Tracking the digital health gap in elderly: A study in Italian remote areas

Abstract

The Covid-19 pandemic has provided a major innovative thrust to public services regarding their digitization to continue providing an effective response to the population's needs and to reduce management costs. However, there has been a partial lack of those welfare policies that can provide an adequate response to the elderly segment of the population, which is most affected by the introduction of new technologies into the public sphere.

This study analyses the digital gap in health in the elderly living in remote areas of Italy and investigates the use of digital devices for health purposes. It compares the use of digital solutions for health with people's common digital competencies and their willingness to use them. A descriptive analysis of the sample was constructed to verify the different responses of the elderly by age, gender, educational qualification, and geographic area. Furthermore, regression analyses have been conducted to test whether there is any dependent effect among the elderly's characteristics or geographic areas.

The results highlight the existence of a potential digital health gap among the elderly in remote areas of Italy both due to infrastructural issues and the lack of digital skills. The latter are positively correlated with educational qualification, such that it is also possible to highlight differences between age groups analysed and shape future welfare policies to reduce digital inequality.

1. Introduction

In recent years, the digitization of services pertaining to public administration has been greatly promoted [1,2]. Indeed, the quest for efficiency (more services in a shorter time) has led to most services becoming digital as well as to the acknowledgement of social aspects and participatory processes requiring changes in business models [3].

In public management, e-government can be seen as a source of public value [4] not only because it fosters a better functionality of services but also because it provides better transparency, thereby increasing accountability and participation from citizens [5]. Moreover, some scholars have also highlighted that digitization has a significant positive economic impact because it streamlines access to information [6,7]. Indeed, it was assumed that the economic sustainability of public systems and welfare policies may depend on the digital transition and its capacity to reduce the costs associated with the provision of public services[8].

Similarly, in the health sector digital transformation has largely favoured guaranteeing services at a universal level. Specifically, the introduction of digital solutions can ease citizens' access to the health system [9], and it can improve management activities through more efficient processes [10].

With the advent of the Covid-19 pandemic, there was a real innovative thrust toward the digitization of services, including those of public health [11], [12], [13], to keep responding to the needs of population, especially during the lockdown [14,15]. Indeed, telemedicine, e-prescriptions and other digital services have been strongly promoted to reduce backlogs [10,16,17]. Hence, in the wake of the pandemic emergency the European Union and single member states have funded substantial projects at both the infrastructure and organisational and training levels to increase the digitization of public services [18], [19], [20].

Despite the interventions made in both social and economic areas, however, only a few welfare policies were addressed to adequate respond to the elderly segment of the population [21] regarding the introduction of recent technologies in the public sphere [22], [23], [24], [25]. Indeed, it is reported that most elderly people lack digital skills, which potentially excludes them from independent participation in public life in sectors like pension, social, and healthcare [26,27].

This phenomenon comes under the concept of the digital divide [28], [29], [30], [31]. The digital divide can be defined as the gap between those who have access to internet and those who do not. This measure highlights inequality in access due to the poor use of technologies [32].

A digital divide may happen for both external factors hindering accessibility to technological tools and for individual features which lead people to not use technologies [33], [34], [35]. Some segments of the population are vulnerable regarding the use of technologies, both with respect to the personal features of the excluded subject [36], [37], [38] and the external characteristics that in any case influence the accessibility to technological tools [33], [34], [35]. The digital divide seems to be linked to a generation factor as highlighted by the national statistical surveys [39,40]. People most penalised by the lack of digital skills turn out to be the elderly and, in particular, those who live alone and in the most remote areas of the peninsula, identified as peripheral and ultraperipheral areas by the national strategy for remote areas classification [41], [42], [43], [44].

On this point, it should be noted that Italy is in twentieth place out of twenty-seven countries in the European Commission's DESI (Digital Economic and Society Index) [45] which yearly compares the digital performance of European countries by analysing four dimensions: (i) connectivity, (ii) human capital, (iii) use of internet services, and (iv) digital public services [46]. Although Italy increased its score regarding providing digital public services and the integration of digital technologies, the human capital area measuring the advanced and basic digital skills possessed by population is particularly poor. This undoubtedly indicates that there is a potential barrier to access to these services due to the lack of digital literacy, which can be amplified when it is in reference to the elderly. This is even more relevant in a country like Italy where the old-age index (ratio of the population aged 65 and over to the population under 15) is quite high (187.6 in 2021) [47] and among the highest in Europe. In addition, there is another issue related to privacy in healthcare, which has led, especially in some cases (e.g. EHR), to very burdensome access procedures requiring multiple passwords with more than one device [45,46], which could represent a barrier also to those who have acquired only basic digital knowledge.

