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Association Between Neighborhood Disadvantage and Fertility Among Pregnancy Planners in the US

Key Points Question Is living in a disadvantaged neighborhood associated with decreased fertility? Findings In this cohort study of 6356 women attempting to conceive without the use of fertility treatment, the probability of conception was reduced 21% to 23% per menstrual cycle when comparing the highest with the lowest deciles of disadvantaged neighborhoods. When disadvantaged neighborhood status was categorized within each state (as opposed to nationally), the results were slightly larger in magnitude. Meaning These findings suggest that investments in disadvantaged neighborhoods may yield positive cobenefits for fertility.


Introduction
In the US, 10% to 15% of reproductive-aged couples experience infertility, defined as the inability to conceive after 12 months of unprotected intercourse. 1 The economic burden of infertility is substantial, with health care costs that exceed $5 billion annually. 2 Likewise, infertility is associated with significant psychosocial consequences. 2,3 To date, there are few established modifiable risk factors to improve fecundability (a couple-based metric of the probability of conception during a menstrual cycle).
Infertility prevention is often discussed with respect to personal behavioral changes that may improve the probability of conception. However, structural, political, and environmental factors may also play a substantial role in fertility. A confluence of these factors is present in a socioeconomically disadvantaged neighborhood environment, defined by the mixture of educational attainment, employment status, household income, and housing quality in a community. 4,5 A growing body of literature demonstrates associations between a disadvantaged neighborhood environment and adverse reproductive health outcomes. [6][7][8][9] One standardized approach for measuring a relative disadvantaged neighborhood environment is the area deprivation index (ADI). 4,10 Greater neighborhood disadvantage, as measured by the ADI, have been consistently associated with adverse health outcomes, such as hospital readmissions. 11 We hypothesized a similar association between a disadvantaged neighborhood environment and fecundability.
There are multiple pathways by which neighborhood disadvantage may reduce fecundability ( Figure 1). Residing in a socioeconomically disadvantaged neighborhood is associated with increased perceived stress, 12 cortisol levels, 13 and allostatic load. 14 Neighborhood exposures are generally chronic, because most people infrequently relocate. 15 Moreover, members of racial and ethnic minority groups are less likely to move to high-opportunity neighborhoods (ie, communities with high opportunity for upward socioeconomic mobility). 16,17 Perceived stress has been associated with  poorer in vitro fertilization outcomes 18 and reduced fecundability among couples attempting spontaneous conception. 19,20 Similar results have also been found for salivary α-amylase, a biomarker of stress levels, and fecundability. 21,22 To our knowledge, no studies of disadvantaged neighborhood environment have directly examined fertility-related outcomes in the US.
State economic policies may particularly influence the day-to-day lives of its residents, including programs that can exacerbate or mitigate socioeconomic disparities. 23 Many policies have measured effects on health, such as improved life expectancy in states with health-protective programs (eg, policies that support civil rights, promote the environment, and invest in underresourced communities.) 24 However, members of marginalized racial and ethnic minority groups are more likely to reside in socioeconomically disadvantaged neighborhoods owing to structural racism enacted through residential segregation, systematic disinvestment in neighborhood infrastructure, and associated disparate exposure to environmental toxicants. [25][26][27][28] Equitably implemented policies to improve disadvantaged neighborhoods may have positive benefits for health outcomes that have strong sociodemographic disparities, including fertility. [29][30][31] In this US prospective cohort study of couples attempting pregnancy, we examined the association between residence in a disadvantaged neighborhood and fecundability. We classified disadvantaged neighborhood status at the national level (ie, ranking within the nation) and state level (ie, ranking within its own state) to understand contextual variation.

Study Population
Pregnancy Study Online (PRESTO) is an online preconception cohort study of couples attempting spontaneous conception. 32  address that could not be accurately geocoded, and 105 whose census block group lacked an ADI score. These exclusions yielded an analytic sample of 6356 participants.

Area Deprivation Exposure Assessment
We used the 2015 ADI to estimate limitations of neighborhood disadvantage. 4,10,11 Briefly, the ADI was constructed using sociodemographic data from the 2011-2015 American Community Survey 5-year means, and the score includes population indicators related to educational attainment, housing, employment, and poverty. 4,10 Higher scores represent neighborhoods with greater disadvantage. We obtained 2 ADI scores for each census block group: national percentile ranking (1-100) and within-state decile ranking (1-10). We hypothesized that the within-state ADI would be more strongly associated with fecundability because it reduces confounding from dissimilar state contexts (eg, social policy, cost of living). Using participants' geocoded baseline addresses, we assigned each residential address to its corresponding census block group and linked it to its ADI score.

