Declining number of general practitioners can impair influenza vaccination uptake in Italy

Declining number of general practitioners can impair influenza vaccination uptake in Italy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Declining number of general practitioners can impair influenza vaccination uptake in Italy Alexander Domnich, Francesco Lapi, Andrea Orsi, Ignazio Grattagliano, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4024663/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background As in many other countries, seasonal influenza vaccination coverage in Italian older adults is insufficient. In Italy, most influenza vaccine doses are administered by general practitioners (GPs), whose number has been declining in recent years. In parallel, the number of patients per GP and consequent GP workload increased dramatically. In this longitudinal study, we aimed to test whether influenza vaccination coverage may be affected by the increased GP workload. Methods The study outcome was the influenza vaccination coverage rate in adults aged ≥ 65 years and registered in 20 Italian regions over the last 23 years. The independent variable of interest was GP workload, proxied as the proportion of GPs with more than 1,500 patients, which is an imposed normative ceiling. By adopting an econometric approach, different specifications of fixed- and random-effects panel regression models were run to assess the association of interest, when adjusted for potential confounders. Results Over the last two decades, most regions showed a negative association between influenza vaccination coverage rates and the density of GPs with a high number of patients. This latter negative association was confirmed ( P < 0.05) in different panel model specifications. In particular, in the fully adjusted two-way fixed-effects model, which explained 72.6% of the variance, each 10% increase in the number of GPs with more than 1,500 patients was associated with a 1.7% decrease in influenza vaccination coverage. However, this association was present only in region-years where at least 18% of GPs were deemed overloaded. Conclusions In the upcoming years, the number of Italian GPs is projected to decline further. At the same time, the aging Italian population will determine an even greater workload for GPs. This study demonstrated that increased GP workload may partially explain the spatiotemporal variation in influenza vaccination uptake in the Italian elderly. With the imperative of increasing or at least maintaining influenza vaccination coverage rates, several short- and mid-term initiatives should be implemented in order to optimize GP workload during seasonal immunization campaigns. Influenza Vaccination General practitioner Older adults Italy Figures Figure 1 Background Older adults are the primary target group for seasonal influenza vaccination (SIV) [ 1 ]. Indeed, approximately 90% of influenza-related deaths occur in individuals aged ≥ 65 years [ 2 , 3 ]. Although its effectiveness is still suboptimal and varies significantly from year to year [ 4 ], SIV remains of high value-for-money. For instance, a recent United States (US) model has established that in older adults SIV remains a cost-effective intervention even if vaccine effectiveness is as low as 4% [ 5 ]. Despite the well-known benefits of annual immunization [ 1 ], SIV coverage (SIVC) rates in older adults are insufficient in most jurisdictions [ 6 ]. Recommendations by medical doctors play a crucial role in promoting SIV uptake [ 7 ]. For Italian adults, general practitioner (GP) is the most important source of trustworthy information on SIV and receiving GP’s advice is associated with the greatest effect on the actual SIV receipt [ 8 ]. Indeed, in Italy the overwhelming majority of SIV doses are administered by GPs who are remunerated for each vaccine dose administered [ 9 ]. On a regional basis, additional monetary incentives are is usually provided for GPs who achieve prespecified SIVC goals among their patients (e.g., 75%). A Cochrane review [ 10 ] has shown that financial incentives to physicians is effective in increasing SIV uptake. On the other hand, this provider-based measure alone may not be sufficient. Thus, Italy has never reached a minimum recommended SIVC goal of 75% and the latest available estimate (season 2022/2023) for older adults was only 56.7% [ 11 ]. Contextually, Italian GPs are self-employed professionals who have their own list of patients, which should not exceed a ceiling of 1,500 subjects. However, thanks to numerous local laws and measures to guarantee primary care access, this limit is often not respected [ 12 ]. In parallel with a steady decrease in the number of GPs per capita, there was a substantial increase in the quota of GPs surpassing the normative threshold of 1,500 patients. This latter fact may imply a situation of working overload leading to the loss of efficiency and poorer quality of assistance [ 13 , 14 ]. In the context of fiscal federalism, Italian regions are jeopardized by inequalities in several healthcare macro-indicators, including GP density [ 13 ] and SIVC in older adults [ 11 ]. Considering both a relatively high mean age of the currently operating GPs and the actual number of GP trainees, it has been estimated that from 2021 to 2025 the number of Italian GPs will decrease by 9% [ 15 ]. In turn, the constantly aging Italian population will likely lead to a further increase in the quota of “overloaded” GPs, especially for what concerns the relative proportion of older adults. In this study, we hypothesized that the large number of patients per GP could lead to poorer SIV uptake outcomes. This could result from a number of concurrent conditions, including less time for identification of eligible patients, less efficient vaccination counseling and even SIV deprioritization for eligible patients judged to be at lower risk for influenza. To test this hypothesis, we aimed to analyze the longitudinal association between regional SIVC rates in older adults and the density of GPs with a high number of patients. Methods Study design, population and data sources We adopted an econometric panel data approach, which is particularly useful for establishing an ecological association (or lack of association) between the spatiotemporal distribution of GPs and SIVC in older adults. By combining both cross-sectional and time-series components, panel data are more efficient at detecting and measuring effects that are not captured by pure cross-sectional and trend analyses [ 16 ]. Our longitudinal panel consisted of single Italian regions ( N = 20) that were followed for 23 (1999–2021) consecutive years. The selected period was determined by data availability: the Italian Ministry of Health started to systematically collect and report data on SIVC in 1999 [ 11 ], while the indicators of GP density were available (as of March 2024) for the period 1995–2021 [ 17 ]. The study population consisted of older adults aged ≥ 65 years. This population is a major target for SIV and during the entire study period older adults were offered SIV free-of-charge [ 18 ]. The aggregated regional data used in the study came from freely available reports and data flows [ 11 , 17 , 19 ]; therefore, no ethical approval was thought. Study variables The study outcome was SIVC rate defined as the proportion (%) of older adults aged ≥ 65 years living in a region i and who received SIV in a year t . Of 460 possible estimates, 454 (98.7%) data points were available [ 11 ]. To maintain balancedness of the panel structure, six missing SIVC rates were imputed as an average of the previous and following years. The imputed values were excluded in a sensitivity analysis by performing unbalanced regressions. The independent variable of interest was the proportion (%) of GPs with more than 1,500 patients to the total number of GPs working in a region i and year t and henceforth referred to as GP 1500+ . Notably, a US ecological study [ 20 ] demonstrated that the state-level SIVC in older adults had the strongest positive association with health care access (defined as having any form of health insurance), while other factors, such as ethnic origin, income and education levels showed no association. In contrast to the US, the Italian Health Service guarantees universal access to healthcare [ 21 ] and SIV is fully reimbursed for all older adults [ 18 ]. In this regard, in our study the variable of GP 1500+ may be seen as a proxy of primary care access. According to the available systematic reviews [ 7 , 22 , 23 ], SIV uptake in older adults is determined by a plethora of factors, from social structural to healthcare-related determinants. These latter factors may confound the association of interest. Following consultation of the available systematic evidence [ 7 , 22 , 23 ] and data availability, a set of covariates was selected and collected. For socio-demographic factors, we considered the proportions of the “oldest old” (i.e., % of subjects aged ≥ 75 years to the number of adults aged ≥ 65 years) and immigrant older adults (i.e., % of foreign-born subjects aged ≥ 65 years to the total number of adults aged ≥ 65 years). While older age is a well-established and strong positive predictor of SIV uptake, immigrant populations often report lower SIV uptake [ 7 , 22 ]. Since having comorbidities increases the likelihood of SIV receipt [ 7 , 22 , 23 ], we also considered the regional medicine consumption rate in older adults (% to all subjects aged ≥ 65 years). Gross domestic product (GDP) per capita was used to correct for the well-known North–South divide [ 24 ] in terms of regional socio-economic wealth. Financial healthcare resources were proxied as public health expenditure (% to GDP). Population density (inhabitants per km 2 ) was used as a proxy of the regional urbanization pattern. Finally, considering the known effects of both influenza A(H1N1)pdm09 and COVID-19 on increasing SIVC [ 11 ], models were also adjusted for these two pandemic events (years 2009 and 2020). All study variables are summarized in Table 1 . Table 1 Descriptive statistics of the study variables Variable Definition Mean SD Median Min Max Ref SIVC Influenza vaccination coverage in