# Strengthening Primary Health Care: The New Contribution of Midwives to Gynecological Care in France

Quitterie Roquebert, Henri Panjo, Carine Franc

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Quitterie Roquebert, Henri Panjo, Carine Franc. Strengthening Primary Health Care: The New Contribution of Midwives to Gynecological Care in France. Health Policy, 2025, 10.1016/j.healthpol.2025.105397. hal-05237524

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# Strengthening primary health care: The new contribution of midwives to gynecological care in France

Quitterie Roquebert<sup>a, ∗</sup>, Henri Panjo<sup>b</sup>, Carine Franc<sup>b, †</sup>

ARTICLE INFO

Keywords:

Primary care

Midwives' skills expansion

Panel data

Health reform

Territorial inequalities of access

A B S T R A C T

Many OECD countries are implementing reforms that redefine the roles of healthcare professionals to improve access to care and enhance the efficiency of the healthcare sector. In 2009, in a context of a shortage of gynecological primary care, France introduced a reform that expanded midwives' scope of practice to include gynecological care for non-pregnant women, alongside their traditional roles in pregnancy and childbirth. This paper explores the effect of this reform on women's healthcare utilization and examines how this effect varies according to women's characteristics. Using administrative data on healthcare utilization among French women, we analyze changes in the probability of consulting a midwife between 2007 and 2017 among non-pregnant women aged 15 to 55. Our results show an increase in midwives' use following the reform, particularly among recently pregnant women and those living in areas with poor access to healthcare services. Overall, the effect of the reform extending the primary care roles of health professionals on the use of care depends strongly on the provision of information about these new skills to the general public. Furthermore, we show that this reform is likely to strengthen the gynecological primary care supply in disadvantaged areas.

# 1. Background

For decades, women's sexual health has been a public policy concern across the world [1]. While sexual health is defined as "*a state of physical, emotional, mental and social well-being in relation to sexuality*" (World Health Organization (WHO) working definition [2]), sexual healthcare involves a wide range of health services including sexual information and education (contraception, sexually transmitted infections, physiology), access to contraception, and medical care related to genital health (screening and treatment of diseases) [3]. Sexual health inequalities are widespread and economically precarious women and those from underprivileged backgrounds are much less likely to have regular gynecological care. This limited access restricts their use of contraception increasing the risk of unwanted pregnancies [4-8] and lower their participation in preventive healthcare, resulting in reduced breast and cervical cancer screening rates [7,9,10].

The WHO has repeatedly presented the development of primary care as a solution to reduce healthcare access inequalities. In many OECD countries, policies have been implemented to extend the professional roles of certain medical or paramedical professionals to fill gaps or

reinforce primary care supply [11–20]. In the field of sexual health, continuity of care for women before, during, and after pregnancy, as well as throughout their lives, has become a major health priority [21]. In 2009, France's HSPT (Hôpital, Patient, Santé Territoire) law extended the scope of midwifery: midwives have been allowed to provide gynecological care to non-pregnant women, in addition to their traditional roles in pregnancy and childbirth. These expanded roles cover contraception (e.g., prescribing pills, inserting intra-uterine devices, implants) and cancer screening (cervical and breast) and the midwifery training was reinforced accordingly. This change has been possible due to the organization of the healthcare system in France, in which, midwives are a medical profession, with a high degree of autonomy and responsibility similar to doctors for the medical services they are allowed to provide. Their fees are regulated and have been standardized for doctors and midwives for gynecological procedures since 2016. While GPs play a central role in primary care provision and act as gatekeeper for many medical specialties, for sexual health, women can directly meet any gynecologist or midwives without seeing a GP first.

These changes have likely influenced the use of sexual healthcare by
introducing midwives as a new regular providers of gynecological care,

<footnote><sup>a</sup> Université de Strasbourg, Université de Lorraine, CNRS, BETA, Strasbourg, France</footnote>

<footnote><sup>b</sup> Centre for Research in Epidemiology and Population Health, French National Institute of Health and Medical Research, (INSERM U1018), Université Paris-Saclay</footnote>

<footnote>Université, Paris-Sud, UVSQ, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: carine.franc@inserm.fr (C. Franc).</footnote>
offering two key advantages: (1) a rapidly growing workforce—with a 3% annual increase between 2012 and 2017, and a rise in those working in private practice (exclusively or alongside a hospital position) from 20% in 2012 to 34% in 2021—while the number of office-based gynecologists declined significantly [22,23]; and (2) a strong public regulation: midwives are not allowed to charge extra fees, unlike doctors (specifically gynecologists), and their practice locations are also regulated: as they are not allowed to set up in already ‘well-endowed’ areas, they are encouraged to set-up in underserved areas, where they can join pluri-professional health centers and/or receive financial incentive to set up.

