Descriptive statistics
Table
1 reports the distribution of cesarean deliveries according to prenatal care utilization characteristics. French prenatal care includes at least 7 prenatal visits, which begin during the first trimester. Hence, a woman who has her first prenatal care visit in the second or third trimester starts clinical care late and thus has less prenatal care. Three ultrasounds are recommended: the first trimester ultrasound estimates nuchal translucency thickness to assess the probability of chromosomal abnormalities, while the second or third trimester scan screens for morphologic malformations and anomalies. Prenatal educational services include a prenatal interview offered during the first trimester and prenatal education sessions that usually take place during the third trimester. Cesarean delivery rates do not differ significantly according to the trimester of the first prenatal visit. Among women who have more than the 3 recommended ultrasounds during their pregnancy, 30.4% have cesarean deliveries, significantly higher than the rate around 21% for women who undergo 3 or fewer ultrasounds. Women with no nuchal translucency scan or no morphologic ultrasound have significantly lower cesarean delivery rates, respectively 21.1% and 17%. Women who do not attend an early prenatal interview have a cesarean delivery rate of 23.7%, significantly higher than the 22.3% rate for women who do. Similarly, the cesarean delivery rate for women not attending prenatal education is significantly higher: 24.9% versus 21.9% for those who did. In the subsample of low-risk women, the only difference according to prenatal care use that remains significant in the cesarean delivery rates for this subsample is that based on participation in prenatal education.
Table
1 also shows the variations in the cesarean delivery rates as a function of the socioeconomic characteristics of the women and their partners. Available socioeconomic data apply the official socioeconomic classification of the French national institute of statistics and economic studies (Institut National de la Statistique et des Etudes Economiques (INSEE)) and thus identify the socioeconomic level of each household accurately. The cesarean delivery rates do not differ significantly by family situation or healthcare coverage. However, women with lower educational levels have significantly higher rates of cesarean deliveries: 24.9% for those who only completed primary school, 25.2% for those who did some secondary school, and 25.4% for those who did complete secondary school (i.e., women having reached the final year of secondary school, whether or not they obtained the baccalaureate degree), while the most highly educated women (i.e., post-secondary education) have a cesarean delivery rate of only 22.8%. Similarly, this rate is significantly higher among women working jobs requiring lower skills: 26.7% for manual workers and 24.9% for office, sales, or service staff; compared with higher skills: 23.4% for intermediate (i.e., technical and associate professional) occupations, and 22.7% for managers and higher intellectual workers. The cesarean delivery rate is 25.1% for unemployed women but 24% for working patients. The number of cesarean deliveries also differs according to the partner’s socioeconomic level. Cesarean delivery rates are significantly higher for women whose partners are not working: 24.9% for women with partners that are not in the labor force (i.e., students, apprentices, homemakers, retirees, those on parental leave, and others neither working nor looking for work), and 24.7% when partners are unemployed, whereas 23.6% when partners are working; or have low-skilled jobs: more than 24% when partners have low-skilled jobs, while around 23% when partners have a high-skilled occupation. Moreover, the preliminary statistics for the low-risk subsample are similar, which means epidemiologic factors alone do not explain the socioeconomic factors affecting cesarean delivery use.
As shown above, the only cesarean rates that differ significantly in both the full and low-risk subsamples are those related to participation in prenatal education (Table
1). Moreover, nearly half the women in our entire population do not participate in this care (Table
1). We present these participation rates according to socioeconomic characteristics in Table
2. Single women have a significantly lower participation rate: 38.4% compared with 55.2%. Similarly, uninsured patients participate significantly less often, at rates of 46.6% versus 55.1%, as do women with lower versus higher educational levels. Prenatal education participation rates are 21.9% for women with a primary school education, 32.7% for those with some secondary education, 45% for those who did complete secondary school, and 63.3% for those with some post-secondary education. Similarly, low-skilled women participate at significantly lower rates: 35.4% for manual workers, 40.9% for farmers, and 53.3% for office, sales, or service staff, but 67.1% for women in managerial and higher intellectual/professional occupations. Women out of (versus in) the labor market have significantly lower rates of prenatal education participation: 34% for women not in the labor force, 47.4% for those unemployed, while 60.8% for working women. Results are similar for partners: participation is significantly lower when the partner is not working nor has a low-skilled job. Specifically, the prenatal education participation rate is 35.6% when partners are manual workers, 43.3% when farmers, and 50% when office, sales, or service staff, but 64.4% when they have managerial and higher intellectual occupations; 37.5% and 40.5% when partners are unemployed and out of the labor force, respectively, while 56.3% when they work. The same disparities in prenatal education participation according to socioeconomic variables appear in the low-risk subsample.
