Abstract
Aims/Introduction
We aimed to examine the proportion of subsequent clinic visits for persons screened as having hyperglycemia based on glycated hemoglobin (HbA1c) levels at screening and the presence/absence of hyperglycemia at health checkups before 1 year of the screening among those without previous diabetes‐related medical care and attending regular clinic visits.
Materials and methods
This retrospective cohort study used the 2016–2020 data of Japanese health checkups and claims. The study analyzed 8,834 adult beneficiaries aged 20–59 years without regular clinic visits who had never received diabetes‐related medical care and whose recent health checkups showed hyperglycemia. The rates of 6‐month subsequent clinic visits after health checkups were evaluated according to HbA1c levels and the presence/absence of hyperglycemia at checkups a year before.
Results
The overall clinic visit rate was 21.0%. The HbA1c‐specific rates were 17.0, 26.7, 25.4 and 28.4% for <7.0, 7.0–7.4, 7.5–7.9 and ≥8.0% (64 mmol/mol), respectively. Persons with hyperglycemia at a previous screening had lower clinic visit rates than those without hyperglycemia, particularly in the HbA1c category of <7.0% (14.4% vs 18.5%; P < 0.001) and 7.0–7.4% (23.6% vs 35.1%; P < 0.001).
Conclusions
The overall rate of subsequent clinic visits among those without previous regular clinic visits was <30%, including for participants with HbA1c ≥8.0%. Persons with previously detected hyperglycemia had lower clinic visit rates, despite requiring more health counseling. Our findings might be useful for designing a tailored approach to encourage high‐risk individuals to seek diabetes care through clinic visits.
Keywords: Diabetes mellitus, Diagnostic screening programs, Office visits
This study examined the proportion of subsequent clinic visits for persons screened as having hyperglycemia by the glycated hemoglobin levels at the screening and the presence/absence of hyperglycemia at health checkups a year before among those without previous diabetes‐related medical care and regular clinic visits. The overall rate of subsequent clinic visits was <30%, even for glycated hemoglobin≥8.0%. Persons with previously detected hyperglycemia (Group A) had lower clinic visit rates than those without (Group B), despite requiring more health counseling.
INTRODUCTION
Diabetes mellitus causes a large burden of disease worldwide 1 , including Japan 2 . It increases vascular complications, decreases quality of life and increases medical costs 3 , 4 , 5 . According to the 2016 Japanese National Health and Nutrition Survey, approximately 10 million people in Japan had diabetes mellitus 6 ; of these, 23.1% did not receive any treatment 6 .
Nationwide health checkup that includes blood tests for hyperglycemia allows identifying patients with undiagnosed diabetes. The Industrial Safety and Health Act requires employers to carry out annual health checkups for their employees to monitor their health status for the prevention of cardiovascular and metabolic diseases 7 . Furthermore, the Act on Assurance of Medical Care for Elderly People requires that all employer‐ and community‐based social health insurance programs carry out health checkups for insured persons aged 40–74 years to prevent metabolic syndrome 8 . These health checkups include tests for blood glucose and glycated hemoglobin (HbA1c). If hyperglycemia is detected, the insurer will notify them of the positive result via a letter or directly contact a clerk or a public health nurse, and recommend visits for definitive diagnosis and treatment 9 .
A retrospective cohort study published in 2014 reported that 65% of the participants with hyperglycemia failed to visit a clinic within 6 months 10 . Because early intervention for suspected type 2 diabetes is effective in inhibiting disease progression 11 , and a previous study showed a decrease in HbA1c levels among those who had follow‐up clinic visits after health checkups 12 , improving the approach to recommending clinical visits is imperative. In a recent study, the frequency of clinic visits in the year before the health checkup was associated with the likelihood of follow‐up clinic visits after health checkups 13 . In this analysis, the patient sample was heterogeneous in terms of access to medical services; thus, it is quite possible that those who had been followed up by a physician for other chronic diseases received diabetes care after it was detected during health checkups.
To develop a targeted approach to facilitate participants to visit clinics, it is essential to understand factors associated with lower clinic visit rates, especially among those without regular clinic visits and less likely to attend the follow‐up clinic visit after the health checkups. As reported previously, glycemic levels at recent health checkups should influence clinic visit rates through the strength of a recommendation to visit a clinic; however, whether the relationship is monotonous remains unknown. In addition, the clinic visit rates might vary depending on whether the visit was recommended for the first time (or whether the participants had previously been advised to do so because of hyperglycemia).
Within this context, the present study aimed to examine the proportion of persons with hyperglycemia at health checkups who subsequently visited clinics, whether the proportions differ depending on HbA1c levels at the recent health checkups and how the proportions change depending on whether hyperglycemia was detected at checkups a year before.
MATERIALS AND METHODS
Study design
The present retrospective cohort study was carried out using the JMDC Claims Database (JMDC‐CD) of JMDC Inc. (Tokyo, Japan) from 2016 to 2020, including data from health checkups and medical/pharmaceutical claims. The overview of this study is described in Figure 1.
Figure 1.
Overview of the study. *Inclusion criteria: (i) hyperglycemia at the recent (fiscal year [FY]2018) health checkup; (ii) glycemic test results available for the FY2017 and FY2018 health checkups; (iii) age 20–59 years; (iv) insured employee; (v) certified throughout the study period (FY2016–FY2019). FY20XX, fiscal year April 20XX to March 20XX + 1. HbA1c, glycated hemoglobin.
