Skip to main content

Socioeconomic status and hospitalization in the very old: a retrospective study



Socioeconomic status could affect the demand for hospital care. The aim of the present study was to assess the role of age, socioeconomic status and comorbidity on acute hospital admissions among elderly.


We retrospectively examined the discharge abstracts data of acute care hospital admissions of residents in Rome aged 75 or more years in the period 1997–2000. We used the Hospital Information System of Rome, the Tax Register, and the Population Register of Rome for socio-economic data. The rate of hospitalization, modified Charlson's index of comorbidity, and level of income in the census tract of residence were obtained. Rate ratios and 95% confidence limits were computed to assess the relationship between income deciles and rate of hospitalization. Cross-tabulation was used to explore the distribution of the index of comorbidity by deciles of income. Analyses were repeated for patients grouped according to selected diseases.


Age was associated with a marginal increase in the rate of hospitalization. However, the hospitalization rate was inversely related to income in both sexes. Higher income was associated with lower comorbidity. The same associations were observed in patients admitted with a principal diagnosis of chronic condition (diabetes mellitus, heart failure, chron obstructive pulmonary disease) or stroke, but not hip fracture.


Lower social status and associated comorbidity, more than age per se, are associated with a higher rate of hospitalization in very old patients.

Peer Review reports


As the population ages, patients over 64 account for a continuously growing proportion of acute hospital care [1]. In Lazio, the region surroundings Rome, people over 64 accounted for 44.6% of hospital stays in 1996 and 49.3% in 2002 [2]. Concern has been raised about the economic implications of the "geriatric epidemic", and the older people are considered to be responsible for an extraordinary consumption of health care resources [3]. However, a recent analysis limited to patients who died in hospital in the UK showed that age was not associated with a greater number of days spent in the hospital in the year preceding death [4]. Furthermore, a German study demonstrated that the average number of hospital days for the last year of life was stable in people who die between 50 and 90 years of age, but significantly higher for persons who die at younger ages [5]. Thus, the growing number of older people in the general population, and not increased use of health care resource by individual old patients, seems to be responsible for the impact ageing has on health care expenditures. However, this conclusion stems from studies that used only age as a potential determinant of hospital bed utilization.

Besides being a relevant correlate of self-rated health status, functional status, morbidity and mortality [69], social inequalities could also affect the demand for acute hospital care. Rates of any hospitalization is higher in subjects with low socioeconomic status (SES) than among those living in high SES neighborhood aged 25 to 74 years [10]. Furthermore, the risk of hospitalization for heart failure among those aged 45 to 64 years is 39% greater in the most versus least socially and economically deprived subjects in a population, irrespective of baseline cardio-respiratory status and cardiovascular risk factors [11]. However, such a strict relationship between low SES and hospitalization is less certain in people aged 75 and over. Because comorbidity dramatically increases with age [12], comorbidity and not SES could represent the major determinant of the need of hospital care in the very old.

We planned the present study to evaluate whether socioeconomic status, as measured by proxy variable such as area-based income, affects acute hospitalization rates also in very old people.


Data source and selection criteria

We examined the discharge abstracts data of acute care hospital admissions of residents in Rome aged 75+ years in the period 1997–2000. Discharge abstract data are routinely collected by the regional Hospital Information Systems (HIS) and include: patient demographic data, admission and discharge dates, admission referral source, discharge status, up to six discharge diagnoses (ICD-9-CM), up to six hospital procedures (ICD-9-CM), regional code of the facility, up to four in-hospital transfers, and date of in-hospital transfer. The information system covers all hospitals in the region and includes also hospitalizations of residents occurred outside the region. The study protocol was approved by the Ethical Committee of the Local Health Authority RME, Rome, Italy.

