Abstract
Background
Essential hypertension incurs substantial cardiovascular morbidity and mortality, particularly in primary prevention settings. General practitioners (GPs) play a pivotal role in the management of uncomplicated hypertension in primary care, yet variations exist among GPs. The determinants shaping GPs’ antihypertensive drugs (AD) prescription patterns in the setting of hypertension remain ambiguous.
Objectives
This investigation sought to examine the correlation between GP characteristics and professional activities on the prescribing patterns of ADs within the context of uncomplicated hypertension management.
Methods
A secondary data analysis utilizing a sample of 2,165 GPs was conducted in Normandy, France, in 2019. The ratio of AD prescriptions to overall prescription volume was computed for each GP. GPs were classified as ‘low’ or ‘high’ AD prescribers based on the median of this ratio. The ratio was examined in relation to GPs’ demographic and professional variables such as age, gender, practice setting, years of experience, consultation frequency, the demographics and socioeconomic status of their patient panels, and prevalence of chronic conditions in patients. These associations were explored using both univariate and multivariate analyses.
Results
GPs categorized as low prescribers had a mean age of 51.3 ± 11.2 years and were predominantly female (56%). In multivariate logistic regression, low-prescriber status correlated with urban practice location (OR: 1.47, 95% CI: 1.14–1.88), younger GP age (OR: 1.87, 95% CI: 1.42–2.44), younger patient population (OR: 3.39, 95% CI: 2.77–4.15), higher consultation rate per patient (OR: 1.33, 95% CI: 1.11–1.61), greater proportion of low-income patients (OR: 1.44, 95% CI: 1.17–1.76), and a lower prevalence of diabetes mellitus (OR: 0.72, 95% CI: 0.59–0.88) throughout 2019.
Conclusion
Low AD prescription rates are associated with younger, urban-based GPs who experienced higher patient consultation rates in 2019 and managed populations with lower income profiles.
Clinical Implications
The prescription of antihypertensive medications might be significantly influenced by GPs' professional attributes and activities within the French healthcare setting.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12875-026-03193-w.
Keywords: Primary care, Hypertension, Control, Antihypertensive drugs
Introduction
Hypertension is a considerable risk factor for cardiovascular morbidity and mortality [1]. In 2015, it was estimated that approximately 1.13 billion individuals globally were affected by hypertension [2]. In the interest of curbing the incidence of cardiovascular diseases (CVD), it is vital to detect and control this noteworthy risk factor among general population. Furthermore, antihypertensive drugs (AD) have demonstrated efficacy in regulating blood pressure (BP). Controlled hypertensive patients receiving AD treatment exhibit similar risks of major CVD events to those in normotensive individuals [3]. Accordingly, a reduction in systolic BP by 10 mmHg correlates with a 20% decrease in the risk of major CVD [4]. Global hypertension prevalence remains relatively unchanged across different ages and sexes, while awareness and management have improved in many nations [5].
In this context, hypertension control is suboptimal among the general population, a situation particularly relevant to France [6]. Data from the PURE study indicated that only 40.6% of hypertensive individuals receive treatment, and a mere 32.5% of those treated achieve BP control [7]. In 2015, the FLAHS study in France demonstrated that 20% of hypertensive individuals were untreated and just half of those treated had controlled BP [8]. Remarkably, hypertension is the foremost reason for primary care consultations globally [1]. Besides, general practitioners (GPs) are on the front line when it comes to managing cardiovascular risk, including hypertension, particularly in primary prevention. Vallée et al. demonstrated that GP consultations were associated with an increase in treatment adequacy (OR: 1.03 ; 95% CI: 1.01;1.05, p = 0.048), whereas this was not the case with second-line specialists (OR: 0.88 ; 95% CI: 0.64;1.12, p = 0.288) [6].
In another context, GPs‘ characteristics and practice-related activities may influence drug prescription patterns and account for uncontrolled disease. For example, Mercier et al. had previously demonstrated that these types of characteristics could influence the prescription of antidepressive drugs in general practice [9]. Also, several studies have already examined GP characteristics of low/high antibiotics prescriptions [10, 11]. However, this type of study has never been conducted on ADs in primary care. Thus, we compare here GPs with high versus low AD prescription rates in Normandy, region of northern France.
