30:2308740.

13.

Etz

Zyzanski

Gonzalez

, et al. A new comprehensive measure of high-value aspects of primary care. Ann Fam Med. 2019;17:221-230.

14.

Zyzanski

Gonzalez

O’Neal

, et al. Measuring primary care across 35 OECD countries. Ann Fam Med. 2021;19:547-552.

15.

Latifovic

Etz

Ramji

Glazier

Kiran

How the novel person-centered primary care measure performs in Canada. J Am Board Fam Med. 2022;35:751-761.

16.

Adler

Vasiliadis

Bickell

The relationship between continuity and patient satisfaction: a systematic review. Fam Pract. 2010;27:171-178.

17.

Baker

Freeman

Haggerty

Bankart

Nockels

KH.

Primary medical care continuity and patient mortality: a systematic review. Br J Gen Pract. 2020;70:e600-11.

18.

Pereira Gray

Sidaway-Lee

Whitaker

Evans

. Which methods are most practicable for measuring continuity within general practices? Br J Gen Pract. 2023;73:279-282.

19.

van Walraven

Oake

Jennings

Forster

. The association between continuity of care and outcomes: a systematic and critical review. J Eval Clin Pract. 2010;16:947-956.

20.

Pereira Gray

Sidaway-Lee

White

Thorne

Evans

PH.

Continuity of care with doctors—a matter of life and death? A systematic review of continuity of care and mortality. BMJ Open. 2018;8:e021161.

21.

Hung

Cramer

Pollack

Gross

Wang

S.-Y.

Primary care physician continuity, survival, and end-of-life care intensity. Health Serv Res. 2022;57:853-862.

22.

Sandvik

Hetlevik

Blinkenberg

Hunskaar

Continuity in general practice as predictor of mortality, acute hospitalization, and use of out-of-hours care: a registry-based observational study in Norway. Br J Gen Pract. 2022;72:e84-e90.

23.

McDonald

Ronksley

Cook

, et al. The impact of primary care clinic and family physician continuity on patient health outcomes: a retrospective analysis from Alberta, Canada. Ann Fam Med. 2024;22:223-229.

24.

Haggerty

Reid

Freeman

, et al. Continuity of care: a multidisciplinary review. BMJ. 2003;327:1219-1221.

25.

Aller

Vargas

Garcia-Subirats

, et al. A tool for assessing continuity of care across care levels: an extended psychometric validation of the CCAENA questionnaire. Int J Integr Care. 2013;13:e050.

26.

Burch

Walter

Stewart

Bower

Patient reported measures of continuity of care and health outcomes: a systematic review. BMC Prim Care. 2024;25:309.

27.

Baker

Freeman

Boulton

, et al. Continuity of Care: Patients’ and Carers’ Views and Choices in Their Use of Primary Care Services. NCCSDO; 2005.

28.

Hersch

Klemenhagen

Adam

Measuring continuity in primary care: how it is done and why it matters. Fam Pract. 2024;41:60-64.

29.

Saultz

Lochner

Interpersonal continuity of care and care outcomes: a critical review. Ann Fam Med. 2005;3:159-166.

30.

Bentler

Morgan

Virnig

Wolinsky

FD.

Do claims-based continuity of care measures reflect the patient perspective?

Med Care Res Rev. 2014;71:156-173.

31.

Bentler

Morgan

Virnig

Wolinsky

FD.

The association of longitudinal and interpersonal continuity of care with emergency department use, hospitalization, and mortality among Medicare beneficiaries. PLoS One. 2014;9:e115088.

32.

Cabana

Jee

SH.

Does continuity of care improve patient outcomes?

J Fam Pract. 2004;53:974-980.

33.

RJC Research. (n.d.). Monitor (in Japanese). Accessed October 28, 2025. https://www.rjc.co.jp/monitor/monitor.html

34.

Zhao

Zhou

Shen

Nawaz

Ren

, et al. Rural and urban differences in patient experience in China: a coarsened exact matching study from the perspective of residents. BMC Health Serv Res. 2021;21:330.

35.

Iqbal

Thompson

Wilson

Patient satisfaction with general practice in urban and rural areas of Scotland. Rural Remote Health. 2021;21:6634.

36.

Henning-Smith

Hernandez

Neprash

Lahr

Differences by rurality in satisfaction with care among Medicare beneficiaries. J Rural Health. 2021;37:114-123.

37.

Fortin

Almirall

Nicholson

Development of a research tool to document self-reported chronic conditions in primary care. J Comorb. 2017;7:117-123.

38.

Wilensky

Frank

Mizraji

Tzur

Goldstein

Almoznino

. Periodontitis and metabolic syndrome: statistical and machine-learning analytics of a nationwide study. Bioengineering. 2023;10:1384.

39.

Johnston

Jones

Manley

. Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure. Qual Quant. 2018;52:1957-1976.

40.

Barker

Steventon

Deeny

SR.

Association between continuity of care in general practice and hospital admissions for ambulatory care sensitive conditions: cross sectional study of routinely collected, person level data. BMJ. 2017;356:j84.

41.

Weinhold

Gurtner

. Rural–urban differences in determinants of patient satisfaction with primary care. Soc Sci Med. 2018;212:76-85.

42.

Bodenheimaer

Laing

BY.

The teamlet model of primary care. Ann Fam Med. 2007;5:457-461.

43.

Rodriguez

Marshall

Rogers

Safran

DG.

Primary care physician visit continuity: a comparison of patient-reported and administratively derived measures. J Gen Intern Med. 2008;23:1499-1502.

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