This paper examines whether expanding midwives' scope of practice impacted women's healthcare use in France. Do women consult midwives for their new skills in primary care, and which women are most likely to do so? In recent decades, countries like the USA, Australia, the Netherlands, and Belgium have expanded the role of midwives in both pregnancy-related and gynecological care [24–28]. However, most research has focused on the effects on the pregnancy-related care [24] highlighting the competition between midwives, general practitioners, and gynecologists for primary obstetric care for low-risk pregnancies [11,25,29]. This competition is influenced by disparities in training, patient awareness, and professional recognition [30,31]. In terms of health outcomes, studies have also shown that increased autonomy for midwives (less physician supervision) can reduce labor induction and cesarean rates [16]. More broadly, our paper focuses on the expansion of midwives' roles in gynecological care for non-pregnant women [24], including contraceptive practices. Recent studies in this area have shown positive experience experiences for non-postpartum women in the Netherlands [26]. Our work contributes to the broader literature on how new professional roles affect healthcare characteristics and utilization [12,17,19,20]. This echoes recent papers suggesting that specialist nurses and advanced nurse practitioners can improve health-care utilization and outcomes, patient information and satisfaction and, possibly, cost containment in several countries like Australia, the USA, Canada and European countries [17,32–34].

This article examines the changes in the use of midwives in France following their expanded roles in primary care after 2009. Using administrative data, we analyze the evolution of midwife consultations over a 10-year period (2007 to 2017) among non-pregnant women aged 15 to 55, focusing on the individual characteristics that may explain variations in the use of these new skills. We show that overall use remains low, but midwife consultations increased significantly during the period. This increase is particularly noteworthy among recently pregnant women, suggesting awareness raised by midwives during pregnancy. Additionally, the increase is greater in remote rural areas, where primary care supply, including gynecological primary care, is low.

## 2. Methods

### 2.1. Data sources

We used data from the *Échantillon Généraliste des Bénéficiaires (EGB)*, a rolling sample of individuals covered by French public health insurance representing 1/97th of the *Système National des Données de Santé (SNDS)* and covering 98% of the French population. Collected since 2005, the EGB includes data from over 700,000 patients in 2017, recording outpatient healthcare utilization (consultations, laboratory tests, etc.) and patients characteristics (sex, date of birth, location, etc.) including chronic illnesses status [35]. The access to this data was approved by the French data protection agency (*Commission Nationale Informatique et Libertés*, CNIL, approval number 01-054).

### 2.2. Study sample

The sample consisted of women aged 15–55 years identified in the EGB from 2005 to 2017. The age range corresponds to the WHO

definition of reproductive age (15 to 49 years), extended to include the menopausal period (45–55 years). In 2017, the database included 166,774 women from metropolitan France and Corsica. The panel is unbalanced due to women entering or exiting the age range and the phased inclusion of the different public health insurance plans. Data for salaried workers (about 90% of the population) are available all the way back to 2005 while data for self-employed and farmers start in 2011. Civil servants and students were also included during this period. All women were analyzed, controlling for insurance fund type.

### 2.3. Variable of interest

We aim to investigate the evolution of midwife utilization by women outside of pregnancy by analyzing changes in the determinants in this use. Our variable of interest is therefore the probability of consulting a midwife outside of pregnancy. To determine periods of healthcare use outside of pregnancy, we reconstructed pregnancy periods in our data using pregnancy outcomes (childbirth, abortion, miscarriage) to estimate the start date (Appendix 1A for more details). We then include the 90-days postpartum period within the pregnancy period, since midwife consultations after childbirth primarily focus on postnatal care, including pelvic floor exercises, breast-feeding, and may also cover contraception. Moreover, this 90-day period aligns with maternity leave in France, which ranges from 16 weeks (including 10 weeks after birth) for the first two children to 26 weeks (including 18 weeks after birth) for subsequent children. Note also that in France, 85% of women extend their period of maternity leave after their first or second child up to 150 days [36].

### 2.4. Independent variables

To analyze the factors influencing midwife use by women outside of pregnancy, we consider key determinants of healthcare utilization. These include indicators of health status (age, chronic conditions, healthcare expenses), socioeconomic factors (insurance type, share of extra fees, Public Fund type), and geographic variables reflecting local healthcare supply (territory typology and regions):

* age on January 1 of a given year, grouped in 5-year intervals;

* dummy variable for long term disease (LTD) (=1 if reported during the year);

* outpatient care expenses during the year, grouped by quintile (lowest quintile includes women with zero expenses);

* proportion of extra fees in total outpatient expenses as a proxy for standard of living (highest two deciles vs. others);

* type of public fund: salaried workers, farmers, the self-employed, civil servants and students;

* dummy variable for woman receiving free health insurance (public scheme for the very poor);

* 13 regions of residence (metropolitan France)

* categorical variable for places of residence (PR) based on healthcare access and socioeconomic characteristics using a French classification of areas [37]; the six categories are: i) suburban areas: limited access to primary healthcare services; ii) remote rural areas: particularly poor healthcare access; iii) tourism and retirement areas: relatively good access to healthcare services; iv) deprived urban and rural areas: high levels of socioeconomic deprivation; v) socioeconomically heterogenous city centers: very mixed populations and abundant healthcare resources; vi) socioeconomically privileged towns and suburban areas: relatively good primary healthcare provision.

The final variable is the time elapsed since the last pregnancy (LP), if any. Indeed, periods of pregnancy are occasions for women to meet midwives and be informed about their new roles. The five categories of this variable, evaluated on January 1 are: “NoP” if the woman had never
been pregnant on this date (since January 1, 2005 for women included in the study population since this date, since the inclusion date for the others), “Preg” if woman had a pregnancy period on January 1 of year t, “≤ 12 months” if the woman had at least one day of pregnancy in the preceding 12 months; “12 to 24 months” if the woman had at least one day of pregnancy 12 to 24 months before the current date, and finally, “> 24 months” if the woman had at least one day of pregnancy >24 months prior to the current date (See Appendix 1B for a graphic description of LP).