The primary objective of this study was to examine the correlation between GP characteristics and professional activities on the prescribing patterns of ADs within the context of uncomplicated hypertension management.
Methods
The report follows the STROBE (Strengthening the Reporting of Observational studies in Epidemiology) guidelines [12, 13], the study protocol was conceptualized and registered at ClinicalTrials.gov (NCT06517134, 24/07/2024).
Study design
The National Health Data System (Système national des données de santé, SNDS) data, analyzed by the Regional Medical Service Directorate of Normandy (Direction régionale du service médical Normandie, DRSM Normandie), served as the study’s foundation. The SNDS aims to augment the utilization of health data. It is rooted in the National Health Insurance Information System (Système national d’information inter-régimes de l’Assurance Maladie, SNIIRAM), which archives health insurance reimbursements, hospital statistics (Program of Medicalization of Information Systems database, PMSI), vital statistics from the epidemiology center on medical causes of death (CépiDc of Inserm), and data concerning disability from the Departmental Centers for Disabled People (MDPH) and the National Fund for Solidarity and Autonomy (CNSA). The SNDS offers a comprehensive perspective on the care trajectories of the entire population, with up to two decades of historical data. The raw data is anonymized by the French health insurance system, which collects this data. Statistical analyses were performed internally by the DRSM Normandie), and only the final results were sent to researchers in a completely anonymous manner.
Accordingly, all pertinent data on GP demographics and drug prescribing are routinely captured, ensuring exhaustiveness and reliability of the dataset.
Selection and characteristics of included GPs
The included pool for this study comprised GP characteristics and prescription practices within the Normandy region in northwestern France during the entire year 2019. With a resident population of 3,325,032 as of January 1, 2019, and a total of 2,165 registered GPs, the study was well-situated to offer a representative analysis.
In order to focus on conventional full-time primary GP care, practitioners whose primary professional focus included specialties such as osteopathy, acupuncture, homeopathy, or nutrition were excluded. Furthermore, GPs with fewer than 300 annual consultations/visits (less than one per day), fewer than 2,000 annual drug prescriptions, no patients with any of the 30 chronic diseases that qualify for no-charge treatment under French law, or solely very low-income patients (eligible for no-charge GP visits as defined by French legislation) were also excluded.
Data collection and outcome measures
Data were collected in two principal categories to facilitate analysis: GP demographics and medical practice activities.
The first category encompassed the age and gender of GPs, the urban or rural classification of the practice location (based on the GP’s clinic postal code and DATASANTE tables from the French National Institute for Statistics and Economic Studies, INSEE), and the number of years in practice.
The second category contained descriptors of GPs’ medical activities throughout 2019, including the frequency of home visits and consultations, the total number of registered patients, the average number of visits or consultations per patient, the average patient age, the number of very low-income patients (“free of charge” patients), and the prevalence of registered chronic diseases (notably diabetes mellitus and CVDs). All patients who had at least one visit to their GP in 2019 were included, but minors were omitted. Prescription metrics, such as the total number of prescription orders, the quantity of drugs prescribed, and the total number of patients with AD prescriptions (specifically, those with more than five AD prescription orders per year), were also collated. ADs were classified as per the Anatomical Therapeutic Chemical (ATC) classification system, including ATC codes C02, C03, C07, C08, and C09.
Defining the AD prescription ratio
Given the lack of a universally accepted metric for gauging AD prescription levels, an AD prescription ratio was developed. For each GP, the annual prescription volumes of individual AD agents were converted into Defined Daily Dose (DDD) units, which were then aggregated to determine each GP’s total annual AD prescription volume for 2019. The AD prescription ratio was calculated by dividing this figure by the overall quantity of all reimbursed medicines prescribed during the same period. This study ensured to exclude all AD prescriptions tied to chronic conditions as outlined by French law (cerebrovascular accidents, heart failure, coronary disease, peripheral arterial disease, renal insufficiency), focusing the evaluation exclusively on the prescription patterns related to uncomplicated hypertension. Thus, the AD prescription ratio measured the proportional level of antihypertensive medication prescribed. GPs were distinguished as ‘low prescribers’ if their AD prescription ratio fell below the median value, and as ‘high prescribers’ if the ratio was above the median as in a previous study on antidepressive drugs [9].
