Age–period–cohort effects on suicide mortality in Andalusia, Spain (2000–2024): demographic masking and sustained pandemic excess

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To analyse suicide mortality trends in Andalusia, Spain (2000–2024) through age–period–cohort (APC) decomposition, identify trend change-points, and quantify pandemic-associated excess mortality. Methods. This population-based time-series study used data from the Spanish National Statistics Institute (INE; heading 098, ICD-10 X60–X84, Y87.0). Age-standardised rates (European Standard Population 2013) were computed by sex. The analytical framework comprised segmented joinpoint regression, Prais–Winsten modelling, an APC model with the intrinsic estimator, and a log-linear counterfactual model projecting the 2000–2019 trend onto 2020–2024. Sensitivity analyses included quasi-Poisson correction and bootstrap resampling. Results. Overall, 18,350 suicide deaths were recorded (77.4% male; male-to-female ratio 3.43:1). Demographic masking was evident: crude rates remained stable (estimated annual percentage change [EAPC] = − 0.12%, p = 0.615), whereas age-standardised rates declined significantly (EAPC = − 0.98%, p < 0.001), revealing that population ageing conceals a real risk decline. Joinpoint regression identified an abrupt break in 2019–2020 (EAPC = + 26.6%, p = 0.002) followed by stabilisation at elevated levels. The APC model (R² = 0.983) disclosed an ascending age–risk gradient, a period nadir in 2015–2019, and declining cohort risk from pre-1935 generations onwards. Counterfactual analysis estimated 783 excess deaths over 2020–2024 (observed/expected = 1.23; 95% CI 1.20–1.27; p < 0.001), concentrated among those aged 15–64. Conclusion. The underlying downward trend was abruptly interrupted in temporal coincidence with the pandemic, producing a sustained level shift persisting five years without attenuation. These findings support age-targeted, geographically tailored prevention strategies. suicide mortality trends age–period–cohort COVID-19 Spain population ageing Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Suicide remains a major public health challenge worldwide. According to the most recent Global Burden of Disease estimates, approximately 727,000 people die by suicide each year, accounting for 1.1% of all deaths globally and ranking as the third leading cause of death among those aged 15–29 years [ 1 , 2 ]. Although the global age-standardised suicide mortality rate declined by 36% between 2000 and 2019, this progress has been unevenly distributed: whilst many high-income countries have achieved sustained reductions, certain regions and population subgroups exhibit stable or rising trends [ 2 , 3 ]. Within Europe, a well-established geographical gradient persists, with higher rates in northern and eastern countries and comparatively lower rates in the Mediterranean south [ 4 , 5 ]. Nevertheless, age-specific analyses reveal that rates increase sharply with age across most European nations, exceeding 50 per 100,000 in men aged 85 and over in several western countries [ 5 ]. Spain has traditionally occupied an intermediate-to-low position in the European ranking. However, recent figures signal a concerning trajectory. The number of suicide deaths rose from 3,158 in 2000 to 4,227 in 2022 — the highest on record — before declining slightly to 3,953 in 2024 [ 6 , 7 ]. Suicide has become the leading cause of external death among those aged 15–29, with men accounting for approximately 75% of all deaths [ 7 , 8 ]. Andalusia, the most populous autonomous community (~ 8.5 million inhabitants), presents characteristics that warrant specific investigation: it is comparable in population to mid-sized European countries such as Austria or Switzerland, and previous studies have identified it — alongside Galicia — as a region with suicide rates consistently above the national average, with an inland–coastal gradient suggesting higher risk in rural provinces [ 8 , 9 ]. Age–period–cohort (APC) analysis constitutes a fundamental epidemiological tool for disentangling the mechanisms underlying temporal mortality trends [ 10 ]. It separates three conceptually distinct components: age effects (biological and cumulative risk), period effects (contextual influences affecting all age groups simultaneously), and birth cohort effects (generational differences attributable to shared formative experiences) [ 10 , 11 ]. In Spain, previous APC studies have been conducted exclusively at the national level: Cayuela et al. analysed 1984–2018 [ 12 ], and Martínez-Alés et al. covered 2000–2019 stratified by migratory status [ 13 ]. Neither descended to the sub-national level, included post-2019 data, nor incorporated a counterfactual model to quantify pandemic-associated excess mortality. The COVID-19 pandemic constituted an unprecedented disruptive event with documented consequences for population mental health, including increases in depression, anxiety, and suicidal behaviour [ 14 , 15 ]. International evidence on its impact on suicide mortality has been mixed: whilst most high-income countries did not register significant increases during 2020, Spain was identified as one of the few where the rise was statistically significant, reversing the prior downward trend [ 14 , 16 ]. Critically, the majority of these studies were limited to the first 12–18 months of the pandemic, without covering the complete post-pandemic period or addressing differential impact by age and sex at the sub-national level [ 17 , 18 ]. A significant knowledge gap therefore exists. No study has applied a complete APC analysis to suicide mortality in Andalusia, nor has any covered a 25-year window encompassing four distinct socioeconomic and health contexts — pre-crisis (2000–2007), economic recession (2008–2013), recovery (2014–2019), and the pandemic and post-pandemic period (2020–2024) — whilst simultaneously quantifying excess mortality through a counterfactual framework and examining provincial disparities. This study aimed to analyse the evolution of suicide mortality in Andalusia, 2000–2024, by integrating age-standardised trend analysis, APC decomposition with the intrinsic estimator, counterfactual estimation of pandemic-associated excess mortality, and examination of geographical and sex-based disparities. METHODS Study design and data sources This was a population-based ecological time-series study of all deaths recorded under heading 098 (suicide and intentional self-harm; International Classification of Diseases, 10th Revision [ICD-10] codes X60–X84, Y87.0) of the Spanish National Statistics Institute (INE) reduced cause-of-death list in the autonomous community of Andalusia during the period 2000–2024 [ 19 , 20 ]. There were no changes in the correspondence between heading 098 and the ICD-10 codes throughout the study period. Two analytical databases were constructed from publicly available INE microdata tables. The first (provincial database) contained, for each combination of year (2000–2024), province (eight provinces and the regional aggregate), and sex, the number of suicide deaths, the denominator population (municipal register at 1 January of each year), and the crude mortality rate per 100,000 person-years (675 records). The second (age-specific database) contained, for each combination of year, age group, and sex, the mortality rate per 100,000 person-years for the whole of Andalusia (975 records). The original INE age categories were regrouped into 13 intervals: ≤14, 15–29, 30–39, and five-year groups from 40–44 to 80–84, plus ≥ 85 years [ 21 ]. Cross-validation confirmed a maximum discrepancy of 0.005 per 100,000 between calculated and INE-published rates. Four historical periods were defined a priori : pre-crisis (2000–2007), economic recession (2008–2013), recovery (2014–2019), and pandemic/post-pandemic (2020–2024). Age standardisation Age-standardised rates (ASR) were calculated by the direct method [ 21 ] using the 2013 European Standard Population (ESP2013) as reference [ 22 ]. ESP2013 weights, originally defined for five-year groups, were regrouped to match the 13 study age intervals, preserving a total weight of 100,000. The estimated annual percentage change (EAPC) was derived from log-linear regression (ln[rate] = α + β × year; EAPC = [e β − 1] × 100) for both crude and standardised rates. Joinpoint regression Segmented joinpoint regression was applied to the ASR series to identify statistically significant trend change-points [ 23 ]. Models with 0 to 3 joinpoints were evaluated and the most parsimonious was selected using the Bayesian information criterion (BIC). Joinpoint significance was tested by Monte Carlo permutation with 4,999 replicates. The EAPC with 95% confidence interval (CI) was estimated for each segment. The average annual percentage change (AAPC) was calculated as the time-weighted mean of the segmental EAPCs [ 24 ]. Prais–Winsten regression The overall EAPC for the full period was additionally estimated by iterative Prais–Winsten regression [ 25 ], which corrects for first-order serial autocorrelation (AR[ 1 ]). This procedure was applied to the global sex-stratified series and to each of the 13 age groups separately. Age–period–cohort model An APC model was fitted to decompose the simultaneous effects of age, historical period, and birth cohort on suicide mortality [ 10 , 11 ]. A Lexis table was constructed with 9 five-year age groups (40–44 to 80–84) and 5 quinquennial periods (2000–2004 to 2020–2024), yielding 13 implicit birth cohorts. Extreme age groups (≤ 39 and ≥ 85 years) were excluded to avoid instability from sparse counts and the open-ended upper interval. The classical identification problem (the exact linear dependency age + cohort = period) [ 10 ] was addressed using the intrinsic estimator (IE) proposed by Yang, Fu, and Land [ 26 , 27 ]. The IE resolves the indeterminacy by projecting the parameter vector onto the orthogonal complement of the null space of the design matrix, yielding the unique minimum-norm L² solution via the Moore–Penrose pseudoinverse. This solution does not depend on arbitrary identification constraints and provides estimable non-linear effects (curvatures) that form the basis for substantive interpretation [ 27 ]. The model was formulated on the log-mean rates of each Lexis cell: ln( r ap ) = µ + α a + β p + γ c where r ap is the mean rate for age group a in period p , µ is the intercept, α a the age effect, β p the period effect, and γ c the cohort effect. Parameters were normalised under a sum-to-zero constraint. Deviance R² was computed as a goodness-of-fit measure. Analyses were conducted for both sexes combined and separately for men and women. Counterfactual analysis of pandemic impact To quantify excess suicide mortality during 2020–2024 relative to the pre-pandemic trend, a counterfactual model was employed [ 14 , 17 ]. A log-linear regression was fitted on the 2000–2019 training period and projected onto 2020–2024, with 95% prediction intervals. Expected deaths were obtained by applying the projected rate to the observed population of each year. The cumulative excess was expressed as the absolute difference (observed − expected), the relative excess (percentage), and the observed/expected ratio (O/E) with 95% CI, with significance assessed by the exact Poisson test. The analysis was stratified by sex. To assess the sensitivity of the estimated excess to the model assumptions, the counterfactual was re-estimated with two alternative training windows: a reduced window (2005–2019) and a window restricted to the last pre-pandemic joinpoint segment (2014–2019). Additionally, a generalised additive model (GAM) with a penalised cubic spline was fitted to verify that the excess was not an artefact of the log-linear trend assumption. Age-specific rate ratios (RR) comparing the 2020–2024 and 2015–2019 quinquennia were computed with 95% CI based on the log-normal approximation to the Poisson rate ratio [ 21 ], to identify the age groups with statistically significant relative increases. Sensitivity and robustness analyses Overdispersion was assessed by the dispersion index (variance/mean) of annual deaths by sex and tested against the Poisson distribution. The overdispersion parameter (φ̂) was incorporated into the APC model by re-estimation under a quasi-Poisson framework, scaling standard errors by √φ̂. The IE model was re-fitted with two alternative age-range specifications (restricted: 40–79; extended: 30–84) to verify the stability of period and cohort effects. Bootstrap 95% CI for all APC effects were obtained from 1,000 non-parametric resamples. Cook’s distance was computed for each Lexis cell to detect influential observations. Full sensitivity results are reported in Online Resources 6 and 7. Software All analyses were performed in Python 3.12 (Google Colaboratory). Core libraries included NumPy 2.0, pandas 2.2, SciPy 1.16, matplotlib 3.10, and statsmodels 0.14. The joinpoint, Prais–Winsten, and IE algorithms were implemented ad hoc following published formulations [ 23 , 25 , 27 ]. Source code is available from the corresponding author upon request. Ethical considerations This study used exclusively publicly available aggregate data from the INE [ 19 , 20 ]; no individual-level data were processed. Ethics committee approval was not required under Spanish legislation (Ley 14/2007, Article 2) [ 28 ]. The study adhered to the RECORD reporting guidelines [ 29 ]. Use of large language models In accordance with ICMJE recommendations and Springer policy on the use of generative artificial intelligence [ 30 ], large language models (Claude, Anthropic; ChatGPT, OpenAI; Gemini, Google) were employed as assistive tools for the generation