Data sources
JMDC‐CD is a database of employment‐based health insurance. It includes data on health checkups, and medical/pharmaceutical claims of insured employees and their dependent family members 14 (approximately 14 million people) in accordance with the Industrial Safety and Health Act and the Act on Assurance of Medical Care for Elderly People. Data from health checkups and medical/pharmaceutical claims were anonymously linked.
Participants undergo physical examination and assessments during the health checkup, including bodyweight, blood pressure, blood tests and physician assessment. They also complete a lifestyle questionnaire that includes smoking and drinking habits. Thus, data from health checkups include the participants' demographics, examination results and self‐reported lifestyle habits. Details of the participants' socioeconomic status, including income, educational level, marital status and race/ethnicity, were unavailable. When there were multiple health checkup results in one fiscal year (FY), we selected the earliest result with hyperglycemia.
Study population
The inclusion criteria were as follows: (i) positive for hyperglycemia (defined as fasting blood sugar ≥126 mg/dL, simple blood sugar ≥200 mg/dL or HbA1c ≥6.5% [48 mmol/mol]) during FY2018 (April 2018–March 2019); (ii) available glycemic test results for FY2017 (April 2017–March 2018) and FY2018 health checkups; (iii) aged 20–59 years (young participants aged <20 years might react differently because of the influence of their parents; participants aged >60 years might exit employer‐based social health insurance plans during the follow‐up period [many Japanese companies have a retirement age of 60 years]); (iv) an insured employee (dependent families were excluded, because their attendance at health checkups is not mandatory; their attendance rate is low compared with insured employees 15 ); and (v) insured during the study period from 1 April 2016 to 31 March 2020 (FY2016–FY2019).
Participants who had diabetes‐related clinic visits from April 2016 to the month of recent (FY2018) health checkup were excluded, because the present study focused on the participant's first clinic visit. Previous clinic visits were identified from the diagnosis, prescription, and treatment information obtained from medical/pharmaceutical claims data. Furthermore, participants who had six or more outpatient clinic visits during FY2017–FY2018 health checkup date; who self‐reported at the FY2018 health checkup for taking insulin injection or medication for hypertension, hyperglycemia or dyslipidemia; who had a history of stroke, heart attack or renal failure; or who had received dialysis were excluded. These participants were likely to have regular clinic visits, which would have a higher clinic visit rate after health checkups. Furthermore, those with a hemoglobin level of <12 g/dL (to assure the accuracy of HbA1c results) and those without HbA1c results at the FY2018 health checkup were also excluded.
Main outcomes
In the present study, the main outcome was clinic visit within 6 months after the FY2018 health checkup, and it is defined as the observed diabetes‐related diagnosis, prescription or treatment, as evaluated from the medical/pharmaceutical claims data. Diabetes was defined as International Classification of Diseases 10th revision codes E10–E14. The drug prescription for diabetes was defined as A10 according to the Anatomical Therapeutic Chemical system, excluding A10X (aldose reductase inhibitors). Medical treatment costs are reimbursed based on Japan's national health insurance fee schedule. We used codes from this schedule to determine whether diabetes was the primary disease for treatment (e.g., HbA1c examination, self‐injection management and gestational diabetes management).
Glycemic level
Most health checkups used HbA1c to measure the glycemic level. To identify potential confounders and effect modifiers, we treated the HbA1c level at the FY2018 health checkup as the main predictor. The HbA1c level was grouped into four categories: <7.0% (53 mmol/mol), 7.0–7.4% (53–57 mmol/mol), 7.5–7.9% (58–63 mmol/mol) and ≥8.0% (64 mmol/mol).
Results for hyperglycemia at previous health checkups
The participants were divided into two groups according to their hyperglycemia status at the previous (FY2017) health checkup: group A had hyperglycemia, whereas group B had no hyperglycemia. The definition of hyperglycemia at the FY2017 health checkup was the same as that at the FY2018 health checkup (i.e., fasting blood sugar ≥126 mg/dL, simple blood sugar ≥200 mg/dL or HbA1c ≥6.5% [48 mmol/mol]). The study population did not include those who had previous clinic visits related to diabetes. Thus, group A consisted of participants with hyperglycemia at the FY2017 health checkups, but did not have clinic visits.
Covariates
Covariates were collected from the FY2018 health checkup results, including the demographic, clinical and lifestyle characteristics of the participants. All the following variables were treated as categorical variables: age (20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54 and 55–59 years); body mass index (<25, 25–29 and ≥ 30 kg/m2); hypertension (systolic blood pressure [SBP] <140 mmHg and diastolic blood pressure [DBP] <90 mmHg, 140 mmHg ≤ SBP < 160 mmHg and DBP <90 mmHg or SBP <160 mmHg and 90 mmHg ≤ DBP < 100 mmHg, and SBP ≥160 mmHg or DBP ≥100 mmHg); low‐density lipoprotein cholesterol (<140, 140–159 or ≥160 mg/dL); triglyceride [TG] and high‐density lipoprotein [HDL] cholesterol level (negative [TG ≤150 mg/dL and HDL <40 mg/dL], positive [TG <150 mg/dL or HDL ≤40 mg/dL]); liver dysfunction (negative [aspartate aminotransferase <51 IU/L, alanine aminotransferase <51 IU/L and γ‐glutamyl transpeptidase <101 IU/L], positive [aspartate aminotransferase ≤51 IU/L or alanine aminotransferase ≤51 IU/L or γ‐glutamyl transpeptidase ≤101 IU/L]); proteinuria measured by urine dipstick (−, ± and ≥1+). Smoking (yes or no) and frequency of alcohol consumption (every day, sometimes and rarely) were self‐reported. The cut‐off levels for blood pressure and hypertension stages were based on the 2019 Japanese Society of Hypertension Guidelines for the Management of Hypertension 16 .
