The effect of healthcare delivery privatisation onavoidable mortality: longitudinal cross-regional resultsfrom Italy, 1993e2003

Cecilia Quercioli,1 Gabriele Messina,1 Sanjay Basu,2,3,4 Martin McKee,4 Nicola Nante,1

David Stuckler4,5

ABSTRACTBackground During the 1990s, Italy privatiseda significant portion of its healthcare delivery. Theauthors compared the effectiveness of private and publicsector healthcare delivery in reducing avoidable mortality(deaths that should not occur in the presence of effectivemedical care).Methods The authors calculated the average rate ofchange in age-standardised avoidable mortality rates in19 of Italy’s regions from 1993 to 2003. Multivariateregression models were used to analyse the relationshipbetween rates of change in avoidable mortality andlevels of spending on public versus private healthcaredelivery, controlling for potential demographic andeconomic confounders.Results Greater spending on public delivery of healthservices corresponded to faster reductions in avoidablemortality rates. EachV100 additional public spending percapita on NHS delivery was independently associatedwith a 1.47% reduction in the rate of avoidable mortality(p¼0.003). In contrast, spending on private sectorservices had no statistically significant effect onavoidable mortality rates (p¼0.557). A higher percentageof spending on private sector delivery was associatedwith higher rates of avoidable mortality (p¼0.002). Theauthors found that neither public nor private sectordelivery spending was significantly associated withnon-avoidable mortality rates, plausibly because non-avoidable mortality is insensitive to healthcare services.Conclusion Public spending was significantly associatedwith reductions in avoidable mortality rates over time,while greater private sector spending was not at theregional level in Italy.

INTRODUCTIONItaly’s healthcare system underwent significantchanges in the early 1990s. The Servizio SanitarioNazionale (National Health Service, abbreviatedSSN) was created in 1978 as a nationwide servicefunded predominantly by general taxation, basedlargely on state provision and free at the point ofuse.1 In the 1990s, continuous increases in health-care costs and perceived inefficiency created pres-sure to introduce major reforms, including increasesin the role of the private sector in provision anddecentralisation of health policy responsibilities toregional administrations.2 3 To varying degrees,healthcare delivery was shifted from the publicsystem to the private sector.4 5 Financing for theservice continued to be public, coming mainly fromtax revenues, but over time, the public share of

total finance decreased from 80.5% of the 1980 to72.6% of the 2000.6 The creation of ‘internalmarkets’ and competition within regions were seenas a means to reduce waste and improve theresponsiveness of healthcare providers to patients’demands.4 7

How would these significant reforms be expectedto affect healthcare performance? The existingliterature on ownership of healthcare delivery iscomplex and suffers from many limitations.8e10 Itis dominated by data from American hospitals andmuch uses Medicare data, which cover only thoseaged 65 years or older. Many of the papers arereanalyses of the same data sets. A 2002 systematicreview concluded that for-profit hospitals producedsignificantly worse outcomes than not-for-profitones.11 However, the authors of a recent meta-regression that sought to address the limitations ofthe earlier work were more guarded and concludedsimply that ‘studies representative of the USA asa whole tend to find lower quality among FPs (for-profits) than private non-profits’.12 Limited researchexists from outside the USA. A study in one prov-ince of China found no difference in mortalityaccording to hospital ownership,13 while anAustralian study found better outcomes followingmyocardial infarction in private compared withpublic hospitals, which seemed to be associatedwith the considerably more intensive investigationand treatment in the latter.14 More recently, severalstudies have been undertaken in the UK looking atthe analogous issue of competition in provision ofcare within a largely publicly owned system andhealth outcomes (specifically mortality frommyocardial infarction). These studies purport toshow an association. Although cited extensively bypoliticians in support of radical reforms to theEnglish NHS, it remains highly controversial, withcritics noting considerable methodological prob-lems with the data, the measure of competition andthe outcome used, the plausibility of the findings,and that the findings display the inverse of theauthors’ interpretationdthat outcomes worsenedin non-competitive areas15 and the authors havepointed out that “Researchers have little influenceover how politicians use their academic work”,16

in particular noting that price competition isassociated with worse outcomes.17

There are two main benefits to studyingthe Italian case of privatisation of healthcaredelivery. First, Italy essentially undertook a ‘quasi-natural experiment’ with privatisation and decen-tralisation.18 Such measures are currently being

