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Abstract

Introduction

Preterm birth (PTB) is a leading global cause of neonatal morbidity and mortality. While individual maternal chronic conditions are established risk factors, the role of maternal multimorbidity remains underexplored. This study aimed to examine the association between maternal multimorbidity and PTB, and to identify chronic conditions associated with the occurrence and severity of PTB.

Methods

A retrospective matched case–control study was conducted at Archbishop Makarios III Hospital in Nicosia, Cyprus. The sample included 978 singleton live births, consisting of 489 preterm cases (<37 weeks) matched 1:1 with 489 term controls (≥37 weeks) by maternal age and country of origin. Data were extracted from patient’s medical records. Multimorbidity was defined as the presence of two or more chronic conditions. Conditional logistic regression assessed associations with PTB, and binary logistic regression examined predictors of extreme/very PTB (<32 weeks) versus moderate/late PTB (32 to <37 weeks).

Results

Maternal multimorbidity was associated with increased odds of PTB (aOR = 1.80; 95% CI: 1.16–2.79; p=0.009). Hypertension (aOR=4.26; 95% CI: 1.84–9.86), kidney disease (aOR=3.67; 95% CI: 1.01–13.30), thrombophilia (aOR=3.53; 95% CI: 1.14–10.88), thyroid disorders (aOR = 1.77; 95% CI: 1.05–2.98), and allergies (aOR=1.82; 95% CI: 1.12–2.99) were independently associated with PTB. Diabetes was inversely associated with extreme PTB (aOR=0.19; 95% CI: 0.10–0.92).

Conclusions

Maternal multimorbidity and several chronic conditions are significant and independent predictors of PTB. These findings underscore the importance of comprehensive antenatal screening and integrated care for women with multiple health conditions. Tailored risk assessment strategies may help reduce the burden of PTB, particularly in populations with rising rates of chronic disease.

Keywords

multimorbidity preterm birth maternal health chronic diseases Cyprus

Introduction

Preterm birth (PTB), defined as delivery before 37 completed weeks of gestation, remains a leading cause of neonatal mortality and long-term morbidity worldwide.^1–3 Each year, more than 15 million infants are born preterm, with the highest burden observed in low- and middle-income countries. Despite advances in perinatal care, PTB continues to pose major clinical and public health challenges, contributing to long-term complications such as neurodevelopmental impairments, chronic respiratory disease, and increased healthcare utilization⁴

Multimorbidity, defined as the presence of two or more chronic health conditions in the single individual,⁵ is increasingly common among women of reproductive age. This trend is attributed to delayed childbearing, changing lifestyles, and enhanced diagnostic capacity.⁶ Recent population-based studies show that multimorbidity in pregnancy is relatively common. In the United Kingdom, the prevalence of pre-existing multimorbidity among pregnant women ranged from about 20% to 46%, depending on the data source and definition used.⁷ Similarly, data from Northern Ireland between 2012 and 2020 reported multimorbidity rates of 18–23%, with complex multimorbidity (≥3 organ systems) affecting up to 6% of pregnancies.⁸ Although individual conditions such as hypertension, diabetes, thyroid disorders, psychological disorders and Hepatitis B virus (HBV) infection have been associated with PTB risk,^9–12 their combined effect through maternal multimorbidity may pose an even greater risk. The cumulative burden may result in additive or synergistic physiological stress, contributing to systemic inflammation, placental dysfunction, and medically indicated early delivery.¹³

Yet, multimorbidity remains understudied as distinct risk factor in obstetric research. Most existing studies focus on single conditions, potentially underestimating the complexity of maternal health.^14–16 A recent systematic review found that maternal multimorbidity is associated with a significantly increased risk of PTB (OR = 4.28; 95% CI: 2.23–6.34).¹⁷ Moreover, limited attention has been paid to how multimorbidity influences the severity or timing of PTB, particularly in distinguishing extreme or very PTB from moderate or late preterm deliveries.¹⁸ Addressing this gap is essential for improving antenatal risk stratification and guiding more integrated, patient-centered care models, especially in health systems adapting to rising rates of chronic disease among reproductive-age women.¹⁹

In Cyprus, the PTB rate was reported at 13.1% in 2014, among the highest in Europe.²⁰ This elevated prevalence, along with an aging maternal population and evolving health risk profiles, underscores the need to better understand maternal risk factors contributing to PTB. In Cyprus, more than one quarter (∼28.6%) of the adult general population is estimated to live with multimorbidity, with rates rising to nearly 70% among the elderly.²¹ Studying these associations in Cyprus provides a valuable opportunity to expand evidence base. As a small, high-income Mediterranean country with universal health coverage and a rising prevalence of maternal comorbidities, Cyprus offers a relevant model for similar populations. Although a previous study has examined individual maternal conditions,²⁰ none have addressed the cumulative impact of multiple chronic conditions on PTB risk.

