Effectiveness of primary care-based chronic disease management program on glycated hemoglobin levels

doi:10.21203/rs.3.rs-4209917/v1

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Effectiveness of primary care-based chronic disease management program on glycated hemoglobin levels

https://doi.org/10.21203/rs.3.rs-4209917/v1

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Background: Diabetes is one of the most common causes of cardiovascular disease and has led to death over time. For better management of glycated hemoglobin levels, which is a well-known index in diagnosing diabetes, many countries have been launching chronic disease management programs. Therefore, this study aimed to evaluate the effectiveness of the primary care-based chronic disease management integrated pilot program (PCDMP) in controlling glycated hemoglobin (HbA1c) levels in Korea. Nation-wide data from the 2019–2021 Korea National Health and Nutrition Examination Survey were used.

Methods: We analyzed nationwide health examination data combined with various regional data sources. Using appropriate inclusion criteria for this study, a total of 13,901 individuals were suitable for the analysis. A generalized linear mixed model was applied to consider the clustered structure of the regional level data, where individual-level data, containing demographic characteristics and health-related information, were nested.

Results: Individuals living in areas with a low PCDMP participation (< 30.37%) had odds (95% CI, 1.08–1.82) of exhibiting glycated hemoglobin levels ≥ 6.5% greater than did those residing in areas with a high PCDMP participation (≥ 30.37%). According to the stratified analysis of HbA1c levels, people residing in areas with low PCDMP participation had significantly greater odds of having an HbA1c > 6.5%, which may indicate diabetes mellitus.

Conclusion: The lower the number of internal medicine clinics in an area participating in PCDMP was, the greater the possibility of an HbA1c > 6.5%. Our findings advocate the need for the government to pay attention to chronic disease management programs, which may lead individuals to have normal HbA1c levels (< 6.5%).

diabetes

glycated hemoglobin

HbA1c

primary care

chronic disease management

Chronic diseases are significant global health challenges that contribute to morbidity and mortality worldwide. Among chronic conditions, diabetes mellitus is a leading cause of cardiovascular disease and death. Various factors affect the development of diabetes, such as obesity, unhealthy diets, and aging [1, 2]. Approximately 463 million adults worldwide were living with diabetes in 2019, and this number is expected to increase to 700 million by 2045, according to the International Diabetes Federation (IDF) [3].

Hemoglobin A1c (HbA1c), a glycated form of hemoglobin, is a critical marker for monitoring long-term glucose control in individuals with diabetes [4]. Elevated HbA1c levels are indicators of poor glycemic control and an increased risk of diabetes-related complications [5]. Thus, maintaining appropriate HbA1c levels is a fundamental goal in diabetes treatment. The management of chronic diseases such as diabetes requires various approaches, including lifestyle modifications, pharmacological interventions, and patient education. Therefore, chronic disease management programs have emerged as a promising strategy for improving the care and outcomes of individuals living with diabetes [6].

In South Korea, the ‘primary care-based chronic disease management integrated pilot program (PCDMP)’ was initiated in 2019 to integrate the ‘community-based primary care pilot project’ started in 2014 and the ‘chronic disease management charges pilot project’ started in 2016 to improve primary care-based and patient-centered medical systems [7].

Recent research has shown a growing interest in evaluating the effectiveness of chronic disease management programs in lowering HbA1c levels among individuals with diabetes. Numerous studies have explored the association between participation in such programs and glycemic control, with varying results. Some studies have reported significant reductions in HbA1c levels among program participants [8, 9, 10], while others have found more modest or inconclusive effects [11]. However, no nationwide study has examined the impact of participation in a pilot program at a clinic in a residential area on HbA1c levels. Therefore, this study aimed to assess the effects of participation in a pilot program on hemoglobin A1c levels in each residential area using a multilevel model.

Data and study population

This study integrated three nation-wide big data sources. The first was the 2019–2021 Korea National Health and Nutrition Examination Survey (KNHANES), a cross-sectional and national survey in which individual-level data were gathered annually to evaluate the health status, health behavior, and nutritional status of the South Korean population to provide basic data for developing nationwide health policies [12]. Our study needed both individual-level and region-level information to describe the relationship between regional PCDMP participation rates and individual HbA1c levels, so we integrated the first dataset with two datasets from two big data sources. The second dataset, for region-level data, was the 2021 medical use statistics by region from the National Health Insurance Service (NHIS) [13]. The last dataset includes the participation rate of internal medicine clinics in the PCDMP manually calculated using data from the PCDMP website by the Korea Health Promotion Institute (KHEPI) and the Health Insurance Review & Assessment Service (HIRA) bigdata open portal [14].