1Dipartimento di Fisiopatologia,Medicina Sperimentale e SanitaPubblica, Universita degli Studidi Siena, Siena, Italy2Department of Medicine,University of California SanFrancisco, San Francisco,California, USA3Division of General InternalMedicine, San FranciscoGeneral Hospital, San Francisco,California, USA4Department of Public Healthand Policy, London School ofHygiene & Tropical Medicine,London, UK5Department of Sociology,University of Cambridge,Cambridge, UK

Correspondence toDr Cecilia Quercioli, Departmentof Public Health, University ofSiena, Via Aldo Moro 2, 53100Siena, Italy; quercioli2@unisi.it

Accepted 26 June 2012

Quercioli C, Messina G, Basu S, et al. J Epidemiol Community Health (2012). doi:10.1136/jech-2011-200640 1 of 7

Research report JECH Online First, published on September 29, 2012 as 10.1136/jech-2011-200640

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considered by England and Greece, among others. Because of thevaried degree of privatisation among different regions during theperiod 1993e2003, with almost no increase in private sectordelivery in Puglia as a fraction of total delivery but a rise of 9percentage points in Lombardy and Veneto regions (figure 1), theoutcomes between regions can be directly compared aftercontrolling for region-specific variables. Second, the economiccontext of existing studies differs from that in Italy. Forexample, Medicare (the Australian health insurance system)pays only a contribution towards care in private hospitals with

patients paying the rest, either directly or via insurance. Hence,the funding for private care in Australia is rather more generousthat publicly provided care. It is plausible that, in Italy, givenfixed funding per case (using the Diagnosis Related Groups,a prospect payment system19), the need to extract profits andmoney to repay loans from private facilities on the one handcould be a incentive to improve efficiency but on the other handcould determine a reduction in the amount available for deliv-ering high-quality patient care. The Italian privatisation exper-iment potentially has important lessons for other countries

Figure 1 Variations across Italy’sregions. Panel A: change in privatesector delivery, 2003 vs 1993. Panel B:average rate of reduction in avoidablemortality, 1993e2003.

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considering similar reforms as they seek to address budget defi-cits following the 2008 financial crisis.

In this study, we examine whether the share of privatedelivery of healthcare in Italy affected each region’s progress inone indicator of health system performance: avoidable mortality.As defined by Rutstein et al,20 avoidable mortality comprises‘deaths that should not occur in the presence of effectivemedical care’.

METHODSPopulation numbers by age, gender and region and number ofdeaths by age, gender, region and cause of death in the period1993e2003 were extracted from computer files of the ItalianBureau of Statistics (ISTAT). Mortality data were classified usingthe 9th Revision of the International Classification of Diseases(ICD-9) for the years 1993e2002 and using the 10th RevisionICD-10 for the year 2003. We adjusted the number of deaths in2003 using the bridge coding system ICD-9/ICD-10 developedby the ISTAT.21 The period 1993e2003 was chosen because itcovers the years of the main SSN reforms and because at thetime of the analysis regional mortality data for 2004 and 2005were not yet available. Avoidable mortality was categorisedfollowing the methods of Rutstein et al,20 updated by Nolte andMcKee.22 Types of death included in the mortality statistic arelisted in box 1. To avoid potential confounding by age and toensure a fair comparison across avoidable and non-avoidablemortality clusters, both mortality rates were age standardised tothe European standard population.23

Regional data on healthcare expenditure and on socioeco-nomic-lifestyle indicators (Gross Domestic Product per capita,percentage of people with university degree, and smoking rates)were obtained from the Health for AlldItaly database and fromdatabases/publications of ISTAT and the Italian Ministry forHealth.24 25