This study aims to examine the association between maternal multimorbidity and the risk of PTB in a hospital-based matched sample in Cyprus, representing the main public maternity service of the country, treating the majority of PTBs. Secondary objectives include evaluating the contribution of specific chronic conditions to PTB and assessing whether multimorbidity or individual conditions are associated with PTB severity.

Materials and methods

Study design and setting

This retrospective matched case-control was conducted at Archbishop Makarios III Hospital in Nicosia, Cyprus, the national referral center for maternal and neonatal care, operating under the State Health Services Organization (SHSO). It houses one of the country’s tertiary-level Neonatal Intensive Care Units (NICU), where most preterm births in Cyprus are centralized. A case-control design was chosen as it offers an efficient approach to studying rare outcomes by comparing affected and unaffected individuals.²² The study adheres to the Strengthening of the Reporting of Observational Studies in Epidemiology (STROBE) guidelines for observational research.²³

Participants and matching

The study sample included women who delivered at Archbishop Makarios III Hospital between January 2019 and December 2022. Exclusion criteria were stillbirths, multiple gestations, and incomplete medical documentation. Eligible cases of PTB were identified using the hospital’s birth registry, based on patient identification numbers and delivery dates. Each case was individually matched to one control (1:1 ratio) based on maternal age (±3 years) and country of origin. These matching variables were selected to account for biological and sociodemographic factors associated with PTB risk. Controls were defined as women delivering term infants (≥37 weeks of gestation) during the same period. An overview of participant selection and matching is presented in Figure 1.

Figure 1.

Flow diagram of participant inclusion and matching.

Data sources

All data were retrospectively extracted from hospital-based paper medical records. A standardized data extraction form was developed to ensure consistency and minimize abstraction errors across reviewers. Records that lacked key information in essential domains such as maternal medical history were excluded from analysis. The data collection process was conducted between January 2024 and April 2025 and covered births from January 2019 through December 2022.

Variables and definitions

The primary outcome of interest was PTB, defined as any live birth occurring before 37 completed weeks of gestation. Controls were defined as term births, occurring at or beyond 37 weeks of gestation (≥37 weeks). Among PTBs, a further classification distinguished between extreme to very preterm births, defined as births before 32 completed and moderate to late preterm births defined as births from 32 weeks to before the completion of 37 weeks.

The primary exposures were maternal multimorbidity and maternal medical history. Multimorbidity was defined as the co-existence of two or more chronic health conditions during or prior to pregnancy.²⁴ Chronic conditions were identified through structured entries in medical records and included diabetes, hypertension, cardiovascular disease, kidney disease, thyroid disorders, thrombophilia, epilepsy, autoimmune disorders, psychological disorders, HBV, hepatitis C virus (HCV), human immunodeficiency virus (HIV).²⁵ These conditions were selected because they were pre-specified within the medical record system however, clinicians also had the option to record additional conditions not included in the predefined list. Each condition was recorded as a binary variable (present/absent) and carried equal weight in the definition of multimorbidity. No condition clustering or weighting was applied.

Several covariates were included in the multivariable models based on their theoretical and empirical relevance to PTB. Maternal age was analysed as a continuous variable and used for matching. Country of origin was categorized as Cypriot or non-Cypriot and included in matching. Parity was categorized into four groups: nulliparous (0 prior live births), primiparous (1), multiparous (2–4), and grand multiparous (≥5). Maternal body mass index (BMI) was calculated from pre-pregnancy or first-trimester weight and height and treated as a continuous variable. Maternal smoking status during pregnancy was extracted from prenatal care documentation and categorized as a binary variable (yes/no). All data were entered and managed using a secure, encrypted Microsoft Excel spreadsheet developed and maintained by the SHSO’s Research and Innovation Centre. Access was restricted to the lead investigator and authorized study personnel.

Statistical analysis

Descriptive statistics were calculated to characterize the sample for maternal multimorbidity, individual chronic conditions and PTB. In the primary matched analysis (preterm vs. term births), binary variables were compared using McNemar’s test. For the secondary analysis comparing extreme to very preterm with moderate to late preterm births (an unmatched comparison), chi-squared or Fisher’s exact tests were applied depending on cell size.