subjects aged ≥ 65 years, % 58.2 9.9 58.7 23.6 79.0 [ 11 ] GP 1500+ General practitioners (GPs) with > 1500 patients, % of registered GPs 21.9 12.5 17.8 4.6 65.4 [ 17 ] Oldest Older adults aged ≥ 75 years, % of subjects aged ≥ 65 years 48.9 3.4 48.9 38.8 55.4 [ 17 ] Immig Immigrants aged ≥ 65 years, % of subjects aged ≥ 65 years 0.8 0.6 0.6 0.0 2.8 [ 19 ] Med Medicine consumption rate, % of subjects aged ≥ 65 years 78.1 5.5 79.0 56.5 88.2 [ 17 ] GDP Gross domestic product (GDP) per capita, € 25,632 7116 25,473 12,384 43,969 [ 17 ] PHExp Public health expenditure, % of GDP 7.4 1.9 6.9 4.2 12.2 [ 17 ] Dens Population density, inhabitants per km 2 179.6 108.3 161.2 36.4 444.0 [ 17 ] Pand Dummy for pandemic (H1N1pdm09 and COVID-19) years 0.1 0.3 0 0 1 – SD, standard deviation. Table 1 should be placed here. Data analysis and modeling strategy Several techniques may be used to model panel data [ 16 , 25 ]. The simplest approach is to apply a pooled ordinary least squares (OLS) regression. Polled OLS, however, ignores the clustered nature of panel data and may cause biased and inconsistent estimations. Fixed-effects (FE) panel models take advantage of the longitudinal data structure by eliminating unobserved time-invariant region-specific factors. The FE approach is a typical choice for modeling longitudinal data from different locations. To account for time-varying (but region-invariant) omitted variables, a full set of year dummies was also included [ 25 , 26 ]. Statistical significance ( P < 0.05) of the region and time FEs was assessed by means of the F test. In summary, the following two-way FE regression model was computed: VC i,t = β GP ∙GP 1500 + i,t + Σ( β C ∙C i,t ) + α i + γ t + ε i,t , for i = 1…20 and t = 1999…2021, where β s are regression coefficients; GP 1500+ is a proportion of GPs with > 1,500 patients; C is a set of the above-described confounders; α is the unobserved time-invariant region effect; γ is the unobserved region-invariant time effect; i is a region; t is a year; and ε is the error term. Since the relationship between GP 1500+ and SIVC is not necessarily linear, a FE model specification with GP 1500+ split into quartiles was also assessed [ 27 ]. Contrary to FE estimation, random-effects (RE) panel models assume that regional effects are randomly distributed and are not correlated with the regressors used. While RE estimation is typically more efficient and associated with higher precision, eventual correlation of regional effects with the regressors would produce inconsistent coefficients. To formally confirm the a priori selected FE estimator, the Hausman’s specification test was used [ 16 , 25 , 28 ]. In all models, continuous variables that were not percentages were log e -transformed [ 27 ]. As panel model diagnostics suggested the presence of both cross-sectional dependence (Pesaran’s test: P < 0.001) and serial correlation (Breusch-Godfrey/Wooldridge’s test: P < 0.001), the Arellano’s heteroskedasticity and autocorrelation consistent (HAC) standard errors (SEs) were used [ 28 ]. All analyses were performed in R environment (R Foundation for Statistical Computing; Vienna, Austria) using the “plm” package v. 2.6-3 [ 28 ]. Results When the panel data were pooled in a simple linear model (Fig. 1A), there was a weak but highly significant ( P < 0.001) negative association between GP 1500+ and SIVC. However, the model fit was poor with an R 2 of only 4%. A more detailed local analysis (Fig. 1B) confirmed that over a period of 23 years most regions displayed a negative relationship between GP 1500+ and SIVC, although this association was significant only in some northern and central regions. Conversely, the southern region of Calabria was a clear outlier, showing a significant positive association. The observed regional heterogeneity justified application of the panel analysis. Figure 1 Country-level (A) and regional (B) correlation on the association between influenza vaccination coverage and distribution of general practitioners with > 1,500 patients In an unadjusted FE model (Table 2 ), each 10% increase in GP 1500+ was associated with a 1.8% ( β = -0.18) drop in SIVC. As expected, individual regional effects were highly significant ( P < 0.001). When the model was fully adjusted, magnitude of the estimate increased ( β = -0.32). Although the Hausman’s test rejected ( P < 0.001) the consistency of RE estimators, RE models provided similar regression coefficients. A two-way FE model specification showed a significant joint time effect and its fully adjusted specification explained 72.6% of within variance. It should be noted that independently of model specification, the association between GP 1500+ and SIVC was constantly negative and statistically significant (Table 2 ). Table 2 Panel analysis on the association between seasonal influenza vaccination coverage rates in older adults aged ≥ 65 years and distribution of general practitioners with more than 1,500 patients Variable Parameter Pooled OLS models RE models Region FE models Two-way FE models Unadjusted Adjusted Unadjusted Adjusted Unadjusted Adjusted Unadjusted Adjusted GP 1500+ β -0.16 -0.32 -0.18 -0.32 -0.19 -0.32 -0.20 -0.17 HAC SE ( P ) 0.03 (< 0.001) 0.05 (< 0.001) 0.04 (< 0.001) 0.12 (0.006) 0.04 (< 0.001) 0.14 (0.021) 0.09 (0.028) 0.08 (0.027) Oldest β – 0.13 – -0.77 – -1.17 – 0.35 HAC SE ( P ) – 0.23 (0.58) – 0.50 (0.12) – 0.58 (0.045) – 0.63 (0.58) Immig β – -2.01 – -2.55 – -3.60 – 2.74 HAC SE ( P ) – 1.33 (0.13) – 2.00 (0.20) – 2.38 (0.13) – 2.52 (0.28) Med β – 0.06 – -0.05 – -0.15 – 0.04 HAC SE ( P ) – 0.12 (0.61) – 0.23 (0.83) – 0.22 (0.50) – 0.12 (0.76) GDP a β – 18.36 – 39.58 – 62.54 – -7.90 HAC SE ( P ) – 3.23 (< 0.001) – 5.56 (< 0.001) – 10.54 (< 0.001) – 14.71 (0.59) PHExp β – 2.11 – 4.30 – 3.60 – 1.40 HAC SE ( P ) – 0.43 (< 0.001) – 0.90 (< 0.001) – 1.44 (0.013) – 0.84 (0.097) Dens a β – 4.69 – 4.87 – 17.77 – -16.45 HAC SE ( P ) – 0.73 (< 0.001) – 2.46 (0.048) – 19.52 (0.36) – 22.28 (0.46) Pand β – 6.82 – 5.01 – 6.47 – – HAC SE ( P ) – 1.30 (< 0.001) – 1.42 ( 1500 patients; HAC SE, heteroskedasticity and autocorrelation consistent standard errors; Immig, proportion of foreign-born older adults; Med, medicine consumption rate in older adults; Oldest, proportion of adults aged ≥ 75 years to those aged ≥ 65 years; OLS, ordinary least squares; Pand, pandemic years; PHExp, public health expenditure; RE, random effects. Table 2 should be placed here. The above-described results were robust in the sensitivity analysis: no significant changes occurred when six missing SIVC rates were excluded (Additional file 1: Table S1 ). When testing for a non-linear relationship between GP 1500+ and SIVC, it emerged that the significantly negative association was present only when GP 1500+ was above the median of 17.8%. In particular, the fully adjusted FE model showed that compared with GP 1500+ in the first quartile (4.6–13.3%), the association between GP 1500+ and SIVC was significant only for the estimates falling into the third (17.8–26.8%) and fourth (26.8–65.4%) quartiles with the regression coefficients of -4.71 (HAC SE = 1.98; P = 0.018) and − 6.05 (HAC SE = 3.07; P = 0.049), respectively. Conversely, GP 1500+ rates lying in the second quartile (13.3–17.8%) showed no significant association ( β = -1.49; HAC SE = 1.76; P = 0.40). Discussion In this study, we showed a significant inverse relationship between the proportion of likely overloaded GPs and SIV uptake rates in older adults. This association was robust to different model specifications and assumptions. From 1999 to 2021, the number of GPs in Italy fell from 8.3 to 6.8 per 10,000 inhabitants. In parallel, GP 1500+ increased from 15.7–42.1% [ 17 ]. Therefore, our models predict that maintaining the status quo in GP 1500+ would result in a steady decline in SIVC. In turn, population-level modeling studies [ 18 , 29 , 30 ] clearly indicate a negative association between SIVC rates in older adults and influenza-associated mortality. Despite its public health relevance, policy research has paid little attention to GP supply-side constraints in determining SIV uptake and, to our knowledge, few studies have scrutinized the link between physician workload and vaccination. One US study [ 31 ] investigated how the county-level supply of family physicians is related to SIV and established that an increase in physician supply by 1‰ was associated with a 58% (95% CI 49–67%) increase in the odds of receiving SIV. Notably, the effect size was larger for some ethnic groups, rural areas and unemployed individuals [ 31 ]. This latter finding could imply that increasing the local-level GP supply may alleviate disparities in SIV uptake. In a Dutch cross-sectional single-year survey, significantly higher SIVC rates were observed in those general practices that have a smaller number of patients per full-time practice assistant [ 32 ]. A later longitudinal study by the same research group reached the same conclusion: GPs with greater number of patients and thus a greater workload recorded 0.7% ( P < 0.05) lower SIV uptake rates among their patients than their colleagues with fewer number of patients [ 33 ]. Among several other tasks, GPs are also expected to provide preventive care to their patients [ 34 ]. GP’s perceived time needed to accomplish various types of consultation is greater than the actual allocated time for these tasks [ 35 ]. An Australian focus group study [ 36 ] revealed that a heavy GP workload and the complexity of managing patients with chronic diseases are the main GP-related barriers to SIV. Analogously, a recent systematic review of qualitative studies on vaccination [ 37 ] hesitation among health professionals highlighted the crucial role of time constraints, which are the main extrinsic factors influencing general vaccine hesitancy. In particular, physicians often reported that the time spent on vaccination may reduce the time for other practices judged as a higher priority. In Italy, it has been estimated that the average duration of individual vaccination counseling is approximately 15 minutes [ 38 ]. Levi et al. [ 39 ] reported that the majority of Italian GPs (68.6%) implement a purely opportunistic approach to get their patients vaccinated against