Fecundability Outcome Assessment
On the baseline questionnaire, participants reported the number of menstrual cycles during which they had been attempting conception ("For how many cycles have you been trying to get pregnant?") and whether they had regular menstrual cycles ("Within the past couple of years, has your menstrual period been regular?"). On the baseline and follow-up questionnaires, participants provided their LMP date. Participants who reported that their cycles were regular (ie, predictable within a few days) were asked about their typical cycle length. Participants with irregular cycles were asked for the number of menses per year and the estimated number of days until their next menses, which we used to estimate cycle length.

Covariate Assessment
On the baseline questionnaire, we ascertained sociodemographic data (age, educational attainment, annual household income, and race and ethnicity), reproductive and contraceptive history (parity, history of infertility, last method of contraception), daily multivitamin/folic acid use, and factors related to intensity of trying to conceive (intercourse frequency, doing something to improve chances of conception [eg, charting menstrual cycles]). Racial and ethnic data were collected to account for the historical marginalization of some populations, including disparities in fertility and subsequent access to fertility treatment. For national and state-level ADI rankings, individual sociodemographic characteristics are standardized to age distribution of the cohort at baseline, with the exception of age.

Main Analysis
Data were analyzed from October 1, 2019, to May 24, 2022. We used the Andersen-Gill data structure to account for left truncation owing to delayed entry into the risk set. 33 We used proportional probabilities regression models to calculate fecundability ratios (FRs) and 95% CIs comparing each category of exposure with the reference group (ie, polytomous categorical exposure by decile using indicator variables). All models included indicator variables for menstrual cycle at risk to account for the decline in population fecundability with increasing attempt time. We also fit restricted cubic splines to examine the potential for nonlinear associations between ADI and fecundability.
In adjusted models, we controlled for clinical factors associated with fertility as precision conception (ie, timing intercourse and/or ovulation testing), and year of enrollment (2013 through 2019). We hypothesized that individual demographic and socioeconomic attributes (eg, educational attainment, income, race and ethnicity) may act along the causal pathway of the association between ADI and fecundability more strongly than operating as confounders; therefore, we did not adjust for these characteristics in the primary adjusted model, although we explored their influence in sensitivity analyses, described hereinafter.
We used multiple imputation to impute missing data on exposure, outcome, and covariates with fully conditional specifications methods and statistically combined 20 data sets to generate FRs and 95% CIs. 34 Women without follow-up questionnaires (758 [11.9%]) were assigned 1 cycle of follow-up, with pregnancy status multiply imputed at that cycle. Missingness ranged from 0 (eg, age) to 3% (household income). All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc).

Sociodemographic Subgroup Analysis
We stratified by individual demographic and socioeconomic variables that could modify the association between disadvantaged neighborhood environment at the state level and fecundability, including annual household income (<$50 000/y vs Ն$150 000/y) and educational attainment (<16 vs Ն16 years, corresponding to college graduates). The sample size did not allow for reasonable disaggregation by racial and ethnic identity.

Sensitivity Analysis
We conducted several sensitivity analyses. First, we restricted the analysis to participants who

Main Statistical Analysis
For the nationally ranked ADI, we observed an inverse association between the ADI and fecundability among participants who resided in a neighborhood with an ADI greater than 60. For instance, the FR  Table 2). For the restricted cubic splines for the national-level ADI metric, we observed an approximately linear inverse association. These results correspond to a 19% and 21% reduction in fecundability, respectively.

JAMA Network Open | Environmental Health
At the state-level ranking, we found an inverse association between the ADI and fecundability among the participants who resided in a neighborhood with an ADI greater than 5. When comparing the most disadvanted neighborhoods (state-level ADI, 10) with the least disadvantaged neighborhoods (state-level ADI, 1), the FR was 0.75 (95% CI, 0.63-0.90) in unadjusted models and 0.77 (95% CI, 0.65-0.92) in adjusted models, corresponding to a 25% and 23% reduction inf fecundability, respectively (Table 2). For the restricted cubic splines for the state-level ADI metric, we observed little evidence of an association below 4 and an approximately linear association beyond 4 ( Figure 2).

