When Will I Go Into Labor?

1. Overview

The calculator provides a spontaneous labor onset timing estimate for an individual pregnancy using a single, smooth hazard-based model that covers both preterm and term/late-term timing. Spontaneous labor onset means labor beginning on its own before a planned induction or planned cesarean. The model is probabilistic: it combines (i) a population baseline calibrated to singleton labor timing, (ii) additive day-shifts for user-supplied factors applied as horizontal shifts, and (iii) a pre-37-week probability determined from registry rates and covariate odds ratios, injected via the same hazard curve. Calculator entries are processed locally in the browser and are not sent to our server.

Scheduled induction, planned cesarean, emergency cesarean, and final delivery route are treated as outside the modeled endpoint. Some source studies use birth or delivery outcomes rather than directly observed spontaneous labor onset, so the implemented effect sizes are approximate translations into a spontaneous-labor timing curve.

2. Baseline hazard and parametric form

Source data. The baseline timing is anchored to population analyses of low-risk singleton pregnancies (e.g., Smith et al., Kaplan–Meier time-to-event), with median spontaneous or non-elective timing near 40 weeks and realistic right-tail mass (PubMed links below). The current engine parameterizes a flexible exponential hazard that reproduces these features after calibration.

Parametric form. For gestational age in days t (LMP-based), we model the instantaneous timing hazard as an exponential function of time with horizontal shift:

$$ \lambda(t \mid X) = h_0(X)\,\exp\{\gamma(X)\,[(t - s(X)) - 259]\}, \qquad t \ge 150 \text{ d}. $$

Here $$h_0(X)$$ and $$\gamma(X)$$ are calibrated to meet baseline survival and tail-shape constraints, and $$s(X) = \mu^*(X) - \mu_{\text{baseline}}$$ is a horizontal shift parameter that incorporates all additive day-shifts. The baseline mean for a reference mother is

$$ \mu_0 = 276.5 \, \text{days}. $$

There is no explicit variance parameter in this formulation; dispersion arises from the integrated hazard shape combined with the horizontal shift.

3. User-level covariates and day-shifts

Predictors listed below are incorporated as either (a) additive day-shifts $$\Delta \mu(X)$$ to the target spontaneous labor onset mean or (b) multiplicative odds ratios that adjust the preterm probability $$\pi(X)$$ (Section 4). The values used in the current release are shown in the right-hand column.

All additive effects are combined to form the target spontaneous labor onset mean $$ \mu^*(X) = \mu_0 + \Delta \mu(X) $$ and implemented as a horizontal shift $$ s(X) = \mu^*(X) - \mu_{\text{baseline}} $$. Odds-ratio effects modify the preterm probability $$\pi(X)$$. There is no separate variance parameter in the current release.

4. Preterm (< 37 weeks) probability

Population weight. The U.S. obstetric-estimate preterm birth rate was 10.4% in 2016 (Shapiro-Mendoza et al.), and recent national estimates remain about 10.4%. Because published preterm evidence often combines spontaneous and medically indicated deliveries, this term should be read as an approximate early spontaneous-labor weighting, not as an individualized estimate of medically indicated preterm delivery. For an individual, the model multiplies the baseline odds by published odds ratios for declared risk factors (e.g., OR 2.6 for smoking, OR 1.6 for Black ethnicity, OR 1.8 for interpregnancy interval < 12 months). The resulting probability $$\pi(X)$$ is truncated to 1-50%.

Injection via the hazard curve. Rather than a separate preterm kernel, the same exponential hazard with horizontal shift is calibrated so that survival at 37 weeks matches the target preterm mass:

$$ S(259 \mid X) = \exp\{-H(259 \mid X)\} = 1 - \pi(X), $$

where the integrated hazard is

$$ H(t \mid X) = \int_{150+s(X)}^{t} \lambda(u \mid X)\,du = \frac{h_0(X)}{\gamma(X)}\Bigl[\exp\{\gamma(X)\,[(t-s(X))-259]\} - \exp\{\gamma(X)\,[150-259]\}\Bigr]. $$

5. Solving for parameters and the full distribution

Calibration. The model first grid-searches over $$\gamma$$ to find a stable curve shape and provisional $$h_0$$ that satisfies the target early spontaneous-labor mass and tail-shape guardrails. It then applies the horizontal shift $$s(X)$$ to align the mean with $$\mu^*(X)$$ and recalculates $$h_0$$ analytically so that the final shifted curve still satisfies $$S(259)=1-\pi(X)$$.