## 2.5. Empirical strategy

The longitudinal nature of the data allows for reconstructing pregnancy periods (See Appendix 1B) and controlling for individual fixed-effects. However, repeated measures may introduce correlations between sampled covariates. We therefore use a generalized estimating equation (GEE) probit regression model [38], a widely used model in repeated measures analysis. This semiparametric approach specifies the model via the likelihood of the marginal distribution and a working correlation matrix, which accounts for the pairwise within-subject associations and is referred to an "unstructured" working correlation matrix [39]. This estimated matrix provides weights for the combination of correlated responses as the regression coefficients. These coefficients have interesting properties, especially for large study samples [38]. For inference, standard errors are computed using the so-called robust "sandwich" estimator of the covariance matrix of the estimated parameters. This provides correct standard errors and thus correct p-values for large sample sizes [39].

We estimate the probability of consulting a midwife outside of pregnancy in year t, denoted $p(\text{use}_{it} = 1)$ where $i$ ($i = 1, ..., N$) represents the woman index, and $t$ ($t = 1, ..., 11$) spans 2007–2017. Although the data covers 2005–2018, the 'last pregnancy' variable (LP), defined in terms of years ($t-1$) and ($t-2$), is therefore unavailable for 2005–2006. Additionally, retrospective identification of pregnancy period based on delivery dates precludes the use of 2018 data (because this would require birth data from 2019). Appendix 2 details the empirical strategy and models. Three models were successively fitted including the individual variables for woman $i$ in year $t$ (age, LTD, type of place of residence, etc.), individual fixed variables in year $t$ (region, public Fund affiliation, etc.) and the variable describing the last pregnancy of woman $i$ in year $t$.

The three models differ in the way the time effect is specified: Model 1 includes 11 separate year-effects, while Model 2, using piecewise linear time effects, averages across two periods: pre-reform (2007–2009, $TE_{0709}$) and post-reform (2010–2017, $TE_{1017}$). To account for time effect heterogeneity, we build thirdly two models including interactions between post-reform time ($TE_{1017}$) and either time since last pregnancy ($LP_{it}$) of woman $i$ in year $t$ (Model 3a) or place of residence profile ($PR_{it}$) of women $i$ in year $t$ (Model 3b). Finally, to compare models performance, we use the quasi-likelihood criterion ($QIC_u$) [40,41]. While the Akaike information criterion (AIC) is widely used, it is based on likelihood and the asymptotic properties of the maximum likelihood estimator (MLE) [42]. However, since GEE do not assume a specific distribution and no likelihood is defined, we compute the $QIC_u$ to identify the optimal covariates subset. The preferred model is the one with the lowest $QIC_u$ value is preferred.

# 3. Results

## 3.1. Descriptive statistics

The study population consists of more than 166,000 women in 2017, of whom 29.6% were over 40 years old and 19.2% were under 25 years old (Table 1). About one in ten women (9.6%) had free health insurance. More than nine out of ten women had used at least one outpatient treatment in 2017 (doctor’s consultation, medication, etc.) and 8.3%

<table><caption>**Table 1**
Main characteristics of the study population in 2007 and 2017.</caption><thead><tr><th></th><th>2007</th><th>2017</th><th>2007</th><th>2017</th></tr></thead><tbody><tr><td></td><td>N = 127,636</td><td>N = 166,404</td><td>N = 127,636</td><td>N = 166,404</td></tr><tr><td>Age by 5-years-class (%)</td><td colspan="4">Free Complementary Health Insurance (CMU-C) (%)</td></tr><tr><td>[15-20]</td><td>9.6</td><td>10.3</td><td>Yes</td><td>9.2</td><td>9.6</td></tr><tr><td>[20-25]</td><td>9.0</td><td>8.9</td><td>Long Term Disease (%)</td><td></td><td></td></tr><tr><td>[25-30]</td><td>12.8</td><td>12.4</td><td>Yes</td><td>7.3</td><td>8.3</td></tr><tr><td>[30-35]</td><td>13.3</td><td>12.8</td><td>Outpatient Care consumption (at least €1) (%)</td><td></td><td></td></tr><tr><td>[35-40]</td><td>14.2</td><td>13.1</td><td>Yes</td><td>89.3</td><td>91.1</td></tr><tr><td>[40-45]</td><td>13.6</td><td>12.9</td><td colspan="3">Out of pregnancy period</td></tr><tr><td>[45-50]</td><td>12.9</td><td>13.6</td><td>Yes</td><td>86.9</td><td>88.7</td></tr><tr><td>[50-55]</td><td>14.5</td><td>16.0</td><td>Pregnancy period in the year (at least a day) (%)</td><td></td><td></td></tr><tr><td>Type of Public Fund (%)</td><td></td><td></td><td>Yes</td><td>9.8</td><td>8.8</td></tr><tr><td>Salaried workers</td><td>92.9</td><td>86.0</td><td>Pregnancy period since 2005 (at least a day) (%)</td><td></td><td></td></tr><tr><td>Civil servants / students</td><td>2.0</td><td>6.1</td><td>Yes</td><td>12.1</td><td>32.9</td></tr><tr><td>Farmers</td><td>1.3</td><td>2.8</td><td colspan="3">Last pregnancy index (%)</td></tr><tr><td>Self-employed</td><td>3.8</td><td>5.2</td><td>Pregnancy period at 01/01</td><td>4.9</td><td>4.5</td></tr><tr><td>Taxonomy of French Territories (%)</td><td></td><td></td><td>≤12 months</td><td>2.8</td><td>4.4</td></tr><tr><td>Heterogenous cities</td><td>28.4</td><td>30.6</td><td>[12-24] months</td><td>-</td><td>3.6</td></tr><tr><td>Periurban</td><td>21.9</td><td>21.2</td><td>>24 months</td><td>-</td><td>18.5</td></tr><tr><td>Rural and remote</td><td>12.1</td><td>11.0</td><td>No pregnancy</td><td>92.3</td><td>69.1</td></tr><tr><td>Urban deprived</td><td>11.5</td><td>10.7</td><td>Midwife care use (at least 1€) (%)</td><td></td><td></td></tr><tr><td>Retirement and tourism</td><td>7.4</td><td>7.1</td><td>Yes</td><td>2.8</td><td>6.9</td></tr><tr><td>Privileged suburbs</td><td>18.7</td><td>19.4</td><td colspan="3">Out of pregnancy period</td></tr><tr><td></td><td></td><td></td><td>Yes</td><td>0.8</td><td>3.1</td></tr></tbody><tfoot><tr><td colspan="6">Reading grid: in 2007, 9.6% of the study population were aged between 15 and 20 years; in 2007, 0.8% of women used midwifery care outside of pregnancy period against 3.1% of them in 2017. In 2017, 11% of women in the study population lived in a rural or remote area.</td></tr><tr><td colspan="6">Source: EGB 2007-2017, CNAM.</td></tr></tfoot></table>