Statistical analysis
The present analysis investigated the effect of GPs characteristics and practice patterns on their AD prescription ratios. We dichotomized the AD prescription ratio, employing the median value as a boundary to distinguish between higher and lower prescribing GPs. Determinants of low AD prescription rates among GPs were investigated. Univariate analysis was performed using Pearson’s chi-square test or Fisher’s exact test, as deemed appropriate. Predictors exhibiting an association with the AD prescription ratio at a significance level of p < 0.05 were subjected to further multivariate analysis. Logistic regression, with a backward elimination approach, was employed for multivariate analysis. No confounding factors were anticipated. Analyses were conducted utilizing NCSS software and SAS (Statistical Analysis System).
Results
Demographics and professional activities of general practitioners (Table 1; Fig. 1)
Table 1.
Characteristics of 2,165 general practitioners included in the analysis with the dichotomized ratio (<0.077 for lower prescribers and ≥0.077 for higher prescribers)
| Low prescribers (n = 1,082) | High prescribers (n = 1,083) | p-value | All prescribers (n = 2,165) | |||||
|---|---|---|---|---|---|---|---|---|
| n | % | n | % | n | % | |||
| Gender | Female | 480 | 44.4% | 377 | 34.8% | p < 0.001 | 857 | 39.6% |
| Male | 602 | 55.6% | 706 | 65.2% | 1,308 | 60.4% | ||
| Practice location | Rural | 141 | 13.0% | 244 | 22.5% | p < 0.001 | 385 | 17.8% |
| Urban | 941 | 87.0% | 839 | 77.5% | 1,780 | 82.2% | ||
| Mean | SD | Mean | SD | Mean | SD | |||
|---|---|---|---|---|---|---|---|---|
| Age of GPs | 51.3 | 11.2 | 54.7 | 11.0 | p < 0.001 | 53.0 | 11.3 | |
| Cumulative years of medical practice | 17.5 | 11.8 | 20.6 | 12.6 | p < 0.001 | 19.1 | 12.3 | |
| Number of consultations and home visits | 5,268 | 2,054 | 5,085 | 1,816 | p = 0.37 | 5,177 | 1,971 | |
| Number of different patients per year | 1,696 | 742 | 1,591 | 573 | p = 0.43 | 1,643 | 665 | |
| Number of consultations and home visits for each patient | 3.22 | 0.75 | 3.26 | 0.66 | p = 0.055 | 3.2 | 0.7 | |
| Mean age of patients | 50.0 | 4.7 | 53.2 | 3.9 | p < 0.001 | 51.6 | 4.6 | |
| Number of patients with low income | 112 | 124 | 72 | 67 | p < 0.001 | 92 | 101 | |
| Number of patients with chronic disease | 368 | 153 | 385 | 139 | p = 0.007 | 377 | 146 | |
| Number of patients with diabetes mellitus | 95 | 45 | 101 | 42 | p = 0.001 | 98 | 44 | |
| Number of patients with cardiovascular disease | 132 | 63 | 149 | 61 | p < 0.001 | 140 | 63 | |
| AD volume of reimbursement (number of units) | 1,265 | 710 | 2,115 | 948 | p < 0.001 | 1,690 | 939 | |
| Total volume of reimbursement (number of units) | 20,213 | 10,375 | 22,485 | 9,404 | p < 0.001 | 21,349 | 9963 | |
| Antihypertensive prescription ratio | 6.1% | 1.3% | 9.4% | 1.5% | p < 0.001 | 7.7% | 2.2% |
AD Antihypertensive drugs, GPs General practitioners, SD Standard deviation
Fig. 1.