Income data

As a surrogate of individual socioeconomic status, we considered the income level of the population living in the census tract (CT) of residence. A median familiar equivalent income index has been derived for each of the 5736 census tracts (CT) of Rome (average population = 480 inhabitants) [13]. In synthesis, data relative to income earned in 1998 (tax returns of the year 1999) were extracted from the Italian Tax Register for all residents of Rome as of the 1st of January 1998. A record linkage between the Tax Register and the Population Register of Rome connected family status information to income data for each subject, then the family equivalent income, weighted for the number of family members was calculated. Data were aggregated at the CT level, and the median value for each CT was calculated. Due to confidentiality of information, only details about income for each CT were available in our study database

In order to obtain categorical values for the income indicator, we calculated the deciles of the income distribution (1st decile very underprivileged, 10th decile very well off) on the basis of the whole adult population.

Hospitalization rates

We computed age-standardised rates of hospitalization (per 1000 inhabitants) by gender and income decile for the three age groups ≥ 75 years, 75–84 years, and ≥ 85 years. The cut off of 75 years was chosen because it marks a dramatic increase in the prevalence of comorbidity and disability [14]. We used all persons residing in Rome at the 1st of January, for each year under study, as the denominator population. All rates were directly standardised using the population of Italy for 1998 as reference. In addition to overall hospitalization, we analysed the following: diabetes (ICD-IX code 250), heart failure (ICD-IX code 428), stroke (ICD-IX codes 431, 432, 434, 436), Chronic Obstructive Pulmonary Disease (COPD) (ICD-IX codes 491, 492), and hip fracture (ICD-IX code 820). Three of these conditions (COPD, heart failure, diabetes mellitus) may be considered as ambulatory care sensitive conditions, i. e. high hospitalization rate for these chronic conditions suggests that community health care is inappropriate [15]. Instead, stroke and hip fracture are acute conditions requiring hospitalization irrespective of primary health care. It should be noted that we took into account only hospitalizations in acute care public or private hospitals and not in rehabilitation or day hospital.

To quantify the burden of comorbidity, i. e. of diseases coexisting with the main disease, during the hospital admission, we computed for each hospitalized subject a modified version of the Charlson Index of comorbidity: individual diagnoses codified according to ICD-9-CM [16] were given a score proportional to the diagnosis-related risk of death [16, 17]. For each patient the final score was obtained by summing the scores of individual diagnoses. For instance, a patient having COPD as the main disease and peripheral vascular disease (codes 443.9, 441–441.9, 785.4, V43.4) and diabetes with chronic complications (codes 250.4–250.6) as comorbid diseases had a Charlson index of 3 corresponding to the sum of 1, the score of peripheral vascular disease, and 2, the score of diabetes with chronic complications. A detailed list of diagnoses and corresponding scores is available in the reference [17].

We used Rate Ratios (RRs) to compare hospital admission rates among income deciles, using the first income decile (the lowest) as the reference group. Confidence intervals (CI) were calculated at the 95% level of significance by using the standard error of the age-adjusted rates. We used multiple linear regression analysis to evaluate the association between the log transformation of duration of hospital stay with income deciles among men and women. Age was considered in the regression models. Statistical analysis was performed using STATA 8 statistical software package.


Age-standardised rates of hospitalization by income decile, separately for males and females, are reported in Table 1. There was only a marginal increase in the hospitalization rate from 75–84 to 85+ year old people in males and in females. The difference was lower when fatal hospital admissions (admissions which ended with a fatal outcome) were excluded from the analysis (hospitalization rates per 1000 (CI 95%): males 75–84 years = 386 (384–389), 85+years = 384 (379–389); females 75–84 years = 285 (284–287), 85+ years = 309 (306–312)). On the other hand, the rate of hospitalization dramatically increased for decreasing income in both sexes and age groups. The increase in hospitalisation rate from 75–84 to over 85 years was greater in the lowest than in the highest SES group. For instance, such an increase for women in the 1st decile of SES (481-397 = 84) was greater than for women in the 10th decile of SES (283-246 = 37).

Table 1 Hospitalization rates (per 1000) by age group, income decile, and gender.