Univariable analyses were conducted using conditional logistic regression for the matched case-control comparison, and binary logistic regression for the unmatched subgroup analysis. Crude odds ratios (ORs), 95% confidence intervals (CIs), and corresponding p-values were reported. Multivariable models were constructed to examine the associations between maternal multimorbidity and PTB, while controlling for chronic conditions (e.g. diabetes, hypertension, thyroid disease, HBV, psychiatric disorders) and adjusting for covariates (maternal age, country of origin, parity, BMI, and smoking status). Conditional logistic regression was used for the primary matched analysis (preterm vs. term), and binary logistic regression was used for the secondary subgroup analysis.

Multicollinearity was assessed using Variance Inflation Factor (VIF) and tolerance values, with VIF >5 and tolerance <0.2 considered indicative of collinearity. Interaction terms were also explored using binary logistic regression to assess for effect modification. Where significant interactions were identified, we include them in the final model and interpret them. Statistical significance was set at p<0.05 for all analyses, and all tests were two-sided. Statistical analyses were performed using IBM SPSS Statistics version 30.0 (IBM Corp., Armonk, NY, USA).

Results

Out of 3648 live births conducted during the study period, 469 cases (<37 weeks) were excluded due to incomplete documentation (n = 178), multiple pregnancies (n = 180), and stillbirths (n = 111). After these exclusions, 978 singleton live births with complete data were included in the final analysis (489 cases and 489 matched controls) (Figure 1). A post hoc power analysis of the multivariable logistic regression model confirmed that the available sample size provided sufficient statistical power (>99% at α = 0.05) to detect the observed associations.

Maternal sociodemographic and health behaviors

The median maternal age of the study sample was 30 years, and the median BMI was 25.85 kg/m². Of the 978 participants, 512 were of Cypriot origin and 466 were non-Cypriots. Of the participants, 56 women had primary or no education, 345 had secondary education, and 548 had tertiary education. A total of 174 women reported smoking during or before pregnancy. Regarding parity, 457 were nulliparous (0 live births), 324 were primiparous (1 live birth), 187 were multiparous (2–4 live births), and 9 were grand multiparous (≥5 live births).

Maternal multimorbidity and health conditions

Table 1 summarizes the distribution of maternal health conditions across the study sample, stratified by PTB status and by the degree of prematurity. Multimorbidity was observed in 14.3% of women with PTB compared to 8.6% among term births, indicating a significantly higher burden of coexisting chronic conditions in the PTB group (p=0.005).

Table 1.

Information about maternal medical history and multimorbidity in the overall population by prematurity status (Preterm birth vs. Term birth) and by degree of prematurity (Extreme to very preterm vs. Moderate to late preterm).