Statistical analysis
Participants' characteristics were compared by their HbA1c levels at the FY2018 health checkup. The clinic visit rates were calculated according to groups (A or B), as defined by HbA1c level at FY2018 health checkup and hyperglycemia status at FY2017 health checkup (this measurement is strictly a proportion rather than a rate; however, we refer to it as the rate for convenience). To estimate the adjusted clinic visit rates, a multivariable regression analysis was carried out with the interaction term between FY2017 hyperglycemia status and FY2018 HbA1c level. A modified Poisson regression model was used to estimate the relative risk 17 . For the multivariable regression, data were restricted to participants with no missing data (n = 7,652). For the sensitivity analysis, we carried out three analyses. First, we estimated the clinic visit rates for participants who had same hyperglycemia status at both FY2016 and FY2017 health checkups (i.e., hyperglycemia was detected at both health checkups [group A'] or hyperglycemia was not detected at both health checkups [group B']). Second, we carried out restricted cube spline regression analyses to address non‐linear associations using continuous HbA1c levels. We stratified the samples according to the presence/absence of hyperglycemia at checkups before 1 year (group A/B), and created two models instead of creating one model with an interaction term. Third, we carried out a sensitivity analysis using multiple imputation techniques to address bias caused by missingness based on the missing‐at‐random assumption. We created a model for missingness according to age, sex and the presence/absence of hyperglycemia at checkups before 1 year (group A/B) using ordered logistic regressions; subsequently, we estimated the model‐based predicted clinic visit rates according to HbA1c levels and group A/B.
To assess the influence of clinic visits on subsequent glycemic control, we described the degree of HbA1c change between the FY2018 and FY2019 health checkups, according to the hyperglycemia status at the FY2017 health checkup and HbA1c level at the FY2018 health checkup.
The tests of significance were two‐tailed, with an α‐level of 0.05. All analyses were carried out using STATA version 16.1 (StataCorp, College Station, TX, USA). Complete data analysis was carried out, because the level of missing data was low (11.8% at most).
RESULTS
Participant flow and baseline characteristics stratified by the glycemic level at the recent health checkup
Figure 2 presents the flowchart of participant selection. The inclusion criteria initially selected 65,387 individuals who: (i) had hyperglycemia results from the FY2018 health checkup; (2) were aged 20–59 years; (iii) were insured employees; and (iv) were insured throughout FY2016–FY2019. We excluded 50,675 individuals who had diabetes‐related clinic visits from the start of FY2016 to the FY2018 health checkup date, leaving 14,712 eligible participants. We further excluded those who had: (i) six or more outpatient clinic visits from the start of the FY2017 to the FY2018 health checkup date; (ii) self‐reported insulin injection, or taking medication for hypertension, hyperglycemia or dyslipidemia; (iii) self‐reported history of stroke, heart attack, renal failure or dialysis; (iv) hemoglobin level <12 g/dL; and (v) missing values for HbA1c. Finally, 8,834 participants formed the study sample.
Figure 2.
Participant flow. *Inclusion criteria: (i) hyperglycemia at the fiscal year (FY)2018 health checkup; (ii) glycemic test results available for the FY2017 and FY2018 health checkups; (iii) age: 20–59 years; (iv) insured employee; and (v) certified throughout the study period (FY2016–FY2019). **The Japanese fiscal year begins in April and ends in March. ***“Clinic visit related to diabetes” was defined as a record of either diabetes‐related diagnosis or medication/treatment. FY20XX, fiscal year April 20XX to March 20XX + 1. HbA1c, glycated hemoglobin.
Table 1 presents the characteristics of each group based on HbA1c level at the FY2018 health checkup (<7.0% [n = 5,392], 7.0–7.4% [n = 1,095], 7.5–7.9% [n = 623], ≥8.0% [n = 1,724]). The most common age categories for HbA1c level of <7.0% and 7.0–7.4% was 50–54 years, and for HbA1c level 7.5–7.9% and ≥8.0% was 45–49 years. The proportion of women was higher in HbA1c level 7.5–7.9% (8.7%) than in <7.0% (7.9%), 7.0–7.4% (6.1%) and ≥8.0% (8.4%).
Table 1.
