The effect of changing pregnancy intentions on preconception health behaviors: a prospective cohort study

PURPOSE

Pregnancy intentions are associated with preconception health behaviors but are understudied among female adolescent and young adult (AYA) cancer survivors. Preconception health is critical for survivors because they face unique risks to fertility and pregnancy from late effects of cancer treatments. This study prospectively assessed the effect of pregnancy intention on physical activity (PA) and smoking behaviors among female AYA survivors.

METHODS

A cohort of 1049 female AYA survivors were recruited between 2013 and 2017. Participants were 18-39 years and had completed primary cancer treatment. Longitudinal mixed effects analysis was conducted on participants who completed at least 2 of 4 questionnaires over 1.5 years. Two measures were used to capture multiple dimensions of pregnancy intention. The pregnancy intention score (PIS) captured wanting and planning dimensions and represented a scaled response of low to high intention. The trying dimension captured urgent intention and ranged from not trying, ambivalent (neither attempting nor avoiding pregnancy), and trying now. Intention change was assessed between each consecutive time points. Final analysis was conducted with multiple imputations.

RESULTS

Survivors with increased intention measured by trying was associated with increased PA over time (adjusted B [95%CI]: 0.3 [0.01, 0.5]) compared to survivors with no changes or decreased trying intention. PIS was not significantly associated with preconception behaviors. No measure of intention was associated with smoking behavior.

CONCLUSIONS

Increasingly urgent pregnancy intention (trying dimension) was associated with higher preconception PA.

IMPLICATIONS FOR CANCER SURVIVORS

Screening for immediate intentions can identify AYA survivors in need of early preconception health promotion.

Perceived and Objective Fertility Risk Among Female Survivors of Adolescent and Young Adult Cancer

Importance

Fertility is important to many survivors of adolescent and young adult (AYA) cancer, yet data on this population's fertility perceptions and their alignment with objective infertility risk are scant.

Objective

To assess whether estimated treatment gonadotoxicity and posttreatment menstrual pattern are associated with higher infertility risk perception.

Design, Setting, and Participants

This retrospective cohort study included female young adult survivors of cancer diagnosed between ages 15 and 39 years were recruited between March 25, 2015, and September 24, 2018, from 2 state cancer registries, social media, and clinician referrals to participate in a study of posttreatment ovarian function. Data analysis occurred between March 1 and September 1, 2022.

Exposures

Participants reported their menstrual pattern. Estimated treatment gonadotoxicity was ascertained through medical record review.

Main Outcomes and Measures

Participants reported infertility risk perception and were categorized as increased risk (feeling less fertile or unable to become pregnant) or no increased risk (feeling more or as fertile) compared with female individuals their age. Objective infertility risk was determined by estimated gonadotoxicity, menstrual pattern, and ovarian reserve testing of self-collected dried blood spots. Multivariable logistic regression identified factors associated with perceived infertility and underestimation or overestimation of infertility risk.

Results

This study included 785 female participants with a mean (SD) age of 33.2 (4.8) years at enrollment and 25.9 (5.7) years at diagnosis. Most participants self-identified their race and ethnicity as White (585 [74.5%]) and non-Hispanic (628 [78.7%]). Most participants (483 [61.5%]) perceived a higher risk of infertility compared with female participants their age. Prior exposure to moderate- or high-gonadotoxicity treatments was associated with higher odds of perceiving increased infertility risk compared with exposure to low-gonadotoxicity treatments (adjusted odds ratio [AOR], 2.73 [95% CI, 1.87-3.97] and 15.39 [95% CI, 5.52-42.96], respectively). Amenorrhea and irregular cycles were associated with higher odds of perceiving increased infertility risk (AOR, 3.98 [95% CI, 2.13-7.41] and 1.69 [95% CI, 1.19-2.40], respectively). Perceived infertility risk had minimal agreement with objective risk (κ = 0.19). Multiparity (AOR, 4.17 [95% CI, 2.61-6.64]) was associated with increased odds of underestimation, while older age (AOR, 0.94 [95% CI, 0.89-0.98]), endocrine comorbidity (AOR, 0.35 [95% CI, 0.18-0.69]), and prior infertility (AOR, 0.16 [95% CI, 0.07-0.38]) were associated with lower odds of underestimation. Multiparity (AOR, 0.48 [95% CI, 0.27-0.86]), breast cancer (AOR, 0.38 [95% CI, 0.20-0.73]), and skin cancer (AOR, 0.24 [95% CI, 0.11-0.51]) were associated with lower odds of overestimation.

