Error in Figure/Table
In the original publication [1], there was a mistake in Figure 1, Table 1, Table 2, Table 3 and Table S1 as published. In this study, one-to-one matching pairs between parents in birth data and men and women in the Census data from Japan were included in the study population via data linkage. Data linkage was conducted by writing programming codes using a statistical software. However, some of the many-to-one matching pairs were included in the study population because of programming errors by the author. Therefore, the author wishes to publish a result that corrects this error. The study population decreased from 782,536 to 777,086 after the correction, and the numeric values in the tables and figure need to be corrected accordingly. The corrected Figure 1, Table 1, Table 2, Table 3 and Table S1 appear below. The author states that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.
Text Correction
There was an error in the original publication [1]. The mistake is explained in the previous section. Corrections have been made to the Abstract, Materials and Methods, and Results. The original and corrected texts are provided below.
| Page | Original | Corrected |
| Page 1, Abstract, line 8 | 782,536 | 777,086 |
| Page 1, Abstract, line 9 | 5.09 and 5.20 | 5.07 and 5.21 |
| Page 2, Data Linkage, line 15 | 782,536 | 777,086 |
| Page 4, Results, lines 2–3 | 311,050 in 2000 to 217,968 in 2020 | 308,994 in 2000 to 216,637 in 2020 |
| Page 4, Results, line 11 | 5.09 and 5.20 | 5.07 and 5.21 |
| Page 4, Results, line 24 | −0.618 | −0.609 |
| Page 4, Results, line 28 | 0.853 | 0.854 |
Figure 1.
The flowchart of the data selection process.
Table 1.
Number of births for each attribute by year.
| Year | |||
|---|---|---|---|
| 2000 | 2010 | 2020 | |
| Total | 308,994 (100.0) | 251,455 (100.0) | 216,637 (100.0) |
| Maternal age group | |||
| 19 years or less | 9607 (3.1) | 5076 (2.0) | 2013 (0.9) |
| 20–24 years | 72,551 (23.5) | 50,407 (20.0) | 31,218 (14.4) |
| 25–29 years | 112,295 (36.3) | 82,313 (32.7) | 65,429 (30.2) |
| 30–34 years | 81,107 (26.2) | 69,971 (27.8) | 66,501 (30.7) |
| 35–39 years | 29,172 (9.4) | 36,087 (14.4) | 40,761 (18.8) |
| 40 years or more | 4262 (1.4) | 7601 (3.0) | 10,715 (4.9) |
| Gender | |||
| Female | 149,954 (48.5) | 122,360 (48.7) | 105,734 (48.8) |
| Male | 159,040 (51.5) | 129,095 (51.3) | 110,903 (51.2) |
| Parity | |||
| Primiparous | 156,453 (50.6) | 125,412 (49.9) | 104,657 (48.3) |
| Multiparous | 152,541 (49.4) | 126,043 (50.1) | 111,980 (51.7) |
| Household occupation | |||
| Farmer | 20,371 (6.6) | 8193 (3.3) | 4175 (1.9) |
| Self-employed | 30,261 (9.8) | 21,016 (8.4) | 17,089 (7.9) |
| Full-time worker 1 | 116,984 (37.9) | 96,872 (38.5) | 75,969 (35.1) |
| Full-time worker 2 | 100,111 (32.4) | 89,426 (35.6) | 92,264 (42.6) |
| Other occupations | 34,218 (11.1) | 25,703 (10.2) | 21,046 (9.7) |
| Unemployed | 3624 (1.2) | 3910 (1.6) | 1721 (0.8) |
| Missing | 3425 (1.1) | 6335 (2.5) | 4373 (2.0) |
| Paternal educational level | |||
| Junior high school | 36,536 (11.8) | 21,616 (8.6) | 13,555 (6.3) |
| High school | 167,938 (54.3) | 109,471 (43.5) | 75,470 (34.8) |
| Technical school or junior college | 34,399 (11.1) | 34,600 (13.8) | 27,607 (12.7) |
| University or graduate school | 66,594 (21.6) | 66,058 (26.3) | 72,419 (33.4) |
| Missing | 3527 (1.1) | 19,710 (7.8) | 27,586 (12.7) |
| Maternal educational level | |||
| Junior high school | 25,841 (8.4) | 16,964 (6.7) | 9896 (4.6) |
| High school | 173,690 (56.2) | 106,675 (42.4) | 71,571 (33.0) |
| Technical school or junior college | 83,233 (26.9) | 72,275 (28.7) | 54,595 (25.2) |
| University or graduate school | 22,671 (7.3) | 36,647 (14.6) | 53,626 (24.8) |
| Missing | 3559 (1.2) | 18,894 (7.5) | 26,949 (12.4) |
| Gestational age | |||
| Term birth | 294,936 (95.5) | 239,867 (95.4) | 206,784 (95.5) |
| Preterm birth | 13,969 (4.5) | 11,548 (4.6) | 9821 (4.5) |
| Missing | 89 (0.0) | 40 (0.0) | 32 (0.0) |
| Birthweight | |||
| >= 2, 500 g | 285,929 (92.5) | 230,548 (91.7) | 199,587 (92.1) |
| < 2500 g | 23,042 (7.5) | 20,876 (8.3) | 17,023 (7.9) |
| Missing | 23 (0.0) | 31 (0.0) | 27 (0.0) |
Table 2.