The same trend in age-standardised rates of hospitalization by income decile was observed in patients with diabetes mellitus (1st vs 10th income decile, males RR = 2.59, 95% CI = 2.05–3.27, females RR = 4.92, 95% CI = 4.07–5.94), heart failure (males RR = 2.32, 95% CI = 2.04–2.63, females RR = 3.28, 95% CI = 2.95–3.65), COPD (males RR = 4.31, 95% CI = 3.74–4.97, females RR = 3.28, 95% CI = 2.85–3.77) or stroke (males RR = 1.93, 95% CI = 1.74–2.13, females RR = 2.07, 95% CI = 1.91–2.25), but not in those with hip fracture (males RR = 0.89, 95% CI = 0.73–1.10, females RR = 1.07, 95% CI = 0.97–1.18) (Figure 1).

Figure 1

Rate of hospitalization for selected conditions in a population aged over 74 years.

In both men and women, there was a decreasing cumulative number of days spent in the hospital with the increase in area-based income. The median length of stay was 12 (SD 12.8) and 12.9 (SD 14.4) days, for men and women respectively, among those in the lowest income decile, and 9.7 (SD 11.5) and 11.3 (SD 13.2) days among those in the highest income decile. When we adjusted for age in the multivariate linear regression analysis, the strong statistically significant inverse relationship remained (p < 0.001).

When comorbidity was examined among hospitalized individuals, higher income was associated with low comorbidity in both genders (Table 2 and 3).

Table 2 Distribution of Charlson index (%) by principal diagnosis and income decile in males.
Table 3 Distribution of Charlson index (%) by principal diagnosis and income decile in females.


Our data show that lower social status, more than age, is correlated with the rate of hospitalization in a population older than 74 years. A longer hospital stay was also detected in the lowest socioeconomic group when compared with those in the upper income category. Comorbidity also was greater in low income patients admitted to the hospital. Thus, socioeconomic inequalities are relevant to explain differences in health care use also in a very old population.

In keeping with our findings, a study conducted in UK showed that an elderly population tenants had a higher institutionalisation rate than owner-occupiers, who represent a higher income population [18]. Furthermore, lower socioeconomic status has been reported to be associated with excess hospitalization of diabetic patients for hypoglycaemia or hyperglycaemia, and this association was independent of age and comorbidity, but it was stronger in middle aged than in elderly diabetics [19]. Analogously, lower income Congestive Heart Failure patients are known to experience a greater rate of hospitalization [20].

The inverse association between income level and hospitalization rate may reflect two concurrent phenomena: higher incidence and prevalence of diseases among people in less advantaged conditions, and inadequate community care, especially secondary care, among poor people resulting in higher demand for hospitalization. The former phenomenon is testified by lower comorbidity and healthier life style characterizing high income subjects in developed countries [21]. Compliance to prescribed treatment is greater in more educated and affluent patients, and this might further reduce both the risk of stroke and the need for hospital care [22]. Furthermore, better income is associated with more efforts to fight modifiable risk factors such as obesity and smoking as well as with lesser exposition to stressors [2325], which are an important risk factor for several diseases. Finally, education per se has been suggested to have a protective role against cognitive decline [26]. Besides being negatively associated with selected risk factors for chronic diseases, higher income also is a marker of better access to the health care facilities [27]. Interestingly, low income and poor education are associated with greater use of primary care services and lesser of secondary care services, i. e. with a gap between primary and hospital care [28, 29]. This conclusion is likely true even for an universal and free health system such as the Italian Health National System, because the proper use of health care resources largely depends upon patient factors, which in turn partly reflect education and social status, rather than on supply factors [30]. Furthermore, the access to freely available services may be hampered by logistic barriers (such as long waiting lists, poor availability of selected services in some health care district, or mobility problems) which affluent people can more easily overcome [31].