had a long-term disease (LTD status). A slightly smaller proportion of women had a pregnancy period in 2017 (8.8%) compared to 2007 (9.8% for 127,636 women in 2007), which is consistent with the decline in the number of births in France over this period [45]. Overall, 69.1% of women had zero days of pregnancy over the entire studied period (from 2007 to 2017).

The share of women who consulted a midwife at least once in the year more than doubled between 2007 and 2017, from 2.8% to 6.9% (Table 1, Fig. 1), with the proportion of women consulting a midwife outside of pregnancy rising from 0.8% to 3.1%, most of the increase occurring after 2012. In contrast, the probability of consulting a gynecologist decreased significantly (by 5 percentage points) over the period, particularly after 2013. Among women who consulted a gynecologist, the number of consultations remained relatively flat. The probability of consulting a general practitioner and the number of consultations (among those who consulted a general practitioner) remained relatively stable over the study period.

Fig. 2 compares the evolution of the share of women who consulted a midwife outside of pregnancy during the study period in terms of last pregnancy status (a) and place of residence of women (b). Overall, the share of women consulting a midwife outside of pregnancy increases for all categories, but the increase is stronger for women recently pregnant (1.9 in 2007 to 7.7% in 2017 for women having being pregnant in the last 12 months) and for women living in rural areas (about 0.8% in 2007 to 4.3% in 2017).
<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Utilization of midwives, gynecologists and general practitioners over the period 2007–2017. **Interpretation:** in 2017, 6.9% of women had at least one consultation with a midwife and 3.1% of women consulted a midwife outside of pregnancy. In 2017, nearly 8 out of 10 women (79.8%) had at least one consultation with a GP outside of pregnancy and these women had on average almost 5 consultations with a GP in that year.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Percentage of women consulting a midwife outside of pregnancy in a given year depending on last pregnancy period and place of residence. **Interpretation:** in 2017, 7.7% of women who had at least one day of pregnancy within the preceding 12 months consulted a midwife outside of pregnancy during that year, whereas the equivalent proportion in 2007 was 1.85% (a); and 4.9% of women who lived in remote rural areas consulted a midwife outside of pregnancy during that year whereas the equivalent proportion in 2007 was 0.7%. In privileged suburbs, the equivalent proportions were 2.43% in 2017 and 0.88% in 2007 (b).</figcaption></figure>

