Environmental effects on mental health

EHR data on 8,949 pregnant women from an urban aca demic medical center from 2015 to 2017 were extracted. The cohort inclusion and exclusion criteria are described in Fig. 1. We excluded patients whose ages were below 18 or above 45, had no encounter recorded in the EHR from 1 year prior to pregnancy to 1 year after delivery, or missing home locations
information. We extracted patient information including gender, age, race, ethnicity, body mass index (BMI), marital status, outpatient and inpatient diagnoses, outpatient and in patient pre scription medication orders, and corresponding encounter dates from the EHR data. Patient age was calculated as the time difference between the birth date and first
prenatal checkup date. The gestational week was calculated using the date of delivery and the specific gestational age at prenatal checkups. Marital status was defined as single (single, divorced, widowed, unknown), and married, as extracted from unstructured clinical notes using regular expression. The trimester of each Page 3 of 11 event was determined using the difference in time between each event and delivery.

Translation:

Electronic health record (EHR) data was collected from 8,949 pregnant women at a city-based teaching hospital between 2015 and 2017. The study focused on patients aged 18 to 45 who had EHR records available from one year before pregnancy through one year after giving birth and whose home addresses were known. Information gathered included gender, age, race, ethnicity, BMI, marital status, diagnoses, medication orders, and corresponding dates. Age was calculated from the patient’s birth date and first prenatal visit. Gestational age was determined using delivery dates and prenatal checkup records. Marital status was categorized as single or married based on notes in the EHR. Each medical event was also assigned to a specific trimester of pregnancy by comparing its date with the delivery date.

The built environment, referring to the surroundings and physical artifacts of where humans live, is considered to be one of the five major social determinants of health (SDoH) [1]. The built environment is strongly as sociated with our way of life through determining the housing quality, mode of transportation, and exposure to pollutants, among others. Poor built environment has been reported to lead to adverse effects on physical and mental health by disrupting sleep, hindering healthy lifestyles, and lowering access to healthcare [2–5]. There is a gender difference in the association between the built environment and health.

Translation:

The built environment meaning the places we live, the buildings around us, and the physical spaces we use is one of the five main social factors that affect health. It shapes how we live by influencing the quality of our housing, how we get around, and what pollutants we’re exposed to. Research has shown that a poor built environment can harm both physical and mental health by disrupting sleep, making it harder to live a healthy lifestyle, and limiting access to healthcare. Studies also suggest that the way the built environment affects health can differ between men and women.

Table1shows the descriptive statistics of the study cohort where continuous variables are presented as mean (standard deviation (SD)), and categorical variables are presented as N  (%in total cohort). The average age of our patient population was 33.7 years (SD=4.59). Nearly half (49.27%) of the patients were White, and the majority were married (86.7%) and had Commercial insurances (84.0%). Over 3% of the cohort were diagnosed with PPD. A total of 3,900 (43.6%) and 482 (5.4%) patients had at least one ED visit pre-and post-delivery. We identified 3 clusters with 1,934 (cluster 1), 4,129 (cluster 2), and2,886 (cluster 3) patients, respectively, based on their clinical event sequences. For the primary outcome of PPD,6.72%of the women in cluster 1 had a diagnosis of PPD within 1 year after childbirth, which was higher than clusters 2 (2.66%) and 3 (1.14%) (P<.001).

Translation:

The study group’s characteristics are shown in the table. The average age of patients was 33.7 years. Nearly half (49.27%) were White, the majority were married (86.7%) and had Commercial health insurance (84.0%). Over 3% were diagnosed with PRD. In total, 43.6% visited the emergency department before delivery and 5.4% visited after. Patients were grouped into three clusters based on their medical history. The primary outcome was postpartum depression (PPD). The rate of PPD within a year after childbirth was 6.72% in the first group, which was significantly higher than in the second group (2.66%) and the third group (1.14%).

There were two major findings in this study. Three clusters of prenatal health and healthcare utilization patterns were discovered from a cohort of women whose pregnancies were managed entirely or partially in an urban academic medical center from 2015 to 2017. The distribution of the primary outcome, PPD, was significantly different across the clusters. Clinically, the clusters differed in maternal age, BMI, marital status, medication use, chronic conditions, and complications during pregnancy. In addition, we found that the cluster membership was associated with built environment factors related to walkability, access to retail re sources, air quality, and neighborhood income equality. These findings contribute to the growing body of evidence that the built environment in the community confers an impact on the trajectories of health and health service utilization during pregnancy. The associations found between retail, land use and the study outcomes among the pregnant cohort are novel and important contributions to the literature.

Translation:

This study found two main things. First, pregnant women treated at a city hospital from 2015 to 2017 could be grouped into three distinct patterns based on their prenatal health and how much they used healthcare. These groups had very different rates of postpartum depression (PPD). The groups also differed in characteristics like the mother’s age, BMI, marital status, medication use, and health issues during pregnancy. Second, the group a woman fell into was linked to features of her neighborhood’s built environment, like how walkable it is, access to stores, air quality, and income equality in the area. This adds to the evidence that where a person lives can affect their health and healthcare use during pregnancy. The connections found between neighborhood features like stores and land use and pregnancy outcomes are new and important findings.

Based on the article’s findings, future research should focus on translating the connections between neighborhood design and maternal health into real-world solutions. The logical next step is to move from observation to action by designing and testing targeted interventions. For example, studies could evaluate whether programs that improve access to prenatal care in underserved neighborhoods, create safe walking paths, or provide vouchers for healthy food directly lead to better pregnancy outcomes and lower rates of postpartum depression in the identified high-risk groups. Furthermore, this work needs to be expanded beyond a single urban hospital to see if these patterns hold true in rural areas, different healthcare systems, and more diverse populations over time. Crucially, researchers should also investigate why these links exist by examining the specific pathways, such as stress levels, physical activity, or social support, through which factors like walkability and air quality actually affect a mother’s health. Ultimately, the goal of follow-up research should be to use these insights to build personalized care plans for at-risk mothers and to inform housing, transportation, and urban planning policies that create healthier communities for all families.

Based on the text provided, the most challenging aspect to understand was the specific methodology used to create the three distinct patient clusters. The article states that clusters were formed based on “clinical event sequences,” but it does not clearly explain what specific events were analyzed (e.g. specific diagnoses, types of medication orders, or timing of emergency department visits) or what computational technique (like a specific machine learning algorithm) was used to group thousands of patients into these meaningful patterns. Without this detail, it is difficult to fully grasp how the researchers moved from raw electronic health record data to the final, interpretable clusters that showed such strong differences in outcomes, making the core analytical step of the study somewhat opaque.