At variance from chronic diseases and stroke, the rate of hospitalization for hip fracture was not associated with income, as if the risk of fall were independent from socio-economic status. This finding is unlikely to suffer from "collection bias" because hip fracture requires hospital care and, thus, the recorded figures cannot be biased by alternative home care. Thus, it is a true finding which contrasts with most of previous observations showing that different measures of income are inversely correlated with the incidence of hip fracture [3234]. However, admissions for both hip fracture and appendicitis, two non-ambulatory care-sensitive conditions, have been reported to be independent from socioeconomic status in a population based cohort study of diabetic patients study [19]. Also an ecological English study found no association between income and hip fracture, but an inverse one between income and risk of fall [35]. Differences in studied population, income quantification and study design might account for the observed discrepancy. Furthermore, elderly Italians, even the least affluent, seem to be characterized by better dietary patterns and nutritional status than elderly living in other European countries [36], and this might smooth the income-related difference in the risk of traumatic hip fracture [34, 37]. Finally, the fact that hospitalization for stroke, an acute event like hip fracture, was inversely related to income does not conflict with evidence pertaining to hip fracture because the two conditions have different profiles of risk. Overall, the awareness of cardiovascular and metabolic risk factors for stroke is more widespread than that of risk factors for osteoporosis and hip fracture: for example, hypertension was regularly treated in 75% of hypertensive patients, while only 56.3% of patients with osteoporosis received active treatment in former studies [38, 39]. The well proved association between higher socio-economic status and better control of risk factors for stroke might translate in more effective prevention of stroke in affluent people [40].

Some limitations of this study should be cited. First, the Charlson's index quantifies comorbidity and was available only among hospitalized subjects. Therefore, it was not possible to evaluate the effect of income while "adjusting" for comorbidity level. Furthermore, Charlson's index only to some extent assesses the severity of illness, which might be relevant to explain the observed pattern of hospitalization. Indeed, computing an index of disease severity would require a detailed clinical information which is not available on administrative databases. Second, we had no information on the type or cost of care as a function of age. However, there is a consistent evidence that aging is associated with under treatment of many conditions [4143]. Accordingly, the rate of hospitalization and the cumulative number of days spent in the hospital are expected to provide a good measure of the cost of hospital care for the elderly, but they might overestimate the costs for the very old. Third, it cannot be excluded that socio-economic inequalities also indirectly affect the access to hospital care, e. g. subjects living alone might experience some delay in care for acute conditions and, thus, would have greater chances of dying at home without living any track in the Hospital Information System. Unfortunately, we had no information on the living arrangement and, then, could not take into consideration this potential source of bias. Eventually, we rated SES on the basis of an area-based measure because individual data were not available due to privacy issues. This might lead to some misclassification of SES. However, since the census blocks in Rome are rather small (few hundreds of inhabitants), the misclassification, if any, is likely to be minor. Furthermore, area-based socio-economic measures were proved to detect socio-economic disparities in mortality among subjects over 65 [44].


Our findings add to previous observations by showing that income can conveniently target subjects at greater risk of hospitalization even in the very old population, while age per se cannot. Accordingly, measures of income might help targeting older people who could benefit the most from dedicated health care programs. Income data are easily available in administrative databases for the whole population and qualify as a cumulative index of health status or health risk, whereas medical databases are not so capillary in most countries and, thus, provide information on a minority of the population.

Efforts are needed to identify factors mediating the relationship between income and health status. Interventions contrasting individual mediators are highly desirable, but, in a broader perspective, attempts at removing social inequalities would be the main health care intervention. Such an intervention would decrease the need for hospital care, and this would translate in an important saving of resources. Thus, physicians, health care managers and political authorities should be aware that medical and social dimensions interact to determine health status and health care needs also in the very old. This underscores the need for a comprehensive view of the health needs and an integrated approach to them.


  1. 1.

    Victor CR, Higginson I: Effectiveness of care for older people: a review. Qual Health Care. 1994, 3: 210-216.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Capon A, Mastromattei A, Di Lallo A, Simeoni S, Guasticchi G: Rapporto sullo stato di salute della popolazione anziana nel Lazio, anno 2002. []

  3. 3.

    Cohen GD: Health care at an advanced age. Myths and misconceptions. Ann Intern M. 1994, 121: 146-147.