Some studies [[48], [49], [50], [51]] have argued that the advent of the Covid-19 pandemic generated new inequalities (also because of the acceleration of the digital transition) and reinforced existing ones, especially regarding the welfare sectors that most affect the older part of the population, such as healthcare. In fact, over-65 s need more access to health services than young adults because half of them suffer from more than one chronic disease [52,53]. Despite the lower digital literacy in contrast to younger people, over-65 s were more involved in the introduction of new technologies in healthcare [54,55], including the use of digital services for overcoming the limitations created by the recent health emergency [56,57]. Moreover, this was particularly evident among those living in remote areas, who already in the pre-pandemic period had greater difficulties in accessing healthcare facilities [11,58] because of both the distance from the closest urban centres and the infrastructural limitations of transportation and telecommunications also caused by geographic issues of the country [33].

Considering these premises, this paper explores the digital gap in health services among people aged over-65 living in Italian remote areas. In particular, the study relies on a paper-based survey in the remote areas of four Italian regions: Tuscany, Lombardy, Veneto, and Calabria. The study analyses the willingness to use technology for health purposes as well as exploring the factors affecting access to digital solutions for health.

2. Methodology

2.1. Questionnaire and sample

The sampling strategy involved enrolment based on the elderly population (over 65 years old) living in municipalities identified as remote areas in four Italian regions: Tuscany, Lombardy, Veneto, and Calabria. These regions were selected according to the four geographic macro areas of northwest, northeast, center and south of Italy on the basis of voluntary participation of the local counters for the largest trade union of Italian retirees. These counters administered the questionnaires to people who accessed their services in the municipalities of remote areas as classified according to the SNAI (Strategia nazionale Aree Intern National Strategy for Inner Areas) classification. Criteria are based on the accessibility indicator calculated in terms of minutes of travel time to reach essential school, health, and rail transport services. The distance to these services assumes the classification of municipalities as either urban pole or belt, intermediate, peripheral, and ultra-peripheral areas. The last three areas represent the remote areas, accounting for 60% of the national territory and 52% of the municipalities [59].

The sampling strategy recalled a simple random type without repetition. It was constructed to be regionally representative of the population over 65 years living in remote areas and accessible to the local counters, with a confidence level of 95 percent and a margin of error of 5 percent. The sample size needed was approximately 400 respondents per region.

Enrolment was conducted through the local counters on a first-come first-serve basis. To ensure the randomness of enrolment, the survey was conducted in periods far from the appointments that could only concern a category of the target population. The first people who entered to use the services at the headquarters were enlisted. In order to guarantee the randomness of selection, starting from a select date, personnel were assigned to administer the questionnaire to all those who arrived at the counter. To ensure territorial representation, people to be enrolled per counter should have been a minimum of 28. This procedure was necessary because of the lack of information on the users accessing the counter. It was assumed that in a period far from deadlines for specific categories, the probability of accessing the counter would be the same among users.

The survey was administered in paper form (PAPI - Paper And Pencil Interview) to overcome any difficulties of access for those with digital deficiencies. It was also administered in fonts that were not too small in order to facilitate reading, as well as being printed in a front and back format. It was administered between December 2021 and March 2022.

The questionnaire, designed together with the trade union, was based on an analysis of the relevant literature [9,48,[60], [61], [62], [63]] and other surveys investigating digital competencies. It consists of twenty multiple-choice questions with the possibility of selecting multiple responses including the characteristics of the respondents and whether they had digital competencies in general or for health reasons.

The structure of the questionnaire can be divided into four sections: (i) biographical data and health conditions, (ii) respondents’ access to digital tools, (iii) use of digital technology for health reasons, and (iv) propensity to use digital technologies for health reasons. The first group of questions concerned the characteristics of the respondents concerning age, gender, educational level, and the geographical area from which they came. We did not apply the racial/ethnic criterion because in remote areas of Italy the vast majority of the elderly are of Italian origin [47]. For this reason, an initial descriptive analysis of the sample was conducted both based on these characteristics and by cross-referencing these with some of the responses.