## 3.2. Analysis of the determinants of midwife care for non-pregnant women

Table 2 presents the results obtained with **Model 1**. The results show

a growing trend in the probability of midwife consultations outside pregnancy over the study period, particularly after 2011. Indeed, compared to the reference year 2009, the year in which the reform was implemented, the probit coefficients for the following years are all
<table><caption>**Table 2**
Determinants of the probability of consulting a midwife outside pregnancy from 2007 to 2017.</caption><thead><tr><th></th><th>Model 1 beta [95%CI]<sup>a</sup></th></tr></thead><tbody><tr><td><strong>Last pregnancy index (LP)</strong></td><td></td></tr><tr><td>Preg</td><td>0.294*** [0.264,0.325]</td></tr><tr><td>≤12 months</td><td>0.251*** [0.221,0.280]</td></tr><tr><td>]12-24] months</td><td>Ref</td></tr><tr><td>>24 months</td><td>-0.086*** [-0.116,-0.055]</td></tr><tr><td>NoP</td><td>0.042** [-0.013,-0.071]</td></tr><tr><td><strong>Year (Time effect)</strong></td><td></td></tr><tr><td>2007</td><td>-0.016 [-0.048,0.016]</td></tr><tr><td>2008</td><td>-0.026 [-0.056,0.004]</td></tr><tr><td>2009</td><td>Ref</td></tr><tr><td>2010</td><td>0.018 [-0.011,0.047]</td></tr><tr><td>2011</td><td>0.055*** [0.025,0.086]</td></tr><tr><td>2012</td><td>0.104*** [0.075,0.134]</td></tr><tr><td>2013</td><td>0.170*** [0.140,0.199]</td></tr><tr><td>2014</td><td>0.280*** [0.251,0.309]</td></tr><tr><td>2015</td><td>0.368*** [0.340,0.397]</td></tr><tr><td>2016</td><td>0.486*** [0.459,0.513]</td></tr><tr><td>2017</td><td>0.618*** [0.591,0.645]</td></tr><tr><td><strong>Taxonomy of French Territories</strong></td><td></td></tr><tr><td>Heterogenous cities</td><td>-0.061*** [-0.085,-0.037]</td></tr><tr><td>Periurban</td><td>0.076*** [0.051,0.102]</td></tr><tr><td>Rural and remote</td><td>0.052*** [0.022,0.082]</td></tr><tr><td>Urban deprived</td><td>-0.024 [-0.059,0.011]</td></tr><tr><td>Retirement and tourism</td><td>-0.004 [-0.040,0.031]</td></tr><tr><td>Privileged suburbs</td><td>Ref</td></tr><tr><td><strong>Age (years)</strong></td><td></td></tr><tr><td>[15-20]</td><td>-0.740*** [-0.777,-0.702]</td></tr><tr><td>[20-25]</td><td>-0.287*** [-0.313,-0.261]</td></tr><tr><td>[25-30]</td><td>-0.067*** [-0.086,-0.048]</td></tr><tr><td>[30-35]</td><td>Ref</td></tr><tr><td>[35-40]</td><td>-0.151*** [-0.170,-0.131]</td></tr><tr><td>[40-45]</td><td>-0.452*** [-0.470,-0.427]</td></tr><tr><td>[45-50]</td><td>-0.682*** [-0.711,-0.653]</td></tr><tr><td>[50-55]</td><td>-0.784*** [-0.815,-0.754]</td></tr><tr><td><strong>Long Term Disease</strong></td><td></td></tr><tr><td>No</td><td>Ref</td></tr><tr><td>Yes</td><td>-0.293*** [-0.324,-0.261]</td></tr><tr><td><strong>Outpatient care expenses</strong></td><td></td></tr><tr><td>No consumption and Q1</td><td>Ref</td></tr><tr><td>Q2</td><td>0.370*** [0.347,0.393]</td></tr><tr><td>Q3</td><td>0.555*** [0.532,0.577]</td></tr><tr><td>Q4</td><td>0.730*** [0.706,0.753]</td></tr><tr><td>Q5</td><td>0.887*** [0.862,0.911]</td></tr><tr><td><strong>Type of Public Fund</strong></td><td></td></tr><tr><td>Salaried workers</td><td>Ref</td></tr><tr><td>Civil servants/Students</td><td>0.038 [-0.000,0.076]</td></tr><tr><td>Farmers</td><td>0.018 [-0.028,0.064]</td></tr><tr><td>Sel-employed</td><td>0.110*** [0.078,0.143]</td></tr><tr><td><strong>Free Complementary Health Insurance (CMU-C)</strong></td><td></td></tr><tr><td>No</td><td>Ref</td></tr><tr><td>Yes</td><td>-0.102*** [-0.125,-0.079]</td></tr><tr><td><strong>% of extra fees in outpatient care expenses</strong></td><td></td></tr><tr><td>&lt;Q9</td><td>Ref</td></tr><tr><td>Q9</td><td>-0.173*** [-0.200,-0.146]</td></tr><tr><td>Q10</td><td>-0.375*** [-0.408,-0.342]</td></tr><tr><td><strong>Model Statistics</strong></td><td></td></tr><tr><td>Quasi-likelihood criterion (QICu)</td><td>210,673.7</td></tr><tr><td>Number of observations (N)</td><td>1587,340</td></tr><tr><td>Number of women (n)</td><td>207,267</td></tr><tr><td><strong>Number of observations by woman</strong></td><td></td></tr><tr><td>Min / Max</td><td>1 / 11</td></tr><tr><td>Median [p25%; p75%]</td><td>9 [4; 11]</td></tr><tr><td>Women with 11 observations (%)</td><td>40.8%</td></tr><tr><td>Models are adjusted for the region of residence; * p < 0.05, ** p < 0.01, *** p < 0.001</td></tr><tr><td><sup>a</sup>beta [95%CI]: Probit estimates and 95% confidence intervals</td><td></td></tr><tr><td colspan="2"><strong>Reading grid:</strong> Compared to 2009 (ref), the probability of consulting a midwife outside pregnancy is significantly higher in 2017 (β=0.618; p < 0.001)</td></tr></tbody></table>

positive and significant from 2011 onward, indicating a gradual increase in the probability of consulting a midwife outside pregnancy between 2009 and 2017: while in 2011, the probability of consultation is slightly higher than in 2009 (β=0.055; p < 0.001), with an increasing linear trend over time, it is significantly much higher in 2017 (β=0.618; p < 0.001) compared to 2009.

0.001) compared to 2009. Before the reform, in 2007 and 2008, the time effect shows slightly negative but non-significant coefficients indicating a stable or slightly lower probability of consultation compared to 2009 (Table 2).