Flow chart of the study. * 145 and 163 GPs were excluded for retired and stopped activity respectively ** 222 GPs had particular exercise, 87 had less than 300 consultation in 2019, 2 didn’t have any chronic patient treated or very low-income patient and 44 had less than 2,000 prescription order in 2019
Of the 2,828 GPs initially identified, 308 were retired or had ceased practicing in 2019, and 355 were excluded based on the application of exclusion criteria. Ultimately, 2,165 GPs were included in the study (as depicted in Fig. 1). The mean age of the included GPs was 53.0 ± 11.3 years; 857 (39.6%) of whom were female. The majority practiced in urban settings (1,780 out of 2,165, accounting for 82.2%) with an average tenure of 19.1 ± 12.3 years. Annually, they conducted 5,177 ± 1,941 consultations and home visits, with 1,643 ± 665 unique patients attending more than one consultation or visit per year. There were, on average, 3.2 ± 0.7 consultations per patient per year.
The patient cohort, comprising 2,113,543 individuals, had a mean age of 51.6 ± 4.6 years, with a gender composition of 1,177,861 women (55.7%) and 935,682 men (44.3%).
AD prescription ratios and determinants of AD prescription in uni- and multi-variate analyses (Table 2)
Table 2.
Univariate and multivariate analysis of variables independently associated with the dichotomized ratio (< 0.077 for lower prescribers and ≥ 0.077 for higher prescribers)
| Low prescribers (n = 1,082) | High prescribers (n = 1,083) | Univariate analysis | Multivariate analysis | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| n | % | n | % | OR | 95% CI | p-value | OR | 95% CI | p-value | |
| Gender | ||||||||||
| Male | 602 | 46.0% | 706 | 54.0% | 1 | NS | ||||
| Female | 480 | 56.0% | 377 | 44.0% | 1.49 | [1.26;1.78] | p < 0.001 | |||
| Practice location | ||||||||||
| Rural | 141 | 36.6% | 244 | 63.4% | 1 | |||||
| Urban | 941 | 52.9% | 839 | 47.1% | 1.94 | [1.55;2.44] | p < 0.001 | 1.47 | [1.14;1.88] | p = 0.003 |
| Age of GPs | ||||||||||
| ≥ 60 yrs | 319 | 40.9% | 461 | 59.1% | 1 | 1 | ||||
| 50–59 yrs | 307 | 50.9% | 296 | 49.1% | 1.50 | [1.21;1.86] | 1.37 | [1.09;1.72] | ||
| 40–49 yrs | 222 | 56.9% | 168 | 43.1% | 1.91 | [1.49;2.44] | 1.65 | [1.26;2.14] | ||
| < 40 yrs | 234 | 59.7% | 158 | 40.3% | 2.14 | [1.67;2.74] | p < 0.001 | 1.87 | [1.42;2.44] | p < 0.001 |
| Cumulative years of medical practice | ||||||||||
| ≥ 30 yrs | 215 | 40.1% | 321 | 59.9% | 1 | |||||
| 20–29 yrs | 264 | 52.9% | 296 | 52.9% | 1.33 | [1.05;1.69] | ||||
| 10–19 yrs | 243 | 39.9% | 161 | 39.9% | 2.25 | [1.73;2.93] | ||||
| < 10 yrs | 360 | 45.9% | 305 | 45.9% | 1.76 | [1.40;2.22] | p < 0.001 | |||
| Number of consultations and home visit | ||||||||||
| < median | 539 | 49.8% | 543 | 50.2% | 1 | NS | ||||
| ≥ median | 543 | 50.1% | 540 | 49.9% | 1.01 | [0.86;1.20] | p = 0.88 | |||
| Number of different patients per year | ||||||||||
| < median | 536 | 49.6% | 545 | 50.4% | 1 | NS | ||||
| ≥ median | 546 | 50.4% | 538 | 49.6% | 1.03 | [0.87;1.22] | p = 0.71 | |||
| Number of consultations and home visits for each patient | ||||||||||
| < median | 545 | 50.4% | 536 | 49.6% | 1 | |||||
| ≥ median | 537 | 49.5% | 547 | 50.5% | 0.97 | [0.82;1.14] | p = 0.003 | |||
| Mean age of patients | ||||||||||
| ≥ 50 yrs | 518 | 37.7% | 855 | 62.3% | 1 | 1 | ||||
| < 50 yrs | 564 | 71.2% | 228 | 28.8% | 4.08 | [3.38;4.93] | p < 0.001 | 3.39 | [2.77;4.15] | p < 0.001 |
| Number of patients with low income | ||||||||||
| < median | 465 | 43.1% | 615 | 56.9% | 1 | 1 | ||||
| ≥ median | 617 | 56.9% | 468 | 43.1% | 1.74 | [1.47;2.07] | p < 0.001 | 1.44 | [1.17;1.76] | p < 0.001 |
| Number of patients with chronic disease | ||||||||||
| < median | 589 | 54.6% | 490 | 45.4% | 1 | NS | ||||
| ≥ median | 493 | 45.4% | 593 | 54.6% | 0.69 | [0.58;0.82] | p < 0.001 | |||
| Number of patients with diabetes mellitus | ||||||||||
| < median | 584 | 54.4% | 490 | 45.6% | 1 | 1 | ||||
| ≥ median | 498 | 45.7% | 593 | 54.3% | 0.70 | [0.60;0.83] | p < 0.001 | 0.72 | [0.59;0.88] | p < 0.001 |
GP General practitioner, yrs Years
The mean AD prescription ratio among GPs was 0.077. Thus, low and high prescribing GPs were categorized based on whether their mean AD ratio was less than 0.077 or greater than 0.077, respectively.