Private delivery was calculated as the sum of the out-of-pocket health expenditure per capita and the health expenditureper capita of the SSN for services delivered by private providers(including outpatient specialist care and hospital care). Publicdelivery was calculated as the sum of the public expenditure forservices provided directly by the SSN (per capita), the pharma-ceutical expenditure provided by SSN (per capita), the expen-diture per capita for primary care (General Practitioners),

expenditure for public outpatient specialist care but notincluding administrative expenditure of SSN (per capita). We didnot include administrative expenditure because this is an ‘over-head’ and does not directly affect the care received by patients;however, the results were found not to have been qualitativelychanged by this step. Some regions may have more or lessoverhead costs, depending on the efficiency of care organisation,which could consequently dilute the effects of public caredelivery, which we sought to evaluate.Given that the ability of a health system to reduce deaths

from any cause depends on numerous complex regional, andoften historical, factors ranging from availability of healthcareproviders to social determinants of mortality, it is more relevantto look at rates of change in regional mortality rather thancompare absolute levels of mortality among regions. This isespecially important in Italy, given the geographical, economicand cultural diversity of the country, and because each regionbegan privatising healthcare delivery at different initial levels ofavoidable mortality. Consequently, we have evaluated the rate ofprogress in reducing avoidable mortality over time by holding‘between-region variations’ constant, using a ‘fixed effects’model that accounts for the proportion of differences in rates ofmortality change that are due to baseline differences betweenregions.26 The fixed effects framework offers similar advantagesto those with a difference-in-difference approach in so far as itevaluates the variations within a region attributable to thepolicy; the difference-in-difference approach is a special caseinvolving a binary treatment variable where a policy is ‘turnedon’ at the time of implementation. Importantly, both difference-in-difference and fixed effects models hold constant between-group heterogeneity, in the case of the former by differencingand in the latter by time demeaning the data.To check the specificity of our findings, we also tested the

association of public and private delivery with non-avoidablemortality rates, which we hypothesised would not be associatedwith changes in the nature of healthcare delivery. Using non-avoidable mortality rates also has the important advantage ofdetecting potential confounding; if rates of public delivery acrossregions were confounded by an unobserved factor, which unre-lated to the health system, such as lower tobacco use or betterhealth status, they would manifest as a significant associationwith non-avoidable mortality rates.Thus, our initial statistical model was as follows:

Avoidable Mortalityi;t ¼ a þ bPublic Deliveryi;t

þ bPrivate Deliveryi;t þ t þ mi þ 3i;t

(1)

Here, i is region and t is year. All the economic variables aremeasured in euro current prices. m is the region dummy variable(controlling for region-specific differences), t is a time trend and3 is the error term. Both public and private deliveries wereincluded in the model to assess their independent effects. Wheretwo covariates are highly correlated, such an modelling approachcan potentially give rise to unstable estimates (‘multi-collinearity ’). There is speculation that public and privatedelivery may be correlated either negatively (‘crowd out’) orpositively (‘crowd in’). However, using the most recent year ofdata, we found evidence for neither of these possibilities(r¼0.028, p¼0.91). Additionally, in the second step, we investi-gated the effect of greater private sector share of delivery,measured as private delivery as a fraction of total delivery. Ineach model, we included a time trend to adjust for averagereductions in mortality rates that applied evenly to Italy’s

Box 1 Categories of avoidable mortality

Ages 0e14: intestinal infections, whooping cough, measles(1e14) and all respiratory diseases except pneumonia andinfluenza (1e14).Ages 0e44: malignant neoplasm of cervix uteri and body ofcervix, leukaemia.Ages 0e49: diabetes and asthma.Ages 0e74: tuberculosis; other infections (diphtheria, tetanus,septicaemia and poliomyelitis); syphilis; malignant neoplasm ofcolon and rectum, skin, breast, cervix uteri and testis; Hodgkin’sdisease; diseases of the thyroid; epilepsy; deficiency anaemia;chronic rheumatic heart disease; hypertensive disease; ischaemicheart disease (half of deaths); cerebrovascular disease; influenza;pneumonia; peptic ulcer; appendicitis; abdominal hernia; choleli-thiasis and cholecystitis; nephritis and nephrosis; benign pros-tatic hyperplasia; misadventures to patients; maternal death;congenital cardiovascular and digestive nomalies; and perinataldeaths, all causes, except stillbirths.

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regions but were unrelated to changes to variations in healthsystem delivery. In a series of further robustness checks, weincluded adjustments for additional time-varying factors,including gross domestic product per capita (a measure ofincome) measured in euro constant price, the percentage ofpopulation with tertiary education and the percentage of theadult population using tobacco.