influenza. Conversely, only 45% of GPs perform some form of vaccination counseling. We believe that the main strength of this study lies in its ability to capture the heterogeneity among all the Italian regions, which have a high degree of autonomy for healthcare decisions, over a sufficiently long time period. At the same time, the spatiotemporal econometric approach adopted to study the relationship between GP 1500+ and SIVC may be also seen as a major study limitation, as it uses aggregated population-level data and thus may be prone to the ecological fallacy. Second, although the FE models remove time-invariant region-specific and/or time-varying region-invariant factors, our results may be still affected by the omitted variable bias, i.e., our model specification did not consider a relevant variable that explains both SIVC and GP 1500+ . One such variable could be the ratio between solo (practices with one GP) and team (associated practices with more GPs) practices. Third, owing to the data availability constraints, our analysis was based on Italian regions, which correspond to the European NUTS (nomenclature of territorial units for statistics) 2 middle level of administrative divisions. A more granular approach (e.g., using the lowest NUTS-3 level corresponding to provinces) could determine a further gain in statistical power and potentially unveil other important associations. Conclusions In conclusion, the Organization for Economic Co-operation and Development (OECD) has selected SIVC in older adults as one of few healthcare quality indicators in primary care [ 40 ]. In Italy, following some increase in SIVC at the beginning of the COVID-19 pandemic, SIVC rates started to decline [ 11 ]. In this study, we showed that the high number of patients per GP may partly explain spatiotemporal variation in SIVC rates. The problem of GP shortage has almost no immediate solutions and requires complex structural changes and political willingness. For the time being and in the imperative of increasing (or at least maintaining) SIVC in principal target groups, the workload of GPs should be optimized. Such boosting strategies could include the provision of assistants to support GPs during the SIV campaign; implementation of an interconnected IT environment able to manage both vaccination reminders for eligible patients and once-only data registration; improvements in often inefficient vaccine supply and distribution chains. We believe that these initiatives, which can be implemented in short and mid-term, will aid in enhancing SIV uptake. Declarations Acknowledgements None. Authors’ contributions AD and GI conceived and designed the study. AD and AO performed data analyses and drafted the manuscript. FL, IG, AR and GG interpreted the results and critically revised the manuscript. Funding This research received no funding. Availability of data and materials All data used in the study are in public domain and the corresponding references have been provided in the manuscript. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare the following financial interests or personal relationships, which may be considered as potential competing interests. AD provided consultation and/or received speaker fees from CSL Seqirus, GSK and SD Biosensor. AO provided consultation and/or received speaker fees from CSL Seqirus, Moderna, Novavax and SD Biosensor. GI provided consultation and/or received grants to conduct experimental and/or observational studies for GSK, Sanofi, MSD, CSL Seqirus and Pfizer. FL provided consultancies in protocol preparation for epidemiological studies and data analyses for CSL Seqirus, Moderna, AstraZeneca and Pfizer. IG, AR and CC provided clinical consultancies for CSL Seqirus, Moderna, AstraZeneca and Pfizer. References World Health Organization (WHO). Vaccines against influenza: WHO position paper – May 2022. Wkly Epidemiol Rec. 2022;97(19):185–208. Thompson WW, Shay DK, Weintraub E, Brammer L, Cox N, Anderson LJ, et al. Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA. 2003;289(2):179–86. Rosano A, Bella A, Gesualdo F, Acampora A, Pezzotti P, Marchetti S, et al. Investigating the impact of influenza on excess mortality in all ages in Italy during recent seasons (2013/14-2016/17 seasons). Int J Infect Dis. 2019;88:127–34. Belongia EA, Simpson MD, King JP, Sundaram ME, Kelley NS, Osterholm MT, et al. Variable influenza vaccine effectiveness by subtype: a systematic review and meta-analysis of test-negative design studies. Lancet Infect Dis. 2016;16(8):942–51. Kim DeLuca E, Gebremariam A, Rose A, Biggerstaff M, Meltzer MI, Prosser LA. Cost-effectiveness of routine annual influenza vaccination by age and risk status. Vaccine. 2023;41(29):4239–48. Organization for Economic Co-operation and Development (OECD). Influenza vaccination rates. 2023. https://data.oecd.org/healthcare/influenza-vaccination-rates.htm . Accessed 6 Mar 2024. Kohlhammer Y, Schnoor M, Schwartz M, Raspe H, Schäfer T. Determinants of influenza and pneumococcal vaccination in elderly people: a systematic review. Public Health. 2007;121(10):742–51. Domnich A, Orsi A, Ogliastro M, Trombetta CS, Scarpaleggia M, Ceccaroli C, et al. Exploring missed opportunities for influenza vaccination and influenza vaccine co-administration patterns among Italian older adults: a retrospective cohort study. Eur J Public Health. 2023;33(6):1183–7. Domnich A, Grassi R, Fallani E, Costantini G, Panatto D, Ogliastro M, et al. Increasing influenza vaccination uptake by sending reminders: A representative cross-sectional study on the preferences of Italian adults. Vaccines (Basel). 2023;11(10):1601. Thomas RE, Lorenzetti DL. Interventions to increase influenza vaccination rates of those 60 years and older in the community. Cochrane Database Syst Rev. 2018;5(5):CD005188. Italian Ministry of Health. Influenza vaccination coverage. 2023. https://www.salute.gov.it/portale/influenza/dettaglioContenutiInfluenza.jsp?lingua=italiano&id=679&area=influenza&menu=vuoto . Accessed 6 Mar 2024. Garattini L, Nobili A, Badinella Martini M, Mannucci PM. The role of general practitioners in the EU: time to draw lessons from a too wide range? Intern Emerg Med. 2023;18(2):343–6. Italian Institute of Statistics. Quality of services. 2021. https://www.istat.it/it/files//2021/10/12.pdf . Accessed 6 Mar 2024. Colombo A, Bassani G. Carenza di medici: ma per quale SSN? Dati, riflessioni e proposte dalla formazione [Lack of doctors, but for what System? Shortage of clinicians in Italy and Lombardy and reflections on structural constrains in training]. Ig Sanita Pubbl. 2019;75(5):385–402. Italian National Agency for Regional Healthcare Services. 2023. Report on general practitioners. https://www.agenas.gov.it/images/agenas/mmg/Dati_MMG_Revisione_4.0_full.pdf . Accessed 6 Mar 2024. Hsiao C. Panel data analysis—advantages and challenges. Test. 2007;16:1–22. Italian Institute of Statistics. Health for all. 2024. https://www.istat.it/it/archivio/14562 . Accessed 6 Mar 2024. Domnich A, Orsi A, Panatto D, Ogliastro M, Barca A, Bert F, et al. Population-level benefits of increasing influenza vaccination uptake among Italian older adults: results from a granular panel model. Front Public Health. 2023;11:1224175. Italian Institute of Statistics. Demographics in numbers. 2024. https://demo.istat.it/ . Accessed 6 Mar 2024. Chiu APY, Dushoff J, Yu D, He D. Patterns of influenza vaccination coverage in the United States from 2009 to 2015. Int J Infect Dis. 2017;65:122–7. Signorelli C, Odone A, Oradini-Alacreu A, Pelissero G. Universal Health Coverage in Italy: lights and shades of the Italian National Health Service which celebrated its 40th anniversary. Health Policy. 2020;124(1):69–74. Nagata JM, Hernández-Ramos I, Kurup AS, Albrecht D, Vivas-Torrealba C, Franco-Paredes C. Social determinants of health and seasonal influenza vaccination in adults ≥ 65 years: a systematic review of qualitative and quantitative data. BMC Public Health. 2013;13:388. Roller-Wirnsberger R, Lindner S, Kolosovski L, Platzer E, Dovjak P, Flick H, et al. The role of health determinants in the influenza vaccination uptake among older adults (65+): a scope review. Aging Clin Exp Res. 2021;33(8):2123–32. Toth F. How health care regionalisation in Italy is widening the North-South gap. Health Econ Policy Law. 2014;9(3):231–49. Arellano M, Honoré B. Panel data models: some recent developments. In: Heckman JJ, Leamer E, editors. Handbook of econometrics volume 5. Amsterdam: Elsevier; 2021. pp. 3229–96. Imai K, Kim IS. On the use of two-way fixed effects regression models for causal inference with panel data. Political Anal. 2021;29(3):405–15. Goldhaber-Fiebert JD, Lipsitch M, Mahal A, Zaslavsky AM, Salomon JA. Quantifying child mortality reductions related to measles vaccination. PLoS ONE. 2010;5(11):e13842. Croissant Y, Millo G. Panel data econometrics in R: The plm package. J Stat Soft. 2008;27:1–43. Fallani E, Orsi A, Signori A, Icardi G, Domnich A. An exploratory study to assess patterns of influenza- and pneumonia-related mortality among the Italian elderly. Hum Vaccin Immunother. 2021;17(12):5514–21. Newall AT, Nazareno AL, Muscatello DJ, Boettiger D, Viboud C, Simonsen L, et al. The association between influenza vaccination uptake and influenza and pneumonia-associated deaths in the United States. Vaccine. 2024. 10.1016/j.vaccine.2024.01.089 . Gai Y, Gu NY. Relationship between local family physician supply and influenza vaccination after controlling for individual and neighborhood effects. Am J Infect Control. 2014;42(5):500–5. Hak E, Hermens RP, van Essen GA, Kuyvenhoven MM, de Melker RA. Population-based prevention of influenza in Dutch general practice. Br J Gen Pract. 1997;47(419):363–6. Hak E, Hermens RP, Hoes AW, Verheij TJ, Kuyvenhoven MM, van Essen GA. Effectiveness of a co-ordinated nation-wide programme to improve influenza immunisation rates in The Netherlands. Scand J Prim Health Care. 2000;18(4):237–41. Fiscella K, Epstein RM. So much to do, so little time: care for the socially disadvantaged and the 15-minute visit. Arch Intern Med. 2008;168(17):1843–52. von dem Knesebeck O, Koens S, Marx G, Scherer M. Perceptions of time constraints among primary care physicians in Germany. BMC Fam Pract. 