Upon stratification, high prescribers were significantly older (p < 0.001) and had a more extended practice duration compared to their low-prescribing counterparts (p < 0.001). Additionally, high prescribers were more commonly male (p < 0.001) and practiced predominantly in rural areas (p < 0.001).
Patients of high prescribers were characteristically older (p < 0.001), had a higher income status (p < 0.001), and presented more frequently with chronic diseases (p = 0.007), and diabetes mellitus (p = 0.001).
Univariate analysis indicated that low prescribing was correlated with factors such as urban practice location (OR: 1.94, 95% CI: 1.55;2.44, p < 10− 3 ), younger age of GPs (OR: 2.14, 95% CI: 1.67;2.74, p < 10− 3 ), younger patient age (OR: 4.08, 95% CI: 3.38;4.93, p < 10− 3 ), greater proportion of low-income patients (OR: 1.74, 95% CI: 1.47;2.07, p < 10− 3 ), fewer diabetes mellitus patients (OR: 0.70, 95% CI: 0.60;0.83, p < 10− 3 ), the gender of the GP (OR: 1.49, 95% CI: 1.26;1.78, p < 10− 3 ), and fewer patients with chronic diseases (OR: 0.69, 95% CI: 0.58;0.82, p < 10− 3). Parameters such as number of consultations/visits per patient, total number of consultations/visits, number of patients with more than one consultation/visit, years of practice by the GP, number of AD prescriptions, total number of drug prescriptions, number of hypertensive patients, ratios of hypertensive, low-income, chronically ill, and diabetes mellitus -affected patients did not exhibit any association with AD prescribing patterns of GPs.
In the multivariate logistic regression analysis, factors associated with low AD prescribing included urban practice setting (OR: 1.47, 95% CI: 1.14;1.88, p = 0.003), younger GP age (OR: 1.87, 95% CI: 1.42;2.44, p < 10− 3 ), younger patient cohort (OR: 3.39, 95% CI: 2.77;4.15, p < 10− 3 ), a higher frequency of patient consultations (OR: 1.33, 95% CI: 1.11;1.61, p = 0.003), a larger proportion of low-income patients (OR: 1.44, 95% CI: 1.17;1.76, p = 0.001), and a reduced number of diabetes mellitus patients (OR: 0.72, 95% CI: 0.59;0.88, p = 0.001).
Discussion
Principal findings
The multivariate logistic regression revealed that low-prescribing GPs were characteristically practicing in urban settings, were younger, had younger patient populations, recorded higher consultation frequencies, served more low-income patients, and treated fewer patients with diabetes mellitus.
