Spss software version 17.1 for windows

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SPSS software version 17.1 for Windows is a statistical analysis software package designed to assist with data management, analysis, and visualization. It provides a wide range of statistical procedures and techniques to help users understand and interpret data.

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SPSS software for Windows, version 17.0 SPSS for Windows, version 17.0 SPSS software for Windows 17.0 SPSS 17.1 for Windows SPSS for Windows software (version SPSS for Windows 17.0 SPSS Windows version 17.0 SPSS software version 17.0 SPSS for Windows (version SPSS for Windows software

7 protocols using spss software version 17.1 for windows

Cost Analysis of Patient Cohort

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Characteristics of the study population were analyzed using descriptive statistics: quantitative variables were described by median, mean, and standard deviation (±SD), while categorical variables were described by count and percentage. The significance of differences between mean costs was verified using nonparametric test for independent samples.
Linear regression analysis was also used to investigate the impact of sociodemographic and clinical characteristics on the dependent cost variables. All analyses were performed using SPSS software version 17.1 for Windows (SPSS Inc, Chicago, IL, USA).

Guerriero F., Orlando V., Monetti V.M., Russo V, & Menditto E. (2017). Biological therapy utilization, switching, and cost among patients with psoriasis: retrospective analysis of administrative databases in Southern Italy. ClinicoEconomics and Outcomes Research: CEOR, 9, 741-748.

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Trends in Imaging Utilization and Costs for Cancer Diagnosis

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Descriptive statistics were used to define patients' demographics and comorbid conditions stratified by year of diagnosis. χ² statistics for trend and linear-by-linear association were used to compare distributions of categorical and continuous variables, respectively. Statistical significance was defined as a two-sided P-value <0.05. The number of routine and new imaging tests per thousand patients by year from diagnosis was calculated and the percentage of patients who received one or more procedures was plotted. For each year of diagnosis, the costs of imaging procedures are expressed in euros as mean cost per patient. Changes in number of procedures and in related cost per patient from 2001 to 2010 are expressed as mean annual rate increases. We estimated the mean annual increases separately for each imaging test using a generalised linear model with a Poisson count distribution and log link for counts, and a log link and normal distribution for costs. All mean annual increase estimates were adjusted for age, geographic location and the Charlson Comorbidity Index. Modelling and statistical analysis were carried out using R version 2.12.1 (IBM SPSS Statistics - Integration Plug-In for R for SPSS Statistics 20 software) and the SPSS software version 17.1 for Windows (SPSS Inc., Chicago, IL, USA).

De Placido S., De Angelis C., Giuliano M., Pizzi C., Ruocco R., Perrone V., Bruzzese D., Tommasielli G., De Laurentiis M., Cammarota S., Arpino G, & Arpino G. (2017). Imaging tests in staging and surveillance of non-metastatic breast cancer: changes in routine clinical practice and cost implications. British Journal of Cancer, 116(6), 821-827.

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Spatial Analysis of Antibiotic Prescription Rates

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The age-adjusted prevalence rates were categorized into quintiles and mapped by the patient’s municipality of residence. Values were presented as mean ± standard deviation (STD). The coefficient of variation (CV) was also calculated as a measure of dispersion (CV = STD/mean). Confidence intervals (CIs) were not calculated as they were not relevant due to the high number of individuals in the study population. The relationship between prevalence rates for children, adults, and the older adults was estimated using the non-parametric Spearman rank correlation test.
A logistic regression analysis was performed for each of the most common antibiotic classes to evaluate the association between receiving an antibiotic prescription and gender, age group and municipality type.
All analyses were performed using SPSS software version 17.1 for Windows (SPSS Inc, Chicago, IL, USA), and a p-value of <0.05 was considered to be statistically significant. Maps for antibiotic prevalence rates were generated by a custom script that uses an Application Programming Interface (API) offered by MapBox (

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Orlando V., Monetti V.M., Moreno Juste A., Russo V., Mucherino S., Trama U., Guida A, & Menditto E. (2020). Drug Utilization Pattern of Antibiotics: The Role of Age, Sex and Municipalities in Determining Variation. Risk Management and Healthcare Policy, 13, 63-71.