    CAS  Article  Google Scholar 

  4. 4.

    Dixon T, Shaw M, Frankel S, Ebrahim S: Hospital admissions, age, and death: retrospective cohort study. BMJ. 2004, 328: 1288-10.1136/bmj.38072.481933.EE.

    Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Busse R, Krauth C, Schwartz FW: Use of acute hospital beds does not increase as the population ages: results from a seven year cohort study in Germany. J Epidemiol Community Health. 2002, 56: 289-293. 10.1136/jech.56.4.289.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Huisman M, Kunst AE, Andersen O, Bopp M, Borgan JK, Borrell C, Costa G, Deboosere P, Desplanques G, Donkin A, Gadeyne S, Minder C, Regidor E, Spadea T, Valkonen T, Mackenbach JP: Socioeconomic inequalities in mortality among elderly people in 11 European populations. J Epidemiol Community Health. 2004, 58: 468-475. 10.1136/jech.2003.010496.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    von dem Knesebeck O, Verde PE, Dragano N: Education and health in 22 European countries. Soc Sci Med. 2006, 63: 1344-1351.

    Article  PubMed  Google Scholar 

  8. 8.

    Huisman M, Kunst A, Deeg D, Grigoletto F, Nusselder W, Mackenbach J: Educational inequalities in the prevalence and incidence of disability in Italy and the Netherlands were observed. J Clin Epidemiol. 2005, 58: 1058-1065. 10.1016/j.jclinepi.2004.12.011.

    Article  PubMed  Google Scholar 

  9. 9.

    Huisman M, Kunst AE, Mackenbach JP: Socioeconomic inequalities in morbidity among the elderly; a European overview. Soc Sci Med. 2003, 57: 861-873. 10.1016/S0277-9536(02)00454-9.

    Article  PubMed  Google Scholar 

  10. 10.

    Taylor CB, Ahn D, Winkleby MA: Neighborhood and individual socioeconomic determinants of hospitalization. Am J Prev Med. 2006, 31: 127-134. 10.1016/j.amepre.2006.03.025.

    Article  PubMed  Google Scholar 

  11. 11.

    Stewart S, Murphy NF, McMurray JJ, Jhund P, Hart CL, Hole D: Effect of socioeconomic deprivation on the population risk of incident heart failure hospitalisation: an analysis of the Renfrew/Paisley Study. Eur J Heart Fail. 2006, 8: 856-863. 10.1016/j.ejheart.2006.02.008.

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Anderson G, Horvath J: Chronic Conditions: Making the Case for Ongoing Care. 2002, Princeton, NJ: Robert Wood Johnson Foundation's Partnership for Solutions

    Google Scholar 

  13. 13.

    Petrelli A, Gnavi R, Marinacci C, Costa G: Socioeconomic inequalities in coronary heart disease in Italy: a multilevel population-based study. Soc Sci Med. 2006, 63: 446-456. 10.1016/j.socscimed.2006.01.018.

    Article  PubMed  Google Scholar 

  14. 14.

    Ferrucci L, Guralnik JM, Simonsick E, Salive ME, Corti C, Langlois J: Progressive versus catastrophic disability: a longitudinal view of the disablement process. J Gerontol. 1996, 51: M123-M130.

    CAS  Article  Google Scholar 

  15. 15.

    Bindman AB, Grumbach K, Osmond D, Komaromy M, Vranizan K, Lurie N, Billings J, Stewart A: Preventable hospitalizations and access to health care. JAMA. 1995, 274: 305-311. 10.1001/jama.274.4.305.

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Deyo RA, Cherkin DC, Ciol MA: Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992, 45: 613-619. 10.1016/0895-4356(92)90133-8.

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Charlson ME, Pompei P, Ales KL, Mackenzie CR: A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chron Dis. 1987, 40: 373-383. 10.1016/0021-9681(87)90171-8.