PDF and CDF. Once calibrated, the unconditional density and distribution are

$$ f(t \mid X) = \lambda(t \mid X)\,S(t \mid X), \qquad F(t \mid X)=1-S(t \mid X), \quad t \ge 150. $$

6. Conditioning on "still pregnant today"

If a user is already g days into pregnancy, the displayed curve is conditional on survival to g:

$$ f^*(t \mid X, g) = \frac{ \lambda(t \mid X) \exp\{-\!\int_{g}^{t} \lambda(u \mid X)du\} }{ S(g \mid X) } \, 1_{(t>g)}, $$

with $$ S(g \mid X)=\exp\{-H(g \mid X)\} $$ where all hazard functions include the horizontal shift $$s(X)$$. All probabilities shown in the app (e.g., "chance of spontaneous labor in the next 7 days") are computed from this conditional PDF.

7. Outputs

• Most-probable window = narrowest interval containing 50% of the remaining spontaneous labor onset probability mass.
• Chart percentile markers: P10, median/P50, and P90.
• Preterm warning: if the remaining chance of spontaneous labor before 37 weeks is at least 15%, the page shows a higher-risk banner.

8. Limitations

The model relies on published effect sizes and registry summaries rather than raw patient-level datasets; rare interactions may be missed. Scheduled induction, planned cesarean, emergency cesarean, and delivery route are not modeled. Inputs such as cervical length or fetal fibronectin are not yet incorporated, but the modular code allows future integration. The current hazard specification does not include an explicit variance parameter; dispersion is governed by the calibrated hazard shape and horizontal shift. The horizontal-shift approach eliminates the need for re-fitting $$h_0$$ and $$\gamma$$ when only the mean changes, improving computational efficiency.

9. Disclaimer

The calculator is for informational purposes only and must not guide medical decision-making. Users with concerns about early labor, late pregnancy, induction, cesarean, or planned delivery should consult their healthcare provider.

Important note on effect sizes: The effect sizes implemented here reflect our best interpretation of the peer-reviewed literature. They are not exact, but they are directionally correct given current evidence. Different model specifications are possible which can change the estimated labor timing probabilities.

References

1. Smith GC. Hum Reprod 2001 16:1497-1502 – baseline gestational length (PubMed)
2. Oberg AS et al. Am J Epidemiol 2013 177:531-540 – genetic & maternal factors in post-term (PubMed)
3. Shapiro-Mendoza CK, Barfield WD, Henderson Z, et al. CDC Grand Rounds: Public Health Strategies to Prevent Preterm Birth. MMWR Morb Mortal Wkly Rep. 2016; 65(32):826–830 (CDC).
4. Patel RR et al. Int J Epidemiol 2004 33:107-113 – ethnic variation in gestation (PubMed)
5. Selvaratnam RJ et al. Objective measures of smoking and caffeine intake and the risk of adverse pregnancy outcome. International Journal of Epidemiology. 28 Sept 2023 (IJE)
6. Levine LD, Bogner HR, Hirshberg A, et al. Term induction of labor and subsequent preterm birth. American Journal of Obstetrics and Gynecology. 2014; 210(4):354.e1-354.e8 (AJOG)
7. Purisch SE, Gyamfi-Bannerman C. Epidemiology of preterm birth. Seminars in Perinatology. 2017; 41(7):387-391 (Seminars in Perinatology)
8. Oliver-Williams C, Fleming M, Wood AM, Smith G. Previous miscarriage and the subsequent risk of preterm birth in Scotland, 1980-2008: a historical cohort study. BJOG. 2015 Oct;122(11):1525-34. doi: 10.1111/1471-0528.13276. Epub 2015 Jan 28. PMID: 25626593; PMCID: PMC4611958 (PubMed)
9. Erickson EN et al. NPJ Digit Med 2023 6:153 – smart-ring physiology model (PubMed)
10. Han Z et al. Int J Epidemiol 2011 40:65-75 – underweight & PTB meta-analysis (Oxford Academic)
11. Hamilton BE, Osterman MJK, Gregory ECW. Births: Provisional Data for 2025. Vital Statistics Rapid Release. 2026 Apr;(43):1-7 (CDC/NCHS)
12. Martin JA, Osterman MJK. Induction of Labor Increases in the United States: 2016 to 2024. NCHS Data Brief. 2026 Mar;(554):1-12 (CDC/NCHS)