Regression results
We present, first, the effects of regular prenatal care on the cesarean delivery rate. Controlling for epidemiologic and hospital characteristics in columns 1 and 2 of Table
3, we find that prenatal care utilization affects the probability of cesarean deliveries. The period of the first prenatal clinical visit does not affect this probability. Ultrasound care does, however: women undergoing more than 3 ultrasounds have a 31% higher probability of cesarean deliveries. The nuchal translucency ultrasound does not appear to affect cesarean delivery rates. However, women who do not have the morphologic ultrasound have a cesarean delivery probability 25% lower than those who do. Interestingly, women who do not participate in prenatal education are 33% more likely to have cesarean deliveries, although attendance at the early prenatal interview has no clear effect on this probability. When we use the subsample with available socioeconomic variables, which enables us to take these characteristics into account, the results are the same, in both hospital fixed and random effects specifications (columns 5 and 6 of Table
3).
Table 3
Effects of prenatal care and socioeconomic status on cesarean delivery use, logit model 1 (odds ratios)
Prenatal care |
First prenatal visit |
Second trimester | 1.10 (0.129) | 1.09 (0.129) | | | 1.06 (0.174) | 1.05 (0.169) | 1.75c(0.509) | 1.67c(0.464) | 1.40 (0.573) | 1.34 (0.524) |
Third trimester | 1.06 (0.252) | 1.05 (0.249) | | | 0.46 (0.259) | 0.46 (0.258) | 1.92 (1.379) | 1.93 (1.386) | 2.24 (1.528) | 2.27 (1.543) |
Obstetric ultrasounds |
< 3 | 1.02 (0.062) | 1.02 (0.061) | | | 1.05 (0.190) | 1.05 (0.190) | 1.04 (0.329) | 1.06 (0.337) | 0.87 (0.326) | 0.88 (0.334) |
≥ 4 | 1.31a(0.057) | 1.31a(0.058) | | | 1.32a(0.090) | 1.32a(0.091) | 1.12 (0.113) | 1.11 (0.110) | 1.10 (0.121) | 1.09 (0.118) |
No nuchal translucency ultrasound | 0.98 (0.076) | 0.99 (0.074) | | | 0.96 (0.130) | 0.97 (0.129) | 0.95 (0.293) | 1.00 (0.289) | 1.01 (0.355) | 1.06 (0.345) |
No morphology ultrasound | 0.75b(0.108) | 0.75b(0.108) | | | 0.69a(0.076) | 0.69a(0.076) | 0.82 (0.221) | 0.78 (0.205) | 0.71 (0.235) | 0.67 (0.218) |
No early prenatal interview | 1.06b(0.049) | 1.06 (0.049) | | | 1.05 (0.048) | 1.05 (0.047) | 1.00 (0.079) | 1.01 (0.077) | 0.97 (0.093) | 0.98 (0.092) |
No prenatal education | 1.33a(0.022) | 1.33a(0.022) | | | 1.39a(0.038) | 1.39a(0.038) | 1.25a(0.068) | 1.26a(0.062) | 1.22b(0.098) | 1.22a(0.092) |
Woman’s socioeconomic level |
Single | | | 0.77a(0.071) | 0.77a(0.072) | 0.72c(0.129) | 0.72c(0.131) | 0.44a(0.113) | 0.44a(0.115) | 0.48a(0.119) | 0.48a(0.122) |
Uninsured | | | 1.06 (0.078) | 1.06 (0.078) | 1.10 (0.094) | 1.10 (0.094) | 0.91 (0.306) | 0.92 (0.311) | 0.67 (0.247) | 0.68 (0.251) |
Education |
Primary school | | | 1.23c(0.140) | 1.22c(0.141) | 1.19 (0.172) | 1.19 (0.173) | 1.45 (0.578) | 1.45 (0.582) | 1.47 (0.603) | 1.46 (0.595) |
Some secondary school | | | 1.33a(0.059) | 1.33a(0.059) | 1.29a(0.083) | 1.29a(0.084) | 1.34b(0.161) | 1.35b(0.164) | 1.32b(0.151) | 1.34b(0.155) |
Completed secondary school | | | 1.29a(0.039) | 1.29a(0.039) | 1.24a(0.035) | 1.24a(0.034) | 1.27a(0.081) | 1.28a(0.084) | 1.29a(0.082) | 1.30a(0.085) |
Occupation |
Manual worker | | | 1.26a(0.097) | 1.26a(0.097) | 1.33a(0.131) | 1.33a(0.131) | 1.55 (0.492) | 1.55 (0.495) | 1.47 (0.553) | 1.50 (0.570) |
Office, sales, or service staff | | | 1.13a(0.042) | 1.13a(0.042) | 1.12a(0.041) | 1.12a(0.041) | 1.14c (0.080) | 1.14c(0.081) | 1.12c(0.072) | 1.12c(0.071) |
Farmer | | | 1.24 (0.455) | 1.25 (0.457) | 1.03 (0.426) | 1.04 (0.431) | 1.29 (0.877) | 1.32 (0.892) | 1.80 (1.186) | 1.82 (1.194) |
