Anteo Di Napoli

Ageing of patients on chronic dialysis: Effects on mortality—A 12-year study

BACKGROUND During the last few decades the mean age of subjects on chronic dialysis (CD) significantly increased. The effects of these changes on mortality rates, causes of death, early and long-term predictors for mortality have not yet been clarified. We analysed this issue through a study performed over a period of 12 years. METHODS We studied 8977 patients enrolled on the Lazio Dialysis Registry (Italy) in 1995-2006. We analysed annual mortality rates, causes of death, probability and determinants of the survival. RESULTS The overall mortality rate was 14.6 deaths (95% CI: 14.2-14.9) per 100 person-years on CD, remaining essentially unchanged over the 12-year period, despite a 5-year increase in the median age. A reduction in mortality was found for patients >74 years from 29.8 (95% CI: 24.8-34.9) in 1995 to 22.5 (95% CI: 20.0-25.1) in 2006. No statistically significant differences were found over time in annual mortality rates by cause of death. The probability of survival was 0.86 1 year after starting dialysis and 0.33 after 12 years. We found a higher association between haematocrit levels, serum albumin, self-sufficiency and survival within 1 year of dialysis and between diabetes and survival after 1 year. CONCLUSIONS The finding that the mortality rate of CD patients did not change over the last 12 years, despite concomitant and significant ageing of patients, supports the public health policy of providing CD to very old subjects. The identification of the clinical factors that predict survival underlines the role of clinicians in preventing and treating these conditions after the start of CD.

Hepatic and extra-hepatic sequelae, and prevalence of viral hepatitis C infection estimated from routine data in at-risk groups.

BackgroundConcerns about the hepatitis C virus (HCV) are due to the high risk of chronic liver disease and poor treatment efficacy. Synthesizing evidence from multiple data sources is becoming widely used to estimate HCV-infection prevalence. This paper aims to estimate the prevalence of HCV infection, and the hepatic and extrahepatic sequelae in at-risk groups, using routinely collected data in the Lazio region, Italy.MethodsHCV laboratory surveillance and dialysis, hospital discharge, and drug-user registers were used as information sources to identify at-risk groups and to estimate HCV prevalence and sequelae.Full name and birth date were used as linkage keys for the various health registries. Prevalence was estimated as the percentage of cases within the general population and the at-risk groups, with 95% confidence intervals (95% CI) from 1997 to 2001. The risk of sequelae was estimated through a follow-up of hospital discharges up to December 31, 2004 and calculated as the prevalence ratio in HCV-positive and HCV-negative people, within each at-risk group, with 95% CI.ResultsThere were 65,127 HCV-infected people in the study period; the prevalence was 1.24% (95%CI = 1.23%-1.25%) in the whole population, higher in males and older adults. Drug users (35.1%; 95%CI = 34.6-35.7) and dialysis patients (21.1%; 95%CI = 20.2%-22.0%) showed the highest values. Medical procedures with little exposure to blood resulted in higher estimates, ranging between 1.3% and 3.4%, which was not conclusively attributable to the surgical procedures. Cirrhosis, hepatocellular carcinoma and encephalopathy were the most frequent hepatic sequelae; cryoglobulinaemia and non-Hodgkins lymphoma were the most frequent extrahepatic sequelae.ConclusionsSynthesising data from multiple routine sources improved estimates of HCV prevalence and sequelae in dialysis patients and drug users, although prevalence validity should be assessed in survey and sequelae need a well-defined longitudinal approach.

Differences in Influenza Vaccination Coverage between Adult Immigrants and Italian Citizens at Risk for Influenza-Related Complications: A Cross-Sectional Study.

Background Due to their increased vulnerability, immigrants are considered a priority group for communicable disease prevention and control in Europe. This study aims to compare influenza vaccination coverage (IVC) between regular immigrants and Italian citizens at risk for its complications and evaluate factors affecting differences. Methods Based on data collected by the National Institute of Statistics during a population-based cross-sectional survey conducted in Italy in 2012–2013, we analysed information on 42,048 adult residents (≥ 18 years) at risk for influenza-related complications and with free access to vaccination (elderly residents ≥ 65 years and residents with specific chronic diseases). We compared IVC between 885 regular immigrants and 41,163 Italian citizens using log-binomial models and stratifying immigrants by area of origin and length of stay in Italy (recent: < 10 years; long-term: ≥ 10 years). Results IVC among all immigrants was 16.9% compared to 40.2% among Italian citizens (vaccination coverage ratio (VCR) = 0.42, 95% confidence interval (CI): 0.36–0.49). Adjusting for sex, age and area of residence, this difference was greatly reduced but remained statistically significant (VCR = 0.71, 95 CI: 0.61–0.81). Further adjustment for socio-economic factors (education, occupation, family composition and economic status) and a composite indicator of health-services utilization did not affect the difference (VCR = 0.78, 95% CI: 0.68–0.90). However, after adjustments, only long-term immigrants from Africa (VCR = 0.49, 95% CI: 0.28–0.85) and recent immigrants (VCR = 0.58, 95% CI: 0.43–0.78) showed a significantly different IVC compared to Italian citizens. Conclusions Differences in demographic characteristics, socio-economic conditions and health-services utilization explained the reduced IVC in most long-term immigrants compared to Italian citizens. By contrast, these differences did not explain the reduced IVC in long-term immigrants from Africa and recent immigrants. This suggests that IVC in these sub-groups is affected by other informal barriers (e.g., cultural and linguistic) that need to be investigated to promote effective immunization access strategies.