and debugging of Python code used in the statistical analyses. All outputs were critically reviewed and validated by the investigators, who assume full responsibility for the content of this work. No AI tool is listed as an author or cited as a source. RESULTS Overall suicide mortality During 2000–2024, 18,350 suicide deaths were recorded in Andalusia (14,206 men, 77.4%; 4,144 women, 22.6%; male-to-female ratio 3.43:1). The annual mean was 734 deaths (range 640 in 2019 to 849 in 2021). The Andalusian population grew by 18.5% over the study period (from 7.29 million in 2000 to 8.63 million in 2024). Demographic masking of underlying trends The mean crude rate for both sexes was 9.03 per 100,000 (SD 0.75); the age-standardised rate (ASR) was 9.28 (SD 1.02). Log-linear trend analysis of the crude rates yielded non-significant EAPCs in all three sex strata (both sexes: −0.12%, p = 0.615; men: −0.22%, p = 0.400; women: +0.22%, p = 0.479), suggesting apparent stability. By contrast, the ASR declined significantly in both sexes (EAPC = − 0.98%, 95% CI − 1.45 to − 0.51, p < 0.001) and in men (EAPC = − 1.33%, 95% CI − 1.85 to − 0.81, p < 0.001), whereas in women the decline was non-significant (EAPC = − 0.55%, p = 0.088). Prais–Winsten correction for first-order autocorrelation produced negligible changes (< 0.06 percentage points in all strata). This divergence arose from progressive population ageing. At the start of the series (2000), the Andalusian age structure was younger than the ESP2013 reference and ASRs exceeded crude rates (both sexes: 11.25 vs 9.62). By 2024, the relationship had reversed: crude rates exceeded ASRs (9.81 vs 9.11). In consequence, demographic ageing masked a genuine decline in age-adjusted suicide risk behind apparently stable crude rates (Fig. 1 ; Online Resources 1 and 9). Joinpoint regression Joinpoint analysis of the ASR series for both sexes identified three change-points (2014, 2019, 2020) defining four segments (Fig. 2 ). Between 2000 and 2014, rates declined at − 1.32% per year (95% CI − 1.94 to − 0.70, p < 0.001). The decline accelerated between 2014 and 2019 (EAPC = − 3.94%, 95% CI − 5.89 to − 1.96, p = 0.001). An abrupt reversal occurred between 2019 and 2020 (+ 26.6%, 95% CI + 10.80 to + 44.55, p = 0.002), coinciding with the onset of the COVID-19 pandemic and the national lockdown. From 2020 to 2024, rates stabilised at the elevated post-rupture level (EAPC = − 0.38%, p = 0.970). Monte Carlo permutation testing (4,999 replicates) confirmed the significance of the joinpoints ( p = 0.003). In men, the pattern was qualitatively similar (joinpoints at 2014, 2019, 2021), with a sustained decline until 2019, a sharp increase between 2019 and 2021 (EAPC = + 16.64%, p = 0.001), and subsequent stabilisation. In women, the model identified two joinpoints (2009, 2010) but permutation testing did not confirm their significance ( p = 0.148), suggesting weaker evidence of structural trend changes in female suicide rates. Age-specific trend analysis over the full period revealed significant declines concentrated in older groups (≥ 65 years: EAPCs between − 1.37% and − 3.79%, all p < 0.05) and in the 15–29 age group (EAPC = − 2.10%, p < 0.001), whereas rates in the 30–64 range remained essentially stable. Age–period–cohort decomposition The APC model (intrinsic estimator; Lexis table: 9 age groups × 5 periods, 13 cohorts) explained 98.3% of the total variance for both sexes (R² = 0.986 men, 0.904 women) (Fig. 3 ). Age effects showed a monotonic ascending gradient. The relative risk nearly doubled from 40–44 years (RR = 0.710) to 80–84 years (RR = 1.387) for both sexes. The gradient was steeper in men (RR 0.686 to 1.670) than in women, who exhibited a plateau between 55 and 84 years (RR 1.019–1.128). Period effects showed the lowest rate ratio in the 2015–2019 quinquennium (RR = 0.890), 11% below the grand mean. The pandemic quinquennium (2020–2024) was associated with a rebound to levels comparable to the early 2000s (RR = 1.020), but did not exceed the peak observed in 2005–2009 (RR = 1.057). A qualitatively similar pattern was found in men (nadir 2015–2019: RR = 0.879). Cohort effects showed a declining risk gradient from the oldest to younger cohorts. Generations born before 1935 carried elevated risk (peak ~ 1920: RR = 1.358), followed by a sustained decline through cohorts born 1935–1975 (nadir ~ 1945–1955: RR ≈ 0.86). From the ~ 1960 cohort onwards, risk stabilised near the mean (~ 1980: RR = 1.004). In women, an exception was noted: the ~ 1975 cohort exhibited a moderately elevated risk (RR = 1.084), the highest among female cohorts born after 1955. Sensitivity analyses confirmed the robustness of the APC model. Two alternative age-range specifications (40–79 and 30–84) replicated the period nadir in 2015–2019 (RR 0.890–0.893) with inter-model correlations exceeding 0.98. Bootstrap confidence intervals (1,000 resamples) and quasi-Poisson correction for overdispersion did not materially alter the results (Online Resources 6 and 7). Counterfactual analysis of pandemic excess The log-linear model fitted to 2000–2019 confirmed a significant pre-pandemic decline in both sexes (EAPC = − 0.93%, p = 0.002) and men (EAPC = − 1.07%, p = 0.001). Projection onto 2020–2024 yielded 3,351 expected deaths for both sexes (Table 1 ). Against 4,134 observed deaths, the estimated cumulative excess was 783 deaths (+ 23.4%; O/E = 1.23, 95% CI 1.20–1.27, p < 0.001). In men, the excess was 622 deaths (O/E = 1.24, 95% CI 1.20–1.29, p < 0.001); in women, 175 deaths (O/E = 1.22, 95% CI 1.14–1.30, p < 0.001). Table 1 Counterfactual analysis of suicide mortality: pandemic period (2020–2024) versus pre-pandemic trend, Andalusia Panel A. Observed vs expected deaths by year and sex Year (phase) Obs. (n) Exp. (n) 95% pred. CI Excess (n) Excess (%) O/E p Both sexes 2020 (Lockdown) 793 677.1 (580.0–790.5) 115.9 + 17.1% 1.17 < 0.001 2021 (De-escalation) 849 672.7 (575.0–786.9) 176.3 + 26.2% 1.26 < 0.001 2022 (Post-pandemic) 830 668.4 (570.1–783.7) 161.6 + 24.2% 1.24 < 0.001 2023 (Post-pandemic) 815 667.9 (568.3–785.0) 147.1 + 22.0% 1.22 < 0.001 2024 (Post-pandemic) 847 665.3 (564.6–783.9) 181.7 + 27.3% 1.27 < 0.001 Cumulative 2020–2024 4,134 3,351.4 — 782.6 + 23.4% 1.23 (1.20–1.27) < 0.001 Men 2020 (Lockdown) 576 516.4 (436.8–610.6) 59.6 + 11.5% 1.12 0.005 2021 (De-escalation) 661 512.1 (432.1–606.9) 148.9 + 29.1% 1.29 < 0.001 2022 (Post-pandemic) 651 508.3 (427.9–603.8) 142.7 + 28.1% 1.28 < 0.001 2023 (Post-pandemic) 631 506.8 (425.5–603.6) 124.2 + 24.5% 1.25 < 0.001 2024 (Post-pandemic) 650 503.7 (421.7–601.6) 146.3 + 29.0% 1.29 < 0.001 Cumulative 2020–2024 3,169 2,547.3 — 621.7 + 24.4% 1.24 (1.20–1.29) < 0.001 Women 2020 (Lockdown) 217 158.3 (126.4–198.3) 58.7 + 37.1% 1.37 < 0.001 2021 (De-escalation) 188 158.0 (125.7–198.5) 30.0 + 19.0% 1.19 0.011 2022 (Post-pandemic) 179 157.6 (125.0–198.6) 21.4 + 13.6% 1.14 0.050 2023 (Post-pandemic) 184 158.2 (125.1–200.1) 25.8 + 16.3% 1.16 0.024 2024 (Post-pandemic) 197 158.4 (124.8–201.1) 38.6 + 24.4% 1.24 0.002 Cumulative 2020–2024 965 790.5 — 174.5 + 22.1% 1.22 (1.14–1.30) < 0.001 Panel B. Counterfactual model parameters (log-linear 2000–2019) Sex EAPC (%) 95% CI p R² RMSE Both sexes −0.93% (− 1.44 to − 0.43) 0.002 0.422 0.067 Men −1.07% (− 1.62 to − 0.53) 0.001 0.451 0.072 Women −0.52% (− 1.25 to + 0.22) 0.183 0.097 0.097 Panel C. O/E ratio by year and sex Year Both sexes Men Women Phase 2020 1.17 1.12 1.37 Lockdown 2021 1.26 1.29 1.19 De-escalation 2022 1.24 1.28 1.14 Post-pandemic 2023 1.22 1.25 1.16 Post-pandemic 2024 1.27 1.29 1.24 Post-pandemic a Expected deaths: projection of the log-linear model ln(Rate) = α + β × Year, fitted to 2000–2019 and projected onto 2020–2024, multiplied by observed population. b Exact Poisson test (H₀: observed = expected). 95% pred.: 95% prediction interval for a new observation. O/E observed/expected ratio; values > 1 indicate excess mortality. EAPC estimated annual percentage change; RMSE root mean square error (log scale). The excess was sustained throughout the five-year period, with annual O/E ratios ranging from 1.17 (2020) to 1.27 (2024) and no significant attenuation trend (slope = + 0.016/year, p = 0.250). In every year from 2021 onwards, the observed count clearly exceeded the upper 95% prediction interval; in 2020, it fell marginally above the upper bound. Sensitivity analyses using alternative training windows (2005–2019; 2014–2019) and a GAM with penalised cubic spline yielded consistent O/E ratios (range 1.23–1.42, all p < 0.001). A notable gender asymmetry was observed in 2020: the O/E ratio reached 1.37 in women—the highest value in the entire series—compared with 1.12 in men, suggesting a disproportionate initial impact of the pandemic context on female suicide mortality. Differential impact by age group Age-specific rate ratios comparing the 2020–2024 and 2015–2019 quinquennia revealed that the pandemic-period excess was concentrated in adults aged 15–64 (Table 2 ). Six of twelve age groups showed significant increases: 15–29 (RR = 1.29, 95% CI 1.07–1.54, p = 0.007), 30–39 (RR = 1.29, 95% CI 1.12–1.48, p < 0.001), 40–44 (RR = 1.23, p = 0.002), 45–49 (RR = 1.23, p < 0.001), 50–54 (RR = 1.19, p < 0.001), and 60–64 (RR = 1.28, p 0.50), indicating that the long-term downward trend in older adults continued through the pandemic period uninterrupted. Provincial disparities, age-specific analyses, the male-to-female ratio, and sensitivity results are reported in Online Resources 2 to 8. Table 2 Rate ratios of suicide mortality by age group: pandemic period (2020–2024) versus pre-pandemic period (2015–2019), Andalusia Panel A. Rate ratios by age group (both sexes) Age group Rate 2015–2019 Rate 2020–2024 RR 95% CI p Sig. 15–29 3.15 4.05 1.29 (1.07–1.54) 0.007 ** 30–39 6.53 8.39 1.29 (1.12–1.48) < 0.001 ** 40–44 8.87 10.93 1.23 (1.08–1.41) 0.002 ** 45–49 10.72 13.20 1.23 (1.12–1.35) < 0.001 ** 50–54 12.10 14.44 1.19 (1.12–1.27) < 0.001 ** 55–59 12.21 14.03 1.15 (0.96–1.37) 0.127 — 60–64 11.04 14.09 1.28 (1.10–1.47) < 0.001 ** 65–69 11.00 12.81 1.16 (0.92–1.47) 0.198 — 70–74 12.65 13.22 1.04 (0.88–1.25) 0.625 — 75–79 16.95 16.12 0.95 (0.70–1.29) 0.746 ↓ 80–84 19.91 20.57 1.03 (0.80–1.34) 0.803 — ≥ 85 10.74 11.69 1.09 (0.81–1.45) 0.568 — Panel B. Rate ratios by sex (all ages) Sex Rate 2015–2019 Rate 2020–2024 RR 95% CI p Both sexes 7.94 9.69 1.22 (1.17–1.28) < 0.001 Men 12.26 15.07 1.23 (1.17–1.30) < 0.001 Women 3.73 4.46 1.19 (1.09–1.31) < 0.001 Panel C. Absolute and relative change by age group Age group Rate 2015–2019 Rate 2020–2024 Δ abs. Δ rel. (%) Ranking 15–29 3.15 4.05 + 0.90 + 28.6% 9 30–39 6.53 8.39 + 1.86 + 28.5% 5 40–44 8.87 10.93 + 2.05 + 23.1% 4 45–49 10.72 13.20 + 2.49 + 23.2% 2 50–54 12.10 14.44 + 2.33 + 19.3% 3 55–59 12.21 14.03 + 1.82 + 14.9% 6 60–64 11.04 14.09 + 3.05 + 27.6% 1 65–69 11.00 12.81 + 1.81 + 16.5% 7 70–74 12.65 13.22 + 0.57 + 4.5% 11 75–79 16.95 16.12 −0.83 −4.9% 12 80–84 19.91 20.57 + 0.66 + 3.3% 10 ≥ 85 10.74 11.69 + 0.94 + 8.8% 8 a Rate: crude suicide mortality rate per 100,000 (quinquennial mean). b RR: rate ratio = rate 2020–2024 / rate 2015–2019. 95% CI based on the log-normal approximation to the Poisson rate ratio [ 21 ]. c Δ abs.: rate difference (2020–2024 minus 2015–2019) per 100,000. d Ranking by absolute increment (1 = largest increase). **: significant increase (p < 0.05); ↓: non-significant decrease; —: no significant change. DISCUSSION This study provides the first population-based analysis of suicide mortality in Andalusia spanning a full quarter-century of consolidated data (2000–2024), integrating age–period–cohort decomposition, segmented trend analysis, counterfactual modelling, and assessment of geographic and gender disparities. The principal findings were that age-standardised rates declined significantly but this decline was masked by population ageing; that an abrupt pandemic-coincident reversal led to an estimated 783 excess deaths during 2020–2024, concentrated in adults aged 15–64 and with a disproportionate initial impact on women; and that the APC model revealed descending cohort effects and a period nadir in 2015–2019. Demographic masking The progressive divergence between crude and age-standardised rates constitutes a methodologically important finding. While crude rates appeared stable (EAPCs near zero, all non-significant), ASRs declined significantly in both sexes and in men. This discrepancy arises because population ageing shifts the denominator towards older groups with inherently higher suicide rates, inflating the crude aggregate and offsetting the underlying decline in age-adjusted risk. This phenomenon has been described for other causes of death but has rarely been documented explicitly for suicide in Spain. Cayuela et al. reported declining age-adjusted rates nationally (EAPC − 0.5% in men, 1984–2018) [ 12 ], and Bertuccio et al. confirmed similar trends across most Western European countries [ 5 ]. Our findings align with this broader pattern and underscore a practical implication: surveillance reports and media coverage that rely exclusively on absolute counts or crude rates risk conveying a misleadingly alarmist—or, in certain periods, misleadingly reassuring—picture. Direct age-standardisation should be the default metric for monitoring suicide mortality trends [ 31 ]. The pandemic rupture The joinpoint analysis revealed a “step-change” pattern: after 14 years of sustained decline that accelerated during the economic recovery (2014–2019: EAPC − 3.94%), rates surged by 26.6% in a single year (2019–2020) and stabilised at that elevated level through 2024. This finding is consistent with the international collaboration led by Pirkis et al., which identified Spain as one of the few high-income countries