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Descriptive Analysis of Polypharmacy

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Characteristics of the study population were analysed using descriptive statistics: quantitative variables were described by means and standard deviations while categorical variables were described by counts and percentages. In the case of categorical variables, crosstabulations with chi-square tests were used for comparing the differences between age group and polypharmacy group. All analyses were performed using SPSS software version 17.1 for Windows (SPSS Inc., Chicago, IL, USA). Statistically significance was set up at p-value < 0.05

Guerriero F., Orlando V., Tari D.U., Di Giorgio A., Cittadini A., Trifirò G, & Menditto E. (2016). How healthy is community-dwelling elderly population? Results from Southern Italy. Translational Medicine @ UniSa, 13, 59-64.

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Mapping Antibiotic Prevalence and Consumption

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The age-adjusted prevalence rates were categorized into quintiles and mapped by the patient’s municipality of residence. Antibiotic consumption (DID) was also mapped for the different municipalities.
Differences in prevalence rates between each LHU and the standard population were expressed as prevalence ratios (PRs).
PRs indicate whether the prevalence rate at LHU level was higher or lower than that of the standard population. Confidence intervals (CIs) were computed using standard methods (at 95% confidence level).35
Univariate and multivariate logistic regression models were conducted to evaluate 1) the association between the highest and lowest antibiotic prevalence rates (ie, highest vs lowest quintile of prevalence) and 2) some determinants such as municipality type (rural or urban), average annual income level per capita, and number of general practitioners (GPs) and average annual medication consumption per 1000 inhabitants.
All analyses were performed using the SPSS software Version 17.1 for Windows (SPSS Inc., Chicago, IL, USA), and a P-value of <0.05 was considered to be statistically significant. Maps for antibiotic prevalence rates were generated by a custom script that uses an Application Programming Interface (API) offered by MapBox (www.mapbox.com).

Russo V., Monetti V.M., Guerriero F., Trama U., Guida A., Menditto E, & Orlando V. (2018). Prevalence of antibiotic prescription in southern Italian outpatients: real-world data analysis of socioeconomic and sociodemographic variables at a municipality level. ClinicoEconomics and Outcomes Research: CEOR, 10, 251-258.

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Prevalence Rate Differences by Gender

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Differences in the prevalence rate between males and females were expressed as crude and age-adjusted RR with 95% confidence interval (CI) (ratio of the prevalence in females and males). Age standardization was performed by direct standardization, where the Italian population recorded on 1 January 2018 (29,427,607 males, 31,056,366 females, 60,483,973 total, according to http://demo.istat.it/pop2018/index.html [29 ]) was used as the standard population. Age was categorized into the following groups: 0–6, 7–14, 15–24, 25–34, 35–44, 45–54, 55–64, 65–74, 75–84, and ≥ 85 years.
Ninety-five percent CI of crude and age-adjusted Risk Ratios (RRs) were computed using standard methods [30 ]. Data management was performed with Microsoft SQL server (version 2018) (Penton, USA, Fort Collins, Colorado), and all analyses were performed using the SPSS software version 17.1 for Windows (SPSS Inc., Chicago, IL, USA). A p-value of <0.05 denoted statistically significant differences.

Orlando V., Mucherino S., Guarino I., Guerriero F., Trama U, & Menditto E. (2020). Gender Differences in Medication Use: A Drug Utilization Study Based on Real World Data. International Journal of Environmental Research and Public Health, 17(11), 3926.

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Prevalence of Antidepressant Use in Older Adults

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Prevalent users were calculated as subject 65 years of age or older receiving at least one prescription of AD. Prevalence of AD use was evaluated per calendar year and it was calculated as the number of prevalent users divided by the number of all resident subjects alive in the same year. Prevalence rates were expressed as percentage. Prevalence rates were stratified by year and age group (65-74; 75-84; >85). Patterns of use of drug class for cardiovascular prevention were calculated and stratified by age group and calendar year.
Characteristics of the study population were analyzed using descriptive statistics: quantitative variables were described by means and standard deviations while categorical variables were described by counts and percentages. Chi square test for trend was used to assess the statistical significance among patients exposed and not exposed to ADs for patients’ characteristics (age, gender, pattern prescription) and years. All analyses were performed using SPSS software version 17.1 for Windows (SPSS Inc, Chicago, IL, USA).

Orlando V., Guerriero F., Putignano D., Monetti V.M., Tari D.U., Farina G., Illario M., Iaccarino G, & Menditto E. (2016). Prescription Patterns of Antidiabetic Treatment in the Elderly. Results from Southern Italy. Current Diabetes Reviews, 12(2), 100-106.

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