A multistate model to evaluate COPD progression integrating drugs consumption data and hospital databases

Background The increase in costs related to the diagnosis and treatment of chronic-degenerative diseases requires a better knowledge of the true care pathway of patients. The study objective was to explore, using multi-state modeling, how analyses of drug prescriptions and data obtained from hospital discharge sheets can be used in combination to build a model of patients health care pathway in a non experimental setting. The model was applied to Chronic Obstructive Pulmonary Disease (COPD). Methods Based on the GOLD guidelines, access to hospitalization for COPD and prescription pharmaceuticals were awarded to seven transients states theoretically progressive. The intensity of transition were estimated with the non-parametric method proposed by Aalen and Johansen for multi-state Markov models non-homogeneous in time. Results The COPD patients included in the study are 111190. Patients admitted with a diagnosis of non acute COPD had a growing probability over time of needing prescriptions for inhaled corticosteroids (ICS) or the set combination of long-acting beta-agonists (LABA) and ICS; they also had a rising probability of an exacerbation. The use of ICS alone or in combination with LABA delays a hospital admission for acute respiratory failure by about 6 months, as compared to short-acting beta-agonists or anticholinergics. Conclusion The probabilities of a transition and their distribution in relation to time, sex, age and clinical status can be a helpful tool for those operating in the health care sector, who are called upon to carry out decisions from the standpoints of both efficacious clinical management and an efficient use of resources

Short-term forecast in the early stage of the COVID-19 outbreak in Italy. Application of a weighted and cumulative average daily growth rate to an exponential decay model

To estimate the size of the novel coronavirus (COVID-19) outbreak in the early stage in Italy, this paper introduces the cumulated and weighted average daily growth rate (WR) to evaluate an epidemic curve. On the basis of an exponential decay model (EDM), we provide estimations of the WR in four-time intervals from February 27 to April 07, 2020. By calibrating the parameters of the EDM to the reported data in Hubei Province of China, we also attempt to forecast the evolution of the outbreak. We compare the EDM applied to WR and the Gompertz model, which is based on exponential decay and is often used to estimate cumulative events. Specifically, we assess the performance of each model to short-term forecast of the epidemic, and to predict the final epidemic size. Based on the official counts for confirmed cases, the model applied to data from February 27 until the 17th of March estimate that the cumulative number of infected in Italy could reach 131,280 (with a credibility interval 71,415-263,501) by April 25 (credibility interval April 12 to May 3). With the data available until the 24st of March the peak date should be reached on May 3 (April 23 to May 23) with 197,179 cumulative infections expected (130,033-315,269); with data available until the 31st of March the peak should be reached on May 4 (April 25 to May 18) with 202,210 cumulative infections expected (155.235-270,737); with data available until the 07st of April the peak should be reached on May 3 (April 26 to May 11) with 191,586 (160,861-232,023) cumulative infections expected. Based on the average mean absolute percentage error (MAPE), cumulated infections forecasts provided by the EDM applied to WR performed better across all scenarios than the Gompertz model. An exponential decay model applied to the cumulated and weighted average daily growth rate appears to be useful in estimating the number of cases and peak of the COVID-19 outbreak in Italy and the model was more reliable in the exponential growth phase

In-Hospital Mortality in Non-COVID-19-Related Diseases before and during the Pandemic: A Regional Retrospective Study

Italy was one of the nations most affected by SARS-CoV-2. During the pandemic period, the national government approved some restrictions to reduce diffusion of the virus. We aimed to evaluate changes in in-hospital mortality and its possible relation with patient comorbidities and different restrictive public health measures adopted during the 2020 pandemic period. We analyzed the hospital discharge records of inpatients from public and private hospitals in Apulia (Southern Italy) from 1 January 2019 to 31 December 2020. The study period was divided into four phases according to administrative restriction. The possible association between in-hospital deaths, hospitalization period, and covariates such as age group, sex, Charlson comorbidity index (CCI) class, and length of hospitalization stay (LoS) class was evaluated using a multivariable logistic regression model. The risk of death was slightly higher in men than in women (OR 1.04, 95% CI: 1.01–1.07) and was lower for every age group below the >75 years age group. The risk of in-hospital death was lower for hospitalizations with a lower CCI score. In summary, our analysis shows a possible association between in-hospital mortality in non-COVID-19-related diseases and restrictive measures of public health. The risk of hospital death increased during the lockdown period

Lung cancer and COPD rates in Apulia: a multilevel multimember model for smoothing disease mapping

<p>Abstract</p> <p>Background</p> <p>If spatial representations of hospitalization rates are used, a problem of instability arises when they are calculated on small areas, owing to the small number of expected and observed cases. Aim of this study is to assess the effect of smoothing, based on the assumption that hospitalization rates, when calculated at the municipal level, may be influenced by both the neighboring municipalities and the health service organization, as well as by environmental risk factors associated with the disease under study.</p> <p>Methods</p> <p>To smooth rates we hypothesize that each municipality belongs to two independent hierarchical levels; at one of these levels subjects may belong to a plurality of superior hierarchical objects. Two different models, so-called Multilevel Multimembership Models, are fitted. In the first the structure of random effects was: the municipality heterogeneity, the spatial dependence of the municipalities and the local health service organization. In the second we replaced the local health service organization effect with the environmental risk effect for each municipality area.</p> <p>The models were applied to spatially represent the rates of hospitalization for lung cancer and chronic obstructive pulmonary disease, determined through the hospital discharge forms recorded in Apulia for the year 2006.</p> <p>Results</p> <p>The effect of smoothing was greater in smaller municipalities and in those with a more unstable Risk Adjusted Rate (RAR) due to the small number of cases and of population at risk. When a hierarchical level representing the ASL is inserted, the model fits the data better.</p> <p>Conclusion</p> <p>Maps of hospitalization rates for lung cancer and chronic obstructive pulmonary disease, shaded with the rates obtained at the end of the smoothing procedure, change the visual picture of the disease distribution over the whole territory, and if detected by the model, seem to express a geographical distribution pattern in specific areas of the region. In the case of lung cancer, the models show a clear difference between RAR and smoothed RAR. The inclusion of a random effect indicating the ASL contributed to improve the graphic representation of the results, whereas the environmental risk was not found to be a better hierarchical level than the municipality for fitting of the model.</p