Characteristics	Overall (n=978)	Prematurity status			Degree of prematurity (n=489)
Characteristics	Overall (n=978)	Preterm birth (<37 weeks) (n=489)	Term birth (>37 weeks) (n=489)	p-value	Extreme to very preterm (<32 weeks) (n=142)	Moderate to late preterm (32 to <37 weeks) (n=347)	p-value
Multimorbidity, n (%) (n=978)
Yes	112 (11.5)	70 (14.3)	42 (8.6)	0.005 ^a	21 (14.8)	49 (14.1)	0.848^b
No	866 (88.5)	419 (94.5)	447 (97.6)	0.005 ^a	121 (85.2)	298 (85.9)	0.848^b
Diabetes^d, n (%) (n=978)
Yes	59 (6.0)	25 (5.1)	34 (7.0)	0.283^a	2 (1.4)	23 (6.6)	0.021 ^c
No	919 (94.0)	464 (94.9)	455 (93.0)	0.283^a	140 (98.6)	325 (93.4)	0.021 ^c
Cardiovascular diseases, n (%) (n=978)
Yes	32 (3.3)	21 (4.3)	11 (2.2)	0.100^a	5 (3.5)	16 (4.6)	0.806^c
No	946 (96.7)	468 (95.7)	478 (97.8)	0.100^a	137 (96.5)	331 (95.4)	0.806^c
Epilepsy, n (%) (n=978)
Yes	9 (0.9)	5 (1.0)	4 (0.8)	1.00^a	2 (1.4)	3 (0.9)	0.631^c
No	969 (99.1)	484 (99.0)	485 (99.2)	1.00^a	140 (99.1)	344 (98.6)	0.631^c
Hypertension, n (%) (n=977)
Yes	41 (4.2)	32 (6.5)	9 (1.8)	<0.001 ^a	10 (7.0)	22 (6.3)	0.776^b
No	936 (95.8)	457 (93.5)	479 (98.2)	<0.001 ^a	132 (93.0)	325 (93.7)	0.776^b
Kidney disease, n (%) (n=975)
Yes	16 (1.6)	12 (2.5)	4 (0.8)	0.057^a	4 (2.8)	8 (2.3)	0.752^c
No	959 (98.4)	476 (97.5)	483 (99.2)	0.057^a	138 (97.2)	338 (97.7)	0.752^c
Thrombophilia, n (%) (n=978)
Yes	18 (1.8)	14 (2.9)	4 (0.8)	0.031 ^a	5 (3.5)	9 (2.6)	0.560^c
No	960 (98.2)	475 (97.1)	485 (99.2)	0.031 ^a	137 (96.5)	338 (97.4)	0.560^c
Thyroid disease, n (%) (n=978)
Yes	73 (7.5)	45 (9.2)	28 (5.7)	0.047 ^a	13 (9.2)	32 (9.2)	0.981^b
No	905 (92.5)	444 (90.8)	461 (94.3)	0.047 ^a	129 (90.8)	315 (90.8)	0.981^b
Allergies, n (%) (n=978)
Yes	82 (8.4)	52 (10.6)	30 (6.1)	0.015 ^a	15 (10.6)	37 (10.7)	0.974^b
No	896 (91.6)	437 (89.4)	459 (93.9)	0.015 ^a	127 (89.4)	310 (89.3)	0.974^b
Psychological disorders, n (%) (n=978)
Yes	32 (3.2)	17 (3.5)	15 (3.1)	0.860^a	1 (0.7)	16 (4.6)	0.030 ^c
No	946 (96.7)	472 (96.5)	474 (96.9)	0.860^a	141 (99.3)	331 (95.4)	0.030 ^c
Autoimmune diseases, n (%) (n=978)
Yes	22 (2.2)	15 (3.1)	7 (1.4)	0.134^a	3 (2.1)	12 (3.5)	0.570^c
No	956 (97.8)	474 (96.9)	482 (98.6)	0.134^a	139 (97.9)	335 (96.5)	0.570^c
Hepatitis B virus (HBV), n (%) (n=977)
Positive	11 (1.1)	7 (1.4)	4 (0.8)	0.508^a	4 (2.8)	3 (0.9)	0.202^b
Negative	966 (98.9)	481 (98.6)	485 (99.2)	0.508^a	138 (97.2)	343 (99.1)	0.202^b
Hepatitis C virus (HCV), n (%) (n=977)
Positive	7 (0.7)	5 (1.0)	2 (0.4)	0.453^a	3 (2.1)	2 (0.6)	0.150^b
Negative	970 (99.3)	483 (99.0)	487 (99.6)	0.453^a	139 (97.9)	344 (99.4)	0.150^b
Human immunodeficiency virus (HIV), n (%) (n=977)
Positive	4 (0.4)	3 (0.6)	1 (0.2)	0.625^a	1 (0.7)	2 (0.6)	1.00^b
Negative	973 (99.6)	485 (99.4)	488 (99.8)	0.625^a	141 (99.3)	344 (99.4)	1.00^b

^aNotes: Bold values indicate statistical significance (p value < 0.05).Differences between cases (preterm birth) and controls (term birth) were analysed using McNemar’s Test for paired nominal data.

^bDifferences between categories of preterm birth (extreme to very preterm vs. moderate to late preterm) were analysed using chi-squared test.

^cDifferences between categories of preterm birth (extreme to very preterm vs. moderate to late preterm) were analysed using Fisher’s Exact Test.

^dIncluding diabetes Type I and Type II.

Among women who delivered preterm, hypertension was significantly more prevalent (6.5%) compared to term deliveries (1.8%) (p < 0.001). Thrombophilia was also more common among preterm cases (2.9%) than controls (0.8%) (p=0.031). Thyroid disorders were reported in 9.2% of PTBs compared to 5.7% of term births (p=0.047), and allergies were more frequent in PTBs (10.6%) than term deliveries (6.1%) (p=0.015). Other conditions such as kidney disease (2.5% vs. 0.8%, p=0.057) and cardiovascular diseases (4.3% vs. 2.2%, p=0.100) tended to be more common among preterm cases, though these differences did not reach statistical significance.

When comparing extreme to very PTB (<32 weeks) with moderate to late very PTB (32 to <37 weeks), diabetes was significantly less common in the extreme preterm group (1.4% vs. 6.6%, p=0.021), while psychological disorders were more frequent in moderate to late PTBs (4.6% vs. 0.7%, p=0.030). Other conditions, including cardiovascular diseases, kidney disease, thrombophilia, thyroid disease, allergies, epilepsy, autoimmune diseases infectious diseases (HIV, HBV, HCV) and multimorbidity, were similarly distributed between the two PTB subgroups, with no significant differences observed (p > 0.05) (Table 1).