External validity
Prior research on the drivers influencing GPs’ AD prescribing practices is scarce. Notably, younger GPs in our cohort had potentially a penchant for discriminating between genuine hypertension and white-coat hypertension (WCH) using home BP measurement (HBPM), which might account for lower prescription rates. Mangiavillano et al., in a cross-sectional survey of GPs in the French Auvergne region post the widespread adoption of HBPM, found that younger GPs more frequently utilized HBPM both for diagnosing (OR: 0.97 95% CI: 0.95;0.98, p < 0.001) and managing hypertension (OR: 0.98 95% CI: 0.97;0.99, p = 0.027). Conversely, data by Mancia et al. suggest that WCH, traditionally considered benign, can be a precursor to hypertension, as evidenced by a 42.6% rate of hypertension onset in patients with WCH over a 10-year observation period [14]. In fact, it is difficult to determine if AD prescription rates among the samples can be considered appropriate or inappropriate. So, among younger GPs, many might also have younger patients, hence lower prevalence of hypertension, which can be suggested by the results were young GP, young patients and lower prevalence of diabetes were all associated with lower AD prescription ratios.
We had hypothesized that few factors were correlated with the rate of AD prescription. In all case, it is well-documented that BP control is suboptimal within the general population, a situation that is prevalent in France [6]. In this view, Sandoya-Olievra et al. have shown that only 69% of BP measurement are validated in primary care in an observational, cross-sectional study conducted in 5 health centers in Maldonado, Uruguay, in 2015 [15]. Moreover, Richard et al. demonstrated in qualitative and quantitative studies that the majority of patients are in favor of or indifferent to the absence of BP measurement in general practice [16, 17]. So, general practice could be more efficient by measuring BP less frequently but better. Nevertheless, a substantial correlation exists between the number of GP consultations and effective BP management. In a cross-sectional analysis of the ESTEBAN survey, Vallée et al. demonstrated that among a representative sample of 396 subjects from the French population, GP consultations were associated with a marginal increase in treatment adequacy (OR: 1.03 ; 95% CI: 1.01;1.05, p = 0.048), whereas cardiologist consultations did not significantly affect treatment (OR: 0.88 ; 95%CI: 0.64;1.12, p = 0.288) [6]. This underscores the pivotal role of GPs in managing hypertension. Treatment inertia, defined as the reluctance of medical practitioners to modify existing treatment despite suboptimal BP control, may account for the observed variance in prescribing practices [18]. However, it is difficult to differentiate between therapeutic inertia and appropriate inaction. Patil et al. shown that in 90 recruited patients, 66% of uncontrolled patients underwent therapeutic intensification, while non-intensification in the others was justified by BP imbalances linked to transient factors [19]. Furthermore, the quality of the physician-patient relationship has been recognized as a crucial factor influencing medication adherence [20, 21]. Here, the fact that the lower prevalence of diabetes was associated with lower prescription ratio and the lack of association with the number of patients with chronic disease suggest that the clinical inertia could be not the problem.
The association between low AD prescribers and socioeconomic status (SES) was evident, most markedly so in the association with patients of low-income (OR: 1.44, 95% CI: 1.17;1.76). The literature indicates a clear link between hypertension and SES. Leng et al., in a meta-analysis of 51 studies conducted in 2010, highlighted that lower SES was correlated with elevated hypertension risk, with significant odds ratios for income (pooled OR: 1.19 ; 95% CI: 0.96;1.46), occupation (pooled OR: 1.31 ; 95% CI: 1.04;1.64), and education (pooled OR: 2.02 ; 95% CI: 1.55;2.63) [22]. In a more contemporary study, Cherfan et al. described an enhanced prevalence of unhealthy behaviors and hypertension risk associated with lower incomes (6.4% versus 3.6%, p < 0.001) within 86,448 participants of the French CONSTANCES cohort [23]. The positive relationship found between lower income and low prescription rates may be linked to issues related to SES. In addition, our work did not specifically study the prescriptions of second-line healthcare professionals such as cardiologists. However, the most affluent populations consume more secondary care [24]. However, we feel that this factor has little influence on our results, since only subjects with uncomplicated hypertension were considered here. In fact, subjects with CVD or chronic kidney failure were deliberately excluded from the analysis, as they by definition consulted more second-line health care professionals.