Craft/trades worker or entrepreneur | | | 1.10 (0.080) | 1.10 (0.080) | 1.14c(0.090) | 1.14c(0.090) | 1.16 (0.281) | 1.15 (0.275) | 1.18 (0.354) | 1.17 (0.347) |
Intermediate (technical) | | | 1.13a(0.052) | 1.13a(0.052) | 1.13c(0.075) | 1.13c(0.074) | 1.19a(0.058) | 1.20a(0.064) | 1.25a(0.078) | 1.26a(0.085) |
Work status |
Unemployed | | | 1.14b(0.066) | 1.14b(0.066) | 1.14c(0.080) | 1.14c(0.080) | 0.81 (0.111) | 0.81 (0.114) | 0.74b(0.099) | 0.74b(0.101) |
Not in labor force | | | 0.94 (0.041) | 0.94 (0.041) | 0.91 (0.058) | 0.91 (0.058) | 1.01 (0.111) | 1.01 (0.108) | 1.01 (0.113) | 1.01 (0.116) |
Partner’s socioeconomic level |
Occupation |
Manual worker | | | 1.15a(0.057) | 1.15a(0.057) | 1.08 (0.067) | 1.08 (0.067) | | | | |
Office, sales, or service staff | | | 1.13a(0.033) | 1.13a(0.033) | 1.11a(0.043) | 1.11a(0.043) | | | | |
Farmer | | | 1.06 (0.218) | 1.06 (0.217) | 1.05 (0.273) | 1.05 (0.271) | | | | |
Craft/trades worker or entrepreneur | | | 1.10 (0.075) | 1.10 (0.074) | 1.06 (0.087) | 1.06 (0.086) | | | | |
Intermediate (technical) | | | 1.07 (0.054) | 1.07 (0.055) | 1.09 (0.095) | 1.09 (0.096) | | | | |
Work status |
Unemployed | | | 1.01 (0.080) | 1.01 (0.080) | 1.11 (0.114) | 1.11 (0.115) | | | | |
Not in labor force | | | 1.17a(0.050) | 1.17a(0.050) | 1.12b(0.064) | 1.12b(0.064) | | | | |
Epidemiologic controls | Yes | Yes | Yes | Yes | Yes | Yes | No | No | No | No |
Hospital controls | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Year fixed effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Hospital effects | Fixed | Random | Fixed | Random | Fixed | Random | Fixed | Random | Fixed | Random |
Residence fixed effects | No | No | No | No | No | No | No | No | Yes | Yes |
N (observations) | 68,314 | 68,314 | 58,324 | 58,324 | 41,141 | 41,141 | 9507 | 9507 | 8020 | 8020 |
Table
3 also shows in columns 3 and 4 the effects of the socioeconomic status of each partner on the use of cesarean deliveries while controlling for epidemiologic and hospital characteristics. Familial situation has a significant effect: single women are 23% more likely to have a cesarean delivery compared to those who do not. Insurance coverage, however, does not affect this probability. More interestingly, compared with the most highly educated women, patients with primary, some secondary, or completed secondary education are respectively 22–23%, 33% and 29% more likely to have a cesarean delivery. Similarly, women working at low-skilled jobs have a higher probability of cesarean deliveries: compared with the most highly qualified women, this probability is 26% higher for manual workers, and 13% higher for office, sales, or service staff as well as for workers with intermediate occupations. Moreover, unemployed women have a probability of a cesarean delivery 14% higher than that of working women. Likewise, the partner’s socioeconomic level affects the woman’s probability of a cesarean delivery, which is 15% higher for women whose partners are manual workers, and 13% higher when they are service workers. This probability is 17% higher when the partner is not in the labor force, versus is working. Adjustment for hospitals’ fixed or random effects does not change these results, which are similar even in the subsample with available prenatal care variables (columns 5 and 6 of Table
3). According to our study, low socioeconomic status significantly and substantially increases the probability of cesarean deliveries. Our results are therefore consistent with the previous literature from high-income countries, as detailed above in the Introduction.