Questions on internet access were used to ascertain whether infrastructural difficulties were also evident among the respondents. Those on digital skills and the use of technological devices were used to ascertain whether there was a digital divide.

With reference to the health sector, specific questions were asked to understand whether digital technologies were already being used for health reasons and whether there was a propensity to use digital health services in the future. Hence, we consider the digital health gap by comparing whether substantial differences exist between the use of digital services for general or health purposes. The questionnaire, in Italian, is available as supplementary material in appendix 1.

2.2. Data analysis

The analysis was performed using the R 4.1.1 statistical software. Concerning the propensity to use new technologies, a multivariate logistic regression model was constructed, in which the dependent variable measures the willingness to test new technologies (the variable is coded ‘new’). The independent variables that were selected are ‘region’, ‘gender’, ‘age’, ‘educational qualification’, ‘civil status’, and ‘internet use at home’. The reference categories of the selected independent variables are as follows: Calabria, female, 65–74, diploma, single, and inability to use the internet. Excluded from the analysis were those who, when surveyed about the possibility of connecting to the internet from home, reported that they were unable to do so due to a lack of connectivity in the area where they live. This is because the aim is to verify not merely whether there is a digital divide between different groups of the population according to the available connection in the area where they live but also to assess whether there is a digital health gap in the elderly living in remote areas compared with those who used internet, but not for health purposes.

Next, a likelihood ratio test was conducted to check the goodness of fit of our model to the null model, for which a better result was obtained, with a p-value smaller than 0.0001. Whether there was a difference in the coefficient between the different categories of the same independent variable in the model was tested. Significance is tested with the Wald test, for which our null hypothesis is that there is no statistically significant difference. For the variables ‘region’ and ‘age’, no significant differences emerged. In contrast, regarding the variable educational qualification, significant differences emerged: this shows that there is a statistically significant difference not only relative to the reference category diploma but also between all the other categories of the education variable.

We repeated the previous analysis only on those who had an internet connection at home and used it. This exclusion helped us to better focus on the individual factors influencing the digital health gap. Hence, infrastructural issues or accessibility to technological tools no longer appeared among the dependent variables of the regression.

The actual use of digital technology for health reasons focuses on the online reservation for the Covid-19 vaccine. It can be considered a benchmark because the reservation was supposed to be made exclusively online. This was an extraordinary example of homogeneous need among the entire population during 2020–2021. Moreover, the online reservation was identified as the main way (if not the only one) to have access to the Covid-19 vaccine. In fact, all regions had to provide this service to make the (strongly recommended) vaccine reservation.

For this, a binomial logistic regression model was constructed, in which the dependent variable is ‘vaccination health device’. The independent variables are the same as in the previous model: ‘region’, ‘gender’, ‘age’, ‘educational qualification’, ‘civil status’, and ‘internet use at home’. Regarding the latter, responses reporting that there were connectivity problems at home were eliminated from the analysis. The likelihood ratio test shows that the fit of our model is better than the null model.

The Wald test was conducted to check whether there are differences in the coefficients between the levels of the same variable. As in the previous analysis, the regression was performed twice, namely, with or without the variable related to internet connection.

3. Results

The total number of respondents was 2372, divided regionally as follows: Calabria – 759, Lombardy – 570, Tuscany – 388, and Veneto – 655. The data are gender balanced, with 50.9% women and 49.1% men. Concerning age, the percentage of respondents in the 65–74 age group was 54.8%, in the 75–84 age group 34.2%, and 10.9% in the 85+ age group. Age was then related to the respondents' educational qualification.

The majority (85.5%) of the respondents in the 85+ age group have only obtained a primary school qualification, which is also in consideration of the historical period experienced. Even in the 75–84 age group, about half (51.8%) have only obtained a primary school qualification. This decreases in the 65–74 class (16.3% for primary school), in which most respondents have obtained a secondary school qualification (41.9%). In this class, the percentage of graduates also increases (31.6%). The percentage of university graduates remains particularly low in all three classes.