Among 22,559 individuals who completed KNHANES, we excluded ones who had been diagnosed with diabetes by doctors. Then, subjects with age < 19 are omitted as well as individuals whose HbA1c levels missing. The STROBE (Strengthening The Reporting of Observational Studies in Epidemiology) diagram in Fig. 1 depicts the inclusion and exclusion criteria for the study population used in our study. At all, a total of 13,901 individuals were included in the study. There was no need for additional ethical approval because all participants provided informed consent in advance, and the data were available to the public.

Measures

The outcome variable was the HbA1c percentage, the best benchmark for tracking glycemic control and a potential diabetes indicator. Then, we generated a binary variable indicating an HbA1c level > 6.5% as high HbA1c and an HbA1c level \(\le\) 6.5 as low HbA1c [15]. On the other hand, for stratified analysis based on HbA1c level, we generated another categorical variable with 3 levels as low, middle, and high HbA1c when the HbA1c level of an individual is \(\le\) 5.7%, > 5.7% and < 6.5%, and > 6.5%, respectively [16]. The variable of interest was the regional participation rate of the PCDMP, observed by the KHEPI. The participation rate ranged from 0–62.07% and was determined by dividing the number of internal medicine clinics participating in the PCDMP by the total number of internal medicine clinics for each of the 17 regions in Korea. A binary variable was formed based on the criterion that residential areas with a participation rate greater than the median (30.37%) indicate a high participation and areas with a participation rate less than the median are considered to indicate a low participation.

For individual-level covariates, socioeconomic variables (sex, age, household income, occupation), health behavior patterns (drinking and smoking), and health condition factors (body mass index [BMI] and chronic disease status) were included. The health behavior patterns are categorized by the severity of drinking and smoking. Then, BMI is divided into obesity, normal, and low weight, and chronic disease status has 4 subgroups: hypertension, hyperlipidemia, both, and none. Moreover, the regional-level covariates were residential area, number of hospital beds per 1000 people, number of clinics per 1000 people, and number of doctors per 1000 people. They are binary variables indicating whether a residential area’s value is greater than or less than the median of each variable.

Statistical analysis

Descriptive statistics were expressed as frequencies (n) and percentages (%), and we investigated the association between HbA1c and socioeconomic, health behavior, and health condition characteristics using Pearson’s chi-square tests. We performed a multilevel analysis to examine the association between PCDMP participation and HbA1c. We conducted the multilevel logistic regression model that allows a random intercept for region and fixed effects for all individual-level covariates as well as region-level covariates. For sensitivity analysis, we conducted the multilevel multinomial logistic regression analysis to investiate the association between PCDMP participation and HbA1c stratified by the HbA1c levels. The main findings are displayed as adjusted odds ratios (aORs) and 95% confidence intervals (CIs), and the statitistical significance is decided by controlling type 1 error rate at 5%. For all the statistical analyses, SAS version 9.4 (SAS Institute, Inc.; Cary, NC, USA) was used, and p-value < 0.05 is set as a significant level at a 95% confidence level.

Figure 1 presents the STROBE flow diagram describing the study population. We initially acquired 22,559 individuals from KNHANES. From the population, 3,868 people were excluded due to age criteria. Then, another 2,793 individuals who had been diagnosed with diabetes by doctors were excluded. From the remaining 15,898 population, missing values in HbA1c levels are excluded (n = 381). Finally, after calculating the regional PCDMP participation rate, we only included 13,901 individuals who live in regions where the PCDMP participation rate is not 0%.