Table 6 in
Appendix presents the effects of the epidemiologic and hospital control variables on cesarean delivery use. Many epidemiologic control risk factors are significantly associated with a higher probability of cesarean deliveries: age, nulliparity, previous cesarean, diabetes, hypertension, eclampsia or preeclampsia, fetal growth restriction, placental bleeding, other obstetric pathology, preterm and post-term delivery, abnormal presentation, induced labor, low and high birth weight; as are some hospital control characteristics: private ownership status, high equipment level, working-day delivery, low hospital size, and low FTE obstetricians per bed. As expected, the effects of these variables are similar to those found in previous studies.
Our results clearly demonstrate that, all else being equal, both prenatal care and socioeconomic position influence the cesarean decision. Because the data have been anonymized, women are not identifiable within the study period. We cannot thus use the fixed effects for women that would allow us to control for the unobservable heterogeneity between them, which may correlate with cesarean delivery use and either prenatal care utilization or socioeconomic status. However, we do control for many observed characteristics of women and their partners. Furthermore, we also performed several additional sensitivity analyses to consider potential relevant confounding factors. First, one natural and straightforward explanation of our results might be the epidemiologic factors: the low-income women and those with high utilization rates of ultrasound care or low rates of prenatal educational care may also be the at-risk population. For example, women with various epidemiologic risk factors require more obstetric ultrasounds than traditionally recommended. Furthermore, the low-income population is also the population most at risk in terms of co-morbidity and diagnosis. As a robustness check, we examine the low-risk subsample to further control for medical severity (see details on the low-risk subsample for cesarean delivery use above in the Methods). These results are presented in columns 7 and 8 of Table
3. In this subsample with fewer observations, we include woman’s socioeconomic characteristics only, to avoid the consequences of the very likely collinearity between the socioeconomic variables of the woman and her partner on the efficiency of the estimates. When we focus on low-risk women, the effects of the obstetric ultrasound care are no longer significant, but the effects of prenatal education remain significantly associated with a higher probability of a cesarean delivery for non-participants. In addition, the woman’s socioeconomic position has the same effects on the cesarean delivery probability in this subsample as in the overall population: the lower the socioeconomic indicator, the higher the cesarean delivery rate. Using the partner’s socioeconomic variables produces similar findings (Table 7 in
Appendix).
Woman’s preferences are another important factor to consider: those who choose to undergo substantial ultrasonographic examinations as well as those who prefer not to attend prenatal education may also prefer a cesarean delivery. To control for the woman’s preference, we can use unplanned deliveries, assuming that if the woman has a preference for a cesarean delivery, the obstetrician would plan this. The data available here do not provide any information about women’s preferences. This assumption can be debated, but appears reasonable to us. The existing literature reports that women’s preferences affect the rates of elective cesarean deliveries [
19,
46‐
48]. Reasons identified as influencing a woman’s decision to request an elective cesarean delivery include cultural factors, fear of pain during labor and delivery, previous experience, and interactions with health care professionals [
47]. The woman’s choice can be a primary indication for planned elective cesarean deliveries, but is not an indication for those that are not planned [
48].