2019;20(1):142. Zwar N, Hasan I, Harris M, Traynor V. Barriers and facilitators to influenza vaccination among high-risk groups aged less than 65 years - views from general practitioners and practice nurses. Aust N Z J Public Health. 2007;31(6):558–61. Prieto-Campo Á, Batista AD, Magalhães Silva T, Herdeiro MT, Roque F, Figueiras A, et al. Understanding vaccination hesitation among health professionals: a systematic review of qualitative studies. Public Health. 2024;226:17–26. Costantino C, Caracci F, Brandi M, Bono SE, Ferro A, Sannasardo CE, et al. Determinants of vaccine hesitancy and effectiveness of vaccination counseling interventions among a sample of the general population in Palermo, Italy. Hum Vaccin Immunother. 2020;16(10):2415–21. Levi M, Bonanni P, Biffino M, Conversano M, Corongiu M, Morato P, et al. Influenza vaccination 2014–2015: Results of a survey conducted among general practitioners in Italy. Hum Vaccin Immunother. 2018;14(6):1342–50. Organization for Economic Co-operation and Development (OECD). 2015. Health care quality indicators - Primary care. https://www.oecd.org/els/health-systems/hcqi-primary-care.htm . Accessed 6 Mar 2024. Additional Declarations Competing interest reported. The authors declare the following financial interests or personal relationships, which may be considered as potential competing interests. AD provided consultation and/or received speaker fees from CSL Seqirus, GSK and SD Biosensor. AO provided consultation and/or received speaker fees from CSL Seqirus, Moderna, Novavax and SD Biosensor. GI provided consultation and/or received grants to conduct experimental and/or observational studies for GSK, Sanofi, MSD, CSL Seqirus and Pfizer. FL provided consultancies in protocol preparation for epidemiological studies and data analyses for CSL Seqirus, Moderna, AstraZeneca and Pfizer. IG, AR and CC provided clinical consultancies for CSL Seqirus, Moderna, AstraZeneca and Pfizer. 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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-4024663","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":278832575,"identity":"3e1e72e6-6736-4705-a205-5d300978180b","order_by":0,"name":"Alexander Domnich","email":"data:image/png;base64,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","orcid":"","institution":"Ospedale Policlinico San Martino","correspondingAuthor":true,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Domnich","suffix":""},{"id":278832576,"identity":"abb5b5ca-80b2-4fa7-b9be-c4ebdf9b6c56","order_by":1,"name":"Francesco Lapi","email":"","orcid":"","institution":"Health Search, Italian College of General Practitioners and Primary Care","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Lapi","suffix":""},{"id":278832577,"identity":"b9777788-b908-46ad-8fcf-d0280d01f23d","order_by":2,"name":"Andrea Orsi","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"prefix":"","firstName":"Andrea","middleName":"","lastName":"Orsi","suffix":""},{"id":278832578,"identity":"93115d89-4170-4d45-a28d-35efd3d3652d","order_by":3,"name":"Ignazio Grattagliano","email":"","orcid":"","institution":"Italian College of General Practitioners and Primary Care","correspondingAuthor":false,"prefix":"","firstName":"Ignazio","middleName":"","lastName":"Grattagliano","suffix":""},{"id":278832579,"identity":"443d923d-daa7-410d-94eb-b3a1ae8e00aa","order_by":4,"name":"Alessandro Rossi","email":"","orcid":"","institution":"Italian College of General Practitioners and Primary Care","correspondingAuthor":false,"prefix":"","firstName":"Alessandro","middleName":"","lastName":"Rossi","suffix":""},{"id":278832580,"identity":"cc9c0ff9-1635-4d5d-bec2-cfb001ffe4e2","order_by":5,"name":"Claudio Cricelli","email":"","orcid":"","institution":"Italian College of General Practitioners and Primary Care","correspondingAuthor":false,"prefix":"","firstName":"Claudio","middleName":"","lastName":"Cricelli","suffix":""},{"id":278832581,"identity":"af111c72-bfc1-4acc-abb7-601f14cb5ffc","order_by":6,"name":"Giancarlo Icardi","email":"","orcid":"","institution":"University of Genoa","correspondingAuthor":false,"prefix":"","firstName":"Giancarlo","middleName":"","lastName":"Icardi","suffix":""}],"badges":[],"createdAt":"2024-03-07 13:30:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4024663/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4024663/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52622820,"identity":"a43a95fc-1be1-4446-9500-0ce17d990b3f","added_by":"auto","created_at":"2024-03-13 17:16:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":587018,"visible":true,"origin":"","legend":"\u003cp\u003eCountry-level (A) and regional (B) correlation on the association between influenza vaccination coverage and distribution of general practitioners with \u0026gt;1,500 patients\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4024663/v1/07247eb47fd598fecd935a28.png"},{"id":52624975,"identity":"505bab17-990d-4b17-b150-8a199543c1fe","added_by":"auto","created_at":"2024-03-13 17:32:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":440201,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4024663/v1/d4cd4cc7-67fe-46a3-bb51-2ecb3b772c85.pdf"},{"id":52622819,"identity":"c572dcc0-f197-4c0e-a2de-8e5a1afbbd99","added_by":"auto","created_at":"2024-03-13 17:16:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16879,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4024663/v1/23568d947e58a515ff4b1df1.docx"}],"financialInterests":"Competing interest reported. The authors declare the following financial interests or personal relationships, which may be considered as potential competing interests. AD provided consultation and/or received speaker fees from CSL Seqirus, GSK and SD Biosensor. AO provided consultation and/or received speaker fees from CSL Seqirus, Moderna, Novavax and SD Biosensor. GI provided consultation and/or received grants to conduct experimental and/or observational studies for GSK, Sanofi, MSD, CSL Seqirus and Pfizer. FL provided consultancies in protocol preparation for epidemiological studies and data analyses for CSL Seqirus, Moderna, AstraZeneca and Pfizer. IG, AR and CC provided clinical consultancies for CSL Seqirus, Moderna, AstraZeneca and Pfizer.","formattedTitle":"Declining number of general practitioners can impair influenza vaccination uptake in Italy","fulltext":[{"header":"Background","content":"\u003cp\u003eOlder adults are the primary target group for seasonal influenza vaccination (SIV) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Indeed, approximately 90% of influenza-related deaths occur in individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although its effectiveness is still suboptimal and varies significantly from year to year [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], SIV remains of high value-for-money. For instance, a recent United States (US) model has established that in older adults SIV remains a cost-effective intervention even if vaccine effectiveness is as low as 4% [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Despite the well-known benefits of annual immunization [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], SIV coverage (SIVC) rates in older adults are insufficient in most jurisdictions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecommendations by medical doctors play a crucial role in promoting SIV uptake [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For Italian adults, general practitioner (GP) is the most important source of trustworthy information on SIV and receiving GP\u0026rsquo;s advice is associated with the greatest effect on the actual SIV receipt [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Indeed, in Italy the overwhelming majority of SIV doses are administered by GPs who are remunerated for each vaccine dose administered [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. On a regional basis, additional monetary incentives are is usually provided for GPs who achieve prespecified SIVC goals among their patients (e.g., 75%). A Cochrane review [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] has shown that financial incentives to physicians is effective in increasing SIV uptake. On the other hand, this provider-based measure alone may not be sufficient. Thus, Italy has never reached a minimum recommended SIVC goal of 75% and the latest available estimate (season 2022/2023) for older adults was only 56.7% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContextually, Italian GPs are self-employed professionals who have their own list of patients, which should not exceed a ceiling of 1,500 subjects. However, thanks to numerous local laws and measures to guarantee primary care access, this limit is often not respected [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In parallel with a steady decrease in the number of GPs per capita, there was a substantial increase in the quota of GPs surpassing the normative threshold of 1,500 patients. This latter fact may imply a situation of working overload leading to the loss of efficiency and poorer quality of assistance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the context of fiscal federalism, Italian regions are jeopardized by inequalities in several healthcare macro-indicators, including GP density [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and SIVC in older adults [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Considering both a relatively high mean age of the currently operating GPs and the actual number of GP trainees, it has been estimated that from 2021 to 2025 the number of Italian GPs will decrease by 9% [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In turn, the constantly aging Italian population will likely lead to a further increase in the quota of \u0026ldquo;overloaded\u0026rdquo; GPs, especially for what concerns the relative proportion of older adults. In this study, we hypothesized that the large number of patients per GP could lead to poorer SIV uptake outcomes. This could result from a number of concurrent conditions, including less time for identification of eligible patients, less efficient vaccination counseling and even SIV deprioritization for eligible patients judged to be at lower risk for influenza. To