The research team, led by Andrzej Ciborek, combined two complementary data sources that are rarely analysed together. The first came from Statistics Poland: annual counts of live births recorded by the mother’s place of residence, which capture the true demographic behaviour of the regional population. The second consisted of delivery records from all 34 maternity facilities in the voivodeship, stratified by perinatal reference level from Level I, the smallest units offering basic obstetric care, up to Level III, the tertiary centres equipped for the most complex and high-risk pregnancies. Separating these two streams of data allowed the authors to distinguish demographic change in the population from structural change in the healthcare network itself, a distinction that many regional analyses conflate.

The numbers tell a striking story. Live births in Greater Poland rose from 36,561 in 2013 to a high point of 40,450 in 2017, a brief echo of the so-called mini-baby boom that followed pro-family policy announcements and a favourable age structure among women of reproductive age. Then the reversal began. By 2023, births had fallen to 27,532, a reduction of 31.9 percent from the 2017 peak. Ordinary least-squares regression over the period 2017 to 2023 confirmed that the decline was statistically robust and remarkably consistent, with a fitted slope of minus 2,118 births per year, a 95 percent confidence interval running from minus 2,429 to minus 1,807, a p-value below 0.001 and an R-squared of 0.973. In practical terms, more than 97 percent of the year-to-year variation in the region’s birth count is captured by a simple linear downward trend, indicating a steady erosion rather than a series of random shocks.

The total fertility rate in the voivodeship followed the same trajectory, sliding from 1.48 children per woman in 2017 to 1.33 in 2023. Both values sit well below the replacement level of roughly 2.1, the rate at which each generation exactly replaces the previous one in the absence of migration. The regional picture, grim as it is, still looks marginally better than the national one: Poland’s national total fertility rate fell to just 1.099 in 2024, among the lowest figures ever recorded for the country. This gap illustrates an important nuance in European demography. Greater Poland, with its mix of the Poznań metropolitan area, mid-sized cities and a broad agricultural hinterland, has historically been more demographically resilient than eastern regions of Poland, yet even this comparatively advantaged region is now aging and shrinking in its childbearing population.

The delivery network data revealed a second, equally consequential finding. Between 2017 and 2023, the number of deliveries fell by 40.3 percent at Level I facilities, by 30.3 percent at Level II units and by 29.2 percent at Level III centres. At first glance, the numbers suggest that small rural hospitals lost business far faster than the large referral centres. But the sensitivity analysis tells a subtler story. When the researchers restricted the facility analysis to units that had operated continuously throughout the study period, the Level I decline shrank to 33.0 percent, a figure much closer to the losses at higher levels of care. The gap between the raw and adjusted figures was explained by the permanent closure of four peripheral Level I units. In other words, the apparent collapse of the smallest maternity hospitals reflected not a disproportionately greater fall in demand in rural areas but the deliberate consolidation of the network, as struggling units were shuttered and their catchment populations redirected elsewhere.

This distinction matters enormously for planning. If the decline in deliveries had been genuinely steeper in peripheral areas, planners might have concluded that rural obstetric services were becoming redundant and could safely be allowed to wither. The corrected picture suggests instead that demand fell broadly evenly across the region, and that the closure of small units was a structural response to falling volumes and the economics of running low-throughput maternity wards. Each closure, however, increases the distance that a pregnant woman must travel to reach care, raising questions about access to emergency obstetric services in sparsely populated areas, particularly for time-critical complications where minutes matter for both maternal and neonatal outcomes.

The study also tested a hypothesis that dominates public debate about fertility: that economic conditions drive birth rates. Using regional income data, the researchers computed Spearman rank correlations between income and the number of births, both on the raw levels and after first-differencing, a statistical technique that removes the common time trend and asks whether the two series still move together. The answer was a clear no. On levels, the correlation was negative and statistically insignificant, with rho equal to minus 0.370 and a p-value of 0.293. After differencing, rho fell to 0.167 with a p-value of 0.668. The data, in short, did not support an independent income-fertility association once the shared downward drift was taken out. That does not mean income is irrelevant to childbearing decisions at the individual level, but it cautions against the simplistic expectation that regional prosperity alone can reverse a fertility decline shaped by deep structural forces, including the shrinking cohort of women of reproductive age, changed life-course preferences and the postponement of parenthood.

Methodologically, the paper is a model of transparency for an ecological study. The authors followed the STROBE and RECORD reporting guidelines, which govern observational studies using routinely collected health data, and ran sensitivity analyses that excluded the year 2020, when the COVID-19 pandemic disrupted both healthcare utilisation and conception patterns, and restricted the facility analysis to continuously operating units. Because the study relied exclusively on aggregated administrative and publicly available statistical data, with no individual-level records, the authors note that ethics committee approval was not required under Polish law. The correspondence between the demographic series, based on place of residence, and the facility series, based on place of delivery, provides an unusually coherent picture: the population series tracked the region’s demographic reality, while the facility series tracked how that reality was absorbed by the hospital network.

