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Record W4399790791 · doi:10.1111/ppe.13100

Pre‐existing conditions and pregnancy: A call to action for multidisciplinary, patient‐centred care

2024· article· en· W4399790791 on OpenAlexafffundabout
Hilary K. Brown

Bibliographic record

VenuePaediatric and Perinatal Epidemiology · 2024
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsThe Scarborough HospitalPublic Health OntarioUniversity of Toronto
FundersCanada Research Chairs
KeywordsMedicinePregnancyMultidisciplinary approachHealth carePrenatal careAnxietyDiseaseFamily medicineCall to actionNursingPsychiatryPopulationEnvironmental health

Abstract

fetched live from OpenAlex

The World Health Organization recommends a risk-oriented strategy to the delivery of prenatal care, including the provision of routine care to all women and additional or specialised care to those with moderate-to-severe diseases or complications.1 However, a growing number of studies highlight a lack of structured models of care for women requiring specialised care during pregnancy. Women with pre-existing conditions such as diabetes mellitus, cardiovascular disease and rheumatoid arthritis report fragmentation across their obstetric care providers and other specialists, with significant issues related to organisation of care and communication between providers.2 Obstetric care providers similarly report that current care structures do not encourage interprofessional communication or shared deliberation.3 Given the lack of existing structures to provide multidisciplinary, patient-centred care to pregnant women with pre-existing conditions, one might imagine that having such a condition in pregnancy is uncommon. However, in this issue of Paediatric and Perinatal Epidemiology, Lundborg and colleagues4 show this is not the case—in fact, one in four women enter pregnancy with a pre-existing condition. Lundborg and colleagues explored temporal changes in the prevalence of pre-existing conditions in pregnant women over a 20-year period in British Columbia, Canada. Using linked physician and hospital data covering 99% of livebirths and stillbirths in the province, they found 26.2% of women had a pre-existing condition recorded in the 5 years before conception, with the most common diagnoses being for depressive disorders, anxiety disorders, bipolar disorder, chronic hypertension and diabetes mellitus. Notably, while the overall prevalence of having a pre-existing condition remained relatively stable over time, prevalence rates of anxiety, bipolar, psychosis, and eating disorders increased considerably, as did rates of diabetes mellitus, stroke, systemic lupus erythematosus, multiple sclerosis and chronic kidney disease. Age–period–cohort analyses also revealed a birth cohort effect whereby increases in psychiatric disorders over time were particularly striking for women born after 1985. Lundborg and colleagues' study makes an important contribution to the literature, with comprehensive ascertainment of pre-existing conditions using validated algorithms applied to population-based data. Some limitations of the analysis point to priorities for future research. For example, while the authors included a comprehensive list of conditions that are relevant for pregnancy outcomes, such as severe maternal and neonatal morbidity/mortality, they did not include asthma, migraine or thyroid disorders. The omission of migraine is especially important. A recent umbrella review found migraine was a significant risk factor for preeclampsia (pooled OR 2.05, 95% CI 1.47–2.84) and preterm birth (pooled OR 1.26, 95% CI 1.21–1.32).5 Migraine is one of the most common causes of disability in reproductive-aged women and commonly co-occurs with other chronic conditions,5 making it an important consideration in prenatal care that should be included in population estimates of pre-existing conditions in pregnancy. The authors were also limited in their ability to measure the chronicity of the conditions. Conditions such as diabetes mellitus and cardiovascular disease are reasonably captured using data from healthcare encounters over a 5-year lookback period and can be assumed to still be present during the pregnancy. Ascertainment of psychiatric disorders, such as depressive and anxiety disorders which may remit and relapse, is more complex. Nevertheless, knowledge of a person's psychiatric history, even if symptoms were not present at the time of pregnancy, is still relevant, given risks of perinatal relapse and associated complications and treatment decisions.6 Interestingly, ascertainment of psychiatric disorders in health administrative data is also heavily influenced by factors that affect a person's likelihood of seeking care, such as health literacy, social acceptability, and service availability. While the increase in psychiatric disorders over time is concerning, future research could examine the influence of such factors on this trend. Another limitation that impacts many studies using health administrative data, including this one, is the inability to measure condition severity. Therefore, in the