Characteristics and results at the fiscal year 2018 health checkup of 8,834 participants with hyperglycemia, stratified by the glycated hemoglobin level
| HbA1c level at FY2018 health checkup | P‐value | |||||
|---|---|---|---|---|---|---|
| <7.0% n = 5,392 | 7.0–7.4% n = 1,095 | 7.5–7.9% n = 623 | ≥8.0% n = 1,724 | Total n = 8,834 | ||
| SD (%) | SD (%) | SD (%) | SD (%) | SD (%) | ||
| Mean age (SD), years | 48.7 (6.9) | 48.5 (6.6) | 48.4 (6.7) | 47.1 (7.1) | 48.3 (6.9) | <0.001 |
| Age groups, n (%) | ||||||
| 20–24 years | 17 (0.3%) | 2 (0.2%) | 0 (0.0%) | 1 (0.1%) | 20 (0.2%) | <0.001 |
| 25–29 years | 78 (1.4%) | 7 (0.6%) | 9 (1.4%) | 34 (2.0%) | 128 (1.4%) | |
| 30–34 years | 114 (2.1%) | 31 (2.8%) | 19 (3.0%) | 64 (3.7%) | 228 (2.6%) | |
| 35–39 years | 242 (4.5%) | 48 (4.4%) | 21 (3.4%) | 128 (7.4%) | 439 (5.0%) | |
| 40–44 years | 890 (16.5%) | 191 (17.4%) | 120 (19.3%) | 334 (19.4%) | 1,535 (17.4%) | |
| 45–49 years | 1,412 (26.2%) | 286 (26.1%) | 165 (26.5%) | 471 (27.3%) | 2,334 (26.4%) | |
| 50–54 years | 1,433 (26.6%) | 305 (27.9%) | 157 (25.2%) | 428 (24.8%) | 2,323 (26.3%) | |
| 55–59 years | 1,206 (22.4%) | 225 (20.5%) | 132 (21.2%) | 264 (15.3%) | 1,827 (20.7%) | |
| Sex, n (%) | ||||||
| Men | 4,968 (92.1%) | 1,028 (93.9%) | 569 (91.3%) | 1,580 (91.6%) | 8,145 (92.2%) | 0.13 |
| Women | 424 (7.9%) | 67 (6.1%) | 54 (8.7%) | 144 (8.4%) | 689 (7.8%) | |
| BMI category, n (%) † | ||||||
| <25 kg/m2 | 2,227 (41.3%) | 310 (28.3%) | 178 (28.6%) | 493 (28.6%) | 3,208 (36.3%) | <0.001 |
| 25–29 | 2,146 (39.8%) | 480 (43.9%) | 274 (44.1%) | 756 (43.9%) | 3,656 (41.4%) | |
| ≥30 | 1,016 (18.9%) | 304 (27.8%) | 170 (27.3%) | 473 (27.5%) | 1,963 (22.2%) | |
| Hypertension, n (%) † | ||||||
| SBP <140 mmHg and DBP <90 mmHg | 3,559 (66.0%) | 681 (62.3%) | 355 (57.1%) | 997 (57.9%) | 5,592 (63.4%) | <0.001 |
| (140 ≤ SBP < 160 and DBP <90) or (SBP <160 and 90 ≤ DBP < 100) | 1,254 (23.3%) | 274 (25.1%) | 161 (25.9%) | 426 (24.7%) | 2,115 24.0 | |
| SBP ≥160 or DBP ≥100 | 576 (10.7%) | 138 (12.6%) | 106 (17.0%) | 300 (17.4%) | 1,120 (12.7%) | |
| LDL cholesterol groups, n (%) † | ||||||
| <140 mg/dL | 2,909 (54.0%) | 484 (44.3%) | 289 (46.5%) | 772 (44.9%) | 4,454 (50.5%) | <0.001 |
| 140–159 | 1,193 (22.1%) | 264 (24.2%) | 136 (21.9%) | 392 (22.8%) | 1,985 (22.5%) | |
| ≥160 | 1,287 (23.9%) | 345 (31.6%) | 196 (31.6%) | 555 (32.3%) | 2,383 (27.0%) | |
| TG/HDL, n (%) † | ||||||
| TG <150 and HDL ≥40 | 2,925 (54.3%) | 469 (43.0%) | 283 (45.5%) | 679 (39.6%) | 4,356 (49.4%) | <0.001 |
| TG ≥150 or HDL <40 | 2,462 (45.7%) | 622 (57.0%) | 339 (54.5%) | 1,034 (60.4%) | 4,457 (50.6%) | |
| Liver dysfunction, n (%) † | ||||||
| AST <51 IU/L and ALT <51I U/L and GGT <101 IU/L | 3,494 (64.8%) | 576 (52.7%) | 329 (52.9%) | 1,047 (60.9%) | 5,446 (61.7%) | <0.001 |
| AST ≥51 IU/L or ALT ≥51 IU/L or GGT ≥101 IU/L | 1,896 (35.2%) | 516 (47.3%) | 293 (47.1%) | 672 (39.1%) | 3,377 (38.3%) | |
| Proteinuria, n (%) † | ||||||
| −, ± | 5,010 (94.3%) | 977 (90.2%) | 550 (89.1%) | 1,399 (82.2%) | 7,936 (91.1%) | <0.001 |
| +, ++, +++ | 305 (5.7%) | 106 (9.8%) | 67 (10.9%) | 302 (17.8%) | 780 (8.9%) | |
| Smoking, n (%) † | ||||||
| Yes | 2,407 (46.1%) | 519 (48.8%) | 274 (45.2%) | 803 (47.5%) | 4,003 (46.6%) | 0.30 |
| No | 2,819 (53.9%) | 545 (51.2%) | 332 (54.8%) | 887 (52.5%) | 4,583 (53.4%) | |
| Alcohol consumption, n (%) † | ||||||
| Everyday | 1,521 (32.1%) | 246 (25.4%) | 130 (23.8%) | 313 (20.5%) | 2,210 (28.4%) | <0.001 |
| Sometimes | 1,597 (33.7%) | 330 (34.1%) | 199 (36.4%) | 560 (36.7%) | 2,686 (34.5%) | |
| Rarely | 1,627 (34.3%) | 393 (40.6%) | 217 (39.7%) | 654 (42.8%) | 2,891 (37.1%) | |
| Hyperglycemia at FY2017 health checkup | ||||||
| Yes (group A) | 1,960 (36.4%) | 804 (73.4%) | 539 (86.5%) | 1,604 (93.0%) | 4,907 (55.5%) | <0.001 |
| No (group B) | 3,432 (63.6%) | 291 (26.6%) | 84 (13.5%) | 120 (7.0%) | 3,927 (44.5%) | |
| No. clinic visits within 6 months, n (%) | ||||||
| Yes | 919 (17.0%) | 292 (26.7%) | 158 (25.4%) | 490 (28.4%) | 1,859 (21.0%) | <0.001 |
| No | 4,473 (83.0%) | 803 (73.3%) | 465 (74.6%) | 1,234 (71.6%) | 6,975 (79.0%) | |
Significance was tested using the χ2‐test for categorical variables and anova for continuous variables.