where suicide mortality increased significantly during the early pandemic months [ 14 ]. Lantos and Nyári, analysing 27 EU-linked countries, similarly identified Spain—alongside Ireland and Hungary—as a country where the 2020 increase reversed a previously declining trend [ 32 ]. However, the distinctive contribution of the present study lies in demonstrating that the excess was not a transient phenomenon confined to the lockdown year but a sustained level shift persisting for at least five years. The counterfactual analysis reinforces this conclusion. Against 3,351 expected deaths, 4,134 were observed during 2020–2024 (O/E = 1.23, 95% CI 1.20–1.27, p < 0.001), with annual O/E ratios showing no attenuation trend. Notably, the lowest relative excess occurred in 2020 (O/E = 1.17)—the year of strictest lockdown—while the highest values were recorded in 2021 and 2024 (O/E 1.26–1.27). This temporal pattern is more consistent with a delayed and sustained response, in which the psychosocial consequences of the pandemic (prolonged unemployment, unresolved grief, mental health deterioration, service disruption) manifested with a time lag and have persisted beyond the resolution of the health emergency [ 17 , 33 ]. Martínez-Alés et al. documented an 11% excess during April–December 2020 but their analysis was limited to the first pandemic year [ 16 ]; our data extend this observation and show that the excess not only persisted but amplified in subsequent years. These findings contrast with the experience of most high-income countries, where systematic reviews found no significant pandemic-related increases [ 15 , 34 ]. The singularity of the Andalusian pattern may relate to context-specific factors: a mental health workforce below the European average [ 33 ], a higher prevalence of adverse social determinants (unemployment, job precariousness), and a pandemic response in which suicide prevention was not explicitly prioritised. Age, period, and cohort effects The APC model explained 98.3% of the variance, indicating that the three temporal dimensions capture virtually all heterogeneity in suicide rates. The ascending age gradient—risk nearly doubling between ages 40–44 and 80–84—is consistent with the international literature documenting age-related accumulation of biological, psychosocial, and healthcare-related risk factors [ 5 , 10 , 35 ]. The bimodal profile in men (peaks at 45–54 and ≥ 75 years) suggests at least two distinct vulnerability patterns: one linked to the midlife crisis and one to frailty and isolation in later life. Period effects confirmed that 2015–2019 carried the lowest contextual risk of the entire series (RR = 0.890), likely reflecting the confluence of economic recovery, progressive expansion of community mental health services, and growing social awareness of suicide prevention [ 36 ]. The pandemic quinquennium (2020–2024: RR = 1.020) reversed this trend but did not exceed the peak of 2005–2009, suggesting that, from a quinquennial perspective, the impact was moderate when averaged with underlying generational effects—though this should not obscure the abrupt annual-level rupture documented by joinpoint analysis. Cohort effects showed declining risk from pre-1935 generations—who experienced childhood during the Spanish Civil War and its aftermath—through cohorts born 1945–1955, with subsequent stabilisation. This pattern is consistent with prior national studies [ 12 , 13 , 37 ]. A noteworthy finding was the moderately elevated risk in women of the ~ 1975 cohort (RR = 1.084), the first generation of full female labour-force participation in Spain, who may have faced the concurrent demands of professional life and traditional caregiving roles amid increasing job precariousness—a hypothesis that warrants verification through individual-level studies. Differential impact by age and gender The concentration of the pandemic-period excess in adults aged 15–64, with the largest relative increases in the 15–29 and 30–39 age groups (RR = 1.29), is particularly concerning given that these groups contribute the greatest number of potential years of life lost. Meanwhile, older adults (≥ 70 years) showed no significant change, suggesting that protective mechanisms sustaining their long-term decline—improved treatment of geriatric depression, expansion of social services—continued to operate during the pandemic. The marked gender asymmetry in 2020 (O/E = 1.37 in women vs 1.12 in men) is consistent with significant increases in female suicide documented in Japan [ 40 ], South Korea [ 41 ], and other settings [ 42 ], attributed to lockdown-related psychosocial determinants disproportionately affecting women: increased caregiving burden, exposure to domestic violence, and precarious employment [ 38 , 39 ]. That this asymmetry attenuated in 2021–2024 suggests it was specific to the strict lockdown conditions rather than a sustained shift in the gender dynamics of suicide. Policy implications These findings have direct implications for suicide prevention planning at both regional and national levels. The Programa de prevención de la conducta suicida en Andalucía 2023–2026 [ 36 ] establishes a comprehensive framework encompassing surveillance, training, clinical care, and postvention. Our results provide the empirical basis to orient at least four priorities within this programme and its national counterpart, the Plan de Acción para la Prevención del Suicidio 2025–2027 [ 43 ]. First, the sustained excess among young and middle-aged adults identifies these groups as priority targets for preventive interventions, warranting resource allocation proportional to their mortality burden. Second, the documented demographic masking underscores the need for surveillance systems to incorporate age-standardised rates as the reference metric, alongside absolute counts. Third, the persistence of excess mortality through 2024 indicates that the pandemic’s sequelae on suicidal behaviour have not remitted; the response must therefore move beyond emergency measures to address structural determinants of risk—access to mental health services, employment, housing, and social support networks. The Andalusian programme could benefit from adopting counterfactual indicators (O/E ratios) as evaluation tools, complementing conventional trend metrics. Fourth, the present study exhausts the analytical potential of publicly available INE data and, in doing so, empirically demonstrates where the official statistical system falls short. The Institutes of Legal Medicine and Forensic Sciences (Institutos de Medicina Legal y Ciencias Forenses) of Andalusia —one per province—constitute the natural complementary source. These Institutes hold individualised data on each death, including method, circumstances, clinical history, prior attempts, precise geolocation, and the simultaneous disaggregation by age, sex, and province that INE public tables do not provide. Establishing the Institutes as a systematic data source for suicide surveillance would enable second-generation studies with microdata, opening the door to analyses of methods, contextual risk factors, and predictive models that aggregate statistical sources can never offer. Strengths and limitations Strengths include the 25-year observation window (among the longest for a subnational European analysis), data through 2024, a population comparable to medium-sized European countries, methodological triangulation, and exclusive use of publicly available sources. Several limitations apply. The ecological design precludes individual-level causal inference. INE mortality data are subject to under-reporting, although stable ICD-10 correspondence throughout the period reduces the likelihood of classification artefacts. Provincial analyses relied on crude rates because publicly available tables do not disaggregate deaths simultaneously by age, sex, and province—a limitation of the statistical system, not of the study design. The counterfactual model assumes log-linear trend continuation, though sensitivity analyses confirmed the robustness of the excess. The intrinsic estimator shares with all APC models the inability to fully separate linear effects, though the non-linear curvatures—which underpin substantive interpretation—remained stable across specifications. CONCLUSION Suicide mortality in Andalusia during 2000–2024 exhibited a genuine underlying decline once adjusted for population ageing, but this trend was abruptly interrupted in coincidence with the COVID-19 pandemic. The estimated excess of 783 deaths over five consecutive years, without attenuation, configures a sustained level shift demanding an equally sustained preventive response. The results support: (a) adopting age-standardised rates as the reference metric in surveillance; (b) prioritising adults aged 15–64 as intervention targets; (c) establishing the Institutes of Legal Medicine and Forensic Sciences as a systematic data source for second-generation studies with individualised microdata; and (d) evaluating prevention policies through counterfactual indicators. These elements should inform both the Programa de prevención de la conducta suicida en Andalucía 2023–2026 [ 36 ] and the national Plan de Acción para la Prevención del Suicidio [ 43 ]. Declarations Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Competing interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval This study was based exclusively on publicly available aggregate mortality and population data from the Spanish National Statistics Institute (INE) [19, 20]. No individual-level data were processed. In accordance with Spanish legislation (Ley 14/2007 de Investigación Biomédica, Article 2) and the guidelines of the Spanish Bioethics Committee, studies conducted on anonymised aggregate data from public sources do not require research ethics committee approval [28]. Consent to participate Not applicable. Consent to publish Not applicable. Availability of data and materials All data used in this study are publicly available from the Spanish National Statistics Institute (INE): mortality data from the cause-of-death statistics [19] and population data from the municipal register series [20]. Source code is available from the corresponding author upon reasonable request. Author contributions Conceptualization: Carlos Romero-Olóriz, Miguel Guerrero Díaz; Methodology: Carlos Romero-Olóriz; Software: Carlos Romero-Olóriz; Formal analysis: Carlos Romero-Olóriz; Investigation: Carlos Romero-Olóriz, Esperanza López Hidalgo, María Victoria Villalba Soria; Data curation: Carlos Romero-Olóriz; Writing – original draft: Carlos Romero-Olóriz; Writing – review and editing: Esperanza López Hidalgo, María Victoria Villalba Soria, Miguel Guerrero Díaz; Resources: Esperanza López Hidalgo; Visualization: Carlos Romero-Olóriz; Supervision: Esperanza López Hidalgo, Miguel Guerrero Díaz. All authors read and approved the final manuscript. Use of generative AI Large language models (Claude, Anthropic; ChatGPT, OpenAI; Gemini, Google) were used as assistive tools for code generation and debugging, as detailed in the Methods section. All outputs were critically reviewed and validated by the authors, who assume full responsibility for the content of this work. 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PLoS ONE 17:e0273637. https://doi.org/10.1371/journal.pone.0273637 Bellizzi S, Lorettu L, Nivoli A et al (2022) Suicide of women and girls during the COVID-19 pandemic. Int J Gynaecol Obstet 157:742–743. https://doi.org/10.1002/ijgo.14146 Comisionado de Salud Mental (2025) Plan de Acción para la Prevención del Suicidio 2025–2027. Ministerio de Sanidad, Madrid. https://www.sanidad.gob.es /areas/calidadAsistencial/estrategias/saludMen tal/docs/Plan_de_accion_para_la_prevencion_d el_suicidio_2025_2027.pdf Additional Declarations No competing interests reported. Supplementary Files OnlineResourcelegends.docx APCSuicAndSPPEESM3.pdf APCSuicAndSPPEESM2.pdf APCSuicAndSPPEESM1.pdf APCSuicAndSPPEESM7.pdf APCSuicAndSPPEESM4.pdf APCSuicAndSPPEESM5.pdf APCSuicAndSPPEESM6.pdf APCSuicAndSPPEESM8.pdf APCSuicAndSPPEESM9.