Unplanned deliveries also include only deliveries for which the patient did not know her mode of delivery in advance. Because most women still perceive prenatal education simply as preparation for vaginal delivery, women with a planned cesarean delivery may choose not to attend. Focusing on unplanned deliveries may thus control for these two factors. Columns 7 and 8 of Table
3 report the effects of prenatal care utilization on cesarean delivery use in the low-risk subsample that includes only unplanned deliveries. Even when the mode of delivery is not planned, prenatal education is still a highly significant variable. Therefore, neither women’s preference nor their advance knowledge of their mode of delivery explains the effects of prenatal education on cesarean deliveries.
Furthermore, income level is a factor that explains woman’s access to prenatal care [
25,
49], and women with low socioeconomic positions may face constraints in their access to health care that may explain their high use of cesarean deliveries. Since no healthcare access variable is available in our data, we use the woman’s town of residence, available for some observations. We do not include town of residence fixed effects in the first regressions because this variable is not available for all observations. Columns 9 and 10 of Table
3 show the effects of socioeconomic status on the probability of a cesarean delivery for low-risk women, with dummy variables included for town of residence. Cesarean delivery is still most prevalent for women with low, compared with high, socioeconomic status. We therefore cannot interpret lack of access to obstetric care as the explanation for the effects of socioeconomic status on cesarean delivery use. Moreover, including fixed effects for town of residence also allows us to control for area-based socioeconomic differences. Individual and area-based socioeconomic conditions are different aspects of socioeconomic position, and both dimensions may affect the probability of a cesarean delivery [
50]. Indeed, individual socioeconomic characteristics may capture some effects of area-based socioeconomic conditions, as women with low socioeconomic positions mostly live in less affluent areas. When we include dummy variables for the woman’s town of residence, the effects of socioeconomic status on the probability of cesarean deliveries persist. Individual socioeconomic status is thus an independent socioeconomic factor affecting cesarean delivery rates. The results are again the same even when we use the partner’s socioeconomic characteristics (Table 7 in
Appendix).
Because prenatal education participation significantly affects cesarean deliveries, we sought to assess in column 1 of Table
4 whether or not socioeconomic conditions affect its utilization during pregnancy, while taking epidemiologic characteristics into account. Familial situation and healthcare coverage do not significantly affect the probability of this participation, but educational level does. Specifically, compared to the women with the most education, the probability of attendance at prenatal education for those with primary schooling only, some secondary school, and who completed secondary school is respectively 47%, 42%, and 27% lower. Likewise, the women with the fewest skills are least likely to participate in prenatal education. Compared with the managers (i.e., the most highly skilled women), women who are crafts/trades workers or entrepreneurs are 13% less likely to participate, and those working as office, sales, or service staff 11% less likely. Further, compared with working women, those who are not in the labor force as well as unemployed ones are respectively 29% and 14% less likely to participate. The same is true for women whose partners have low-skilled jobs or do not work: the probability of participation is 26% lower when the partner is a manual worker or a farmer, and 15% lower if he is an office, sales, or service worker, as well as 26% lower if unemployed, and 22% lower if he is not in the labor force.
Table 4
Effects of socioeconomic status on prenatal education utilization, logit model 2 (odds ratios)
Woman’s socioeconomic level |
Single | 1.07 (0.134) | 0.52a(0.087) | 0.50a(0.088) |
Uninsured | 1.06 (0.111) | 1.37 (0.475) | 1.34 (0.578) |
Education |
Primary school | 0.53a(0.106) | 0.61 (0.189) | 0.72 (0.221) |
Some secondary school | 0.58a(0.054) | 0.43a(0.088) | 0.45a(0.117) |
Completed secondary school | 0.73a(0.038) | 0.62a(0.086) | 0.60a(0.098) |
Occupation |
Manual worker | 0.90 (0.128) | 0.63 (0.279) | 0.45 (0.231) |
Office, sales, or service staff | 0.89a(0.023) | 0.65a(0.041) | 0.63a(0.049) |
Farmer | 0.75 (0.170) | 0.49 (0.358) | 0.51 (0.382) |
Craft/trades worker or entrepreneur | 0.87a(0.046) | 0.58a(0.062) | 0.60a(0.081) |
Intermediate (technical) | 0.94 (0.061) | 0.84 (0.152) | 0.77c(0.118) |
Work status |
Unemployed | 0.86a(0.032) | 0.77c(0.107) | 0.80c(0.095) |
Not in labor force | 0.71a(0.034) | 0.57a(0.043) | 0.62a(0.080) |
Partner’s socioeconomic level |
Occupation |
Manual worker | 0.74a(0.050) | | |
Office, sales, or service staff | 0.85a(0.027) | | |
Farmer | 0.74b(0.109) | | |
Craft/trades worker or entrepreneur | 0.91c(0.045) | | |
Intermediate (technical) | 0.92b(0.039) | | |
Work status |
Unemployed | 0.74a(0.051) | | |
Not in labor force | 0.78a(0.052) | | |
Epidemiologic controls | Yes | No | No |
Year fixed effects | Yes | Yes | Yes |
Hospital effects | Fixed | Fixed | Fixed |
Residence fixed effects | No | No | Yes |
N (observations) | 48,042 | 7064 | 6033 |
Table 8 in
Appendix presents the effects of the epidemiologic control variables on prenatal education participation. Several epidemiologic control factors are significantly associated with a lower probability of prenatal education attendance: younger and older age, multiparity, diabetes, fetal growth restriction, other obstetric pathology, preterm delivery, non-spontaneous onset of labor, cesarean delivery, and low birth weight.