Predictors of PTB (preterm vs. term birth)

Univariable logistic regression (Table 2) showed that maternal multimorbidity was associated with increased odds of PTB (OR=1.85; 95% CI: 1.21–2.82; p=0.004). Among individual conditions, hypertension conferred the strongest risk, with over threefold higher odds of PTB (OR=3.56; 95% CI: 1.70–7.45; p < 0.001). Thrombophilia (OR=3.50; 95% CI: 1.15–10.63; p=0.027) and kidney disease (OR=3.67; 95% CI: 1.02–13.14; p=0.046) were also significantly associated with increased PTB likelihood. Moderate associations were found for thyroid disorders (OR=1.70; 95% CI: 1.03–2.83; p=0.037) and allergies (OR=1.85; 95% CI: 1.15–2.98; p=0.012).

Table 2.

Univariable conditional and binary logistic regression of maternal multimorbidity and medical history in the overall population, by prematurity status (preterm birth vs. term birth) and by degree of prematurity (extreme to very preterm vs. moderate to late preterm).

Characteristics	Prematurity status (preterm birth vs. term birth)		Degree of prematurity (extreme to very vs. moderate to late)
Characteristics	OR (95% CI)	p-value	OR (95% CI)	p-value
Multimorbidity
Yes vs. No	1.85 (1.21-2.82)	0.004	1.06 (0.61-1.84)	0.848
Diabetes^a
Yes vs. No	1.39 (0.81-2.36)	0.230	0.20 (0.10-0.87)	0.032
Cardiovascular disease
Yes vs. No	2.00 (0.94-4.27)	0.074	0.76 (0.27-2.10)	0.591
Epilepsy
Yes vs. No	1.25 (0.34-4.66)	0.739	1.64 (0.27-9.91)	0.591
Hypertension
Yes vs. No	3.56 (1.70-7.45)	<0.001	1.12 (0.52-2.43)	0.776
Kidney disease
Yes vs. No	3.67 (1.02-13.14)	0.046	1.23 (0.36-4.13)	0.744
Thrombophilia
Yes vs. No	3.50 (1.15-10.63)	0.027	1.37 (0.45-4.16)	0.588
Thyroid disease
Yes vs. No	1.71 (1.03-2.83)	0.037	0.99 (0.50-1.95)	0.981
Allergies
Yes vs. No	1.85 (1.15-2.98)	0.012	0.99 (0.53-1.87)	0.974
Autoimmune diseases
Yes vs. No	2.14 (0.87-5.26)	0.096	0.60 (0.18-2.17)	0.438
Psychological disorders
Yes vs. No	1.13 (0.57-2.27)	0.724	0.15 (0.02-1.12)	0.064
Hepatitis B virus (HBV)
Positive vs. Negative	2.00 (0.50-8.00)	0.327	3.31 (0.73-15.00)	0.120
Hepatitis C virus (HCV)
Positive vs. Negative	2.50 (0.49-12.89)	0.273	3.71 (0.61-22.46)	0.153
Human immunodeficiency virus (HIV)
Positive vs. Negative	3.00 (0.31-28.84)	0.341	1.22 (0.11-13.56)	0.872

Abbreviations: OR, Odds Ratio; CI, Confidence Interval.

Reference category for comparison of prematurity status = Term birth (gestational week at delivery ≥37 weeks).

Reference category for comparison of degree of prematurity = moderate to late (32 to <37 weeks).

For Diabetes, CVD, Hypertension, Kidney disease, Thrombophilia, Thyroid disease, Allergies, Autoimmune diseases psychological disorders, HIV, HBV and HCV the reference category was No.

Conditional logistic regression was used for the comparison of prematurity status (preterm births vs. term birth), and binary logistic regression for the comparison of degree of prematurity (extreme to very preterm vs. moderate to late preterm). Bold indicate statistically significant at a p < 0.05.

^aIncluding diabetes Type I and Type II.

After adjusting for maternal age, country of origin, parity, BMI, education, and smoking status (Table 3), women with multimorbidity had approximately 80% higher odds of PTB compared to those without multimorbidity (aOR=1.80; 95% CI: 1.16–2.79; p=0.009). Hypertension remained strongly associated with PTB (aOR=4.26; 95% CI: 1.84–9.86; p<0.001), followed by kidney disease (aOR=3.67; 95% CI: 1.01–13.30; p=0.048) and thrombophilia (aOR=3.53; 95% CI: 1.14–10.88; p=0.028). Thyroid disease (aOR=1.77; 95% CI: 1.05–2.98; p=0.033) and allergies (aOR=1.82; 95% CI: 1.12–2.99; p=0.017) were also significant.