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 May, 2026 Reviews received at journal 04 May, 2026 Reviews received at journal 02 May, 2026 Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 22 Apr, 2026 Reviewers agreed at journal 21 Apr, 2026 Reviewers invited by journal 21 Apr, 2026 Editor assigned by journal 17 Apr, 2026 Submission checks completed at journal 16 Mar, 2026 First submitted to journal 13 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9114122","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628634162,"identity":"bf7668c8-4989-4d90-a9bb-56f6e5813b2c","order_by":0,"name":"Carlos Romero-Olóriz","email":"","orcid":"","institution":"Andalusian Emergency Health Centre 061 (CES-061)","correspondingAuthor":false,"prefix":"","firstName":"Carlos","middleName":"","lastName":"Romero-Olóriz","suffix":""},{"id":628634164,"identity":"dfbefd30-7ebb-4c67-951f-092d345062f1","order_by":1,"name":"Esperanza López Hidalgo","email":"","orcid":"","institution":"Institute of Legal Medicine and Forensic Sciences of Malaga","correspondingAuthor":false,"prefix":"","firstName":"Esperanza","middleName":"López","lastName":"Hidalgo","suffix":""},{"id":628634165,"identity":"3421053c-cb7b-4b17-b4ea-da00ac63412a","order_by":2,"name":"María Victoria Villalba Soria","email":"","orcid":"","institution":"Institute of Legal Medicine and Forensic Sciences of Malaga","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"Victoria Villalba","lastName":"Soria","suffix":""},{"id":628634166,"identity":"e7420419-67f8-4e05-baf2-b45bdd28a839","order_by":3,"name":"Miguel Guerrero Díaz","email":"data:image/png;base64,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","orcid":"","institution":"University Hospital Virgen de la Victoria","correspondingAuthor":true,"prefix":"","firstName":"Miguel","middleName":"Guerrero","lastName":"Díaz","suffix":""}],"badges":[],"createdAt":"2026-03-13 11:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9114122/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9114122/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108491173,"identity":"32406078-26fb-424e-a113-d4563b50bb96","added_by":"auto","created_at":"2026-05-05 09:52:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7909251,"visible":true,"origin":"","legend":"\u003cp\u003eCrude versus age-standardised (ESP2013) suicide mortality rates by sex, Andalusia, 2000–2024. The three panels display the comparison between crude rates (dashed lines, square markers) and directly age-standardised rates using the 2013 European Standard Population (solid lines, circle markers) for both sexes, men, and women. Shaded areas between curves reflect the magnitude and direction of the difference attributable to population age structure. Background colour bands delimit the four historical periods: pre-crisis (2000–2007), economic recession (2008–2013), recovery (2014–2019), and pandemic/post-pandemic (2020–2024). At the start of the period, age-standardised rates exceeded crude rates (difference in 2000: −1.63 for both sexes; −4.23 for men per 100,000); by 2024, the relationship had reversed (+0.70 and +0.74, respectively). \u003cem\u003eEAPC\u003c/em\u003eestimated annual percentage change; \u003cem\u003eASR\u003c/em\u003e age-standardised rate. Note: Y-axis scales differ across panels to enhance readability\u003c/p\u003e","description":"","filename":"APCSuicAndSPPEFig1.png","url":"https://assets-eu.researchsquare.com/files/rs-9114122/v1/e9990087086cd0362d2c3da5.png"},{"id":108210743,"identity":"a32d25ee-1b24-4427-9a7e-4933161fccce","added_by":"auto","created_at":"2026-04-30 13:42:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":9745705,"visible":true,"origin":"","legend":"\u003cp\u003eJoinpoint regression on age-standardised suicide mortality rates by sex, Andalusia, 2000–2024. The three panels display the results of segmented joinpoint regression applied to directly age-standardised rates (ESP2013) for both sexes, men, and women. Points represent observed rates; solid lines represent estimated trend segments from a continuous piecewise log-linear model, with identified joinpoints numbered. Background colour bands delimit the four historical periods. For both sexes, three joinpoints (2014, 2019, 2020) defined four segments: sustained decline 2000–2014 (EAPC = −1.3%, p \u0026lt; 0.05), accelerated decline 2014–2019 (EAPC = −3.9%, p \u0026lt; 0.05), abrupt increase 2019–2020 (EAPC = +26.6%, p \u0026lt; 0.05), and stabilisation 2020–2024 (EAPC = −0.4%, not significant); AAPC = −0.69% (95% CI −3.9 to +12.7). In men, three joinpoints (2014, 2019, 2021) yielded a similar pattern. In women, two joinpoints (2009, 2010) were identified but did not reach significance on permutation testing (p = 0.148). \u003cem\u003eEAPC\u003c/em\u003eestimated annual percentage change; \u003cem\u003eAAPC\u003c/em\u003e average annual percentage change; *p \u0026lt; 0.05; \u003cem\u003eESP2013\u003c/em\u003e 2013 European Standard Population. Note: Y-axis scales differ across panels to enhance readability\u003c/p\u003e","description":"","filename":"APCSuicAndSPPEFig2.png","url":"https://assets-eu.researchsquare.com/files/rs-9114122/v1/c8992ba9c09ef8a62f4d3fa2.png"},{"id":108803689,"identity":"a41d994b-414e-484a-b0e8-c1b55e320cb6","added_by":"auto","created_at":"2026-05-08 15:03:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10894908,"visible":true,"origin":"","legend":"\u003cp\u003eAge–period–cohort effects from the intrinsic estimator model by sex, Andalusia, 2000–2024. Rate ratios (RR) relative to the global mean (RR = 1.0, dashed horizontal line) for both sexes (left), men (centre), and women (right). The model was fitted to a Lexis table of 9 five-year age groups (40–44 to 80–84) × 5 quinquennial periods (2000–2004 to 2020–2024), yielding 13 birth cohorts (~1920–1980). Deviance R² = 0.983 (both sexes), 0.986 (men), 0.904 (women). Background colour bands in period panels delimit the four historical periods. Top row (age effect): in men, risk increased monotonically from RR = 0.69 at 40–44 to RR = 1.67 at 80–84; in women, a plateau was observed between 55 and 84 years (RR 1.02–1.13). Middle row (period effect): risk peaked in 2005–2009 (RR = 1.06), reached its nadir in 2015–2019 (RR = 0.89), and rebounded moderately in 2020–2024 (RR = 1.02). Bottom row (cohort effect): cohorts born ~1920–1935 carried elevated risk (peak RR = 1.36), followed by sustained decline through ~1945–1955 (RR ≈ 0.85) and stabilisation in later cohorts. Shaded grey bands in the both-sexes cohort panel identify birth cohorts by the major historical events that shaped their formative years: cohorts born ~1920–1935 experienced the Spanish Civil War (1936–1939) and the subsequent autarky period during childhood and adolescence, marked by material deprivation, social disruption, and political repression; cohorts born ~1945–1955 reached adulthood during Spain's economic development period (desarrollismo) and the transition to democracy (1975–1982), associated with improved living standards and social modernisation. \u003cem\u003eRR\u003c/em\u003e rate ratio; coefficients subject to the sum-to-zero identification constraint Σα = Σβ = Σγ = 0. Note: Y-axis scales differ across panels to enhance readability\u003c/p\u003e","description":"","filename":"APCSuicAndSPPEFig3.png","url":"https://assets-eu.researchsquare.com/files/rs-9114122/v1/052951320d47f7cf6eb48dc0.png"},{"id":108809096,"identity":"19bd1585-a7e5-476c-88a9-d1bb3fc258e5","added_by":"auto","created_at":"2026-05-08 15:49:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":20148556,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9114122/v1/1d090204-b496-4e47-baf4-d0214ff36eaf.pdf"},{"id":108210742,"identity":"adf085f3-6a3e-4163-a3dd-4dc12e19545d","added_by":"auto","created_at":"2026-04-30 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15:01:10","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":783460,"visible":true,"origin":"","legend":"","description":"","filename":"APCSuicAndSPPEESM5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9114122/v1/cf26e631a92a08f608c81502.pdf"},{"id":108210751,"identity":"d7508c02-af23-4757-a56a-0fc80ed3272f","added_by":"auto","created_at":"2026-04-30 13:42:54","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":647303,"visible":true,"origin":"","legend":"","description":"","filename":"APCSuicAndSPPEESM6.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9114122/v1/912e8a10e5a828a1daffb9aa.pdf"},{"id":108491969,"identity":"d656a4e2-84e6-467b-b21f-d55ec5633b26","added_by":"auto","created_at":"2026-05-05 09:56:26","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":235707,"visible":true,"origin":"","legend":"","description":"","filename":"APCSuicAndSPPEESM8.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9114122/v1/135e256fd8366f7d5c6e7ff1.pdf"},{"id":108210754,"identity":"01d2a499-5a1b-46bd-8e40-c573382416f3","added_by":"auto","created_at":"2026-04-30 13:42:54","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1147450,"visible":true,"origin":"","legend":"","description":"","filename":"APCSuicAndSPPEESM9.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9114122/v1/ca2dc22d77484f4b28b188fe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Age–period–cohort effects on suicide mortality in Andalusia, Spain (2000–2024): demographic masking and sustained pandemic excess","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSuicide remains a major public health challenge worldwide. According to the most recent Global Burden of Disease estimates, approximately 727,000 people die by suicide each year, accounting for 1.1% of all deaths globally and ranking as the third leading cause of death among those aged 15\u0026ndash;29 years [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although the global age-standardised suicide mortality rate declined by 36% between 2000 and 2019, this progress has been unevenly distributed: whilst many high-income countries have achieved sustained reductions, certain regions and population subgroups exhibit stable or rising trends [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Within Europe, a well-established geographical gradient persists, with higher rates in northern and eastern countries and comparatively lower rates in the Mediterranean south [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Nevertheless, age-specific analyses reveal that rates increase sharply with age across most European nations, exceeding 50 per 100,000 in men aged 85 and over in several western countries [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSpain has traditionally occupied an intermediate-to-low position in the European ranking. However, recent figures signal a concerning trajectory. The number of suicide deaths rose from 3,158 in 2000 to 4,227 in 2022 \u0026mdash; the highest on record \u0026mdash; before declining slightly to 3,953 in 2024 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Suicide has become the leading cause of external death among those aged 15\u0026ndash;29, with men accounting for approximately 75% of all deaths [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Andalusia, the most populous autonomous community (~\u0026thinsp;8.5\u0026nbsp;million inhabitants), presents characteristics that warrant specific investigation: it is comparable in population to mid-sized European countries such as Austria or Switzerland, and previous studies have identified it \u0026mdash; alongside Galicia \u0026mdash; as a region with suicide rates consistently above the national average, with an inland\u0026ndash;coastal gradient suggesting higher risk in rural provinces [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAge\u0026ndash;period\u0026ndash;cohort (APC) analysis constitutes a fundamental epidemiological tool for disentangling the mechanisms underlying temporal mortality trends [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It separates three conceptually distinct components: age effects (biological and cumulative risk), period effects (contextual influences affecting all age groups simultaneously), and birth cohort effects (generational differences attributable to shared formative experiences) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In Spain, previous APC studies have been conducted exclusively at the national level: Cayuela et al. analysed 1984\u0026ndash;2018 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and Mart\u0026iacute;nez-Al\u0026eacute;s et al. covered 2000\u0026ndash;2019 stratified by migratory status [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Neither descended to the sub-national level, included post-2019 data, nor incorporated a counterfactual model to quantify pandemic-associated excess mortality.\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic constituted an unprecedented disruptive event with documented consequences for population mental health, including increases in depression, anxiety, and suicidal behaviour [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. International evidence on its impact on suicide mortality has been mixed: whilst most high-income countries did not register significant increases during 2020, Spain was identified as one of the few where the rise was statistically significant, reversing the prior downward trend [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Critically, the majority of these studies were limited to the first 12\u0026ndash;18 months of the pandemic, without covering the complete post-pandemic period or addressing differential impact by age and sex at the sub-national level [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA significant knowledge gap therefore exists. No study has applied a complete APC analysis to suicide mortality in Andalusia, nor has any covered a 25-year window encompassing four distinct socioeconomic and health contexts \u0026mdash; pre-crisis (2000\u0026ndash;2007), economic recession (2008\u0026ndash;2013), recovery (2014\u0026ndash;2019), and the pandemic and post-pandemic period (2020\u0026ndash;2024) \u0026mdash; whilst simultaneously quantifying excess mortality through a counterfactual framework and examining provincial disparities.\u003c/p\u003e \u003cp\u003eThis study aimed to analyse the evolution of suicide mortality in Andalusia, 2000\u0026ndash;2024, by integrating age-standardised trend analysis, APC decomposition with the intrinsic estimator, counterfactual estimation of pandemic-associated excess mortality, and examination of geographical and sex-based disparities.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and data sources\u003c/h2\u003e \u003cp\u003eThis was a population-based ecological time-series study of all deaths recorded under heading 098 (suicide and intentional self-harm; International Classification of Diseases, 10th Revision [ICD-10] codes X60\u0026ndash;X84, Y87.0) of the Spanish National Statistics Institute (INE) reduced cause-of-death list in the autonomous community of Andalusia during the period 2000\u0026ndash;2024 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. There were no changes in the correspondence between heading 098 and the ICD-10 codes throughout the study period.