The sum of the numbers of the participants does not match because of missing values.
ALT, alanine aminotransferase; anova, analysis of variance; AST, aspartate aminotransferase; BMI, body mass index; DBP, diastolic blood pressure; GGT, γ‐glutamyl transpeptidase; HbA1c, glycated hemoglobin; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; SBP, systolic blood pressure; SD, standard deviation; TG, triglyceride.
Clinic visit rates by the glycemic level at the recent health checkup and the presence of hyperglycemia at the previous health checkup
The clinic visit rates within 6 months were 21.0% in total, and 17.0, 26.7, 25.4 and 28.4% for HbA1c levels of <7.0, 7.0–7.4, 7.5–7.9 and ≥8.0%, respectively (Table 1). Figure 3 presents the clinic visit rates within 6 months, stratified by the FY2018 HbA1c level and FY2017 hyperglycemia status. Group A had a lower clinic visit rate than group B, especially in the HbA1c <7.0% category (14.4% vs 18.5%; P < 0.001) and the 7.0–7.4% category (23.6% vs 35.1%; P < 0.001), with less significance in the 7.5–7.9% category (24.1% vs 33.3%; P = 0.07). The difference in rates was the least in the HbA1c of ≥8.0% category (28.2% vs 30.8%; P = 0.54).
Figure 3.
Crude clinic visits rates after the presence of hyperglycemia at the fiscal year (FY)2018 health checkup, by the glycemic level at the FY2018 health checkup and the presence of hyperglycemia at the FY2017 health checkup. Group A: Those who had hyperglycemia at the FY2017 health checkup. Group B: Those who did not have hyperglycemia at the FY2017 health checkup. Significant differences were found between groups A and B (shown in the figure as ***P < 0.001), as well as within group A among different glycemic levels (<7.0% vs 7.0–7.4% [P < 0.001], <7.0% vs 7.5–7.9% [P < 0.001], <7.0% vs ≥8.0% [P < 0.001], 7.0–7.4% vs ≥8.0% [P = 0.016]) and group B (<7.0% vs 7.0–7.4% [P < 0.001], <7.0% vs 7.5–7.9% [P = 0.001], <7.0% vs ≥8.0% [P = 0.001]). Error bars show 95% confidence intervals. Significance was tested using the χ2‐test. The values in parentheses refer to the number in each category. FY20XX, fiscal year April 20XX to March 20XX + 1. HbA1c, glycated hemoglobin.
Adjusted clinic visit rates by the glycemic level at the recent health checkup and the presence of hyperglycemia at the previous health checkup
Figure 4 presents the adjusted clinic visit rates within 6 months, stratified by the FY2018 HbA1c level and FY2017 hyperglycemia status. Group A was less likely to have clinic visit compared with group B, especially when the HbA1c level was <8.0% (HbA1c <7.0%: 14.1% vs 18.3%; P < 0.001; HbA1c 7.0–7.4%: 23.8% vs 32.6%; P = 0.005; HbA1c 7.5–7.9%: 23.9% vs 36.1%; P = 0.022). No difference was found between group A and group B in the HbA1c of ≥8.0% category (28.4% vs 29.9%; P = 0.76). These rates were obtained using the modified Poisson regression, and the absence of hyperglycemia at the FY2017 health checkup (Group B), HbA1c of 7.0–7.9%, age of 30–44 and 50–54 years, body mass index of ≥30 kg/m2, TG of ≥150 mg/dL or HDL of <40 mg/dL and liver dysfunction were associated with higher clinic visit rates compared with their respective reference group (Table 2).
Figure 4.
Adjusted clinic visit rates after the presence of hyperglycemia at the fiscal year (FY)2018 health checkup by the glycemic level at the FY2018 health checkup and the presence of hyperglycemia at the FY2017 health checkup. Group A: Those who had hyperglycemia at the FY2017 health checkup. Group B: Those who did not have hyperglycemia at the FY2017 health checkup. Significant differences were found between groups A and B (shown in the figure as *P < 0.05; **P < 0.01; ***P < 0.001), as well as within group A among different glycemic levels (<7.0% vs 7.0–7.4% [P < 0.001], <7.0% vs 7.5–7.9% [P < 0.001], <7.0% vs ≥8.0% [P < 0.001], 7.0–7.4% vs ≥8.0% [P = 0.025]) and group B (<7.0% vs 7.0–7.4% [P < 0.001], <7.0% vs 7.5–7.9% [P < 0.001], <7.0% vs ≥8.0% [P = 0.003]). Error bars show 95% confidence intervals. Significance was assessed using modified Poisson regression. The value in parentheses refers to the number in each category. FY20XX, fiscal year April 20XX to March 20XX + 1. HbA1c, glycated hemoglobin.