The implications reach well beyond obstetrics. A region whose births have fallen by nearly a third in six years is a region whose population is aging rapidly, and the study frames its findings explicitly around the dual challenge of adaptive perinatal network planning and geriatric-care expansion. Fewer children mean fewer paediatric wards, fewer school places and, eventually, fewer young workers; more elderly residents mean rising demand for long-term care, geriatric medicine, chronic-disease management and social services. The authors are careful to characterise these as planning implications rather than tested outcomes, a scientifically honest framing, but the direction of the demographic arithmetic is not in doubt. Healthcare systems built around a growing or stable young population cannot simply be repurposed overnight, and the closures of maternity units already underway show how difficult and politically sensitive the transition can be.

For other regions of Central and Eastern Europe, Greater Poland may function as a leading indicator. The post-1990 transition intensified below-replacement fertility across the region, and Poland’s national fertility rate of 1.099 in 2024 is among the lowest in the European Union. The Greater Poland analysis demonstrates that sub-national data, properly modelled, can reveal dynamics that national statistics conceal: the uneven geography of facility closures, the timing of turning points and the degree to which economic variables do or do not track birth trends. As the authors’ collaboration between Kalisz University and Charité underscores, the questions raised here are European in scope, and the answers will shape how health systems across the continent adapt to a future in which the cradle is quieter but the ranks of the elderly grow with every passing year.

Subject of Research: Fertility decline, population ageing and perinatal care network contraction in the Greater Poland Voivodeship, 2013–2023

Article Title: Fertility decline and population ageing in the Greater Poland Voivodeship, 2013–2024: a longitudinal analysis of implications for regional health and social services planning

Article References: Ciborek, A., Chęcińska-Maciejewska, Z., Bogdański, A., & Krauss, H. (2026). Fertility decline and population ageing in the Greater Poland Voivodeship, 2013–2024: a longitudinal analysis of implications for regional health and social services planning. BMC Public Health. https://doi.org/10.1186/s12889-026-29263-3

Image Credits: AI Generated

DOI: 10.1186/s12889-026-29263-3

Keywords: fertility decline, population ageing, total fertility rate, Greater Poland Voivodeship, perinatal care network, live births, health services planning, Poland, demographic change, longitudinal analysis, maternity facility closures, public health

The study, led by Teddy Lazebnik of the University of Haifa and Jönköping University, together with Svetlana Bunimovich-Medrazitsky of Ariel University and Ariel Alexi of Bar-Ilan University, addresses a gap that has long been overlooked in pandemic preparedness planning. While demographers and economists have extensively studied how pandemics change the decision to have children—through economic uncertainty, social disruption, and shifting risk perceptions—far less attention has been paid to how the pathogens themselves damage the biological machinery of reproduction. The new work builds a bridge between these two worlds by embedding fertility physiology directly into the mathematics of epidemic spread.

At the heart of the model is an extended version of the classic Susceptible-Infected-Recovered (SIR) framework, the workhorse of mathematical epidemiology since the 1920s. In its basic form, the SIR model sorts a population into three compartments—susceptible, infected, and recovered—and tracks how individuals flow between them through infection and recovery. The researchers expanded this structure considerably. Their version incorporates two genders, three age groups (children, adults, and the elderly), birth and death dynamics, an exposed state to capture incubation periods, and separate asymptomatic and symptomatic infectious states. Crucially, adults who recover from symptomatic infection face a defined probability of developing physiological fertility impairment, which reduces their residual reproductive capacity by a fixed proportion.

The biological motivation is grounded in clinical evidence from the COVID-19 pandemic. Studies cited by the authors found that severe COVID-19 infection was associated with marked decreases in ovarian reserve, hormonal disturbances, and endometrial abnormalities in women—key factors contributing to infertility. In men, the virus significantly reduced sperm quality and caused sex hormone disruption, with effects on sperm concentration and motility that were most pronounced during the illness stage. Research also showed that SARS-CoV-2 can infect the ovary, uterus, vagina, and placenta through the ACE2 receptor, potentially causing infertility, menstrual disorders, and fetal distress. These are not merely statistical correlations; they represent a physiological pathway by which a pathogen can permanently alter an individual’s reproductive capacity.