Moreover, in the 65–74 class about 69.5% are able to connect to the internet from their homes, while this percentage drops to 31.3% for the 75–84 class and 8.4% for the 85+ class. Furthermore, what emerges from the sample analysed is that 10.7% of respondents do not have access to the internet due to the lack of a network connection. This quantifies the infrastructural problem related to connectivity in the inner areas covered by the analysis. If we exclude from the analysis those who do not have an internet connection, in the 65–74 class about 76% declare that they are able to surf the internet from their homes. This percentage drops to 36.2% for the 75–84 class and 9.6% for the 85+ class.

Finally, we analysed the percentage of those who, having an internet connection at home and being able to use it, did or did not reserve the Covid-19 vaccine online. Interestingly, 41% of the 65–74 age class who answered that they are able to surf the internet and own a smartphone did not reserve a Covid-19 vaccination online. This percentage rises to 49.7% in the 75–84 age group and 61.5% in the over 85 age group. That highlights how there is a potential digital health gap in the elderly population since a considerable number of people in all three age groups do not use the internet for health reasons. For further information, refer to appendix 3 to 5.

Table 1 shows the logistic function for the propensity to use new technological tools. As can be verified, the variables 'gender', 'civil status', and 'age' are not significant. Even the geographical area indicated with the variable 'region' is not significant except for the Veneto category, showing how the propensity to use digital technologies is not linked to territorial location and any regional cultural factors. Instead, the 'educational qualification' turns out to be significant, being able to detect a significant effect between the variable and the propensity to use digital devices. Indeed, as already confirmed in other studies, it appears that as the level of education increases, so does the aptitude for technology. Being in possession of an internet connection and knowing how to use it also increases the propensity of the variable 'internet use at home' being significant.

Table 1.

Multivariate logistic regression model for propensity to use new technological tools.

Variable	Estimate	Standard Error	OR	P-value
Intercept	0.89035	0.29376	2.4359770	0.00244 ⁎⁎
Region lombardy	−0.04842	0.16365	0.9527342	0.76733
Region tuscany	0.26322	0.24357	1.3011108	0.27984
Region veneto	0.28458	0.14899	1.3292088	0.27984
Gender male	−0.08108	0.12748	0.9221170	0.52475
Age ([75]–84)	−0.08571	0.14672	0.9178597	0.55910
Age (85+)	−0.39373	0.22386	0.6745378	0.07861
Educational qualification_primary	−1.27502	0.21999	0.2794244	6.80e-09 ⁎⁎⁎
Educational qualification_secondary	−0.55762	0.20275	0.5725706	0.00596 ⁎⁎
Educational qualification_degree	1.85517	0.73483	6.3927928	0.01158 *
Civil status_divorced	0.19868	0.44309	1.2197951	0.655386
Civil status_separed	0.35871	0.38472	1.4314833	0.35114
Civil status_married	0.22248	0.21295	1.2491696	0.29613
Civil status_widow	−0.01782	0.22931	0.9823409	0.93807
Internet at home_yes	1.24196	0.15253	3.4623845	3.88e-16 ⁎⁎⁎

Note:.

^⁎⁎⁎p < 0.

^⁎⁎p < 0.001.

^⁎p < 0.01 .p < 0.05.

Table 2 shows the logistic function for the propensity to use new technological tools, but compared to Table 1, only those who stated that they had an internet connection at home and were able to connect are considered in the analysis.

Table 2.

Multivariate logistic regression model for propensity to use new technological tools in respondent with Internet connection at home and able to connect.

Variable	Estimate	Standard error	OR	P-value
Intercept	2.18152	0.41522	8.8598023	1.49e-07 ⁎⁎⁎
Region lombardy	0.17412	0.28438	1.1901958	0.540359
Region tuscany	0.17878	0.35234	1.1957580	0.611868
Region veneto	−0.24955	0.25515	0.7791541	0.328047
Gender male	−0.17963	0.21270	0.8355769	0.398373
Age (75–84)	0.03274	0.24262	1.0332858	0.892643
Age (85+)	0.89711	1.07950	2.4525069	0.405950
Educational qualification_primary	−1.41951	0.32703	0.2418318	1.42e-05 ⁎⁎⁎
Educational qualification_secondary	−0.93465	0.25463	0.3927224	0.000242 ⁎⁎⁎
Educational qualification_degree	2.14456	1.02885	8.5383037	0.037122 *
Civil status_divorced	0.47344	0.59547	1.6055124	0.426570
Civil status_separed	1.25259	0.62679	3.4994047	0.045671 *
Civil status_married	0.50931	0.36898	1.6641487	0.167491
Civil status_widow	0.28924	0.43495	1.3354114	0.506053

Note:.