Table 2.
Adjusted incidence rate ratio for clinic visit rates by glycated hemoglobin group and hyperglycemia status
| Rate ratio | P‐value | 95% CI | ||
|---|---|---|---|---|
| 2017 screening hyperglycemia status | ||||
| Yes (group A) | 0.77 | <0.001 | 0.67 | 0.89 |
| No (group B) | (Reference) | |||
| HbA1c | ||||
| <7.0% | (Reference) | |||
| 7.0–7.4% | 1.78 | <0.001 | 1.47 | 2.17 |
| 7.5–7.9% | 1.97 | <0.001 | 1.43 | 2.73 |
| ≥8.0% | 1.64 | 0.003 | 1.19 | 2.25 |
| FY2017 screening hyperglycemia status * HbA1c levels | ||||
| Yes * <7.0% | (Reference) | |||
| Yes * 7.0–7.4% | 0.95 | 0.69 | 0.73 | 1.23 |
| Yes * 7.5–7.9% | 0.86 | 0.44 | 0.59 | 1.26 |
| Yes * ≥8.0% | 1.23 | 0.24 | 0.87 | 1.75 |
| Age | ||||
| 20–24 years | 0.59 | 0.44 | 0.16 | 2.21 |
| 25–29 years | 0.74 | 0.13 | 0.50 | 1.09 |
| 30–34 years | 0.53 | 0.001 | 0.36 | 0.77 |
| 35–39 years | 0.73 | 0.007 | 0.58 | 0.92 |
| 40–44 years | 0.85 | 0.03 | 0.74 | 0.99 |
| 45–49 years | 0.89 | 0.06 | 0.78 | 1.00 |
| 50–54 years | 0.87 | 0.04 | 0.77 | 0.99 |
| 55–59 years | (Reference) | |||
| Sex | ||||
| Men | (Reference) | |||
| Women | 1.06 | 0.51 | 0.89 | 1.26 |
| BMI | ||||
| <25 kg/m2 | (Reference) | |||
| 25–29 kg/m2 | 0.94 | 0.26 | 0.85 | 1.04 |
| ≥30 kg/m2 | 0.87 | 0.04 | 0.76 | 0.99 |
| Hypertension | ||||
| SBP <140 mmHg and DBP <90 mmHg | (Reference) | |||
| (140 ≤ SBP < 160 and DBP <90) or (SBP <160 and 90 ≤ DBP < 100) | 1.03 | 0.60 | 0.93 | 1.14 |
| SBP ≥160 or DBP ≥100 | 0.98 | 0.82 | 0.86 | 1.13 |
| LDL cholesterol | ||||
| <140 mg/dL | (Reference) | |||
| 140–159 mg/dL | 1.00 | 0.98 | 0.90 | 1.11 |
| ≥160 mg/dL | 0.95 | 0.39 | 0.86 | 1.06 |
| TG/HDL | ||||
| TG <150 mg/dL and HDL ≥40 mg/dL | (Reference) | |||
| TG ≥150 mg/dL or HDL <40 mg/dL | 1.13 | 0.008 | 1.03 | 1.24 |
| Liver dysfunction | ||||
| AST <51 IU/L and ALT <51 IU/L and GGT <101 IU/L | (Reference) | |||
| AST ≥51 IU/L or ALT ≥51 IU/L or GGT ≥101 IU/L | 1.13 | 0.01 | 1.03 | 1.24 |
| Proteinuria | ||||
| −, ± | (Reference) | |||
| +, ++, +++ | 1.14 | 0.07 | 0.99 | 1.31 |
| Smoking | ||||
| Yes | (Reference) | |||
| No | 1.08 | 0.10 | 0.99 | 1.18 |
| Alcohol consumption | ||||
| Every day | (Reference) | |||
| Sometimes | 1.10 | 0.12 | 0.98 | 1.23 |
| Rarely | 1.07 | 0.25 | 0.95 | 1.20 |
The asterisks indicate the interaction term between FY2017 hyperglycemia status and FY2018 HbA1c level.
ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; CGT, gamma‐glutamyl transferase; CI, confidence interval; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; SBP, systolic blood pressure; TG, triglycerides.
The first sensitivity analysis (restricted to those who had the same hyperglycemia status at both FY2016 and FY2017 health checkups, n = 6,306) showed more differences in rates between groups A' and B′ compared with the main analysis (Figure S1). The second sensitivity analysis delineated restricted cube spline curves about the non‐linear association between continuous HbA1c levels and clinic visit rates. In both curves, the clinic visit rates monotonically increased with the increase in HbA1c levels until HbA1c level reached 8%; group A showed a gradual upward trend after reaching a plateau at HbA1c levels of 8–10%, whereas group B showed a peak around HbA1c level of 8% and then showed a monotonic decline (Figure S2). The third sensitivity analysis used multiple imputations (Figure S3) and trends similar to Figure 3.
