Different Effects for Different Questions: An Illustration Using Short Cervix and the Risk of Preterm Birth

Overview

Journal Int J Gynaecol Obstet

Publisher Wiley

Specialty Gynecology & Obstetrics

Date 2022 Jul 28

PMID 35899762

Authors

Rachael K Ross

Stephen R Cole

Daniel Westreich

Jessie K Edwards

Patrick Musonda

Bellington Vwalika

Margaret P Kasaro

Joan T Price

Jeffrey S A Stringer

Affiliations

Soon will be listed here.

Abstract

Objective: To illustrate the difference between exposure effects and population attributable effects.

Methods: We examined the effect of mid-pregnancy short cervical length (<25 mm) on preterm birth using data from a prospective cohort of pregnant women in Lusaka, Zambia. Preterm birth was live birth or stillbirth before 37 weeks of pregnancy. For estimation, we used multivariable regression and parametric g-computation.

Results: Among 1409 women included in the analysis, short cervix was rare (2.4%); 13.6% of births were preterm. Exposure effect estimates were large (marginal risk ratio 2.86, 95% confidence interval [CI] 1.80-4.54), indicating that the preterm birth risk was substantially higher among women with a short cervix compared with women without a short cervix. However, the population attributable effect estimates were close to the null (risk ratio 1.06, 95% CI 1.02-1.10), indicating that an intervention to counteract the impact of short cervix on preterm birth would have minimal effect on the population risk of preterm birth.

Conclusion: Although authors often refer to "the" effect, there are actually different types of effects, as we have illustrated here. In planning research, it is important to consider which effect to estimate to ensure that the estimate aligns with the research objective.

References

Morgenstern H, Bursic E . A method for using epidemiologic data to estimate the potential impact of an intervention on the health status of a target population. J Community Health. 1982; 7(4):292-309. DOI: 10.1007/BF01318961. View

Spiegelman D, Hertzmark E . Easy SAS calculations for risk or prevalence ratios and differences. Am J Epidemiol. 2005; 162(3):199-200. DOI: 10.1093/aje/kwi188. View

Brookhart M, Schneeweiss S, Rothman K, Glynn R, Avorn J, Sturmer T . Variable selection for propensity score models. Am J Epidemiol. 2006; 163(12):1149-56. PMC: 1513192. DOI: 10.1093/aje/kwj149. View

Westreich D, Cole S, Young J, Palella F, Tien P, Kingsley L . The parametric g-formula to estimate the effect of highly active antiretroviral therapy on incident AIDS or death. Stat Med. 2012; 31(18):2000-9. PMC: 3641816. DOI: 10.1002/sim.5316. View

Ahern J . Population Intervention Measures to Connect Research Findings to Policy. Am J Public Health. 2016; 106(12):2152-2153. PMC: 5105025. DOI: 10.2105/AJPH.2016.303494. View

Greenland S, Robins J . Identifiability, exchangeability, and epidemiological confounding. Int J Epidemiol. 1986; 15(3):413-9. DOI: 10.1093/ije/15.3.413. View

Westreich D, Mooney S . Number (of Whom?) Needed to Treat (with What?): Exposures, Population Interventions, and the Number Needed to Treat. Epidemiology. 2019; 30 Suppl 2:S55-S59. DOI: 10.1097/EDE.0000000000001061. View

Ahern J, Colson K, Margerson-Zilko C, Hubbard A, Galea S . Predicting the Population Health Impacts of Community Interventions: The Case of Alcohol Outlets and Binge Drinking. Am J Public Health. 2016; 106(11):1938-1943. PMC: 5055784. DOI: 10.2105/AJPH.2016.303425. View

Iams J, Goldenberg R, Meis P, Mercer B, Moawad A, Das A . The length of the cervix and the risk of spontaneous premature delivery. National Institute of Child Health and Human Development Maternal Fetal Medicine Unit Network. N Engl J Med. 1996; 334(9):567-72. DOI: 10.1056/NEJM199602293340904. View

10.

Andersen H, Nugent C, Wanty S, Hayashi R . Prediction of risk for preterm delivery by ultrasonographic measurement of cervical length. Am J Obstet Gynecol. 1990; 163(3):859-67. DOI: 10.1016/0002-9378(90)91084-p. View

11.

Austin P, Laupacis A . A tutorial on methods to estimating clinically and policy-meaningful measures of treatment effects in prospective observational studies: a review. Int J Biostat. 2012; 7(1):6. PMC: 3404554. DOI: 10.2202/1557-4679.1285. View

12.

Westreich D . From Patients to Policy: Population Intervention Effects in Epidemiology. Epidemiology. 2017; 28(4):525-528. PMC: 5453818. DOI: 10.1097/EDE.0000000000000648. View

13.

To M, Skentou C, Royston P, Yu C, Nicolaides K . Prediction of patient-specific risk of early preterm delivery using maternal history and sonographic measurement of cervical length: a population-based prospective study. Ultrasound Obstet Gynecol. 2006; 27(4):362-7. DOI: 10.1002/uog.2773. View

14.

Martens E, Pestman W, de Boer A, Belitser S, Klungel O . Systematic differences in treatment effect estimates between propensity score methods and logistic regression. Int J Epidemiol. 2008; 37(5):1142-7. DOI: 10.1093/ije/dyn079. View

15.

Edwards J, Cole S, Lesko C, Mathews W, Moore R, Mugavero M . An Illustration of Inverse Probability Weighting to Estimate Policy-Relevant Causal Effects. Am J Epidemiol. 2016; 184(4):336-44. PMC: 4983650. DOI: 10.1093/aje/kwv339. View

16.

Rose G . Sick individuals and sick populations. Int J Epidemiol. 2001; 30(3):427-32; discussion 433-4. DOI: 10.1093/ije/30.3.427. View

17.

Hassan S, Romero R, Vidyadhari D, Fusey S, Baxter J, Khandelwal M . Vaginal progesterone reduces the rate of preterm birth in women with a sonographic short cervix: a multicenter, randomized, double-blind, placebo-controlled trial. Ultrasound Obstet Gynecol. 2011; 38(1):18-31. PMC: 3482512. DOI: 10.1002/uog.9017. View

18.

Taipale P, Hiilesmaa V . Predicting delivery date by ultrasound and last menstrual period in early gestation. Obstet Gynecol. 2001; 97(2):189-94. DOI: 10.1016/s0029-7844(00)01131-5. View

19.

Hui S, Chor C, Lau T, Lao T, Leung T . Cerclage pessary for preventing preterm birth in women with a singleton pregnancy and a short cervix at 20 to 24 weeks: a randomized controlled trial. Am J Perinatol. 2012; 30(4):283-8. DOI: 10.1055/s-0032-1322550. View

20.

Harel O, Mitchell E, Perkins N, Cole S, Tchetgen Tchetgen E, Sun B . Multiple Imputation for Incomplete Data in Epidemiologic Studies. Am J Epidemiol. 2017; 187(3):576-584. PMC: 5860387. DOI: 10.1093/aje/kwx349. View