Women who are in the immediate post-pregnancy period (Preg) have significantly the highest probability of consulting a midwife (β=0.294; p < 0.001) compared to those whose last pregnancy was between 12 and 24 months ago. Interestingly, while women whose last pregnancy was >24 months ago show slightly lower probability of consultation (β=-0.086; p < 0.001), women who have never been pregnant (NoP) also have significantly a higher probability of consulting a midwife (β=0.042; p < 0.01).

Access to midwives appears to vary by territory. Compared to privileged suburbs—the territory with the highest average availability of healthcare services (reference in the model)—women living in heterogeneous cities have a significantly lower probability of consulting a midwife (β=-0.061; p < 0.001), while conversely, those living in peri-urban areas (β=0.076; p < 0.001) and rural and remote areas (β=0.052; p < 0.001) have a higher probability of consultation.

Women aged between 30 and 35 years (reference age group) were the most likely to consult a midwife outside of pregnancy, the coefficients for all other age groups being all negative and significant. Younger women have a significantly lower probability of consultation, especially those aged 15-20 (β=-0.740; p < 0.001), while after 35 years old the probability of consultation decreases with a more pronounced decline from 40 years onward (for 50-55 years, β=-0.784; p < 0.001). In short, age has an inverted U-shaped effect on the probability of consulting a midwife outside of pregnancy.

In terms of health status, women with a long-term disease have a significantly lower probability of consulting a midwife outside of pregnancy (β=-0.293; p < 0.001), all other things being equal, and besides, a woman whose healthcare expenditures increase has a rising probability of consulting a midwife outside of pregnancy. Indeed, comparing to women with no consumption or a very low healthcare expense (reference), women, whose outpatient health expenses are the highest (Q5), were significantly the most likely to consult a midwife outside of pregnancy (β=0.887; p < 0.001). Finally, women bearing the highest proportion of extra fees had a lower probability of consulting midwives outside of pregnancy (for the last decile, β=-0.375; p < 0.001).

Table 3 shows the results for Models 2, 3a, and 3b, which all help better understand the effect of time on the probability of consulting a midwife outside of pregnancy. Like Model 1, Models 2, 3a, and 3b are adjusted for women's characteristics (age, LTD, etc.) (See Appendix 3 for complete results).

Model 2 allows for potential changes around the reform (2009), providing two average linear time effects, one 'pre-reform' (2007-2009) slightly negative and significant (β=-0.042; p < 0.001), and the other 'post-reform' (2009-2017) significantly positive (β=0.085; p < 0.001). Model 2 is more parsimonious than Model 1 and yields the same interpretation. These results suggest, as in Model 1, that there is a significant linear increase in the use of midwives after the reform, on the probit scale. While the effects of the last pregnancy index and of the French taxonomy of leaving areas are similar to those previously obtained, Model 2 provides the basis for constructing Models 3a and 3b, which examine how the effect of time on the probability of consulting a midwife outside of pregnancy (after the reform), is modified by whether or not women became pregnant during the period studied (Model 3a) and by the area of residence (Model 3b).

Model 3a shows that the probability of consulting a midwife outside of pregnancy increased significantly for all women after the reform (2009-2017), but with different magnitudes depending on pregnancy history: the average annual increase was the greater for women who had a pregnancy period which was >12 months ago (β=0.128; p < 0.001) or 2 years in the past (β=0.122; p < 0.001), these coefficients being non-significantly different from each other (see Appendix 4a). While the increase in midwife consultations after 2009 benefited all women, the
post-pregnancy group (β=0.055; *p* < 0.001) and never-pregnant women (β=0.082; *p* < 0.001) saw more moderate increases.

**Model 3b** shows that the probability of consulting a midwife outside of pregnancy increased in all geographical areas after the reform (2009–2017), but that the magnitude of the average annual increase was dependent on the type of women living areas: particularly large among women living in remote and rural areas (β=0.119; *p* < 0.001), substantial among those living in periurban areas (β=0.090; *p* < 0.001) and deprived urban areas (β=0.093; *p* < 0.001), and smaller in socioeconomically privileged areas and cities centers (β=0.066; *p* < 0.001).

## 4. Discussion

Our results suggest that the demand for midwifery care by non-pregnant women increased in response to midwives' expanded role in primary care in 2009. From 2019 to 2017, the probability of consulting a midwife out of pregnancy quadrupled, particularly after 2012. This is consistent with the growing number of midwives, especially self-employed ones (+3% annually between 2012 and 2017 [22]) and the fact that 2012 marked the first cohort of midwives actually trained to provide general gynecological care as part of their studies.

We show that the increase in midwife consultations is particularly pronounced among recently pregnant women, highlighting the need for clear public awareness of midwives expanded primary care role beyond pregnancy monitoring. Pregnancy consultations provide women an opportunity to learn about the midwives' new scope of practice, boosting

the probability of post-pregnancy gynecological care. Even women who had not experienced a pregnancy during the study period became more likely to consult a midwife after 2009. Midwives seem to be their best "sales representatives," using pregnancy as an opportunity to showcase their skills and build trusted patient relationships that benefit both sides [30,31].