Difference in HbA1c between the recent and subsequent year's health checkups by clinic visits, glycemic level at recent health checkup, and presence of hyperglycemia at the previous health checkup
Figure 5 presents the change in HbA1c between the FY2018 and FY2019 health checkups stratified by the presence/absence of clinic visit, FY2018 HbA1c level and FY2017 hyperglycemia status. Among participants who had clinic visits, HbA1c decreased at the FY2019 health checkup compared with the FY2018 health checkup, especially in the HbA1c of ≥7.0% category (group A: −0.05, −0.34, −0.74 and −2.55%; group B: −0.05, −0.60, −0.99 and −2.55%, respectively, for HbA1c levels of <7.0, 7.0–7.4, 7.5–7.9 and ≥8.0%).
Figure 5.
Changes in glycated hemoglobin (HbA1c) levels between the fiscal year (FY)2018 and FY2019 checkups by the presence/absence of follow‐up visit, the presence of hyperglycemia at the previous (FY2017) checkup and glycemic revel at the recent (FY2018) checkup. Group A: Those who had hyperglycemia at the FY2017 checkup. Group B: Those who did not have hyperglycemia at the FY2017 checkup. Significant difference was found between those with and without clinic visits among group A (<7.0% [P < 0.001], 7.0–7.4% [P < 0.001], 7.5–7.9% [P < 0.001] and ≥8.0% [P < 0.001]) and group B (no clinic visit: <7.0% [P < 0.001], 7.0–7.4% [P < 0.001], 7.5–7.9% [P = 0.002] and ≥8.0% [P < 0.001]; shown in the figure as **P < 0.01; ***P < 0.001). Error bars show 95% confidence intervals. Significance was assessed using two‐sided t‐test. The value in parentheses refers to the number in each category. FY20XX, fiscal year April 20XX to March 20XX + 1.
DISCUSSION
The overall rate of subsequent clinic visits was 21.0%, which was lower than expected, with <30% even for HbA1c ≥8.0%. These findings did not change after adjusting for covariates including age; sex; body mass index; hypertension; low‐density lipoprotein cholesterol, TG and HDL‐cholesterol levels; liver dysfunction; proteinuria; smoking; and alcohol consumption. To our knowledge, this is the first study to estimate the situations of clinic visits by the presence/absence of hyperglycemia in the health checkups a year before among those without previous regular clinic visits. As expected, the present study confirmed that persons who attended clinic visits had improved HbA1c at their subsequent health checkups. The first sensitivity analysis (restricted to those who had the same hyperglycemia status at both FY2016 and FY2017 health checkups) showed similar, but enhanced, differences compared to the main analysis. The restricted cube spline curves, created for the second sensitivity analysis, exhibited comparable characteristics to those depicted in Figure 3. The third sensitivity analysis used multiple imputations and indicated the robustness of the model against the consideration of missingness based on the missing‐at‐random assumption.
Our estimated clinic visit rate was lower than the previous estimates made by Tsujimura et al. 10 (no clinic visit within 6 months after the health checkup: 65.9% for men, 59.4% for women). This difference might be due to variations in the definitions of clinic visit and exclusion criteria. Tsujimura et al. included glucose measurement in their definition of clinic visit, and we did not. Recently, Okada et al. 13 showed that fewer physician visits in the previous year, lower HbA1c levels and no history of antidyslipidemic or antihypertensive treatment were associated with fewer clinic visits. They included the presence/absence of hyperglycemia in the health checkups a year before as a variable in their candidate predictors; however, the Lasso regression estimating the proportion of follow‐up clinic visits did not choose the variable in the final model. The discrepancy in the findings between that study and the present study was probably due to the inclusion or exclusion of persons with regular clinic visits. In the present study, the low subsequent clinic visit rate indicated a systematic problem in the health checkup system, which mainly failed to encourage participants with detected hyperglycemia to consult a doctor unless they had already attended regular clinic visits for other chronic diseases.
The positive association between the HbA1c levels at the health checkup and the clinic visits in the present study was also reported by Tsujimura et al. 10 and Okada et al., 13 whereas the influence of HbA1c level on the subsequent clinic visit rates hit a low value ceiling and was <30% even for HbA1c ≥8.0%. We added further evidence that previous health checkup results can affect the subsequent clinic visit.
To date, it was unclear who should be targeted or what is a practical approach for enhancing clinic visits after health checkups. The present study showed that among persons who had no diabetes‐related clinical visits, the overall clinic visit rate after the health checkup was far lower than expected, with <30% even for HbA1c of ≥8.0%. This result is meaningful, as it showed who should be targeted and what is a practical approach for enhancing clinic visits. It indicates the importance of communicating the severity of hyperglycemia to the participants, particularly among those with high HbA1c levels. Notably, persons found having hyperglycemia at screening a year before had lower clinic visit rates than those not having hyperglycemia at that time. These populations might be a target for government incentives that encourage major employer‐based insurance programs to encourage employees with abnormal findings at health checkups to visit clinics. Currently, Japan's National Medical Care Plan is in the process of revision. “Untreated patients with diabetes who visited medical clinics after being recommended to do so after health checkup (number or ratio of patients)” is being proposed as a candidate of performance indicator of the health system for diabetes 18 . This further indicates the importance of the present findings for guiding Japanese health policy.