Preterm birth rate (%) by year and parental educational level.
| Year | |||
|---|---|---|---|
| 2000 | 2010 | 2020 | |
| Total | 13,597 (4.51) | 10,246 (4.56) | 8357 (4.52) |
| Paternal educational level | |||
| Junior high school | 1892 (5.27) | 1045 (5.04) | 686 (5.21) |
| High school | 7446 (4.50) | 4959 (4.68) | 3366 (4.57) |
| Technical school or junior college | 1439 (4.24) | 1456 (4.33) | 1187 (4.39) |
| University or graduate school | 2820 (4.28) | 2786 (4.32) | 3118 (4.39) |
| Maternal educational level | |||
| Junior high school | 1397 (5.52) | 854 (5.28) | 488 (5.07) |
| High school | 7834 (4.58) | 4845 (4.72) | 3248 (4.70) |
| Technical school or junior college | 3438 (4.18) | 3055 (4.35) | 2388 (4.45) |
| University or graduate school | 928 (4.13) | 1492 (4.16) | 2233 (4.24) |
Table 3.
Results of the slope index of inequality and relative index of inequality for the preterm birth rate depending on parental educational level.
| 2000 | 2010 | 2020 | |
|---|---|---|---|
| Estimates (95%CI) | Estimates (95%CI) | Estimates (95%CI) | |
| Slope index of inequality | |||
| Paternal educational level | −0.609 (−0.924, −0.293) | −0.620 (−0.976, −0.264) | −0.489 (−0.876, −0.103) |
| Maternal educational level | −1.024 (−1.344, −0.705) | −1.061 (−1.422, −0.700) | −0.967 (−1.353, −0.580) |
| Relative index of inequality | |||
| Paternal educational level | 0.854 (0.795, 0.918) | 0.867 (0.800, 0.939) | 0.886 (0.812, 0.967) |
| Maternal educational level | 0.779 (0.723, 0.838) | 0.773 (0.713, 0.839) | 0.784 (0.719, 0.856) |
| CI, confidence intervals | |||
| 1. Gender, parity, household occupation, and maternal age group were adjusted in the analysis. | |||
| 2. Estimates for the slope index of inequality, which was calculated using a binomial model with an identity link function, can be interpreted as the absolute risk difference between the highest and lowest educational levels. | |||
| 3. Estimates for the relative index of inequality, which was calculated using a log-binomial model, can be interpreted as the risk ratio between the highest and lowest educational levels. | |||
Table S1.
Results of the slope index of inequality and relative index of inequality for the preterm birth rate depending on parental educational level using an imputation method.
| 2000 | 2010 | 2020 | |
|---|---|---|---|
| Estimates (95%CI) | Estimates (95%CI) | Estimates (95%CI) | |
| Slope index of inequality | |||
| Paternal educational level | −0.602 (−0.913, −0.290) | −0.542 (−0.879, −0.206) | −0.496 (−0.851, −0.141) |
| Maternal educational level | −0.975 (−1.291, −0.660) | −0.986 (−1.329, −0.644) | −0.734 (−1.092, −0.377) |
| Relative index of inequality | |||
| Paternal educational level | 0.855 (0.796, 0.918) | 0.882 (0.818, 0.950) | 0.885 (0.817, 0.959) |
| Maternal educational level | 0.788 (0.733, 0.847) | 0.789 (0.731, 0.852) | 0.832 (0.767, 0.901) |
| CI, confidence intervals | |||
| 1. Gender, parity, household occupation, and maternal age group were adjusted in the analysis. | |||
| 2. Estimates for the slope index of inequality, which was calculated using a binomial model with an identity link function, can be interpreted as the absolute risk difference between the highest and lowest educational levels. | |||
| 3. Estimates for the relative index of inequality, which was calculated using a log-binomial model, can be interpreted as the risk ratio between the highest and lowest educational levels. | |||
Footnotes
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Reference
- 1.Okui T. Analysis of an Association between Preterm Birth and Parental Educational Level in Japan Using National Data. Children. 2023;10:342. doi: 10.3390/children10020342. [DOI] [PMC free article] [PubMed] [Google Scholar]