As a diagnostic test, we removed regions that had large year-to-year fluctuations in avoidable mortality of >20%, whichwould be implausible in the absence of external shocks. Thisonly occurred in Aosta Valley, which has a very small population

of about 120 000 inhabitants, and as consequence a very smallnumber of deaths resulting in statistically non-sensical vari-ability. Thus, we excluded it from the model because its largefluctuations in avoidable mortality would compose a substantialfraction of overall variation in the data set in percentage changeterms and could thus qualitatively change our findings.Data were analysed using STATAV.10.2 StataCorp LP, College

Station, Texas 77845 USA. Standard errors were clustered byeach region to reflect non-independence of sampling. Modelcoefficients were presented as semi-elasticities (ie, as percentagechanges) to facilitate interpretation.

Figure 2 Public and private healthcaredelivery and progress in reducingavoidable mortality, 1993e2003.Panel A: relationship between averagelevels of spending on public healthcaredelivery and average rate of reduction inavoidable mortality. Panel B:relationship between average levels ofspending on private healthcare deliveryand average rate of reduction inavoidable mortality.

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RESULTSFirst, we evaluated the relationship between each region’s overallprogress in reducing age-standardised avoidable mortality ratesand average levels of spending on public and private delivery percapita (figure 2). Increased public spending on SSN delivery wascorrelated with an increase in the rate of decline in avoidablemortality between 1993 and 2003 (r¼0.33, figure 2, panel A).This association was weaker for spending in the private sector(r¼0.01, figure 2, panel B). Neither public nor private sources ofdelivery were associated with reductions in non-amenablemortality (rSSN¼0.13, rprivate¼�0.17, respectively).i

However, these correlations only allow for a comparison of 19data points since the progress over time (the slope) is based onan estimate derived from the longitudinal data, and both publicand private spending are correlated with each other and withGross Domestic Product (GDP). Our statistical model made itpossible to assess the independent associations of public andprivate spending with avoidable and non-avoidable mortalityrates over time, while correcting for potential bias from the non-random implementation of privatisation across Italy’s regions.

Table 1 shows the results of our longitudinal fixed-effectstatistical models. We found that each V100 additional spendingon public sector services was associated with 1.47% lower ratesof avoidable mortality over the 1993e2003 period (p¼0.003). Incontrast, private sector spending had no statistically significanteffect on avoidable mortality during the period (p¼0.557). Takentogether, these results suggest that greater reliance on privatespending is not associated with an increase in mortality buta slower rate of progress in reducing avoidable morality thanpublic sector spending, an observation consistent with theunadjusted scatter plots in figure 2. We found that a higherpercentage of spending on private sector delivery was associatedwith higher rates of avoidable mortality (0.34%, p<0.01),consistent with this finding; again, private sector spending wasfound to have no effect on non-avoidable mortality rates (0.15%,p¼0.24).

To correct for further potential confounders in the underlyinghealth status of the population, we introduced further controlsfor education (measured by the percentage of the populationwith a university degree) and smoking (measured as thepercentage of adults who smoke). As shown in table 2, none ofthe results was changed, and neither education nor smoking wassignificantly correlated with amenable or non-amenablemortality rates. The lack of any correlation in the case of thelatter likely reflects the long lag period between smoking andsubsequent risks of lung cancer and cardiovascular diseasemortality.

Analysis of two regional matched case studiesTo put the magnitude of these findings in perspective, wecompare, for the year 2003, Tuscany (a region with about 35.1%of all spending delivered through the private sector) withLombardy (where about 49.1% is provided by the private sector).Lombardy has greater GDP per capita than Tuscany (wV22 980vs V19 944 per capita), but its rates of avoidable mortality areabout 10% higher than Tuscany’s (63.7 vs 57.2 per 100 000,respectively). Overall, Lombardy spends more on healthcarethan Tuscany, yet Tuscany spends about 26% more on publicdelivery (V917.4 vs V1155.4 per capita, respectively). Of course,these two regions differ in other historical aspects; however,

these differences in avoidable mortality outcomes appear at leastpartly attributable to their differential investment in publichealthcare delivery.As a further comparison, we could investigate the change in

regions matched on similar levels of avoidable mortality rates andpublic delivery in 1993. Two closely matched neighbouringregions are Trentino and Friuli. In 1993, Trentino and Friuli hadsimilar levels of avoidable mortality (94.2 vs 95.5 per 100 000,respectively). Trentino had slightly higher private sector delivery(33.3% vs 30.7% in Friuli), but by 2003, the situation had reversedso that Friuli had 38% private sector delivery versus 35.2% inTrentino. In both regions, avoidable mortality rates fell, but theydid so more greatly in Trentino (37% vs 32%), where publicspending had risen more greatly (from V729 to V1336 per capitavs V748 to V1212 per capita in Friuli during this same period).