test this hypothesis, we aimed to analyze the longitudinal association between regional SIVC rates in older adults and the density of GPs with a high number of patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design, population and data sources\u003c/h2\u003e \u003cp\u003eWe adopted an econometric panel data approach, which is particularly useful for establishing an ecological association (or lack of association) between the spatiotemporal distribution of GPs and SIVC in older adults. By combining both cross-sectional and time-series components, panel data are more efficient at detecting and measuring effects that are not captured by pure cross-sectional and trend analyses [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur longitudinal panel consisted of single Italian regions (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20) that were followed for 23 (1999\u0026ndash;2021) consecutive years. The selected period was determined by data availability: the Italian Ministry of Health started to systematically collect and report data on SIVC in 1999 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], while the indicators of GP density were available (as of March 2024) for the period 1995\u0026ndash;2021 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe study population consisted of older adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years. This population is a major target for SIV and during the entire study period older adults were offered SIV free-of-charge [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe aggregated regional data used in the study came from freely available reports and data flows [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]; therefore, no ethical approval was thought.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy variables\u003c/h2\u003e \u003cp\u003eThe study outcome was SIVC rate defined as the proportion (%) of older adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years living in a region \u003cem\u003ei\u003c/em\u003e and who received SIV in a year \u003cem\u003et\u003c/em\u003e. Of 460 possible estimates, 454 (98.7%) data points were available [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. To maintain balancedness of the panel structure, six missing SIVC rates were imputed as an average of the previous and following years. The imputed values were excluded in a sensitivity analysis by performing unbalanced regressions.\u003c/p\u003e \u003cp\u003eThe independent variable of interest was the proportion (%) of GPs with more than 1,500 patients to the total number of GPs working in a region \u003cem\u003ei\u003c/em\u003e and year \u003cem\u003et\u003c/em\u003e and henceforth referred to as GP\u003csub\u003e1500+\u003c/sub\u003e. Notably, a US ecological study [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] demonstrated that the state-level SIVC in older adults had the strongest positive association with health care access (defined as having any form of health insurance), while other factors, such as ethnic origin, income and education levels showed no association. In contrast to the US, the Italian Health Service guarantees universal access to healthcare [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and SIV is fully reimbursed for all older adults [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In this regard, in our study the variable of GP\u003csub\u003e1500+\u003c/sub\u003e may be seen as a proxy of primary care access.\u003c/p\u003e \u003cp\u003eAccording to the available systematic reviews [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], SIV uptake in older adults is determined by a plethora of factors, from social structural to healthcare-related determinants. These latter factors may confound the association of interest. Following consultation of the available systematic evidence [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and data availability, a set of covariates was selected and collected. For socio-demographic factors, we considered the proportions of the \u0026ldquo;oldest old\u0026rdquo; (i.e., % of subjects aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years to the number of adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years) and immigrant older adults (i.e., % of foreign-born subjects aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years to the total number of adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years). While older age is a well-established and strong positive predictor of SIV uptake, immigrant populations often report lower SIV uptake [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Since having comorbidities increases the likelihood of SIV receipt [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], we also considered the regional medicine consumption rate in older adults (% to all subjects aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years). Gross domestic product (GDP) per capita was used to correct for the well-known North\u0026ndash;South divide [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] in terms of regional socio-economic wealth. Financial healthcare resources were proxied as public health expenditure (% to GDP). Population density (inhabitants per km\u003csup\u003e2\u003c/sup\u003e) was used as a proxy of the regional urbanization pattern. Finally, considering the known effects of both influenza A(H1N1)pdm09 and COVID-19 on increasing SIVC [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], models were also adjusted for these two pandemic events (years 2009 and 2020). All study variables are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003eDescriptive statistics of the study variables\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIVC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfluenza vaccination coverage in subjects aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.9\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\u003e23.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGP\u003csub\u003e1500+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral practitioners (GPs) with \u0026gt;\u0026thinsp;1500 patients, % of registered GPs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOldest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOlder adults aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years, % of subjects aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmigrants aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years, % of subjects aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedicine consumption rate, % of subjects aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGross domestic product (GDP) per capita, \u0026euro;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25,473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12,384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43,969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHExp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePublic health expenditure, % of GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePopulation density, inhabitants per km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e161.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e444.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDummy for pandemic (H1N1pdm09 and COVID-19) years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eSD, standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003eshould be placed here.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData analysis and modeling strategy\u003c/h2\u003e \u003cp\u003eSeveral techniques may be used to model panel data [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The simplest approach is to apply a pooled ordinary least squares (OLS) regression. Polled OLS, however, ignores the clustered nature of panel data and may cause biased and inconsistent estimations. Fixed-effects (FE) panel models take advantage of the longitudinal data structure by eliminating unobserved time-invariant region-specific factors. The FE approach is a typical choice for modeling longitudinal data from different locations. To account for time-varying (but region-invariant) omitted variables, a full set of year dummies was also included [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) of the region and time FEs was assessed by means of the \u003cem\u003eF\u003c/em\u003e test. In summary, the following two-way FE regression model was computed:\u003c/p\u003e \u003cp\u003eVC\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eβ\u003c/em\u003e\u003csub\u003eGP\u003c/sub\u003e∙GP\u003csub\u003e1500\u0026thinsp;+\u0026thinsp;\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;Σ(\u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003eC\u003c/em\u003e\u003c/sub\u003e∙C\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e) + \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eγ\u003c/em\u003e\u003csub\u003e\u003cem\u003et\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;\u003cem\u003eε\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,t\u003c/em\u003e\u003c/sub\u003e,\u003c/p\u003e \u003cp\u003efor \u003cem\u003ei\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u0026hellip;20 and \u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1999\u0026hellip;2021, where \u003cem\u003eβ\u003c/em\u003es are regression coefficients; GP\u003csub\u003e1500+\u003c/sub\u003e is a proportion of GPs with \u0026gt;\u0026thinsp;1,500 patients; C is a set of the above-described confounders; \u003cem\u003eα\u003c/em\u003e is the unobserved time-invariant region effect; \u003cem\u003eγ\u003c/em\u003e is the unobserved region-invariant time effect; \u003cem\u003ei\u003c/em\u003e is a region; \u003cem\u003et\u003c/em\u003e is a year; and \u003cem\u003eε\u003c/em\u003e is the error term.