^⁎⁎⁎p < 0

^⁎⁎p < 0.001.

^⁎p < 0.01 .p < 0.05.

The variables 'gender', 'civil status', and 'age' are also not significant in this analysis. As can be seen, even the variable 'region' is not significant. The results of this analysis show that there are no geographical barriers to the propensity of the population to use new technologies. On the other hand, educational qualification is significant, confirming what was previously reported on the significant effect between the level of education and the dependent variable.

Table 3 shows the logistic function of the actual use of digital technology for health reasons, focusing on the online reservation for the Covid-19 vaccine. As can be seen, all three 'region' categories are significant for the reference category Calabria, showing that there are differences related to geographical variation in the use of technology for health reasons. Online reservation for vaccinations is significantly lower in Calabria than in the other regions considered. 'Gender' is not significant, nor is 'civil status'. On the other hand, the result for 'educational qualification' is certainly relevant, in which all categories are significant and there is an effect between the use of technologies for health reasons and the level of education, positive or negative, for the reference category 'diploma'. Belonging to the oldest age group is also significant. No collinearity problems appear in our analysis. Also certainly significant is the fact of having the connection at home and using it.

Table 3.

Multivariate logistic regression model for the use of digital technology for health reasons, focusing on the online reservation for the Covid-19 vaccine.

Variable	Estimate	Standard error	OR	P-value
Intercept	−1.906310	0.345184	0.1486278	3.34e-08 ⁎⁎⁎
Region lombardy	0.718246	0.179529	2.0508331	6.32e-05 ⁎⁎⁎
Region tuscany	1.613947	0.234245	5.0225985	5.58e-12 ⁎⁎⁎
Region veneto	0.654847	0.169821	1.9248475	0.000115 ⁎⁎⁎
Gender male	−0.090297	0.136708	0.9136599	0.508927
Age (75–84)	−0.002205	0.163157	0.9977979	0.989220
Age (85+)	−1.549801	0.601965	0.2122902	0.010037 *
Educational qualification_primary	−1.080310	0.228933	0.3394901	2.37e-06 ⁎⁎⁎
Educational qualification_secondary	−0.600254	0.154919	0.5486724	0.000107 ⁎⁎⁎
Educational qualification_degree	0.553098	0.236368	1.7386310	0.019284 *
Civil status_divorced	0.381405	0.398931	1.4643400	0.339038
Civil status_separed	0.151617	0.364324	1.1637147	0.677293
Civil status_married	0.186889	0.264596	1.2054938	0.479989
Civil status_widow	−0.065670	0.308378	0.9364397	0.831363
Internet at home_yes	1.832147	0.212193	6.2472876	<2e-16 ⁎⁎⁎

Note:

^⁎⁎⁎p < 0

^⁎⁎p < 0.001.

^⁎p < 0.01 .p < 0.05.

Table 4 shows the logistic regression of the actual use of digital technology for health purposes, focusing on the online reservation for the Covid-19 vaccine, but compared to Table 3 we consider in the analysis only those who stated that they were able to connect to the internet from home. The results show that for the variable 'region' all the categories are significant for Calabria reference category. The actual online reservation for the Covid-19 vaccine is significantly higher in Veneto (OR = 1.6), Lombardy (OR = 2.16), and Tuscany (OR = 5.02) than in Calabria. Despite organisational differences in the regional healthcare systems, the four regions in the study performed similarly regarding the Covid-19 vaccination campaign. They provided online platforms, which were the priority reservation system. In the absence of access to such platforms, all regions provided the possibility to reserve by telephone or in pharmacies.

Table 4.

Multivariate logistic regression model for the actual use of digital technology for health reasons, focusing on the online reservation for the Covid-19 vaccine in respondents with internet connection at home and who are able to connect.