Strengths and limitations
While no formal sample size calculation was undertaken, this investigation comprised a substantial sample and utilized a comprehensive regional database. The AD prescription ratio developed specifically for this study, reflecting a novel approach initially employed in the context of antidepressant or antibiotics prescriptions [9, 10, 11], permits the isolation of AD prescription patterns from the confounding factor of overall prescription volume. By excluding AD prescriptions for CVDs (e.g., stroke, peripheral artery disease, heart failure, myocardial infarction) and chronic kidney dysfunctions, this study concentrated on uncomplicated hypertensive prescriptions. Nevertheless, this study did not capture referrals for diagnosis and treatment nor could it ascertain whether prescriptions were initiated in primary or secondary care settings. So, It is not possible to assess whether patients real need the AD therapy by the presented data. The diagnosis and management of hypertension, particularly the methodology of BP measurement, remain unexplored within this dataset. The fact that factors such as continuity of care and patient acceptance could not be taken into account in this medical-economic database are two limitations to be considered in future work. Additionally, the degree of patient adherence to AD therapy was not ascertainable through this study format. Finally, the assessment of low-quality practice was not one of the study’s objectives. Therefore, we do not draw any conclusions about being a low prescriber of ADs. However, this is a link that could potentially be explored in future studies. In order to differentiate between lower and higher prescribers, we used a method already described in another study with the median approach [9]. We could also have compared the highest quartile or quintile to the lowest quartile or quintile, as is sometimes the case.
Conclusions
This investigation identified low-prescribing GPs factors. In order to comprehensively describe prescribing patterns for AD drugs in general practice, future research should evaluate in detail all aspects of the GP consultation, with a particular focus on diagnostic and management patterns for this common pathology.
Supplementary Information
Acknowledgements
In memory of Dr Francis KUHN, who was fully committed to this project.
Statement on the use of generative AI and AI-assisted technologies in the writing process
In preparing this work, the authors used SciRevise to improve the readability of this manuscript. After using this tool, the authors reviewed and corrected the content as necessary and assume full responsibility for the content of the publication.
Abreviations
- AD
Antihypertensive drugs
- ATC
Anatomical Therapeutic Chemical
- BP
Blood pressure
- CépiDc
Center on medical causes of death
- CNSA
National Fund for Solidarity and Autonomy
- CVD
Cardiovascular diseases
- DDD
Defined Daily Dose
- DRSM
Direction régionale du service médical Normandie
- GP
General practitioner
- HBPM
Home BP measurement
- INSEE
French National Institute for Statistics and Economic Studies
- MDPH
Maison départemental des personnes handicapées
- PMSI
Program of Medicalization of Information Systems database
- SAS
Statistical Analysis System
- SES
Socioeconomic status
- SNDS
Système national des données de santé
- SNIIRAM
Système national d'information inter-régimes de l'Assurance Maladie
- STROBE
Strengthening the Reporting of Observational studies in Epidemiology
- WCH
White-coat hypertension
Authors’ contributions
XH conceptualized the project; XH, and SD conducted the statistical data analysis; AR, MM, and XH composed the manuscript; DL, JA, and RD undertook manuscript revision for critical conceptual content and directed the research ensemble; All Authors partook in manuscript critique, input of comments, and conferred their approval on the concluded text, assuming comprehensive accountability for its integrity and the scientific veracity presented.
Funding
No funds were required to carry out this study.
Data availability
The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
No ethics approval and consent to participate formular were required by the Local Research Ethics Committee (CLER) of University Caen Normandy (France). Our research was conducted on human data in accordance with the Declaration of Helsinki (https://www.wma.net/policies-post/wma-declaration-of-helsinki/).
Consent for publication
All co-authors have all reviewed and approved this manuscript.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.OMS, Hypertension. Disponible sur: https://www.who.int/fr/news-room/fact-sheets/detail/hypertension.