\u003c/p\u003e \u003cp\u003eTwo analytical databases were constructed from publicly available INE microdata tables. The first (provincial database) contained, for each combination of year (2000\u0026ndash;2024), province (eight provinces and the regional aggregate), and sex, the number of suicide deaths, the denominator population (municipal register at 1 January of each year), and the crude mortality rate per 100,000 person-years (675 records). The second (age-specific database) contained, for each combination of year, age group, and sex, the mortality rate per 100,000 person-years for the whole of Andalusia (975 records). The original INE age categories were regrouped into 13 intervals: \u0026le;14, 15\u0026ndash;29, 30\u0026ndash;39, and five-year groups from 40\u0026ndash;44 to 80\u0026ndash;84, plus\u0026thinsp;\u0026ge;\u0026thinsp;85 years [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Cross-validation confirmed a maximum discrepancy of 0.005 per 100,000 between calculated and INE-published rates.\u003c/p\u003e \u003cp\u003eFour historical periods were defined \u003cem\u003ea priori\u003c/em\u003e: pre-crisis (2000\u0026ndash;2007), economic recession (2008\u0026ndash;2013), recovery (2014\u0026ndash;2019), and pandemic/post-pandemic (2020\u0026ndash;2024).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAge standardisation\u003c/h3\u003e\n\u003cp\u003eAge-standardised rates (ASR) were calculated by the direct method [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] using the 2013 European Standard Population (ESP2013) as reference [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. ESP2013 weights, originally defined for five-year groups, were regrouped to match the 13 study age intervals, preserving a total weight of 100,000. The estimated annual percentage change (EAPC) was derived from log-linear regression (ln[rate] = α\u0026thinsp;+\u0026thinsp;β\u0026thinsp;\u0026times;\u0026thinsp;year; EAPC = [e\u003csup\u003eβ\u003c/sup\u003e \u0026minus; 1] \u0026times; 100) for both crude and standardised rates.\u003c/p\u003e\n\u003ch3\u003eJoinpoint regression\u003c/h3\u003e\n\u003cp\u003eSegmented joinpoint regression was applied to the ASR series to identify statistically significant trend change-points [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Models with 0 to 3 joinpoints were evaluated and the most parsimonious was selected using the Bayesian information criterion (BIC). Joinpoint significance was tested by Monte Carlo permutation with 4,999 replicates. The EAPC with 95% confidence interval (CI) was estimated for each segment. The average annual percentage change (AAPC) was calculated as the time-weighted mean of the segmental EAPCs [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003ePrais–Winsten regression\u003c/h3\u003e\n\u003cp\u003eThe overall EAPC for the full period was additionally estimated by iterative Prais\u0026ndash;Winsten regression [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which corrects for first-order serial autocorrelation (AR[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]). This procedure was applied to the global sex-stratified series and to each of the 13 age groups separately.\u003c/p\u003e\n\u003ch3\u003eAge–period–cohort model\u003c/h3\u003e\n\u003cp\u003eAn APC model was fitted to decompose the simultaneous effects of age, historical period, and birth cohort on suicide mortality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. A Lexis table was constructed with 9 five-year age groups (40\u0026ndash;44 to 80\u0026ndash;84) and 5 quinquennial periods (2000\u0026ndash;2004 to 2020\u0026ndash;2024), yielding 13 implicit birth cohorts. Extreme age groups (\u0026le;\u0026thinsp;39 and \u0026ge;\u0026thinsp;85 years) were excluded to avoid instability from sparse counts and the open-ended upper interval.\u003c/p\u003e \u003cp\u003eThe classical identification problem (the exact linear dependency age\u0026thinsp;+\u0026thinsp;cohort\u0026thinsp;=\u0026thinsp;period) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] was addressed using the intrinsic estimator (IE) proposed by Yang, Fu, and Land [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The IE resolves the indeterminacy by projecting the parameter vector onto the orthogonal complement of the null space of the design matrix, yielding the unique minimum-norm L\u0026sup2; solution via the Moore\u0026ndash;Penrose pseudoinverse. This solution does not depend on arbitrary identification constraints and provides estimable non-linear effects (curvatures) that form the basis for substantive interpretation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The model was formulated on the log-mean rates of each Lexis cell:\u003c/p\u003e \u003cp\u003eln(\u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003eap\u003c/em\u003e\u003c/sub\u003e) = \u0026micro;\u0026thinsp;+\u0026thinsp;α\u003csub\u003ea\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003ep\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;γ\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003er\u003c/em\u003e\u003csub\u003e\u003cem\u003eap\u003c/em\u003e\u003c/sub\u003e is the mean rate for age group \u003cem\u003ea\u003c/em\u003e in period \u003cem\u003ep\u003c/em\u003e, \u0026micro; is the intercept, α\u003csub\u003ea\u003c/sub\u003e the age effect, β\u003csub\u003ep\u003c/sub\u003e the period effect, and γ\u003csub\u003ec\u003c/sub\u003e the cohort effect. Parameters were normalised under a sum-to-zero constraint. Deviance R\u0026sup2; was computed as a goodness-of-fit measure. Analyses were conducted for both sexes combined and separately for men and women.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCounterfactual analysis of pandemic impact\u003c/h2\u003e \u003cp\u003eTo quantify excess suicide mortality during 2020\u0026ndash;2024 relative to the pre-pandemic trend, a counterfactual model was employed [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A log-linear regression was fitted on the 2000\u0026ndash;2019 training period and projected onto 2020\u0026ndash;2024, with 95% prediction intervals. Expected deaths were obtained by applying the projected rate to the observed population of each year. The cumulative excess was expressed as the absolute difference (observed\u0026thinsp;\u0026minus;\u0026thinsp;expected), the relative excess (percentage), and the observed/expected ratio (O/E) with 95% CI, with significance assessed by the exact Poisson test. The analysis was stratified by sex.\u003c/p\u003e \u003cp\u003eTo assess the sensitivity of the estimated excess to the model assumptions, the counterfactual was re-estimated with two alternative training windows: a reduced window (2005\u0026ndash;2019) and a window restricted to the last pre-pandemic joinpoint segment (2014\u0026ndash;2019). Additionally, a generalised additive model (GAM) with a penalised cubic spline was fitted to verify that the excess was not an artefact of the log-linear trend assumption.\u003c/p\u003e \u003cp\u003eAge-specific rate ratios (RR) comparing the 2020\u0026ndash;2024 and 2015\u0026ndash;2019 quinquennia were computed with 95% CI based on the log-normal approximation to the Poisson rate ratio [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], to identify the age groups with statistically significant relative increases.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSensitivity and robustness analyses\u003c/h3\u003e\n\u003cp\u003eOverdispersion was assessed by the dispersion index (variance/mean) of annual deaths by sex and tested against the Poisson distribution. The overdispersion parameter (φ̂) was incorporated into the APC model by re-estimation under a quasi-Poisson framework, scaling standard errors by \u0026radic;φ̂. The IE model was re-fitted with two alternative age-range specifications (restricted: 40\u0026ndash;79; extended: 30\u0026ndash;84) to verify the stability of period and cohort effects. Bootstrap 95% CI for all APC effects were obtained from 1,000 non-parametric resamples. Cook\u0026rsquo;s distance was computed for each Lexis cell to detect influential observations. Full sensitivity results are reported in Online Resources 6 and 7.\u003c/p\u003e\n\u003ch3\u003eSoftware\u003c/h3\u003e\n\u003cp\u003eAll analyses were performed in Python 3.12 (Google Colaboratory). Core libraries included NumPy 2.0, pandas 2.2, SciPy 1.16, matplotlib 3.10, and statsmodels 0.14. The joinpoint, Prais\u0026ndash;Winsten, and IE algorithms were implemented \u003cem\u003ead hoc\u003c/em\u003e following published formulations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Source code is available from the corresponding author upon request.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthical considerations\u003c/h2\u003e \u003cp\u003eThis study used exclusively publicly available aggregate data from the INE [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; no individual-level data were processed. Ethics committee approval was not required under Spanish legislation (Ley 14/2007, Article 2) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The study adhered to the RECORD reporting guidelines [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eUse of large language models\u003c/h2\u003e \u003cp\u003eIn accordance with ICMJE recommendations and Springer policy on the use of generative artificial intelligence [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], large language models (Claude, Anthropic; ChatGPT, OpenAI; Gemini, Google) were employed as assistive tools for the generation and debugging of Python code used in the statistical analyses. All outputs were critically reviewed and validated by the investigators, who assume full responsibility for the content of this work. No AI tool is listed as an author or cited as a source.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOverall suicide mortality\u003c/h2\u003e \u003cp\u003eDuring 2000\u0026ndash;2024, 18,350 suicide deaths were recorded in Andalusia (14,206 men, 77.4%; 4,144 women, 22.6%; male-to-female ratio 3.43:1). The annual mean was 734 deaths (range 640 in 2019 to 849 in 2021). The Andalusian population grew by 18.5% over the study period (from 7.29\u0026nbsp;million in 2000 to 8.63\u0026nbsp;million in 2024).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDemographic masking of underlying trends\u003c/h2\u003e \u003cp\u003eThe mean crude rate for both sexes was 9.03 per 100,000 (SD 0.75); the age-standardised rate (ASR) was 9.28 (SD 1.02). Log-linear trend analysis of the crude rates yielded non-significant EAPCs in all three sex strata (both sexes: \u0026minus;0.12%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.615; men: \u0026minus;0.22%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.400; women: +0.22%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.479), suggesting apparent stability. By contrast, the ASR declined significantly in both sexes (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.98%, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;1.45 to \u0026minus;\u0026thinsp;0.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and in men (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.33%, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;1.85 to \u0026minus;\u0026thinsp;0.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas in women the decline was non-significant (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.55%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.088). Prais\u0026ndash;Winsten correction for first-order autocorrelation produced negligible changes (\u0026lt;\u0026thinsp;0.06 percentage points in all strata).\u003c/p\u003e \u003cp\u003eThis divergence arose from progressive population ageing. At the start of the series (2000), the Andalusian age structure was younger than the ESP2013 reference and ASRs exceeded crude rates (both sexes: 11.25 vs 9.62). By 2024, the relationship had reversed: crude rates exceeded ASRs (9.81 vs 9.11). In consequence, demographic ageing masked a genuine decline in age-adjusted suicide risk behind apparently stable crude rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Online Resources 1 and 9).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eJoinpoint regression\u003c/h2\u003e \u003cp\u003eJoinpoint analysis of the ASR series for both sexes identified three change-points (2014, 2019, 2020) defining four segments (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Between 2000 and 2014, rates declined at \u0026minus;\u0026thinsp;1.32% per year (95% CI\u0026thinsp;\u0026minus;\u0026thinsp;1.94 to \u0026minus;\u0026thinsp;0.70, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The decline accelerated between 2014 and 2019 (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.94%, 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;5.89 to \u0026minus;\u0026thinsp;1.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). An abrupt reversal occurred between 2019 and 2020 (+\u0026thinsp;26.6%, 95% CI\u0026thinsp;+\u0026thinsp;10.80 to +\u0026thinsp;44.55, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), coinciding with the onset of the COVID-19 pandemic and the national lockdown. From 2020 to 2024, rates stabilised at the elevated post-rupture level (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.38%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.970). Monte Carlo permutation testing (4,999 replicates) confirmed the significance of the joinpoints (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn men, the pattern was qualitatively similar (joinpoints at 2014, 2019, 2021), with a sustained decline until 2019, a sharp increase between 2019 and 2021 (EAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;16.64%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), and subsequent stabilisation. In women, the model identified two joinpoints (2009, 2010) but permutation testing did not confirm their significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.148), suggesting weaker evidence of structural trend changes in female suicide rates.