The general features of the study population are displayed in Table 1. A total of 617 people (4.44%) out of the 13,901 people eligible for the analysis had high HbA1c, as indicated by an HbA1c level of 6.5% or higher. However, only individuals who were not diagnosed with diabetes by doctors were included. According to previous reports, obese men older than 60 years with high incomes, a blue-collar job, smoking, not drinking, both hypertension and hyperlipidemia and poor subjective health status were more likely to have high HbA1c. Furthermore, a proportion of individuals living in rural cities with low PCDMP participation, low number of doctors, high number of hospital beds, and high number of clinics per 1000 people had high HbA1c.

Table 1. General characteristics of the study population
Characteristics		HbA1c
		TOTAL		High		Low		*P value*
		N	%	N	%	N	%
		13,901	100	617	4.44	13,284	95.56
*Region Level*
PCDMP participation								0.002
	High (> 30.37%)	7,485	53.85	298	3.98	7,187	96.02
	Low (≤ 30.37%)	6,416	46.15	319	4.97	6,097	95.03
Region								0.007
	Metropolitan city	6,346	45.65	249	3.92	6,097	96.08
	Rural	7,555	54.35	368	4.87	7,187	95.13
Hospital beds per 1000 people								0.020
	High (> 33.23)	4,757	34.22	238	5.00	4,519	95.00
	Low (≤ 33.23)	9,144	65.78	379	4.14	8,765	95.86
Clinics per 1000 people								<.001
	High (> 0.09)	3,775	27.16	204	5.40	3,571	94.60
	Low (≤ 0.09)	10,126	72.84	413	4.08	9,713	95.92
Doctors per 1000 people								0.005
	High (> 2.58)	7,485	53.85	298	3.98	7,187	96.02
	Low (≤ 2.58)	6,416	46.15	319	4.97	6,097	95.03
*Individual Level*
Sex								<.001
	Male	6,083	43.76	328	5.39	5,755	94.61
	Female	7,818	56.24	289	3.70	7,529	96.30
Age								<.001
	19~39	4,091	29.43	45	1.10	4,046	98.90
	40~49	2,633	18.94	92	3.49	2,541	96.51
	50~59	2,621	18.85	145	5.53	2,476	94.47
	over 60	4,556	32.77	335	7.35	4,221	92.65
Income
	High	2,290	16.47	161	7.03	2,129	92.97
	Middle	7,208	51.85	318	4.41	6,890	95.59
	Low	4,403	31.67	138	3.13	4,265	96.87
Occupation								<.001
	White-collar	3,637	26.16	110	3.02	3,527	96.98
	Pink-collar	1,882	13.54	67	3.56	1,815	96.44
	Blue-collar	3,087	22.21	189	6.12	2,898	93.88
	Housewife or Inoccupation	5,295	38.09	251	4.74	5,044	95.26
BMI								<.001
	Obesity	4,784	34.41	383	8.01	4,401	91.99
	Normal	8,392	60.37	222	2.65	8,170	97.35
	Low weight	725	5.22	12	1.66	713	98.34
Drinking								0.034
	Frequently	5,936	42.70	249	4.19	5,687	95.81
	Occasionally	4,115	29.60	169	4.11	3,946	95.89
	None	3,850	27.70	199	5.17	3,651	94.83
Smoking								<.001
	Current smoker	2,322	16.70	122	5.25	2,200	94.75
	Ex-smoker	3,143	22.61	173	5.50	2,970	94.50
	None	8,436	60.69	322	3.82	8,114	96.18
Chronic Disease Status								<.001
	Only hypertension	1648	11.86	136	8.25	1,512	91.75
	Only hyperlipidemia	1103	7.93	57	5.17	1,046	94.83
	Both	1312	9.44	120	9.15	1,192	90.85
	None	9839	70.78	304	3.09	9,535	96.91

Table 2 presents the results of the multilevel analysis for the association between regional participation in the PCDMP and individual HbA1c. According to the full model, adjusting individual and region-level covariates, the regional participation of the PCDMP had a significant effect on HbA1c. In detail, the odds of an individual exhibiting high HbA1c were 1.4 times greater for individuals residing in areas with a low PCDMP participation than those living in areas with a high PCDMP participation. (95% CI, 1.08–1.82)