Table 3.

Multivariable conditional and binary logistic regression of maternal multimorbidity and medical history in the overall population, by prematurity status (preterm birth vs. term birth) and by degree of prematurity (extreme to very preterm vs. moderate to late preterm).

Characteristics	Prematurity status (preterm birth vs. term birth)		Degree of prematurity (extreme to very vs. moderate to late)
Characteristics	aOR (95% CI)	p-value	aOR (95% CI)	p-value
Multimorbidity
Yes vs. No	1.80 (1.16-2.79)	0.009	1.09 (0.61-1.93)	0.770
Diabetes^a
Yes vs. No	1.28 (0.89-2.29)	0.198	0.19 (0.10-0.92)	0.031
Cardiovascular disease
Yes vs. No	1.96 (0.91-4.25)	0.086	0.65 (0.23-1.84)	0.413
Epilepsy
Yes vs. No	1.36 (0.35-5.20)	0.656	2.74 (0.39-19.12)	0.311
Hypertension
Yes vs. No	4.26 (1.84-9.86)	<0.001	1.29 (0.57-2.91)	0.574
Kidney disease
Yes vs. No	3.67 (1.01-13.30)	0.048	1.25 (0.36-4.38)	0.723
Thrombophilia
Yes vs. No	3.53 (1.14-10.88)	0.028	1.32 (0.42-4.17)	0.641
Thyroid disease
Yes vs. No	1.77 (1.05-2.98)	0.033	0.95 (0.46-1.94)	0.883
Allergies
Yes vs. No	1.82 (1.12-2.99)	0.017	0.90 (0.47-1.75)	0.760
Autoimmune diseases
Yes vs. No	2.09 (0.83-5.27)	0.118	0.50 (0.14-1.82)	0.289
Psychological disorders
Yes vs. No	1.13 (0.57-2.27)	0.675	0.14 (0.02-1.07)	0.057
Hepatitis B virus
Positive vs. Negative	1.90 (0.47-7.77)	0.371	5.29 (1.11-25.25)	0.037
Hepatitis C virus
Positive vs. Negative	1.77 (0.32-9.75)	0.511	3.68 (0.57-23.58)	0.169
Human immunodeficiency virus
Positive vs. Negative	1.88 (0.19-18.53)	0.589	1.22 (0.11-13.56)	0.872

Abbreviations: aOR, adjusted Odds Ratio; CI, Confidence Interval.

All variables were adjusted for maternal age, country, parity, BMI, education, and smoking.

Reference category for comparison of prematurity status = term birth (gestational week at delivery ≥37 weeks).

Reference category for comparison of degree of prematurity = moderate to late (32 to <37 weeks).

For Diabetes, CVD, Hypertension, Kidney disease, Thrombophilia, Thyroid disease, Allergies, Autoimmune diseases psychological disorders, HIV, HBV and HCV the reference category was No.

Conditional logistic regression was used for the comparison of prematurity status (Preterm births vs. Term birth), and binary logistic regression for the comparison of Degree of Prematurity (Extreme to very preterm vs. Moderate to late preterm). Bold indicate statistically significant at a p < 0.05.

^aDiabetes includes Type I and Type II.

Predictors of degree of prematurity (extreme to very vs. moderate to late PTB)

Women with diabetes were significantly less likely to deliver before 32 weeks, with the odds of extreme PTB being approximately 80% lower compared to those without diabetes (OR=0.20; 95% CI: 0.10–0.87; p=0.032) (Table 2). In the multivariable logistic regression analysis (Table 3), the inverse association between diabetes and extreme PTB remained statistically significant. Specifically, diabetes was associated with a lower adjusted odds of extreme PTB (aOR=0.19; 95% CI: 0.10–0.92; p=0.031). In contrast, HBV positivity was identified as a significant independent predictor factor, with HBV-positive women exhibiting over five times higher adjusted odds of extreme PTB compared to HBV-negative women (aOR=5.29; 95% CI: 1.11–25.25; p=0.037).

Discussion

Principal findings

This study examined the association between maternal multimorbidity and medical history, with the risk and severity of PTB. The results confirm that PTB is a multifactorial outcome shaped by chronic maternal conditions. Multimorbidity emerged as an independent risk factor, along with specific chronic diseases and certain infectious conditions. These findings extend existing evidence and provide context-specific insights into high-risk maternal profiles.