Furthermore, our findings suggest that midwives' expanded skills have strengthened the availability of gynecological primary healthcare in underserved areas. The increase in midwives' consultations for non-pregnant women is more pronounced in remote and rural areas, where healthcare access—especially to specialist—is limited [43]. This finding suggests that prior shortages in routine gynecological care are being partly addressed by the ability of midwives to provide these services. In contrast, the smaller increase in the use of midwives outside of pregnancy in privileged areas reflects less initial rationing and raises questions about competition between providers with overlapping skills [16, 25,29]. Regulatory measures over the past 15 years likely reinforced these trends. While doctors can freely establish practices, midwives are incentivized to settle in underserved areas. Since 2007 (reinforced in 2012), restrictions prevent new self-employed midwives from setting up in “over-supplied” areas unless replacing a departing colleague, while those in “under-supplied” areas receive subsidies for equipment and operating costs for 3 years, along with reduced social charges. Additionally, during this period, financial incentives were also implemented to encourage healthcare professionals to join multi-disciplinary centers, specifically in underserved areas. Consequently, these structures

<table><caption>**Table 3**
Analysis of the time effect heterogeneity on the probability of consulting a midwife outside pregnancy.</caption><thead><tr><th></th><th>Model 2 beta [95%CI]</th><th>Model 3a beta [95%CI]</th><th>Model 3b beta [95%CI]</th></tr></thead><tbody><tr><td><strong>Last pregnancy index (LP)</strong></td><td></td><td></td><td></td></tr><tr><td>Preg</td><td>0.293*** [0.263,0.323]</td><td>0.646*** [0.570,0.721]</td><td>0.293*** [0.263,0.323]</td></tr><tr><td>&lt;12 months</td><td>0.250*** [0.221,0.280]</td><td>0.401*** [0.324,0.479]</td><td>0.251*** [0.221,0.280]</td></tr><tr><td>]12-24] months</td><td>Ref</td><td>Ref</td><td>Ref</td></tr><tr><td>&gt;24 months</td><td>-0.087*** [-0.117,-0.057]</td><td>-0.089 [-0.180,0.002]</td><td>-0.090*** [-0.120,-0.059]</td></tr><tr><td>NoP</td><td>0.042** [0.014,0.071]</td><td>0.286*** [0.212,0.360]</td><td>0.043** [0.014,0.072]</td></tr><tr><td><strong>Average Time effect 2007-2009</strong></td><td>-0.042*** [-0.056,-0.029]</td><td>-0.026*** [-0.040,-0.012]</td><td>-0.042*** [-0.056,-0.029]</td></tr><tr><td><strong>Average Time effect 2009-2017</strong></td><td>0.085*** [0.082,0.088]</td><td></td><td></td></tr><tr><td><strong>Taxonomy of French Territories</strong></td><td></td><td></td><td></td></tr><tr><td>Heterogenous cities</td><td>-0.061*** [-0.085,-0.037]</td><td>-0.060*** [-0.084,-0.036]</td><td>-0.083*** [-0.124,-0.043]</td></tr><tr><td>Periurban</td><td>0.077*** [0.052,0.102]</td><td>0.078*** [0.053,0.103]</td><td>-0.026 [-0.067,0.015]</td></tr><tr><td>Rural and remote</td><td>0.052*** [0.022,0.083]</td><td>0.054*** [0.024,0.084]</td><td>-0.186*** [-0.240,-0.132]</td></tr><tr><td>Urban deprived</td><td>-0.024 [-0.059,0.010]</td><td>-0.024 [-0.059,0.011]</td><td>-0.143*** [-0.199,-0.087]</td></tr><tr><td>Retirement and tourism</td><td>-0.002 [-0.037,0.033]</td><td>-0.001 [-0.037,0.034]</td><td>-0.122*** [-0.180,-0.063]</td></tr><tr><td>Privileged suburbs</td><td>Ref</td><td>Ref</td><td>Ref</td></tr><tr><td><strong>Time effect (2009-2017) by LP</strong></td><td></td><td></td><td></td></tr><tr><td>Time effect 2009-2017: Preg</td><td></td><td>0.055*** [0.050,0.061]</td><td></td></tr><tr><td>Time effect 2009-2017: &lt;12 months</td><td></td><td>0.102*** [0.094,0.109]</td><td></td></tr><tr><td>Time effect 2009-2017:]12-24] months</td><td></td><td>0.128*** [0.116,0.140]</td><td></td></tr><tr><td>Time effect 2009-2017: &gt;24 months</td><td></td><td>0.122*** [0.112,0.132]</td><td></td></tr><tr><td>Time effect 2009-2017: NoP</td><td></td><td>0.082*** [0.078,0.085]</td><td></td></tr><tr><td><strong>Time effect (2009-2017) by Taxonomy</strong></td><td></td><td></td><td></td></tr><tr><td>Time effect 2009-2017: Heterogenous cities</td><td></td><td></td><td>0.072*** [0.067,0.077]</td></tr><tr><td>Time effect 2009-2017: Periurban</td><td></td><td></td><td>0.090*** [0.085,0.095]</td></tr><tr><td>Time effect 2009-2017: Rural and remote</td><td></td><td></td><td>0.119*** [0.112,0.127]</td></tr><tr><td>Time effect 2009-2017: Urban deprived</td><td></td><td></td><td>0.093*** [0.085,0.101]</td></tr><tr><td>Time effect 2009-2017: Retirement and tourism</td><td></td><td></td><td>0.094*** [0.085,0.102]</td></tr><tr><td>Time effect 2009-2017: Privileged suburbs</td><td></td><td></td><td>0.066*** [0.060,0.072]</td></tr><tr><td><strong>Model Statistics</strong></td><td></td><td></td><td></td></tr><tr><td>Quasi-likelihood criterion (QICu)</td><td>210,856.2</td><td>210,568.1</td><td>210,600.1</td></tr><tr><td>Number of observations (N)</td><td></td><td>1587,340</td><td></td></tr><tr><td>Number of women (n)</td><td></td><td>207,267</td><td></td></tr><tr><td><strong>Number of observations by woman</strong></td><td></td><td></td><td></td></tr><tr><td>Min / Max</td><td></td><td>1 / 11</td><td></td></tr><tr><td>Median [p25%; p75%]</td><td></td><td>9 [4; 11]</td><td></td></tr><tr><td>Women with 11 observations (%)</td><td></td><td>40.8%</td><td></td></tr><tr><td colspan="4">Models are adjusted for the region of residence, age, LTD, Outpatient care expenses, type of Public Fund, Free complementary health insurance, % of extra fees in outpatient care expenses; *p < 0.05, **p < 0.01, ***p < 0.001</td></tr><tr><td colspan="4"><sup>a</sup>beta [95%CI]: Probit estimates and 95% confidence intervals</td></tr><tr><td colspan="4"><strong>Reading Grid:</strong> The average annual 'pre-reform' (2007-2009) time effect is slightly negative and significant β=-0.042 and the average annual 'post-reform' (2009-2017) time effect is significantly positive β=0.085 (model 2). In model 3a, the main effect of the last pregnancy index (LP) is to be interpreted before the reform (2007-2009); in model 3b the main effect of the taxonomy of the French territories is to be interpreted before the reform (2007-2009).</td></tr></tbody></table>
developed more rapidly in these types of areas, providing the population with a broader range of primary care services, and more than half of them include one or more midwives.