Again, to better control for differences in epidemiologic characteristics that may explain these findings, we perform the same estimates in the low-risk subsample (see details on the low-risk subsample for prenatal care participation above in the Methods). The results are presented in column 2 of Table
4. When we focus on low-risk patients, women in low socioeconomic positions remain less likely to participate in prenatal education. Moreover, we also include the woman’s town of residence as a fixed effect in column 3 of Table
4, which made it possible to take geographic differences across women into account, including those related to health care access and to area-based socioeconomic status. Indeed, individual socioeconomic variables can capture disparities in healthcare access as well as the variables based on area-based socioeconomic conditions. Our finding show that, regardless of differences in prenatal care access and area-based socioeconomic situation, women with low, compared with high, socioeconomic status are less likely to use prenatal education. Results using partner’s socioeconomic variables are very similar (Table 9 in
Appendix).
Because our results show that socioeconomic status significantly affects prenatal education participation (Table
4), we went on to examine the effects of interaction terms for both of these variables on cesarean delivery use. We thus study whether or not the effects of prenatal education vary by socioeconomic status, which we are unable to study from the results of Table
3. For each occupation, we seek to compare the impact of socioeconomic status on those with and without prenatal education. Therefore, we add dummy variables for prenatal education crossed with the dummy variables for the woman’s occupation, as well as dummy variables for no prenatal education crossed with the dummy variables for the woman’s occupation. This exclude from the regression the global effects of prenatal education and of occupation type; when we consider all types of occupation and the constant, however, it introduce a strict collinearity problem. We therefore include a constraint in the model: the sum of all the crossed occupation and prenatal education variables equals 0. The results are presented as odds ratios. Table 10 in
Appendix provides the results as coefficients. This model also enables us to answer the question: Do the effects of prenatal education vary for different occupation types? We do so by comparing the coefficient values between different occupation types crossed with prenatal education. Table
5 reports the results for model 1 with the variables described here. In low and intermediate occupational category (manual workers, office, sales, or service staff, and intermediate (technical) occupations), the women who do not participate in prenatal education are more likely to have cesarean deliveries than those who do participate. Moreover, in response to the question of whether the effects of prenatal education vary for different occupation types, we see that for managerial or higher intellectual occupations as well as intermediate (technical) occupations, prenatal education has a negative cumulative effect with socioeconomic status on the probability of cesarean deliveries (compared to the average effect of the sample). Interaction terms for prenatal education and other available socioeconomic variables yield the same results (Table 11 in the
Appendix presents the results as odds ratios. However, the results as coefficients are available upon request).