To translate this biology into population-level dynamics, the researchers formulated a system of ordinary differential equations describing how individuals move through epidemiological states within each gender and age group. The birth function in their model is particularly elegant: it calculates the average fertility potential of adult male-female pairings, weighting fertility-impaired individuals at a fraction of normal fertility, and caps total births by the smaller adult gender group. The team proved several mathematical properties of the system, including that solutions exist and remain unique, that all state variables stay non-negative, and that the population cannot blow up in finite time—ensuring the model behaves sensibly across its parameter space.

Because the coupled system does not admit a closed-form analytical solution, the researchers solved it numerically using an agent-based simulation approach. Each agent in the simulation carries an age, gender, epidemiological state, and fertility status, and the population evolves through discrete daily time steps over a ten-year horizon. Fertility impairment is treated as an absorbing state during adulthood: once an adult enters the fertility-impaired compartment, they remain there until aging out of the reproductive window. This assumption reflects the study’s focus on long-term physiological damage rather than transient illness effects.

To ground the simulations in reality, the team calibrated the model using demographic data from ten major cities worldwide, including Delhi, Shanghai, Paris, Istanbul, London, Toronto, Tel Aviv, New York, São Paulo, and Berlin. These cities span an enormous range of baseline fertility rates—from Tel Aviv’s 3.0 children per woman to Shanghai’s 1.28—and vary widely in age structure, from Delhi’s 37.2 percent children to Shanghai’s 52.1 percent elderly. The epidemiological parameters were drawn from published COVID-19 literature, with infection rates, recovery rates, and immunity decay calibrated to observed pandemic behavior. The fertility reduction parameter was set conservatively at 4 to 6 percent, benchmarked against the roughly 4 percent drop in U.S. births observed in 2020 and similar cross-country declines at the pandemic’s trough.

The simulation results reveal a striking asymmetry between the immediate and long-term consequences of a pandemic. When the researchers compared scenarios with and without physiological fertility impairment, the impaired scenario produced substantially larger and more persistent declines in total fertility rate. Moreover, the decline did not simply mirror the epidemic curve—it continued to grow for years after the pandemic peaked, eventually reaching a plateau in most cities. Some cities, such as São Paulo, showed continued upward trends in fertility decline even at the end of the simulation horizon, suggesting that the demographic ripple effects of a single outbreak can extend well beyond the immediate crisis period.

Sensitivity analysis identified which parameters matter most for the magnitude of fertility decline. The recovery rate from disease emerged as the single most significant predictor, followed by the proportional fertility reduction caused by the pathogen and the average infection rate—all statistically significant at the 5 percent level. This finding carries a clear policy implication: interventions that accelerate treatment and shorten disease duration may protect demographic stability more effectively than measures targeting infection rates alone. The analysis also revealed highly non-linear interactions between parameters, particularly between the exposed-to-infected transition rate and the infection rate, where moderate transition speeds combined with high infection rates produced the steepest fertility declines.

Perhaps the most sobering result concerns cities that already have low baseline fertility. The model showed that pandemics disproportionately affect regions with already declining fertility trends, amplifying existing demographic pressures rather than simply adding a uniform shock. In cities like Shanghai, where the total fertility rate already sits well below replacement level, even a moderate infection-induced fertility decline compounds an existing trajectory toward population contraction. The authors conclude that policymakers should explicitly incorporate fertility considerations into pandemic preparedness strategies, treating the physiological reproductive consequences of infection as a first-order demographic concern rather than a secondary afterthought. As the world confronts the twin challenges of emerging pathogens and unprecedented fertility decline, this framework offers a quantitative tool for anticipating—and potentially mitigating—the long-term demographic costs of the next pandemic.

Subject of Research: Mathematical modeling of infection-induced physiological fertility decline during pandemics

Article Title: Modeling the effects of pandemics on physiology-related fertility decline in society

Article References: Lazebnik, T., Bunimovich-Medrazitsky, S., & Alexi, A. (2026). Modeling the effects of pandemics on physiology-related fertility decline in society. Frontiers in Epidemiology, 6, Article 1740829. https://doi.org/10.3389/fepid.2026.1740829

Image Credits: AI Generated

DOI: 10.3389/fepid.2026.1740829

Keywords: pandemic, fertility decline, SIR model, epidemiology, COVID-19, demographics, total fertility rate, agent-based simulation, reproductive health, population dynamics, mathematical modeling, public health policy

The study assembles an unusually rich set of official data for England’s local authorities. It combines 2024 local-authority total fertility rates from the Office for National Statistics with Census 2021 measures of the share of residents holding Level 4 or above qualifications, roughly equivalent to a degree or higher. To this it adds residence-based housing affordability ratios, earnings data, the English Indices of Deprivation 2019, internal migration estimates, official 2022-based population projection outputs and local-authority boundary files. All of these sources are publicly available, meaning the analysis relies on no restricted or newly collected personal data. The design allows the education-fertility relationship to be tested not just as a statistical average but as a geographic pattern.