Progression of liver cirrhosis to HCC: an application of hidden Markov model

<p>Abstract</p> <p>Background</p> <p>Health service databases of administrative type can be a useful tool for the study of progression of a disease, but the data reported in such sources could be affected by misclassifications of some patients' real disease states at the time. Aim of this work was to estimate the transition probabilities through the different degenerative phases of liver cirrhosis using health service databases.</p> <p>Methods</p> <p>We employed a hidden Markov model to determine the transition probabilities between two states, and of misclassification. The covariates inserted in the model were sex, age, the presence of comorbidities correlated with alcohol abuse, the presence of diagnosis codes indicating hepatitis C virus infection, and the Charlson Index. The analysis was conducted in patients presumed to have suffered the onset of cirrhosis in 2000, observing the disease evolution and, if applicable, death up to the end of the year 2006.</p> <p>Results</p> <p>The incidence of hepatocellular carcinoma (HCC) in cirrhotic patients was 1.5% per year. The probability of developing HCC is higher in males (OR = 2.217) and patients over 65 (OR = 1.547); over 65-year-olds have a greater probability of death both while still suffering from cirrhosis (OR = 2.379) and if they have developed HCC (OR = 1.410). A more severe casemix affects the transition from HCC to death (OR = 1.714). The probability of misclassifying subjects with HCC as exclusively affected by liver cirrhosis is 14.08%.</p> <p>Conclusions</p> <p>The hidden Markov model allowing for misclassification is well suited to analyses of health service databases, since it is able to capture bias due to the fact that the quality and accuracy of the available information are not always optimal. The probability of evolution of a cirrhotic subject to HCC depends on sex and age class, while hepatitis C virus infection and comorbidities correlated with alcohol abuse do not seem to have an influence.</p

Hospitalization for COPD in Puglia: the role of hospital discharge database to estimate prevalence and incidence

Background and aim. Chronic Obstructive Pulmonary Disease (COPD), although largely preventable, is a great health burden in all the countries worldwide. Statistics of morbidity and mortality of COPD show the need for correct management of the disease. Chronic Obstructive Respiratory Diseases (DRG 88) are in 9th place for discharge in in-patient hospital admission. It is necessary to establish specific indicators which are efficacious and relevant for the patient, the doctor and the health manager. This study will analyse the information in respect of hospital admissions (Hospital discharge database) in Puglia for the period 2000-2005. Methods. The analysis was carried out utilising the Puglia Region hospital patient discharge database, selecting those patients with admission for chronic respiratory disease as principal or secondary diagnosis. Results. Chronic respiratory diseases are more frequent in males and in people over 45 years old with frequency increasing with age. Geographical distribution shows that there are greater rates of hospitalisation in big cities and in the neighbourhood of industrial areas. Although the trend over time is slight. A higher percentage of re-admission has been found for patients with COPD, and the interval between the two admissions occurs within one or two months; the diagnosis at the second admission is the same as for the first. 10.6% of discharge forms report one diagnosis, especially in patients older than 65 years of age. Little could be said about diagnostic procedures because these are not reported on the discharge form. Conclusion. Hospitalisation data confirms expectations regarding age and sex of patients. The high hospitalisation rates indicate that in-patients care still remains the only viable treatment for COPD and other chronic respiratory diseases. The high number of exacerbations reflect the absence of out-patients service or community care, and the same diagnosis in more than one episode shows the lack of efficiency of health services and disease management. This data is necessary to understand disease distribution and the modification of disease management in order to reduce health care costs, to increase efficacy in disease control and to limit repeated exacerbation and so to obtain the maximum benefit for the patients

Caries experience among adolescents in southeast Italy

Abstract Background/purpose According to WHO recommendations, 12-year-olds are considered an important target group for evaluating the level of dental caries among children with permanent teeth, and are often chosen for international comparisons. The aim of the present study was to evaluate the current oral health status of 12-year-old children in southeast Italy, stratified by gender and residential area. Materials and methods The survey was conducted on 431 children enrolled by multistage cluster sampling. A dental caries experience index (decayed, missing, and filled permanent teeth; DMFT) was recorded at schools by a team of examiners trained at the start of the study. Statistical analyses by Chi-square, Fisher's exact, and Wilcoxon tests were performed using SAS version 9.1 software for PCs. We applied the Zero-Inflated Negative Binomial regression model in the STATA package. Results Caries prevalence was recorded in 38.3% of the sample. Estimated means and 95% confidence intervals of the DMFT index by gender were: 1.15 (0.91–1.39) for males, 1.26 (1.02–1.5) for females, and 1.21 (1.03–1.39) for the total sample. The D component of the index was dominant. The mean number of caries found in southeast Italy was significantly higher than the national mean ( t =3.125, P=0.002), but significantly lower than the mean for south Italy ( t =−2.125, P=0.03). Results of the regression model showed that only the mother and father's nationality and educational level contributed to the DMFT. Conclusions The oral health situation of 12-year-old children from southeast Italy seems to be in line with that in other Western European countries

EBPH is Back for a Global Audience

We are proud to announce the re-starting of the Journal Epidemiology Biostatistics and Public Health (EBPH), made possible thanks to the Milano University Press (MUP), the new publisher of the journal