As expected, the effects of other women's characteristics are consistent across all models. Women aged 30–35 are the most likely to consult a midwife outside of pregnancy possibly due to greater awareness of midwives' extended scope of practice among younger generations of women. Additionally, the rapid decline in gynecologists [23,44], may push younger women to seek alternative providers, including midwives [6,44,45]. Midwives can only provide gynecological care to women without long-term diseases, which explain why those with chronic conditions are less likely to consult them. For instance, diabetes mellitus-linked to pregnancy complications and contraceptive risks-, often requires specialized follow-up by a gynecologist [44,46,47]. Moreover, women with conditions like obesity are known to be reluctant to access gynecological care and prevention [10,48]. Lastly, controlling for other covariables, high healthcare utilization may signal a proactive approach to health, yet women facing the highest proportion of extra fees are less likely to consult midwives outside of pregnancy [49]. Conversely, women covered by the free complementary health insurance-scheme dedicated for the poorest-, also consult midwives less often, consistent with findings that women of lower socioeconomic status have reduced access to gynecological care and screenings [10].

This study has some limitations. First, despite carefully identifying non-pregnancy periods, some women may have received postpartum care longer than assumed, potentially exaggerating the effect of a recent pregnancy on the probability of consulting a midwife for non-pregnancy related reasons. Second, the dataset lacks details on medical services provided by midwives (contraception, cervical screening, vaccination, etc.), which would help understand how midwives' activities have expanded. Observing these services would clarify whether midwives complement or substitute gynecologists in gynecological primary care. More broadly, this raises questions about cooperation or competition between these professions, warranting further research.

## 5. Conclusion

Since the 2009 expansion of midwives' scope to gynecological care, their consultations among non-pregnant women have significantly increased, particularly among recently pregnant women and those in the most disadvantaged areas. This reform's effects depend on several parameters. First, information mechanisms—for both patients and healthcare professionals—play a crucial role in its implementation. The first awareness campaign only took place in 2016, facing strong opposition from physicians' groups, raising questions about cooperation versus competition between different health professionals providing overlapping medical services in a context of fee-for-service payment mechanism. No further campaigns have followed once again. Second, geographic accessibility to care has improved, reinforced by regulations on professional settlement for midwives and incentives to join multi-professional health centers in underserved areas.

More than a decade after the reform and despite these advances, major financial and geographic barriers to women's healthcare persist. Awareness of midwives' evolving role remains limited, despite successive expansions: in 2016 (medical abortion), 2022 (sexually transmitted infections screening and treatment), and 2023 (vaccination), with potential future extensions. Strengthening regulatory measures, incentives for professional cooperation, and adapting midwifery training could further improve access to gynecological primary care. However, more efforts are needed to enhance public awareness and information dissemination.

## CRediT authorship contribution statement

**Quitterie Roquebert:** Writing – review & editing, Methodology.
**Henri Panjo:** Software, Methodology, Formal analysis, Data curation.

**Carine Franc:** Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Formal analysis, Conceptualization.

## Declaration of competing interest

**Declaration of Competing Interest:** The authors have no conflict of interest to declare.

## Acknowledgements

The authors thank Virginie Ringa and Magali Dumontet for her valuable suggestions and insights, as well as all the participants in the GYMS team seminar (February 2023), the S-PRI Seminar (November 2023) and the CREM seminar (January 2024), which include economists, sociologists, geographers, GPs and midwives. Finally, the authors thank two anonymous reviewers for their insightful suggestions.

## Funding

This research did not receive any specific grant from Funding agencies in the public, commercial, or not-for-profit sector.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.healthpol.2025.105397.

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