\u003c/p\u003e \u003cp\u003eSince the relationship between GP\u003csub\u003e1500+\u003c/sub\u003e and SIVC is not necessarily linear, a FE model specification with GP\u003csub\u003e1500+\u003c/sub\u003e split into quartiles was also assessed [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eContrary to FE estimation, random-effects (RE) panel models assume that regional effects are randomly distributed and are not correlated with the regressors used. While RE estimation is typically more efficient and associated with higher precision, eventual correlation of regional effects with the regressors would produce inconsistent coefficients. To formally confirm the \u003cem\u003ea priori\u003c/em\u003e selected FE estimator, the Hausman\u0026rsquo;s specification test was used [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn all models, continuous variables that were not percentages were log\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e-transformed [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. As panel model diagnostics suggested the presence of both cross-sectional dependence (Pesaran\u0026rsquo;s test: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and serial correlation (Breusch-Godfrey/Wooldridge\u0026rsquo;s test: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the Arellano\u0026rsquo;s heteroskedasticity and autocorrelation consistent (HAC) standard errors (SEs) were used [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll analyses were performed in R environment (R Foundation for Statistical Computing; Vienna, Austria) using the \u0026ldquo;plm\u0026rdquo; package v. 2.6-3 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eWhen the panel data were pooled in a simple linear model (Fig.\u0026nbsp;1A), there was a weak but highly significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) negative association between GP\u003csub\u003e1500+\u003c/sub\u003e and SIVC. However, the model fit was poor with an \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e of only 4%. A more detailed local analysis (Fig.\u0026nbsp;1B) confirmed that over a period of 23 years most regions displayed a negative relationship between GP\u003csub\u003e1500+\u003c/sub\u003e and SIVC, although this association was significant only in some northern and central regions. Conversely, the southern region of Calabria was a clear outlier, showing a significant positive association. The observed regional heterogeneity justified application of the panel analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e Country-level (A) and regional (B) correlation on the association between influenza vaccination coverage and distribution of general practitioners with \u0026gt;\u0026thinsp;1,500 patients\u003c/p\u003e \u003cp\u003eIn an unadjusted FE model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), each 10% increase in GP\u003csub\u003e1500+\u003c/sub\u003e was associated with a 1.8% (\u003cem\u003eβ\u003c/em\u003e = -0.18) drop in SIVC. As expected, individual regional effects were highly significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). When the model was fully adjusted, magnitude of the estimate increased (\u003cem\u003eβ\u003c/em\u003e = -0.32). Although the Hausman\u0026rsquo;s test rejected (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) the consistency of RE estimators, RE models provided similar regression coefficients. A two-way FE model specification showed a significant joint time effect and its fully adjusted specification explained 72.6% of within variance. It should be noted that independently of model specification, the association between GP\u003csub\u003e1500+\u003c/sub\u003e and SIVC was constantly negative and statistically significant (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003ePanel analysis on the association between seasonal influenza vaccination coverage rates in older adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years and distribution of general practitioners with more than 1,500 patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePooled OLS models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRE models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eRegion FE models\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eTwo-way FE models\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUnadjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGP\u003csub\u003e1500+\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAC SE (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12 (0.006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.14 (0.021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.09 (0.028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.08 (0.027)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOldest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAC SE (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58 (0.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.63 (0.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eImmig\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAC SE (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33 (0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.38 (0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.52 (0.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAC SE (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12 (0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23 (0.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.22 (0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.12 (0.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGDP\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-7.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAC SE (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.23 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.56 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.54 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14.71 (0.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePHExp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAC SE (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.90 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.44 (0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.84 (0.097)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDens\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-16.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAC SE (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.73 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.46 (0.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.52 (0.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e22.28 (0.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAC SE (\u003cem\u003eP\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42 (\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.03 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003e Log\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e-transformed.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eDens, population density; FE, fixed effects; GDP, gross domestic product per capita; GP\u003csub\u003e1500+\u003c/sub\u003e, proportion of general practitioners with \u0026gt;\u0026thinsp;1500 patients; HAC SE, heteroskedasticity and autocorrelation consistent standard errors; Immig, proportion of foreign-born older adults; Med, medicine consumption rate in older adults; Oldest, proportion of adults aged\u0026thinsp;\u0026ge;\u0026thinsp;75 years to those aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years; OLS, ordinary least squares; Pand, pandemic years; PHExp, public health expenditure; RE, random effects.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cem\u003eshould be placed here.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe above-described results were robust in the sensitivity analysis: no significant changes occurred when six missing SIVC rates were excluded (Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhen testing for a non-linear relationship between GP\u003csub\u003e1500+\u003c/sub\u003e and SIVC, it emerged that the significantly negative association was present only when GP\u003csub\u003e1500+\u003c/sub\u003e was above the median of 17.8%. In particular, the fully adjusted FE model showed that compared with GP\u003csub\u003e1500+\u003c/sub\u003e in the first quartile (4.6\u0026ndash;13.3%), the association between GP\u003csub\u003e1500+\u003c/sub\u003e and SIVC was significant only for the estimates falling into the third (17.8\u0026ndash;26.8%) and fourth (26.8\u0026ndash;65.4%) quartiles with the regression coefficients of -4.71 (HAC SE\u0026thinsp;=\u0026thinsp;1.98; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and \u0026minus;\u0026thinsp;6.05 (HAC SE\u0026thinsp;=\u0026thinsp;3.07; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), respectively. Conversely, GP\u003csub\u003e1500+\u003c/sub\u003e rates lying in the second quartile (13.3\u0026ndash;17.8%) showed no significant association (\u003cem\u003eβ\u003c/em\u003e = -1.49; HAC SE\u0026thinsp;=\u0026thinsp;1.76; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.40).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we showed a significant inverse relationship between the proportion of likely overloaded GPs and SIV uptake rates in older adults. This association was robust to different model specifications and assumptions. From 1999 to 2021, the number of GPs in Italy fell from 8.3 to 6.8 per 10,000 inhabitants. In parallel, GP\u003csub\u003e1500+\u003c/sub\u003e increased from 15.7\u0026ndash;42.1% [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, our models predict that maintaining the status quo in GP\u003csub\u003e1500+\u003c/sub\u003e would result in a steady decline in SIVC. In turn, population-level modeling studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] clearly indicate a negative association between SIVC rates in older adults and influenza-associated mortality.