\u003c/p\u003e \u003cp\u003eAge-specific trend analysis over the full period revealed significant declines concentrated in older groups (\u0026ge;\u0026thinsp;65 years: EAPCs between \u0026minus;\u0026thinsp;1.37% and \u0026minus;\u0026thinsp;3.79%, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and in the 15\u0026ndash;29 age group (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.10%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas rates in the 30\u0026ndash;64 range remained essentially stable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAge\u0026ndash;period\u0026ndash;cohort decomposition\u003c/h2\u003e \u003cp\u003eThe APC model (intrinsic estimator; Lexis table: 9 age groups \u0026times; 5 periods, 13 cohorts) explained 98.3% of the total variance for both sexes (R\u0026sup2; = 0.986 men, 0.904 women) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAge effects showed a monotonic ascending gradient. The relative risk nearly doubled from 40\u0026ndash;44 years (RR\u0026thinsp;=\u0026thinsp;0.710) to 80\u0026ndash;84 years (RR\u0026thinsp;=\u0026thinsp;1.387) for both sexes. The gradient was steeper in men (RR 0.686 to 1.670) than in women, who exhibited a plateau between 55 and 84 years (RR 1.019\u0026ndash;1.128).\u003c/p\u003e \u003cp\u003ePeriod effects showed the lowest rate ratio in the 2015\u0026ndash;2019 quinquennium (RR\u0026thinsp;=\u0026thinsp;0.890), 11% below the grand mean. The pandemic quinquennium (2020\u0026ndash;2024) was associated with a rebound to levels comparable to the early 2000s (RR\u0026thinsp;=\u0026thinsp;1.020), but did not exceed the peak observed in 2005\u0026ndash;2009 (RR\u0026thinsp;=\u0026thinsp;1.057). A qualitatively similar pattern was found in men (nadir 2015\u0026ndash;2019: RR\u0026thinsp;=\u0026thinsp;0.879).\u003c/p\u003e \u003cp\u003eCohort effects showed a declining risk gradient from the oldest to younger cohorts. Generations born before 1935 carried elevated risk (peak\u0026thinsp;~\u0026thinsp;1920: RR\u0026thinsp;=\u0026thinsp;1.358), followed by a sustained decline through cohorts born 1935\u0026ndash;1975 (nadir\u0026thinsp;~\u0026thinsp;1945\u0026ndash;1955: RR\u0026thinsp;\u0026asymp;\u0026thinsp;0.86). From the ~\u0026thinsp;1960 cohort onwards, risk stabilised near the mean (~\u0026thinsp;1980: RR\u0026thinsp;=\u0026thinsp;1.004). In women, an exception was noted: the ~\u0026thinsp;1975 cohort exhibited a moderately elevated risk (RR\u0026thinsp;=\u0026thinsp;1.084), the highest among female cohorts born after 1955.\u003c/p\u003e \u003cp\u003eSensitivity analyses confirmed the robustness of the APC model. Two alternative age-range specifications (40\u0026ndash;79 and 30\u0026ndash;84) replicated the period nadir in 2015\u0026ndash;2019 (RR 0.890\u0026ndash;0.893) with inter-model correlations exceeding 0.98. Bootstrap confidence intervals (1,000 resamples) and quasi-Poisson correction for overdispersion did not materially alter the results (Online Resources 6 and 7).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCounterfactual analysis of pandemic excess\u003c/h2\u003e \u003cp\u003eThe log-linear model fitted to 2000\u0026ndash;2019 confirmed a significant pre-pandemic decline in both sexes (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.93%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and men (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.07%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Projection onto 2020\u0026ndash;2024 yielded 3,351 expected deaths for both sexes (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Against 4,134 observed deaths, the estimated cumulative excess was 783 deaths (+\u0026thinsp;23.4%; O/E\u0026thinsp;=\u0026thinsp;1.23, 95% CI 1.20\u0026ndash;1.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In men, the excess was 622 deaths (O/E\u0026thinsp;=\u0026thinsp;1.24, 95% CI 1.20\u0026ndash;1.29, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); in women, 175 deaths (O/E\u0026thinsp;=\u0026thinsp;1.22, 95% CI 1.14\u0026ndash;1.30, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCounterfactual analysis of suicide mortality: pandemic period (2020\u0026ndash;2024) versus pre-pandemic trend, Andalusia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel A. Observed vs expected deaths by year and sex\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear (phase)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eObs. (n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eExp. (n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e95% pred. CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eExcess (n)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eExcess (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eO/E\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBoth sexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020 (Lockdown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e677.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(580.0\u0026ndash;790.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;17.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021 (De-escalation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e672.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(575.0\u0026ndash;786.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e176.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;26.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022 (Post-pandemic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e668.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(570.1\u0026ndash;783.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;24.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023 (Post-pandemic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e667.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(568.3\u0026ndash;785.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e147.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;22.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024 (Post-pandemic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e665.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(564.6\u0026ndash;783.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e181.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;27.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCumulative 2020\u0026ndash;2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4,134\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3,351.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e782.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;23.4%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.23 (1.20\u0026ndash;1.27)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020 (Lockdown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e516.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(436.8\u0026ndash;610.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;11.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021 (De-escalation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e512.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(432.1\u0026ndash;606.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e148.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;29.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022 (Post-pandemic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e508.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(427.9\u0026ndash;603.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e142.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;28.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023 (Post-pandemic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e506.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(425.5\u0026ndash;603.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;24.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024 (Post-pandemic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e503.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(421.7\u0026ndash;601.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e146.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;29.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCumulative 2020\u0026ndash;2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3,169\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2,547.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e621.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;24.4%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.24 (1.20\u0026ndash;1.29)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWomen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020 (Lockdown)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(126.4\u0026ndash;198.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;37.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021 (De-escalation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(125.7\u0026ndash;198.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;19.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022 (Post-pandemic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(125.0\u0026ndash;198.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;13.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023 (Post-pandemic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(125.1\u0026ndash;200.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;16.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024 (Post-pandemic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(124.8\u0026ndash;201.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;24.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCumulative 2020\u0026ndash;2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e965\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e790.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026mdash;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e174.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e+\u0026thinsp;22.1%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.22 (1.14\u0026ndash;1.30)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel B. Counterfactual model parameters (log-linear 2000\u0026ndash;2019)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEAPC (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eR\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRMSE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth sexes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u0026minus;\u0026thinsp;1.44 to \u0026minus;\u0026thinsp;0.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.07%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u0026minus;\u0026thinsp;1.62 to \u0026minus;\u0026thinsp;0.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(\u0026minus;\u0026thinsp;1.25 to +\u0026thinsp;0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel C. O/E ratio by year and sex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBoth sexes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eWomen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003ePhase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLockdown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDe-escalation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePost-pandemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePost-pandemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePost-pandemic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e Expected deaths: projection of the log-linear model ln(Rate) = α\u0026thinsp;+\u0026thinsp;β\u0026thinsp;\u0026times;\u0026thinsp;Year, fitted to 2000\u0026ndash;2019 and projected onto 2020\u0026ndash;2024, multiplied by observed population.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e Exact Poisson test (H₀: observed\u0026thinsp;=\u0026thinsp;expected). 95% pred.: 95% prediction interval for a new observation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eO/E observed/expected ratio; values\u0026thinsp;\u0026gt;\u0026thinsp;1 indicate excess mortality. EAPC estimated annual percentage change; RMSE root mean square error (log scale).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe excess was sustained throughout the five-year period, with annual O/E ratios ranging from 1.17 (2020) to 1.27 (2024) and no significant attenuation trend (slope\u0026thinsp;=\u0026thinsp;+\u0026thinsp;0.016/year, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.250). In every year from 2021 onwards, the observed count clearly exceeded the upper 95% prediction interval; in 2020, it fell marginally above the upper bound. Sensitivity analyses using alternative training windows (2005\u0026ndash;2019; 2014\u0026ndash;2019) and a GAM with penalised cubic spline yielded consistent O/E ratios (range 1.23\u0026ndash;1.42, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eA notable gender asymmetry was observed in 2020: the O/E ratio reached 1.37 in women\u0026mdash;the highest value in the entire series\u0026mdash;compared with 1.12 in men, suggesting a disproportionate initial impact of the pandemic context on female suicide mortality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDifferential impact by age group\u003c/h2\u003e \u003cp\u003eAge-specific rate ratios comparing the 2020\u0026ndash;2024 and 2015\u0026ndash;2019 quinquennia revealed that the pandemic-period excess was concentrated in adults aged 15\u0026ndash;64 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Six of twelve age groups showed significant increases: 15\u0026ndash;29 (RR\u0026thinsp;=\u0026thinsp;1.29, 95% CI 1.07\u0026ndash;1.