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Breeze E, Sloggett A, Fletcher A: Socioeconomic and demographic predictors of mortality and institutional residence among middle aged and older people : results from the longitudinal study. J Epidemiol Community Health. 1999, 53: 765-774.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Booth GL, Hux JE: Relationship between avoidable hospitalizations for diabetes mellitus and income level. Arch Intern Med. 2003, 163: 101-106. 10.1001/archinte.163.1.101.

    Article  PubMed  Google Scholar 

  20. 20.

    Philbin EF, Dec GW, Jenkins PL, Di Salvo TG: Socioeconomic status as an independent risk factor for hospital readmission for heart failure. Am J Cardiol. 2001, 87: 1367-1371. 10.1016/S0002-9149(01)01554-5.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Kim S, Symons M, Popkin BM: Contrasting socioeconomic profiles related to healthier lifestyle in China and the United States. Am J Epidemiol. 2004, 159: 184-191. 10.1093/aje/kwh006.

    Article  PubMed  Google Scholar 

  22. 22.

    Mojtabai R, Olfson M: Medication costs, adherence, and health outcomes among Medicare beneficiaries. Health Aff (Millwood). 2003, 22: 220-229. 10.1377/hlthaff.22.4.220.

    Article  Google Scholar 

  23. 23.

    Kahn HS, Williamson DF, Stevens JA: Race and weight change in US women: the roles of socioeconomic and marital status. Am J Public Health. 1991, 81: 319-323.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Siahpush M, McNeill A, Borland R, Fong GT: Socioeconomic variations in nicotine dependence, self-efficacy, and intention to quit across four countries: findings from the International Tobacco Control (ITC) Four Country Survey. Tob Control. 2006, 15 (Suppl 3): iii71-75. 10.1136/tc.2004.008763.

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Lantz PM, House JS, Mero RP, Williams DR: Stress, life events, and socioeconomic disparities in health: results from the Americans' Changing Lives Study. J Health Soc Behav. 2005, 46: 274-288.

    Article  PubMed  Google Scholar 

  26. 26.

    Lee S, Buring JE, Cook NR, Grodstein F: The relation of education and income to cognitive function among professional women. Neuroepidemiology. 2006, 26: 93-101. 10.1159/000090254.

    Article  PubMed  Google Scholar 

  27. 27.

    Caiazzo A, Cardano M, Cois E, Costa G, Marinacci C, Spadea T, Vannoni F, Venturini L: Inequalities in health in Italy. Epidemiol Prev. 2004, 28 (3 Suppl): i-ix. 1–161

    PubMed  Google Scholar 

  28. 28.

    Dunlop S, Coyte PC, McIsaac W: Socio-economic status and the utilisation of physicians' services: results from the Canadian National Population Health Survey. Soc Sci Med. 2000, 51: 123-133. 10.1016/S0277-9536(99)00424-4.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Morris S, Sutton M, Gravelle H: Inequity and inequality in the use of health care in England: an empirical investigation. Soc Sci Med. 2005, 60: 1251-1266. 10.1016/j.socscimed.2004.07.016.

    Article  PubMed  Google Scholar 

  30. 30.

    Van der Heyden JH, Demarest S, Tafforeau J, Van Oyen H: Socio-economic differences in the utilisation of health services in Belgium. Health Policy. 2003, 65: 153-165. 10.1016/S0168-8510(02)00213-0.

    CAS  Article  PubMed  Google Scholar 

  31. 31.

    Iezzoni LI, McCarthy EP, Davis RB, Siebens H: Mobility impairments and use of screening and preventive services. Am J Public Health. 2000, 90: 955-961.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Bacon WE, Hadden WC: Occurrence of hip fractures and socioeconomic position. J Aging Health. 2000, 12: 193-203. 10.1177/089826430001200203.

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Farahmand BY, Persson PG, Michaelsson K, Baron JA, Parker MG, Ljunghall S, Swedish hip fracture group: Socioeconomic status, marital status and hip fracture risk: a population-based case-control study. Osteoporos Int. 2000, 11: 803-808. 10.1007/s001980070060.