\u003c/p\u003e \u003cp\u003eDespite its public health relevance, policy research has paid little attention to GP supply-side constraints in determining SIV uptake and, to our knowledge, few studies have scrutinized the link between physician workload and vaccination. One US study [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] investigated how the county-level supply of family physicians is related to SIV and established that an increase in physician supply by 1\u0026permil; was associated with a 58% (95% CI 49\u0026ndash;67%) increase in the odds of receiving SIV. Notably, the effect size was larger for some ethnic groups, rural areas and unemployed individuals [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. This latter finding could imply that increasing the local-level GP supply may alleviate disparities in SIV uptake. In a Dutch cross-sectional single-year survey, significantly higher SIVC rates were observed in those general practices that have a smaller number of patients per full-time practice assistant [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A later longitudinal study by the same research group reached the same conclusion: GPs with greater number of patients and thus a greater workload recorded 0.7% (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) lower SIV uptake rates among their patients than their colleagues with fewer number of patients [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong several other tasks, GPs are also expected to provide preventive care to their patients [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. GP\u0026rsquo;s perceived time needed to accomplish various types of consultation is greater than the actual allocated time for these tasks [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. An Australian focus group study [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] revealed that a heavy GP workload and the complexity of managing patients with chronic diseases are the main GP-related barriers to SIV. Analogously, a recent systematic review of qualitative studies on vaccination [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] hesitation among health professionals highlighted the crucial role of time constraints, which are the main extrinsic factors influencing general vaccine hesitancy. In particular, physicians often reported that the time spent on vaccination may reduce the time for other practices judged as a higher priority. In Italy, it has been estimated that the average duration of individual vaccination counseling is approximately 15 minutes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Levi et al. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] reported that the majority of Italian GPs (68.6%) implement a purely opportunistic approach to get their patients vaccinated against influenza. Conversely, only 45% of GPs perform some form of vaccination counseling.\u003c/p\u003e \u003cp\u003eWe believe that the main strength of this study lies in its ability to capture the heterogeneity among all the Italian regions, which have a high degree of autonomy for healthcare decisions, over a sufficiently long time period. At the same time, the spatiotemporal econometric approach adopted to study the relationship between GP\u003csub\u003e1500+\u003c/sub\u003e and SIVC may be also seen as a major study limitation, as it uses aggregated population-level data and thus may be prone to the ecological fallacy. Second, although the FE models remove time-invariant region-specific and/or time-varying region-invariant factors, our results may be still affected by the omitted variable bias, i.e., our model specification did not consider a relevant variable that explains both SIVC and GP\u003csub\u003e1500+\u003c/sub\u003e. One such variable could be the ratio between solo (practices with one GP) and team (associated practices with more GPs) practices. Third, owing to the data availability constraints, our analysis was based on Italian regions, which correspond to the European NUTS (nomenclature of territorial units for statistics) 2 middle level of administrative divisions. A more granular approach (e.g., using the lowest NUTS-3 level corresponding to provinces) could determine a further gain in statistical power and potentially unveil other important associations.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, the Organization for Economic Co-operation and Development (OECD) has selected SIVC in older adults as one of few healthcare quality indicators in primary care [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In Italy, following some increase in SIVC at the beginning of the COVID-19 pandemic, SIVC rates started to decline [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this study, we showed that the high number of patients per GP may partly explain spatiotemporal variation in SIVC rates. The problem of GP shortage has almost no immediate solutions and requires complex structural changes and political willingness. For the time being and in the imperative of increasing (or at least maintaining) SIVC in principal target groups, the workload of GPs should be optimized. Such boosting strategies could include the provision of assistants to support GPs during the SIV campaign; implementation of an interconnected IT environment able to manage both vaccination reminders for eligible patients and once-only data registration; improvements in often inefficient vaccine supply and distribution chains. We believe that these initiatives, which can be implemented in short and mid-term, will aid in enhancing SIV uptake.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAD and GI conceived and designed the study. AD and AO performed data analyses and drafted the manuscript. FL, IG, AR and GG interpreted the results and critically revised the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in the study are in public domain and the corresponding references have been provided in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare the following financial interests or personal relationships, which may be considered as potential competing interests. AD provided consultation and/or received speaker fees from CSL Seqirus, GSK and SD Biosensor. AO provided consultation and/or received speaker fees from CSL Seqirus, Moderna, Novavax and SD Biosensor. GI provided consultation and/or received grants to conduct experimental and/or observational studies for GSK, Sanofi, MSD, CSL Seqirus and Pfizer. FL provided consultancies in protocol preparation for epidemiological studies and data analyses for CSL Seqirus, Moderna, AstraZeneca and Pfizer. IG, AR and CC provided clinical consultancies for CSL Seqirus, Moderna, AstraZeneca and Pfizer.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization (WHO). Vaccines against influenza: WHO position paper \u0026ndash; May 2022. Wkly Epidemiol Rec. 2022;97(19):185\u0026ndash;208.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson WW, Shay DK, Weintraub E, Brammer L, Cox N, Anderson LJ, et al. Mortality associated with influenza and respiratory syncytial virus in the United States. JAMA. 2003;289(2):179\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosano A, Bella A, Gesualdo F, Acampora A, Pezzotti P, Marchetti S, et al. Investigating the impact of influenza on excess mortality in all ages in Italy during recent seasons (2013/14-2016/17 seasons). Int J Infect Dis. 2019;88:127\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBelongia EA, Simpson MD, King JP, Sundaram ME, Kelley NS, Osterholm MT, et al. Variable influenza vaccine effectiveness by subtype: a systematic review and meta-analysis of test-negative design studies. Lancet Infect Dis. 2016;16(8):942\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim DeLuca E, Gebremariam A, Rose A, Biggerstaff M, Meltzer MI, Prosser LA. Cost-effectiveness of routine annual influenza vaccination by age and risk status. Vaccine. 2023;41(29):4239\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization for Economic Co-operation and Development (OECD). Influenza vaccination rates. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.oecd.org/healthcare/influenza-vaccination-rates.htm\u003c/span\u003e\u003cspan address=\"https://data.oecd.org/healthcare/influenza-vaccination-rates.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 6 Mar 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKohlhammer Y, Schnoor M, Schwartz M, Raspe H, Sch\u0026auml;fer T. Determinants of influenza and pneumococcal vaccination in elderly people: a systematic review. Public Health. 2007;121(10):742\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDomnich A, Orsi A, Ogliastro M, Trombetta CS, Scarpaleggia M, Ceccaroli C, et al. Exploring missed opportunities for influenza vaccination and influenza vaccine co-administration patterns among Italian older adults: a retrospective cohort study. Eur J Public Health. 2023;33(6):1183\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDomnich A, Grassi R, Fallani E, Costantini G, Panatto D, Ogliastro M, et al. Increasing influenza vaccination uptake by sending reminders: A representative cross-sectional study on the preferences of Italian adults. Vaccines (Basel). 2023;11(10):1601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThomas RE, Lorenzetti DL. Interventions to increase influenza vaccination rates of those 60 years and older in the community. Cochrane Database Syst Rev. 2018;5(5):CD005188.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eItalian Ministry of Health. Influenza vaccination coverage. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.salute.gov.it/portale/influenza/dettaglioContenutiInfluenza.jsp?lingua=italiano\u0026amp;id=679\u0026amp;area=influenza\u0026amp;menu=vuoto\u003c/span\u003e\u003cspan address=\"https://www.salute.gov.it/portale/influenza/dettaglioContenutiInfluenza.jsp?lingua=italiano\u0026amp;id=679\u0026amp;area=influenza\u0026amp;menu=vuoto\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 6 Mar 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarattini L, Nobili A, Badinella Martini M, Mannucci PM. The role of general practitioners in the EU: time to draw lessons from a too wide range? Intern Emerg Med. 2023;18(2):343\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eItalian Institute of Statistics. Quality of services. 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.istat.it/it/files//2021/10/12.pdf\u003c/span\u003e\u003cspan address=\"https://www.istat.it/it/files//2021/10/12.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 6 Mar 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eColombo A, Bassani G. Carenza di medici: ma per quale SSN? Dati, riflessioni e proposte dalla formazione [Lack of doctors, but for what System? Shortage of clinicians in Italy and Lombardy and reflections on structural constrains in training]. Ig Sanita Pubbl. 2019;75(5):385\u0026ndash;402.