examination of the prevalence of ‘any pre-existing condition’, a woman with mild, well-controlled diabetes mellitus, for example, is weighted equally as one with severe cardiovascular disease. Notably, commonly used weighted indices like the Charlson comorbidity index were created in ageing, hospitalised patients and are therefore inappropriate for use in reproductive-aged women, while obstetric comorbidity indices usually include both conditions arising before (e.g., chronic hypertension) and during pregnancy (e.g., gestational hypertension), also making them inappropriate for use in the current context.7 Efforts to create weighted indices of pre-existing conditions according to their severity, including their associated risks of adverse outcomes such as severe maternal and neonatal morbidity/mortality, could be useful for understanding the relative importance of various pre-existing conditions in pregnancy-related prevalence studies. Another consideration relates to Lundborg and colleagues' study population. While the authors used the conventional denominator of livebirths and stillbirths ≥22 weeks gestation, it is possible that the burden of pre-existing conditions is greater in the entire pregnant population—that is, including pregnancies ending in a miscarriage or induced abortion. Measurement of miscarriage in health administrative data is challenging because many losses occur without an associated healthcare encounter, and many more occur before the pregnancy is even clinically detectable.8 This means studies that look at all pregnant women only capture a subset with a recognised pregnancy—that is, pregnancies resulting in a healthcare encounter. Nevertheless, examination of trends in the prevalence of pre-existing conditions among such recognised pregnancies is critical given that miscarriage and induced abortion make up a substantial proportion of all pregnancies and are elevated in women with some pre-existing conditions.9, 10 Knowledge of the prevalence of pre-existing conditions among women going into a pregnancy—regardless of its outcome—is important for informing preconception and early prenatal care. Lundborg and colleagues' study provides important epidemiologic data that will be useful for informing preconception and prenatal care in Canada and elsewhere. Given that one in four women enter pregnancy with a pre-existing condition, this study highlights an urgent need for robust preconception care strategies aimed at chronic disease prevention by addressing upstream social determinants of health and lifestyle factors such as nutrition and exercise, as well as efforts to optimise disease management and provide resources for pregnancy planning, including medication counselling, among women with an existing condition. Findings also have implications for care of women with pre-existing conditions during pregnancy. As described by Lundborg and colleagues, this includes the need for structured multidisciplinary, patient-centred care approaches to improve communication and cooperation across obstetric care providers and other specialists. Such approaches require an understanding of women's social needs (e.g., poverty), which often accompany chronic illness, and the clinical needs of those with multiple co-occurring conditions. Multidisciplinary, person-centred models of care are increasingly being used for cancer, cardiovascular disease and medical complexity in older populations. Lundborg and colleagues' study shows pregnancy is a missed opportunity for the development and use of similar models of care in obstetric settings. Finally, the high, and rising, prevalence of psychiatric disorders—particularly in women born after 1985—is concerning and points to a need for collaborative mental healthcare approaches in preconception and obstetric care settings to prevent and manage mental illness. As risk factors for pre-existing conditions in pregnancy, such as older maternal age and obesity, continue to rise, efforts aimed at preventing and managing these conditions across the reproductive life course, including during pregnancy, will become increasingly important. Hilary K. Brown is supported by a Tier 2 Canada Research Chair in Disability and Reproductive Health (2019-00158). None to declare. Hilary K. Brown is an Associate Professor at the University of Toronto in the Department of Health & Society and the Dalla Lana School of Public Health. Dr Brown holds a Tier 2 Canada Research Chair in Disability & Reproductive Health. Her research programme uses epidemiologic methods to examine maternal and child health and mental health across the life course, focusing on populations with disabilities and chronic illness, health equity and the social determinants of health. Her most recent research efforts aimed to understand the pregnancy outcomes and care experiences of women with multiple chronic conditions. Not applicable.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.070
GPT teacher head0.390
Teacher spread0.320 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2024
Admission routes3
Has abstractyes

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