Table 2. Adjusted odds ratios of HbA1c by characteristics of individual- and region-level (multilevel model)
Variables		HbA1c
		Full model
		aOR	95% CI
*Region Level*
PCDMP participation
	High (> 30.37%)	1.00
	Low (≤ 30.37%)	1.40	(1.08	1.82)
Region
	Metropolitan city	1.00
	Rural	0.86	(0.66	1.13)
Hospital bed per 1000 people
	High (> 33.23)	1.00
	Low (≤ 33.23)	0.90	(0.75	1.09)
Clinic per 1000 people
	High (> 0.09)	1.00
	Low (≤ 0.09)	1.03	(0.83	1.27)
*Individual Level*
Sex
	Male	1.00
	Female	0.75	(0.59	0.96)
Age
	19~39	1.00
	40~49	3.02	(2.10	4.36)
	50~59	4.84	(3.41	6.88)
	over 60	5.63	(3.96	8.00)
Income
	High	1.00
	Middle	1.50	(1.15	1.97)
	Low	1.22	(0.99	1.52)
Occupation
	White-collar	1.00
	Pink-collar	0.92	(0.67	1.27)
	Blue-collar	1.05	(0.81	1.37)
	Housewife or Inoccupation	1.01	(0.77	1.32)
BMI
	Obesity	2.99	(2.51	3.55)
	Normal	1.00
	Low weight	0.75	(0.41	1.35)
Drinking
	Frequently	0.97	(0.78	1.20)
	Occasionally	1.13	(0.90	1.41)
	None	1.00
Smoking
	Current smoker	1.38	(1.05	1.82)
	Ex-smoker	1.06	(0.82	1.37)
	None	1.00
Chronic Disease Status
	Only hypertension	1.38	(1.09	1.73)
	Only hyperlipidemia	1.06	(0.79	1.44)
	Both	1.42	(1.11	1.81)
	None	1.00
aOR, adjusted odds ratio, CI, confidence interval, SE, standard error
*Doctor variable are a linear combination of other variables as shown.
*Referece: HbA1c = abnormal

The results of the sensitivity analysis by HbA1c level are shown in Table 3. People living in areas with low participation of PCDMP were 1.08 times (95% CI, 0.96-1.21) and 1.40 times (95% CI, 1.08-1.82) more likely to develop middle HbA1c and high HbA1c, respectively. In other words, there is a greater probability of developing middle or high HbA1c than low HbA1c in residential areas where few clinics participate in the PCDMP.

Table 3. Results of subgroup analysis stratified by dependent variables
Variables		HbA1c
		Low (HbA1c ≤ 5.7%)	Middle (5.7% < HbA1c ≤ 6.5%)		High (HbA1c > 6.5%)
		OR	OR	95% CI	OR	95% CI
PCDMP participation
	High (> 30.37%)	1
	Low (≤ 30.37%)		1.08	(0.96 - 1.21)	1.4	(1.08 - 1.82)
aOR, adjusted odds ratio, CI, confidence interval
*p<.0001

This study investigated how PCDMP affects individuals' health outcomes, specifically, their HbA1c levels. As described earlier, the PCDMP aims to increase patient acceptance and establish a chronic disease management system in local clinics [11]. Additionally, we utilized HbA1c levels > 6.5% as a high HbA1c indicator [17]. To evaluate the PCDMP as a well-established program and progress to become an official program of the nation, we should investigate whether participating in the PCDMP is helpful for individuals living in such residential areas in terms of HbA1c. For instance, many studies have explored the effect of a PCDMP on the risk of complications and managing hypertension in hypertension patients in South Korea [18, 19] and concluded that the program had positive effects on the health of hypertension patients.

This study, on the other hand, examined the effects of participating in the PCDMP on HbA1c using mixed-effects logistic regression. The main findings of this study are as follows: First, people living in areas with low PCDMP participation have odds of exhibiting high HbA1c, which is 1.40 times greater than that of residents in areas with high PCDMP participation. Second, the likelihood of having high HbA1c was significantly greater in those living in areas with low PCDMP participation. In other words, living in areas with a low PCDMP increases the risk of exhibiting high HbA1c. Our findings are similar to those of previous studies investigating the effects of participating in PCDMP on the health behaviors of hypertension patients [17, 18]. One of the early studies on the effect of the PCDMP on the risk of complications in patients with hypertension in Korea found that the hazard ratio was significantly lower for patients participating in the PCDMP than for patients not participating in the program for all 4 complications—hypertension, myocardial infarction, stroke, chronic kidney disease, and heart failure [17].