Conclusions and Relevance

In this cohort study, survivors of AYA cancer had high rates of perceiving increased infertility risk but frequently overestimated or underestimated their risk. These findings suggest that counseling on infertility risk throughout survivorship may reduce misalignment between perceptions and actual risk, decrease fertility-related psychological distress, and inform family planning decisions.

Phenotypes of disease severity in a cohort of hospitalized COVID-19 patients: Results from the IMPACC study

BACKGROUND

Better understanding of the association between characteristics of patients hospitalized with coronavirus disease 2019 (COVID-19) and outcome is needed to further improve upon patient management.

METHODS

Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) is a prospective, observational study of 1164 patients from 20 hospitals across the United States. Disease severity was assessed using a 7-point ordinal scale based on degree of respiratory illness. Patients were prospectively surveyed for 1 year after discharge for post-acute sequalae of COVID-19 (PASC) through quarterly surveys. Demographics, comorbidities, radiographic findings, clinical laboratory values, SARS-CoV-2 PCR and serology were captured over a 28-day period. Multivariable logistic regression was performed.

FINDINGS

The median age was 59 years (interquartile range [IQR] 20); 711 (61%) were men; overall mortality was 14%, and 228 (20%) required invasive mechanical ventilation. Unsupervised clustering of ordinal score over time revealed distinct disease course trajectories. Risk factors associated with prolonged hospitalization or death by day 28 included age ≥ 65 years (odds ratio [OR], 2.01; 95% CI 1.28-3.17), Hispanic ethnicity (OR, 1.71; 95% CI 1.13-2.57), elevated baseline creatinine (OR 2.80; 95% CI 1.63- 4.80) or troponin (OR 1.89; 95% 1.03-3.47), baseline lymphopenia (OR 2.19; 95% CI 1.61-2.97), presence of infiltrate by chest imaging (OR 3.16; 95% CI 1.96-5.10), and high SARS-CoV2 viral load (OR 1.53; 95% CI 1.17-2.00). Fatal cases had the lowest ratio of SARS-CoV-2 antibody to viral load levels compared to other trajectories over time (p=0.001). 589 survivors (51%) completed at least one survey at follow-up with 305 (52%) having at least one symptom consistent with PASC, most commonly dyspnea (56% among symptomatic patients). Female sex was the only associated risk factor for PASC.

INTERPRETATION

Integration of PCR cycle threshold, and antibody values with demographics, comorbidities, and laboratory/radiographic findings identified risk factors for 28-day outcome severity, though only female sex was associated with PASC. Longitudinal clinical phenotyping offers important insights, and provides a framework for immunophenotyping for acute and long COVID-19.

FUNDING

NIH.

Anti-nucleocapsid antibody levels and pulmonary comorbid conditions are linked to post-COVID-19 syndrome

BACKGROUNDProlonged symptoms after SARS-CoV-2 infection are well documented. However, which factors influence development of long-term symptoms, how symptoms vary across ethnic groups, and whether long-term symptoms correlate with biomarkers are points that remain elusive.METHODSAdult SARS-CoV-2 reverse transcription PCR-positive (RT-PCR-positive) patients were recruited at Stanford from March 2020 to February 2021. Study participants were seen for in-person visits at diagnosis and every 1-3 months for up to 1 year after diagnosis; they completed symptom surveys and underwent blood draws and nasal swab collections at each visit.RESULTSOur cohort (n = 617) ranged from asymptomatic to critical COVID-19 infections. In total, 40% of participants reported at least 1 symptom associated with COVID-19 six months after diagnosis. Median time from diagnosis to first resolution of all symptoms was 44 days; median time from diagnosis to sustained symptom resolution with no recurring symptoms for 1 month or longer was 214 days. Anti-nucleocapsid IgG level in the first week after positive RT-PCR test and history of lung disease were associated with time to sustained symptom resolution. COVID-19 disease severity, ethnicity, age, sex, and remdesivir use did not affect time to sustained symptom resolution.CONCLUSIONWe found that all disease severities had a similar risk of developing post-COVID-19 syndrome in an ethnically diverse population. Comorbid lung disease and lower levels of initial IgG response to SARS-CoV-2 nucleocapsid antigen were associated with longer symptom duration.TRIAL REGISTRATIONClinicalTrials.gov, NCT04373148.FUNDINGNIH UL1TR003142 CTSA grant, NIH U54CA260517 grant, NIEHS R21 ES03304901, Sean N Parker Center for Allergy and Asthma Research at Stanford University, Chan Zuckerberg Biohub, Chan Zuckerberg Initiative, Sunshine Foundation, Crown Foundation, and Parker Foundation.