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), 30\u0026ndash;39 (RR\u0026thinsp;=\u0026thinsp;1.29, 95% CI 1.12\u0026ndash;1.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 40\u0026ndash;44 (RR\u0026thinsp;=\u0026thinsp;1.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002), 45\u0026ndash;49 (RR\u0026thinsp;=\u0026thinsp;1.23, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), 50\u0026ndash;54 (RR\u0026thinsp;=\u0026thinsp;1.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 60\u0026ndash;64 (RR\u0026thinsp;=\u0026thinsp;1.28, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). By contrast, no group aged\u0026thinsp;\u0026ge;\u0026thinsp;70 experienced a significant change (RR range 0.95\u0026ndash;1.09, all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.50), indicating that the long-term downward trend in older adults continued through the pandemic period uninterrupted. Provincial disparities, age-specific analyses, the male-to-female ratio, and sensitivity results are reported in Online Resources 2 to 8.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRate ratios of suicide mortality by age group: pandemic period (2020\u0026ndash;2024) versus pre-pandemic period (2015\u0026ndash;2019), Andalusia\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePanel A. Rate ratios by age group (both sexes)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRate 2015\u0026ndash;2019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRate 2020\u0026ndash;2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eSig.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.07\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.12\u0026ndash;1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.08\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.12\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.12\u0026ndash;1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.96\u0026ndash;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.10\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.92\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.88\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.70\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.80\u0026ndash;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.81\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel B. Rate ratios by sex (all ages)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRate 2015\u0026ndash;2019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRate 2020\u0026ndash;2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eRR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ep\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth sexes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.17\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.17\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.09\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePanel C. Absolute and relative change by age group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRate 2015\u0026ndash;2019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eRate 2020\u0026ndash;2024\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eΔ abs.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eΔ rel. (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eRanking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;28.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;28.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;23.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;23.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;19.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;14.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;27.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;16.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;4.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;4.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80\u0026ndash;84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u0026thinsp;8.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e Rate: crude suicide mortality rate per 100,000 (quinquennial mean).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003e RR: rate ratio\u0026thinsp;=\u0026thinsp;rate 2020\u0026ndash;2024 / rate 2015\u0026ndash;2019. 95% CI based on the log-normal approximation to the Poisson rate ratio [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ec\u003c/sup\u003e Δ abs.: rate difference (2020\u0026ndash;2024 minus 2015\u0026ndash;2019) per 100,000.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ed\u003c/sup\u003e Ranking by absolute increment (1\u0026thinsp;=\u0026thinsp;largest increase).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e**: significant increase (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); \u0026darr;: non-significant decrease; \u0026mdash;: no significant change.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study provides the first population-based analysis of suicide mortality in Andalusia spanning a full quarter-century of consolidated data (2000\u0026ndash;2024), integrating age\u0026ndash;period\u0026ndash;cohort decomposition, segmented trend analysis, counterfactual modelling, and assessment of geographic and gender disparities. The principal findings were that age-standardised rates declined significantly but this decline was masked by population ageing; that an abrupt pandemic-coincident reversal led to an estimated 783 excess deaths during 2020\u0026ndash;2024, concentrated in adults aged 15\u0026ndash;64 and with a disproportionate initial impact on women; and that the APC model revealed descending cohort effects and a period nadir in 2015\u0026ndash;2019.\u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDemographic masking\u003c/h2\u003e \u003cp\u003eThe progressive divergence between crude and age-standardised rates constitutes a methodologically important finding. While crude rates appeared stable (EAPCs near zero, all non-significant), ASRs declined significantly in both sexes and in men. This discrepancy arises because population ageing shifts the denominator towards older groups with inherently higher suicide rates, inflating the crude aggregate and offsetting the underlying decline in age-adjusted risk. This phenomenon has been described for other causes of death but has rarely been documented explicitly for suicide in Spain. Cayuela et al. reported declining age-adjusted rates nationally (EAPC\u0026thinsp;\u0026minus;\u0026thinsp;0.5% in men, 1984\u0026ndash;2018) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and Bertuccio et al. confirmed similar trends across most Western European countries [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Our findings align with this broader pattern and underscore a practical implication: surveillance reports and media coverage that rely exclusively on absolute counts or crude rates risk conveying a misleadingly alarmist\u0026mdash;or, in certain periods, misleadingly reassuring\u0026mdash;picture. Direct age-standardisation should be the default metric for monitoring suicide mortality trends [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eThe pandemic rupture\u003c/h2\u003e \u003cp\u003eThe joinpoint analysis revealed a \u0026ldquo;step-change\u0026rdquo; pattern: after 14 years of sustained decline that accelerated during the economic recovery (2014\u0026ndash;2019: EAPC\u0026thinsp;\u0026minus;\u0026thinsp;3.94%), rates surged by 26.6% in a single year (2019\u0026ndash;2020) and stabilised at that elevated level through 2024. This finding is consistent with the international collaboration led by Pirkis et al., which identified Spain as one of the few high-income countries where suicide mortality increased significantly during the early pandemic months [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Lantos and Ny\u0026aacute;ri, analysing 27 EU-linked countries, similarly identified Spain\u0026mdash;alongside Ireland and Hungary\u0026mdash;as a country where the 2020 increase reversed a previously declining trend [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, the distinctive contribution of the present study lies in demonstrating that the excess was not a transient phenomenon confined to the lockdown year but a sustained level shift persisting for at least five years.\u003c/p\u003e \u003cp\u003eThe counterfactual analysis reinforces this conclusion. Against 3,351 expected deaths, 4,134 were observed during 2020\u0026ndash;2024 (O/E\u0026thinsp;=\u0026thinsp;1.23, 95% CI 1.20\u0026ndash;1.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with annual O/E ratios showing no attenuation trend. Notably, the lowest relative excess occurred in 2020 (O/E\u0026thinsp;=\u0026thinsp;1.17)\u0026mdash;the year of strictest lockdown\u0026mdash;while the highest values were recorded in 2021 and 2024 (O/E 1.26\u0026ndash;1.27). This temporal pattern is more consistent with a delayed and sustained response, in which the psychosocial consequences of the pandemic (prolonged unemployment, unresolved grief, mental health deterioration, service disruption) manifested with a time lag and have persisted beyond the resolution of the health emergency [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Mart\u0026iacute;nez-Al\u0026eacute;s et al. documented an 11% excess during April\u0026ndash;December 2020 but their analysis was limited to the first pandemic year [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]; our data extend this observation and show that the excess not only persisted but amplified in subsequent years.\u003c/p\u003e \u003cp\u003eThese findings contrast with the experience of most high-income countries, where systematic reviews found no significant pandemic-related increases [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The singularity of the Andalusian pattern may relate to context-specific factors: a mental health workforce below the European average [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], a higher prevalence of adverse social determinants (unemployment, job precariousness), and a pandemic response in which suicide prevention was not explicitly prioritised.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eAge, period, and cohort effects\u003c/h2\u003e \u003cp\u003eThe APC model explained 98.3% of the variance, indicating that the three temporal dimensions capture virtually all heterogeneity in suicide rates. The ascending age gradient\u0026mdash;risk nearly doubling between ages 40\u0026ndash;44 and 80\u0026ndash;84\u0026mdash;is consistent with the international literature documenting age-related accumulation of biological, psychosocial, and healthcare-related risk factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The bimodal profile in men (peaks at 45\u0026ndash;54 and \u0026ge;\u0026thinsp;75 years) suggests at least two distinct vulnerability patterns: one linked to the midlife crisis and one to frailty and isolation in later life.\u003c/p\u003e \u003cp\u003ePeriod effects confirmed that 2015\u0026ndash;2019 carried the lowest contextual risk of the entire series (RR\u0026thinsp;=\u0026thinsp;0.890), likely reflecting the confluence of economic recovery, progressive expansion of community mental health services, and growing social awareness of suicide prevention [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The pandemic quinquennium (2020\u0026ndash;2024: RR\u0026thinsp;=\u0026thinsp;1.020) reversed this trend but did not exceed the peak of 2005\u0026ndash;2009, suggesting that, from a quinquennial perspective, the impact was moderate when averaged with underlying generational effects\u0026mdash;though this should not obscure the abrupt annual-level rupture documented by joinpoint analysis.\u003c/p\u003e \u003cp\u003eCohort effects showed declining risk from pre-1935 generations\u0026mdash;who experienced childhood during the Spanish Civil War and its aftermath\u0026mdash;through cohorts born 1945\u0026ndash;1955, with subsequent stabilisation. This pattern is consistent with prior national studies [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. A noteworthy finding was the moderately elevated risk in women of the ~\u0026thinsp;1975 cohort (RR\u0026thinsp;=\u0026thinsp;1.084), the first generation of full female labour-force participation in Spain, who may have faced the concurrent demands of professional life and traditional caregiving roles amid increasing job precariousness\u0026mdash;a hypothesis that warrants verification through individual-level studies.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eDifferential impact by age and gender\u003c/h2\u003e \u003cp\u003eThe concentration of the pandemic-period excess in adults aged 15\u0026ndash;64, with the largest relative increases in the 15\u0026ndash;29 and 30\u0026ndash;39 age groups (RR\u0026thinsp;=\u0026thinsp;1.29), is particularly concerning given that these groups contribute the greatest number of potential years of life lost. Meanwhile, older adults (\u0026ge;\u0026thinsp;70 years) showed no significant change, suggesting that protective mechanisms sustaining their long-term decline\u0026mdash;improved treatment of geriatric depression, expansion of social services\u0026mdash;continued to operate during the pandemic.\u003c/p\u003e \u003cp\u003eThe marked gender asymmetry in 2020 (O/E\u0026thinsp;=\u0026thinsp;1.37 in women vs 1.12 in men) is consistent with significant increases in female suicide documented in Japan [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], South Korea [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and other settings [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], attributed to lockdown-related psychosocial determinants disproportionately affecting women: increased caregiving burden, exposure to domestic violence, and precarious employment [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. That this asymmetry attenuated in 2021\u0026ndash;2024 suggests it was specific to the strict lockdown conditions rather than a sustained shift in the gender dynamics of suicide.