Comparison with existing literature

Multimorbidity and PTB

Our findings (aOR=1.80; 95% CI: 1.16–2.79) are consistent with previous studies demonstrating that maternal multimorbidity increases the risk of PTB. A large population-based cohort study from Japan reported a similar association (aOR = 1.50; 95% CI: 1.33–1.69),²⁶ while a Scottish record-linkage study found an aOR of 1.64 (95% CI: 1.48–1.82).²⁷ A recent systematic review and meta-analysis identified a pooled OR of 4.28 (95% CI: 2.23–6.34), indicating a stronger overall effect across diverse populations.¹⁷ These findings demonstrate a consistent positive association between multimorbidity and PTB across different populations and healthcare systems, despite variations in study design and multimorbidity definitions. The higher pooled estimate likely reflects the inclusion of broader multimorbidity definitions and more heterogeneous populations. Collectively, these studies, together with our findings from Cyprus, reinforce the global relevance of maternal multimorbidity as an independent risk factor for PTB. The mechanisms underlying these associations may involve chronic systemic inflammation, oxidative stress, vascular dysfunction, and neuroendocrine dysregulation, all of which contribute to premature parturition pathways.^17,26 Beyond biological pathways, multimorbidity often leads to polypharmacy, increasing the risk of adverse drug interactions that may affect fetal health.²⁸ These pregnancies also require more intensive antenatal care and complex decisions, often prompting earlier interventions due to overlapping risks.¹⁷

Individual chronic conditions and PTB

Although the primary focus of this study was multimorbidity, several individual chronic conditions also showed significant associations with PTB, consistent with prior evidence. These conditions likely act synergistically to elevate risk among women with multiple comorbidities.

Maternal hypertension was significantly associated with increased risk of PTB, aligning with findings from systematic reviews and large registry-based studies that report up to fivefold higher odds among women with preeclampsia or gestational hypertension.^29–31 Placental malperfusion, endothelial dysfunction, and angiogenic imbalance are recognized mechanisms contributing to placental pathology and early delivery.^32–34 Although antenatal care enables early detection and management, the timing and severity of hypertensive disorders often necessitate preterm delivery.^35,36

Thrombophilia was a significant predictor of PTB, consistent with prior studies linking prothrombotic states to adverse pregnancy outcomes.^36–38 Inherited and acquired forms promote placental thrombosis and infarction, which impairs oxygen and nutrient exchange.^38,39 Women with thrombophilia often receive low molecular weight heparin and/or aspirin, with close monitoring via ultrasound and Doppler studies.⁴⁰

Kidney disease was strongly associated with increased PTB risk, consistent with evidence linking chronic kidney disease (CKD) to adverse outcomes such as fetal growth restriction, hypertension, and preterm delivery.^41–43 CKD is marked by inflammation, oxidative stress, endothelial dysfunction, placental impairing and trophoblast invasion.^41,42 These high-risk pregnancies require close monitoring of renal function, blood pressure, and fetal growth, with early delivery often needed for maternal or fetal indications.⁴⁴ Polypharmacy is common, increasing the risk of drug interactions and complicating perinatal care.⁴⁵

Thyroid disorders were significantly associated with increased PTB risk, consistent with research linking both overt and subclinical dysfunction to adverse pregnancy outcomes.^46–48 Thyroid hormones regulate trophoblast proliferation, invasion, and angiogenesis, all crucial for placental development.^47,48 In hypothyroidism, impaired trophoblast function and reduced angiogenic signaling may restrict placental perfusion, while autoimmune thyroid diseases are associated with elevated TPO antibodies and pro-inflammatory cytokines at the maternal–fetal interface, potentially triggering early parturition.^49,50 Poorly controlled thyroid imbalances may also affect uterine contractility and cervical ripening.⁵¹

Allergic conditions, including asthma and atopic disorders, were moderately associated with increased PTB risk, aligning with studies linking allergic inflammation to adverse obstetric outcomes^52,53 These disorders involve chronic inflammation that may result in uterine contractility, and cervical ripening.⁵³ Psychosocial stress and socioeconomic barriers may further reduce control of allergies and result in adverse complications in pregnancy.⁵⁴ Heightened surveillance may lower the threshold for preterm delivery in suspected fetal compromise^52,53

HBV infection was significantly associated with extreme PTB, consistent with studies linking chronic HBV to preterm delivery.^55,56 Mechanisms involve impaired immune tolerance at the maternal–fetal interface, increased cytokines, and altered NK cell function, potentially triggering early labor.^55,56 In advanced disease, hepatic dysfunction and coagulopathy may impair placental oxygen and nutrient transfer.^57,58 In limited-resource settings, early delivery may follow concerns over maternal or fetal compromise.^57,58