Association between pregnancy intention and preconception health behaviors

BACKGROUND

Female adolescent and young adult (AYA) cancer survivors face higher infertility and pregnancy risks than peers with no cancer history. Preconception health behaviors such as physical activity (PA), tobacco smoking, and alcohol intake influence reproductive outcomes. In general populations, pregnancy intention is positively associated with healthy preconception behaviors, but it has not been studied among AYA survivors. The authors hypothesized that higher pregnancy intention would be associated with healthier behaviors, especially among AYA survivors with perceived infertility risk.

METHODS

A cross-sectional analysis was conducted with data collected between 2013 and 2017 from 1071 female AYA survivors aged 18 to 39 years who had completed their primary cancer treatment and enrolled in an ovarian function study. Self-reported intention dimensions were measured as a pregnancy intention score (PIS) and trying now to become pregnant. Multivariable linear (PA), binary (smoking), and ordinal (alcohol use) logistic regressions were used to estimate associations between intentions and preconception behaviors, with adjustments made for demographic and cancer characteristics. Effect modification by perceived infertility risk was assessed.

RESULTS

The mean PIS was 1.1 (SD, 0.77) on a 0 to 2 scale (2 = high intention), and 8.9% were attempting pregnancy now. A higher PIS was associated with increased PA (β, 0.08; 95% CI, 0.11-1.04), whereas ambivalence in pregnancy intention was associated with lower alcohol consumption (odds ratio, 0.72; 95% CI, 0.55-0.95). Pregnancy intentions were not associated with smoking. Perceived infertility risk strengthened the relationship between PIS and PA (P < .05).

CONCLUSIONS

Pregnancy intentions were associated with some healthier preconception behaviors in AYA survivors. Medical professionals caring for AYA survivors may consider pregnancy intention screening to guide conversations on preconception health.

Profiles of a Health Information-Seeking Population and the Current Digital Divide: Cross-Sectional Analysis of the 2015-2016 California Health Interview Survey

Background

Internet use for health information is important, given the rise of electronic health (eHealth) that integrates technology into health care. Despite the perceived widespread use of the internet, a persistent “digital divide” exists in which many individuals have ready access to the internet and others do not. To date, most published reports have compared characteristics of internet users seeking health information vs nonusers. However, there is little understanding of the differences between internet users seeking health information online and users who do not seek such information online. Understanding these differences could enable targeted outreach for health interventions and promotion of eHealth technologies.

Objective

This study aims to assess population-level characteristics associated with different types of internet use, particularly for seeking online health information.

Methods

The 2015-2016 California Health Interview Survey datasets were used for this study. Internet use was classified as never used the internet (Never use), ever used the internet but not to search for health information in the last 12 months (Use not for health), and ever used the internet and have used it to search for health information in the last 12 months (Use for health). Weighted multinomial logistic regression was used to assess sociodemographic and health characteristics associated with types of internet use. Findings are reported as odds ratios (ORs) with 95% CIs.

Results

Among 42,087 participants (weighted sample of 29,236,426), 19% reported Never Use of the internet, 27.9% reported Use not for health, and 53.1% reported Use for health. Compared to Never Use individuals, Use for health individuals were more likely to be younger (OR: 0.1, 95% CI 0.1-0.2 for ≥60 years vs <60 years), female (OR: 1.6, 95% CI 1.3-1.9 compared to males), and non-Hispanic white (OR: 0.54, 95% CI 0.4-0.7 for Latinos and OR: 0.2, 95% CI 0.2-0.4 for African Americans) and have a higher socioeconomic status (>400% of Federal Poverty Guidelines; OR: 1.3, 95% CI 1.4-2.4). Overall, characteristics for the Use not for health and Use for health groups were similar, except for those with lower levels of education and respondents not having visited a physician in the last year. For these two characteristics, the Use not for health group was more similar to the Never Use group.

Conclusions

Our findings indicate that a digital divide characterized by sociodemographic and health information exists across three types of users. Our results are in line with those of previous studies on the divide, specifically with regard to disparities in use and access related to age, race/ethnicity, and socioeconomic status. Disparities in online health-seeking behavior may reflect existing disparities in health care access extending into a new era of health technology. These findings support the need for interventions to target internet access and health literacy among Never Use and Use not for health groups.