DISCUSSIONOur statistical analysis revealed that greater investment inpublic health service delivery increased the rate of regionalprogress in avoidable mortality rates in Italy. In contrast, privatesector delivery had no statistically significant effect on avoidablemortality. As a control, neither public nor private sector deliveryhad an effect on non-avoidable mortality rates, plausibly becausesuch rates are not significantly determined by healthcare provi-sion. These findings suggest that greater private health sectordelivery is not adverse to health system performance, but suchexpenditure comes at a cost of foregone investment in publicservice delivery that could have delivered greater reductions inrates of avoidable mortality.Italy offers a valuable laboratory for assessing the conse-

quences of widely debated but largely unevaluated ideas forhealthcare reform. As with any statistical analysis, however,ours has important limitations. First, there are only 20 regions

Table 1 Associations of V100 per capita additional public and privatehealthcare delivery expenditure with amenable and non-amenablemortality rates, Italy, 19 regions, 1993e2003

Amenablemortality

Non-amenablemortality

V100 per capita additional public delivery �1.47%** (0.43) 0.19% (1.16)

V100 per capita additional private delivery 0.47% (0.79) 2.16% (1.82)

Region-years 209 209

R2-within region 0.947 0.546

*p<0.05, **p<0.01, ***p<0.001.Robust standard errors in parentheses clustered by region to reflect non-independence ofsampling. Statistical b-coefficients presented as semi-elasticities. Models also control fortime trends and region-specific fixed effects.

Table 2 Robustness test of associations of public and privatehealthcare delivery with avoidable and non-avoidable mortality, Italy, 19regions, 1993e2003

Avoidablemortality

Non-avoidablemortality

V100 higher spending on public delivery �1.54%*** (0.38) 0.31% (1.36)

V100 higher spending on private delivery 0.24% (0.87) 2.26% (1.84)

V100 higher gross domestic product percapita

0.082% (0.074) �0.17%*** (0.044)

Percentage of population with a universitydegree

0.26 (0.63) 0.18 (0.68)

Percentage of adults who use tobacco �0.076 (0.19) 0.72 (0.39)

Region-years 209 209

R2-within region 0.947 0.546

*p<0.05, **p<0.01, ***p<0.001.Robust standard errors in parentheses clustered by region to reflect non-independence ofsampling. Models also control for time trends and region-specific fixed effects.

iAs rates of change in non-amenable mortality and amenable mortality were onlymoderately correlated (r¼0.21), it is likely that factors outside the formal healthsystem are driving such progress.

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within Italy, and breaking the country into smaller geographicunits would produce instability in death rates due to smallpopulation sizes, as is the case with Aosta, the smallest of theregions. The small number of regions implies that the modelmay lack sufficient statistical power to capture subtle gains frominvesting in private sector delivery. However, we were able toidentify significant associations between public health sectordelivery and avoidable mortality, suggesting that any positiveeffect of private sector delivery would be of smaller magnitudethan the effect of the public spending. Second, coincidentpolitical changes and social welfare reforms were present at thetime of health system reform, which could be an uncontrolledsource of bias in the data. Italy’s reforms took place at a time ofprivatisation of savings banks and private contracting forwelfare services.18 Thus, an analysis of the apparent success ofthe privatisation of the banks has concluded that this was, atleast in part, a consequence of other developments taking placeat the time.27 To correct for these potential confounders, wecontrolled for longstanding and relatively fixed cultural andhistorical differences across regions, such as the North/Southdifferences, using 18 regional dummy variables, and wecontrolled for additional time-varying differences in eachregion’s wealth, educational attainment and smoking prevalence(as an indicator of health status). Our principal measure ofhealth system performance, avoidable mortality, likely over-comes such potential confounding of macro-economic develop-ments associated with privatisation, as unlike non-avoidablemortality, avoidable mortality is relatively insensitive to changesin health status and is less affected by macro-economic policies(as one illustration, we observed that non-avoidable wassignificantly associated with GDP per capita but avoidablemortality was not). Nonetheless, there is potential for effects ofresidual time-varying confounders that we have not captured.However, it is likely that such confounders would not be specificto the avoidable mortality and would thus manifest as affectingnon-avoidable mortality. Third, we used just one index ofhealthcare system performance, avoidable mortality, whilereducing such deaths is obviously important, it is not the onlygoal of health systems, and future analyses can evaluate othermeasures of interest, such as access to healthcare, health statusand efficiency. Furthermore, avoidable mortality captures theperformance of the health system as a whole, including primarycare, which is not the focus of our research. It is difficult toenvisage how the quality of primary care might be correlatedwith the share of private outpatient specialist care and hospitalcare. Thus, if variation is random, it is likely to attenuate theassociations we have identified. Fourth, although we adjustedfor differences in composition by age-standardising mortalitydata, we were unable to do so for other variables.