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Zingmond DS, Soohoo NF, Silverman SL: The role of socioeconomic status on hip fracture. Osteoporos Int. 2006, 17: 1562-1568. 10.1007/s00198-006-0161-7.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    West J, Hippisley-Cox J, Coupland CA, Price GM, Groom LM, Kendrick D, Webber E: Do rates of hospital admission for falls and hip fracture in elderly people vary by socio-economic status?. Public Health. 2004, 118: 576-581. 10.1016/j.puhe.2004.02.006.

    CAS  Article  PubMed  Google Scholar 

  36. 36.

    Huijbregts PPCW, Feskens EJM, Rasanen L, Fidanza F, Nissinen A, Menotti A, Kromhout D: Quality of dietary pattern and 20-year mortality in elderly men in Finland, Italy and The Netherlands. BMJ. 1997, 315: 13-17.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Huang Z, Himes JH, McGovern PG: Nutrition and subsequent hip fracture risk among a national cohort of white women. Am J Epidemiol. 1996, 144: 124-134.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Onder G, Gambassi G, Landi F, Pedone C, Cesari M, Carbonin PU, Bernabei R: Investigators of the GIFA Study (SIGG-ONLUS): Trends in antihypertensive drugs in the elderly: the decline of thiazides. J Hum Hypertens. 2001, 15: 291-297. 10.1038/sj.jhh.1001176.

    CAS  Article  PubMed  Google Scholar 

  39. 39.

    Onder G, Pedone C, Gambassi G, Landi F, Cesari M, Bernabei R, Investigators of the GIFA Study: Treatment of osteoporosis among older adults discharged from hospital in Italy. Eur J Clin Pharmacol. 2001, 57: 599-604. 10.1007/s002280100370.

    CAS  Article  PubMed  Google Scholar 

  40. 40.

    Cox AM, McKevitt C, Rudd AG, Wolfe CD: Socioeconomic status and stroke. Lancet Neurol. 2006, 5: 181-188. 10.1016/S1474-4422(06)70351-9.

    Article  PubMed  Google Scholar 

  41. 41.

    Charlson M, Peterson JC: Medical comorbidity and late life depression : what is known and what are the unmet needs?. Biol Psychiatry. 2002, 52: 226-235. 10.1016/S0006-3223(02)01422-1.

    Article  PubMed  Google Scholar 

  42. 42.

    Quadrelli SA, Roncoroni A: Features of asthma in the elderly. J Asthma. 2001, 38: 377-389. 10.1081/JAS-100000259.

    CAS  Article  PubMed  Google Scholar 

  43. 43.

    Dale DC: Poor prognosis in elderly patients with cancer: the role of biased undertreatment. Support Oncol. 2003, 1 (4Suppl2): 11-17.

    Google Scholar 

  44. 44.

    Rehkopf DH, Haughton LT, Chen JT, Waterman PD, Subramanian SV, Krieger N: Monitoring socioeconomic disparities in death: comparing individual-level education and area-based socioeconomic measures. Am J Public Health. 2006, 96: 2135-2138. 10.2105/AJPH.2005.075408.

    Article  PubMed  PubMed Central  Google Scholar 

Pre-publication history

  1. The pre-publication history for this paper can be accessed here:

Download references


The authors thank Margaret Becker for her editorial assistance.

Author information



Corresponding author

Correspondence to Andrea Corsonello.

Additional information

Competing interests

The author(s) declare that they have no competing interests.

Authors' contributions

RAI, CA, FF, VB, and CAP planned and conducted the study, performed the statistical analysis, and drafted the first version of the manuscript. AC contributed to the study design and to the final version of the manuscript.

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Antonelli-Incalzi, R., Ancona, C., Forastiere, F. et al. Socioeconomic status and hospitalization in the very old: a retrospective study. BMC Public Health 7, 227 (2007).

Download citation


  • Chronic Obstructive Pulmonary Disease
  • Census Tract
  • Hospitalization Rate
  • Hospital Information System
  • Hospital Information System