The central statistical result is stark. In adjusted models that control for housing affordability, earnings and deprivation, the share of highly educated residents remains strongly and negatively associated with the local total fertility rate. In other words, places with more degree-holders have fewer births per woman, and this is not simply because educated people live in expensive or deprived areas. The education gradient survives when the usual economic suspects are held constant. This matters because much demographic research treats the education-fertility link as a property of individuals aggregated without regard to place; here it appears as a property of places themselves.

To test whether these patterns are geographically organised, the study applies local indicators of spatial association, a technique introduced by geographer Luc Anselin that identifies clusters of similar values among neighbouring areas. The results show that raw fertility rates, educational composition and even the residuals of adjusted fertility models all remain spatially clustered. Neighbouring local authorities tend to resemble one another in their fertility levels and in the strength of the education association. Statistical dependence of this kind violates the assumptions of ordinary regression, which is why the study turns to spatial autoregressive models that explicitly account for neighbouring areas influencing one another.

The low-fertility, high-education regime is most clearly visible in London. Boroughs including Camden, Islington, Westminster, Southwark, Lambeth and Wandsworth form a concentrated block of places where high qualification levels and very low period fertility coincide. These are areas characterised by dense professional labour markets, high housing costs and large populations of young adults in education and early-career employment. The clustering suggests that postponement behaviour is transmitted or reinforced locally, through shared labour markets, housing markets and social environments, rather than emerging independently in each place from a national average trend.

The spatial autoregressive analysis adds a technical but important nuance. Impact decomposition in these models separates the effect of local education levels from the effect of education in neighbouring areas. The education association splits into a direct local component and an indirect spillover component, meaning that a local authority’s fertility is shaped partly by the educational composition of the places around it. This supports what the author calls a spatial-demographic interpretation of fertility postponement: the phenomenon behaves like a regional system, not a collection of isolated jurisdictions. Policies aimed at supporting families may therefore need to be designed at spatial scales larger than a single borough.

A second strand of the study carries a warning for anyone who uses official population projections in local statistical models. Projection outputs, such as projected working-age growth and old-age dependency ratios, already encode assumptions about migration, age structure and fertility. Using them as independent explanatory variables in regression models risks circularity, because the projected outcomes are partly built from the very behaviours being explained. The study therefore analyses projection models separately, as projection-sensitivity exercises rather than causal claims. This methodological caution is relevant well beyond England, wherever local researchers combine official forecasts with current outcomes.

The appendix diagnostics reinforce this distinction. For the current-fertility spatial autoregressive model, residual spatial autocorrelation is reduced but not fully eliminated, which the author reads as evidence that current fertility retains genuine spatial structure deserving substantive analysis. For the projection-sensitivity models, residuals are much closer to spatial independence, consistent with the interpretation that projected outcomes are strongly shaped by spatially organised demographic assumptions rather than by the covariates in the models. In plain terms, current fertility has a geography of its own, while projections largely inherit the geography of their assumptions.

The broader significance lies in what this means for how England understands its demographic future. If low fertility is concentrated in identifiable urban regimes, then national averages conceal sharp local divergence, and the places driving the decline are also the places absorbing much of the country’s internal migration and economic growth. Fertility postponement has long been linked to rising educational enrolment and the expansion of higher education, and the persistence of a two-child family ideal across Europe suggests that much of the decline reflects timing rather than completed family size. But the spatial clustering found here indicates that timing decisions are made in contexts, and those contexts differ profoundly across the country.

For planners, the message is that fertility postponement is not only a life-course process aggregated to places but a regional-planning issue. Housing affordability, labour market structure and educational composition interact across administrative boundaries, and the spillover effects documented in the study mean that neighbouring authorities share demographic fates. For demographers, the message is methodological: spatial dependence must be modelled, and official projections must be handled with care to avoid treating assumed futures as observed data. As England’s birth rates continue to fall, the geography of that decline, clustered in educated urban England and radiating through neighbouring areas, may prove as consequential as its overall magnitude.

Subject of Research: Spatial patterns of fertility postponement and their relationship to education across English local authorities

Article Title: Spatial regimes of fertility postponement in England : education, local clustering and projection sensitivity across local authorities

Article References: Bhagat, S. (2026). Spatial regimes of fertility postponement in England : education, local clustering and projection sensitivity across local authorities. Journal of Population Research, 43(4), Article 32. https://doi.org/10.1007/s12546-026-09440-2

Image Credits: AI Generated

DOI: 10.1007/s12546-026-09440-2

Keywords: fertility postponement, total fertility rate, spatial demography, education gradient, local authorities, England, London, LISA, spatial autoregressive model, population projections, housing affordability, place-based demography