Notwithstanding these limitations, our study does suggestthat, at least in the Italian context, investment in public sectordelivery is more successful in reducing deaths amenable tohealthcare than a similar investment in private provision. Theseresults are consistent with the experience of the USA, with itspredominance of market mechanisms in healthcare, where therehas been much slower progress in reducing avoidable mortalitythan in European health systems.22 28 In summary, there aredifferences in health outcomes, at population level, according towhether delivery is in the public or private sector when eachoperate under the same funding regime. These findings haveimportant implications for those countries like England that areconsidering introducing a much more mixed market in healthcare.

Contributors CQ had the idea for the article, contributed to the literature research,contributed to the study design, collected the data, contributed to the data analysisand reviewed the drafts of the manuscripts. GM collaborated to collect and to managedata, collaborated to data analysis, helped to conceptualise ideas and reviewed thedrafts of the manuscripts. NN collaborated in collection of the data and to the idea forthe study, helped to conceptualise ideas and revised the drafts of the manuscript. SB,MM and DS contributed to the analysis and drafted the article. All the authors havecontributed to conception of the article, drafting and revision of the content.

Funding The authors declare that they are independent from any funders and that thestudy was carried out within their institutional work without any support from anythird party.

Competing interests NN declares that he is the owner of a small private hospital.

Provenance and peer review Not commissioned; externally peer reviewed.

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What is already known on this subject

< In the 1990s, continuous increases in healthcare costs andperceived inefficiency created pressure to increase in the roleof the private sector in healthcare provision in many countries,including Italy.

< The impact of health services competition and healthcaredelivery privatisation on healthcare performance iscontroversial.

< No study has investigated whether private care delivery hasan effect on outcomes at a population level.

What this study adds

< In this study, we examine whether the share of privatedelivery of healthcare in Italy affected each region’s progressin one indicator of health system performance: avoidablemortality. In fact, Italy essentially undertook a ‘quasi-naturalexperiment’ with privatisation and decentralisation, withvaried degrees of privatisation among different regions.

< We found that greater spending on public delivery of healthservices corresponded to faster reductions in avoidablemortality rates. Each V100 additional public spending percapita on NHS delivery was associated with a 1.47%reduction in the rate of avoidable mortality.

< In summary, there are differences in health outcomes, atpopulation level, according to whether delivery is in the publicor private sector when each operates under the same fundingregime. These findings have important implications for thosecountries like England that are considering introducing a muchmore mixed market in healthcare.

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15. Pollock A, Macfarlane A, Kirkwood G, et al. No evidence that patient choice in theNHS saves lives. Lancet 2012;378:2057e60.

16. Bloom N, Cooper Z, Gaynor M, et al. In defence of our research on competition inEngland’s National Health Service. Lancet 2012;378:2064e5; author reply 2065e6.

17. Charlesworth A, Cooper Z. Making competition work in the English NHS: the casefor maintaining regulated prices. J Health Serv Res Policy 2011;16:193e4.

18. Bach S. Decentralization and privatization in municipal services: the case of healthservices. Working Paper, Sectoral Activities Programme. Vol. 164. Geneva:International Labour Office, 2000.

19. 3m Information Systems, Definition Manual: All Patient Refined Diagnosis RelatedGroups. Wallingford, CT: 3M Health Information Systems, 2003.

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