Table 5
Effects of prenatal care and socioeconomic status on cesarean delivery use, interaction terms for prenatal care and socioeconomic status, logit model 1 (odds ratios)
Crossed dummy variables for woman’s occupation and prenatal education participation |
Manual worker × No prenatal education | 1.28b(0.131) | 1.28b(0.130) |
Manual worker × Prenatal education | 1.16 (0.196) | 1.16 (0.198) |
Office, sales, or service staff × No prenatal education | 1.16c(0.097) | 1.16c(0.097) |
Office, sales, or service staff × Prenatal education | 0.86c(0.069) | 0.85c(0.069) |
Farmer × No prenatal education | 1.10 (0.621) | 1.11 (0.629) |
Farmer × Prenatal education | 0.76 (0.227) | 0.76 (0.228) |
Craft/trades worker or entrepreneur × No prenatal education | 1.11 (0.187) | 1.11 (0.187) |
Craft/trades worker or entrepreneur × Prenatal education | 0.91 (0.131) | 0.90 (0.131) |
Intermediate (technical) occupation × No prenatal education | 1.20b(0.102) | 1.20b(0.101) |
Intermediate (technical) occupation × Prenatal education | 0.84a(0.050) | 0.84a(0.050) |
Managerial or higher intellectual occupation × No prenatal education | 1.10 (0.067) | 1.10 (0.066) |
Managerial or higher intellectual occupation × Prenatal education | 0.74a(0.060) | 0.74a(0.059) |
Epidemiologic and hospital controls | Yes | Yes |
Other prenatal care and socioeconomic variables | Yes | Yes |
Year fixed effects | Yes | Yes |
Hospital effects | Fixed | Random |
N (observations) | 41,141 | 41,141 |
Discussion
We investigate a rarely studied question in this paper: how patient care throughout pregnancy affects mode of delivery. We use a rich and large database of delivery information for the years 2008–2014, which allows us to take many patient- and hospital-level characteristics that may affect obstetric practices into account. We estimate multilevel logit models, first to study the effects of prenatal care on cesarean delivery and then to assess whether socioeconomic status influences prenatal care and specifically participation in prenatal education, which appears to affect mode of delivery significantly.
Our primary results show that prenatal education affects cesarean delivery rates. The probability of a cesarean delivery increases by 20 to 40% for women who do not participate in prenatal education. Two mechanisms may explain this finding. On the one hand, prenatal education may improve women’s knowledge about mode of delivery. Several studies report that women are not aware of the risks and benefits of birth procedures, and that this lack of knowledge results in some of them choosing cesarean rather than vaginal delivery [
51,
52]. Indeed, a substantial proportion of women continue to consider the cesarean as the safest method of delivery, especially for the child, even though epidemiologic studies demonstrate conclusively that cesarean, compared to vaginal, deliveries increase maternal morbidity in this and future pregnancies and do not improve infant health. On the other hand, prenatal education may have positive psychological effects on pregnant women, who are often anxious about giving birth. Indeed, fear of childbirth is strongly associated with performance of cesarean deliveries [
53].
Since a significant proportion of obstetricians are willing to proceed with a cesarean delivery if requested [
54,
55], the woman’s choice is an important determinant of cesarean deliveries to consider: the number of patient-request cesarean deliveries is currently estimated at 4–18% of the total [
56]. Women attending prenatal education may be more aware of the risks of cesarean deliveries and less affected by fear of giving birth, and may therefore ask less often for a cesarean delivery. Moreover, a well-informed patient is likely to be better able to respond to the information she receives from the obstetrician and to participate in the decision process about method of delivery than someone less informed. This may affect physician and hospital incentives to perform more cesarean deliveries.
Moreover, we find that socioeconomic status influences uptake of prenatal education. Using several individual socioeconomic indicators, our results confirm that low socioeconomic women are more likely to have cesarean deliveries and further show that women in this subgroup have a lower probability of participating in prenatal education. For example, compared to the women with the most education, women who have no post-secondary schooling have at least a 20% lower probability of attendance at prenatal education. The problem of socioeconomic disparities in method of delivery is the focus of much attention, especially in view of the difficulty in modifying socioeconomic differences by public policy interventions. Since utilization of prenatal education may be a factor substantially more susceptible to change, our finding is of interest.
In order to address the possible individual level self-selection into prenatal education participation, our empirical model includes a large set of available covariates. We also perform several robustness analyses that allow many potential confounding factors to be taken into account. However, we cannot fully control for all differences between women who choose to attend prenatal education and women who do not, including in their risk for cesarean delivery. Hence, our results do not allow any causal inferences. Future research exploring the effect of implementation or promotion of such care programs on mode of delivery is thus highly recommended. If our observed associations are confirmed to be causal, a straightforward implication would be that public policies promoting participation rates for prenatal education and targeting this promotion primarily at low-income women could lead to real reductions in cesarean delivery rates.