Our findings reinforce the multifactorial nature of PTB. Future research should employ large, prospective cohort designs to distinguish spontaneous from medically indicated PTB and to explore how different multimorbidity patterns or disease clusters influence pregnancy outcomes across populations. Improved harmonization of definitions and data collection methods would enhance comparability and enable meta-analytic synthesis across settings. From a clinical perspective, integrated antenatal care models that incorporate early screening and coordinated management of chronic conditions are essential for identifying and mitigating risk early in pregnancy. Given that cardiometabolic conditions were among the most frequent and influential comorbidities in our study, preconception and early antenatal screening could facilitate timely intervention and targeted management. This requires closer collaboration between obstetricians, primary care physicians, and specialists, particularly for women with multiple long-term conditions. At the policy level, strengthening primary care systems and promoting preconception screening for chronic diseases could support earlier intervention and reduce disparities in maternal health outcomes. Based on our findings, practical recommendations include the adoption of integrated and coordinated care pathways for women with multimorbidity to improve pregnancy outcomes. Addressing socioeconomic barriers to care and ensuring continuity between preconception, antenatal, and postnatal services remain critical for improving maternal and perinatal outcomes among women with multimorbidity.

Strengths and limitations

A key strength of this study is its matched case–control design, which controlled for maternal age and country of origin, thereby reducing sociodemographic confounding. The use of conditional logistic regression appropriately accounted for this matched structure. Focusing on multimorbidity rather than isolated conditions enabled a broader assessment of cumulative risk, while stratifying PTB by severity provided insight into how specific conditions influence both risk and degree of prematurity. However, as a retrospective study, potential biases include selection bias due to incomplete or differential record availability, information bias from inconsistent documentation of medical conditions, and residual confounding from unmeasured variables. Although conditional logistic regression mitigates bias related to matching, it assumes accurate specification of matching factors and independence between exposure and selection, which may be challenged by retrospective data. Another limitation is the lack of information distinguishing spontaneous from medically indicated preterm births in the medical records, which prevented separate analyses of these subtypes. This constraint limits the ability to explore whether multimorbidity differentially affects the underlying mechanisms of PTB. Lastly, as a single-centre study involving a high-risk population, generalisability is limited.

Conclusions

This study shows that maternal multimorbidity and chronic conditions such as hypertension, thrombophilia, kidney disease, thyroid disorders, and allergies are significantly associated with increased odds of PTB in Cyprus. HBV infection was also linked to PTB severity. Early identification and monitoring of high-risk women can guide timely interventions. Reducing Cyprus’s high PTB rates will require coordinated, multidisciplinary care and public health strategies promoting chronic disease awareness, screening, and proactive management.

Footnotes

ORCID iD

Konstantinos Giannakou

Ethical considerations

Ethical approval was obtained from the Cyprus National Bioethics Committee (EEBK EΠ 2022.01.300) and the Research and Innovation Centre of the SHSO (Ref: 02/23).

Consent to participate

This study was conducted using previously collected data. No direct contact with participants occurred, and no identifiable personal information was used. The requirement for informed consent was waived by the Cyprus National Bioethics Committee, as the study involves minimal risk and complies with applicable ethical standards.

Consent for publication

No personal identifying information is included in the manuscript, and the data presented is in aggregate form. This study adheres to ethical standards for research publication, ensuring the privacy and confidentiality of participants.

Author contributions

LM: Conceptualization, Investigation, Methodology, Formal analysis, Visualization, Writing – Original Draft Preparation. MK: Methodology, Writing – Review & Editing. DL: Methodology, Writing – Review & Editing. KG: Conceptualization, Investigation, Validation, Formal analysis, Methodology, Project administration, Supervision, Writing – Original Draft Preparation. All the authors take responsibility for all aspects of reliability and freedom from bias of the data presented and their discussed interpretation. All authors read and approved of the final manuscript.

Funding

This research received no funding.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.*

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Maternal multimorbidity and chronic conditions as predictors of preterm birth: A matched case–control study in Cyprus

Abstract

Introduction

Methods

Results

Conclusions

Keywords

Introduction

Materials and methods

Study design and setting

Participants and matching

Data sources

Variables and definitions

Statistical analysis

Results

Maternal sociodemographic and health behaviors

Maternal multimorbidity and health conditions

Predictors of PTB (preterm vs. term birth)

Predictors of degree of prematurity (extreme to very vs. moderate to late PTB)

Discussion

Principal findings

Comparison with existing literature

Multimorbidity and PTB

Individual chronic conditions and PTB

Strengths and limitations

Conclusions

Footnotes

ORCID iD

Ethical considerations

Consent to participate

Consent for publication

Author contributions

Funding

Declaration of conflicting interests

Data Availability Statement

References