An introduction to the evaluation of a diagnostic test: sensitivity, specificity, predictive value

Starting from a 2×2 cross tabulation of “test response” vs “true disease status” it is possible to calculate the sensitivity, the specificity, the positive predictive value and the negative predictive value of that test. Sensitivity is the probability that a person with the disease will be correctly classified by the test. Specificity is the probability that a person without the disease will be correctly classified by the test. Positive predictive value is the probability that a person with a positive test result is affected by the disease. Negative predictive value is the probability that a person with a negative test result is not affected by the disease. Sensitivity and specificity are characteristic of the test, while predictive values are influenced by the prevalence of the disease in the tested population.

Likelihood ratio of positive and negative diagnostic test result and Bayes theorem

How much more likely is a positive test to be found in a patient with a pathological condition than in a patient without it? How much more likely is a negative test to be found in a patient without a pathological condition than in a patient with it? To give an answer to these questions, it is necessary to consider the positive and negative likelihood ratio. The Fagan nomogram is a simple method which can be used to easily obtain estimates of the post-test probability of disease for any given combination of the pre-test probability of disease and likelihood ratio.

Evaluation of diagnostic tests in parallel and in series

Multiple diagnostic tests are often required in clinical setting, evaluating the combination of their results to diagnose a disease or a condition. Multiple tests can be performed in parallel or in series. In parallel testing, two or more tests are applied to a patient at the same time. If any of the test results are positive, then the patient is considered to be positive. In series testing, the tests are performed sequentially, but only the individuals that are positive to the initial test are retested. A patient is only considered to be positive if all tests are positive. Using tests in parallel improves the negative predictive value, as it increases the sensitivity and reduces false negatives. Using tests in series improves the positive predictive value, as it increases specificity and reduces false positives.

The variation in the health status of immigrants and Italians during the global crisis and the role of socioeconomic factors

BackgroundThe effects of the recent global economic and financial crisis especially affected the most vulnerable social groups. Objective of the study was to investigate variation of self-perceived health status in Italians and immigrants during the economic global crisis, focusing on demographic and socioeconomic factors.MethodsThrough a cross-sectional design we analyzed the national sample of multipurpose surveys “Health conditions and use of health services” (2005 and 2013) conducted by the Italian National Institute of Statistics (ISTAT). Physical Component Summary (PCS) and Mental Component Summary (MCS) scores, derived from SF-12 questionnaire, were assumed as study outcome, dichotomizing variables distribution at 1st quartile. Prevalence rate ratios (PRR) were estimated through log-binomial regression models, stratified by citizenship and gender, evaluating the association between PCS and MCS with surveys’ year, adjusting for age, educational level, employment status, self-perceived economic resources, smoking habits, body mass index.ResultsFrom 2005 to 2013 the proportion of people not employed or reporting scarce/insufficient economic resources increased, especially among men, in particular immigrants. Compared with 2005 we observed in 2013 among Italians a significant lower probability of worse PCS (PRR = 0.96 both for males and females), while no differences were observed among immigrants; a higher probability of worse MCS was observed, particularly among men (Italians: PRR = 1.26;95%CI:1.22–1.29; immigrants: PRR = 1.19;95%CI:1.03–1.38). Self-perceived scarce/insufficient economic resources were strongly and significantly associated with worse PCS and MCS for all subgroups. Lower educational level was strongly associated with worse PCS in Italians and slightly associated with worse MCS for all subgroups. Being not employed was associated with worse health status, especially mental health among men.ConclusionsOur findings support the hypothesis that economic global crisis could have negatively affected health status, particularly mental health, of Italians and immigrants. Furthermore, results suggest socioeconomic inequalities increase, in economic resources availability dimension. In a context of public health resources’ limitation due to financial crisis, policy decision makers and health service managers must face the challenge of equity in health.

Measures of association in medicine and epidemiology

The measures of association evaluate the direction and magnitude of the relationship between two variables. The “effect size” of the association is described with a variety of coefficients when variables are expressed as continuous data (e.g. Pearson correlation coefficient), while if the association refers to categorical variables, the strength of the relationship between these variables is measured through difference in proportions, relative risk, odds ratio. Tests of significance evaluate statistically whether the relationship between the variables is significant or not.

A brief guide to the measures of impact used in epidemiology and public health

In epidemiology, to evaluate how strongly a risk factor is associated with a disease, measures of association, such as relative risks and odds ratios, are used. The attributable risk (AR) is related to the relative risk but, while relative risk represents the ratio of the incidence rates, AR is the difference between these rates. Measures of impact such as AR estimate how much of the disease would be prevented if the risk factor was removed from the population. AR is the difference in the disease rates in exposed and unexposed individuals to a risk factor. AR is calculated from prospective data (cohort study) and may be calculated as the difference in cumulative incidences (risk difference) or incidence densities (rate difference), reflecting the absolute excess risk of the disease in the exposed group compared with the non-exposed group. AR quantifies how much the risk in the exposed group is attributable to the exposure. Population attributable risk (PAR) is the incidence of a disease in the population that would be eliminated removing the exposure to a risk factor. PAR is calculated by subtracting the incidence in the unexposed from the incidence in the total population. Population etiological fraction (FEpop.) is the fraction of disease occurrence in the population that would not have occurred if the risk factor had not been present in the population.

Reliability assessment of a measure: the kappa statistic

The Kappa coefficient is a measure of inter-rater agreement. This statistic measures observed agreement greater than chance and can range from -1 to 1. A value of zero indicates statistical independence and a value of 1 indicates a perfect agreement between observers. The value of Kappa is influenced by the prevalence of the evaluated condition: two observers can have a high observed agreement but low Kappa if the prevalence is very high or very low (paradox of the Kappa statistic).