Variable	Estimate	Standard error	OR	P-value
Intercept	−0.306650	0.272476	0.7359083	0.260412
Region lombardy	0.771302	0.175259	2.1625804	1.08e-05 ⁎⁎⁎
Region tuscany	1.613973	0.225577	5.0227249	8.38e-13 ⁎⁎⁎
Region veneto	0.484946	0.162317	1.6240868	0.002811 ⁎⁎
Gender male	−0.002724	0.131429	0.9972793	0.983462
Age (75–84)	−0.188731	0.154489	0.8280089	0.221840
Age (85+)	−1.965326	0.581663	0.1401101	0.000728 ⁎⁎⁎
Educational qualification_primary	−1.633178	0.214103	0.1953078	2.38e-14 ⁎⁎⁎
Educational qualification_secondary	0.793279	0.149077	0.4523591	1.03e-07 ⁎⁎⁎
Educational qualification_degree	0.596623	0.232711	1.8159763	0.0100354 *
Civil status_divorced	0.688672	0.391652	1.9910690	0.078683
Civil status_separed	0.373439	0.350974	1.4527219	0.287325
Civil status_married	0.328416	0.248806	1.3887666	0.186845
Civil status_widow	−0.086979	0.289785	0.9166962	0.764063

Note:

^⁎⁎⁎p < 0.

^⁎⁎p < 0.001.

^⁎p < 0.01 .p < 0.05.

The Wald test shows that there are statistically significant differences between all the regions. It can therefore be seen that the failure to reserve the Covid-19 vaccine on the part of the elderly population is correlated with certain geographic areas, and it can be assumed that this variation is also determined by the type of platform used regionally for this online service. 'Gender' and 'civil status' are not significant, while a correlation with 'educational qualification' is found, and the effect could be positive (OR = 1.8 for degree) or negative (OR = 0.19 for primary e OR = 0.45 for secondary) with respect to the reference category (diploma). Belonging to the oldest age group is also significant (OR = 0.14), but the variable 'age' could be particularly correlated with 'educational qualification'. Since education seems to significantly influence the propensity to use new technologies, also for health reasons, it was decided to also consider a model in which the region–education interaction was present as a covariate. The results obtained are similar in terms of significance and conclusions. The decision to use the model without iteration as the main result of the analysis was made through Akaike's information criterion [64]. The results of the model with the interaction are available as supplementary material (appendices 6 to 9).

4. Discussion

Some studies refer to the difficulties in accessing the internet and related technologies [65] in reference to the elderly population [50] but in a separate way. Instead, this study examines the problems of the digital health gap among the elderly population in the inner areas, bringing together all the aforementioned factors [55,60,66,67].

Especially regarding inner areas, there are no specific studies dealing with those who live in the areas furthest from urban centres in Italy since it is evident that public services in these areas are particularly limited [68,69] not only with respect to the health sector [70] but also as an objective infrastructural problem due to the lack of connection, which makes it difficult to overcome the digital divide. It should be noted that in our study, 10.7% of the respondents stated that they did not have the internet at home, thus also confirming connectivity inconveniences already reported for both fixed and mobile technologies [71].

As for the emerging findings, it should be noted that this study shows that the propensity to use the new digital tools is greater in people who have a higher level of education, whether the respondents have an internet connection at home or not. This, indeed, consistent with the literature on digital inequality [27,72,73] confirms other studies and could provide a stimulating link for policymakers with future public policies since as age decreases, the degree of education increases and thus the propensity to use new technologies. Recently, with National Recovery and Resilience Plan, Italy invested in training initiatives especially for the elderly.

In light of what has already emerged in other studies [74], [75], [76], facilitating internet access through the creation of stable broadband connectivity even in peripheral and ultra-peripheral areas tends to favor greater use of digital technologies also for welfare-related demands such as pension provision and healthcare. Indeed, in Italy, the implementation of telecommunications infrastructures has recently been planned through economically significant investment plans, such as the National Recovery and Resilience Plan [77]. Indeed, it provides for major investments to ensure the coverage of the entire territory with ultra-wideband networks to enable citizens and companies to seize the benefits of digitization and more generally to fully realize the gigabit society goal.

