Dataset Information

Partitioning the population attributable fraction for a sequential chain of effects.

ABSTRACT:

Background

While the population attributable fraction (PAF) provides potentially valuable information regarding the community-level effect of risk factors, significant limitations exist with current strategies for estimating a PAF in multiple risk factor models. These strategies can result in paradoxical or ambiguous measures of effect, or require unrealistic assumptions regarding variables in the model. A method is proposed in which an overall or total PAF across multiple risk factors is partitioned into components based upon a sequential ordering of effects. This method is applied to several hypothetical data sets in order to demonstrate its application and interpretation in diverse analytic situations.

Results

The proposed method is demonstrated to provide clear and interpretable measures of effect, even when risk factors are related/correlated and/or when risk factors interact. Furthermore, this strategy not only addresses, but also quantifies issues raised by other researchers who have noted the potential impact of population-shifts on population-level effects in multiple risk factor models.

Conclusion

Combined with simple, unadjusted PAF estimates and an aggregate PAF based on all risk factors under consideration, the sequentially partitioned PAF provides valuable additional information regarding the process through which population rates of a disorder may be impacted. In addition, the approach can also be used to statistically control for confounding by other variables, while avoiding the potential pitfalls of attempting to separately differentiate direct and indirect effects.

SUBMITTER: Mason CA

PROVIDER: S-EPMC2572052 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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Publications

Partitioning the population attributable fraction for a sequential chain of effects.

Mason Craig A CA Tu Shihfen S

Epidemiologic perspectives & innovations : EP+I 20081002

<h4>Background</h4>While the population attributable fraction (PAF) provides potentially valuable information regarding the community-level effect of risk factors, significant limitations exist with current strategies for estimating a PAF in multiple risk factor models. These strategies can result in paradoxical or ambiguous measures of effect, or require unrealistic assumptions regarding variables in the model. A method is proposed in which an overall or total PAF across multiple risk factors i ...[more]

PMID: 18831748

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Project description:Chlamydia trachomatis infection is highly prevalent among young women in the United States. Prevention of long-term sequelae of infection, including tubal factor infertility, is a primary goal of chlamydia screening and treatment activities. However, the population-attributable fraction of tubal factor infertility associated with chlamydia is unclear, and optimal measures for assessing tubal factor infertility and prior chlamydia in epidemiological studies have not been established. Black women have increased rates of chlamydia and tubal factor infertility compared with White women but have been underrepresented in prior studies of the association of chlamydia and tubal factor infertility.The objectives of the study were to estimate the population-attributable fraction of tubal factor infertility associated with Chlamydia trachomatis infection by race (Black, non-Black) and assess how different definitions of Chlamydia trachomatis seropositivity and tubal factor infertility affect population-attributable fraction estimates.We conducted a case-control study, enrolling infertile women attending infertility practices in Birmingham, AL, and Pittsburgh, PA, during October 2012 through June 2015. Tubal factor infertility case status was primarily defined by unilateral or bilateral fallopian tube occlusion (cases) or bilateral fallopian tube patency (controls) on hysterosalpingogram. Alternate tubal factor infertility definitions incorporated history suggestive of tubal damage or were based on laparoscopic evidence of tubal damage. We aimed to enroll all eligible women, with an expected ratio of 1 and 3 controls per case for Black and non-Black women, respectively. We assessed Chlamydia trachomatis seropositivity with a commercial assay and a more sensitive research assay; our primary measure of seropositivity was defined as positivity on either assay. We estimated Chlamydia trachomatis seropositivity and calculated Chlamydia trachomatis-tubal factor infertility odds ratios and population-attributable fraction, stratified by race.We enrolled 107 Black women (47 cases, 60 controls) and 620 non-Black women (140 cases, 480 controls). Chlamydia trachomatis seropositivity by either assay was 81% (95% confidence interval, 73-89%) among Black and 31% (95% confidence interval, 28-35%) among non-Black participants (P < .001). Using the primary Chlamydia trachomatis seropositivity and tubal factor infertility definitions, no significant association was detected between chlamydia and tubal factor infertility among Blacks (odds ratio, 1.22, 95% confidence interval, 0.45-3.28) or non-Blacks (odds ratio, 1.41, 95% confidence interval, 0.95-2.09), and the estimated population-attributable fraction was 15% (95% confidence interval, -97% to 68%) among Blacks and 11% (95% confidence interval, -3% to 23%) among non-Blacks. Use of alternate serological measures and tubal factor infertility definitions had an impact on the magnitude of the chlamydia-tubal factor infertility association and resulted in a significant association among non-Blacks.Low population-attributable fraction estimates suggest factors in addition to chlamydia contribute to tubal factor infertility in the study population. However, high background Chlamydia trachomatis seropositivity among controls, most striking among Black participants, could have obscured an association with tubal factor infertility and resulted in a population-attributable fraction that underestimates the true etiological role of chlamydia. Choice of chlamydia and tubal factor infertility definitions also has an impact on the odds ratio and population-attributable fraction estimates.

Project description:BackgroundSevere maternal morbidity is an important proxy for maternal mortality. Population attributable fraction is the proportion of a disease that is attributable to a given risk factor and can be used to estimate the reduction in the disease that would be anticipated if a risk factor was reduced or eliminated.ObjectiveWe sought to determine the population-attributable fraction (PAF) of potentially modifiable risk factors for severe maternal morbidity.Study designWe used a retrospective cohort of 86,260 delivery hospitalizations from Magee-Womens Hospital, Pittsburgh, PA for this analysis (2003-2012). Severe maternal morbidity was defined as any of the following: Centers for Disease Control and Prevention International Classification of Diseases 9th Revision diagnosis and procedure codes for the identification of maternal morbidity; prolonged postpartum length of stay (defined as >3 standard deviations beyond the mean length of stay: >3 days for vaginal deliveries and >5 days for Cesarean deliveries); or maternal intensive care unit admission. We used multivariable logistic regression with generalized estimating equations to estimate the association of prepregnancy overweight or obesity, maternal age ≥35 years, preexisting hypertension, preexisting diabetes, excessive gestational weight gain, smoking, education, and marital status with severe maternal morbidity. We then calculated the PAF for each risk factor. We also examined the impact of modest reductions and elimination of risk factors on the PAF of severe maternal morbidity.ResultsThe overall rate of severe maternal morbidity was 2.0%. Overweight and obesity, maternal age ≥35 years, preexisting hypertension, excessive gestational weight gain, and lack of a college degree had PAF ranging from 4.5% to 13%. If all risk factors were eliminated, 36% of cases could have been prevented. Modest reductions in the prevalence of excessive BMI and advanced maternal age had minimal impact on preventing severe maternal morbidity. Smoking during pregnancy and marital status were not associated with severe maternal morbidity.ConclusionsOur data suggest maternal morbidity can be reduced by modifying common, individual-level risk factors. Nevertheless, the majority of cases were not attributable to the patient level risk factors we examined. These data support the need for large studies of patient-, provider-, system- and population-level factors to identify high-impact interventions to reduce maternal morbidity.

Dataset Information

Partitioning the population attributable fraction for a sequential chain of effects.

Background

Results

Conclusion

Publications

Partitioning the population attributable fraction for a sequential chain of effects.

Similar Datasets

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