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003ePolicy implications\u003c/h2\u003e \u003cp\u003eThese findings have direct implications for suicide prevention planning at both regional and national levels. The \u003cem\u003ePrograma de prevenci\u0026oacute;n de la conducta suicida en Andaluc\u0026iacute;a 2023\u0026ndash;2026\u003c/em\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] establishes a comprehensive framework encompassing surveillance, training, clinical care, and postvention. Our results provide the empirical basis to orient at least four priorities within this programme and its national counterpart, the \u003cem\u003ePlan de Acci\u0026oacute;n para la Prevenci\u0026oacute;n del Suicidio 2025\u0026ndash;2027\u003c/em\u003e [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFirst, the sustained excess among young and middle-aged adults identifies these groups as priority targets for preventive interventions, warranting resource allocation proportional to their mortality burden. Second, the documented demographic masking underscores the need for surveillance systems to incorporate age-standardised rates as the reference metric, alongside absolute counts. Third, the persistence of excess mortality through 2024 indicates that the pandemic\u0026rsquo;s sequelae on suicidal behaviour have not remitted; the response must therefore move beyond emergency measures to address structural determinants of risk\u0026mdash;access to mental health services, employment, housing, and social support networks. The Andalusian programme could benefit from adopting counterfactual indicators (O/E ratios) as evaluation tools, complementing conventional trend metrics. Fourth, the present study exhausts the analytical potential of publicly available INE data and, in doing so, empirically demonstrates where the official statistical system falls short. The Institutes of Legal Medicine and Forensic Sciences (Institutos de Medicina Legal y Ciencias Forenses) of Andalusia \u0026mdash;one per province\u0026mdash;constitute the natural complementary source. These Institutes hold individualised data on each death, including method, circumstances, clinical history, prior attempts, precise geolocation, and the simultaneous disaggregation by age, sex, and province that INE public tables do not provide. Establishing the Institutes as a systematic data source for suicide surveillance would enable second-generation studies with microdata, opening the door to analyses of methods, contextual risk factors, and predictive models that aggregate statistical sources can never offer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eStrengths include the 25-year observation window (among the longest for a subnational European analysis), data through 2024, a population comparable to medium-sized European countries, methodological triangulation, and exclusive use of publicly available sources.\u003c/p\u003e \u003cp\u003eSeveral limitations apply. The ecological design precludes individual-level causal inference. INE mortality data are subject to under-reporting, although stable ICD-10 correspondence throughout the period reduces the likelihood of classification artefacts. Provincial analyses relied on crude rates because publicly available tables do not disaggregate deaths simultaneously by age, sex, and province\u0026mdash;a limitation of the statistical system, not of the study design. The counterfactual model assumes log-linear trend continuation, though sensitivity analyses confirmed the robustness of the excess. The intrinsic estimator shares with all APC models the inability to fully separate linear effects, though the non-linear curvatures\u0026mdash;which underpin substantive interpretation\u0026mdash;remained stable across specifications.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eSuicide mortality in Andalusia during 2000\u0026ndash;2024 exhibited a genuine underlying decline once adjusted for population ageing, but this trend was abruptly interrupted in coincidence with the COVID-19 pandemic. The estimated excess of 783 deaths over five consecutive years, without attenuation, configures a sustained level shift demanding an equally sustained preventive response. The results support: (a) adopting age-standardised rates as the reference metric in surveillance; (b) prioritising adults aged 15\u0026ndash;64 as intervention targets; (c) establishing the Institutes of Legal Medicine and Forensic Sciences as a systematic data source for second-generation studies with individualised microdata; and (d) evaluating prevention policies through counterfactual indicators. These elements should inform both the \u003cem\u003ePrograma de prevenci\u0026oacute;n de la conducta suicida en Andaluc\u0026iacute;a 2023\u0026ndash;2026\u003c/em\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and the national \u003cem\u003ePlan de Acci\u0026oacute;n para la Prevenci\u0026oacute;n del Suicidio\u003c/em\u003e [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was based exclusively on publicly available aggregate mortality and population data from the Spanish National Statistics Institute (INE) [19, 20]. No individual-level data were processed. In accordance with Spanish legislation (Ley 14/2007 de Investigaci\u0026oacute;n Biom\u0026eacute;dica, Article 2) and the guidelines of the Spanish Bioethics Committee, studies conducted on anonymised aggregate data from public sources do not require research ethics committee approval [28].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to publish\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are publicly available from the Spanish National Statistics Institute (INE): mortality data from the cause-of-death statistics [19] and population data from the municipal register series [20]. Source code is available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Carlos Romero-Ol\u0026oacute;riz, Miguel Guerrero D\u0026iacute;az; Methodology: Carlos Romero-Ol\u0026oacute;riz; Software: Carlos Romero-Ol\u0026oacute;riz; Formal analysis: Carlos Romero-Ol\u0026oacute;riz; Investigation: Carlos Romero-Ol\u0026oacute;riz, Esperanza L\u0026oacute;pez Hidalgo, Mar\u0026iacute;a Victoria Villalba Soria; Data curation: Carlos Romero-Ol\u0026oacute;riz; Writing \u0026ndash; original draft: Carlos Romero-Ol\u0026oacute;riz; Writing \u0026ndash; review and editing: Esperanza L\u0026oacute;pez Hidalgo, Mar\u0026iacute;a Victoria Villalba Soria, Miguel Guerrero D\u0026iacute;az; Resources: Esperanza L\u0026oacute;pez Hidalgo; Visualization: Carlos Romero-Ol\u0026oacute;riz; Supervision: Esperanza L\u0026oacute;pez Hidalgo, Miguel Guerrero D\u0026iacute;az. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUse of generative AI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLarge language models (Claude, Anthropic; ChatGPT, OpenAI; Gemini, Google) were used as assistive tools for code generation and debugging, as detailed in the Methods section. All outputs were critically reviewed and validated by the authors, who assume full responsibility for the content of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization (2025) Suicide. Fact sheet. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/suicide\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/suicide\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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Ministerio de Sanidad, Madrid. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sanidad.gob.es /areas/calidadAsistencial/estrategias/saludMen tal/docs/Plan_de_accion_para_la_prevencion_d el_suicidio_2025_2027.pdf\u003c/span\u003e\u003cspan address=\"https://www.sanidad.gob.es\n/areas/calidadAsistencial/estrategias/\nsaludMental/docs/Plan_de_accion_para_\nla_prevencion_del_suicidio_2025_2027.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"social-psychiatry-and-psychiatric-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sppe","sideBox":"Learn more about [Social Psychiatry and Psychiatric Epidemiology](http://link.springer.com/journal/127)","snPcode":"127","submissionUrl":"https://submission.nature.com/new-submission/127/3","title":"Social Psychiatry and Psychiatric Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"suicide, mortality trends, age–period–cohort, COVID-19, Spain, population ageing","lastPublishedDoi":"10.21203/rs.3.rs-9114122/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9114122/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose.\u003c/h2\u003e \u003cp\u003eTo analyse suicide mortality trends in Andalusia, Spain (2000\u0026ndash;2024) through age\u0026ndash;period\u0026ndash;cohort (APC) decomposition, identify trend change-points, and quantify pandemic-associated excess mortality.\u003c/p\u003e\u003ch2\u003eMethods.\u003c/h2\u003e \u003cp\u003eThis population-based time-series study used data from the Spanish National Statistics Institute (INE; heading 098, ICD-10 X60\u0026ndash;X84, Y87.0). Age-standardised rates (European Standard Population 2013) were computed by sex. The analytical framework comprised segmented joinpoint regression, Prais\u0026ndash;Winsten modelling, an APC model with the intrinsic estimator, and a log-linear counterfactual model projecting the 2000\u0026ndash;2019 trend onto 2020\u0026ndash;2024. Sensitivity analyses included quasi-Poisson correction and bootstrap resampling.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eOverall, 18,350 suicide deaths were recorded (77.4% male; male-to-female ratio 3.43:1). Demographic masking was evident: crude rates remained stable (estimated annual percentage change [EAPC]\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.12%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.615), whereas age-standardised rates declined significantly (EAPC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.98%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), revealing that population ageing conceals a real risk decline. Joinpoint regression identified an abrupt break in 2019\u0026ndash;2020 (EAPC\u0026thinsp;=\u0026thinsp;+\u0026thinsp;26.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) followed by stabilisation at elevated levels. The APC model (R\u0026sup2; = 0.983) disclosed an ascending age\u0026ndash;risk gradient, a period nadir in 2015\u0026ndash;2019, and declining cohort risk from pre-1935 generations onwards. Counterfactual analysis estimated 783 excess deaths over 2020\u0026ndash;2024 (observed/expected\u0026thinsp;=\u0026thinsp;1.23; 95% CI 1.20\u0026ndash;1.27; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), concentrated among those aged 15\u0026ndash;64.\u003c/p\u003e\u003ch2\u003eConclusion.\u003c/h2\u003e \u003cp\u003eThe underlying downward trend was abruptly interrupted in temporal coincidence with the pandemic, producing a sustained level shift persisting five years without attenuation. These findings support age-targeted, geographically tailored prevention strategies.\u003c/p\u003e","manuscriptTitle":"Age–period–cohort effects on suicide mortality in Andalusia, Spain (2000–2024): demographic masking and sustained pandemic excess","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-30 13:42:48","doi":"10.21203/rs.3.rs-9114122/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-15T11:59:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T14:23:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T14:01:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-24T00:57:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307565621019616062518337743890518381238","date":"2026-04-23T15:48:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106603522141489300255224369595920117579","date":"2026-04-23T07:26:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"174732107386648123666455499059523206159","date":"2026-04-23T03:03:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"261958684717512271158670867387779990720","date":"2026-04-22T05:49:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"306001292181868118791551643467052024326","date":"2026-04-21T15:27:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-21T13:37:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-17T06:39:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-16T09:18:57+00:00","index":"","fulltext":""},{"type":"submitted","content":"Social Psychiatry and Psychiatric Epidemiology","date":"2026-03-13T11:11:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"social-psychiatry-and-psychiatric-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sppe","sideBox":"Learn more about [Social Psychiatry and Psychiatric Epidemiology](http://link.springer.com/journal/127)","snPcode":"127","submissionUrl":"https://submission.nature.com/new-submission/127/3","title":"Social Psychiatry and Psychiatric Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"211ad483-8a9d-4b37-8104-0ebab61956aa","owner":[],"postedDate":"April 30th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-15T11:59:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T14:23:20+00:00","index":20,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-02T14:01:50+00:00","index":19,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T12:09:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-30 13:42:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9114122","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9114122","identity":"rs-9114122","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}