- 2.NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants. Lancet Lond Engl. 2017;389:37–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Lanti M, Puddu PE, Vagnarelli OT, et al. Antihypertensive treatment is not a risk factor for major cardiovascular events in the Gubbio residential cohort study. J Hypertens. 2015;33:736–44. discussion 744. [DOI] [PubMed] [Google Scholar]
- 4.Ettehad D, Emdin CA, Kiran A, et al. Blood pressure Lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. Lancet Lond Engl. 2016;387:957–67. [DOI] [PubMed] [Google Scholar]
- 5.NCD Risk Factor Collaboration (NCD-RisC). Long-term and recent trends in hypertension awareness, treatment, and control in 12 high-income countries: an analysis of 123 nationally representative surveys. Lancet Lond Engl. 2019;394:639–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Vallée A, Grave C, Gabet A, Blacher J, Olié V. Treatment and adherence to antihypertensive therapy in france: the roles of socioeconomic factors and primary care medicine in the ESTEBAN survey. Hypertens Res Off J Jpn Soc Hypertens. 2021;44:550–60. [DOI] [PubMed] [Google Scholar]
- 7.Chow CK, Teo KK, Rangarajan S, et al. Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries. JAMA. 2013;310:959–68. [DOI] [PubMed] [Google Scholar]
- 8.Girerd X, Hanon O, Pannier B, Mourad JJ, Vaïsse B. [Determinants of controlled hypertension in patients treated with antihypertensive drugs in france: the French league against hypertension survey (FLAHS 2015)]. Ann Cardiol Angeiol (Paris). 2016;65:219–22. [DOI] [PubMed] [Google Scholar]
- 9.Mercier A, Benichou J, Auger-Aubin I, et al. How do GP practices and patient characteristics influence the prescription of antidepressants? A cross-sectional study. Ann Gen Psychiatry. 2015;14:3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Patel A, Pfoh ER, Misra Hebert AD, et al. Attitudes of high versus low antibiotic prescribers in the management of upper respiratory tract infections: a mixed methods study. J Gen Intern Med. 2020;35:1182–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Arias P, Matta M, Strazzulla A, Le Mener C, Gallien S, Diamantis S. Identifying general practitioners’ antibiotic prescribing profiles based on National health reimbursement data. Open Forum Infect Dis. 2024;11:ofae172. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.STROBE statement. STROBE. Available on: https://www.strobe-statement.org/.
- 13.von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344–9. [DOI] [PubMed] [Google Scholar]
- 14.Mancia G, Bombelli M, Facchetti R, et al. Long-term risk of sustained hypertension in white-coat or masked hypertension. Hypertension. 2009;54:226–32. [DOI] [PubMed] [Google Scholar]
- 15.Sandoya-Olivera E, Ferreira-Umpiérrez A, Machado-González F. Quality of blood pressure measurement in community health centres. Enfermeria Clin. 2017;27:294–302. [DOI] [PubMed] [Google Scholar]
- 16.Bouchon C, Favier J, Trefond J, Vaillant-Roussel H, Vorilhon P, Richard A. [Arterial blood pressure not systematically measured during consultations in general practice: doctors’ and patients’ differing views and feelings]. Exercer. 2022;33:4–10. [Google Scholar]
- 17.Richard A, Trefond J, Lambert C, Balandreaud G, Vaillant-Roussel H. Patients’ acceptance of less blood pressure measurement in consultation: a cross-sectional study in general practice. Fam Pract. 2023;40:55–60. [DOI] [PubMed] [Google Scholar]
- 18.Lebeau J-P, Cadwallader J-S, Aubin-Auger I, et al. The concept and definition of therapeutic inertia in hypertension in primary care: a qualitative systematic review. BMC Fam Pract. 2014;15:130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Patil SJ, Wareg NK, Hodges KL, Smith JB, Kaiser MS, LeFevre ML. Home blood pressure monitoring in cases of clinical uncertainty to differentiate appropriate inaction from therapeutic inertia. Ann Fam Med Annals Family Med. 2020;18:50–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Burnier M. Drug adherence in hypertension. Pharmacol Res. 2017;125:142–9. [DOI] [PubMed] [Google Scholar]
- 21.Burnier M, Egan BM. Adherence in hypertension. Circ Res. 2019;124:1124–40. [DOI] [PubMed] [Google Scholar]
- 22.Leng B, Jin Y, Li G, Chen L, Jin N. Socioeconomic status and hypertension: a meta-analysis. J Hypertens. 2015;33:221–9. [DOI] [PubMed] [Google Scholar]
- 23.Cherfan M, Vallée A, Kab S, et al. Unhealthy behavior and risk of hypertension: the CONSTANCES population-based cohort. J Hypertens. 2019;37:2180–9. [DOI] [PubMed] [Google Scholar]
- 24.Lorant V, Boland B, Humblet P, Deliège D. Equity in prevention and health care. J Epidemiol Community Health. 2002;56:510–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.