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eItalian National Agency for Regional Healthcare Services. 2023. Report on general practitioners. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.agenas.gov.it/images/agenas/mmg/Dati_MMG_Revisione_4.0_full.pdf\u003c/span\u003e\u003cspan address=\"https://www.agenas.gov.it/images/agenas/mmg/Dati_MMG_Revisione_4.0_full.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 6 Mar 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsiao C. Panel data analysis\u0026mdash;advantages and challenges. Test. 2007;16:1\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eItalian Institute of Statistics. Health for all. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.istat.it/it/archivio/14562\u003c/span\u003e\u003cspan address=\"https://www.istat.it/it/archivio/14562\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 6 Mar 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDomnich A, Orsi A, Panatto D, Ogliastro M, Barca A, Bert F, et al. Population-level benefits of increasing influenza vaccination uptake among Italian older adults: results from a granular panel model. Front Public Health. 2023;11:1224175.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eItalian Institute of Statistics. Demographics in numbers. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://demo.istat.it/\u003c/span\u003e\u003cspan address=\"https://demo.istat.it/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 6 Mar 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiu APY, Dushoff J, Yu D, He D. Patterns of influenza vaccination coverage in the United States from 2009 to 2015. Int J Infect Dis. 2017;65:122\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSignorelli C, Odone A, Oradini-Alacreu A, Pelissero G. Universal Health Coverage in Italy: lights and shades of the Italian National Health Service which celebrated its 40th anniversary. Health Policy. 2020;124(1):69\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagata JM, Hern\u0026aacute;ndez-Ramos I, Kurup AS, Albrecht D, Vivas-Torrealba C, Franco-Paredes C. Social determinants of health and seasonal influenza vaccination in adults\u0026thinsp;\u0026ge;\u0026thinsp;65 years: a systematic review of qualitative and quantitative data. BMC Public Health. 2013;13:388.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoller-Wirnsberger R, Lindner S, Kolosovski L, Platzer E, Dovjak P, Flick H, et al. The role of health determinants in the influenza vaccination uptake among older adults (65+): a scope review. Aging Clin Exp Res. 2021;33(8):2123\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eToth F. How health care regionalisation in Italy is widening the North-South gap. Health Econ Policy Law. 2014;9(3):231\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArellano M, Honor\u0026eacute; B. Panel data models: some recent developments. In: Heckman JJ, Leamer E, editors. Handbook of econometrics volume 5. Amsterdam: Elsevier; 2021. pp. 3229\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImai K, Kim IS. On the use of two-way fixed effects regression models for causal inference with panel data. Political Anal. 2021;29(3):405\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldhaber-Fiebert JD, Lipsitch M, Mahal A, Zaslavsky AM, Salomon JA. Quantifying child mortality reductions related to measles vaccination. PLoS ONE. 2010;5(11):e13842.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCroissant Y, Millo G. Panel data econometrics in R: The plm package. J Stat Soft. 2008;27:1\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFallani E, Orsi A, Signori A, Icardi G, Domnich A. An exploratory study to assess patterns of influenza- and pneumonia-related mortality among the Italian elderly. Hum Vaccin Immunother. 2021;17(12):5514\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewall AT, Nazareno AL, Muscatello DJ, Boettiger D, Viboud C, Simonsen L, et al. The association between influenza vaccination uptake and influenza and pneumonia-associated deaths in the United States. Vaccine. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.vaccine.2024.01.089\u003c/span\u003e\u003cspan address=\"10.1016/j.vaccine.2024.01.089\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGai Y, Gu NY. Relationship between local family physician supply and influenza vaccination after controlling for individual and neighborhood effects. Am J Infect Control. 2014;42(5):500\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHak E, Hermens RP, van Essen GA, Kuyvenhoven MM, de Melker RA. Population-based prevention of influenza in Dutch general practice. Br J Gen Pract. 1997;47(419):363\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHak E, Hermens RP, Hoes AW, Verheij TJ, Kuyvenhoven MM, van Essen GA. Effectiveness of a co-ordinated nation-wide programme to improve influenza immunisation rates in The Netherlands. Scand J Prim Health Care. 2000;18(4):237\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFiscella K, Epstein RM. So much to do, so little time: care for the socially disadvantaged and the 15-minute visit. Arch Intern Med. 2008;168(17):1843\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon dem Knesebeck O, Koens S, Marx G, Scherer M. Perceptions of time constraints among primary care physicians in Germany. BMC Fam Pract. 2019;20(1):142.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZwar N, Hasan I, Harris M, Traynor V. Barriers and facilitators to influenza vaccination among high-risk groups aged less than 65 years - views from general practitioners and practice nurses. Aust N Z J Public Health. 2007;31(6):558\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrieto-Campo \u0026Aacute;, Batista AD, Magalh\u0026atilde;es Silva T, Herdeiro MT, Roque F, Figueiras A, et al. Understanding vaccination hesitation among health professionals: a systematic review of qualitative studies. Public Health. 2024;226:17\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCostantino C, Caracci F, Brandi M, Bono SE, Ferro A, Sannasardo CE, et al. Determinants of vaccine hesitancy and effectiveness of vaccination counseling interventions among a sample of the general population in Palermo, Italy. Hum Vaccin Immunother. 2020;16(10):2415\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevi M, Bonanni P, Biffino M, Conversano M, Corongiu M, Morato P, et al. Influenza vaccination 2014\u0026ndash;2015: Results of a survey conducted among general practitioners in Italy. Hum Vaccin Immunother. 2018;14(6):1342\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization for Economic Co-operation and Development (OECD). 2015. Health care quality indicators - Primary care. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.oecd.org/els/health-systems/hcqi-primary-care.htm\u003c/span\u003e\u003cspan address=\"https://www.oecd.org/els/health-systems/hcqi-primary-care.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 6 Mar 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Influenza, Vaccination, General practitioner, Older adults Italy","lastPublishedDoi":"10.21203/rs.3.rs-4024663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4024663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAs in many other countries, seasonal influenza vaccination coverage in Italian older adults is insufficient. In Italy, most influenza vaccine doses are administered by general practitioners (GPs), whose number has been declining in recent years. In parallel, the number of patients per GP and consequent GP workload increased dramatically. In this longitudinal study, we aimed to test whether influenza vaccination coverage may be affected by the increased GP workload.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe study outcome was the influenza vaccination coverage rate in adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years and registered in 20 Italian regions over the last 23 years. The independent variable of interest was GP workload, proxied as the proportion of GPs with more than 1,500 patients, which is an imposed normative ceiling. By adopting an econometric approach, different specifications of fixed- and random-effects panel regression models were run to assess the association of interest, when adjusted for potential confounders.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOver the last two decades, most regions showed a negative association between influenza vaccination coverage rates and the density of GPs with a high number of patients. This latter negative association was confirmed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in different panel model specifications. In particular, in the fully adjusted two-way fixed-effects model, which explained 72.6% of the variance, each 10% increase in the number of GPs with more than 1,500 patients was associated with a 1.7% decrease in influenza vaccination coverage. However, this association was present only in region-years where at least 18% of GPs were deemed overloaded.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn the upcoming years, the number of Italian GPs is projected to decline further. At the same time, the aging Italian population will determine an even greater workload for GPs. This study demonstrated that increased GP workload may partially explain the spatiotemporal variation in influenza vaccination uptake in the Italian elderly. With the imperative of increasing or at least maintaining influenza vaccination coverage rates, several short- and mid-term initiatives should be implemented in order to optimize GP workload during seasonal immunization campaigns.\u003c/p\u003e","manuscriptTitle":"Declining number of general practitioners can impair influenza vaccination uptake in Italy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 17:16:20","doi":"10.21203/rs.3.rs-4024663/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8bff8d02-bc5e-41cd-ad01-bee338a432bb","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-16T12:35:03+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-13 17:16:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4024663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4024663","identity":"rs-4024663","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}