Globally, several studies have explored the positive impact of chronic disease management programs in primary care on diabetes management [19, 20]. However, to the best of our knowledge, this is the first study to examine the association between regional PCDMP participation and individual HbA1c levels in nondiabetic individuals utilizing data from local clinics’ PCDMP participation and the KNHANES containing individual health information. Our study is distinguished from early findings in that the regional covariates were adjusted with a multilevel approach using mixed-effects considering the clustering effects of regions in the datasets. Furthermore, external validity would be high when a large sample is included. Our study's significant conclusions included the possibility that PCDMP may help individuals who are not diagnosed with diabetes by doctors not to get diabetes, which may lead to a decreased possibility of reduced healthcare expenses [21]. Consequently, the PCDMP might be an affordable diabetes management approach. On the other hand, research has shown that the PCDMP's diabetes education program and required testing in local healthcare facilities have no appreciable impact on diabetic patients' ability to maintain a healthy blood sugar level, an alternative diabetes index [22].

Nonetheless, this study has a few limitations. First, the KNHANES data used in the study were secondary, and it was not feasible to perform a time-series analysis to monitor changes in individual health status as the survey items differed annually. A second constraint was that residential areas were divided into only 17 regions in Korea when we calculated the participation of the PCDMP. In addition, the current PCDMP is a pilot program run by the Korean government; thus, if it becomes an official program, the procedures and details of the program are subject to change. Third, individuals’ HbA1c levels are divided into two categories, \(\ge\) 6.5% and < 6.5%, where information may be lost about individuals with HbA1c levels of nearly 6.5%. Additionally, diagnosing diabetes is not solely based on HbA1c levels; rather, other tests, including screening tests and glycemia tests, may be used; moreover, there are no perfect guidelines for diabetes diagnosis [23–25]. Finally, in statistical analysis, few regional covariates are linear combinations of other variables, which leads to unreliable and unstable estimates of regression coefficients [26].

At the national level, efforts are still being made to effectively manage chronic illnesses through primary care. The Korean government aims to run integrated national primary care-based chronic disease management programs and regulations that emphasize the positive aspects of the current pilot programs while addressing their shortcomings. This study is significant since it provides a comprehensive evaluation of the efficacy of the PCDMP in Korea, increasing the importance of primary care. By enhancing continuity of care and averting complications, increasing the role of primary diabetes care can greatly enhance the health outcomes of individuals. These findings offer essential information to help improve primary care, which is increasingly important globally.

PCDMP: primary care-based chronic disease management integrated pilot program; HbA1c: glycated hemoglobin; KNHANES: Korea National Health and Nutrition Examination Survey; NHIS: National Health Insurance Service; CI: Confidence interval; IDF: International diabetes federation; KHEPI: Korea Health Promotion Institute; HIRA: Health Insurance Review & Assessment Service; BMI: Body mass index; GLMM: Generalized linear mixed model; aOR: Adjusted odds ratio

Acknowledgments

We would like to thank the members of the Institute of Health Services Research at Yonsei University for their advice on the further development of this study.

Authors’ contributions

Juan Kim and Il Yun made substantial contributions to the concept or design of the work; Juan Kim and Il Yun contributed to the acquisition, analysis, or interpretation of the data; Juan Kim drafted the article; and Eun-Cheol Park and Min Jin Ha critically revised the article for important intellectual content. All the authors approved the version to be published.

Funding

Min Jin Ha was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) [No. 2022R1A2C1091488].

Data availability statement

The datasets analyzed in the present study are publicly available. First, the KNHANES data are available online: https://knhanes.kdca.go.kr. Second, the NHIS data are available online: https://www.nhis.or.kr/nhis/together/wbhaec06900m01.do. Third, the HIRA big data portal data are available online: https://opendata.hira.or.kr/op/opc/olapYadmStatInfoTab4.do.

Ethics approval and consent to participate

As the KNHANES complies with the Declaration of Helsinki and provides publicly accessible data, further ethical approval for the use of these data was not required.

Consent for publication

Not applicable

Competing interests

The authors declare no competing interests to declare.

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Effectiveness of primary care-based chronic disease management program on glycated hemoglobin levels

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