Furthermore, the findings of the study showed how in the reservation for the Covid-19 vaccine, which the entire Italian population who had access to the internet should have been able to book directly online, there was greater usability of certain regional platforms, such as those used by the regions of Tuscany and Veneto, compared to the national one. Given the same digital difficulties in the population for an exclusively digitised reservation service (this is the case for Calabria and Lombardy which shared the same digital platform), the differences may rely on greater readability and usability of the information on the websites of the public administrations [78]. Indeed, the decision to use less complex designs for apps and platforms specifically related to the health sector favours the use of these tools even by those with fewer skills [79,80], thus promoting the reduction of digital inequalities in healthcare [38,81].

Possible limitations could be related to the self-assessment survey being submitted to the elderly, resulting in personal evaluation of the questions. Moreover, it has minimal content because of the need, as already mentioned, to make it easily accessible to this segment of the population from the point of view of both usability and comprehension of the text. For this reason, although it would have been interesting to investigate further interactions, some of them could not be examined. Another limitation might be related to the lack of geospatial information beyond the region of residence/domicile. We are therefore unable to identify the respondent's area of residence, but it would have been interesting to identify inhomogeneities across the territory. In addition, a list of the population under study is not available as a consequence of only people who went personally to the counters in the period selected for the administration of the questionnaire being selected. Among these aspects, it would have been interesting to understand what might have been the potential causes that positively or negatively influenced access to digital vaccination platforms in different geographical areas.

Indeed, it might emerge that there are contextual factors related to specific initiatives carried out in some territories could have affected the propensity to reserve vaccination online. In Tuscany, one factor could be the presence in the territory both of strong associationism and of points of access helping elderly to use digital technologies. This is the case of permanent of temporary health facilities known as Botteghe della Salute [82] and Case di Comunità [83].

For this reason, it would certainly be interesting to proceed with a future mapping of the points of access to digitization in inner areas to verify whether substantial differences emerge in the use behavior of digital technologies and the propensity to get through these in the future if public facilities are easily available to the resident population. Indeed, it will be interesting to check whether the extant welfare policy played some further effects such as supporting digital health skills in the elderly and overwhelming the digital health gap.

Finally, the sample we analysed is representative of the elderly population attending the local counters and residing in the peripheral areas of Italy; however, to better understand the extent of the phenomenon, also in consideration of the territorial and infrastructural conformation of the country, we believe it is possible that geographical variations may be highlighted and that it would be interesting to extend the analysis to a national level, including urban areas as well to ascertain whether there are actual differences in terms of the digital health gap between the elderly population residing in remote or central areas and whether these differences can also be related to other factors, such as the presence of cultural or organisational barriers, the level of infrastructure, and the existence of local welfare policies that favor the use of digital technologies.

5. Conclusion

This study identified the digital health gap in elderly people in Italy, with specific reference to those who live in remote areas. In verifying the digital skills of the elderly population and the propensity to use digital tools, especially for health purposes, it was possible to ascertain how these also vary among them, and it could be stated that, apart from infrastructural problems typical of the more territorially disadvantaged contexts, there is a digital health gap both with the rest of the Italian population and within the same age groups identified in the study.

This study conducted with the elderly in remote areas confirms what is found in other studies on the general population: both age and education are correlated with digital skills. In particular, the results reveal that the 65–74 age group can be considered the reference for planning in the coming years. This is also in view of the higher level of education of this group compared to the other groups.

This evidence can certainly be considered an important achievement for future public welfare policies, which are almost lacking throughout Europe, and delegated to the third sector or trade unions. In view of the possibility of greater digital involvement of the youngest age group among those analysed in the study, starting from the greater presence in the territory of both social and health access points, so thus fostering the technological skills of these individuals, the intergenerational digital inequality could certainly be overcome in the near future, thus increasing the usability of digital public services, also regarding the health sector, among the elderly population, thereby hopefully reducing the digital health gap.

Moreover, this study highlighted that while the use of internet is homogenous across geographic areas, access to the digital health service, mainly the reservation for Covid-19, significantly differs among Italian regions. This means that organisational and community aspects positively affect the reservation for vaccination by the elderly. Differences may be explained by the different platform, the communication on the web, and other important aspects such as the mobilization of the community to help the elderly in booking the vaccination online. Further research could focus on analysing the specific initiatives implemented in different geographical areas and the impact they have made.

Declaration of Competing Interest

The Authors declare that there is no conflict of interest and do not receive a financial support for the research, authorship, and/or publication of this article.