The present study results might benefit public health nurses in insurance programs or companies, as they have access to previous health checkup results, can identify these high‐risk participants and encourage them to visit a clinic. Currently, as some personal health records through smartphone applications are linked to health checkup results, these applications can directly inform the high‐risk participants about the present study results and assist them to attend clinic visits. For example, if the health checkup results are delivered electronically to physicians who care for diseases other than diabetes, the physicians can act on the abnormal finding directly with the patient's consent or refer the patient to another appropriate physician. Given the low clinic visit rate, governments might need to encourage these initiatives, as well as take effective measures to encourage such participants to attend clinic visits.
Data regarding socioeconomic status, including educational level, marital status, race/ethnicity and type of employer‐based health insurance plan indicating the participants' type of occupation, were not available from the database. Further studies involving socioeconomic information are necessary to strengthen our understanding of the same.
The overall rate of subsequent clinic visits was 21.0% and lower than expected, with <30% even for HbA1c of ≥8.0%. These findings did not change after adjustment for covariates. Persons with previously detected hyperglycemia had lower clinic visit rates, despite needing more health counseling. To the best of our knowledge, this is the first study to estimate clinic visit by the presence/absence of hyperglycemia in the health checkups a year before among those without previous regular clinic visits. the present findings might be useful when designing a tailored approach to encourage high‐risk individuals to seek diabetes care through clinic visits.
DISCLOSURE
The authors declare no conflict of interest. Although HM works for the Japanese government, the views expressed in this article are written in a personal capacity and do not represent those of the Japanese government. NT received a collaborative research fund from JMDC Inc. TS received his salary from University of Tsukuba in FY2018 and FY2019 based on the fund. However, this study was not related to the collaborative research project; the authors instead paid for the database use. JMDC Inc. was not involved in the study design, analysis and interpretation of data, writing of the report, or any restrictions regarding submitting the report for publication. By contrast, JMDC Inc. was involved in generic data collection (not specific to the present study purpose). MO received honoraria for lectures from Novartis Pharma K.K., Sanofi K.K. and Eli Lilly Japan K.K; clinical commissioned/joint research grants from Japan Diabetes Society, Novo Nordisk Pharma Ltd., Nippon Boehringer Ingelheim Co., Ltd., MSD K.K., Kyowa Kirin Co., Ltd., Abbott Japan LLC and Sanofi K.K.; scholarship grants from Sumitomo Dainippon Pharma Co., Ltd., Mitsubishi Tanabe Pharma Corporation and Novartis Pharma K.K. KU received honoraria for lectures from Sumitomo Dainippon Pharma Co., Ltd., MSD K.K., Kyowa Kirin Co., Daiichi Sankyo, Nippon Boehringer Ingelheim Co., Ltd., Takeda Pharmaceutical Co., Ltd., Novo Nordisk Pharma Ltd., Mitsubishi Tanabe, AstraZeneca, Ono, Sanofi K.K and Astellas, and clinical research grants from Novo Nordisk Pharma Ltd., Nippon Boehringer Ingelheim Co., Ltd., Takeda Pharmaceutical Co., Ltd., Astellas, Eli Lilly, MSD K.K. and Abbott Japan LLC. These grants and honoraria were not received for this research.
Approval of the research protocol: The data used in this study were anonymized in a structured format. The study protocol was approved by the Institutional Review Board of the National Center for Global Health and Medicine (NCGM‐G‐002096‐03).
Informed consent: N/A.
Registry and the registration no. of the study/trial: N/A.
Animal studies: N/A.
Supporting information
Figure S1 | Adjusted clinic visit rates after considering the presence of hyperglycemia at the fiscal year 2018 health checkup, the presence of hyperglycemia at the fiscal year 2016 and fiscal year 2017 health checkups, and glycemic level at the fiscal year 2018 health checkup.
Figure S2 | Restricted cube spline curve of glycated hemoglobin levels at the fiscal year 2018 checkup for the clinic visit rate according to the presence of hyperglycemia at the fiscal year 2017 health checkup.
Figure S3 | Adjusted clinic visit rates according to the presence of hyperglycemia at the fiscal year 2018 health checkup, the glycemic level at the fiscal year 2018 health checkup and the presence of hyperglycemia at the fiscal year 2017 health checkup using multiple imputation techniques.
ACKNOWLEDGMENTS
The authors thank Mr Takashi Furuno for his data processing. The authors also thank Enago (https://www.enago.jp) for their English language editing.
This study was supported by the Grant of the National Center of Global Health and Medicine (26‐D‐002, Kohjiro Ueki) and JSPS KAKENHI (Grant No. JP19K19451, Takehiro Sugiyama).
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Associated Data
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Supplementary Materials
Figure S1 | Adjusted clinic visit rates after considering the presence of hyperglycemia at the fiscal year 2018 health checkup, the presence of hyperglycemia at the fiscal year 2016 and fiscal year 2017 health checkups, and glycemic level at the fiscal year 2018 health checkup.
Figure S2 | Restricted cube spline curve of glycated hemoglobin levels at the fiscal year 2018 checkup for the clinic visit rate according to the presence of hyperglycemia at the fiscal year 2017 health checkup.
Figure S3 | Adjusted clinic visit rates according to the presence of hyperglycemia at the fiscal year 2018 health checkup, the glycemic level at the fiscal year 2018 health checkup and the presence of hyperglycemia at the fiscal year 2017 health checkup using multiple imputation techniques.