Sociodemographic Subgroup Analyses
We performed several subgroup analyses where we exclusively focused on the state-level ADI ranking (eFigures 1 and 2 in the Supplement). When we stratified by educational attainment, the spline curve was highly attenuated for college graduates compared with the primary model results, whereas there was a largely flat curve with less precision among participants who did not graduate from college. Among participants with annual household incomes less than $50 000, the spline curve showed an inverse association between ADI and fecundability, although imprecise. In contrast, among those with annual household incomes of $150 000 or more, the spline curve was mostly flat.

Sensitivity Analysis
Among participants with fewer than 3 cycles of pregnancy attempt at enrollment, we observed similar results with less precision (eTable 1 in the Supplement). Results were similar across strata of parity, but inverse associations were less precise among parous participants (eTable 2 in the Supplement). When we evaluated the addition of individual socioeconomic variables, adjusting for race and ethnicity and educational attainment yielded partially attenuated results, although the 95%  Graphs are plots of restricted cubic splines, where the observations are trimmed at the first and 99th percentiles. The reference value is the minimum. The knots are located at 20, 40, 60, and 80 in the national spline and 2, 4, 6, and 8 in within-state spline. The blue line indicates fecundability ratio; the shaded gray area, 95% CI. Adjusted regressions contain covariates for age, daily multivitamin or folic acid intake, parity, intercourse frequency, last method of contraception used before attempting pregnancy, doing something to improve the chances of conception, and year of baseline enrollment.
CIs were wider (eTable 3 in the Supplement). Adding household income strongly attenuated the overall association.

Discussion
Using data from a US preconception cohort study, greater limitations of neighborhood disadvantage were associated with reduced fecundability, a couple-based metric of the per-cycle probability of conception. Associations between ADI and fecundability were similar when neighborhood disadvantage was measured relative to the nation or the state, which lends support for the hypothesis that local context may be particularly influential in fertility.
This finding is consistent with literature demonstrating that neighborhood socioeconomic environments influence reproductive health. 6 25,38 For example, higher neighborhood unemployment has been associated with decreased population-level fertility rates. 39 Less affluent areas often have higher concentrations of air pollution, 40 which has been associated with adverse fertility outcomes. [41][42][43][44][45][46][47] There also may be fewer amenities such as green spaces to provide capacity restoration for residents, such as reducing stress. 26, 48 Conversely, urban renewal programs show positive health benefits for local populations, 29 although the effects are mixed when gentrification is taken into account. 49 Our results lend credibility to this hypothesis, because a disadvantaged neighborhood environment was associated with a modest decrease in fecundability in the most disadvantaged areas.
A key challenge in analyzing social determinants of health is deciding when individual

Limitations
This study has some limitations. We assessed exposures based on participants' residential address and matched them to a corresponding census block group. Although the US Census aims for block groups to accurately represent neighborhoods, we do not know how participants interact with their surroundings, including time spent in nearby neighborhoods that may be ranked differently. [56][57][58] We also cannot consider time-activity patterns, housing characteristics, local pollution, or other factors that may yield differences between our exposure metric and participants' true neighborhood exposures. However, the prospective study design allows us to capture residential addresses before pregnancy is observed; thus, exposure misclassification is likely nondifferential with respect to the outcome.
We calculated fecundability using a combination of self-reported variables: pregnancy attempt time at study entry, LMP dates, usual cycle length, and pregnancy status. Each variable is measured with error, yielding some degree of potential outcome misclassification. However, previous work in PRESTO 32,59 has validated LMP dates and usual cycle length and shown that mean gestational age of pregnancy detection is 4 weeks (ie, before a missed period). Therefore, we have evidence that participants are accurately reporting outcome data. Analyses restricted to participants with fewer than 3 cycles of attempted pregnancy at enrollment yield results similar to those of the main model.
Thus, we do not anticipate that outcome misclassification is a large source of bias.
The composition of PRESTO's study population, pregnancy planners who enroll in a prospective cohort via the internet, is unique. Based on a prior validation study, 60 internet-based recruitment methods did not indicate biased etiologic associations. However, pregnancy planners may fundamentally differ from the general population at risk of pregnancy. Many PRESTO participants also have higher socioeconomic status compared with the general US population, per their household income and educational attainment. 50,61 Therefore, our results may not be generalizable to populations of lower socioeconomic status. Future research should include racially and ethnically diverse populations across the socioeconomic spectrum to better understand fecundability disparities.

Conclusions
Our findings underscore the importance of understanding the association between a disadvantaged neighborhood environment and reproductive health. In this study, residence in the most disadvantaged neighborhoods of the US was associated with reduced fecundability, a sensitive metric of fertility. If confirmed in other studies, our results suggest that policies and programs that address socioeconomic inequities may reduce infertility in local communities.